Rural Industrial Entrepreneurship
The Case of Bardhaman District in West Bengal
Subrata Dutta
Promotoren : Professor Dr. Henk Folmer Hoogleraar in de Algemene Economie Wageningen Universiteit, Nederland
Professor Dr. Wim J.M. Heijman Hoogleraar in de Regionale Economie Wageningen Universiteit, Nederland
Co-promotor : Professor Dr. Amita Majumder Economic Research Unit Indian Statistical Institute, Kolkata, India
Promotiecommissie : Professor Dr. Arie Kuyvenhoven, Wageningen Universiteit
Professor Dr. Gerrit Antonides, Wageningen Universiteit
Dr. Han H.L. Oud, Universiteit van Nijmegen
Dr. Sudha Loman, Wageningen Universiteit
Dit onderzoek is uitgevoerd binnen de 'Mansholt Graduate School of Social Sciences'
Rural Industrial Entrepreneurship
The Case of Bardhaman District in West Bengal
Subrata Dutta
Proefschrift ter verkrijging van de graad van doctor op gezag van de rector magnificus van Wageningen Universiteit prof. dr. ir. L. Speelman in het openbaar te verdedigen op woensdag 9 juni 2004 des namiddags te 16.00 uur in de Aula
Subrata Dutta
Rural Industrial Entrepreneurship: The Case of Bardhaman District in West Bengal
Ph.D. Thesis, Wageningen University, Wageningen, The Netherlands, 2004
ISBN 90-8504-046-9
Key words: Rural Development / Rural Industry / Entrepreneurship / Non-farm Sector / Non-agricultural Sector / Culture / LISREL Model / Simultaneous Equations System / Bardhaman (Burdwan) District / West Bengal / India
All rights reserved
To
My mother, my wife (Sumita) and my son (Sagnik)
Acknowledgements
It was Professor Asim Chaudhuri of Rabindra Bharati University, Kolkata, who brought me to academia. So, at the very beginning, I pay my gratitude to him. I remember, as a teacher in Bachelors and Masters, he drew our attention by his fascinating lectures on classical political economy. I had a hidden passion for studying lives but found no sincere indulgence. Four years after I completed my Masters in economics, coincidentally one day I saw Professor Chaudhuri at the library of the United States Information Services at Kolkata and he encouraged me to switch over from non-academia to the area of research. I entered. But the new world was not that interesting to me simply because I lacked something. What was that something? – Something like a clear vision that helps one pierce the depth of ideas first, then bridge the ideas with aims, and finally reach the aims. So, it’s a journey. And making the journey worth is an art, and, of course, science too. That is what I could first perceive when I came in touch with Professor Henk Folmer at Wageningen University in the Netherlands. He is the main supervisor of my study. I owe my gratitude to him since he is the person from whom I learned how a complete research work should be designed and carried out. His repeated critical notes on my drafts stimulated me to improve the thesis a lot. In my eyes, he is a dedicated scientist. Not only as a supervisor, but also as a human being he is a very nice and fantastic person indeed.
I am indebted to my second supervisor Professor Wim Heijman of Wageningen University because he introduced me with Professor Folmer in 2000. I remember that first I started my Ph.D. study at Maastricht School of Management (MSM) in 1999. But I had to look for a supervisor outside of MSM because there was no faculty available in the area of my interest, i.e. development economics. So, I met Professor Heijman while he was visiting MSM as a guest faculty and that was the beginning of our journey. During the period of study, Professor Heijman controlled many aspects of organisation, which have been helpful to carry out the work smoothly. My co-supervisor in India, Professor Amita Majumder of the Economic Research Unit, Indian Statistical Institute, Kolkata, helped and encouraged me a lot during the study. Her valuable guidance right from
vii preparing the questionnaires to completing the thesis considerably enriched the whole work. Without her continuous support, especially during the estimation of the LISREL model, I could not have finished this study. I express my sincere gratitude to Professor Majumder. I am sincerely grateful to Dr. Han Oud of Nijmegen University, the Netherlands, for his valuable suggestions during estimation of the LISREL model. Without his help too, it would not have been possible for me to finish the study. I have learnt a lot from him since he is one of the few experts in LISREL.
I have been introduced with Professor Gerrit Antonides at our department in Wageningen University just two weeks back and I am really thankful to him for his constructive remarks on my thesis. My special thanks go to Dr. Rein Haagsma, Dr. Sudha Loman, Dr. Pierre van Mouche, Dr. Johan van Ophem, and Dr. Jan Rouwendal of our department in Wageningen University for offering me all kinds of study support I requested for and a very nice academic atmosphere at Wageningen. I also convey my thanks to Professor Asok Maiti and Dr. Subhendu Chakrabarti of the Indian Statistical Institute for their moral support. I am also thankful to Dr. Wilbert Houweling of Wageningen University for his several helps.
I am grateful to the Maastricht School of Management (MSM) for their all-out support during this study. Especially, the comments of Professor M. van Beugen and Dr. L. Alcorta on my proposal are gratefully acknowledged. I thank Mr. Jos Linssen and Mr. Ron Soeren for they have taken care of several official aspects of my study. I acknowledge the support of Ashish Kumar (India), S.P. Premaratne (Sri Lanka), Osama Isdudi (Palestine) and Md. Mewan (Sudan) – my Ph.D. batch mates at MSM.
The support I have received from the two great ladies, i.e. two secretaries of our department at Wageningen University – Ms Annelies Coppelmans and Ms Margaret van Wissen, is really unforgettable. It is difficult for me to express my feeling of gratitude towards them in words. Many many thanks to them for what they have done for me. I thank my Ph.D. batch mates at Wageningen—Judith Cornelisse (the Netherlands), Muyeye Chambwera (Zimbabwe), Pius Odunga (Kenya), Hoang Nguyen-Huy (Vietnam),
viii Morteza Chalak (Iran)—for their friendship and support. Especially I want to mention the name of Judith for she has provided me with enormous support. Anybody will be proud of having a friend like Judith. I thank the fieldworkers—Dilip Rana, Pranab Datta, Tirtha Dey, Satrajit Goswami and Shirsendu Gupta—for their participation in data collection in Bardhaman district.
I am deeply shocked that my father – who passed away last December—could not see this book. This dissatisfaction of mine will never be covered up. But the good news is that I have become a father in November last year. My son, Sagnik, has brought new lights to our family. My mother is the source of all my inspiration. My respects go to her for all her love and support. My wife never seriously complained for not providing her with even a little company; rather she provided me with direct helps (along with indirect support) during my study in several ways—sometimes in composing texts in word file, or in inserting data in excel file. I am really thankful to her for her all kinds of support. Special gratitude goes to my three aunts (my mother’s sisters) who have always loved me and taken care of me. I also thank all my other friends and well-wishers in India as well as in the Netherlands.
Subrata Dutta Wageningen, 9 May 2004
ix
x CONTENTS
Acknowledgements vii Contents xi Abstract xvii Map of Bardhaman district xix
1 INTRODUCTION 1 1.1 Introduction 1 1.2 Poverty: The problem 2 1.3 Poverty in India (with special reference to rural poverty) 5 1.4 Rural poverty in West Bengal 8 1.5 Poverty alleviation 11 1.6 Definition of small-scale industries in India 12 1.7 What is misleading? 15 1.8 Definition of rural industry 16 1.9 Definition used in the present study 19 1.10 The objective of the study and the research question 20 1.11 Significance of the study 21 1.12 Organisation of the study 26
2 THE STUDY AREA 29 2.1 Introduction 29 2.2 Murshidabad: the starting place of British regime 30 2.3 Bengal in retrospect 31 2.4 A brief history of Bengali entrepreneurship 33 2.5 Agriculture in West Bengal 37 2.5.1 The growth debate 37 2.6 Industry in Wets Bengal 44
xi 2.7 Urbanisation and infrastructure in West Bengal 48 2.8 Urban centres and small-scale industries in West Bengal 51 2.9 Rural industrialisation and the role of infrastructure in rural towns 55 2.10 Bardhaman’s economy in retrospect: a glimpse 59 2.11 Recent Bardhaman: a glimpse 61 APPENDIX 2.1: Overall description of Bardhaman district 66
3 ISSUES RELATING TO RURAL INDUSTRIES: A REVIEW OF LITERATURE 71 3.1 Introduction 71 3.2 Agriculture-Industry linkage: The classical political economy framework 73 3.2.1 The Physiocrats and Adam Smith 73 3.2.2 Karl Marx 76 3.3 Categorisation of non-farm sector 79 3.4 Kuznets’s growth theory 82 3.5 Relationship between agriculture and industrial growth 86 3.6 Linkages: theories and debate 90 3.7 Japanese experience in brief 102 3.7.1 Agriculture/non-agriculture linkage 102 3.7.2 Transformation from traditional industries to modern industries 104 3.8 Reasons for household participation in rural non-farm activities 105 3.9 Secondary and seasonal employment in non-farm activities 106 3.10 Sectoral composition of rural non-farm employment 107 3.11 Hymer-Resnick model vs. Ranis-Stewart models 113 3.11.1 Hymer-Resnick model 113 3.11.2 The Ranis-Stewart models: colonials and post-colonials 114 3.11.3 Colonial archetypes 119 3.11.4 Post-colonial archetypes 120 3.12 Urbanisation and role of rural infrastructure in rural industrialisation 127 3.13 Conclusions 133
xii APPENDIX 3.1: Distribution of rural workers across non-farm sectors in selected countries, male and female 135 APPENDIX 3.2: Women’s share in total employment by sector in selected countries, rural and urban 136 APPENDIX 3.3: Percentage of non-agricultural workers in rural workforce in India, 1961-1983 137
4 ENTREPRENEURSHIP AND CULTURE 139 4.1 Introduction 139 4.2 Entrepreneurship 143 4.3 Theories of entrepreneurship: Schumpeter and his previous and later 143 4.4 Achievement motive in economic growth: psychological view 153 4.5 Family environment and entrepreneurship 159 4.6 Culture and development 164 4.7 Darwinian principles of evolutionary change and technical culture 174 4.8 Some culturally sensitive models 178 4.8.1 Entrepreneurship among the Amish 179 4.8.2 Entrepreneurship in the indigenous people in Alaska 180 4.8.3 Entrepreneurship in the Canadian Sub-Arctic 180 4.8.4 Entrepreneurship in Laos 180 4.8.5 Entrepreneurship in the kingdom of Lesotho 181 4.9 Conclusions 182 APPENDIX 4.1: Plato, Aristotle and McClelland 184 APPENDIX 4.2: Characteristics of Stationary Societies 187
5 DATA COLLECTION 191 5.1 Introduction 191 5.2 Secondary data 193 5.3 Primary data: sampling procedure 193 5.4 The structure of the questionnaires 195 5.5 Selection and training of field workers 198
xiii 5.6 The pilot survey 198 5.7 The survey 199 5.8 Conclusions 201 APPENDIX 5.1: Questionnaire for farmers who are engaged in non-farm manufacturing activities 202 APPENDIX 5.2: Questionnaire for farmers who are NOT engaged in non-farm manufacturing activities 218
6 DETERMINANTS OF NON-FARM MANUFACTURING ENTRE- PRENEURSHIP OF FARMERS: THEORETICAL CONSIDERA- TIONS AND EMPIRICAL FINDINGS 225 6.1 Introduction 225 6.2 Basic concepts and assumptions of theoretical considerations 226 6.2.1 Rationality and decision making 226 6.2.2 The rational entrepreneur 228 6.3 Non-farm entrepreneurship 231 6.4 Explanatory variables 231 6.4.1 Sex 231 6.4.2 Age and age squared 235 6.4.3 Marital status 239 6.4.4 Children 241 6.4.5 Education 243 6.4.6 Political position of the farmer 245 6.4.7 Financial family support 249 6.4.8 Work-effort or fate 251 6.4.9 Marriage relationship 253 6.4.10 Involvement in agriculture 255 6.4.11 Types of crops 258 6.4.12 Wealth 259 6.4.13 Innovation 261 6.4.14 Risk 264
xiv 6.4.15 Summary of the explanatory variables and descriptive statistics 266 6.4.16 Simultaneity bias 266 6.5 The LISREL model 269 6.5.1 The measurement model 270 6.5.2 The structural model 270 6.5.3 Submodels 272 6.5.4 The theoretical and the sample covariance matrices 274 6.5.5 Identification 277 6.5.6 Estimation of the model 278 6.5.7 Model judgment and model identification 280 6.6 The results 282 6.6.1 The measurement model 282 6.6.2 The structural model 283 6.7 Conclusions 289 APPENDIX 6.1: The Full Results 294
7 CONCLUSIONS 333 7.1 Summary of the study 333 7.2 Policy recommendations 341 7.3 Further research recommendations 343
REFERENCES 345
SAMENVATTING EN CONCLUSIES (SUMMARY AND CONCLUSIONS IN DUTCH) 365
ABOUT THE AUTHOR 371
xv
xvi Abstract
For a living, most of the rural people in developing countries are primarily dependent on agriculture. If the farmers, who have investible surplus generated from agriculture, are interested in non-farm entrepreneurship then rural economy can find an industrial route of development. With this consideration, the study has posed the research question as to what determines non-farm entrepreneurship among farmers and thus attempted to identify the factors that may influence farmer’s non-farm entrepreneurship.
The theoretical part constituted a set of 13 hypotheses which in turn led to formulation of two questionnaires in order to collect data—one questionnaire was for interviewing the farmers who were engaged in non-farm manufacturing activities and the other questionnaire was for interviewing the farmers who were engaged in farming only. So far as the investigation part of the study is concerned Bardhaman district of the state of West Bengal in India was selected because during 1980s and 1990s the state has experienced high agricultural growth compared to the previous decades, which implies that farmers might have been able to gather surplus generated from agricultural development and therefore it was considered interesting to study non-farm entrepreneurship of farmers of West Bengal. Five administrative blocks were randomly selected from the eastern part (agricultural part) of Bardhaman district, and then six panchayats have been randomly selected from each block, and finally 10 farm households were randomly selected from each panchayat, i.e. totally 300 samples were randomly selected for interviews.
The LISREL (LInear Structural RELations) approach was applied to estimate the model which was constituted in the form of simultaneous equations system that included a set of 10 equations (indicating interdependencies between the endogenous and explanatory variables) with a consideration of the hypotheses of the theoretical model; and we applied the LISREL approach, by using its maximum likelihood estimator, since this approach can control for simultaneity bias in the model, and simultaneously deal with latent
xvii variables and the observable variable or, as we may say, can simultaneously estimate the measurement model and the structural model.
The farmers who are married, engaged in producing three crops year, and risk takers have been found to have a relatively high probability to become non-farm entrepreneurs. The farmers who are relatively wealthy and have high levels of education have been found to be less likely in becoming non-farm entrepreneurs whereas age of farmer has indirect positive impact on non-farm entrepreneurship via marriage and indirect negative impact on non-farm entrepreneurship via risk attitude and wealth. The number of children of a farmer has been found to have an insignificant effect on non-farm entrepreneurship, but interestingly non-farm entrepreneurship has been found to have a positive impact on the number of children. Three exogenous variables—viz. age squared, farmer’s primary involvement in agriculture either as a landowner or as a sharecropper, and farmer’s faith in work-effort or fate – have been found to be highly insignificant and therefore have been removed from the structural model. Three explanatory variables – viz. political affiliation of farmer, financial family support, marriage relation, and innovativeness – have also been found to have insignificant impacts on non-farm entrepreneurship.
xviii
Map of Bardhaman District (without scale)
xix Chapter 1
Introduction
1.1 Introduction
The rural economy of the so-called developing countries is currently the subject of much discussion. What is the typical condition of the rural poor in most of the developing countries? In an informal fashion of writing, Schumacher (1973) described that the work opportunities in villages of the developing countries are so restricted that the poor people cannot work their way out of misery. They are under-employed or totally unemployed, and when they do find occasional work their productivity is exceedingly low. Some of them have land, but often too little. Many have no land and no prospect of ever getting any. There is no hope for them in the rural areas and hence they drift into the big cities. The concentration of non-farm sectors in a few urban areas, and the wage gap between rural and urban areas result in a huge rural-urban migration and concentration of unemployed workers in urban areas (Todaro, 1980). Thus rural unemployment becomes urban unemployment. The growing number of unemployment in urban areas necessitates finding a way to create jobs outside agriculture and outside cities focusing on a growth process that would boost the demand for rural non-agricultural activities. Hence creating demand in rural areas for locally produced non-food goods and services becomes an important element in the process of economic development (Mellor, 1976; Bell and Hazell, 1980). Rapid agricultural growth may raise rural income and, consequently, the landowning class may create demand for non-food items. But the demand side alone has the limited scope to change the situation of the rural economy until and unless the increased demand is positively responded by the supply of rural non-farm goods and services, otherwise the increased demand may find route towards the urban industrial goods and services. Agricultural land-owning group has vital role to play in rural industrialisation by investing the surplus, earned from agriculture, to diversified non-farm Introduction activities. From both scientific and policy point of view this is a subject of rural non-farm entrepreneurship. Further expansion of the existing low-productivity rural industrial units as well as low-quality goods will not simply help. The rural land-owning group’s involvement in non-farm activities may have to incorporate a modern outlook since combating the modern, large-scale, urban industrial sector requires a steady shift of the rural land-owning group’s affinity from traditional culture to modern or technical culture. Thus the modernisation of the traditional sector (often synonymous with agricultural sector or rural sector) may go hand in hand with rapid and wide rural industrialisation. Bringing about technological change through innovation in the rural non-agricultural sector is often held to be responsible for modernising the remote villages. In such a perspective, the main focus of this study is to ascertain the determinants of rural non-farm entrepreneurship among the farmers.
From section 1.2 to 1.5 in this chapter, we present the problem statement. From section 1.6 to 1.9 we discuss the definitions of small-scale industry and rural industry. In section 1.10, we set the main objective and the research question for the study. Section 1.11 states the significance of the study and section 1.12 presents the organisation of the whole study.
1.2 Poverty: The Problem
Poverty being an age old curse in the developing countries is a big challenge for the twenty-first century. Poverty and its correlates like illiteracy, malnutrition, ill health etc. offer the people of a poor nation the utter bleakness of the future. The World Development Report (World Bank, 1990), which represents a landmark study on poverty in developing countries, experimented with a choice of two poverty lines: $275 and $370 per person per year, expressed in 1985 purchasing power parity (PPP) prices. The range was chosen to reflect the fact that the poverty lines of some of the poorest nations fall between these two limits. Table 1.1, as furnished by Ray (1998), puts together poverty data from two World Development Reports. In 1990, over one billion individuals were
2 Chapter 1 estimated to earn less than $370 per year ($420 per year at 1990 PPP prices). The picture does not look very hopeful. Only East Asia experienced very high rates of growth and the absolute number of poor declined in five years. And it is observed that the absolute number of poor did not increase in East Europe. But the absolute numbers of the poor for all other regions, as shown in Table 1.1, rose significantly between 1985 and 1990. The overall percentage of people in poverty (at $370 line) was roughly constant over this period at 30% of the population of all developing countries. It is also somewhat clear from the table that why South Asia and Sub-Saharan Africa are to be designated as the areas where mass poverty in the world is geographically concentrated.
Table 1.1: Poverty in developing regions, 1985 and 1990 (using “universal” poverty lines)
1985 1990 Ultra-poor (Under Poor (Under $370) Poor $275) Region HCa HCRb HCa HCRb HCa HCRb (millions) (%) (millions) (%) (millions) (%) Sub-Saharan Africa 120 30 184 48 216 48 East Asia 120 9 182 13 169 11 South Asia 300 29 532 52 562 49 East Europe 3 4 5 7 5 7 Mid. East/N. Africa 40 21 60 31 73 33 L. America/Caribbean 50 12 87 22 108 26 All LDCs 633 18 1051 31 1133 30 Notes for a and b: HC is head count and HCR is head count ratio. A natural measure that appears to mind is simply to count the number of people below the poverty line. One might be interested in the numbers per se or the relative incidence of the poor. In the latter case, one has to divide the number of poor people by the total population of the country or region under consideration. The first measure is known as the head count (HC) and the latter as the head-count ratio (HCR) which is just head count as a fraction of population. Source: Ray (1998: 257), Table 8.1; Original source: World Bank (1990).
3 Introduction
Table 1.2: Rural and urban poverty in selected countries as per World Development Report, published in 1990
Rural Rural poor Infant mortality Access to safe population (% of total (per 1000 live water (% of Region and (% of total poor) births) population) country population) Rural Urban Rural Urban Sub- Saharan Africa Côte d’Ivoire 57 86 121 70 10 30 Ghana 65 80 87 67 39 93 Kenya 80 96 59 57 21 61 Asia India 77 79 105 57 50 76 Indonesia 73 79 74 57 36 43 Malaysia 62 80 - - 76 96 Philippines 60 67 55 42 54 49 Thailand 70 80 43 28 66 56 Latin America Guatemala 59 66 85 65 26 89 Mexico 31 37 79 29 51 79 Panama 50 59 28 22 63 100 Peru 44 52 101 54 17 73 Venezuela 15 20 - - 80 80 Source: Ray (1998: 260), Table 8.2; Original source: World Bank (1990).
Rural poverty in particular is very acute in the developing countries. Poverty in rural areas is significantly higher than that in urban areas. “Even countries with substantial advances in creating an equitable agriculture display higher rural poverty than their national averages” (Ray, 1998: 259). Table 1.2 summarizes rural-urban disparities in poverty, as well as two major indicators of well-being, for selected countries. The other studies provide us with some more information about rural poverty in Asia, particularly in some Indian states. In Asia, the proportion of the population living in absolute poverty in rural areas is 75 % (Mellor, 1995: 9). Agarwal (1986) has noted for all India that the poor are likely to be found in agricultural labourer households. A study has shown that the rural poor in India allocate about 80 per cent of their total expenditure on food, but,
4 Chapter 1 yet they do not get enough nutrition in terms of calories and fat (Dev et al, 1994: 245; Lipton, 1983: 35). Let us now review rural poverty of India in a nutshell.
1.3 Poverty in India (with special reference to rural poverty)
Before independence, the colonial government was primarily concerned with the maintenance of law and order, defence and tax collection and lacked an explicit development policy. Public investment decisions were governed more by profitability considerations than by any concern for long-run growth or equity. The period was marked by economic stagnation, particularly in the agricultural sector. The growth rate of agriculture was around 0.3 per cent per annum in the first half of twentieth century. Aggregate real output increased at a rate of less than two percent per annum during the period 1900-1950. In per capita terms, it was less than half a per cent. There was some growth in the large-scale manufacturing sector which was, however, nullified by the decline of traditional industries. Capital formation was only about six per cent of Net Domestic Product (Dev et al, 1994).
At the time of independence in August 1947, India was faced with problems of rehabilitating the economy disturbed by the Second World War and partition of the country, and of achieving rapid economic growth to emancipate the millions of its population from poverty, hunger and malnutrition. The economy was predominantly agrarian, with large inequalities in the distribution of resource endowments among people and across geographical regions. Unemployment and underemployment was prevalent. And saving, capital formation, income levels and hence living standards were all very low (Dev et al, 1994).
Of course, there have been some improvements during last 50 years after independence. Aggregate economic statistics on the evolution of poverty in India point fairly unambiguously towards steady, albeit slow, progress in the reduction of poverty (Jayaraman and Lanjouw, 1999). The poverty headcount (based on various rounds of the
5 Introduction
National Sample Survey Organisation’s household surveys of consumption) declined from 53% in 1970 to 36% in 1991 (Datt, 1995; Datt and Ravallion, 1996). This decline was driven principally by the fall in poverty in rural areas (from 55% to 37%), where the bulk of the population still lives and where the incidents of poverty have always been highest. The poverty gap in rural areas declined at 1.77% per year between 1951 and 1992 (compared to a fall of 0.82% per year for the rural headcount over this period); and in rural areas the squared poverty gap declined at 2.55% per annum (Datt, 1995). These figures suggest that the poorest of the poor, in per capita consumption terms, have seen the more pronounced improvements.
Other indicators of well-being generally lend support to the notion that living standards have been rising in India over time. Female literacy rates, for example, rose from below 10% in 1950 to 39% by 1990; male literacy rates expanded from 27% to 64% over the same period; life expectancy in India in 1950 was only 32.1 years, by 1990 rising to 59.2 years (Drèze and Sen, 1995). In another study, Drèze and Srinivasan (1996) examine National Sample Survey data for 61 constituent regions (loosely defined in terms of agroclimatic characteristics) in India in 1972/73 and 1987/88. They find little evidence of regional “pockets” where the incidence of poverty has risen.
The picture from secondary data, in terms of direction of change, is thus fairly positive. Much of the impetus behind the decline, albeit slow, of rural poverty over time can be found in the process of agricultural intensification which has taken place, at a varying pace, across rural India. What is equally clear, however, is that the absolute levels of deprivation are still very high. This is true in terms of consumption-based poverty, and also in terms of social indicators. India in the 1990s has been still far from its goal of universal primary education, and freedom from hunger and preventable illness (Jayaraman and Lanjouw, 1999). Even against a background of declining poverty in general, it is clear that at any given moment there are certain subgroups of the population who face a high risk of falling into poverty. Instances of drought (or flood) in rural India are common, and can spell dramatic increases in poverty (Hazell and Ramasamy, 1991). After all, rapid population growth represents one factor which can result in increased
6 Chapter 1 impoverishment. Village studies have noted this as a force which can offset rising productivity, and occasionally result in a decline in per capita incomes (see, for details, Jayaraman and Lanjouw, 1999). The population in the working age-group (15-59 years) is the fundamental supply-side factor in the labour market (Dev et al, 1994: 264). Employment for all the people of this age group is a far cry.
Moreover, one should not be very much encouraged by the incidence of reduction in rural poverty in India for a particular short period of time because rural poverty in India is very dynamic in nature, which may fall in a span of few years and then again may go up in the subsequent years. Mellor and Desai (1985) have provided, primarily based on Ahluwalia’s estimates of the incidence of rural poverty in India for 1956/57 to 1977/78, an opportunity to understand the dynamic behaviour of the poverty variable. Table 1.3 shows this picture.
Table 1.3: Changes in the incidence of rural poverty in India, 1956/57 to 1977/78
Subperiod Changes in the percentage of rural population in poverty
1956/57 to 1960/61 Decreased from 54% to 39% 1960/61 to 1966/67 Increased from 39% to 57% 1966/67 to 1971/72 Decreased from 57% to 41% 1971/72 to 1974/75 Increased from 41% to 50% 1974/75 to 1977/78 Decreased from 50% to 39% Source: Mellor and Desai (1985: 195)
It is clear from the above table that the incidence of rural poverty in India is dynamic, changing substantially even from one year to the next. But, more importantly, it shows that for a period of almost two decades there was neither a rising nor a declining trend in the incidence of Indian rural poverty. This depicts that in independent India the incidence of rural poverty has not consistently experienced a steady decline.
During the period, West Bengal, a state of India and the study area of the present research, was no exception and could not extend substantial well-being to its rural poor. In a study on Bengal (both West Bengal and Bangladesh), van Schendel (1991: 238) has
7 Introduction stated that two-thirds of the rural population of Bengal were utterly poor. Let us now take a slight insight into rural poverty of West Bengal.
1.4 Rural Poverty in West Bengal
In a review of several studies, Chatterjee (1998) has observed a very high head count ratio (per cent), i.e. nearly 80 per cent, for 1973-74 in West Bengal.1 This is, according to him, partly due to the prevalence of drought in that year. The year 1977-78 saw heavy rainfall and floods in many parts of the state and the head count ratio is found to be 76.85 per cent. In 1983, the rainfall was moderate to low. But, after 1983, West Bengal experienced consistently good rainfall. For the years 1973-74, 1977-78 and 1983 even the average standard of living fell below the poverty in rural areas and this again reached the poverty line level in 1986-87 in rural areas, when the head count ratio fell to 60.50 per cent. In 1988-89, the head count ratio became 53.10 per cent. For the signs of sharper decline of rural poverty in the state in the second half of 1980s, the productivity growth of West Bengal agriculture (discussed in Chapter 2) in that period is often seen to be responsible by the scholars. But, in a review of different studies, Rogaly et al (1995) concluded that, due to the effect of agricultural development in West Bengal in 1980s, while many gained increased employment and higher wages, relative poverty has increased and the quality of life of the poorest might have decreased. Besides, Beck (1994), in a case study in the three villages of West Bengal, observes that poverty rose in those villages during the late 1980s, even though he documents an increase in incomes of the poorest households. In his observation in those three villages, employment was available for only half the year at most, so the villagers had to resort to cuts in consumption and supplement their diet by wild foods. Beck asked people, who were regularly hungry, some questions about the amount they ate and shortfalls of food. He came to know from the respondents the followings:
1 For a detailed review of different measurements of poverty for West Bengal, see Chatterjee (1998).
8 Chapter 1
“Food had to be bought every day, as the cash was not available to buy in stocks at cheaper prices. Little of respondents’ food requirements came from ration2 shops. Ration rice, which was about 20 paisa3 cheaper than rice bought locally with cash, was taken by all except three Fonogram4 respondents when it was available, but the ration shop was open only once a week, the supplies erratic and the quality of the rice very bad” (Beck, 1994: 136).
Beck (1994) has also provided a representative daily shopping bill, from a Bithigram (a West Bengal village) household with two adults and two children under six. This shows the amount the household members estimated they would need to suffice for one day, and fits closely with other estimates, when adjusted for household size. The bill is given in Table 1.4.
Table 1.4: A representative daily household shopping bill
Item Amount Price (in rupees) Rice 2 kg 6.50 Potatoes 1 kg 1.40 Oil 50 gm 1.20 Chilli 25 gm 1.00 Lentils 100 gm 1.00 Spices 1.50 Mustard oil .50 Tea leaves .40 Pana 1.00 Khoinib .20 Salt .20 Vegetables 1.00 Total 15.90 a Betel leaf; b A kind of tobacco which is cheaper; Source: Beck (1994: 137)
2 This is public distribution system in India through which commodities are sold at administered prices (lower than the open market prices). 3 Paisa (or, paise) is the unit of rupee, Indian currency. 4 This is a village in West Bengal.
9 Introduction
If both the partners of a couple worked, the woman earned eight rupees a day and the man ten rupees. So they would have enough to meet their daily needs. In Beck’s (1994) observation, employment was available for only half the year at most, so the respondents had to resort to cuts in consumption and supplement their diet by wild foods. It was not a surprise to hear that this household, along with fifty-six of the other fifty-nine households replying, spent more or less all their daily income on food on a regular basis. Only three households out of sixty said that they were getting an adequate amount of food.
The impoverishment is also seen, if the daily wage for the casual labourer in rural West Bengal is looked into. The wage for casual labour is uniform within villages, but it varies across villages considerably (Bardhan and Rudra, 1981, 1986; Rudra, 1982, 1984; Walker and Ryan, 1990; Dasgupta, 1993). Table 1.5 displays a remarkably wide range of wages in a cluster of villages in Illambazar in West Bengal. The daily wage per casual labourer in most of the villages is either Rs. 5.00 or Rs. 3.00 plus 1.5 kg rice. This wage could hardly suffice the subsistence requirements of a small family on daily basis with one member in employment. And, also, this kind of casual employment is not available throughout the year.
Table 1.5: Wage rates in Illambazar cluster, West Bengal
Daily wage rates Number of villages
Cash only Rs. 4.00 4 Rs. 5.00 10 Rs. 6.00 6 Rs. 8.00 1 Cash and kind Rs. 2.00 + 1.5 kg rice 5 Rs. 3.00 + 1.5 kg rice 22 Rs. 3.50 + 1.5 kg rice 1 Rs. 4.00 + 1.5 kg rice 2 Rs. 6.00 + 1 meal 1 Source: Rudra 1984, (Table 2).
10 Chapter 1
1.5 Poverty Alleviation
The poverty alleviation programmes as are undertaken in the state of West Bengal are basically the same ones that are implemented in all other states across India. Most of the poverty alleviation schemes are designed centrally and the implementing agencies for these programmes are the states.
Land in rural areas is an important asset or an important means of production. Everybody in villages does not have land. Access to assets other than land—redistribution of which is somewhat difficult—was thought to be a provider of additional income to the poor. A very wide range of such assets can be listed. They may include livestock, raw material for arts and crafts for artisans, small implements or even a better means of transport (a bullock cart or bicycle or rickshaw). The most important programme which is aimed at providing additional income to the poor by giving them productive assets of the type listed above is the Integrated Rural Development Programme (IRDP). In a review of some case studies, Vyas and Bhargava (1995) find that IRDP has been successful in very special circumstances—where the asset and employment base of the poor is much secure. IRDP has done well in relatively developed and prosperous areas, but its performance in the backward and remote areas has been poor.
One of the major programmes for skill development is Training of Rural Youth for Self- Employment (TRYSEM). The objective of the programme is to provide youth with some skills so that they can be self-employed. In a review of TRYSEM’s performance in the nine states in India, no significant success was observed.5 Vyas and Bhargava (1995) state that TRYSEM is neither linked with industrial policy nor linked with the rural industrialisation process. Since the needs are not identified after any systematic review of demand, the training does not always help the poor to improve their levels of living.
5 The nine states include Andhra Pradesh, Bihar, Gujarat, Haryana, Karnataka, Kerala, Maharashtra, Rajasthan, and West Bengal.
11 Introduction
Two employment generating programmes are National Rural Employment Programme (NREP) and the Rural Landless Employment Generating Programme (RLEGP). These programmes were modified into a combined programme called Jawahar Rozgar Yojana (JRY) in 1989-90. In a study on the state of Bihar, JRY was found to be the most visible rural development programme (see Sharma, 1995). This may be because of the fact, as Parthasarathy (1995) comments, that the JRY gave greater flexibility to village panchayats in the choice of projects. Anyway, Parthasarathy finally found JRY to be inconsistent with the spirit of multi-level planning since it was not adequately related to agricultural development.
Given such a background, it is more or less clear that different employment generating programmes in India need to be given greater importance in order to combat poverty since poverty in the country is still very acute. At the same time, as a poverty alleviation programme, promotion of small industries in a wider scale should be taken into consideration.
1.6 Definition of Small-Scale Industry (SSI) in India:
Since the study is related with the rural industrial sector, we need to define which the rural industries are. Before doing so, first we are to furnish the definitions of the small scale industrial (SSI) units, which have been formulated by the government of India. In the next section, we will discuss the definition of rural industries with a review of existing literature and thereafter we will give our definition of rural industry that has been followed in the present study.
The definitions of small-scale industrial units and ancillary industrial units have undergone several changes in the past. The Government, as they claim, had to make these changes mainly keeping in view the price escalation over the past years. Small-scale industries have been defined in terms of the upper ceiling of investment in plant and machinery (original value) alone since 1966. The investment ceiling for plant and
12 Chapter 1 machinery (original value) which was then fixed at Rs. 7.5 lakhs (Rs. 1 lakh = Rs. 100,000) in the case of SSI units and Rs. 10 lakhs for ancillary units has been revised upwards several times thereafter as detailed below (see Table 1.6):
Table 1.6: Investment Ceiling for Plant & Machinery (Rs. in Lakhs)
Description 1966 1975 1980 1985 1991 1999
Small-scale industries 7.5 10 20 35 60 100** Ancillary industries 10 15 25 45 75 100** Export oriented small- - - - - 75 *** scale industries Tiny enterprises - - 2* 2* 5 25 Small-scale service & - - - - 5 10 business (industry related) enterprises Notes: * Located in rural areas and towns with maximum population of 5 lakhs (i.e. 500,000); ** In 2001, the Government of India has enhanced the investment ceiling from 100 lakhs (i.e. 1 crore) to 500 lakhs (i.e. 5 crores) for those small- scale industries and ancillary industries who are engaged in manufacturing of 41 specified items. *** All small-scale units which export more than 50% of their output are classified as export oriented units. Source: Report on the Second All-India Census of Small-Scale Industrial Units and the official website of the Ministry of Small-Scale Industries, Government of India.
The most recent definitions of small-scale units formulated by the Government of India in 1999 need some more clarifications which are stated below.
1.6.1 Small-Scale Industries:
A small-scale industrial unit is an industrial undertaking in which the investment in fixed assets of plant and machinery,6 whether held on ownership term or on lease or by hire- purchase, does not exceed Rs. 100 lakhs (i.e. 1 crore).
1.6.2 Ancillary Industrial Units:
13 Introduction
An ancillary industrial unit is an industrial undertaking which is engaged or is proposed to be engaged in the manufacture or production of parts, components, sub-assemblies, tooling or intermediates or rendering of services and the undertaking which supplies or renders or proposes to supply or render not more than 50 per cent of its production or services, as the case may, to one or more other industrial undertakings and whose investment in fixed assets in plant and machinery, whether held on ownership terms or on lease or on hire purchase, does not exceed Rs. 100 lakhs (i.e. 1 crore).
[Please note that, in 2001, the Government of India has again enhanced the investment ceiling from 100 lakhs (i.e. 1 crore) to 500 lakhs (i.e. 5 crores) for those small-scale industries and ancillary industries who are engaged in manufacturing of 41 specified items.]
1.6.3 Export oriented small-scale industries:
All small-scale units which export more than 50 per cent of their output are classified as export oriented units.
1.6.4 Tiny Enterprises:
The “tiny” concept was introduced in 1977. As a follow up on “Policy Measures for Promoting and Strengthening Small, Tiny and Village Enterprises” laid in the Parliament on 6 August, 1991 the limit for tiny enterprises was enhanced from Rs. 2 lakhs to Rs. 5 lakhs, irrespective of the location of the unit. In 1999, the definition has been changed again. All small-scale units where investment on plant and machinery (excluding land and building) is upto Rs. 25 lakhs are classified as tiny industries.
6 This does not include the fixed assets of land and building.
14 Chapter 1
1.6.5 Industry Related Small-Scale Service & Business Enterprises (SSSBE):
Industry related service/business enterprises with investment on plant and machinery (excluding land and building) upto Rs. 10 lakhs are entitled to be registered under SSSBE.
1.7 What is Misleading?
It should be noted here that the Government of India has clearly indicated that no small- scale or ancillary industrial undertaking referred to above shall be subsidiary of or owned or controlled by any other industrial undertakings. But, in a study on India’s small-scale industries, Taub and Taub (1989: 17) observed that an indeterminate number of enterprises was included in counting that formally met all the criteria for a small-scale industry but did not fit the larger conception of small-scale units as independent entrepreneurship in action. These were the industries established by large-scale manufacturers as, in effect, wholly-owned subsidiaries of the larger concern. Manufacturers set these units up because they could then take advantage of the some of the benefits available to small-scale units, such as more liberal labour laws and tax advantages. They did not, however, represent a new or additional contribution to the industrial economy, and in that sense their inclusion in statistics on small-scale sector growth, in numbers, output, or whatever, is misleading. Therefore, following this argument, the governmental claim which states that the small-scale sector presently accounts for 40 per cent of the industrial production of the national economy seems to be exaggerating and misleading.
In addition, the associations or organisations of these kinds of small-scale industrial units have very powerful lobbies in the central government. Therefore, they are very strong in influencing the government to formulate the definitions of small-scale industries, i.e. to increase the investment ceiling, in their favour so that they can run their de facto large- or medium-scale units under the purview of small-scale definition in order to exploit all the
15 Introduction governmental benefits given to small-scale sector. As a result, having been very poorly organised and due to having very weak influence on the governmental policy making body, the real small-scale sector or the tiny sector has failed to be the beneficiary of the small-scale industrial policy.
1.8 Definition of Rural Industry
The term ‘rural industry’ (or ‘rural industrialisation’) is a confusing term to define. There is no particular definition. It is also difficult to differentiate between rural and non-rural industries. The term ‘rural industry’ is often considered to be synonymous with cottage industries (which constitute household based petty production activities) and, consequently, ‘rural industrialisation’ with the development and promotion of cottage industries. This view would, however, be too simplistic and narrow (Islam, 1987). Currently, rural industries do not necessarily mean only cottage industries. Manufacturing enterprises using modern machines and tools can also be regarded as rural industries. It is also not necessary that the rural industries must have a direct link with agriculture. Diversification of the rural economy through the introduction and promotion of small- scale manufacturing enterprises (but not necessarily cottage-based ones only) should essentially mean rural industrialisation (Islam, 1987). But the size of the industries is important which means it should be of small-scale type (we will define this ‘small-scale’ notion of rural industry in our own definition presented in the next section). Saith (1991) has argued that no unique and universally appropriate definition can be elicited from the diverse experiences of ‘rural industrialisation’ in the process of economic development.
Often we use the terms like rural non-agricultural activity or rural non-farm activity. Broadly speaking, non-farm or non-agricultural activities can include all those activities which are undertaken outside agriculture. In this sense, rural non-agricultural activity is essentially a residual category (Basant and Kumar, 1989). Unni (1991) defines that non- agricultural activities include all economic activities other than crop production and allied agricultural activities such as animal husbandry, plantations, fishing, forestry etc. They
16 Chapter 1 therefore include agricultural processing and trade (conventionally classified as part of the manufacturing and commerce sectors, respectively), as well as construction, mining, transport, and financial and personal services (Rosegrant and Hazell, 2000). Saith (1992) distinguishes between rural industrial sector and rural non-agricultural sector. According to him, the rural industrial sector constitutes one part of the rural non-agricultural sector. The latter also includes various services, household based petty production activities and non-agricultural labour, which in turn includes work on rural public works programmes and creation of public infrastructure.
In a study on rural industry, UNDP et al (1988) have defined micro-enterprises with 0-4 employees and small enterprises with 5-25 employees, located in the countryside and in villages and towns as rural industry. The study has included manufacturing (the transformation of materials into finished or intermediate physical products) and a few activities, such as metalworking repair shops (which use much the same equipment and skills as their counterparts in pure manufacturing). Production and repairs are often carried out by the same firm. It seems that processing of agricultural products like jam- jelly-pickle making is included into the definition formulated by UNDP et al, but drying of raw agricultural produce—e.g., of grains and tobacco—has been categorically excluded from the their study.
The location factor is also important. The enterprises should be based in rural areas on the one hand, and the process of rural industrialisation has to involve the rural people, either as labourers or as entrepreneurs, on the other. The United Nations defines the term ‘rural’ to include locations with up to 20,000 population, the practical definition varies from country to country. In most cases, some urban industries are also considered by the scholars and policy makers as rural industries. They ignore the fine border between a rural and an urban area. The argument is: “Where transport infrastructure and the marketing and trade network are well-developed, or where general urbanisation is very marked, larger urban areas may be regarded as locations for rural industries, as long as such areas provide a comparable environment to small towns” (UNDP et al, 1988: 12). Often newly settled urban areas (shanty towns) retain many of the rural characteristics of
17 Introduction the migrants from countryside. Small enterprises in such environments should also be considered as rural industries, irrespective of absolute population figures. Islam (1987: 3) argues that “if people in villages have access to employment opportunities available in nearby small rural towns or market centres (or the so-called ‘rural growth centres’, as they are often termed), such locations should also be covered by the term rural, in the present context”. According to Rosegrant and Hazell (2000), rural can be defined as any locality that exists primarily to serve an agricultural hinterland. They argue that, in contrast, urban economies are driven by manufacturing, government or some other economic base independent of agriculture. Given this view, rural areas include all the rural settlements, central market places and towns that are linked together through economic transactions related to the agricultural economy. In this connection, we can refer to Saith (1991) who has emphasized more on the linkage approach than the locational approach. According to him, there could be several types of industries which though located in the smaller urban centres nevertheless display exceedingly high rural linkages, either through a high dependence on rural labour and/or rural raw materials within production processes which are labour and raw-material intensive.
Another concept is ‘village industry’ which is nothing but a quite similar concept to rural industry. The term has got a distinct identity in India since it is pronounced with Mahatma Gandhi’s ideology of economic self-dependence. ‘Village industries’ attracted Mahatma Gandhi’s attention in 1935. At that point of time, there was no specific definition given to village industries. The industries in rural areas processing local raw material for local markets with simple techniques and equipment were categorised under ‘village industries’. After independence, the Khadi and Village Industries Commission (KVIC) was set up by an Act of Parliament in 1956 to foster the development of khadi7 and village industries in rural areas. After that this Act has been amended quite a few times. Village industry in the amended Act of 1987 has been defined as follows:
7 Khadi is an indigenous textile in India. The discovery of charka (spinning wheel) was EUREKA for Mahatma Gandhi who made it an integral part of the freedom movement and later inspired the khadi movement establishing All India Spinners Association. After independence, the Government of India brought khadi and village industries into the mainstream of planning process integrating them with the overall economic development.
18 Chapter 1
“Village industry shall mean an industry which is located in a place with a population not exceeding 10,000 (or such other figure as may be prescribed from time to time) which produces goods and services, with or without the use of electric power, and in which the fixed capital investment per head of artisan/worker does not exceed Rs. 15,000 or such other sum as may be prescribed from time to time.
However, any non-manufacturing facility or unit that may be located in place with population exceeding 10,000 in connection with the sole purpose of promoting, maintaining, assisting (including mother units) or managing a village industry as defined, shall be deemed to be a village industry” (KVIC Report, 1994: 37).
1.9 The Definition Used in the Present Study
In the present study, only manufacturing enterprises are considered. In the literature review, presented above regarding the definitional aspect, we have found four criteria (viz. technology, investment, personnel, and location) that help in defining rural enterprise. Let us now set our definition of rural enterprise considering these four criteria.
● Technology: By technology, we mean the production technique used in the non-farm enterprise. It is broadly divided into two categories—one is primitive and the other is modern. Manually produced goods as well as products produced by hand-made-machines are called to be the products of primitive technology. Things produced by machine-made-machines are called to be the products of modern technology.
● Investment:
19 Introduction
For investment criterion, we follow the Government of India’s definition for tiny enterprises, formulated in 1999 (see Table 1.6 and the corresponding description in this chapter).
● Personnel: This criterion is not strict at all. Any number of employees will be considered.
● Location: As regards location factor, we include village, rural retail market, rural wholesale market, and small rural town in the panchayat area. We exclude only municipality area of the district.
1.10 The Objective of the Study and the Research Question
Absence or presence of entrepreneurship is generally caused by economic conditions on the one hand, and the personal characteristics and family environment as well as the psycho-socio-cultural realities on the other. These conditions or realities vary from region to region and from community to community. In the present study, we are concerned with rural socio-economic conditions of West Bengal. The core purpose of the study is to examine a micro issue i.e. whether or not an individual farmer opts for non-agricultural manufacturing entrepreneurship. We now furnish below the specific objectives of this study:
• We are to examine personal characteristics of the farmer, which influence on his willingness of non-farm entrepreneurship; • We are to understand the influence of economic factors (including financial family support) of the farmer on his willingness to non-farm entrepreneurship; • We are to examine the influence of the farmer’s agricultural/occupational background on his willingness to accept diversified, industrial activity as a profession;
20 Chapter 1
• We are to perceive the influence of political factor on the farmer’s interest towards non-farm entrepreneurship; • We are to see the impact of the psycho-socio-cultural factors on the willingness of the farmer to be a non-farm entrepreneur; • We are to understand the influence of the factors relating to risk and innovation on the farmer’s willingness towards non-farm entrepreneurship.
In this perspective, the main research question that this study poses is:
What are the determinants of non-farm manufacturing entrepreneurship of farmers?
1.11 The Need for the Promotion of Rural Industrial Entrepreneurship (or rural industrialisation): Significance of the Study
Employment generation in rural areas is very important. Government’s popular programmes, which do not have deep-rooted vision of development, cannot reap fruit of sustainable growth. Encouraging policies need to be adopted to channel local capital from agriculture to non-agriculture, given the fact that free labour is ready to be another factor of production. What is needed is to encourage non-farm entrepreneurship among the rural land-owning farmers, because this group constitutes the rural capitalists who can afford to invest into new (diversified) activities and generate employment for the rural poor. Thus, the issue of rural industrial entrepreneurship for the growth of rural non-farm activities and for generating employment provisions assumes special significance. Let us go into more detail.
It is known to us that the most of the developing countries are predominantly agrarian in nature and at the same time it is also known to us that the scope of labour absorption in agriculture is limited (Dunham, 1991: 18; J. Harriss, 1991). So far agriculture and allied activities have been the major sources of employment in the rural areas in India, providing 77 per cent of the total rural employment. But with the declining capacity of
21 Introduction agriculture to absorb labour, the problem of unemployment will persist unless non- agricultural sources of employment are found. Many researchers have noted that rising agricultural production is no longer followed by increases in the demand for labour (for example, Vyas, 1985; Lanjouw and Lanjouw, 1995). The creation of non-farm jobs in rural areas on a large enough scale is possible only by promoting rural industries.
On the other hand, the observable trends over the past few decades in the less developed countries suggest that the industrial sector has been unable to generate employment growth at a rate which can make any appreciable impact on the high levels of unemployment. This is due to the high capital intensity of modern industrial production techniques. Large enterprises are not able to produce adequate spread effects, either in terms of the number of people benefiting from them or in terms of the geographical area covered. Large undertakings are generally concentrated in a few ideal locations and they lead to highly polarized development. They also tend to appropriate a major share of developmental facilities, public economic services, fiscal and financial incentives, etc.
An industrialisation strategy guided by the goal of meeting the needs of the rural poor not only leads to a different composition of products and of techniques, but it also contributes to stopping the drift to the cities. In this sense, rural industrialisation is also relevant for the urban centres, because any effort to treat urban poverty (which is also substantial) without tackling rural poverty is destined to be overwhelmed by increased migration. By raising the level of living of the poor people in the countryside, it reduces the pressure to leave the farmsteads and to expand urban expensive services.
With a few notable exceptions, until two decades ago, little attention was paid to rural industrialisation and rural non-farm activities except in the form of a few passing references to it in the planning exercises and plan documents of most countries. Of late, however, rural industrialisation has become a “fast-moving bandwagon” on which most developing countries think they can hitch a cheap ride to rural development. One of the factors underlying the bandwagon effect, with development planners now suddenly regarding the rural non-agricultural sector as a panacea, has been the massive projection
22 Chapter 1 of the successful, even if exceptional, cases: China and the four (industrialising) East Asian countries. In India, the role of rural industries was underscored by two major contending development paradigms proposed in the early post-independence period. The Gandhian ideology of development gave pride of place to villages and village industries. The ideological and economic essence of this approach was one that was totally rejected by the mainstream Nehru-Mahalanobis ideology of rapid industrialisation based on an accelerated expansion of the modern heavy industries sector.8 But the new-found emphasis has also to do with an increasing awareness of the relative failure of the previous industrialisation-oriented development strategies to alleviate the problems of rural poverty and underemployment, as well as an increasing acquiescene to the narrower political constraints within which future labour-oriented poverty alleviation strategies can be adopted. The following discussion appears to be of some importance at the present conjecture.
Observable trends over the past few decades clearly reveal a worsening in the employment balance in the developing economies. Rates of employment growth—say, at average levels of labour productivity—have fallen far short of the rates of labour force growth. In so far as high (and in many poor countries, rising) growth rates of population, as well as increasing participation rates (especially in countries where the female participation rate had been low to begin with), are likely to be relatively permanent features, the prognosis with regard to the balance is also bleak. On the one hand, the rate of labour transfer into the modern industrial and service sectors has been uniformly low, and in many countries has been unable to fully absorb the net additions to the urban workforce arising from natural increase. This is due partly to the low rates of industrialisation, but is also the result of the high capital intensity of such industrial
8 We can get an idea of Mahalanobis’s (1968) development ideology from one of his articles, where he wrote: “We must be clear….that no radical cure of unemployment and underemployment would be possible without a rapid growth of modern industries. We must produce an increasing quantity of steel every year. We must produce increasing quantities of heavy machinery and electrical equipment. More and more goods would be then produced by increasing utilization of our domestic resources. To do this, we must, of course, steadily and rapidly increase our domestic savings.”
23 Introduction
growth as has taken place. Hence, any notion that industrial expansion will soon siphon off the rural backlog of unemployment and underemployment seems patently unrealistic.
On the other hand, the labour absorptive capacity of agricultural intensification strategies has also proved to be quite limited. In the Asian group, with the exception of Thailand, there are few possibilities of pushing out the cultivation frontier much, and additional irrigation—necessary for raising the multiple cropping intensity—is becoming increasingly expensive in terms of resources. Concomitant agrarian differentiation and structural shifts induced by capital-oriented patterns of technological change have aggravated the situation by intensifying ongoing process of rural marginalisation and proletarianisation of resource-poor rural classes and communities. Indeed, if the Green Revolution were to continue according to current trends, a net loss of employment per hectare could occur once farm mechanization extends to such crucial agricultural operations as harvesting on a wider scale. In view of the declining real earnings of workers, and widening income disparities in town and countryside, the pessimistic scenario acquires a keen political edge from the point of view of governments. In this context, the rural non-farm sector expected to provide cheap jobs for the rural poor.
The developmental rationale and potential role of rural industries are much wider. Though the immediate objective assigned to rural industries is usually rural employment generation, focusing on the landless poor, rural industries can achieve a good deal more. A set of six arguments needs to be mentioned (Saith, 1992).
1. By taking industry to the villages, large-scale rural out-migration can be checked. This reduces the pressure on scarce urban housing, and infrastructural and other services. By the same token, wage costs are lower in rural areas where the marginal costs of migration, town housing and transport, and higher living expenses do not have to be incurred.
2. To the extent that additional employment is created in towns, matching flows of marketed surplus of food grains have to be ensured (assuming rising total real earnings), whereas
24 Chapter 1
extensive rural industrialisation based on the employment of surplus rural labour bypasses the need to induce and manage such transfers. Usually, imperfect or inadequate marketing of food grains leads to increases in food prices which then erode the real entitlements of other poor classes.
3. Rural industrialisation could stop the skill drain from the countryside if it provided a sufficiently lucrative alternative for employment as workers or owner-managers. New rural enterprises could simultaneously generate modern skills in the rural workforce. A parallel argument could be extended to rural investible surpluses, which could be absorbed directly in local income generation instead of being siphoned off through the banking system, lying idle or circulating in mostly non-productive local money-lending circuits.
4. Rural industries could utilize local “slack” resources which are not capable of being used in urban, modern industry. The social cost of such raw materials is extremely low, and the benefits quite high, as in the case of various waste-recycling activities.
5. At a wider level, rural industrialisation can lead an economy to a greater degree of regional and sectoral equality and decentralisation. This aspect might be quite important in systems characterised by geo-natural obstacles to economic integration and where the transport networks are weak or expensive. Here, rural industries could be the agent of localized growth that is not overly dependent on fragile and unpredictable links to the (distant) industrial centre.
6. Rural industrialisation could have significant spin-offs for agricultural development as well. The extension and deepening of the skill profile of the rural workforce is one extremely important factor. Another is the interdependent relationship that rural industrialisation has with the creation of new rural infrastructure, which in turn raises agricultural productivity, e.g. through better roads, canals, storage facilities and so on. These activities are typically best executed on the basis of local knowledge and felt need. Again, almost everywhere, a major element of rural industrialisation is a set of activities
25 Introduction which support agricultural production directly, e.g. the maintenance, repair and progressive improvement of farming and agricultural processing implements. Local small-scale fertilizer plants, brick kilns and stone quarries, local irrigation and power generation schemes also fall into this category, whose benefits deriving are augmented through the dynamic backward and forward linkages generated by such local intersectoral relationships.
This comprehensive listing of the potential roles of rural industrialization explains why it is treated as some sort of panacea in the present debates on strategic reorientations. A note of caution might therefore be in order. It would be quite unwarranted to assume any automaticity in the realization of what are at best the potential benefits of rural industrialisation. As a process, it does not come in the form of a standardized package. A question central to the later part is the identification of conditions which promote or induce entrepreneurship in rural industrial sector. More specifically, identification of the determinants of non-farm entrepreneurship of the land-owning farmers has got major focus in the present study.
1.12 Organisation of the Study
The organisation of the whole study is as follows. Chapter 2 broadly describes the state of West Bengal as the study area. In the latter part, the chapter goes down to Bardhaman district of the state. It gives a brief history of entrepreneurial background of Bengal. It provides description of the state’s agricultural and industrial background. It emphasizes the state’s rural industrialisation as well as urbanisation. Chapter 3 of this book extensively reviews the literature in the field of rural industry. The discussion considerably covers the issue of linkage between agriculture and industry and presents the linkage debate. The chapter incorporates categorization of the non-farm sector. It points out very briefly the Japanese experience of transition. Amongst other issues, the chapter emphasizes the role of urbanisation and development of rural infrastructure in promoting rural industrialisation. Chapter 4 deals with extensive literature pertaining to
26 Chapter 1 the theory of entrepreneurship. It looks at entrepreneurship from the cultural point of view. Chapter 5 presents the story about data collection for this study. Chapter 6 firstly presents the theoretical considerations of the empirical model and secondly analyses data through the LISREL model.9 Chapter 7 presents the conclusions, summary and the recommendations for policy and further research.
9 The full form of LISREL is LInear Structural RELations.
27 Introduction
28 Chapter 2
The Study Area
2.1 Introduction
Our study area is Bardhaman district of West Bengal state in India. India was and still remains to be a vast country. A general discussion on India as a whole cannot give a true picture of the country. In terms of geography, climate, people, culture, and so many other aspects, its different parts are of so diverse nature that a common overall assessment of a particular theme of the country may not provide a complete scene of the play that is happening across the regions or states of India. It may call for a disaggregative level analysis—at least a statewise analysis. But a statewise full analysis of the nation may form such a huge volume that it may take a shape of an epic. We restrict our discussions in the area of the state of West Bengal as well as Bardhaman district. Before doing so, we present a very brief background of European and Indian entrepreneurship in India before independence.
The base of the Indian industry was not so strong since the country had been ruled by the foreign rulers for about two hundred years. “British paramountcy in India in 1900 was very much in evidence. The unchallenged political supremacy of the British favoured, among other things, the exercise of economic power by Europeans resident in India who derived special advantages from linguistic and racial identification with the rulers of India…. One important industry where government patronage was crucial and where Indians were rarely to be found was the engineering industry. Large government contracts for construction and engineering were rarely, if ever, given to Indian firms. Since engineering firms in a poor economy with little industry had to depend mainly on contracts placed by public authorities, there were practically no large Indian firms” (Bagchi, 1970). The Study Area
The European firms did not bring capital from Europe to India. The inflow of foreign private capital into India was roughly zero. The European firms mostly gathered their capital from their earnings in trade, industry, banking, and employment in the army or various government departments in India. Most of the big managing agency houses made their money in trade or as labour contractors. Before the First World War, many Indians from the professional classes had entered the field of industry but, according to Bagchi (1970), industry which was not backed by a large trading and financing organisation was not destined to succeed. Given such an industrial background during the British rule, India started its independent journey in 1947. As we have mentioned earlier, we will restrict our following discussions in West Bengal and then Bardhaman district, not on whole India.
2.2 Murshidabad: starting place of British regime
Why do we start from Murshidabad? The place of Murshidabad in West Bengal assumes historical importance. History of British India started from the place of Murshidabad in Bengal when, through the battle of Plassey in 1757, Robert Clive occupied the place. Murshidabad is a small village located towards north of Kolkata (formerly known as Calcutta) and can be reached after a six-hour train ride. The town was formerly the capital of Bengal. It attained its prosperity by being the centre of silk production as well as the seat of government administration. Those visiting the town will find a number of temples near the place where the Marwaris, residents originally of western Rajasthan (a state of India), used to carry out their commercial activities. These temples were built by the Marwaris themselves. Robert Clive wrote a very interesting account of the town as it was after Plassey battle of 1757:
“The town of Murshidabad was as large as London. The number of its population was large and the inhabitants were rich. Some of them were wealthier than that of London. If they really wanted to expel the
30 Chapter 2
Europeans living there, they could have done it at any time they wished” (see Noboru, 1970: 13).
The silks produced in Murshidabad came to the attention of the British East India Company in 1656, and resulted in its introduction into the European market. Thereafter, silk factories sprung up around the town of Kashimbazar. The Dutch and French immigrants also took part in the movement which eventually converted Murshidabad into a base area for the European traders-turned-powers to carry on their colonial activities. The town prospered for more than 200 years as one of the few commercial centres in East India and became the focal point where the Marwari community built their foundations.
This town which used to enjoy an unprecedented prosperity in the past has become a calm village today. The town itself has gradually collapsed and the industry has declined. The silk factories which had once produced one of the principal export products have disappeared from the town. Its place has been succeeded by the handicraft industry of the village. Moreover, with the loss of its position as the seat of government administration, the handicraft industry which processed ivory products destined for use in the palace is no longer in existence today. Indeed, the prosperity and decline of Murshidabad represents one of the most significant chapters in the history of India. The colonial rule of the British deprived its chance of growing into a large city. The town which was comparable to London in the past has turned into a rural village.
2.3 Bengal in retrospect
Boyce (1987: 3) wrote: “Early travellers to Bengal were struck by its great prosperity. Six centuries ago the Moroccan explorer Ibn Battuta, whose travels took him to Persia, China, Sumatra, and Timbuktu, reported: ‘This is a country of great extent, and one in which rice is extremely abundant. Indeed, I have seen no region of the earth where provisions are so plentiful’ (Yule, 1866: 457).” The French traveller Bernier, who visited Bengal around 1660, recorded similar impressions:
31 The Study Area
“Egypt has been represented in every age as the finest and most fruitful country in the world, and even our modern writers deny that there is any other land so peculiarly favoured by nature; but the knowledge I have acquired of Bengale, during two visits paid to that kingdom, inclines me to believe that the pre-eminence ascribed to Egypt is rather due to Bengale. The latter country produces rice in such abundance that it supplies not only the neighbouring but remote states…. Bengale abounds likewise in sugar…. The three or four sorts of vegetables which, together with rice and butter, form the chief food of the common people, are purchased for the merest trifle, and for single roupie twenty or more good fowls may be bought. Geese and ducks are proportionately cheap. There are also goats and sheep in abundance…. Fish of every species, whether fresh or salt, is of the same profusion. In a word, Bengale abounds with every necessary of life” (Bernier, 1914: 437-9).
Bernier (1914: 439) also described Bengal’s handloom textiles, which then ranked among the world’s greatest industries: “There is in Bengale such a quantity of cotton and silks, that the kingdom may be called the common storehouse for those two kinds of merchandise, not of Hindoustan or the Empire of the Great Mogol only, but of all the neighbouring kingdoms, and even of Europe.” In 1790 Lord Cornwallis, the Governor- General of Bengal appointed by the British East India Company, also paid tribute to the land’s fertility: “We have, by a train of the most fortunate events, obtained the dominion of one of the most fertile countries on the face of the globe, with a population of mild and industrious inhabitants, perhaps equal to, if not exceeding in number, that of all the other British possessions put together” (Firminger, 1917: 542).
The British East India Company was attracted to Bengal above all by the prospect of a monopoly over the lucrative trade in Bengali textiles. With the development of a mechanised textile industry in Britain, however, the British rulers sought to eliminate Bengali competition by means of trade restrictions and the imposition of prohibitive
32 Chapter 2 duties. Not only were Bengali textiles shut out of the British market, but even within India taxes discriminated against local cloth (Lamb, 1955: 468; Sinha, 1970: 11-12, 41- 3). As a result, while industry developed in Britain, it withered in Bengal. The weavers were thrown back on to the land.
The decline of cotton was followed by the rise of the cultivation of jute, the world’s second most important natural fibre, which is used to make rope, Hessian, and carpet backing. In 1947, with the end of British rule in India, the province of Bengal was partitioned between India and Pakistan. East Bengal went to Pakistan with a new name East Pakistan1 (which became independent in 1971 as a sovereign country entitled Bangladesh) and West Bengal fell into the part of India. Spate (1954) has cited West Bengal’s disproportionate urban concentration and food deficit, voicing the accepted wisdom that West Bengal’s agriculture was qualitatively as well as quantitatively inferior to that of East Bengal (now Bangladesh). But, afterwards, according to Boyce (1987), West Bengal’s agriculture no longer seems markedly inferior to that of its eastern neighbour, but Spate’s verdict may well be endorsed by future historians. We will come to this discussion later.
2.4 A Brief History of Bengali Entrepreneurship
Indeed, it was in and around Kolkata that the beginnings of the industrial age in India were seen. Kolkata has a tradition of establishment of industries older than in Mumbai (formerly known as Bombay). The jute industry, perhaps the oldest and largest in terms of generation of employment and export earnings in pre-independence years, was (and continues to be) located in a large number of small towns situated up to 60 kilometres north of Kolkata along the river Hooghly. Kolkata and its sister city Howrah were (and are) home also to a large steel fabricating industry manufacturing bridges, coaches, wagons and other equipments for the railways, mining equipment and machine tools besides repair of ships (Nath, 2000). But there is now a sense of dismay at the depths to
1 East Pakistan was unofficially called as East Bengal.
33 The Study Area which Kolkata and Eastern India have fallen, compared to the growth and prosperity that have touched other regions of India since independence in 1947. The economy of Bengal as well as the rest of India has been in transition since the imposition of British rule over the subcontinent in the later half of the eighteenth century. This transitional history has been well-summarised by Gupta (1991). Earlier, the Dutch had come to Pipli in the province of Orissa in 1627; by 1655, they were trading both at Hooghly and Balasore in Bengal. The French came to India in 1688, when they founded the trading post of Chandernagore, Bengal, in pursuit of Colbert’s decision to establish French trading posts on the mainland of India. French trade with India reached impressive heights with the appointment of Dupleix as governor of Chandernagore in 1732. Chandernagore, which did not have a single ship in 1732, is reported to have “possessed fifteen or twenty vessels in daily use by the Company’s employees when he [Dupleix] left in 1742” (Sinha, 1965: 35-36).
The British, however, outstripped the French and the Dutch. A series of treaties between the British East India Company and the successive Nawabs of Bengal gradually transferred political powers and functions into the hands of the Company. From then onwards European monopoly in industrial activities prevailed in Bengal. It was early in the twentieth century when a movement started whose motive was to break the system of foreign monopoly capitalism and to enable the educated Bengalis to break out of the narrow confines of service and professions into the wider fields of commercial and industrial enterprise. The year 1905 saw the beginnings of the “swadeshi” movement— ostensibly a protest against Curzon’s decision to partition Bengal—whose principal objective was to boycott the use of British goods and develop reliance on indigenous products. Bengalis wanted to escape the narrow sector (narrow in the sense that it excluded industrial and business activities) of the economy to which they had been confined:
“Living on an inelastic income derived partly from landed property and partly from clerical service in government and mercantile offices, the majority of the educated Bengalis found their economic horizon extremely limited. They were
34 Chapter 2
eager to enter the world of business and industry, but lack of capital stood in their way. The big landlords and the successful lawyers and doctors who were in a position to supply the capital were reluctant to put money into the hands of young and untrained persons, and even a man with some training found it difficult to raise sufficient capital, partly because of the poverty of the people and partly because of the fear of powerful foreign competition. The fear was real and was bred from experience. Rabindranath’s brother, Jyotirindranath Tagore, started an Inland River Steam Navigation Service in 1884 to break the exclusive European monopoly of river navigation. His swadeshi venture, however, collapsed from the unequal and unfair competition which he faced from the Flotilla Company managed by the Hoare Miller group. This pathetic yet magnificent gesture by the grandson of Dwarkanath Tagore taught educated Bengalis the lesson that swadeshi ventures were extremely likely to end in debt and financial ruin” (Ray, 1984: 150-151).
The Bengali entrepreneurs did not succeed in their efforts, but the year of 1914 was provided something of a watershed in the realignment of economic power on the Indian subcontinent. In the business world of Kolkata, one saw a remarkable breakthrough for Indian capital. In Kolkata, the investments of Indians rose by 1,609.04 per cent between 1914 and 1922. According to Gupta (1991), European businessmen felt intimidated by the disappearance of the favourable pre-war political climate and by the increasing transfer of power to Indians through constitutional reform. They quickly abandoned their long term policies of reinvesting profits earned in India and began to transfer them to England as quickly as possible.
This was the time when, according to Gupta (1991), Marwaris emerged in Bengal as a leading business community (see also Lamb, 1955). Gupta stated that Indian businessmen, especially Marwaris, had amassed enormous profits during the war. Actually, from the middle of the nineteenth century, migrating Oswal, Agarwal, and Maheshwari traders from Rajasthan in Western India had begun to settle in Kolkata and displaced the Bengalis as the principal collaborators of the expanding British companies.
35 The Study Area
These businessmen kept to their traditional trading activities during the war. After the war, flush with capital, they aggressively began to finance new ventures. Unlike the swadeshi period of a decade earlier, shares in companies were fully subscribed in a very short time. Even Bengali businessmen began to make some headway, especially in such areas as tannery and leather works, chemicals and pharmaceuticals, pottery, cement, and coal mines.
Nath (2000) describes that the jute and steel fabrication industries as well as the tea gardens of Darjeeling and Assam were owned before independence by the Scots. Tea is one of the largest exports of India and Kolkata is the principal packaging and export centre. After independence, the Scots’ place has been taken not by the Bengalis but mainly by the Marwaris. The able and affluent Bengali young men were and continue to be content with becoming executives, lawyers, physicians, scientists and other professionals. But with the exception of the Indian Iron and Steel Company, established by Biren Mukherjee, and some companies manufacturing pharmaceuticals and toiletries no large industrial enterprise was established by a Bengali.
In Bengal, as Kling (1967) points out, the Marwaris and other non-Bengali business castes have dominated modern manufacturing and trade. As a result of the lack of economic opportunity in Rajasthan for the Marwaris and the lack of indigenous entrepreneurship in Bengal, the Marwaris began entering key entrepreneurial positions in trade and finance in Bengal, especially in Kolkata, in the nineteenth century. Kling focuses on the antipathy toward business activity by Bengalis. The “Bengali middle classes were averse to trade and industry and preferred the liberal professions” (Bagchi, 1970: 240). The lack of indigenous entrepreneurship in Bengal stems in part from the discrimination and duplicity of the British in the nineteenth century and the low esteem (of Bengalis) placed on business occupations. In the early nineteenth century, participation of Bengalis in activities competitive with the British, such as international trade, resulted in the subsequent exclusion of Bengalis from modern business. In a number of cases in the 1880s, Bengali firms with established reputations were defrauded by British partners, an action that led many Bengali businessmen to retreat from trade and
36 Chapter 2 commerce. In addition, although Bengalis admired the boldness and daring of the ancient romantic merchant-prince and the modern industrialist, they placed a low value on the profession of traditional businessmen such as the petty trader. The Bengali business classes, having spurned traditional business and being excluded from modern business, could find no middle ground suitable for their talents. This lack of participation in traditional business virtually precluded upward mobility to modern business.
2.5 Agriculture in West Bengal
In a book on agricultural growth in West Bengal and Bangladesh, Boyce (1987) documented the slow growth of agricultural output in West Bengal (and Bangladesh) between the 1950s and end of 1970s. According to his conclusions, the simple exponential growth rate of agricultural output in West Bengal over the period 1949-80 was 1.74 per cent, far below the rate of population growth. Using a slightly different method, he estimated the rates of growth of aggregate agricultural output for the state as 1.42 per cent and 2.25 per cent for the subperiods 1949-64 and 1965-80 respectively. The scenario started changing from 1980s. Let us now turn into the agricultural growth debate of West Bengal.
2.5.1 The growth debate
The West Bengal agriculture started experiencing high growth since the early 1980s. Harriss (1992) observes that a quite dramatic spurt in agricultural production marks the end of ‘agrarian impasse’ of West Bengal. The reason he has marked to the remarkable output growth is the development of private shallow tubewell irrigation. According to him, all this has taken place in the absence of any reform of the agrarian structure. He asserts that the development in agriculture is an outcome of some growth in suitable technology and the reverse situation of the previous kind of extremely unfavourable fertiliser-paddy price ratio. However, Harriss has received counterattack from Sanyal et
37 The Study Area al (1998) who found difficulty in agreeing with the attempt of playing down the role of institutional intervention through land reform measures in accelerating agricultural production in West Bengal in recent times. They held the land reform measures, initiated by the Left Front government, responsible the agricultural growth. “Agriculture in West Bengal was, for the first time, poised for a change when the Left front rule was established in 1977. ‘Operation Barga’ assumed the dimension of a movement in the countryside within a few months of its launching in October 1978. Quick recordings of the names of bargadars and granting legal rights to cultivate land was a major incentive for the marginal and small peasants to raise production. Provisions were also made for institutional credit and subsidies to the sharecroppers and assignees of the vested land to remove their dependency on landlords or money lenders. The impact of these provisions was felt in agricultural investments and output growth in the 1980s” (p. 2979).
Another dimension, which is the most vibrant one, has been added to the debate centring the ‘high growth’ in agriculture in West Bengal—especially concerning the method of calculating the growth rate and the reasons behind the occurrence of such growth. In the following, we are going to try to present the core of the debate.
Using an index number series on aggregate agricultural production, the exponential growth rate for all West Bengal for the period 1981-82 to 1990-91 was an impressive 6.4 per cent per annum (Saha and Swaminathan, 1994). The growth performance is remarkable in comparison to the annual growth rate of agricultural output for the period 1965-80 which was estimated by Boyce (1987: 68) and found to be 2.2 per cent. Some of the notable features of the districtwise pattern of growth in production, as estimated and described by Saha and Swaminathan (1994), are as follows (see Table 2.1). With the exception of Darjeeling and Jalpaiguri, the index of aggregate crop production grew by over 5 per cent annually in all the districts of West Bengal. By any standard, and particularly that of the performance of preceding years, this performance is exceptional. Bankura and Birbhum were ranked at the top with growth rates of 9.5 and 9.3 per cent respectively. Midnapur and Purulia followed with growth rates of 8.5 and 8.4 per cent. Next in rank were Nadia and Howrah. In these six districts, growth rates were higher than
38 Chapter 2 the state average. In relation to growth in 1965-80, there has been a tremendous increase in the growth rate of total output in almost all the districts.
In general, as during 1965-80, the high growth districts are those in the Gangetic plains of West Bengal. The northern sub-Himalayan districts of Jalpaiguri, Darjeeling, Coochbehar and West Dinajpur continued to lag behind the southern districts. A comparison of the position of districts in terms of rates of growth of aggregate crop output in the two periods, 1965-80 and 1981-90, shows a perceptible change in the ranking of districts over time (see Table 2.2, the ranking in which follows Table 2.1). Many of the districts that were ranked in the middle for the period 1965-80 such as Birbhum, Bankura and Midnapur moved to the top of the ranking in the second period. Purulia witnessed a remarkable transformation from its last position in 1965-80 to fourth position in 1981-90.
Table 2.1: Decomposition of growth rates of aggregate agricultural production in West Bengal by district (1965-80 and 1981-90).
1965-80** 1981-82 to 1990-91 District Production Production Area Productivity
West Bengal 2.74 (0.33) 6.4 (0.9) 1.2 (0.2) 5.2 (0.8) Bardhaman 3.85 (0.49) 6.4 (0.9) 1.2 (0.5) 5.1 (0.6) Birbhum 1.63 (0.54) 9.3 (1.9) 1.3 (0.4) 8.0 (1.6) Bankura 2.55 (0.90) 9.5 (1.8) 2.3 (0.5) 7.2 (1.0) Midnapur 2.68 (0.53) 8.5 (1.6) 1.3 (0.2) 7.1 (1.0) Howrah 4.30 (0.93) 7.6 (1.0) 2.7 (0.7) 4.9 (1.0) Hooghly 5.82 (0.60) 6.0 (0.6) 1.7 (0.6) 4.2 (0.8) 24-Parganas* 2.49 (0.68) 6.1 (1.6) 0.5 (0.5) 5.6 (0.9) Nadia 3.95 (0.65) 7.6 (0.9) 2.0 (0.3) 5.5 (0.8) Murshidabad 3.15 (0.57) 5.7 (1.0) 0.6 (0.2) 5.1 (0.9) West Dinajpur 1.19 (0.44) 5.5 (0.8) 0.8 (0.2) 6.2 (1.0) Malda 3.21 (0.52) 5.7 (0.6) 2.3 (0.4) 3.5 (0.7) Jalpaiguri 1.28 (0.56) 2.1 (0.8) 0.26 (0.8) 1.9 (0.9) Darjeeling 1.28 (0.82) 3.0 (0.8) 2.2 (0.8) 0.8 (1.2) Coochbehar 1.04 (0.39) 5.2 (0.6) 1.0 (0.5) 4.2 (0.3) Purulia 0.25 (1.23) 8.4 (3.0) 3.9 (1.5) 4.4 (3.0) Notes: Index number for all crops has base 1981-82 = 100; Standard errors are shown in the parentheses. *Only North 24-Parganas has been considered for the 1981-82 to 1990- 91 period while in the 1965-80 period both north and south parts have been included. Source: Saha and Swaminathan (1994); **Boyce (1987: 259)
39 The Study Area
Table 2.2: Ranking of districts in descending order by rate of growth of aggregate agricultural production in West Bengal, 1965-80 and 1981-90
Rank* 1965-80 1981-90
1 Hooghly Bankura 2 Howrah Birbhum 3 Nadia Midnapur 4 Bardhaman Purulia 5 Malda Nadia 6 Murshidabad Howrah 7 Midnapur Bardhaman 8 Bankura 24-Parganas 9 24-Parganas Hooghly 10 Birbhum Malda 11 Jalpaiguri Murshidabad 12 Darjeeling West Dinajpur 13 West Dinajpur Coochbehar 14 Coochbehar Darjeeling 15 Purulia Jalpaiguri Note: *Based on estimates in Table 2.1. Source: Saha and Swaminathan (1994).
To check the question as to whether or not the agricultural growth occurred has been influenced by exceptionally good rainfall, Saha and Swaminathan (1994) have included an index of rainfall (defined as actual rainfall as a percentage of normal rainfall) in the exponential growth equation.2 The growth estimates have shown that weather elasticities were insignificant in all but four districts. The estimates of growth did not change substantially when adjustments for weather were made. Therefore, according to the authors, the growth performance of agriculture in the 1980s cannot be explained in terms of unusually good weather conditions. However, the growth was traced by the authors as an effect of two reasons. First, improved farming practices and the use of high-yielding varieties (HYV) on a larger scale than before (although, at the same time, the authors found important barriers to HYV adoption) and the second reason which they found the most important one is implementation of the programme of land reform along with decentralisation of power, which was executed by the Left Front government after it had assumed power in 1977. The authors wrote: “The transition in West Bengal’s agricultural production performance (and its excellent comparative performance among the states of
2 See also Dev (1987).
40 Chapter 2
India) occurred after a landmark programme of land reform and after the establishment of new, democratic panchayat institutions in the West Bengal countryside. Panchayats are active in different production-related activities. Panchayats are involved in water management; rural workers were organised to sink shallow tubewells to build contour bunds and to do irrigation-related earthwork of different kinds. Panchayats are reported to be involved in ensuring that cultivators receive electricity for agriculture, and in the allocation of rural credit. Panchayats are expected to distribute ‘mini-kits’ or input packages to small cultivators” (p. A-10).
Saha and Swaminathan’s (1994) thesis have faced vehement criticism from Chattopadhyay and Das (2000) who have argued in favour of including an explanatory variable called as rainfall in the model but questioned the treatment of this variable as a proxy for weather and the consideration of total rainfall index as an aggregative measure. Since volatile fluctuation in the agricultural production data is quite disturbing, these authors have remarked that induction of an additional variable, e.g. rainfall, to minimize the effect of volatile fluctuations in the data on the estimated growth rate is justified. In their own words:
“The inclusion of rainfall as an explanatory variable in the trend equation certainly increases the accuracy of growth rate estimations, but treating this variable as a proxy for weather and estimating total rainfall index as an aggregative measure are likely to introduce measurement error in the variable. As a result, the coefficient of rainfall estimated by the OLS method will be both biased and inconsistent. In other words, the estimated coefficient will not reflect the true effect of rainfall on production. That is why, it seems, Saha and Swaminathan (1994) failed to find any significant effect of rainfall upon agricultural production in West Bengal during 1980s even when the rainfall was quite good during the period and was expected to have positive effect upon production”3 (p. 120).
3 See also Datta Roy (1994).
41 The Study Area
The authors have applied Durbin’s ranking method for measuring the impact of rainfall on agricultural production in order to obtain both unbiased and consistent estimate of the coefficient of rainfall. The coefficients of rainfall have been found to be statistically significant and their regression results conform to their expectation that agricultural production in West Bengal is still dependent on rainfall and fluctuations in rainfall index significantly positively contribute to fluctuations in agricultural production in the state. The authors have alerted that before fitting a trend equation and estimating the growth rates from the trend equation one should carry out necessary adjustments in the data for satisfying the criteria of randomness, non-autocorrelation, homoscedasticity and stationarity of time-series data.4 They argue that the use of R2 or Adj R2 as a measure of goodness-of-fit and, therefore, as a criterion for choosing the best equation is not sufficient. The authors claim that their estimate is more reliable as it satisfies all the econometric criteria. In the result of their estimation (for districtwise growth rates, see Table 2.3), the annual growth rate of agricultural production in West Bengal during 1977- 78 and 1994-95 was found to be 3.65 per cent which is much lower than the growth rate estimated by Saha and Swaminathan for the period 1981-82 to 1990-91.5 In this context, Chattopadhyay and Das (2000) have concluded that “the rate of growth of agricultural production in West Bengal during the Left Front rule has been certainly higher than that during the pre-Left Front rule, but much below the growth rate as estimated by some of
4 The authors have remarked that while most of the researchers report in their analysis the value of D-W statistic as a proof of presence or absence of autocorrelation, they hardly make necessary correction in the model in case autocorrelation is present. According to them, the “properties of randomness and homoscedasticity are hardly checked perhaps on the assumptions that non- randomness of the disturbance term is not a serious drawback and heteroscedasticity is not a problem of time series data. In the time series data on agricultural production the heteroscedasticity may be very much present even when the observations are aggregative provided that one part of the aggregate accounts for a major share in the total and the variability in that part is a function of some variables that change over time, such as unirrigated marginal land brought under cultivation, rainfall, etc. Various studies on the fluctuations in agricultural production have pointed out that the extents of fluctuations have in many cases been either divergent or convergent implying thereby that the condition of homoscedasticity in the agricultural production data is not satisfied (pp. 119-120)” 5 “Further, the increase in the growth rate during this period over the pre-Left Front period (2.08 per cent) by 1.57 percentage point is inconformity with the growth of agricultural inputs used in the state during this second period” (p. 128). But the growth rate of 6.4 per cent, as estimated by Saha and Swaminathan (1994), in West Bengal agriculture appears to be much higher than what can be explained by the amounts of inputs used during this period (Datta Roy, 1994: 1883-84).
42 Chapter 2
Table 2.3: Growth rates of districtwise agricultural production in West Bengal, 1957-58 to 1976-77 and 1977-78 to 1994-95.
Growth rates@ Kinked trend equation Name of the 1957-58 1977-78 to Trend Adj R2 D-W Runs district to 1994-95 break$ 1976-77 Bardhaman 2.786* 3.837* 1.051* .9912* 1.515k 15+ (.0011) (.0013) (.0022) Birbhum 2.086* 2.435* 0.349 .9357* 2.258 c 26*** (.0020) (.0023) (.0039) Bankura 1.269* 4.807* 3.538* .9569* 1.838 c 17+ (.0023) (.0026) (.0044) Midnapur 1.537* 5.074* 3.537* .8753* 2.025 c 21+ (.0043) (.0048) (.0082) Howrah 4.049* 4.220* 0.171 .9151* 1.789 c 17+ (.0044) (.0049) (.0084) Hooghly 4.261* 4.727* 0.466 .9746* 2.010 c 22+ (.0025) (.0028) (.0048) 24-Parganas 2.007** 2.702** 0.695** .8391* 1.785 c 19+ (.0027) (.0031) (.0039) Nadia 3.911* 4.461* 0.550 .9884* 2.509 k 23+ (.0016) (.0018) (.0081) Murshidabad 3.068* 3.583* 0.515 .8973* 1.641 c 16+ (.0038) (.0043) (.0072) West Dinajpur 1.948* 3.144* 1.196* .9754* 1.558 k 17+ (.0017) (.0013) (.0027) Malda 2.742* 3.740* 0.998* .9392* 1.777 c 20+ (.0039) (.0025) (.0044) Jalpaiguri 2.050* 1.484* 0.566 .9214* 2.082 c 20+ (.0021) (.0017) (.0034) Darjeeling 2.513* 3.325* 0.812 .8939* 2.031 c 16+ (.0034) (00.41) (.00670 Coochbehar 1.792* 2.314* 0.522 .8199* 1.851 c 21+ (.0033) (.0037) (.0063) Purulia 0.687 3.325* 2.638* .5622* 1.752 c 21+ (.0071) (.0065) (.0112)
Notes: ***, ** and * are significant at 10, 5, and 1 per cent level, respectively. c indicates presence of no autocorrelation, k implies inconclusive at 5 per cent level and non- autocorrelated at 1 per cent level, + random disturbance term, figures in parentheses are standard errors. Growth rates are in per cent per annum form. @ estimated from equation: ln yt or yt = a + b1D1t + b2D2t, t = actual time – break point. Here D1 = 1 and D2 = 0 for the period of 1957-58 to 1976-77 and D1 = 0 and D2 = 1 for the period of 1977-78 to 1994-95. Growth rate for the period 1957-58 to 1976-77 = estimated b1*100 or (estimated b1/harmonic mean)*100; and growth rate for the period 1977-78 to 1994-95 = estimated $ b2*100 or (estimated b2/harmonic mean)*100. Estimating equation: ln yt or yt = a + b1t + b2D2t, and trend break calculated from estimated b2. Source: Chattopadhyay and Das (2000).
43 The Study Area the early researchers. Further, the conclusion drawn by these researchers that the rainfall did not have any significant effect on agricultural production and, by implication therefore, the higher growth rate achieved during the Left Front rule was due to the government’s land reform policy and ‘panchayati raj’ system, not due to any external factor like the occurrence of good rainfall in most of the years, is not acceptable because of erroneous treatment of rainfall as a variable free of measurement errors and application of inappropriate technique for measurement of the effect of rainfall” (p. 132).
In the above perspective, it seems that the counterarguments presented by Chattopadhyay and Das (2000) deserve some credits over the arguments presented by Saha and Swaminathan (1994), although the thesis of the latter scholars has the merit of introducing the agricultural development of West Bengal through contributing to the literature. However, the debate may continue with regard to the method of measuring actual growth rate of agriculture in West Bengal, but there has already been a consensus among scholars that since the early 1980s West Bengal agriculture has experienced higher output growth than what it has witnessed in the first three decades of the post- independence period.
2.6 Industry in West Bengal
In the following, we mainly present a brief chronological history of industrial performance in West Bengal which was placed at top in British India and then gradually started lagging behind the other leading states. West Bengal was foremost among the Indian states at the time of India’s independence, in terms of concentration of industrial capital in the field of organised manufacturing sector. Her share in all-India stock of capital in the manufacturing sector was 24.6 per cent in 1950. In 1993-94, the share had declined to just 8.8 per cent (Banerjee, 1998). The rate of decline, however, was not uniform. For a comprehensive introductory note on the industrial background of West Bengal, we can refer to Raychaudhuri and Chatterjee (1998: 3061) who note:
44 Chapter 2
“In a less developed economy, industrialisation is synonymous with economic development and one way to boost up industrial activity is public sector investment in capital goods and large-scale manufacturing sector. The much quoted Mahalanobis strategy had the same message for India’s industrialisation. West Bengal’s industrial development also benefited to a large extent from the public sector investment in the decades of 1950s and early 1960s. Although large-scale public sector investment gave a fillip to industrial development in West Bengal, the decline of the same from the mid-1960s led to a big slump and sickness of industries all around. One problem to another—labour unemployment led to the politics of gherao, while the latter led to flight of capital to other states.”
Before independence, as Dasgupta (1998) reviewed, Bengal (including Bangladesh) became the leading industrialised state in the erstwhile British India. In 1921, Bengal accounted for 35.1 per cent of the total number of industrial workers in India. In 1939 (just prior to the Second World War), Bengal continued to be the major industrialised state in India accounting for 28.7 per cent of the total number of industrial workers in British India. In this regard, a comparative picture between Bengal and some other states is shown in Figure 2.1. In 1946, according to the first census of manufacturing industries in India (which covered all factories employing 20 or more workers using power) West Bengal continued to lead other states in terms of its share in total number of industrial workers in India, though in terms of value added in the manufacturing sector, West Bengal was second to Bombay (including Gujarat). This is shown in Table 2.4. In 1948, “West Bengal had a much higher level of employment in the engineering industries than Bombay. In the case of chemicals and food, drink, [and] tobacco, Bombay had marginally higher employment than West Bengal; and in chemicals (leaving out dyeing and bleaching) West Bengal had greater employment. In other industries such as paper and leather, West Bengal had higher employment than Bombay. It is only in textiles, in terms of employment, that Bombay had a clear superiority over West Bengal. Moreover, the industrial structure of West Bengal was more diversified than that of Bombay” (Dasgupta, 1998: 3049).
45 The Study Area
Figure 2.1: Per cent of industrial workers in some states of British India, 1921 and 1939
40
35
30
25 1921 20 1939
per cent 15
10
5
0 Madras Bombay Bengal Uttar Punjab Pradesh
Source: Dasgupta, 1998
Table 2.4: Registered factories and factory employment in major Indian states, 1946
Number of registered Total number Value added factories of employees (Rs. crore)* West Bengal 1218 509120 57.32 Bombay 959 500267 87.66 Madras 1244 144931 15.25 Uttar Pradesh 559 166763 21.71 Bihar 316 93523 19.66 *Rs. 1 crore = Rs. 100,00,000. Source: Dasgupta (1998)
Planned industrialisation began in India from 1951 with the beginning of the First Five- Year Plan. During first 15 years, West Bengal prospered well. In the period between 1951 and 1965, the value of industrial output in West Bengal increased by 287 per cent. Registered factory employment in West Bengal in the same period increased from 6,51,944 to 8,80,270. But West Bengal fell behind Maharashtra in terms of the number of
46 Chapter 2 industrial licences received (Dasgupta, 1998).6 Figure 2.2 illustrates the comparative positions of some major states. Actually, the declining trend-point of industrial performance of West Bengal has been indicated from the data of 1946, as shown in the Table 2.4 which tells us that Bombay (or Maharashtra) had higher value added though West Bengal had higher employment. Dasgupta has noted that this feature prevailed till 1965. Higher value added with lower employment would indicate that industry is more efficient as it generates more non-wage value added which implies greater profitability. Moreover, the Indo-Pak war of 1965 coupled with harvest failures in the country in two consecutive years, 1965-66 and 1966-67, brought in an industrial recession. “The recession of the mid-1960s did not affect all industries uniformly. The major industries that were affected were food processing, textiles and engineering goods industries, in particular metal products and transport equipment industries. Within the transport equipment group, the most adversely affected was the railways equipment industry. After registering an average annual growth rate of 31.8 per cent during 1960-65, the industry registered negative growth rates during 1967, 1968 and 1969, respectively” (Dasgupta, 1998: 3050). The evidences found from the book of Basu (1991: 103) tell us that, in 1959, the share of income earned from registered industry in West Bengal to that of India was 22.2 per cent. In 1963, it became 23.1 per cent. According to him, the share of West Bengal started going down after 1963—initially at slow pace, but afterwards rapidly. In 1986-87, it came down to 7.4 per cent and 6.5 per cent in 1989. Another review made by Raychaudhuri and Chatterjee (1998) suggests that the share of the value added in the industrial sector in West Bengal to that of India as a whole is below 10 percent and shows a consistent decline since 1970s. The trend rate of growth of industrial production as a whole during 1980-81 to 1995-96 for West Bengal lags far behind the corresponding rate for India as a whole. Figure 2.3 gives the comparative picture of the index of industrial production in India and West Bengal.
6 License system has been abolished in 1991.
47 The Study Area
Figure 2.2: Statewise distribution of applications for licence and licences issued, 1956-66
4000
3500
3000
2500 No. of applications 2000 No. of licences issued 1500
1000
500
0
l as h a esh r ihar shtra ad ad ades B a M r Mysore Pr tar P hra Mahar t West Beng nd U A
Source: Dasgupta, 1998
2.7 Urbanisation and infrastructure in West Bengal
The city of Kolkata is the product of the colonial trade. The city did not come out through endogenous development of economic activities. The exogenous factors played crucial role in its emergence. We come to know from the review made by Giri (1998) that industrial growth, primarily based on export-oriented jute industry, took place around Kolkata because of the port facility, internal river transport network based on Ganges and the railway infrastructure. Another external factor which contributed to urbanisation and also urban concentration in West Bengal immediately before independence was huge refugee migration from the eastern part of Bengal (now Bangladesh). The historical perspective of urbanisation in West Bengal suggests that in the pre-independence era
48 Chapter 2 urbanisation process in West Bengal was determined largely by the exogenous factors rather than being a part of the endogenous development of the region (Dasgupta, 1995). “The result was a monocentric urban development concentrated in and around Calcutta [Kolkata], with some urban development in the mining belt of Asansol” in Bardhaman district (Giri, 1998: 3033).
Figure 2.3: Index of industrial production in India and West Bengal, 1980-81 to 1995-96 (Base: 1980-81=100)
300
250
200 India 150 West Bengal Index Index 100
50
0
1 2 3 6 7 8 9 0 2 3 4 5 6 -8 8 8 8 8 9 -9 -9 9 5-8 6-8 84-85 91-9 92-9 980 981- 982- 9 98 98 987- 988-989- 9 9 993 994 995- 1 1 1 1983-841 1 1 1 1 1 1990-911 1 1 1 1
Source: Raychaudhuri and Chatterjee (1998)
After independence, the level of urbanisation in West Bengal was 23.88 per cent in 1951. Although the state had a level of urbanisation above the all-India average (17.29 per cent), it was fourth in rank, preceded by Maharashtra (28.75 per cent), Gujarat (28.23 per cent) and Tamil Nadu (24.35 per cent). However, Giri (1998) observes that West Bengal experienced a slower rate of urbanisation compared to all-India average in the post- independence period. The gap between urbanisation level of West Bengal and all-India average sharply declined from 6.59 percentage points in 1951 to 1.67 percentage points
49 The Study Area in 1991 (see Table 2.5). The comparative picture of the growth rates of urbanisation, calculated from the Table 2.5, between West Bengal and India is shown in the Figure 2.4.
Table 2.5: Urbanisation in West Bengal and India
Urbanisation level West All-India Gap Bengal (1) (2) (1)-(2) 1951 23.88 17.29 6.59 1961 24.45 17.97 6.48 1971 24.75 20.22 4.53 1981 26.47 23.34 3.13 1991 27.39 25.72 1.67 Source: Giri (1998)
In West Bengal, significant diffusion of urban centres has not occurred after independence. According to Bhattacharya (1998), the structural system of the state— nourishing the Kolkata-centric urbanised regions—is found to remain mono-nuclear over time. Strengthening a major primate city like Kolkata and neglecting the rural sector in terms of building infrastructure does not help the economy grow as a whole. Migration of workforce addresses the other side of the issue. In Mellor’s (1995) opinion, unemployment in rural sector creates problem in urban sector. There is a common argument that urbanisation is accompanied by a shift of employment and other inputs from the predominantly rural agricultural sector to the predominantly urban industrial and service sectors. In the third world countries, with high population growth rate and limited scope for extending agriculture, unemployment is pervasive in the rural areas. The wage rate that prevails in rural agriculture is not sufficient to generate demand for non- agricultural commodities. In such a situation, the manufacturing sector plays the role of the prime mover behind the urbanisation process and migration from rural to urban areas takes place. Giri (1998) observed that the rate of urbanisation in West Bengal was closely associated with the change in the proportion of workforce engaged in the manufacturing sector. “During the 1960s the pace of urbanisation was the lowest and the proportion of workers engaged in manufacturing declined from 14.60 per cent in 1961 to 13.87 per cent in 1971. In the 1980s the slowing down of the urbanisation rate was also accompanied by
50 Chapter 2 a decrease in the share of manufacturing. In contrast during the 1970s an increase in the share of manufacturing in the workforce was accompanied by an increased rate of urbanisation” (p. 3037).
Figure 2.4: Rate of growth of urbanisation in West Bengal and India, 1961-1991
18
16 15.43
14 West Bengal 12.52 All India 12
10 10.2
8 6.95 Rate of growth Rate of 6
4 3.93 3.48 2.39 2 1.23 0 1961 1971 1981 1991
Source: Giri (1998)
2.8 Urban Centres and Small-Scale Industries in West Bengal
Of the total number of 45,954 small industrial units in the state of West Bengal, 8,877 units (19.32%) were found to be located in the metropolitan areas, 16,519 units (35.9%) in the urban areas and remaining 20,558 units (44.74%) in the rural areas. Considering the whole volume of the rural areas in the state, the percentage share of the small units located in the rural areas to the total number of small units in the state is not significantly higher than that located in the urban areas to the total number of small units in the state.
51 The Study Area
This is shown in Table 2.6 which gives us more explicit picture. West Bengal consists of 17 districts. The district of Kolkata, which is the capital of the state, is almost completely a metropolitan area. 99.78% units of this district were found to be located in the metropolitan area. Kolkata is surrounded by the three districts viz. Howrah, North 24- Parganas and South 24-Parganas and one can always expect strong metropolitan influence on these surrounding districts. In Howrah, 74.32% of the small scale units were found to be located in urban areas and 25.68% in the rural areas. This urban concentration of small enterprises is also seen in North and South 24-Parganas. The percentage share of the number of small units in the urban areas to the total number of small units in each of these two districts is higher than that of the number small units in the rural areas to the total number small units.
Small industrialisation is related with the degree of urbanisation. Not so many small industries have flourished in those districts of West Bengal which are less urbanised. In other words, there has been no significant rural industrialisation in the rural-led districts of West Bengal. The evidence on urbanisation indicates that much of the growth in factory production and employment is geographically concentrated in a small area of the state. For example, the Kolkata Urban Agglomeration alone accounted for 64.11 per cent and 63.64 per cent respectively of the urban population in the state in 1971 and 1981 (Government of West Bengal, 1987-88: Statistical Appendix, 3). This means that, excluding this Kolkata agglomeration centre, the whole state holds only about 35% urbanisation which indicates a very poor rate to induce industrialisation in the state. The growth of small enterprises was inhibited by less urbanisation in the region.
Further, as can be seen from Table 2.7, West Bengal has an extremely skewed pattern of urbanisation when compared with an industrialised state like Tamil Nadu. Though West Bengal is not too far behind in the average degree of urbanisation, with 26.5 per cent of the population located in urban areas as compared with 33 per cent in Tamil Nadu, only 7 of its 16 districts have more than a fifth of its population in urban centres as compared with 12 out of 16 in Tamil Nadu.
52 Chapter 2
Table 2.6: Dispersal of small enterprises in West Bengal Sl. Name of the Total Enterprises located in No. district number Rural Urban Metropolitan of enter- Number Per cent Number Per cent Number Per cent prises (1) (2) (3) (4) (5) (6) (7) (8) (9) 1 Bankura 1,690 1,435 84.91 2,55 15.09 - - 2 Birbhum 2,088 1,526 73.08 562 26.92 - - 3 Bardhaman 3,280 1,958 59.7 1,322 40.3 - - 4 Kolkata 8,897 3 0.03 17 0.19 8,877 99.78 5 Coochbehar 593 310 52.28 283 47.72 - - 6 Darjeeling 696 247 35.49 449 64.51 - - 7 West Dinajpur 781 4 17 53.39 364 46.61 - - 8 Hooghly 3,049 1,953 64.05 1,096 35.95 - - 9 Howrah 7,369 1,892 25.68 5,477 74.32 - - 10 Jalpaiguri 1,329 1,103 82.99 226 17.01 - - 11 Malda 1,060 828 78.11 232 21.89 - - 12 Midnapur 4,140 3,369 81.38 771 18.62 - - 13 Murshidabad 1,770 1,237 69.89 533 30.11 - - 14 Nadia 1,981 1,257 63.45 724 36.55 - - 15 South 24- 2,455 915 37.27 1,540 62.73 - - Parganas 16 Purulia 1,570 1,040 66.24 530 33.76 - - 17 North 24- 3,206 1,068 33.31 2,138 66.69 - - Parganas TOTAL 45,954 20,558 44.74 16,519 35.95 8,877 19.32 Note: *Percentages with respect to total number of units in column (3). Source: Report on the Second All-India Census of Small-Scale Industrial Units, Department of Small-Scale Industries, Ministry of Industry, Government of India.
If we go down to the level of tehsils/blocks, 63 per cent of the tehsils/blocks in West Bengal have an urbanisation level of less than 10 per cent, while only 28 per cent of the tehsils/blocks in Tamil Nadu are characterised by less than 10 per cent urbanisation. It means that not only is a larger proportion of the population resident in rural areas in West Bengal, but the urban population is squeezed into a few pockets, which are themselves unevenly distributed across the state. Since the manner in which urban areas are defined tends to include segments where there has been a significant development of factory industry, this would mean that even the limited direct and indirect employment effects of the growth of factory production have not touched much of the state’s population and regions. Thus rural sources of employment in general and agricultural employment in particular have to be the means of livelihood for a major part of the region and its population.
53 The Study Area
Table 2.7: Urbanisation in West Bengal and Tamil Nadu as per 1981 census
District Percentage of Number of Number of Number of T/B urban to total Tehsils/ T/B with nil with 0-10% population Blocks urbanisation urbanisation WEST BENGAL 26.5 Coochbehar 6.9 8 2 7 Jalpaiguri 14.1 14 5 8 Darjeeling 27.6 13 7 8 West Dinajpur 11.2 16 9 10 Malda 4.8 10 7 9 Murshidabad 9.4 21 11 12 Nadia 21.6 14 5 8 24-Parganas* 38.9 55 23 26 Kolkata (Metro.) 100.0 Howrah 45.1 12 3 6 Hooghly 29.5 20 9 12 Midnapur 8.5 39 26 31 Bankura 7.6 19 14 16 Purulia 9.0 18 11 13 Bardhaman 29.4 27 10 14 Birbhum 8.3 14 8 9
TAMIL NADU 33.0 Chennai (Metro.) 100.0 Chengalpattu 38.9 12 2 3 North Arcot 23.5 13 1 4 South Arcot 15.7 12 4 6 Dharmapuri 9.4 8 2 5 Salem 28.9 9 1 3 Periyar 22.0 6 0 1 Coimbatore 20.5 6 0 0 Nilgiri 48.9 4 0 0 Madurai 36.2 13 0 3 Tiruchirapalli 26.1 10 0 1 Thanjavur 23.1 16 2 3 Pudukottai 13.3 6 1 4 Ramanathapuram 28.2 18 1 3 Tirunelveli 34.6 14 1 3 Kanniyakumari 17.2 4 1 3 Note: * Here the 1981 Census provides the data for the undivided district of 24-Parganas which was later divided into two districts, i.e. North 24-Parganas and South 24-Parganas. West Bengal Industrial Development Corporation (WBIDC) data individually shows that the rate of urbanisation in North 24-Parganas is 51.23% whereas the rate in South 24-Parganas is only 13.30%, although both the districts are close to Calcutta. This fact correlates with the growth of small enterprises in the way that the number of small enterprises in South 24- Parganas is less than that in the other surrounding districts of Calcutta. Source: Chandrasekhar, 1993. Original source: Census of India, 1981, various reports.
54 Chapter 2
2.9 Rural Industrialisation and the Role of Infrastructure in Rural Towns
In the state of West Bengal, underdeveloped areas are mainly called backward areas which are faced with very less urbanisation. The metropolitan district Kolkata is considered as non-backward area. So are the parts of three other districts surrounding Kolkata. Table 2.8 shows the distribution of units in backward and non-backward areas. It is remarkable here that the non-backward area in the state consisting of only the whole part of Kolkata district and some parts of other three surrounding districts together holds 42.83% share of the total number of small units in the state, whereas the huge backward area in the state holds only 57.17% share. This picture shows a clear urban concentration of small enterprises. The area of Greater Kolkata not only includes Kolkata district and parts of its three surrounding districts but also a substantial part of Hooghly district, which enjoys a quick metropolitan link with Kolkata. The locational advantage of Hooghly, as it is a part of Kolkata agglomeration, gives its small enterprises an urban environment to grow in number (see Table 2.8). Apart from the large Kolkata agglomeration, if we look at the other districts individually in the Table 2.8 then Bardhaman and Midnapur districts naturally draw the attraction for holding the small enterprises more than 3000 in each. This requires further explanation. After the Kolkata agglomeration, another important largest urban area in the state is the Asansol-Durgapur agglomeration. This agglomeration belongs to the district of Bardhaman. The main towns in this district are Asansol, Bardhaman, Durgapur, Burnpur, Kulti, Raniganj and Chittaranjan. Due to its dispersed urban nature, Bardhaman district holds a significant number of small enterprises. In this perspective, only the case of Midnapur district is exceptionally different. It includes only Midnapur town, Kharagpur town, Kharagpur railway settlement and a growing town Haldia. The rate of urbanisation in this district is only 8.5% (see Table 2.7). Yet, according to Table 2.8, the number of small enterprises in this district is exceptionally very high. Apart from Midnapur district, the rural picture in West Bengal is miserable. Rural industrialisation in the state has not been promoted. Infrastructure and services are heavily concentrated in the metropolitan city Kolkata only and the adjacent districts have gained some peripheral impacts. Sufficient infrastructure facilities are not dispersed throughout the state.
55 The Study Area
Table 2.8: Distribution of enterprises in backward and non-backward areas
Sl. Name of the Total Enterprises located in No. district number of Backward area Non-backward area enterprises Number Per cent* Number Per cent* (1) (2) (3) (4) (5) (6) (7) 1 Bankura 1,690 1,690 100 0 0 2 Birbhum 2,088 2,088 100 0 0 3 Bardhaman 3,280 3,280 100 0 0 4 Kolkata 8,897 1 0.01 8,896 99.99 5 Coochbehar 593 593 100 0 0 6 Darjeeling 696 696 100 0 0 7 West Dinajpur 781 781 100 0 0 8 Hooghly 3,049 3,049 100 0 0 9 Howrah 7,369 86 1.17 7,283 98.83 10 Jalpaiguri 1,329 1,329 100 0 0 11 Malda 1,060 1,060 100 0 0 12 Midnapur 4,140 4,140 100 0 0 13 Murshidabad 1,770 1,770 100 0 0 14 Nadia 1,981 1,981 100 0 0 15 South 24- 2,455 181 7.37 2,274 92.63 Parganas 16 Purulia 1,570 1,570 100 0 0 17 North 24- 3,206 1,979 61.73 1,227 38.27 Parganas TOTAL 45,954 26,274 57.17 19,680 42.83 Note: * Percentage with respect to total number of enterprises in column (3). Source: Report on the Second All-India Census of Small-Scale Industrial Units, Department of Small-Scale Industries, Ministry of Industry, Government of India.
Rural industrialisation requires investment on basic infrastructure throughout the state either to help grow rural towns in a geographically dispersed way or to provide rural towns with basic infrastructure facilities. Regardless of agroclimate, the denser the infrastructure, rural town services and population, the greater the earnings from the rural non-farm sector (Reardon et al, 1998). In general, the quality and quantity of physical infrastructure (e.g. roads) and social infrastructure (e.g. schools) tend to be correlated with population density and the development of rural towns. More developed infrastructure and denser population means lower transaction costs for market products (farm or non-farm) and a greater availability of inputs (electricity, water etc.) at a lower cost. Hence, infrastructure quality and quantity have often been identified as key determinants of non-farm business investments. Islam (1987) has also argued that both
56 Chapter 2 physical and social infrastructure play an important role in rural industrialisation. According to him, the contrasting experiences of Taiwan and Korea amply demonstrate this.
In Taiwan province of China, the shift made by rural households to non-farm sources of income began in the late 1960s. Structural reforms in the late 1960s stimulated the spectacular expansion of an outward-oriented export economy. Manufacturing grew by 20 per cent per year, leading the way in the sustained double-digit growth of GNP. The consequent pace of labour absorption in the industrial sector took the steam out of the population pressure on the land frontier. The growth of industry is evenly spread across space—a well-known and much lauded feature of the Taiwanese economy. Urban centres are themselves geographically dispersed and infrastructure is also well distributed, making it possible for industrial estates to flourish in the smaller towns (Reardon et al, 1998).
The opposite picture was seen in the republic of Korea throughout its rapid growth period in the 1960s and 1970s. Why? It was observed that manufacturing activities were concentrated in just two growth poles: Seoul in the north and Pusan in the south, along with the adjacent provinces. The population in the other provinces remained dependent on agricultural occupations. This was because of the fact that infrastructure and services were heavily concentrated in the urban centres. The option of commuting from the countryside was constrained by an inadequate rural road network. Instead, there was considerable migration to the cities (Reardon et al, 1998).
Finally, let us mention the study carried out by Chapman and Wanmali (1981) who have argued that successful development is correlated with an extensive and general regional urbanisation. They noted that urbanisation in India is at too low a level to facilitate the diffusion of a modern commercial sector in traditionally agricultural areas; in particular there are too few towns of the smaller sizes to allow for the proper integration of the urban and rural sectors which hinders modernisation of the latter. In West Bengal, as we have seen that the small scale enterprises are concentrated in Kolkata and its immediate
57 The Study Area satellites, this is a result of strengthening a major primate city which is seen to be the centre of introverted economic systems. Development policy has to take cognizance of the fact that it cannot rely upon nor utilize the urban framework, but has to be directed at the rural level. It calls for a massive scheme of the infrastructure development in rural Bengal which in turn will facilitate the growth and development of rural non-agricultural sector. This also, of course, mirrors Rostow’s attitude towards capital accumulation for the agricultural sector in the early stages of growth. Dasgupta (1993) remarks that it is hard to imagine that investment projects in rural infrastructure could be anything but worthwhile. Mody (1981) appreciates the necessity of resource flows into agriculture during the initial period of development for building rural infrastructure, whether or not these are reversed subsequently. The success of industrialisation will depend to a large extent not only on the capability of agriculture to generate surpluses but also on whether and how these surpluses can be channelled into industrial investment.
2.10 Bardhaman’s economy in retrospect: A glimpse
Bardhaman is a pre-dominantly agricultural district of West Bengal, although some parts of the district are industrially developed. In the early period of the (British) East India Company’s rule Raja of Bardhaman was at the top of the landed society in the district and at the bottom was the cultivator of the soil. In between them there was a group of landed aristocrats who did neither enjoy the status of the Raja nor lived very ordinarily like the cultivators. Commercialisation of agriculture was introduced by the British and the system transformed from subsistence farming to cash crop production—mainly rice. The rich farmers profited from the new system as the prices of food crops rose. But the system did not favour the small farmers. “It forced the small peasant to mortgage his land to the money lenders or rich peasants and ultimately to sell it to the latter. Thus the jotedar-cum-rural creditors possessed large holdings and they became prosperous at the cost of primary producers. But their accumulation of land was not propelled by an urge for the development of large-scale farming on capitalist lines” (A.K. Dutta, 2002: 72). The large farmers had sufficient fund, but they did not invest it for technological up-
58 Chapter 2 gradation or in innovation in order to raise agricultural productivity. They restored to sharecropping cultivation because surplus could easily be extracted through sharecropping which entailed almost no risk on the part of the landlords. Finally, A.K. Dutta remarked that the district of Bardhaman during the British rule represented an agrarian system where the prevalence of a backward form of cultivation and production was found. Thus, from the producer’s side, capitalist farming could not grow in the district.7 From the demand side, lack of purchasing power of the village people gave no fillip to the grain production on the one hand and, on the other, “in the absence of rapid industrialisation, the urban market for agricultural products underwent no expansion” (A.K. Dutta, 2002: 73). Moreover, due to the limited domestic market the cash crops could not bring in sustained long-term development in agricultural sector. Although there was considerable growth of cultivation and exports of cash crops, local farm economy received marginal benefits from it and consequently capitalist farming did not grow since the producer himself was not directly involved in exporting his produces. Rice mills emerged in the district as an agro-based industry, but no other agroindustry developed in the district so far. Rice milling industry was located in semi-urban areas, primarily in subdivisional towns and the district headquarter.
The pressure of population was not felt in Bardhaman during the second half of the 19th century. Population remained almost static till 1921. However, the surplus labour force was found in 20th century Bardhaman. Moreover, the population of the district increased after 1921 (see Table 2.9 which is further placed in Figure 2.5 that shows a steady growth of population after 1921). But the picture of the non-agricultural sector which could have absorbed the surplus labour force was not impressive at all. The only big industry in Bardhaman was coalmining. With the progress of coalmining industry, iron and pottery works emerged in the western part of the district. The iron works started at Barakar in 1875 by Barakar Iron Works Company, Limited, which however collapsed in 1879. About two years later, the government restarted the works and ran them for about eight and a half years. It was renamed as the Bengal Iron and Steel Company Ltd.
7 For a detailed review of literature and discussion on the complex agrarian structure in Bardhaman district during the British rule, see the book of A.K. Dutta (2002).
59 The Study Area
Figure 2.5: Population in Bardhaman, 1891-1951
Table: 2.9: Population in 2500000 Bardhaman, 1891-1951 2000000 Year Population
1891 1391880 1500000 1901 1528290 Population 1911 1533874 1921 1434771 1000000 1931 1575699 1941 1890732 500000 1951 2191667 0 Source: A.K. Dutta (2002) 1880 1900 1920 1940 1960
Besides coal and iron, Raniganj/Asansol subdivision was famous for pottery works. Clay- works were manufactured on a large scale in Raniganj and Durgapur. Messers Burn and Company Limited of Raniganj excelled in using the fire-clay for manufacturing stoneware pipes, red-colour roofing tiles, bricks, and pottery. The pottery works of this company seem to have been started in 1860. The other famous companies were the Raniganj Pipe Works and Bengal Firebrick Syndicate of Kulti. In tiny sector, a special branch of iron and steel works is cutlery in which blacksmiths manufacture knives, scissors etc. The most famous and primary centre of cutlery industry in Bardhaman district was Kanchannagar, a suburb in the south-west of Bardhaman town, five miles away from Bardhaman railway station. It is often said that the cutlery industry in Kanchannagar is five hundred years old. But there is no historical documentation about it. The industry is still traceable at Kanchannagar but it has lost its former glory long ago.
In the past, some other indigenous handicrafts also existed in Bardhaman district. They include weaving industry, brass and bell-metal works, conch-shell manufactures and so on. Weaving occupied an important place in the traditional indigenous cottage industry of the district. Of the three branches of weaving industry—cotton, silk and wool, the first
60 Chapter 2 two flourished in the district. From 1880s the weaving industry in Bardhaman as well as in Bengal faced hard competition from machine-made Manchester cloth and the indigenous industry started decaying. Nonetheless, 18,117 persons were engaged in different sections of cotton weaving industry in Bardhaman in 1891. The chief centres of the weaving industry in the district were Purbasthali, Kalna, and Manteswar in which about 2000 persons were daily employed in 1908-09. According to 1921 census, there were 3942 handlooms in Bardhaman district (see A.K. Dutta, 2002: 203).
In this section, we have tried to present a very brief description of Bardhaman’s economy, mainly in the field of agriculture and industry, in the pre-independence era based on available evidences. The literature suggests that commercial agriculture had not led to the technical transformation of agriculture. The large farmers could increase their landed property, but it did not result in substantial increase in production; rather it had encouraged cultivation in smaller farms by sharecroppers who continued to retain labour- intensive techniques in their production units. On the other hand, the industrial scenario in the district did not show any remarkable picture. Rice milling industry was found to be the most important food-processing industry of Bardhamn. The rice mills in Bardhaman were really big but modernisation in the mills of the district notably began in the 1960s and became widespread in the 1970s. The modernisation took place in the milling plant only; processing of paddy still followed the traditional method.
2.11 Recent Bardhaman: A glimpse
In the present section, we offer the brief and salient aspects of the economy of Bardhaman district in recent time. A detailed overall description of the district is presented in APPENDIX 2.1. To start with, one can always observe that rice is the major crop of West Bengal. During the 1980s and 1990s, rice production in the state increased rapidly. So far as rice production is concerned, Bardhaman is traced as an important district in the state. In Neil Webster’s (1990: 47) version: “It is one of the main rice- producing districts in the region and was the first district to be chosen for the intensive
61 The Study Area
Agricultural Development Programme in the state, in 1962, and thereby became a forerunner in the green revolution.” Saha and Swaminathan (1994) showed that in 1980- 81 and also in 1990-91 shares of Bardhaman district in rice production were the highest in the state (see Table 2.10). Again, Saha and Swaminathan considered the period between 1980-81 and 1990-91and Bardhaman was found to be one of the important rice- producing districts of the state (see Table 2.11).
Table 2.10: Share of Districts in rice Table 2.11: Growth of rice production in production in West Bengal West Bengal (by district), 1980-81 to 1990-91
District Percentage of total District Production rice production 1980-81 1990-91 Midnapur 16.8 14.6 West Bengal 6.19 (4.5) Bardhaman 13.4 13.6 Bardhaman 6.19 (4.7) 24-Parganas 12.4 11.1 Birbhum 6.4 (3.4) Bankura 7.9 8.1 Bankura 7.4 (3.5) Birbhum 7.8 7.8 Midnapur 6.4 (3.6) Hooghly 6.9 5.8 Howrah 7.6 (4.9) WestDinajpur 6.6 7.4 Hooghly 4.1 (2.9) Murshidabad 5.7 7.5 24-Parganas 5.9 (3.4) Purulia 4.7 3.6 Nadia 9.9 (8.2) Jalpaiguri 3.9 2.4 Murshidabad 7.6 (3.9) Malda 3.8 4.7 West Dinajpur 5.7 (5.0) Nadia 3.7 6.7 Malda 6.0 (7.3) Coochbehar 3.7 3.8 Jalpaiguri 1.2 (0.8) Howrah 2.0 2.2 Darjeeling 3.0 (1.9) Darjeeling 0.7 0.7 Coochbehar 4.6 (5.3) All districts 100 100 Purulia 5.7 (2.0) Source: Saha and Swaminathan (1994) Source: Saha and Swaminathan (1994)
More recent data show that yield rates of total cereals (including rice) in Bardhaman district in 1998-1999 and 1999-2000 were found to be 3056 and 2738 kgs/hectare respectively. The yield rates of total cereals in Bardhaman district in both the years were the highest of all districts (Evaluation Wing, Directorate of Agriculture, Government of West Bengal, 2001). This can be depicted as a justification for why we have selected Bardhaman district as our study area. Since Bardhaman district is the main rice/cereal producing district in the state, it may be interesting to examine the farmers’ participation
62 Chapter 2 in non-agricultural, industrial, activities through investment of their surplus generated from agriculture.
Let us now turn towards the industrial picture of the district after independence. Durgapur, located in the district of Bardhaman, had been projected in the fifties and sixties as the second city of West Bengal as far as industries are concerned. During the period covered by the Second and Third Five Year Plans, a number of public sector enterprises were set up in Durgapur. The most important among them was the Durgapur Steel Plant which not only provided employment to thousands of people but also facilitated the setting up of upstream as well as downstream ancillary industrial units. There is also the Alloy Steel Plant in Durgapur, which produces a large number of metal alloys and which also feeds the demand of downstream metal product units. In the chemical sector, Durgapur Chemicals Ltd. is a large plant located in Durgapur. Keeping in view the fact that the district of Bardhaman is agriculturally well developed, the government had set up the Hindustan Fertilizers Ltd. at Durgapur. Another public sector unit at Durgapur is Durgapur Projects Ltd.
The public sector industries are located outside Durgapur too. At Chittaranjan, there is the giant Chittaranjan Locomotive Works which is the largest manufacturer and supplier of railway engines in India. The Indian Iron and Steel Company (IISCO) is situated at Burnpur. The present financial situation of IISCO is not satisfactory. Hindustan Cables at Rupnarayanpur is also a large public sector enterprise. The Eastern Coalfields Ltd., the public sector mining company, is also located in Bardhaman district. However, it is to be noted here that the large public sector undertakings are all concentrated in the two western subdivisions of the district, viz. Durgapur and Asansol, while the three eastern subdivisions, viz. Bardhaman, Kalna, and Katwa, have got very little share of the public investment in the large-scale industrial sector.
In the private sector too, there are a number of large-scale industrial units in Bardhaman district. The major large-scale private industrial units of Bardhaman district are listed below.
63 The Study Area
1. J.K. Paper Mill, J.K. Nagar 2. Raniganj Paper Mill, Raniganj 3. Philips Carbon Black Ltd., Durgapur 4. Burn Standard Co., Durgapur 5. Shanky Wheel, Durgapur 6. IFB Agro, Durgapur 7. Dishergarh Power Supply Corporation, Dishergarh 8. Aluminium Factory, Asansol
Apart from the above, thirteen new large industrial projects were implemented in the district during 1999-2000. The list of the projects implemented along with other details is furnished below:
Table 2.12: Projects implemented in Bardhaman district during 1999-2000
Sl. Name of the company Product Project cost No. (Rs. in crore) 1. Bengal Nestor’s Industries Ltd. Dairy products 3.50 2. Bhaskar Shrachi Alloys Ltd. Casting of iron and steel, and steel ingots 3.00 3. Bhaskar Shrachi Alloys Ltd. Silicon/ferro manganese, ferro silicon 9.90 4. Elegant Commerce Ltd. Heavy rounds 19.60 5. Jai Balaji Sponge (P) Ltd. Sponge iron 3.88 6. Jolla Steel Pvt. Ltd. M.S. Ingot 1.40 7. Khalsa Tubes Pvt. Ltd. Lancing pipes 2.20 8. Maithan Alloys Ltd. Ferro alloys 7.10 9. Sethia Oils Ltd. Refined oil, Vanaspati, fatty acid 7.50 10. Shyam Ferro Alloys Ltd. Ferro alloys plant 11.50 11. Shyama Cast Ltd. Sponge iron 10.00 12. Sova Ispat Alloys (P) Ltd. High carbon ferro manganese alloys 5.37 13. Srinivasa Ferro Alloys Ltd. H.C. ferro manganese and H.C. ferro 5.33 alloys Source: Economic Review, 2000-2001
Several other large- and medium-scale projects are under construction in the district. The list is furnished in the following table:
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Table 2.13: Projects presently under construction in Bardhaman district
Sl. Name of the company Product Project cost No. (Rs. in crore) 1. Adhunik Steel Ltd. Steel ingots/billets 4.50 2. Bardhaman Iron and Steel Co. Pvt. Mini steel plant 2.00 Ltd. 3. Bengal Recycles and Fabrications M.S. ingots 8.72 Pvt. Ltd. 4. Core Ceramics Burnt clay fly-ash 13.75 bricks 5. Corporate Ispat Alloys Ltd. Ferro alloys 7.00 6. D.K. Paul and Co. Pvt. Ltd. Steel ingots 3.16 7. Haldia Steels Ltd. Ferro alloys 5.44 8. Howrah Gases Ltd. Clinker slag, grinding 4.11 unit 9. Howrah Gases Ltd. Sponge iron 2.73 10. Indian Institute of Aviation Ltd. Aviation training centre 8.00 11. Jagadamba Fiscal Services (P) Ltd. Polythene compound of 3.11 all types 12. Jai Balaji Sponje Pvt Ltd. Sponge iron mills, steel 35.00 strips 13. Jalan Cold Storage Pvt. Ltd. Cold storage 3.00 14. Jawala Steel Ltd. M.S. ingots 46.84 15. Larsen and Tubro Ltd. Portland cement 150.00 16. Ma Chandi Durga Ispat (P) Ltd. Ferro alloys 14.00 17. Monnet Ferro Alloys Ltd. H.C. ferro chrome, ferro 8.85 manganese 18. Rashmi Ispat (P) Ltd. PP/HDPE woven fabrics 5.03 19. Rescon Chemicals (P) Ltd. Rice-husk ash nodules 3.33 20. S.D. Tools (P) Ltd. Forging steel 2.00 21. Shyama Cast Ltd. M.S. ingots 3.00 22. Stalberg Gmbh Flux for steel making 27.00 23. Super Semelter Ltd. - 24.04 24. Vinayak Steels (P) Ltd. Sponge iron steel 80.40 casting Source: Economic Review, 2000-2001
65 The Study Area
APPENDIX 2.1: Overall Description of Bardhaman District
Location of Bardhaman District
Bardhaman, sometimes called and spelt as Burdwan (as pronounced by the British), is located between the latitude 23053’ N and 22056’ N and between the longitude 88025’ and 86048’. The district has a total area of 7024 sq. kms. and is bordered by Birbhum district in the north, Murshidabad district in the north-east, Nadia in the east, Hooghly and Bankura in the south and Purulia in the south-east.
Climate and Rainfall
The climate of the district is generally hot and the western part, mainly covered by Asansol and Durgapur Sub-Divisions, is drier than the eastern part which is very fertile. The district enjoys ample monsoon showers and is agriculturally very well developed. Sometimes, it suffers from floods due to the overflowing of Damodar and Ajay rivers during monsoon.
On an average, Bardhaman district experiences the maximum rainfall from July to September. The mean annual rainfall of the district is around 1470 mm. The temperature of the district varies between 50 C and 440 C. April and May are the hottest months and December and January are the coldest. Table 1 furnished below gives us a picture of rainfall of Bardhaman district.
Table 1: Rainfall pattern of Bardhaman district for the period 1997-1999
Year
1997 1998 1999
Actual Rainfall (in 1529 1261 1964 mm) Source: District Statistical Handbook 1997-1999
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Soil
The soil of the part of Bardhaman district is of rich alluvial variety and is perfectly suitable for intensive cultivation of paddy, wheat, potatoes and other crops and vegetables. The soil of the western part of the district is reddish and is not that fertile.
Rivers
The district of Bardhaman is extremely fortunate of being girdled by three major rivers— the Hooghly on the east, the Ajay on the North and the Damodar on the south.
Apart from these three, there are myriads of minor rivers and streams across the district. The Hooghly, the Ajay and the Damodar are all perennial rivers.
Forest
The aggregate forest area of the district is 25196 hectares and the forests are mainly spread over the western part of the district. The forests are mainly covered by Sal and Kendu trees. The main forest products are timber and fuel-wood.
Distribution of Population
Bardhaman is one of the most populous districts in West Bengal. According to the 1991 Census, it has an aggregate population of 6050605, the population density being 861 persons per sq. km. Raniganj, Jamuria, Niamatpur, Dishergarh, Burnpur, Andal and Durgapur are densely populated areas while the blocks of Ketugram, Mangalkote, Galsi II, Khandaghosh, Raina I and II, Bhatar, Ausgram, Jamuria I and II are sparsely populated regions.
67 The Study Area
Administrative Set up
The district comprises five administrative sub-divisions viz Bardhaman Sadar, Durgapur, Asansol, Kalna and Katwa. The Development Blocks coming under the individual Sub- Divisions are as follows:
Bardhaman Sadar: Bardhaman I and II, Ausgram I and II, Bhatar, Memari I and II, Jamalpur, Raina I and II, Khandaghosh, Galsi II. Asansol: Salanpur, Barabani, Raniganj, Jamuria. Durgapur: Galsi I, Andal, Durgapur-Faridpur, Kanksa, Pandabeswar. Katwa: Mangalkote, Ketugram I and II, Katwa I and II. Kalna: Purbasthali1 and 2, Kalna I and II, Monteswar.
In all, there are 31 administrative blocks throughout the 5 Sub-Divisions. The district has 32 police stations, 31 Panchayat Samitis, and 278 Gram Panchayats.
There are nine municipalities (M) and two municipal corporations (MC) in Bardhaman District. The names of municipalities, municipal corporations and the sub-divisions to which they fall under are given in Table 2 below:
Table 2: Distribution of municipal areas in the five sub-divisions
Sub-Division Municipality (M) and municipality corporation (MC) Bardhaman Bardwan (M), Guskara (M), Memari (M)
Asansol Kulti (M) , Raniganj (M), Jamuria (M), Asansol (MC) Kalna Kalna (M)
Katwa Katwa (M), Daihat (M)
Durgapur Durgapur (MC)
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69 The Study Area
70 Chapter 3
Issues Relating to Rural Industries: A Review of Literature
3.1 Introduction
This chapter discusses the literatures which have dealt with the various issues related to rural industries. The role of agriculture in economic development with special reference to industrial development is one of the major points of discussion that has been highlighted in the following sections. More explicitly, a substantial portion of the chapter discusses the interlinkage between agriculture and industry (industry in general and rural industry in particular). The discussion on such interlinkage has got a long tradition, starting from the classicists to the present day economists. During last few decades, many authors have put agricultural development at the central place as an engine of prosperity while discussing early stages of development of a nation. It is known to all that the economies of the most of the developing nations are predominantly agrarian in nature. Agriculture is the main pillar of those economies because most the people of the developing nations live in villages and earn their living from farming and trades related to farming. Rural industries in those economies are often seen to have been very much dependent on agriculture. In the countries like India and Bangladesh, for instance, rice- milling is an example of rural industrial activities. Before rural electrification, labour- intensive dhenki method was used to process rice and hence dhenki constituted the single largest source of non-farm employment for rural women. After electrification, dhenki was replaced by rice milling. In both the cases (i.e. before electrification and after electrification), rural non-farm economy has remained heavily dependent on agriculture as well as agriculture-based off-farm activities except some specialized craftsmanship. A traditional (agricultural) society may gradually start moving towards modernisation through diversification of rural economy, in the first place, from agriculture to agriculture-based industrial activities and thereafter to pure non-agriculture. Therefore, for modernization, industrialization and prosperity of rural sector, diversification of rural Issues Relating to Rural Industries: A Review of Literature
economic activities is very important—especially in those countries where marginal and landless peasants constitute a sizeable population. But diversification of rural agrarian economy, according to some economists, finds viable route of prosperous ladder only after the economy is self-sufficient in producing agricultural output. There are some other scholars who reserve their rights to give agriculture all credit for bringing about successful rural industrialisation. They believe that agricultural growth may be a necessary condition for the development of rural non-farm sector but not a sufficient condition. This debate has been vividly described in this chapter amongst other issues. In this connection, development of village infrastructure is an inevitable issue for a complete discussion on rural industrialisation which, in turn, relates to the issue of urbanisation. In this context, we can refer to Deininger and Olinto (2001: 455) who opine: “There is little doubt that the importance of rural non-farm employment, which in many countries already constitutes an important sector of the rural economy, will greatly increase as agriculture becomes more and more integrated into global markets and as the links between urban and rural areas intensify.”
The organisation of the chapter is as follows. Section 3.2 offers a brief presentation of the discussion on agriculture-industry linkage in the classical political economy. Section 3.3 gives us the categorization of the rural non-farm sector. In section 3.4, Kuznets’s Growth Theory is presented. An econometric model on the relationship between agriculture and industrial growth is presented in section 3.5. Section 3.6 discusses the theories and theoretical debate regarding the rural growth linkages. The role of agriculture in Japanese industrialisation is briefly described in Section 3.7. The next section, i.e. section 3.8, highlights the reasons for household participation in rural non-farm activities. Section 3.9 discusses the non-farm jobs as secondary and seasonal employment. Sectoral composition of rural non-farm employment is described in section 3.10. Section 3.11 offers the Hymer-Resnick Model and Ranis-Stewart Models which raise the agriculture- rural industry linkage debate through the theoretical models. We present relevant discussion on urbanisation in section 3.12. The chapter concludes with the section 3.13.
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3.2 Agriculture-Industry Linkage: The Classical Political Economy Framework
3.2.1 The Physiocrats and Adam Smith
The analysis of the linkages between agriculture and the rest of the economy dates back to Quesnay in 18th century France (Harriss, 1987; Bharadwaj, 1987; Eapen, 1999). Some members of the Physiocratic School laid great stress upon the agricultural sector which produced an economic surplus or net product (“produit net”) and so played a very significant role in economic development (Gide and Rist, 1915).
Quesnay distinguishes three social classes:
I. The first is a productive class consisting entirely of agriculturists—perhaps also of fishermen and miners. II. The second is a proprietary class, including not only landed proprietors, but also any who have the slightest title to sovereignty of any kind in the society. This class is seen as the bearers of the responsibility of survival of feudalism, where the two ideas of sovereignty and property are always linked together.
The identity of this second class needs to be explained further. The classification of basic social groups in the Physiocratic model was made with primary reference to the relation in which each group stood to the net product. The main distinction which they emphasized was that between the ‘productive class’ (i.e. those engaged in agricultural production) and the ‘sterile class’ (i.e. those engaged in non-agricultural activities). The sterile class has been defined in the third category (see below). In the no-man’s-land between these two classes, partaking to some extent of the character of each but belonging definitely to neither, lay the class of proprietors. This class consisted of the landowners, the king, and the clergy, who were assumed to receive, in the form of rent, taxes, and tithes respectively, the value of the net product which agriculture annually yielded. (Meek, 1962: 20)
73 Issues Relating to Rural Industries: A Review of Literature
III. The third is a sterile class, consisting of merchants and manufacturers (mainly constituting self-employed artisans and craftsmen),1 together with domestic servants and members of the liberal professions.
To the Physiocrats, the agriculturists were seen to be the productive class. Hence, the first class, being the only productive class, must supply all that flow of wealth. But agricultural products alone do not suffice for the upkeep of Class I. Gide and Rist (1915) argue that Class I also requires manufactured goods, which it must get from the sterile class. On the other side, salaries obtained by the sterile class are employed in buying the necessaries of life and the raw material of industry.
The most strange part of this theory is that the class III includes manufacturers on the one hand but ignores manufacturers as a productive class on the other (agriculturists were considered to be the only productive class by Quesnay). What led Quesnay to his conclusion, which astonished his contemporaries no less than subsequent generations of economists, was a belief that industry as constituted in France in the seventeenth and eighteenth centuries could make no kind of net contribution to the nation’s tax revenues. Its ‘net product’ (produit net), or taxable capacity, or economic surplus, was zero, which meant that at best, if its support cost the rest of society nothing, it could make no contribution to the military and welfare needs of the state (Eltis, 1988). In less favourable circumstances where industry actually needed to be subsidised or protected, such diversions of real resources would impoverish the primary producing sector which provided the surpluses on which French governments relied. Criticism came up from the fact that manufacturing and commercial states like Venice and Holland had accumulated wealth and power, so how could it possibly be argued that agriculture provided the ultimate source of all wealth and of all net government revenues? Quesnay insisted that taxable industrial and commercial profits could arise only where businesses had managed to achieve elements of monopoly power. Eltis (1988) said that this had arisen in Quesnay’s Europe in a variety of ways. States frequently granted monopoly privileges to political supporters, or else they sold future monopoly rights for current cash, or they allowed corporations with monopoly power to emerge by protecting their own countries’ industries. Such policies were prevalent throughout Europe and they had allowed
1 The artisan (manufacture) sector was believed not to produce any surplus (or net product) and
74 Chapter 3 extremely profitable corporations to emerge. And it was also true that industrial innovators could sell at monopoly prices, but these would disappear as soon as others learned to make the same new products. In addition, France’s great jewellers and furniture makers of the ancien régime had temporary monopoly-rights over their distinguished products which allowed them to sell at home and overseas at very high prices which yielded financial surpluses.
Whatever be the argument, in the context of our core discussion the significance of Quesnay’s theory is that the linkage between class I and class III, i.e. linkage between agriculture and manufacturing (although sterile) has gained recognition in the Physiocratic school of thoughts.2
In contrary to Quesnay, Adam Smith stressed upon manufacturing sector by recognising that industry contributes also to ‘net product.’ While talking about natural progress of wealth or opulence Adam Smith divided the “course” into three different stages. The greater part of the capital of every growing society is, first, directed to agriculture, afterwards to manufactures, and last of all to foreign commerce. Therefore, the great similarity between Quesnay and Adam Smith is that both of them discussed the role of agriculture in the process of economic development. Adam Smith believed that agriculture had the potential to provide a vastly greater economic surplus than industry, but in his judgement the surplus industry offered was not zero (Eltis, 1988). In addition, hence called sterile. 2 In this context, we can refer to Meek (1962: 21-22) who has described such linkage concept from the Physiocrats’ point of view. If the net product is increasing from year to year, the level of agricultural output and, therefore, the general level of economic activity will rise. An increasing net product means, in the first place, that the landowners will spend more on agricultural produce; they will also spend more on manufactured goods, and the makers of these goods, whose incomes are thus increased, will in their turn spend more on agricultural produce; thus the aggregate demand for agricultural produce will increase, output will be stimulated, the net product will increase further, and the general level of economic activity will rise progressively from year to year. An increasing net product means, in the second place, that more will be available for investment. The landowners will have more to spare for investment in improving their land; the agricultural entrepreneurs, in so far as they happen at the time to be sharing in the net product, will have more to spare for maintaining and expanding their fixed and working capital; thus aggregate investment in agriculture will increase, and once again output will be stimulated, the net product will increase further, and the general level of economic activity will rise progressively from year to year. The eventual result of this process will be the attainment of maximum level of output consistent with the state of the country’s resources and the existing techniques. If the net product is decreasing from year to year, on the other hand, a cumulative downward trend in the
75 Issues Relating to Rural Industries: A Review of Literature
industry could be expected to provide external benefits of great importance to the whole economy through the productivity advances associated with the division of labour. The important difference between Smith and Quesnay is that Smith believed industrial profits could include an element of economic surplus in the sense that industrial capitalists can save and invest from their profits, with the result that they have the potential to add to the growth of the economy. Smith’s main objection to Quesnay’s argument is of course its neglect of the enormous advantages a society can obtain from the division of labour which can be taken further in industry than in agriculture. The division of labour in industry leads to more extensive subdivisions of as well as expansion in industrial employment, and hence to the achievement of higher productivity through the invention of superior machinery. Smith argues that a country which successfully develops its industry can attain far more favourable terms of trade between agricultural produce and manufactures than one that is still without a substantial industrial sector. Although it is clear that Smith stressed more on industry than agriculture, he also mentioned of the interlinkage between agriculture and industry. According to him, the great commerce of every civilised society is carried on between the inhabitants of the town and those of the country. “The country supplies the town with the means of subsistence and the materials of manufacture. The town repays this supply by sending back a part of the manufactured produce to the inhabitants of the country” (Adam Smith, 1776: 479).
3.2.2 Karl Marx
In dealing with the process of capitalist development, Karl Marx believed that the capitalist relations would ultimately engulf agriculture as well as industry with a concentration of property in land, the proletarianisation of peasants, large productivity gains of capitalist agriculture, displacement of working capital and labour previously engaged in small-holdings and artisan manufactures.
Marx (1954)3 viewed the capitalistic system of production as the destructive force of rural domestic industry. We quote him from Capital (Vol. 1): “Formerly divided among a
level of economic activity will take place, leading either to complete ruin or to the achievement of a sort of sub-optimum equilibrium at a very low level.
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number of small producers, who cultivated it themselves and with their families spun it in retail fashion, it is now concentrated in the hand of one capitalist, who sets others to spin and weave it for him” (Marx, 1954: 698). Hence, the process started supplying wage- labour to industry. He added: “Formerly, the peasant family produced the means of subsistence and the raw materials, which they themselves, for the most part, consumed. These raw materials and means of subsistence have now become commodities; the large farmer sells them, he finds his market in manufacturers. Yarn, linen, coarse woolen stuffs—things whose raw materials had been within the reach of every peasant family, had been spun and woven by it for its own use—were now transformed into articles of manufacture, to which the country districts at once served for markets. The many scattered customers, whom stray artisans until now had found in the numerous small producers working on their own account, concentrate themselves now into one great market provided for by industrial capital. Thus hand in hand with the expropriation of the self-supporting peasants, with their separation from their means of production, goes the destruction of rural domestic industry, the process of separation between manufacture and agriculture. And only the destruction of rural domestic industry can give the internal market of a country that extension and consistence which the capitalist mode of production requires” (pp. 699-700). The thinning-out of the independent, self-supporting peasants brought about an increase in the number of industrial proletariat in the urban sector. In spite of the smaller number of the cultivators, the soil brought forth as much or more produce, after as before, because the revolution in the conditions of landed property was accompanied by improved methods of culture, greater co-operation, concentration of means of production and because not only were the agricultural wage-labourers put on the strain more intensely, but the field of production on which they worked for themselves, became more and more contracted. With the setting free of a part of the agricultural population, therefore, their former means of nourishment were also set free. The peasants, expropriated and cast adrift, must buy their value in the form of wages, from his new master, the industrial capitalist. Thus, formation of glut of agricultural produce was not possible. “That which holds good of the means of subsistence holds with the raw materials of industry dependent upon home agriculture” (pp. 697-698).
3 This edition was first published in 1954 by Progress Publishers, Moscow. We have quoted Marx
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There are two aspects in Marx’s view described above. Marx believed that agriculture and rural domestic industry go hand in hand, but capitalistic mode of production draws a separation between agriculture and domestic industry since in order to enter into the internal market the capitalistic production requires the destruction of rural domestic industry. Another aspect is implicitly present in the above text and we can find a connection of this subtext to the present situation of many developing countries. In the following, we discuss the second aspect and it may sound relevant in the light of the present discussion regarding rural industry although it is not related with the interlinkage between agriculture and industry.
Marx’s core argument, considering the capitalistic system of production as the destructive force of rural domestic industry, is still very pertinent in the context of the present situation of the many developing countries, especially when we see that the multinationals or the big business houses are entering the markets of rural domestic industry. A glaring example, given by Taori and Singh (1991), is of potato wafers, the demand for which is estimated to be of the order of Rs 400 crore4 out of which Rs 40 crore worth is supplied by the large-scale sector and the remaining still by small entrepreneurs, shopkeepers etc. So many rural entrepreneurs, scattered across the country, are self-employed in the small-scale potato wafer industry. They may lose their incomes if the big companies enter their markets. However, because of aggressive marketing, the demand for potato wafers in the large-scale sector is recording a growth of 30 per cent per annum. The days are not far off when television advertisements will have a tremendous impact on the rural population as well, replacing the local sources of supply. With the onslaught of the modern expensive advertisements on television which covers a large share of the population with novel noises of sales campaign and packaging, the rural industries have a difficult time for surviving.
In the classical political economy framework, the agriculture-industry linkage has been articulated very lucidly. The expansion of non-agricultural activities has been viewed primarily in terms of the changing relationship between agriculture and industry. Agriculture, initially, is the mainspring of all economic activities, including within its fold very simple and primitive forms of industry viz. processing of raw materials within
here from the 1986 edition of the book.
78 Chapter 3
the household or artisan production which may sometimes be a supplement to a peasant agriculture. Industry at this stage is considered more an appendage of agriculture, in which situation one can hardly speak of interlinkages. However, with the growing division of labour and increasing specialisation in the process of capitalist development, industry separates spatially and organisationally from agriculture and the sectoral distinction becomes clearer. Over time the interlinkages between agriculture and industry grow through the progress of technology and diversification of production in terms of inputs and outputs (Eapen, 1999).
3.3 Categorisation of the Non-farm Sector
The discussion so far points to the fact that the non-farm sector does not consist of a homogeneous set of activities in terms of income and productivity levels. While observing the non-homogeneous characteristics, duality in the non-farm sector has been identified by the two researchers in relatively early studies. Mukhopadhyay and Lim (1985) suggested that the rural non-farm sector consists of two sub-sectors. Sub-sector 1 are enterprises run on a more or less stable basis with an eye on surplus generation and growth, using primarily hired labour and a certain degree of technological sophistication. Sub-sector 2 consists of products and/or activities which are often, though not always, seasonal, which are run with the help primarily of unpaid family labour, using rather primitive technology, catering mostly to the local market and responding more to the supply side of the labour market than to the market demand for output. There are both labour supply side and product demand side stimuli to the growth of rural non-farm sector, or ‘push’ and ‘pull’ factors from the point of view of labour absorption. Sub- sector 2 substantially responds to the supply side of the labour market because of the fact that this sub-sector is very much labour intensive whereas rural labour force is primarily dependent on agriculture. Therefore, sub-sector 2 finds an inflow of labour force in the slack agricultural seasons since agricultural labourers lose their jobs in farming during slack period.
4 Rs 1 crore = Rs 10000000.
79 Issues Relating to Rural Industries: A Review of Literature
Rural industry has also been categorised, based more on the products, into three types. First, the production of low quality and cheap variety of goods meeting certain consumption needs, using locally available raw material and primitive techniques. Second, agro-processing industries. Third, transitional location of modern industry in rural areas which leads over time to these areas being absorbed as urban centres (Chandrasekhar, 1993).
Another interesting categorisation is based on the locational versus the linkages approach to rural industrialisation (Saith, 1991). The definition of rural industry in the first approach is primarily based on location in rural areas. It helps as a safety valve to contain urban congestion. In the second approach rural industry is viewed from the point of whether it generates sufficient linkages in the rural sector. In Saith’s (1991) argument, preference is given to the linkage approach, and the sector is understood to include all economic activities which display sufficiently strong rural linkages, irrespective of whether they are located in designated rural areas or not. The following four categories thus emerge:
(a) rural-located, rural-linked; (b) rural-located, urban-linked; (c) urban-located, urban-linked; (d) urban-located, rural-linked.
Only the third category is completely urban. The other three categories, connected either through location or linkage with the rural population, cover a remarkably wide variety of activities and enterprises, ranging from household-based cottage and handicrafts production to relatively medium sized, modern, and complex industrial plants. Which approach—locational or linkage—is found more meaningful depends upon the objectives of policy and the concrete circumstances of the economy. Saith (1991) has argued that it is necessary to emphasize that the generation of rural linkages is not connected with a deep rural location. Whether a locational criterion is super-imposed on the linkage one would depend upon the case with which resources, including the rural labour force, could move from their rural residential locations to urban work-places. The rural industrial sector should be viewed from the rural end, and the key criterion for defining an industrial enterprise or other economic activity as ‘rural’ is whether it generates
80 Chapter 3
significant developmental linkages with the rural sector. According to Saith, one simple index of the intensity of linkage effects could be the percentage of the gross output value of the enterprise that is accounted for by the rural sector either through receipts for rural raw materials purchased by the enterprise, or through income flows received in the form of wages or profits for labour or capital provided by the residents of the rural sector. Restricting the index to the disposition of the value added by the enterprise would be inappropriate since it would exclude the linkage through the raw material purchases made in the rural sector. Not all industries located in designated rural areas would necessarily show high levels of rural linkage. This might be especially true for cases where modern medium or large-scale industrial enterprises are being invited through incentives to relocate their plants in designated rural area. Although these industries are entitled to take the pressure off the urban centre, they might still operate within the high urban linkage effects more or less intact. So the policy makers should be aware of this phenomenon of rural linkage effects.
Now we turn to the three sources of demand, mentioned below, for the products and services of rural non-farm activities. World Bank (1978) has made the following categorisation:
1. Non-food consumer as well as durable (e.g. wooden furniture) goods and services, the demand for which rises as rural incomes increase; 2. Inputs and services to agriculture (including tools and equipment, repair services, transport, processing, and supporting infrastructure and works), the demand for which rises with agricultural development; 3. Manufactured goods and handicrafts, the demand for which stems from markets in other regions or from abroad. This category of products has no direct demand linkage with the local rural area and is mainly produced for the specific target buyers of other regions.
Almost the same kind of categorisation has been made by Islam (1987). According to him, the broad components of the demand for products of rural industries are: (a) household demand, (b) intermediate demand and (c) export demand. A large part of the products of rural industries are meant for final consumption by rural households or use as inputs in the agricultural sector.
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3.4 Kuznets’s Growth Theory
What is the position of the rural non-agricultural sector in the growth models? The models have divided the whole economy into two broad sectors—agriculture and industry—as it was observed in the classical political economy framework. Promotion of the rural non-farm economy as an employment-oriented strategy has assumed no significance in most of the growth theories. Saith (1992) has pointed out that economic theorising within both the classical and neo-classical traditions, as well as analyses of structural transformation along the lines of Clark, Kuznets and Chenery, have generally ignored the existence of the rural non-farm sector. In the broad sweep of the process of economic transformation of an agrarian economy into a modern industrial one, this subsector, sandwiched between two major ones, has implicitly been regarded as a manifestation of incomplete transition to the full-blown industrial economy where the rural sector sheds all its non-farming functions, which are regrouped within the modern sector.
Let us take Kuznets’s views as example. Kuznets has talked about the direct linkage between agriculture and non-agriculture. The important features are:
1. Expansion of the non-agricultural sector is strongly reliant on domestic agriculture, not only for a sustained increase in the supply of food, but also for raw materials used in manufacturing products such as textiles. 2. Because of the strong agrarian bias of the economy during the early stages of economic growth, the agricultural population inevitably forms a substantial proportion of the home market for the products of domestic industry, including the market for producer goods as well as consumer goods. 3. Because the relative importance of agriculture in the economy inevitably declines with economic growth and development, agriculture is seen as a principal source of capital for investment elsewhere in the economy. Thus the development process involves the transfer of surplus capital from agriculture to the non- agricultural sector. Similarly, development also entails the transfer of surplus labour from agriculture to non-agricultural occupations, especially over the long term.
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In the above features, Kuznets has made it clear that during the early stages of development agriculture serves as an engine of non-agricultural growth, but in the long run as non-agricultural sector grows the relative importance of agriculture declines caused by resource transfer from agriculture to non-agriculture. This “long run” relationship between agriculture and non-agriculture has been shown by Kuznets mathematically. He found an inverse relationship between these two sectors in the long run.
An expression derived from Kuznets shows how the agricultural sector’s share of GDP growth is related to the product of agriculture’s initial share of GDP and the relative rates of growth of agricultural and non-agricultural net products (see, for example, Ghatak and Ingersent, 1984). Let
Pa = agricultural net product
Pn = non-agricultural net product P = total national product Then:
P = Pa + Pn (1) and
∂Pa ∂Pn ∂P = Pa + Pn (2) Pa Pn
Writing ra for ∂Pa / Pa and rn for ∂Pn / Pn :
∂P = Pa ra + Pn rn (3)
Therefore, Pa ra = ∂P − Pn rn (4) P r P r and a a = 1− n n (5) ∂P ∂P Substituting for ∂P on the right hand side of equation (5) from equation (3): P r = 1− n n Pa ra + Pn rn P r = a a Pa ra + Pn rn
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1 = ()Pa ra + Pn rn / Pa ra 1 = (6) 1+ Pn rn / Pa ra
Kuznets’s formula expressing an inverse relationship between agriculture’s share of GDP growth ()Pa ra /∂P and the product of the ratio of sectoral shares of GDP ()Pn /Pa and the ratio of sectoral growth rates ()rn /ra is given by equation (6). This equation implicates that in a developing economy where per capita incomes are rising, growth in the agricultural sector can be expected to lag behind non-agricultural sector. It means that in a rapidly growing economy non-agriculture starts playing more important role than agriculture (although Kuznets, as we have seen before the above mentioned mathematical presentation, has recognised the crucial role of agriculture during the early stages of economic development. in the mathematical presentation he has talked about an economy which is not greatly dependent on agriculture, rather where non-agricultural sector has already gained momentum). This may happen for three reasons. First, the demand for food and other agricultural products is generally less income-elastic than the demand for non-agricultural products. Mellor (1976) observed that agricultural labourers in India, represented mainly by landless workers, spend over three-quarters of increments to consumption on agricultural commodities and 59 per cent on foodgrains alone. He also observed that, in successively higher expenditure classes, the proportion of increments to expenditure spent on foodgrains declines rapidly. Second, due to scientific advances and associated technological innovations in agriculture farmers become increasingly reliant on inputs purchased from the non-farm sector of the economy. Third, because the demand for off-farm marketing services—distribution, storage and processing—is more elastic than the demand for agricultural products at the farm gate.
In recognising the development trends making for the declining relative importance of the agricultural sector in the long term, it is necessary to avoid the trap of overlooking the critical importance of domestic agriculture’s product contribution to the maintenance of an adequate rate of economic growth in the short term. This is the trap that a number of developing countries have fallen into in opting for a strategy of rapid industrialisation without parallel development in agriculture (Ghatak and Ingersent, 1984). This kind of
84 Chapter 3 strategy hampers growth process due to the fact that levels of agricultural productivity per capita are too low to sustain rapid industrialisation on the basis of a home market. A direct leap to more advanced modern industry is rendered inefficient on account of a wide array of absorption problems which raise the incremental capital-output ratio to extremely high levels. Due to high capital-output ratio large industries provide no scope for raising huge employment opportunities as well as purchasing power at mass level. On the one hand, these industries are unable to enter the export market and, on the other hand, domestic markets are too narrow to sustain such an industrial sector at anywhere near optimal capacity utilization. In such a situation, agriculture is viewed as a prior phase necessary for laying the basis for industrial acceleration (Saith, 1992). In India, for example, the capital-goods-oriented Mahalanobis industrialisation strategy has been unable to generate employment at an acceptable rate. The debate on whether agricultural development can lay the foundation stone for industrial acceleration is described in section 3.6.
In this perspective, Kuznets’s work is significant for two reasons. First, it gives importance to agricultural growth as an engine of non-agricultural growth. Second, it proves that with the increase in non-agricultural income over time the agriculture’s share to GDP goes down. But a tremendous vacuum shows off in his approach when he remains completely silent about the rural non-agricultural economy as a crucial separate existence in the analysis of the development process. He discusses the non-agricultural (industrial) sector of an economy as a whole but does not involve the critical role of rural non-farm activities during an economy’s transformation from traditional situation to modernisation. It is interesting that the Lewisian framework (very briefly described herewith) too, despite focusing essentially on the analytic of the very transition, ignores the rural non-farm economy, except for an aside which despatches ruined artisanal groups to the same lifestyle and mobility path as surplus labour in agriculture. Lewis model viewed the problem of surplus labour absorption as essentially as one of intersectoral labour transfer through industrialisation which would accelerate capital accumulation and also raise per capita output in the economy since industry, typically, was more productive than agriculture. The steady expansion of the capitalist industrial sector would draw upon the “unlimited supply” of rural labour from a low productivity agricultural sector at a constant real wage to the point labour was no longer infinitely elastic (Lewis, 1954). From this “turning point” which would suggest a tight labour market situation, the
85 Issues Relating to Rural Industries: A Review of Literature benefits of industrial expansion would trickle down to workers and the rural population through higher wage rates. The process of industrialisation, therefore, was expected to remove underdevelopment and rural poverty. This whole framework holds no position for the rural non-farm economy during labour transfer from agriculture to industry.
3.5 The Relationship between Agriculture and Industrial Growth: An Econometric Model
Hwa (1989) has developed a model based on the relationship between agriculture and industrial growth. According to him, the relationship between agriculture and industry is one of interdependence and complementarity. For example, agriculture supports industrialisation by providing a source of labour, capital and raw materials to other sectors, and by generating demand for industrial products. At the same time, agriculture receives from industry modern farm inputs, advanced technologies, and consumption goods to increase its productivity.
For testing the statistical significance of this relationship, Hwa has developed the following non-linear model of the Chenery-Syrquin (1975) type, which relates the rate of
. . industrial growth ( Ι ) to per capita income (YN) and the rate of agricultural growth ( Α ):
. . Ι = f [ Α , lnYN, (lnYN)2] + u, (1)
where u is a randomly distributed error term.
The model is derived in the following way. First, assume that the rates of growth of industry and agriculture are both non-linear functions of per capita income variables:
. 2 Ι = αI lnYN + βI (lnYN) + εI (2) and
. 2 Α = αA lnYN + βA (lnYN) + εA, (3)
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where εI and εA are random errors.
These are simplified reduced form models for the determination of industrial and agricultural growth. The per capita income variables are used as summary measures of the stage of economic development, in addition to being measures of final demand. Alternatively, these regressions can be thought of as establishing ‘norms’ for the rates of growth of industry and agriculture with reference to the stage of economic development, as measured by the level of per capita income. It is possible to test the hypothesis of whether countries with higher industrial growth in relation to ‘normal’ industrial growth are also those with higher agricultural growth with reference to its norm. This can be made by regressing the residuals in (2) on the residuals (3):
εI = γεA + u, γ > 0, (4) where u is a randomly distributed error term.
Substituting (2) and (3) into (4) and rearranging the terms yields:
. . 2 Ι = γ Α + (αI - γαA) lnYN – (γβA - βI) (lnYN) + u. (5)
This equation is the explicit form of (1). It also implies that the disparity between
. . industrial and agricultural growth, Ι – Α , is a second order non-linear function of per capita income.
The estimation of (5) is conducted using two cross-country samples: one consists of 63 countries for the decade of the 1960s and the other has 87 countries for the decade of the 1970s. Both samples include developing as well as developed countries. The estimation results based on the ordinary least square (OLS) method are presented in Table 3.1.
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. Table 3.1: Estimated regression coefficients for annual industrial growth Ι
Independent variables Equation Period lnYN (lnYN)2 . Constant R2 Α I 1960-70 9.277* - 0658* - - 24.331* 0.12 (2.7) (2.8) (2.1) II 1970-79 13.068** - 0.904** - - 40.594** 0.13 (3.4) (3.4) (3.1) III 1960-70 6.548 - 0.458 0.491* - 16.730** 0.18 (1.8) (1.8) (2.1) (2.1) IV 1070-79 9.477** -0.649* 0.722** - 29.873* 0.28 (2.6) (2.6) (4.1) (2.4) Notes: The numbers under the respective coefficients in parentheses are t-statistics. The coefficients with a significance level above 5% are indicated by * and those above 1% by **. The notations have the following meanings: . Ι = The average annual rate of growth of industry, comprised of mining, manufacturing, construction, and electricity, water, and gas. YN = GNP per capita in 1970 and 1979 US dollars respectively, for the 1960-70 sample and the 1970-79 sample. . Α = The average annual rate of growth of agriculture. Source: Hwa (1989)
According to Hwa’s analysis, the regressions with the per capita income variables alone (equations I and II) depict a parabolic curve, a result that indicates that at a relatively low level of income, industrial growth will increase as per capita income increases, and that when per capita income reaches a certain level, the rate of industrial growth will reach a maximum and then taper off. This outcome, as Hwa states, confirms the hypothesis as formulated in (2).
Equations III and IV show that the growth rate of agriculture is a statistically significant variable in explaining industrial growth and that it has raised the R2s significantly for both samples, especially for 1970-79. Although the statistical significance of the per capita income variables were reduced, the results unambiguously confirm the hypothesis that countries with the above-normal performance in industry are also those associated with the above-the-norm performance in agriculture over the development process that is manifested through a continuous rise in per capita income.
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In the above model, Hwa concludes that agricultural development leads to industrial growth via an increase in per capita income. This was his hypothesis too. In testing his hypotheses, he has taken only three explanatory variables as mentioned above. This has practically made his study too narrow to examine the influence of other factors/variables on industrial growth as well. Besides, simultaneity bias in the model has not been taken into consideration. For example, Hwa has not checked whether or not agricultural growth or per capita income could be influenced by industrial development. In all sense, the model failed to form a complete structure. Lastly, or should it have been firstly (!), the model has been termed by Hwa as a non-linear econometric model which is actually nothing but a linear model.
In the same article, Hwa has developed another model, more explicitly a production function model following the Cobb-Douglas production function, based on the relationship between agriculture and overall economic growth.5 Both the models have
5 Hwa has used the following Cobb-Douglas production function following the Balassa (1987) model:
Y = CKαLβelog R (1) where Y = gross domestic product; C = a scale parameter; K = capital stock; L = the labour force; and elog R = the rate of technological change over time, which is taken to be synonymous with productivity change.
Rewriting the variables in (1) in terms of the rate of change over time yields: . . . . Y = α K+ β L+ R. (2) . In the literature of production function analysis, the productivity change ( R ) in production function (2) is frequently treated as a ‘residual’, and the production function is estimated accordingly. The argument presented assumes that the rate of productivity change is positively . . influenced by both the rates of agricultural growth ( Α ) and export growth ( X ) but is negatively . related to the rate of inflation ( P ): . . . . R = a + γ Α + θ X + η P + ε , γ, θ > 0, η < 0, (3) where a is a constant term and ε is a residual, assumed to be randomly distributed. Combining (2) and (3) yields:
...... Y = a + γ Α + α K + β L + γ Α + θ X + η P + ε (4) . where Y = the average annual rate of growth of GDP;
89 Issues Relating to Rural Industries: A Review of Literature reserved no place for rural non-farm economy or rural industrial sector as a vital player in the process of development. The problem lies in the fact that rural non-farm sector is not seen as an independent contributor to economic growth and it is a concept which is not true.
3.6 Linkages: Theories and Debate
It is recognised by many economists that linkage mechanism plays an important role in economic activities. Linkages between agriculture and industry have been focal issue in many academic works. In the following we are going to discuss the views of many authors regarding linkage mechanism.
Hirschman (1958) has developed the concept of production linkages as inducement mechanisms for stimulating economic activities through backward and forward linkage effects. He defines linkage from the standpoint of supply of output to other production sectors and demand for input from other sectors. The backward linkages relate to the input provision or derived demand, i.e., every non-primary economic activity will induce supply of the inputs needed in that activity through domestic production. The forward linkages relate to the output utilisation, i.e., every activity that does not by its nature cater exclusively to final demands, will induce attempts to utilize its outputs as inputs in some new activities. This has been schematically shown in the Figure 3.1. (For a detailed scheme of linkages between agriculture and small scale industries adapted from Harriss (1987) based on a study in North Arcot in India, see Figure 3.2).
. K = the average annual rate of growth of capital, proxied by the average investment rate; . L = the average annual rate of growth of the labour force; . X = the average annual rate of growth of exports; . P = the average annual rate of inflation.
Please note that although equation (4) includes the export and agricultural growth rates as regressors, it is essentially a production function and is not a national income accounting identity.
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Figure 3.1: Forward and backward production linkages
FORWARD PRODUCTION LINKAGE BACKWARD PRODUCTION LINKAGE
ECONOMIC ACTIVITY ECONOMIC ACTIVITY WHICH SUPPLIES ITS OUTPUTS AS WHICH INDUCES DEMAND FOR INPUTS INPUTS
TO FROM
SOME NEW ACTIVITIES DOMESTIC PRODUCTION
It is important to note here that in defining backward linkages the supply of inputs through imports from outside (not always through domestic production) should also be taken into consideration and this may be termed as leakage of “local” growth linkages.
Nurkse (1961) has advocated the basic principle of “linked progress” in the two broad sectors, farming and manufacturing. He describes that in a very poor country a given increase in a manufacturing is likely to require a greater agricultural advance than in one that is not so poor. Conversely, a given increase in food output is likely to support a larger increase in manufacturing in an area where income per head is already fairly high than in one where it is still very low. According to him, the problem of low-income area is this: there is not a sufficient market for manufactured goods in a country where peasants, farm labourers and their families, comprising typically two-thirds to four-fifths of the population, are too poor to buy any factory products, or anything in addition to the little they already buy. There is a lack of real purchasing power reflecting the low productivity in agriculture. The other side of the same coin is that the local economy cannot supply the food needed to sustain the new industrial workers. Therefore, industrial development for domestic markets requires a complementary advance on the farm front,
91 Issues Relating to Rural Industries: A Review of Literature a rise in agricultural productivity. The equilibrium relation between the two rates of advance may vary in the course of time as well as between countries. But farming and manufacturing, as Nurkse emphasises, must move forward together, though not necessarily at the same rate.
The effort to specify the linkages, empirically and quantitatively, between these two sectors is much more recent (Harriss, 1987). Of particular importance was the expenditure linked multiplier effect of growing agricultural income, viz. consumption linkages, in generating rural based, small-scale, labour intensive industries, highlighted by Mellor (1976) in his “new strategy of agriculture-led growth” (see also Johnston and Kilby, 1975). Mellor’s argument is that increased food production based on cost decreasing technology can make large net additions to national income. If this income accrues especially to larger cultivators (say, those in the upper middle deciles), a large portion of it will be spent on non-agricultural goods and services. But Islam (1986) has stressed on egalitarian growth in agriculture. He stated, “…if the benefits of growth in agriculture are not widely distributed, the pattern of consumer demand will not change [in this manner] and hence this source of incentives for non-farm activities may not materialize” (pp. 172). This is a hypothesis which is not tested in Islam’s paper, but it seems to contrast with Mellor’s argument because Mellor finds larger cultivators’ additional income as crucial generator of demand for rural non-farm goods and services whereas egalitarian growth approach of Islam emphasizes wide distribution of agricultural income amongst rural people. Saith’s comments are also significant in this regard. He has described Mellor’s version of “Agriculture First” (AF) strategy as the most complete and internally consistent one. In this connection, Saith has explained: “AF, especially when it is not based on a concentration of resources on the large farmer, is likely to generate diverse forward and backward linkages on the consumption and production sides. These are likely to induce a local supply response from the rural non- farm economy, thus creating a virtuous circle of rural development. Such expansion is also likely to be characterized by much higher labour absorption rates; over time, such a successful rural process is also bound to widen markets for the mainstream industrial sector, and thereby lay the basis for a sustainable form of development based on an expansion of the home market” (1992: 103). In a study on China, Ho (1986) has argued that the interlinkage between agriculture and rural industries followed the classic pattern of enlarging the market for the latter’s products and helping the former’s growth through
92 Chapter 3 a more efficient supply of inputs, including labour. UNDP et al (1988) have stated that consumer demand caused by growth in agricultural income provides the major impetus for rural industrialisation.
Figure 3.2: Agricultural linkages of registered small-scale industries (number of units)
FORWARD PRODUCTION LINKS
Agro-based consumer goods
Machinery Primary Agro-processing
Machinery
Transport
Machinery
AGRICULTURE
Capital goods Inputs
Machinery/Spares Machinery
BACKWARD PRODUCTION LINKS
Source: Adapted from Harriss (1987)
In addition to the above, there is much debate on Mellor’s argument. Harriss (1987) has pointed out that the size of the multiplier effect, which was emphasized by Mellor, not only depends on mere rise in agricultural income but at the same time also depends on: 1) whether the growth linkages of agriculture6 are stronger or weaker than those of industry; 2) whether consumption linkages are stronger or weaker than production linkages and 3) whether local linkages are stronger or weaker than non-local linkages. These points may be clearer from the conclusions of her work. In a complex setting of North Arcot (a
6 Mellor (1976: 161-162) has defined growth linkages of agriculture in the following way: “…the increased efficiency of technologically advanced farming allows a large net increase in national income, which provides the dynamics of growth led by the agricultural sector.”
93 Issues Relating to Rural Industries: A Review of Literature district in the state of Tamil Nadu in India), Harriss (1987) found that the non-farm economy in that agrarian region was led by more than rice (the main crop of the region) and more than agriculture. In particular, an examination of employment and incomes in mercantile, industrial and government activities revealed not only a massive concentration but also the existence of a sizeable economic sector where income and demand for income elastic goods are relatively high. Secondly, while agricultural growth might have stimulated industrial expansion, such expansion was seen in activities which were production linkages, both backward (inputs) and forward (processing),7 and not in activities which could be regarded as consumption linkages. And, thirdly, production activities have been found to have important non-local links. So it was not always local industry that benefited, and that when the industry was local, it was not necessarily small- scale, nor indeed labour intensive. The industry that is generated in the growth linkage process might prove to be overwhelmingly urban.
In a detailed study on Uttar Pradesh in India, Papola (1987) has concluded that rural industrialisation needs to be viewed not merely as an adjunct to agricultural growth but as an independent element of a strategy for generating non-agricultural employment and incomes in rural areas. He suggests that input supplying and servicing activities like manufacture and repair of agricultural implements are likely to develop in rural areas with agricultural growth. But, at the same time, he argues that higher incomes generated by agricultural growth is found to facilitate an improvement in the situation of some rural industrial activities producing goods of general use and capable of adapting to new pattern of demand, but the larger volume of agricultural produce available for processing tends to shift the processing industries to town. Thus, the hypothesis, as formulated by Papola (1987), that agricultural growth leads to industrialisation in rural areas both in terms of diversification and improved performance seems only partially validated in the Indian case. Therefore, he concluded that rural industrialisation “would have to be seen in a perspective wider than that limits it to the needs of and opportunities provided by agricultural growth. In that sense, it has to be a part of the policy and strategy of industrialisation in general, and location and diversification of industries in particular, and not merely a programme of protection and promotion of village and agricultural related industries” (p. 106). Harriss (1991: 455) has argued that “agricultural growth may
7 In the study, the author has found that forward production links are more important than
94 Chapter 3 be a necessary condition for rural diversification…, it is certainly not sufficient.” Dunham (1991) has also argued that, in a dynamic situation where agricultural productivity and incomes are increasing, the potential for local growth in the non-farm sector is likely to depend on a range of circumstances. Important amongst these will be (1) the nature of the particular product or service (and especially whether or not it is technically ‘inferior’8 to those imported); (2) the industrial history of the area; (3) the accessibility or degree of closure of the local economy; and (4) purchasing power and the size of the local market. Dunham stated that if large farms are numerous and there is a high inequitable distribution of assets and income, then Mellor’s thesis seems likely to be justified (because the rich would spend the money for non-food commodities and services). But the situation is not always as clear-cut as that. It is by no means clear that large-scale commercial farms will determine consumer demand for non-farm goods. According to Dunham, the pattern of demand that emerges is likely to be dependent on agrarian structure consisting of three factors mentioned below:
1) Farm size distribution: The reason is that distribution of money income in the rural agrarian sector is broadly consistent with that of land; 2) Household size (the number of mouths): The relatively larger household size would create higher demand for non-farm goods and services provided that the household belongs to upper or upper-middle deciles; 3) Values that shape the consumption behaviour of rural households within a particular area: The traditional households who are always hesitant to accept modernisation may hamper the growth of new industries with innovative product or service concepts.
Chandrasekhar (1993) has also questioned the idea of combined (agriculture and non- agriculture) rural dynamism. In a study on West Bengal, he observed that the demand for manufactures resulting from increases in income above a certain critical minimum is catered to by the urban-based units. Lastly, despite making a lengthy criticism, Dunham ultimately admitted that Mellor’s key study is of contemporary relevance for two reasons. First because expansion of rural non-farm sector, due to its higher employment backward production links.
95 Issues Relating to Rural Industries: A Review of Literature generation provision, is a growing policy priority. And second because many aspects of his macro framework are still very pertinent today. For instance, Mellor advocated a more open economy, stressing comparative advantage in international trade with the import of capital-intensive manufactures and the export of labour-intensive manufactures. He emphasized the need for strong anti-inflationary measures at national level in order to keep food prices down. To promote small-scale industrial sector, Mellor stressed on identification and removal of market bottlenecks—particularly with regard to input supplies and to infrastructure which were observed to be creating uncertainties for local industrial expansion. He favoured less government control and suggested to switch the government’s emphasis from regulation to facility support.
One thing has been very clear from different studies that agricultural development may not be the sufficient condition for rural non-farm growth but for increase in rural non- farm income agricultural development, of course, is regarded to be the necessary condition.9 Why agriculture is so important has been described in a recent study by Asian Development Bank (2000). They have argued that most rural non-farm employment in
8 Urban centres tend to be the locations for manufacturing based on methods of mass production and thus the urban industries have the opportunities to exploit the economies of scale. The conditions for rural industries are different. 9 Based on the particular experience of Latin America, Reardon et al (2001: 397) do not recognize agriculture even as a necessary condition for non-agricultural growth. They say that demand for non-farm goods and services can be driven by “motors” other than the agricultural sector. Demand is driven by any motor that raises local incomes and the pool of investment capital and thus increases in rural non-farm wage and self-employment through production and expenditure linkages. For example, an increase in tourism (service sector) can induce growth in manufactures (e.g., local wine-making) and in agriculture itself. Moreover, the “motor” does not even have to be local, as long as the local economy is “open” in that workers can commute and local farm and non-farm firms can sell to the area where the motor is churning. For example, a mine or a big city in a coastal region could induce non-farm employment growth in the nearby highlands. Behind this tendency in Latin America, Mellor (1989) found an argument. He said that there seems to be a tendency for agriculturally-led growth to be less effective in Latin America than in Asia. The most important reason for that is the tendency for the distribution of land in Latin America to be highly skewed. Since the addition to net national income from technological change in agriculture is distributed largely to landowners, the benefits are skewed to high-income people, with a consequent tendency for the goods comprising the marginal propensities to consume to have a larger import content and a higher capital intensity. The result is smaller domestic employment and income multipliers and greater dependence on foreign markets—which are innately less stable, more risky, and hence have lower net returns—for growth in food output. The consequence that lower efficiency in the conversion of agricultural growth has for overall growth in Latin America is a lower employment multiplier in the non-agricultural sector and hence lower economic returns to investment in agriculture. The solution lies with a broader distribution of land and a greater relative emphasis on technological change for small-holders. But this may be unsatisfactory because of poor land resources and the high costs of distribution to the small- holder part of bimodal agricultural production systems.
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Asia arises in services, trade, and household manufacturing activities. These are dominated by small, part-time, mostly family businesses that are highly labour-intensive. These activities depend to a large extent on local and regional demand. But where is effective demand in poor rural regions? Here is the importance of agricultural growth. It generates enormous purchasing power among the rural population for non-food consumer goods and services and therefore supports rapid growth in services and trade in rural areas, and provides a nascent market for an emerging manufacturing sector. Rapid agricultural growth supplies basic food, raw materials for agro-industry, and exports and frees up foreign exchange for the importation of strategic industrial and capital goods. It releases labour and capital to the non-farm sector. It reduces poverty by increasing labour productivity and employment in rural areas, by generating more remunerative opportunities for rural-urban migration, and by lowering food prices for all.
In a study Asian Development Bank (2000) has observed that while an agricultural revolution was necessary during the early stages of the transformation, not all the countries that experienced successful agricultural revolutions started to industrialise and grow rapidly. Several other key factors are also needed to enable countries to successfully convert agricultural growth into national economic growth. All the important ones are mentioned here:
• Agricultural growth must be equitable, so that it puts increased purchasing power into the hands of the rural masses and not just a privileged few. Although the green- revolution technology was basically scale-neutral, access to rural resources – particularly land – has been a key determinant of the equitability of agricultural growth. • A well-developed infrastructure in rural areas is required to foster the links between the farm and non-farm sectors. Villages need to be connected to local towns so that agricultural inputs and outputs can flow freely and so that people can go shopping. Rural towns also need good infrastructure, especially roads, electricity, schools, sewers, water, and communications, in order to attract new firms and to grow. In particular, rural areas must not be allowed to be left behind in the use of modern communications technology to forge links with larger markets. Since the transaction costs of poor infrastructure are borne unequally by the poor (because poor
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infrastructure increases disparities in rural-urban price relationships), improvements help level the playing field for the most disadvantaged. • As agriculture develops and food security diminishes as a major constraint, countries need to move quickly toward market liberalisation and pro-trade and pro-investment policies. Protecting domestic industries and overhauling exchange rates penalise agricultural growth and impede the development of competitive industries that should be at the forefront of export-led growth. They also shield a country from the new technologies that are embedded in many imports and that have the potential to raise economic efficiency and competitiveness significantly. • As agriculture develops, rural non-farm activities become more dynamic. Strong rural financial institutions are required to mobilise resources and allocate them efficiently to promote a wide array of economic activities. Both farm and non-farm enterprises and households demand a broad range of financial services, including savings facilities, not just credit. • Promotion of a legal and regulatory environment (e.g., to secure property rights and enforce contracts) that will help to promote trade, commerce, and manufacturing is necessary. • Investment in human capital and especially in rural education is necessary to ensure continued productivity increases in agriculture and to allow rural workers to be more readily absorbed in non-agriculture if necessary. Training should be provided in relevant technical, entrepreneurial, and management skills. • Industrialisation policies that foster the development of all kinds of rural non-farm activities and not just manufacturing are needed to be initiated and implemented. Despite the relatively small importance of manufacturing in rural employment, policy makers have been enamoured of this sector; rural industrialisation policies have showered manufacturing firms with all kinds of preferential tax, subsidy, licensing, and regulatory benefits, as well as with targeted and subsidised credit and technical assistance programmes. Moreover, these policies have typically favoured larger capital-intensive manufacturing firms (the lure of the shiny rice mill or shoe factory), and neglected small labour-intensive firms and informal household manufacturing activities. It is necessary to revamp rural industrialisation policies to (a) be more inclusive, perhaps by redefining them as “rural enterprise” policies; and
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(b) to remove all unnecessary subsidies and protective policies that prevent rural firms from becoming competitive in the marketplace.
In the first point mentioned above, Asian Development Bank has focused on uneven distribution of income which is caused by lack of access of rural poor to land resource. But surprisingly they did not talk about extensive land reform as a measure which is an effective instrument to cure uneven distribution of the fruit of agricultural growth and, therefore, to reach equitability. In the third point, Asian Development Bank has argued that: “As agriculture develops and food security diminishes as a major constraint, countries need to move quickly toward market liberalisation and pro-trade and pro- investment policies.” They have argued that any kind of protection impede the development of competitive industries. But there should be a big question mark about this argument. The counter argument is as follows. As agriculture develops, purchasing power of rural people increases. In this situation, if market is open to all, multinationals and the big business sector will naturally tap this new market. Is it possible for small and tiny business sector to compete with the multinationals and big business house? Is it not a competition between two unequal sectors? In this case, authors of Asian Development Bank have ignored this reality and favoured market liberalisation. Conversely, United Nations (1990) have recognised the reality and advocated protection for rural industrial products. They have expressed the following opinion.
Rural industrial products tend to face competition from domestic large- scale industries as well as imported goods. Therefore a large number of potential rural industrial products such as pins, plastics, pencils, cups, etc., in addition to agriculture supporting manufactured goods, may need protection. Favourable fiscal measures are required in this regard (United Nations, 1990: 28).10
10 United Nations (1990: 89) have also opined that, in India, although there is a realisation that some products need to be reserved for rural industries, very few rural industries have been set up producing equipment and inputs for use by the agricultural sector. Excepting the development of some centres for repair and maintenance of implements and engines in rural markets, rural industries based on backward production linkage have not made much progress. The counter argument is found in Mellor (1976: 173) who has argued that the linkages arising from increased foodgrain production cannot have their full stimulative effect on growth unless restraints on expanded production in the domestic consumer goods sector are removed. In this case, according to him, it is institutional deficiencies which are most likely to restrain development. Growth in industrial production may face a constraint from institutional barriers particularly with respect to
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Why is protection needed? Three aspects are very important in this regard. One is poor quality of products produced by rural industries. Next is distribution cost. Lastly, it is publicity cost. So far as quality is concerned, competition between urban (large) and rural (small) industries does not stand at all. It is said that due to inferior quality demand for most of the rural industrial products are income inelastic. Delivering this argument, one should not talk in favour of wiping out small rural enterprises. Actually the rural small- scale sector has a very important place in a developing economy even if the sector produces poor quality goods. The reason is that the poor-quality-goods-producing industries may sometimes play critical role during transition from primitive techniques of production to modernisation. The second thing is about distribution/transaction cost of a product. Large firms supply bulk quantity of goods whereas small scale sector has a limited capacity. Naturally, distribution cost per unit greatly differs between these two sectors. That means, big houses incur lower distribution/transaction cost than the small sector does. This has an impact on pricing of the product. Advertisement or publicity is also an important factor in connection to reaching market. Big houses can afford publicity in the print and electronic media but the rural small scale sector cannot. On the other hand, cultural factors are not that much rigid in India to resist big firms to penetrate a local market as we have seen the reverse in Japan. In the Japanese case, an important factor which operated in an almost unique fashion was the stability of the cultural tastes and consumer behaviour of the population in the face of market contacts with the West. Japan borrowed great deal from its Western competitors, but still managed to retain a powerful preference for traditional forms of consumption biased in favour of local rural- linked community industrial products. The significance of this virtually unique feature becomes evident by comparison with India at a point, say in the mid-nineteenth century, when the two systems had less economic distance separating them (Saith, 1992). British rule in India is considered to be responsible for this comparison between these two nations.
One of the most harmful effects of a foreign rule is the imposition on the conquered peoples of the ideals of the conquerors; and the newly created Indian “bourgeoisie” showed itself during the latter half of the last century
capital, input, and output markets. Public policy must diagnose the bottlenecks and make appropriate adjustments.
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extremely ready to accept European standards and to pour scorn over everything Indian. This was specially so in the case of the arts. To follow European fashions was considered the hall-mark of enlightenment. Consequently the products of indigenous industries suffered… It was perhaps natural for this class to act as it did: it was itself entirely a product of British rule (Gadgil, 1971: 414).11
The Japanese case provides a sharp contrast. Rosovsky (1961: 53 & 86) argues that:
Japan brought to economic development a built-in resistance to the corroding influence of the demonstration effect—the deep commitment to a traditional economy that has continued to be productive to this day. The modern sector has succeeded because it has climbed on to the shoulders of the traditional sector…. Two hundred years of isolation solidified a style of life until it could not be demolished, even by the powerful impact of an industrial revolution (cited in Broadbridge, 1966: 19 and Saith 1992: 50).
We were discussing about market liberalisation. Of course, it is necessary to move toward market liberalisation to encourage the development of competitive industries but the crucial question is: When is it necessary? What is the exact time? Just after agricultural development any attempt toward market liberalisation will simply inhibit growth of rural small-scale sector. Rural enterprises should be provided time to exploit increased local demand which is the result of agricultural growth. In other words, domestic small scale sector should be given opportunity to grow first. At the same time, it is also true that in the current era of market liberalisation it is really tough for the developing countries to protect their markets for their own domestic industries, especially when they are under heavy pressure from the World Bank and the International Monetary Fund (IMF) due to the dependency of developing countries on these institutions for loans. What is the alternative then? We will try to conceptualise an alternative possibility for the development of rural small-scale sector in Chapter 6 focusing on the issue as to whether or not rural small enterprises can grow with the involvement of farmers who have investible surplus generated from agriculture.
11 Cited in Saith (1992: 50).
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3.7 The Japanese Experience in Brief
3.7.1 Agriculture/Non-agriculture Linkage
If a backward Asian country, as Japan was in 1850, is to modernise as rapidly as Japan did, it may seem necessary that it satisfies some conditions. Is a strong linkage between agriculture and rural industry one of them? According to Nakamura (1966), one of the interesting aspects of the growth process involves the transfer of resources, labour and capital, from the traditional sector of a developing economy to the modern sector. More explicitly, growth first starts in the traditional sector and then spreads into the industrial sector. Usually the traditional sector is taken to be synonymous with the agricultural sector. Almost universal acceptance has been accorded to the general proposition that Japan’s industrialisation during the Meiji era was heavily dependent on the rapid growth of agricultural production. Most of the increase in rural industrial activity in Japan— almost entirely financed and operated by rural landlord-merchants—was induced by the existence of surplus capital and by the development of national market including the rural market (since peasants’ real income rose).
Smith (1988) found some evidence of the farm/non-farm linkage during Japanese industrialisation (1750-1920). He saw mainly labour participation in both farm and non- farm activities. Farm families did not engage exclusively in farming; nearly all members worked concurrently at other occupations.
In discussions of Japan’s modern economic growth Meiji resource transfer has deservedly received much attention. The Meiji transfer, however, was not a new development. It was a continuation of a trend that began in the Tokugawa period owing to economic changes of that period. It was indicated that industrial and commercial activity increased during the Tokugawa period, particularly in the rural areas. According to Preston (2000), “Tokugawa was an agricultural society. Over 80 per cent of the population were peasant farmers, scattered across the country in hundreds of small villages. The staple crop was rice, grown in paddy fields which required collective work. In the early years of the shogunate the economy was mainly subsistence as a result of the upheavals of decades of civil wars. However, with stability the economy became
102 Chapter 3 successful and output rose, artisanal skill based manufacture prospered and commercialisation slowly spread.” Beginning about 1800, rural industrial and commercial activity assumed national importance. The significance of the changing structure of rural production is that a continuous transfer of rural labour and capital from agricultural production to non-agricultural production must have taken place. The increase in rural non-agricultural production is also evidence of resource transfer. Sugihara (1996) stated that the traditional historiography has assumed the availability of cheap labour, drawn from the countryside, to be a crucial factor for Japan’s industrialisation. After Meiji Restoration of 1868 a strong central state was created and landlordism developed, and rural savings were transferred, through the land tax and the high rent, to industry for investment. In this way, agriculture provided industry with labour and with capital.12
Almost universal acceptance has been to the general proposition that Japan’s industrialisation during the Meiji era was heavily dependent on the rapid growth of agricultural production. The argument is summarised below.
Agricultural production and real income are said to have risen at more than two percent per year which is somewhat more than twice the 0.9 per cent growth rate of population. Since the agricultural labour force declined somewhat during this period, it is claimed that the speed of growth is attributable to agricultural developments which caused labour productivity to increase at about 2.6 per cent per year. A proposition of major importance is therefore advanced that this remarkable increase in agricultural labour productivity released a cheap and “unlimited” supply of labour to other sectors since most of the population growth was taking place in farm families.
There is an important proposition about Japanese economic development that the speed of growth of agricultural production was responsible for a large transfer of savings to
12 In an article, Robison (1971) has shown that transfers of capital and labour from a lower productivity sector such as agriculture, to a higher productivity sector such as industry, is by itself a distinctive source of economic growth. An explanation of this view is found in Hwa’s (1989) argument which states that rapid agricultural growth makes the situation feasible to increase per capita domestic consumption, exports of agricultural products, and absorption of the agricultural labour force by the industrial sector. Therefore, it enhances the resource transfer from agriculture to industry.
103 Issues Relating to Rural Industries: A Review of Literature other sectors. Landlords are believed to be responsible for most of these savings, and they invested a large part of their savings in the non-agricultural sector as entrepreneurs in their own right.
3.7.2 Transformation from Traditional Industries to Modern Industries
For a low income country, the rural industrial activities hold tremendous potentials. Let us take the classic example of Japan which is regarded to be a success story in small- scale industrial sector. The industrialisation in Japan did not take place overnight. It has a background. The concurrent growth of agriculture, traditional industry and modern industry was a major characteristic of the Meiji industrialisation. Traditional industry gave its all support to the economic growth of Japan. Some evidence is found in the study of Sugihara (1996). He observed that, according to the 1909 Factory Survey report, 72 per cent of factories (defined as a concern employing five or more persons) had no power supply, and 5 per cent operated on the Japanese-style (small) water mill, while the factories equipped with steam engines, gas- or petrol-operated engines or motors, consisted of 23 per cent. Some 58 per cent of factory workers were employed in small factories (employing between five and 100 persons). In addition, production employing less than five persons, which was excluded in the Survey, is estimated to have amounted to 51 per cent of total industrial production.
In the next 50 years, Japan showed tremendous improvements. The main goal of the Meiji State was to narrow the large technological gap between Japan and the West. But the more relevant question was how Western technology and organisation could be absorbed into a non-European economy with a very different commodity and technology mix and institutional framework. Thus, the Meiji government was much more concerned with the exploitation of rural human resources and their technical and managerial knowledge. During the second half of the Meiji period, a large amount of local and central government expenditure went into the establishment of commercial and technical schools, commercial museums and industrial experimental and testing stations.
Between the two World Wars, the rural household economy was slowly transformed into the urban household economy, and traditional small-scale production was slowly
104 Chapter 3 transformed into modern large-scale production. After the disruption of World War II, the process of urbanisation accelerated and the rise of big business became apparent in the 1950s and 1960s. The proportion of city dwellers in the total population raised from 38 per cent in 1950 to 76 per cent in 1975. The Japanese economy shifted its base from the rural household to the urban household at this point, and a persistent rise in wages resulted. Smith (1988) has said that much of Japan’s economic growth in the twentieth century was achieved by the expansion of traditional industries with the aid of relatively modest technical and organisational modifications.
3.8 Reasons for Household Participation in Rural Non-farm Activities
Decisions made by rural households concerning the form and extent to their involvement in rural non-farm activities (either starting enterprises or entering the wage labour market) generally depend on two main factors (Reardon et al, 2000):
■ The incentives offered, such as the relative profitability and risk of farm and rural non- agricultural activities;
■ The household’s capacity (determined by education, income and assets and access to credit, etc.) to undertake such activities.
When opting for undertaking rural non-agricultural activities, farm households may be motivated by:
• “pull” factors, such as better returns in the non-farm sector relative to the farm sector; and • “push” factors, which include in particular:
♠ an inadequate farm output, resulting either from temporary events (e.g. a drought) or longer-term problems (e.g. land constraints); ♠ an absence of or incomplete crop insurance and consumption credit markets (to use as ex-post measures for harvest shortfalls);
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♠ the risks of farming, which induce households to manage income and consumption uncertainties by diversifying and undertaking activities with returns that have a low or negative correlation with those of farming; ♠ an absence or failure of farm input markets or input credit markets, compelling households to pay for farm inputs with their own cash resources.
3.9 Secondary and Seasonal Employment in Non-farm Activities
Non-agricultural activities provide considerable amount of part-time employment to rural population on the one hand. Still much more part-time and periodic non-agricultural work often remains uncaptured because of its seasonal nature on the other (Basant and Kumar, 1989). Many small and landless farmers undertake non-farm work in the slack seasons. Construction and irrigation works are common examples, but repair and maintenance jobs, which can be postponed until the slack season, are important as are a number of processing, service, and commercial activities that expand after the harvest, while the products of a number of manufacturing activities can be stocked (World Bank, 1978). (Non-farm workers, similarly, often undertake farm work as a secondary occupation, of which crop cutting during the peak season and animal husbandry are examples; what follows concentrates on secondary non-farm work, however).
There are large swings in farm and non-farm employment over the agricultural cycle. There is no period when there is no non-farm employment, and non-farm work competes with farm work even during the peak, when it occupies roughly one-third of the working hours of the labour force. World Bank (1978) showed that over the whole cycle, non- farm work accounts for about 40 per cent of working hours; during the slack season, it accounts for 75 per cent of working hours. Seasonal fluctuations of this kind in farm and non-farm work are common in all agricultural regions. In some regions, irrigation obviously acts to increase farm employment relative to non-farm employment in the dry season. Seasonal fluctuations in non-farm employment remain significant, even in these regions, however.
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Secondary employment in non-farm activities is particularly important for the small and landless farmers. Some evidence is available from the World Bank (1978) study. A survey in Pakistan found that 48 per cent of farm families relied on secondary sources of income from non-farm activities, which contributed 23 per cent to total income. For small and landless farm families, however, the percentages are much higher; 70 per cent undertake non-farm work, and the contribution of such work to their incomes is 39 per cent. For small and landless farmers in the Republic of Korea, the contribution of non- farm work to income is 42 per cent; in north Thailand, the contribution is 76 per cent under “less intense” cropping and 42 per cent under intense cropping, illustrating the point previously made: that even in irrigated regions, secondary employment in non-farm activities remains important. In Zambia, for farms of around two hectares, about 22 per cent of total farm income (allowing for subsistence income) is from non-farm work, while the contribution to cash income is about 30 per cent. Although there are marked variations among countries in the extent of secondary employment in non-farm work for farm families, the available information from other countries also confirms that such employment is particularly extensive and important for small and landless farm families.13
3.10 Sectoral Composition of Rural Non-farm Employment
Basant and Kumar (1989) comment that rural households in developing countries are seldom so specialised that the work of all household members throughout the year falls in
13 Some evidence is given by UNDP et al (1988) which are furnished below. The information provided are not clear whether they are regarding secondary or part-time employment, but they seem to be relevant in connection with the present discussion.
In Tanzania, four industrial branches—food products, textiles, wood products and metal products—accounted for 88% of the value added in rural small industrial sector and 75% of employment in manufacturing enterprises in 1978. In Senegal, 20% of artisans are in artistic trades (e.g., jewellery and weaving), 50% in utility trades (clothing, metal wood and leather) and remainder in a variety of services. Non-household grain-milling is a recent activity in which rural women are engaged. In Indonesia, according to 1974 data, five branches accounted for 85% of the total value added in Rural industrial sector; food (44%), wood, rattan and bamboo products (21%), structural clay products (9%), textiles and clothing (6%), and metals and machinery (4%).
Reardon et al (2001) have shown that—in Chile, Colombia, Costa Rica, Honduras, Mexico, Panama and El Salvador—rural non-farm employment, in both absolute and relative terms, has continued to grow rapidly. The Ecuador study by Elbers and Lanjouw (2001) reports that non- farm activities constituted 20% of rural employment in 1974 versus 36.4% in 1994. For data on share of rural non-agricultural workers in India (statewise), please see APPENDIX 3.3.
107 Issues Relating to Rural Industries: A Review of Literature a single economic sector. One of the most critical limitations of aggregate data is their inability to cover (capture) the range of occupations and types of employment status of individuals, let alone of households as economic units. Moreover, comparisons across countries are complicated by differences in definitions of rural areas, of the total work force, and of different non-farm sectors. The general compositional pattern of rural non- farm employment, according to the observation of World Bank (1978), appears to be approximately 20 to 30 per cent in manufacturing; 20 to 35 per cent in services, including government services; 15 to 30 per cent in commerce; 5 to 15 per cent in construction; 5 per cent in transport; and the rest in utilities and other activities. Rosegrant and Hazell (2000) have given a picture of employment shares for a number of South and Southeast Asian countries which is shown in Table 3.1.
Because part-time and temporary employment are important in many agricultural and non-farm activities, the employment data tend to underestimate the importance of some activities, though the bias is likely to be small when expressed in share rather than in absolute terms. Non-farm sectors are not always defined in the same way; the biggest differences tend to arise in the definitions of the “service” and “other” sectors (Bangladesh departs most from other country definitions).
Despite these differences in definitions, the data in Table 3.1 show a remarkably consistent story across countries. The non-farm economy accounts for 40 to 60 per cent of total national employment and the rural non-farm economy accounts for 20 to 50 per cent of total rural employment. Differences between South and Southeast Asian economies are also surprisingly small. While rapidly developing (until recently) economies like Indonesia and Thailand now have very little agricultural employment in their urban areas—only 9.4 and 1.9 per cent, respectively, of total urban employment— the non-farm share of rural employment is not much different from those of other countries. This no doubt reflects the fact that as rural settlements grow and diversify, they soon become classified as urban rather than rural areas in the census data. Even so, the share of non-farm employment in total national employment was not much higher in Indonesia and Thailand in the mid-1990s than in Sri Lanka in 1981 or Pakistan in 1992- 93.
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Table 3.1: Employment shares by activity in rural & urban areas, selected countries (percent) Economy Total Employment Manu- Agri. Nonfarm facturing Transport Trade Services Finance Construction Other Bangladesh (1991) Rural 60.1 39.9 6.8 4.0 …………. 35.4 ………..... 3.3 50.2 Urban 15.1 84.9 8.1 6.5 …………. 31.8 ………..... 3.4 50.1 Total 54.6 45.4 7.3 5.1 …………. 33.8 ………..... 3.3 50.4 Sri Lanka (1981) Rural 55.7 44.3 19.8 8.3 16.5 25.2 1.5 6.6 22.1 Urban 7.3 92.7 16.0 9.7 23.9 28.7 2.8 3.7 15.0 Total 45.2 54.8 18.5 8.8 19.2 26.5 2.0 5.5 19.5 Pakistan (1992-93) Rural 63.8 36.2 19.0 10.4 21.9 26.8 0.7 19.4 1.8 Urban 5.8 94.2 22.6 10.6 28.9 26.0 2.4 7.1 2.4 Total 47.6 52.4 20.8 10.5 25.4 26.4 2.6 13.2 2.1 India (1993-94) Rural 76.9 23.1 30.7 6.9 19.4 …….. 26.8 …….. 11.6 4.6 Urban 17.7 82.3 22.2 12.7 25.9 …….. 38.3 …….. 3.1 2.3 Total 61.5 38.5 28.5 8.4 21.1 …….. 29.8 …….. 9.4 2.8 Philippines (1980) Rural 74.0 26.0 20.9 11.9 13.2 32.1 3.0 11.5 7.4 Urban 18.3 81.7 19.4 11.3 14.9 35.9 7.1 8.1 3.3 Total 51.4 48.6 19.9 11.5 14.3 34.7 5.8 9.2 4.6 Indonesia (1995) Rural 63.1 36.9 23.8 8.2 31.7 24.2 0.5 9.4 2.2 Urban 9.4 90.6 20.0 8.0 30.1 31.1 2.4 6.8 1.6 Total 45.9 54.1 21.8 8.1 30.9 27.9 1.5 8.0 1.8 Thailand (1996) Rural 49.9 50.1 30.3 5.1 22.1 …….. 19.7 …….. 21.5 1.3 Urban 1.9 98.1 22.6 7.0 29.9 …….. 28.8 …….. 9.7 2.0 Total 39.7 60.3 27.6 5.8 24.8 …….. 22.8 …….. 17.4 1.6 Source: Rosegrant and Hazell (2000)
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The composition of the non-farm economy also shows remarkable similarities across countries. Service activities dominate the non-farm economy in both rural and urban areas, followed by manufacturing and trade. Service activities (including much of the “other” activities in Bangladesh and Sri Lanka) are more dominant in the lower income South Asian countries, while trade and manufacturing are about equal in importance to services in the Southeast Asian countries.
Within manufacturing, as observed in the study of World Bank (1978), most rural employment is accounted for by four broad groups of activities: (a) food processing; (b) textiles and wearing apparel; (c) wood, including saw-milling, furniture making, and general carpentry; and (d) metal, including blacksmithing, welding, fabrication, and the making of tools and equipments. All four categories appear to have retained their importance no matter what level of development has been reached in a particular country.
Within each category, however, there are considerable differences among countries. A particularly noticeable feature of manufacturing activities in rural areas (as elsewhere) is their diversity, both with regard to manufacturing techniques used, and with regard to the types and quality of the final product. To a large extent, this diversity is linked to the nature and income level of the markets, which determine both the range and the quality of products in demand. On the supply side, local income levels (labour costs), availability of capital, level of infrastructure, and degree of competition from large scale industries are the most important factors determining production techniques and product types. In Africa, for example, the rural metal working sector is largely confined to blacksmithing and welding. In irrigated regions in Pakistan Punjab and the Indian state of Tamil Nadu, the sector is much more advanced, and includes small scale manufacturing of diesel and electric tubewell pumpsets, an activity that has quickly become an important source of rural employment in recent years. In Taiwan, where the rural metalworking sector has similarly diversified in response to agricultural modernisation, industrial decentralisation, drawing on cheap labour and good infrastructure, has been an additional factor affecting the diversity, as well as the level and growth of employment. Handicrafts, textiles, food and crop processing, carpentry, and wood products similarly differ enormously among regions, with many old crafts declining in importance and new ones emerging as a result of the development of the rural economy. Construction, commercial transportation, and
110 Chapter 3 service activities, which together account for as much as two-thirds of rural non-farm employment, are, by their nature, linked to local markets. As with rural manufacturing, the techniques used, and the types and quality of products and services offered, differ considerably among regions. In construction, half of employment is typically in the construction of dwellings, and farm and other buildings, while the remainder is largely in roads and civil works. In commerce, retail trade accounts for three-quarters of total employment, while the other quarter is in trade and financial services. In services, half of the employment is typically in business, repair, community, personal and various recreational services; and other half—in regions where there is an active development programme—is generally in government, with educational and medical services also being a large employer. (In regions without active development programmes, services such as health and personal care, education, and the training of apprentices are provided for by the families and small businesses themselves, and occupy much of the family’s time.)
Rural towns can be expected to have an employment structure that reflects their economic links to agriculture, while urban towns typically have a more independent economic base. On average, according to Hazell and Haggblade (1993), the non-farm employment share for rural areas increases from 26 per cent to 36 per cent when rural towns are added to the definition of rural areas. Moreover, the non-farm employment share increases quickly with size of locality and is 81 per cent even for rural towns. In India, non-farm employment share also increases sharply with locality size. In rural towns (defined as having populations between 5,000 and 100,000), 76.4 per cent of the work force was employed in non-farm activities in 1971 (see Table 3.2). Services and household manufacturing activities are the most important sources of employment in rural areas, whereas employment in rural towns is more nearly dominated by trade and services. In urban towns, trade and services are also important sources of employment, but manufacturing dominates. Unlike rural areas and towns, manufacturing employment in urban towns is nearly all in formal non-household activity; household manufacturing accounts for a mere 3.9 per cent of total employment.
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Table 3.2: Employment shares by activity and size of Locality, India (per cent)
Economy Total Manufacturing Non-farm Employment Employment Agri Non- Household Non- Transport Trade Services Construction Other farm household India (1971)
Rural 84.9 15.1 21.6 15.7 5.9 15.7 35.3 3.9 2.0
Rural 23.6 76.4 8.6 19.5 10.5 25.4 25.4 4.5 1.8 Towns Urban 4.7 95.3 3.9 30.3 12.0 21.5 27.8 3.5 0.7 Towns Source: Hazell and Haggblade (1991: 518)
Considering all the developing countries of the world, examination of the composition of rural non-farm activities must reveal considerable diversity with respect to sector and function, types and quality of products, and technology of production. Such diversity poses special problems for the design of development assistance, particularly as regards the kinds of intervention required from the public sector. Taken separately, each kind of rural non-farm activity—there are over 20 broad categories—accounts for only a small fraction of rural employment; taken together, however, rural non-farm activities become an important source of employment.
However, Basant and Kumar (1989) make a critical comment that estimates of labour force by economic activity generally provide a classification of workers according to their principal sector of employment or occupation. Such estimates are likely to underestimate the extent of non-agricultural work because it is commonly a secondary source of income on a part-time or seasonal basis in rural areas. Such underestimation is likely to be more in regard to the activities of female workers than for those of male workers (for women’s share in non-farm activities, see APPENDICES 3.1, 3.2 and 3.3).
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3. 11 Hymer-Resnick Model Vs. Ranis-Stewart Models
The interlinkages between agriculture and industry have been presented as a theoretical model by Hymer-Resnick in 1969. The importance of this model lies in the fact that it incorporates three sectors viz. agricultural sector, urban industrial sector, and rural non- farm sector. But, generally speaking, rural non-farm sector did not bear any positive indication in the model and the development process mainly focused on the linkages between export of agricultural products to the urban sector and import of urban manufactured goods to the rural sector. Therefore, although rural non-farm economy finds a place in the Hymer-Resnick model, it is not seriously taken as an important instrument of playing a vital role in the development of home economy. It is the Ranis- Stewart (1993) model which countered the Hymer-Resnick considerations and not only tried to trace the position of the rural non-agricultural economy but also tried to search for the positive role of this sector by developing a series of models based on four situations like unfavourable colonial case, favourable colonial case, unfavourable post- colonial case, and favourable post-colonial case. Let us now turn towards them.
3.11.1 Hymer-Resnick model
In Hymer-Resnick (1969) model, the pre-colonial era was a self-sufficient peasant economy. To serve their own needs, peasants used only labour to produce two types of goods—food and some simple non-agricultural produce. The non-agricultural activities— defined as ‘Z-goods’—consist of the household or village production of handicrafts and services, including some textiles, garments and food processing for village consumption. However, as the colonial era increasingly linked the rural economy with the world economy, two related developments occurred: on the one hand, profitable new opportunities in the form of minerals and cash crops for world markets opened up, offering an alternative use of rural labour, i.e., new export activities developed; and on the other, consumption of imported manufactured goods, produced in the metropolitan centres, became possible and, at times, was mandated. These imported products (M) often were of higher quality and fulfilled a wider range of needs than Z-goods. Consequently, labour was induced (sometimes forced) to move out of the production of (non-traded) Z-goods and into the production of (non-domestically consumed) cash crops
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(AE); the foreign exchange so earned permitted the purchase of imported manufactures which substituted for Z-goods production and permitted the further expansion of AE activities. As a consequence, there was a decline of the Z-goods sector and an expansion of both exports and imports. The production of food for domestic consumption was assumed to be broadly unaffected by (nor to affect) these developments.
0 O The Hymer-Resnick displacement process is illustrated in Figure 3.3. AE Z in quadrant I represents the production possibilities curve as the fixed supply of labour (i.e., all rural labour after the society’s food needs have been met) is allowed to shift between the production of non-traded Z-goods and the production of non-domestically consumed AE goods. Cash crop exports AE are exchanged for imports (M), through international exchange at some terms of trade OPO (quadrant IV). Using a 45O line to project OMO imports onto the horizontal axis (in quadrant II), ZOMO then represents the consumption
0 O possibilities curve, corresponding to the production possibilities curve AE Z , given the terms of trade OPO. A price consumption curve (P.C. Curve) can be constructed in quadrant II representing the locus of all tangencies between the consumer indifference map (between Z and M goods) and alternative terms of trade. Given terms of trade OPO, equilibrium at time zero is then established at 1 for consumption and at 1’ for production; * production of AE is then given by Oa and that of Z-goods by OZ*, representing Z displacement of ZO Z*, compared with a no-trade position. If the terms of trade improve to OP’ (in quadrant IV), the consumption possibilities curve shifts out to ZOM’, and the consumption (production) equilibrium to 22’, showing further Z displacement by M goods, as the substitution effect overcomes the income effect and shifts consumption towards M. If Z is assumed to be an inferior good, both the price and income effects are negative and there is substantial Z displacement in response to further improvements in the terms of trade.
3.11.2 The Ranis-Stewart Models: Colonial and Post-colonial
The Hymer-Resnick model, as described above, was intended primarily to apply to the colonial era. But, according to Ranis-Stewart (1993), even in that era the assumptions made were not universally applicable, either with respect to the likely movement of the
114 Chapter 3 terms of trade or the inferior character of Z-goods. But, especially when extended to the post-colonial era, there are number of assumptions which clearly do not necessarily apply. The following are the major departures on which Ranis-Stewart focus:
1. The Z-goods sector in the Hymer-Resnick model is broadly homogeneous, composed of traditional non-agricultural activities carried out in individual households or at the village level. However, Ranis-Stewart stressed that rural non-agricultural activities, in fact, range from household and village production on a very small scale, producing very simple, low quality products, to small modern factories using mechanical horsepower, sometimes using imported technology, and producing modern higher quality products.
Ranis-Stewart divide the Z-sector into two groups: ZT, covering traditional household and village processes and products, and ZM, covering non-traditional or modernising rural non-agricultural processes and products. The latter are more likely to be located in rural towns, rather than in households or villages. Some ZM activities existed even in the colonial era, at least in some countries, but they clearly may become more important in the post-colonial period. Science and technology inputs, domestic and foreign, contribute to the enhanced productivity of such ZM activities. An example of ZT would be household rice milling or handloom weaving, while metalworking or machinery repair shops would constitute typical ZM activities.
2. A major development of the post-colonial era has been emergence of a modern industrial sector, located predominantly in urban areas. This sector, initially at least, was devoted to replacing imports. Ranis-Stewart call this the U-sector. In the presence of the U-sector, the Hymer-Resnick displacement of Z-goods by imports is no longer a dominant feature; instead it is the U-sector which now displaces Z-goods, actually or potentially. Ranis-Stewart call this displacement of Z-goods by U-goods, U- displacement. The assumption that labour is the only factor of production in non- agriculture must now be dropped as the U-sector imports capital and other inputs in exchange for the AE cash crop exports.
3. It should also be recalled that the agricultural sector (A) is really composed of two sub- sectors: the agricultural cash crop export sector (AE), and a domestically oriented food producing agricultural sector (AD), which has up to now been behind a curtain. In both periods, but especially the post colonial era, domestic agriculture can be assumed to have
115 Issues Relating to Rural Industries: A Review of Literature the potential for dynamic growth as it is subject to productivity-raising technology change. This feature of AD—not incorporated into the Hymer-Resnick model—has two relevant consequences. First, as fewer resources may be required to feed the total population, the sub-sector can, over time, release land and labour for use elsewhere, i.e., the production of more AE and Z-goods, permitting an outward shift of the AEZ production possibility frontier. Second, a dynamic AD can give rise to stronger linkages between domestic agriculture and rural non-agriculture than export agriculture typically provides. Such linkages run both from agriculture to rural non-agriculture and from rural non-agriculture back to agriculture. The former include consumption linkages as well as forward and backward production linkages.
Here, the linkages from agriculture to non-agriculture and vice-versa are required to be critically viewed, because such linkages are not that straightforward as it is usually assumed. To understand this, let us go back to Mellor (1989: 307) again who said that “if the rate of growth of the demand for labour can be accelerated, demand for food can grow rapidly, absorbing the increase in supply induced by technological change. If the food supply increases more rapidly than food demand, real food prices will fall.” That is exactly what has happened in India after the occurrence of the Green Revolution. “The first period of the Green Revolution was one of substantial gains in farm profits. But the rapid gains in production during the late 1970s did not translate into further advances in income because the prices of agricultural products fell” (Binswanger and Quizon, 1989: 116). Mellor states that some reduction in output prices can be absorbed by producers without a decline in output or net income since new technology increases factor productivity. But what does happen when agricultural prices go down beyond Mellor’s “some reduction?” Simply, the farmers will start losing profit margin and, consequently, production will be hampered. “During the early Green Revolution period (1965-66), the real per capita income of the rural population of India rose by about 8 per cent. However, these gains were rapidly eroded. The sobering point is that in 1980-81 real rural per capita income appears to have been only about 2 per cent higher than in 1960-61” (Binswanger and Quizon, 1989: 130). In such a situation, considering open and closed economy, two remedies can be thought of. One, looking for export markets for agricultural produces could be a way out. But the problem may persist if the crops produced in the region are not demanded by the people of the other regions (for example,
116 Chapter 3
Figure 3.3: Unfavourable colonial case (Hymer-Resnick)
Z
I II
Production Consumption Possibilities Curve ZO Possibilities Curves
* 1’ Z 1
2 2’ P.C. Z’ Curve
AE 0 A O O E a* M M
M’
45O
45O O P MO
III IV P’ M’
M Source: Ranis and Stewart (1993)
other neighbouring regions may have also reached self-sufficiency in agricultural production). Second, promotion of rural non-agricultural activities may be another option. Let us explain it further. Rural income rises in the early period of green revolution and that is the high time for at least partial diversification of rural economic activities from agriculture to non-agriculture through transfer of resource like capital, given the fact that surplus agricultural labour force will be absorbed in non-agriculture. If the second
117 Issues Relating to Rural Industries: A Review of Literature option happens to have been accepted in the rural economy, then the people engaged in non-agriculture will create further demand for food products and prices of agricultural goods will be able to retain a certain level for sustaining viable production. In such a situation, linkages from rural non-agriculture to agriculture may work in a positive direction. But this is merely a subject of rural entrepreneurship—which involves a crucial question i.e. whether or not a farmer is willing to invest his surplus capital to non- agriculture. The present study primarily focuses on this issue and we will discuss this issue later in other chapters. Let us now confine ourselves to Ranis-Stewart model again.
Ranis and Stewart consider three dimensions of the linkages from rural non-agriculture to agriculture (which, according to them, have more been neglected). An improvement in agricultural markets or improved internal terms of trade resulting from additional rural non-agricultural activity; improved science and technology knowledge, often embodied in modern inputs; and a change in attitudes among farmers, who, as a result of the growing availability of consumer goods and additional opportunities for investment in rural non-agricultural activities, have a greater incentive to raise productivity and accumulate savings.
4. In the colonial era, the Hymer-Resnick assumption of improving external terms of trade or improving opportunities for AE may have been broadly correct. But in the post- colonial era this does not seem to correspond to the facts; most less developed countries’ terms of trade have either remained constant or deteriorated. If deteriorating terms of trade occur in the Hymer-Resnick model, Z-goods production tends to increase. Ranis- Stewart assume constant terms of trade in their discussion.
Hymer-Resnick model was based on colonial era, whereas Ranis-Stewart have mainly focussed on the post colonial era. But Ranis-Stewart argue that in both the colonial and post-colonial eras, conditions may be relatively favourable or unfavourable for the achievement of rural balanced growth. So, they found it useful to contrast the extremes of the unfavourable colonial archetype (the Hymer-Resnick case) with the more favourable archetype.
118 Chapter 3
3.11.3 Colonial Archetypes
3.11.3.1 The Unfavourable Colonial Archetype: The Hymer-Resnick Case
The distinguishing features of this case are that colonial policies inhibit the development of domestic industry through a combination of mercantilist restrictions by the mother country on the pattern of trade and investment, coupled with the relative neglect of food producing agriculture. The focus of government attention is on the export of minerals and cash crops and the auxiliary overhead services required to bring them to market; less attention is paid to agriculture for domestic consumption. AE is often concentrated in large holdings, yielding an unequal distribution of income and therefore probably relatively weak linkages with non-agriculture, because, in this case, it is assumed that linkages can be strong only when rural income distribution is not extremely skewed. Where the primary export consists largely of minerals the resulting rural income distribution is even more unequal and the rural linkages even weaker. As a combined consequence of these features, the Z-goods sector is likely to be dominated by traditional household goods ZT, with negligible ZM activity. In this context ZT is gradually displaced by imported consumer goods, while domestic agriculture tends to stagnate relatively. This is the Hymer-Resnick prototype, discussed herewith and summarised in Figure 3.3.
3.11.3.2 The Favourable Colonial Archetype
In circumstances more favourable for rural development, the colonial government, for its own reasons, focuses more attention on food producing agriculture (usually because the export and the domestic food crop are one and the same, e.g. rice) and occasionally even resorts to land reform. In these circumstances, the agricultural sector tends to be unimodal, i.e., composed of large numbers of individual small-holders, rather than bimodal as in the unfavourable archetype (i.e., with plantations hiring landless agricultural workers); consequently agricultural income is more equally distributed, yielding stronger linkages with non-agricultural activity. In this favourable case, the colonial government is less restrictive with respect to local entrepreneurs, permitting indigenous industrial development to progress naturally as a consequence of the various linkages.
119 Issues Relating to Rural Industries: A Review of Literature
Under these assumptions, illustrated in Figure 3.4, as agricultural productivity increases, more labour and land are released permitting an outward shift of the production
0 O 0 possibilities curve from AE Z to AE Z’, thereby also leading to an outward shift in the consumption possibilities curve from ZOMO to Z’MO, in the second quadrant. Z is now increasingly made up of ZM which is much more dynamic and innovative in character than the ZT it replaces. As a consequence of both these quantitative and qualitative shifts, the displacement of Z by M goods, predicted by Hymer-Resnick, is considerably weakened and, indeed, may not occur, as modernising rural industries are better able to compete with imports.
Both the substitution and income effects are now likely to be positive, leading to a new equilibrium position at 2 in quadrant II. Consequently, Z-goods production increases relative to the pessimistic Hymer-Resnick case.
3.11.4 Post-colonial Archetypes
3.11.4.1 The Unfavourable Post-colonial Archetype
The major development of the post-colonial era is the emergence of a U-sector, as a consequence of the well-known import substitution policy syndrome. In the unfavourable case, urban industry is encouraged, relative to both Z-goods production and domestic agriculture, by government policies affecting the internal terms of trade, the exchange rate, the interest rate, and the allocation of infrastructure, credit and foreign exchange. The U-sector is mainly composed of large-scale domestic enterprises, public and private, as well as multinational companies. Foreign institutions—both private and public— typically help finance this urban oriented import substitution process. Within agriculture, export crops, whose earnings are needed to help fuel the import substitution process, are usually favoured relative to food crops in terms of research, extension, subsidised inputs availability, etc. Land and income are likely to remain unequally distributed, especially in the AE activities, with increases in purchasing power directed largely towards U-goods. Consequently, rural linkages remain weak, both as a result of the neglect of the food
120 Chapter 3 producing agricultural sector and the U-displacement process which consists in the displacement of Z (and M) goods by the newly developing urban consumer goods industries. While some ZT production will continue—partly due to the persistence of rural poverty and partly the ‘natural’ protection afforded by transport costs (especially in large countries)—the potential development of the ZM sector is severely affected.
3.11.4.2 The Favourable Post-colonial Archetype
In more favourable post-colonial circumstances, the Z-goods sector represents a dynamic element in interaction with a growing food producing agricultural sector. This results from a more favourable environment in a number of dimensions. First, in this case the government does not discriminate against domestic agriculture relative to traditional primary export activities. Consequently, productivity expands in the AD sector and additional resources are released to the rest of the economy. Second, since income generated within AD is more likely to be equally distributed than within AE, especially when land-saving or labour-using technologies are adopted, that sector’s expansion generates stronger potential demand for the output of rural non-agricultural activities. Third, government policy with respect to the allocation of infrastructure and the macro- economic environment are generally more evenhanded between rural and urban industry as well as between agriculture and industry.
121 Issues Relating to Rural Industries: A Review of Literature
Figure 3.4: Favourable colonial case
Z
I II
Z’
2 ZO 1
AE M 0 O MO AE
O 45
PO III IV
M’
Source: Ranis and Stewart (1993)
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Figure 3.5: Unfavourable post-colonial case
Z
I II
ZO
1 2
U, MC A E ' 0 UO U’ AE AE O
III IV
M o k
M ' k
P
MK, MC
Source: Ranis and Stewart (1993)
123 Issues Relating to Rural Industries: A Review of Literature
Figure 3.6: Favourable post-colonial case
Z
ZM I II
ZT 2
1
U, MC AE ' 0 O O U’ AE AE U
III IV
P
MK, MC
Source: Ranis and Stewart (1993)
As a consequence of the above, the rural industrial sector faces relatively more favourable conditions, both from the perspective of demand, via linkages with agriculture, and of supply, via the dynamics of investment and the adoption of new technology. The resulting modernisation transforms Z from a sector largely composed of very small, low productivity, household and village enterprises of the ZT type to one increasingly represented by ZM activities, i.e., composed of small factories using modern (often imported and then adapted) technologies and producing products of a more
124 Chapter 3
uniform (and often higher) quality than the traditional ZT goods, selling at favourable prices relative to the output of the U-sector and imports. The modernised ZM sector may eventually also export, through subcontracting relations with the U-sector as well as through direct channel. ZM gradually displaces ZT and the Z-goods sector retains a substantial, and likely growing, importance, at least until the labour surplus has been eliminated. U-displacement is now much less in evidence, since the U-sector itself is less artificially nurtured and is forced to compete and/or induced to establish complementary relations with the Z-goods sector. At some point the U-sector will begin to supply the ZM with domestically manufactured capital goods, while the ZM sector supplies the U-sector with partly processed agricultural goods and/or serves as a subcontractor for the production of U-goods. Far from exhibiting Hymer-Resnick stagnation, the Z-sector thus becomes one of the dynamic elements in industrial development, with a high rate of capital accumulation, technology change, and employment expansion. This, in turn, acts as a stimulant to agricultural growth, through rural non-agriculture to agriculture linkages. This pattern of development tends to be associated with a high overall rate of growth, a more egalitarian income distribution and regional balance, in contrast to the unfavourable archetype.
Figures 3.5 and 3.6 illustrate these two post-colonial extremes. The horizontal axis to the right of the origin now indicates a combination of U-goods and Mc, imported consumer goods, the proportion of imports in the supply of total consumer goods declining as import substitution proceeds. Earnings from agricultural exports are now spent increasingly on imports of capital goods (Mk) and to a diminishing extent on imports of consumer goods (Mc), both shown on the vertical axis below the origin. The consumption possibilities curve (e.g. ZOUO in Figure 3.5) represents the possible combinations of the consumption of Z-goods and the consumption of U-goods. As illustrated in the diagram, once imported consumer goods have been fully replaced by capital goods imports, the proceeds from agricultural exports are translated, first, into larger amounts of Mk, and then, through domestic production, into larger volumes of U.
In the unfavourable case, shown in Figure 3.5, while the productivity and output of AE increase, the productivity of the Z-sector remains unchanged, shifting the production
O 0 O ' possibilities curve outward, from Z AE to Z AE . The consumption possibilities curve
125 Issues Relating to Rural Industries: A Review of Literature therefore shifts correspondingly, from ZOUO to ZOU’. The substitution effect acts to reduce the consumption of Z-goods and to increase the consumption of U. Since the Z sector remains overwhelmingly ZT, the income effect also tends to be negative, i.e., ZT is likely to be an inferior good. So this too acts to reduce Z-consumption. Consequently, the equilibrium position moves from 1 to 2, with reduced production and consumption of Z- goods.
Finally, Figure 3.6 represents the favourable post-colonial case. Here productivity increase in the AD sector releases land and labour for the enhanced production of AE and Z, and consequently there is an outward shift in the production possibilities curve in quadrant I. Moreover, ZT is gradually replaced by ZM, with an increase in productivity enhancing the potential production of Z goods. The outward shift of AE is, however, relatively smaller than in the unfavourable case, as less attention is paid to that sector and more to domestic agriculture. In this case, the income as well as the substitution effects on Z-goods consumption is positive because of the increasingly modern characteristic of
ZM goods, leading to a new equilibrium at 2 in quadrant II of Figure 3.6. Consequently, both Z-goods production and consumption expand, supported by strong linkages with domestic agriculture as well as expanding complementary relations with the U-sector over time.
Definitely the favourable post-colonial model shows a very positive as well optimistic picture so far as the transformation of ZT to ZM is concerned. This can be achieved through favourable institutional intervention. Heavy investment on rural infrastructure (both hard and soft) may facilitate such transformation. According to Ranis-Stewart, the favourable post-colonial model fits well into Taiwan case. Taiwan was a colony of Japan from 1895 till the end of the World War II. Japan was interested almost exclusively, certainly prior to 1930, in Taiwan’s agricultural output, particularly sugar and rice, as a complement to the Japanese home economy which was beginning to run into food shortages after the turn of the century. As a consequence, Japanese colonial policy was heavily rural-oriented, as evidenced by the 1905 land reform. The attention consistently paid both to physical infrastructure (e.g. irrigation and roads) and organisational infrastructure (e.g. the substantial agricultural research expenditures).
126 Chapter 3
3.12 Urbanisation and the Role of Infrastructure in Rural Industrialisation
To some writers, the connection between industry and the city was, perhaps, too obvious to require further elucidation. Others found a ready explanation at hand. Hoselitz (1951) has argued that the economic development of underdeveloped countries is contingent upon the introduction of industry; industrialisation, in turn, is associated with urban growth. Davis and Golden (1955) have also found no difficulty in maintaining that city makes its own peculiar contribution to the process of economic development, or in asserting the existence of a direct relationship between urbanisation and industrialisation in which the former indicates and stimulates the latter. But, can a city or town grow without the support of infrastructure? In Lampard’s (1955) view, the great urban explosion of the second half of the 19th century was seen to stem directly from improvements in communications which served to concentrate economic opportunities in locations which offered the greatest cost advantages in the procurement, processing, the distribution of goods. He has strongly argued that, in a culture of economic competition, producers always tend to scatter or concentrate according to the principle of minimum cost. Producers prefer urban sites in order to have an access into better transfer facilities, broader and more flexible labour markets, numerous auxiliary business services like banking, insurance, brokerage, utilities, or fire and police protection. Conventional production theory has led many to suppose that economies of scale and mobility of factors would eventually bring all activities into great centres, but the persistence of small-scale plants and widely dispersed towns is not necessarily a token of irrationality (Lampard, 1955). Oberai (1993) has also argued that public sector investment in infrastructure development (power generation, water treatment, transportation systems, etc.) is concentrated in the urban centres in order to exploit economies of scale. Industrial firms located in cities thus reap substantial cost benefits because of their access both to infrastructure and to large and diversified markets for labour and other inputs. Anderson (1971) has argued that whether in ancient, medieval, or modern times, if there is technical invention that leads to social change, its point of beginning tends most likely to be in urban centres.
According to Gilbert and Gugler (1982), the world depression of the 1930s and the effects of the Second World War led to the spontaneous process of industrial expansion in the larger Third World nations. They have argued that the initial stage of
127 Issues Relating to Rural Industries: A Review of Literature industrialisation saw the establishment of companies producing directly for the consumer market. Many of these products were only affordable by the higher income groups in the society, most of whom lived in the major cities. Consequently, most market oriented companies tended to concentrate in the largest cities. Even while talking about the rural industries in China, Gilbert and Gugler (1982) have observed that after 1949 there has been a programme of rural industrialisation but industry has continued to locate in major cities or in nearby satellite towns.
So far as rural small enterprises are concerned, UNDP et al (1988) have stated a distinct opinion. They have argued that small industry is less dependent on the urban locational amenities which are a veritable life-line for large industry development. According to them, in the post-World War II period, development strategies were followed in many developing countries, which tended to concentrate on public investment and ownership to promote large-scale industrialisation. This was the case in Nehru-Mahalanobis strategy in India too (Saith, 1992). Agriculture and other rural activities were often neglected or subordinated to pursuit of modernisation through large-scale industrialisation and urbanisation (Chakravarty, 1987; UNDP et al, 1988). Large firms’ intention is clear. At big cities, commercial and financial services are relatively well developed and a concentration of purchasing power exists. Hence, at early stages of development, most industries act as if urbanisation economies associated with sheer city size are crucial and the intermediate city system suffers (Hamer, 1985). Also, Friedmann (1973) has argued that ‘core-region’ policies have generally stressed large-scale industrial complexes. This strategy overlooks the long-term consequences of building up an entrepreneurial tradition that has its roots in the bazaar (rural trade centre) economy. This calls for an alternative strategy which would aim at deconcentration. A feasible proposition then can be this: the city may be brought into the countryside, not the whole city, to be sure, for the people continue to live on their own farms and in the villages, but vital elements of it should be present in rural areas (Friedmann, 1973). Establishing some vital urban elements to rural areas may be termed as rural urbanisation and this process can gain momentum through infrastructure development. In Bunce’s (1982) view, a recurrent theme in the study of rural settlement problems is stagnation and decline in community infrastructure. Lerner (1958) has argued that rural energies would have to be released by placing villages in better communication with the urban centres. Friedmann and Douglass (1976: 372) suggest that the countryside should be transformed by “introducing and adapting
128 Chapter 3 elements of urbanism to a specific rural setting. This means: instead of encouraging the drift of rural people to cities by investing in cities, encouraging them to remain where they are by investing in rural districts and to transmute existing settlements into a hybrid form we call agropolis or city in the fields.” An agropolitan district is thus defined by Friedmann (1988) as containing, at least, one small urban centre and a population of between 40,000 and 60,000 persons. Agropolitan districts according to this definition are relatively small and the population within such a district can reach the centre within a few hours. Ærøe (1992) too is an advocate of this concept. In Friedmann’s approach, the main purpose of the urban centre is to strengthen its surrounding rural base and to improve the life of the community. Rural development is on the agenda, and the role of the urban centre is to support this development through provision of services and social infrastructure.14 Bunce (1982) also mentions this common principle of concentrating investment in strategic growth centres. The logic of this is that if growth occurs at a number of centres, the surrounding rural areas will benefit through a ripple effect. This is discussed below in more detail.
14 Tangri (1964) says that urbanisation is neither a necessary nor a sufficient condition for economic growth. He argues that “we cannot determine the role of urbanisation without estimating the economic costs or benefits of such urban phenomena as anomie, political and ideological ferment, and transformation of cultural and social values” (Tangri, 1964: 383). So far as the economic cost of urbanisation is concerned, he raises the right question because urbanisation needs a huge investment on infrastructure. There is a category of goods, such as roads, electricity, cables, ports, irrigation canals, and potable water supplies, whose availability is a precondition for the growth of anonymous markets, and for the production and distribution of raw materials and outputs (Rogers and shoemaker, 1971; Brown, L.A. and Lentneck, 1973). They form an economy’s infrastructure. Now, the answer to the question of economic cost of infrastructure is sought into the following discussion. According to Dasgupta (1993), now it is a contingent fact that the production of infrastructure involves large fixed costs relative to the size of the population involved in their use. (Commodities that we have labelled public goods also often satisfy this property). In rural communities of poor countries they are often large relative to average income, which is another way of saying the same thing. If this is the case, then who will invest on infrastructure in rural areas? It can be argued, both analytically and by an appeal to evidence, that the production and use of infrastructure is hampered if decisions are left exclusively to the private sector (Scherer, 1980; Stiglitz and Mathewson, 1986; Tirole, 1988; Panzar, 1989). The reason is that, because of large fixed costs, the average cost of production is less than the marginal cost when the level of output is optimal. This means that setting price equal to marginal cost of production entails losses, something a private producer would wish to avoid. This forms the classical reason for government involvement in the production of infrastructure (Guesnerie, 1975; D. Brown and Heal, 1979, 1983; Beato and Mas-Colell, 1985; Dierker, 1986; and D. Brown, Heller and Starr, 1990). This view was supported by Dasgupta (1993) too. He argues that, for very poor regions, the infrastructure has to be supplied free of charge, the expenditure being financed by general taxation.
129 Issues Relating to Rural Industries: A Review of Literature
Urban population is dependent on the acquisition of an agricultural surplus (Gilbert and Gugler, 1982). Hence an urban centre in a rural setting plays a positive role in rural development. Johnson (1970) has noted that there is one central place for every 16 villages in Europe compared with one for every 157 villages in the Middle East. This gap in the urban size distribution lowers agricultural productivity because there is nowhere to sell the produce and no opportunities for buying consumer goods which might stimulate farmers to increase production. Bendavid-Val (1991) has argued that rural towns are important links in marketing agricultural exports from the area. Their export marketing function may include commodity processing; grading and packaging; storage, bulking, and depot activities; wholesale trade; transportation activities; and the maintenance, repair, and supply services to support these functions. Some argue that agricultural development itself necessarily requires growth in industry and services. Mellor (1976, 1995: 8) has already emphasised this rural dynamism (through forward and backward linkage effects) and strongly argued that “agricultural growth also accelerates non- agricultural growth and therefore will not automatically eliminate the problems of urbanisation.” Here is the importance of small urban centres in the agricultural districts and thus the concept of ‘growth centre’ holds a venerable place in the regional development (Gilbert and Gugler, 1982). A growth centre serves as an agent in an agropolitan district. The growth centre notion conceives of an urban complex containing a series of industrial enterprises. The centre provides a focus for the development of the centre’s hinterland. Industries that consume agricultural products will create a market for the region’s farmers, as will the growing concentration of urban inhabitants. Mellor (1995) argues that where non-agricultural activity is fomenting the growth of secondary urban centres, the better the modern communications the better these firms can compete and look outward. Liedholm et al (1994) have also argued that rural towns help grow small enterprises. They serve as centres for marketing farm produces, for obtaining wage employment, for engaging in non-farm enterprises, and for investing (Bendavid-Val, 1991). Towns, as centres of local trade, are the primary vehicles for multiplying into more livelihoods for more people in the area. Bendavid-val (1991) has argued that rural towns, given a welcoming environment, are logical business locations for many types of entrepreneurs. Considering a link between the large urban centre and the rural areas through market town, Smith (1975) has developed a market chain shown in Figure 3.7. Such a chain calls for a strategy to strengthen the link, as shown in the Figure, through
130 Chapter 3 development of public infrastructure. For another schematic diagram developed on the basis of the overall conceptual discussion presented above, see Figure 3.8.
Figure3.7: Market chain
Urban primary centre
Market town
Rural wholesale market
Rural retail market
Source: Smith (1975)15 Courtesy: Royal Anthropological Institute of Great Britain and Ireland, London.
15 Cited in Whyte (1982: 22).
131 Issues Relating to Rural Industries: A Review of Literature
Figure 3.8: Relationship between urbanisation and rural industrialisation
URBANISATION RURAL INDUSTRIALISATION
requires facilitates
DEVELOPMENT OF RURAL RURAL GROWTH INFRASTRUCTURE creates CENTRES/MARKETS (THROUGH PUBLIC INVESTMENT)
We draw an end to this discussion with an example. On the eve of independence, Punjab (in India) was industrially backward in comparison with the other states of India. But, by the beginning of the 1960s, Punjab had become known as the land of small-scale enterprises (Bhalla, 1995). Technological breakthrough in Punjab agriculture was led by large-scale planned investment in rural infrastructure right from the beginning of planning in 1951. Urbanisation accelerated during 1961 to 1981, particularly in agricultural marketing cum trading towns. The plan investment in Punjab gave top priority to rural infrastructure such as irrigation and power, agriculture, community development, credit, markets, and research and extension, which together constituted nearly 70 percent of the total outlay during all the plans. The central government has made a considerable outlay for direct assistance and for infrastructure. By 1985, nearly all villages were linked by metalled roads, and the proportion of surfaced roads per 100 square kilometres of area far exceeded that for India as a whole. Since agriculture is still the largest sector of the Punjab economy, the extent of urbanisation is limited. But the “notable feature of Punjab’s pattern of development is that many rural areas have acquired urban functions and amenities owing to their fairly well-developed infrastructure and the good road connections between most villages, but have retained their rural characteristics. Urbanisation statistics fail to reflect this important phenomenon” (Bhalla, 1995: 103). There is no single dominant city in Punjab. There are
132 Chapter 3 large numbers of equally important major centres and a host of others running down to smaller and smaller centres that still are important sources of non-agricultural employment. The Punjab experience illustrates that rapid agricultural growth coupled with dispersed infrastructural facilities stimulates growth in other sectors through input, output and consumption linkages.
3.13 Conclusions
This chapter focuses on various issues relating to rural industries. The subjects include rural growth linkages, urbanisation, secondary or seasonal employment in rural non-farm activities, and sectoral composition of rural non-farm employment. Primarily, the chapter emphasizes the interlinkages between agriculture and industry and tries to look for the place where the rural non-farm economy does stand in the existing literature and to ascertain whether the sector is given its due respect for its role in the development of home economy. In this connection, the present chapter has started the discussion on interlinkages from the time of the Physiocrats and covered the linkage debate appeared in literature during the last few decades. While Quesnay was found to have been concentrated in agriculture for its surplus generating role, Adam Smith was found to be an advocate of industry as a leading sector (which adds value through division of labour) in the development of an economy. Marx found capitalistic mode of production as a destructive force of small farming as well as rural cottage industry. According to him, small farming groups turn into wage earners under the capitalistic system and, accordingly, rural home-based industry finds the ruinous path. Thereafter, in this chapter, we present the Kuznets’s model and Hwa’s model on relationship between agriculture and industry, where we find agriculture as a significant engine of development but find no place of rural industry as an important connector of transformation of a traditional sector towards industrialisation. Although Hymer-Resnick model takes the rural non- farm economy into consideration, the model ignores/fails to define particular developmental role of this sector and puts significance to the linkage between rural agricultural sector and urban industrial sector. It is John W. Mellor who has been very renowned for theorizing resource transfer from agriculture to rural industry. Although Mellor gave importance to other factors like rural infrastructure, he recognized agriculture as a primary engine for the development of the overall rural economy in
133 Issues Relating to Rural Industries: A Review of Literature general and for the development of the rural industrial sector in particular. He initiated a debate, but the other scholars have more or less agreed with him with regard to the dynamic role of agriculture in the process of growth of rural economy. Later Ranis- Stewart took up the Hymer-Resnick model and showed the shortcomings of the Hymer- Resnick by developing a series of new models with an objective to pinpoint the position of rural non-agricultural sector in different situations starting from unfavourable colonial case to favourable post-colonial case. In such a discussion on rural industry, we take the issue of urbanisation or rural infrastructure into consideration in a separate section, since urbanisation facilitates the road to industrialisation.
134 Chapter 3
APPENDIX 3.1: Distribution of rural workers across non-farm sectors in selected countries, male and female (per cent)
Country Total Manufac- Transport Trade Services Finance Construction Other Nonfarm turing Bangladesh (1991) Male 100 6.5 4.6 …………..... 39.4 3.7 45.8 ………...... Female 100 8.4 0.4 …………..... 12.2 0.8 78.2 ………...... Sri Lanka (1981) Male 100 18.8 9.8 18.1 20.6 1.6 7.7 23.4 Female 100 24.9 1.6 8.9 46.1 1.4 1.2 15.9 India (1993-94) Male 100 26.8 8.4 21.1 ………. 27.2 12.6 3.8 ………. Female 100 48.7 0.6 14.2 ………. 25.9 7.1 3.2 ………. Philippines (1980) Male 100 16.5 19.0 9.6 22.9 3.1 18.6 10.4 Female 100 27.9 0.6 19.0 46.8 2.8 0.4 2.5 Indonesia (1995) Male 100 19.9 12.5 23.7 26.0 0.7 14.5 2.8 Female 100 30.9 0.3 46.2 21.0 0.2 0.3 1.0 Thailand (1996) Male 100 25.3 8.0 17.8 ………. 17.3 29.9 1.7 ………. Female 100 37.5 1.0 28.2 ………. 23.1 9.7 0.5 ………. Source: Rosegrant and Hazell (2000)
135 Issues Relating to Rural Industries: A Review of Literature
APPENDIX 3.2: Women’s share in total employment by sector in selected countries, rural and urban (per cent)
Country Total Manufac- Transport Trade Services Finance Construction Other Nonfarm turing Bangladesh (1991) Rural 14.7 18.2 1.5 …………..... 5.1 3.9 22.8 ………...... Urban 12.1 15.9 1.4 …………..... 5.7 3.3 15.7 ………...... Sri Lanka (1981) Rural 17.9 22.3 3.4 9.6 32.8 16.1 3.3 12.9 Urban 18.5 24.3 5.5 7.6 33.7 22.9 5.8 11.4 India (1993-94) Rural 19.4 30.3 1.8 14.0 ………. 18.6 11.9 16.9 ………. Urban Philippines (1980) Rural 38.6 51.5 2.0 55.4 56.3 36.5 1.2 13.1 Urban 38.2 34.6 5.3 39.1 56.0 38.9 1.9 15.9 Indonesia (1995) Rural 35.6 46.2 1.1 51.9 30.8 16.9 1.3 15.0 Urban 33.5 35.7 3.0 44.8 37.0 29.0 3.7 11.9 Thailand (1996) Rural 41.3 51.0 8.1 52.7 ………. 48.5 18.5 17.7 ………. Urban 45.4 46.1 14.3 48.4 ………. 56.7 24.5 40.0 ………. Source: Rosegrant and Hazell (2000)
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APPENDIX 3.3: Percentage of non-agricultural workers in rural workforce in India, 1961-1983
States 1961 1971 1972-73 1977-78 1981 1983 Total Share All India 14.3 15.2 16.7 15.8 18.6 Andhra Pradesh 17.8 17.3 17.6 17.0 22.8 Bihar 13.1 10.4 15.3 12.6 16.5 Gujarat 11.8 14.1 13.0 17.4 15.4 Haryana 16.3 21.5 18.3 21.2 22.3 Himachal Pradesh 10.0 17.8 12.2 17.7 12.9 Karnataka 11.7 15.5 15.2 15.4 15.8 Kerala 39.7 38.0 34.6 42.3 36.9 Madhya Pradesh 9.0 9.4 8.6 10.4 10.0 Maharashtra 10.1 12.7 14.2 13.6 14.3 Orissa 12.9 14.9 15.0 14.9 20.9 Punjab 23.9 20.5 18.5 21.1 17.5 Rajasthan 9.0 12.6 11.6 12.9 13.3 Tamil Nadu 17.9 19.1 22.0 18.3 25.4 Uttar Pradesh 12.8 12.3 17.3 12.9 18.0 West Bengal 20.3 18.2 24.4 21.8 26.4
Male All India 16.3 16.3 19.3 19.5 18.3 22.4 Andhra Pradesh 20.0 19.7 21.4 19.7 19.9 25.6 Bihar 14.7 11.3 17.8 16.9 13.6 18.7 Gujarat 15.9 15.8 16.1 15.6 19.4 21.1 Haryana 19.4 21.6 19.9 22.5 24.1 27.8 Himachal Pradesh 15.4 23.4 18.9 22.6 26.6 22.9 Karnataka 13.9 15.9 14.8 16.8 16.4 18.4 Kerala 40.4 38.9 44.3 40.8 42.1 42.2 Madhya Pradesh 11.7 10.8 9.6 10.8 12.8 12.8 Maharashtra 14.8 16.1 17.6 19.6 19.6 20.4 Orissa 12.1 14.1 18.4 15.4 15.9 21.8 Punjab 23.2 20.0 20.6 22.2 21.9 22.5 Rajasthan 11.9 13.5 15.6 17.5 16.4 19.0 Tamil Nadu 20.7 20.9 24.6 26.1 22.4 31.1 Uttar Pradesh 13.9 12.9 18.1 19.8 13.6 21.3 West Bengal 20.0 18.4 22.1 22.3 21.8 26.9
Female All India 10.3 10.6 14.3 11.9 9.7 12.3 Andhra Pradesh 14.8 12.0 13.8 14.6 11.4 18.7 Bihar 9.9 5.7 14.8 11.0 6.9 11.8 Gujarat 4.8 6.4 8.7 5.6 6.8 7.4 Haryana 8.7 21.4 15.6 9.5 7.7 9.7 Himachal Pradesh 3.5 3.7 1.6 1.8 3.5 2.4 Karnataka 7.9 14.2 10.9 12.5 10.9 11.8 Kerala 38.0 35.4 41.4 27.4 38.5 29.1 Madhya Pradesh 5.5 5.5 4.4 5.3 6.5 6.1 Maharashtra 4.4 5.4 6.6 7.8 5.4 7.1 Orissa 14.5 21.1 18.3 14.2 12.2 19.0 Punjab 31.6 65.9 37.0 10.0 14.1 7.4 Rajasthan 4.5 6.4 8.4 4.7 5.2 6.1 Tamil Nadu 13.2 12.9 15.6 16.4 11.1 18.2 Uttar Pradesh 9.3 7.4 15.0 10.9 8.3 10.3 West Bengal 21.8 16.0 43.1 31.3 21.8 24.9 Source: Unni (1991)
137 Issues Relating to Rural Industries: A Review of Literature
138 Chapter 4
Entrepreneurship and Culture
4.1 Introduction
The central problem in growth, as some orthodox economists all assume, is capital formation. Of course, some have stressed ‘technical progress’ or ‘technological creativity’ in their studies related to developing countries. But the studies which have given main thrust on savings and capital formation have implicitly (behind the veil) assumed that sufficient technological creativity to carry forward economic growth is present in all societies. This assumption may fit well the developed countries but not the developing countries at all. In development studies literature, sometimes lack of technological creativity is regarded as an inhibitive factor to growth. In a discussion pertinent to increase in productivity, Hagen (1962) gave priority to technological creativity over capital formation. This was apparent to the first great student of economic growth, Schumpeter, who wrote:
The slow and continuous increase in time of the national supply of productive means and savings is obviously an important factor in explaining the course of economic history through the centuries, but is completely overshadowed by the fact that development consists primarily in employing existing resources in a different way, in doing new things with them, irrespective of whether those resources increase or not (Schumpeter, 1949: 68).
Schumpeter’s statement is true not merely in some vague qualitative way but also in a definite quantitative sense. Robert Solow (1957) has estimated that of the increase in output per man-hour in the United States from 1909 to 1949, not more than 13 per cent Entrepreneurship and Culture was due to increase in capital. Between 87 and 90 per cent was due to other changes, which may be lumped under the broad heading of technological progress (though some of them may not belong there). This statistical estimate implies that if there had been no increase in the quantity of capital used per worker, and changes in productive equipment had been made only by replacing equipment as it wore out with new equipment embodying new ideas, we would have had at least 87 per cent of the increase in output per man-hour that we actually had; only 13 per cent or less is attributable to increase in the quantity of physical plant and equipment used per worker.
Although technological creativity has got some attention in economics, all other entrepreneurial characteristics like achievement motivation, propensity to take risk, arbitrage, organising capability etc. have either been ignored or got relatively less importance in mainstream economics. To fill up this vacuum, entrepreneurship has been considered an interesting subject in the field of psychology since, some may argue, entrepreneurial motivation is primarily generated from the mind of a person concerned, which cannot be incorporated into the theoretical models (production or distribution theory of the firm). Only the Austrian school of economics has been maintaining a series of serious studies of entrepreneurship within the field of economics. In this regard, we will present a brief analytical history of the studies of entrepreneurship within the field of economics in this chapter.
The one side of the story is that entrepreneurship has been neglected in the theories of economic development. The other side is that, while studying entrepreneurship, economists generally do not bother about culture. But, on the contrary, the study of culture tries to hold or make room for all activities of a society in its holistic womb. The definition of culture, most often quoted from Tylor (1871), is: “Culture is that complex whole which includes knowledge, belief, art, morals, law, custom, and any other capabilities and habits acquired by man as a member of society.” It is quite natural that, as Ogburn (1964) has stated, different students will emphasize different aspects of culture as most significant, and in the future important new ideas about culture may be discovered. In the above mentioned definition of culture, Tylor has not reserved any
140 Chapter 4 distinct place for entrepreneurship or entrepreneurial capabilities, but he has kept provision to include “any other capabilities” to “that complex whole” of culture for the future students. Entrepreneurial capability is one of the “any other capabilities” and thus culture is reflected through entrepreneurial act (like other acts) of a set of population. A particular culture has been defined by Redfield (see Ogburn 1964: 3) as “an organized body of conventional understandings, manifest in act and artefact, which, persisting through tradition, characterises a human group.” Thus, in this broad definition of culture, entrepreneurial capability can take its place to “manifest in act” and, consequently, to “characterise a human group.”
Berger (1991) has stated that a more comprehensive approach to entrepreneurship must be interdisciplinary, comparative, and, above all, must take culture seriously. But there is limitation to conduct such an interdisciplinary study and this limitation has been cited by Ogburn (1964: 9-10) who has said that: “There are interrelationships between the parts of modern society as truly as in the cultures of preliterate peoples. But these interrelationships tend to be neglected by modern scientists because of their specialisation in particular fields.” Berger (1991) has roughly distinguished between two camps: economists on the whole are inclined to see entrepreneurship as a variable dependent upon economic factors and largely independent of culture, and scholars from other disciplines tend to see entrepreneurship as a variable deeply embedded in culture. Economists, taking as a given the basic motivation to maximize one’s gain, postulate that entrepreneurial activities will emerge more or less spontaneously when economic conditions are favourable. Hence the members of this camp—the “mainstream” economists of neoclassical frame—emphasize the pre-eminent importance of the availability of capital, access to markets, labour supply, raw materials, and technology. They formulate their analyses in terms of “economic opportunity conditions” and “economic risk,” and their analytical models use a combination of rather narrowly defined, functionally interrelated factors in more or less mechanistic ways.1 In contrast,
1 Entrepreneurship has not received much recognition from the mainstream economists. Moreover, economists (except very few) who have emphasised entrepreneurship for economic development did not try to relate entrepreneurship with culture. This trend has been observed from the early period of economic theories. For instance, priority is given, by the Physiocrats, to
141 Entrepreneurship and Culture anthropologists, historians, psychologists, and sociologists emphasize in varying and often contradictory terms the influence of non-economic factors such as social norms and beliefs, psychological motivations for achievement, the legitimacy of entrepreneurship, questions of social “marginality,” and the “internal fit” between any and all of these in the rise of modern entrepreneurship. According to Berger (1991), because mainstream economists with their clearly defined analytical models currently dominate the discussion and are influential in shaping present-day economic policies, the analysis of entrepreneurship in cultural terms has remained elusive. If non-economic factors are taken into consideration, they are typically those pertaining to the political-economic system, while wider questions of society and culture remain largely ignored. Berger (1991) states that few have tried to enrich their studies from a cross-cultural and historical perspective, and even fewer have attempted to come to grips with the changing nature of cultures and society. Berger strongly argues that since entrepreneurship is embedded in culture, such dynamics must be incorporated into our studies of it.
In this context, our discussion is broadly divided into two sections. First, we try to see how entrepreneurship has been dealt with in the field of economics, and, in the next part, we discuss the issue of entrepreneurship with an interdisciplinary approach (with special reference to India as well as West Bengal). Finally, we end up our discussion with some culturally sensitive models of entrepreneurship and conclusions.
capital. Despite giving priority to capital, they didn’t ignore entrepreneurial capabilities, but cultural factors are totally absent while they talk about entrepreneurial capabilities. They assertively say that the rehabilitation and further development of agriculture was the main precondition of general economic advance. How, then, was this to be achieved? One of the chief hindrances to further development, the Physiocrats recognised, was the prevalence of small-scale, capital starved, subsistence farming. What was required in the countryside above all, they argued, was not men, but wealth, i.e. capital. Simultaneously, Quesnay puts emphasis on entrepreneurial capabilities. He says that the normal income of the agricultural entrepreneur includes a “reward due for the trouble, work, and risks of his enterprise” (see Meek, 1962). But, in this line of discussion, we did not find even a meagre mention of culture in the Physiocratic school of thought.
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4.2 Entrepreneurship
The word ‘entrepreneur’ itself has an interesting history and it appeared first in French according to Encyclopaedia Britannica, long before there was any general concept of an entrepreneurial function. By the early 16th century men engaged in leading military expeditions were referred to as entrepreneurs. From this usage it was easy to move to applying the word ‘entrepreneur’ to other type of adventures. After 1700, ‘entrepreneur’ was a word which was frequently applied by the French to government road, bridge, harbour and fortification contractors. The same term was later applied to architects. Seeing such activities as the entrepreneurial function Bernard F. de. Bolidor, says Hoselitz, defined it as buying labour and material at uncertain prices and selling the resultant product at a contracted price (see Gautam, 1979).
Let us supplement it with Hoselitz’s (1951) words. The word ‘entrepreneur comes from the French word ‘entreprendre’, which means ‘to do something’, and it was originally used in the Middle Ages in the sense of ‘a person who is active, who gets things done’. Swedberg (2000) adds that the first economic theory of entrepreneurship is to be found in a work entitled Essays on the Nature of Commerce in General (circa 1730), written by a Paris banker of Irish extraction, Richard Cantillon (circa 1680-1734), who had a real flair for economic analysis.
Let us now discuss the evolution of the concept of entrepreneurship in economics.
4.3 Theories of Entrepreneurship: Schumpeter and his Previous and Later
The crucial role of entrepreneurship in an economic system has, in many cases, been characterised by private ownership of capital. But the entrepreneur virtually deserves his own identity, different from capitalist. Blaug (1986: 76) remarks: “a businessman need not be a ‘capitalist’ or ‘manager’ but he must be a decision-maker, whether he liked it or not. It is his function and this function alone that deserves the title of ‘entrepreneurship’.”
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The analysis of entrepreneurship should have occupied a central role in the investigations of economists. But, as Blaug (1986: 76-77) has commented with deep despair, “when we open any current text book of elementary economics, we discover that entrepreneurship is hardly mentioned, or mentioned only in passing. Is this some sinister conspiracy of silence…?” Always this was not the case. The strange disappearance of the entrepreneur from the centre of the stage of economic debate has a long history which is brilliantly depicted in Blaug’s work. Barreto (1989) has also done a good deal of job in presenting a similar history, starting from the period of neoclassical era. Following mainly Blaug’s work combined with some arguments of Barreto (1989) and Peneder (2001), here is a summary presented below.
In the book entitled The Wealth of Nations (1776), Adam Smith clearly distinguished the functions between the capitalist and the manager, and he emphasised that “profits” of the capitalist exclude the “wages” of management as payment for “the labour of inspection and direction.” But, Adam Smith did not distinguish between the capitalist as the provider of the “stock” of the enterprise and the entrepreneur as the ultimate decision maker. He used the terms “projector” and “undertaker” as the English equivalents of the French word “entrepreneur” but only as synonymous for the business proprietor. This failure to separate the entrepreneurial function from that of pure ownership of capital became almost common practice of all the English classical economists. Thus, the term “entrepreneur” or any of its English equivalents is totally absent in the writings of David Ricardo and so is the concept of the businessman as the principal agent of economic change.
Blaug comments that this explanation of the neglect of entrepreneurship in English classical political economy appears somewhat unconvincing. Blaug’s argument is as follows.
It is to be that the concept of entrepreneurship—as containing a function quite distinct from that of both the capitalist and the manager—had already been formulated by a renowned French economist of the eighteenth century, Richard Cantillon, who had
144 Chapter 4 written some twenty years before Adam Smith. Cantillon had pointed out the fact that discrepancies between demand and supply in a market create opportunities for someone to buy cheap and to sell dear and this sort of arbitrage performed by some agent brings competitive markets into equilibrium. He called people who take advantage of these unrealised profit opportunities “entrepreneurs”, that is, those who are willing to buy at a certain price and sell at an uncertain price. (Moreover, he noted that action of this kind need not involve manufacture and need not absorb the personal funds of the entrepreneur, although it frequently did.) Anyway, the core argument that Cantillon offers is that entrepreneurship is a matter of foresight and willingness to assume risk, which is not necessarily connected with the employment of labour in some productive process. Cantillon, therefore, left no doubt of the difference between the functions of the entrepreneur and the capitalist.
Adam Smith, as Blaug (1986) stated, read Cantillon but did not take the analysis of entrepreneurship as a serious concern. Similarly, David Ricardo had read the writings of Jean Baptiste Say’s, who leaned heavily on Cantillon in distinguishing between the provision of capital to a business enterprise, on the one hand, and the function of supervision, direction, control, and judgment, on the other. Nevertheless, there is no considerable indication of the special role of entrepreneurship in Ricardo. It is evident that Ricardo and all other leading English classical economists regarded production and the investment of capital as a more or less automatic process, involving no critical decision-making and certainly no risky judgment or imagination of any kind. Ricardo recognised the capitalist’s role to introduce a novel improvement in production. For example, a new machine is liable to reap extra returns. And this is a fact which Ricardo knew very well but this did not lead him to single out the capacity to innovate as the feature which distinguished one capitalist from another.
Blaug states that exactly the same thing is true of Marx. Marx knew well that capitalists can borrow all their capital from banks. That is why he regarded “interest” on capital as a deduction from the “profits” of the enterprise. He also knew that the special skills of managers, including the skills of monitoring and supervising the labour force, can be
145 Entrepreneurship and Culture hired from the labour market. But the entrepreneur is missing in his writings. He never considered whether the residual income left over after paying the interest on borrowed capital and the wages of management corresponds to any particular economic function, for example, the function of buying inputs at certain prices and selling the output at uncertain prices, as a result of which there may be losses rather than profits. He must have thought that either decision-making under uncertainty, which is what is involved in operating a business enterprise, entails no risks, or if it does, there is an unlimited supply of people in a capitalist economy willing to take such risks. Whatever be the case, Marx simply conflated the functions of the capitalist and the entrepreneur and in that sense simply continued the task where Adam Smith and David Ricardo left.
For the first entirely adequate argument in favour of the entrepreneurial role, we must look at the nineteenth-century German economist, Johann von Thünen. Blaug remarks that his remarkable but hopelessly obscure book, The Isolated State, Volume II (published in 1850), defines the gains of the entrepreneur as to which is left over from the gross profits of a business operation after paying (1) the actual or imputed interest on invested capital, (2) the wages of management, and (3) the insurance premium against the calculable risk of losses. The rewards of the entrepreneur, von Thünen went on, are therefore the returns for incurring those risks which no insurance company will cover because they are unpredictable. Since novel action is precisely the condition under which it is impossible to predict the probability of gain or loss, the entrepreneur is “inventor and explorer in his field” (Hébert and Link, 1982: 45-47).2
John Stuart Mill’s Principles of Economics (published in 1848) popularised the term “entrepreneur” among English economists but failed to break the tradition of the Smith- Ricardo concept of the entrepreneur as simply a multifaceted capitalist. The general equilibrium theory of Léon Walras, a central figure in the marginal revolution which ushered in the era of the neoclassical economics, provides a perfect example of how the new microeconomics caused the entrepreneur, as it were, to disappear. Walras tells us that every productive agent in a competitive economy is rewarded according to his
2 Cited in Blaug (1986).
146 Chapter 4 marginal product, that is, the increment of output which is contributed by the marginal unit of that agent. Now suppose that some economic agent hires others to produce a certain product. The hiring agent will be forced by competition to pay all the agents whom he employs their marginal product. Initially, this may leave him with something over and above the marginal product of his own services. If so, this merely induces the hired agents themselves to become the hiring agent because the hired agents get attracted by the positive residual and consequently enter the market as hiring agents. Thus they eliminate the positive residual. If, on the other hand, the residual proves to be negative, the hiring agent ceases to be a residual income recipient and rents the use of his services to others at the value of its marginal product. In either case, the residual always tends to become zero. The hiring agent is, of course, the entrepreneur but Walras assumed that entrepreneurship is not itself a factor of production but rather a function that can be carried out by any agent, say, the capitalist or the salaried manager. In any case, with a zero residual income, the total product is, as neoclassical economists liked to pronounce, exactly “exhausted” when all productive agents are paid their marginal products. When perfect competition exists in the market and when we reach short-run and long-run equilibrium, labour receives “wages” in accordance with the marginal product of labour, capital receives “interest” in accordance with the marginal product of capital goods, but “profits” are eliminated and the entrepreneur, as Walras said, “neither benefits, nor loses” (Hébert and Link, 1982: 63-64).3 For an extended historical presentation of the absence of the entrepreneur in microeconomic theory, covering almost whole neoclassical era and modern theory of the firm, one may consult chapter 2 and chapter 3 of Barreto (1989).
Now we can raise a fundamental question: why does microeconomic theory neglect entrepreneurship? So long as economic analysis is preoccupied with the nature of static equilibrium under conditions of perfect competition, there is simply no room in the theory either for entrepreneurship or for profit as a reward of risks associated with uncertainty. The growing popularity of general equilibrium theory sets the seal on the possibility of theorising entrepreneurship. As a matter of fact, static equilibrium school of thought increasingly became the mainstream economics. Despite valiant attempt to shift
3 Cited in Blaug (1986).
147 Entrepreneurship and Culture microeconomics from static concept to dynamic concept, large parts of modern economics remain trapped in a static framework. Actually the core of neoclassical economics lacked any true theory of the competitive process; what it truly possessed is the theory of the outcome of that process in an equilibrium state. In short, it emphasized equilibrium at the expense of disequilibrium. By assuming that all economic agents have free access to all the information they require for taking decisions, decision-making in the large part of modern economics is trivialised into the mechanical application of mathematical rules for optimisation. No wonder then that the mainstream economics of today is rich in the treatment of consumer behaviour, the profit maximising decisions of business firms (in short-run equilibrium), the theory of wages, the theory of interest, the theory of international trade, etc., but poor in the analysis of technical change, the growth of big business, the causes of the wealth and poverty of nations—and the theory of entrepreneurship (see also Baumol, 1968; Leff, 1979; Kirzner, 1979).
Apart from Blaug’s presentation of arguments, we can refer to Barreto (1989) who analysed the question as to why the entrepreneur is nonexistent into orthodox microeconomic theory. Barreto argues that as soon as the entrepreneur takes its place in the orthodox microeconomic theory, consistency of the theoretical base is violated. This violation is not tolerable for the sake of the building of the theory. The explanation of Barreto is as follows.
1. Why can’t the entrepreneur as innovator be incorporated into the modern theory of the firm? Because the production function exactly describes every possible input-output relationship; because the logic of rational choice requires that the ends and means be known and given. That is, in orthodox theory, the searcher knows what he is looking for, and where to look for it. The introduction of innovation requires relaxing these core assumptions. But this is impossible. If relaxed, then the consistent, interlocking nature of the theory would be destroyed. 2. Why can’t the entrepreneur as uncertainty-bearer be incorporated into the modern theory of the firm? Because perfect information, a basic assumption, guarantees that every agent’s expectations will be exactly fulfilled. The
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introduction of uncertainty demands relaxing this assumption. But this relaxation would prevent the application of the logic of rational choice. Although, by such relaxation, decision-making would have some real meaning, the resulting destruction of the consistent framework of the theory is not acceptable. 3. Why can’t the entrepreneur as arbitrageur be incorporated into the modern theory of the firm? Because perfect information prevents the introduction of ignorance, a requirement for arbitrage. Once again, relaxing this assumption would definitely cause the immediate breakdown of the model, which is too costly.
There is other school of thought, who brings entrepreneurship into theory. Blaug links up the debate. The virtual consensus about the negligence of entrepreneurship has been seriously questioned at several occasions in the twentieth century. The first occasion came with the publication of Frank Knight’s Risk, Uncertainty and Profit (published in 1921), an acknowledged classic of modern economics. Knight started with elaboration of von Thünen’s distinction between “risk” and “uncertainty”. There are many uncertainties in economic life such as the chances of dying at a certain age. Their objective probability can be calculated and correspondingly they can be shifted via insurance to the shoulders of others. Such risks thus become an element in the costs of production, a deduction from and not a cause of profits or losses. There are other uncertainties, however, which can never be reduced to objective measurement because they involve unprecedented situations.
The excellence of Knight’s argument was to show that the presence of true “uncertainty” about the future may allow entrepreneurs to earn positive profits despite perfect competition, long-run equilibrium and “product exhaustion”. Production takes place in anticipation of consumption, and since the demand for factors of production is derived from the expected demand of consumers for output, the entrepreneur is forced to speculate on the price of his final product. But it is impossible to determine the price of the final product without knowing what payments are going to be made to the factors of production. The entrepreneur resolves this dilemma by guessing the price at which output
149 Entrepreneurship and Culture will sell, thereby translating the known marginal physical products to the factors of production into their anticipated marginal value products. Although the factors are hired on a contractual basis and therefore must be paid their anticipated marginal value product, the entrepreneur as a residual income recipient may make a windfall gain if actual receipts happen to be greater than forecasted receipts.
Ten years before the appearance of Knight’s book, the young Schumpeter had contributed a wholly different view of the economic problem in The Theory of Economic Development (published in 1911). In this book, entrepreneurship and its connection with dynamic uncertainty is placed at the centre of economic inquiry. Schumpeter developed his argument by constructing a model of an economy in which technical change of any kind is absent. Such an economy, he contended, would settle down to a repetitive and perfectly routine economic process in which there is no uncertainty about the future. Hence, there would be no profits in such an economy. Turning towards counter argument, Schumpeter (1942) finds that the capitalist system cannot be understood except in terms of the conditions giving rise to entrepreneurship, because the entrepreneur as innovator is the source of all dynamic change in an economy. Schumpeter consequently separates entrepreneurship from other economic functions which may or may not be fulfilled by the same individual. For example, the capitalists are characterized by the ownership of the means of production, the management undertake the administration of a running concern, and the inventor produces ideas, whereas the entrepreneur gets things done (see Peneder, 2001). Distinguishing between “invention” and “innovation”—the discovery of new technical knowledge and its practical application to industry—and defining “innovations” broadly as the introduction of new technical methods, new products, new sources of supply, and new forms of industrial organisation, Schumpeter traced all disrupting economic change to innovations and identified the innovator with the entrepreneur.
After Schumpeter’s theory had arrived, mainstream economic theory has continued to neglect Schumpeter’s writings on entrepreneurship as it continues to neglect Knight’s theory of profits because neither fits in with static equilibrium analysis. The theory of entrepreneurship has, however, been given a new lease of life by the modern Austrian
150 Chapter 4 school, descending from Ludwig von Mises and Friedrich von Hayek. Peneder (2001), amongst others, states that it was Ludwig von Mises who firmly established the idea of the market as an entrepreneurial process, not as an equilibrium state. The market is such an entrepreneurial process which is driven by the daring, imaginative, speculative actions of entrepreneurs who see opportunities for pure profit in the conditions of disequilibrium. In the two books, Competition and Entrepreneurship (published in 1973) and Perception, Opportunity and Profit (1979), a student of von Mises, Israel Kirzner, has sought once again to persuade his fellow economists that the properties of disequilibrium states deserve as much attention as those of equilibrium states. Disequilibria are due to intertemporal and interspatial differences in demand and supply and hence give rise to unrealised profit opportunities. The essence of entrepreneurship, for Cantillon as much as for Kirzner, consists in the personal alertness to such potential sources of gain. There is a subtle change of emphasis in Kirzner’s discussion of entrepreneurship from that of Schumpeter’s: Schumpeter always portrayed the entrepreneur-innovator as a disequilibrating force disturbing a previous equilibrium, whereas Kirzner (1973; 1979) depicts him as seizing upon a disequilibrium situation and working to restore equilibrium. As Demsetz has said (in Blaug, 1986), entrepreneurship in new Austrian theory is “little more than profit maximisation in a context in which knowledge is costly and imitation is not instantaneous.” A more promising approach to the theory of entrepreneurship is offered in a study by Mark Casson who synthesises and extends previous works by Knight, Schumpeter, Kirzner and many others. Casson (1982: 23) defines an entrepreneur as “someone who specializes in taking judgemental decisions about the coordination of scarce resources.” An entrepreneur is someone who reaches a different decision from other people in the face of identical circumstances either because of access to better information or because of a different interpretation of the same information. The entrepreneurial function is, in principle, performed in all societies by individuals whose judgement differs from the norm.
This brief history, presented mainly by Mark Blaug, of the ideas of entrepreneurship gives us a theoretical discussion on entrepreneurship which has virtually deserved an important place in economic development through technological change, but has always
151 Entrepreneurship and Culture been neglected by mainstream economic studies. Another important thing is that the scientists who advocated the positive role of entrepreneurs in economic development did not talk a single word about culture or environment or value in the society. The non- economic factors are completely ignored whereas so far as entrepreneurship is concerned it is such an economic factor which somehow maintains a relationship with the non- economic factors. It is also surprisingly noted that Blaug, in his critical appraisal, himself has neither made any remark about the cultural factors, nor has he tried to raise the issue with relevant literatures. In a different study, at least one attempt of theorisation has been made by Lipset (2000)4 through introduction of “deviants” in anti-entrepreneurial cultures. The role of the group of deviants in breaking the tradition in a society is of immense significance. The logic of value analysis implies that the creation or expansion of roles which are not socially approved in terms of the traditional values should be introduced by social deviants. Lipset argues, while talking about the values and entrepreneurship in Latin America, that this hypothesis is basic to much of the literature dealing with the rise of the businessmen in different traditional societies. Let us go for an elaboration.
We start with Schumpeter. Although Schumpeter did not talk about culture, a meagre, or should we say implicit, is observed while he points out that the key aspect of entrepreneurship, as distinct from being a manager, is the capacity for leadership in innovation, for breaking through the routine and the tradition (see Schumpeter, 1961: 74- 94). The approach which emphasizes the theory of deviance assumes that those who introduce change must be deviants, since they reject the traditional elite’s ways of doing things. As Hoselitz (see in Lipset, 2000: 117) puts it, “a deviant always engages in behaviour which constitutes in a certain sense a breach of the existing order and is contrary to, or at least not positively weighted in the hierarchy of existing social values.” In societies in which the values of the dominant culture are “not supportive of entrepreneurial activity, someone who is relatively outside of the social system may have a particular advantage in entering an entrepreneurial activity. The restraints upon entrepreneurial activity imposed by the network [of social relations] would be less
4 Originally published in 1970.
152 Chapter 4 effective against such a person. Thus, an immigrant may be outside of many of the networks of the nation and freer to engage in entrepreneurial activity” (Kriesberg, 1963).5 The creative role of the deviant, or the outsider, has in part been conceptualised by the term “marginal man,” who for various reasons is partially outside the culture in which he is living, is less socially integrated in the structures which maintain conformity, and is, therefore, not so committed to the established values of the larger order. Hence men of this sort are more likely to be receptive to possibilities for change (see Lipset, 2000).
It is not always the case that innovating entrepreneurs in developing societies must be recruited disproportionately from the ranks of social “deviants.” Rather, it points with Weber to the fact that many minority groups have not shown such propensities (see Lipset, 2000). Clearly the Catholic minorities in England, or other Protestant countries, were much less likely than the general population to engage in entrepreneurial activity. In his analysis of the divergent consequences for economic behaviour of Protestantism and Catholicism, Max Weber pointed to the greater business accomplishments of the Protestant majority as compared to the Catholic minority in Germany. The key issue, as Weber has indicated, is the value system of the various groups involved. Latin America and some other less developed traditional societies are so vulnerable to economic cultural “deviants” because the predominant values of the host culture are in large measure antithetical to rational entrepreneurial orientations. Where national values support economic development, the Weberian emphasis on value would suggest that the innovating business elite would be drawn not from deviants but rather from the “in- groups,” from persons with socially privileged backgrounds (see Lipset, 2000).
4.4 The Achievement Motive in Economic Growth: Psychological Views
The question of why some countries develop rapidly in the economic sphere at certain times and others do not is in itself of great interest. Usually, rapid economic growth has been explained in terms of “external” factors—favourable opportunities for trade,
5 Cited in Lipset (2000: 117).
153 Entrepreneurship and Culture unusual natural resources, or conquests that have opened up new markets or produced internal political stability. But McClelland (1966), a psychologist, has been interested in the internal factors—in the values and motives men have that lead them to exploit opportunities, to take advantage of favourable trade conditions; in short, to shape their own identity.
So far as human motivation is concerned, the question which comes first is: How can human motives be identified? The problem of seeking an answer to this question lies in the fact that what people say about their motives is not a reliable basis for determining what those motives really are. Freud and the other psychoanalysts convinced us, rightly or wrongly, in analyzing the psychopathology of everyday life in terms of dreams and neurotic symptoms.6 McClelland (1966) planned to study human motivation by coding objectively what people spontaneously thought about in their waking fantasies. His method was to collect such free fantasy, in the form of brief stories written about pictures, and to count the frequency with which certain themes appeared. He was able to demonstrate that the frequency with which certain “inner concerns” appeared in these fantasies varied systematically as a function of specific experimental conditions by which he induced motivational states in the subjects. Eventually, he was able to isolate several of these inner concerns, or motives, which, if present in great frequency in the fantasies of a particular person, enabled him to know something about how he would behave in many other areas of life. Chief among these motives was what he termed “the need for Achievement” (n Achievement)—a desire to do well, not so much for the sake of social recognition or prestige, but to attain an inner feeling of personal accomplishment.
6 Freud demonstrated repeatedly that the “obvious” motives—the motives that the people themselves thought they had or that a reasonable observer would attribute to them—were not, in fact, the real motives for their often strange behaviour. By the same token, Freud showed the way to a better method of learning what people’s motives were. He analyzed dreams and free associations: in short, fantasy or imaginative behaviour. Stripped of its air of mystery and the occult, psychoanalysis has taught us that one can learn a great deal about people’s motives through observing the things about which they are spontaneously concerned in their dreams and waking fantasies.
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In this regard, it is worth mentioning the name of the German sociologist, Max Weber, whose perceptive analysis of the connection between Protestantism and the spirit of capitalism had greatly impressed McClelland. Weber’s argument is as follows. The distinguishing characteristic of Protestant business entrepreneurs and of workers, particularly from the pietistic sects, was not that they had invented the institutions of capitalism or good craftsmanship, but that they went about their jobs with a new perfectionist spirit. The Calvinistic doctrine of predestination had forced them to rationalize every aspect of their lives and to strive hard for perfection in the positions in this world to which they had been assigned by God. According to this description of the behaviour of these people given by Weber, McClelland concluded that they must certainly have had a high level of n Achievement. Perhaps the new spirit of capitalism was none other than a high need for achievement—if so, then n Achievement has been responsible, in part, for the extraordinary economic development of the West.
McClelland has made a historical study based on ancient Greece. He coded imaginative literary documents: poetry, drama, funeral orations, letters written by sea captains, epics, etc. Literary documents written during three different historical periods (dealing with similar themes) were found. Three different periods are: 900 B.C.-475 B.C. (the period of economic growth or prosperity); 475 B.C.-362 B.C. (the period of economic climax); and 362 B.C.-100 B.C. (the period of economic decline). Hesiod wrote on farm and estate management in the early period; Xenophon, in the middle period; and Aristotle, in the late period. The measure of economic growth was computed from information supplied by Heichelheim in his Wirtschaftsgeschichte des Altertums. Heichelheim records in detail the locations throughout Europe where the remains of Greek vases of different centuries have been found. Of course, these vases were the principal instrument of Greek foreign trade, since they were containers for olive oil and wine, which were the most important Greek exports. Getting the information about where the vase fragments have been found, McClelland was able to compute the trade area of Athenian Greece for different time periods. His concern was Athenian Greece, because the later period of Hellenistic Greece represented another civilization. That is why he did not consider the period of Hellenistic Greece in his study. Now we go to the results of the study.
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When all the documents had been coded, it was found, as predicted by McClelland, that the level of n Achievement was highest during the period of growth prior to climax of economic development in Athenian Greece. In other words, the maximum n Achievement level preceded the maximum economic level by at least a century. Furthermore, that high level of n Achievement had fallen off by the time of maximum economic prosperity, thus foreshadowing subsequent economic decline. A critical note, relating McClelland’s findings to Aristotle and Plato’s philosophy, is presented in APPENDIX 4.1.
A similar methodology was applied, with the same results, to the economic development of Spain in the sixteenth century and to two waves of economic development in the history of England (one in the late sixteenth century and the other at the beginning of the industrial revolution, around 1800). The n Achievement level in English history (as determined on the basis of dramas, sea captains’ letters, and street ballads) rose, between 1400 and 1800, twice, a generation or two before waves of accelerated economic growth (incidentally, at times of Protestant revival).
McClelland has also tested the hypothesis on modern nations. Again there are two variables. Independent variable is n Achievement manifested in literature and dependent variable is economic development. For independent variable, what type of literary document could be used that would be equally representative of the motivational levels of people in India, Japan, Portugal, Germany, the United States, and Italy? Two cultures may express their achievement motivation in a different literary form. But, by that time McClelland had gained experience through the historical studies (stated above) that certain types of literature usually contain much more achievement imagery than others. This is not too serious as long as time changes are dealt with. He decided to use children’s stories, for several reasons. They exist in standard form in every modern nation. The stories are imaginative. They are not often influenced by temporary political events, if selected from those used in the earliest grades. He collected children’s readers for the second, third, and fourth grades from every country where they could be found for
156 Chapter 4 two time periods, which were roughly centred around 1925 and around 1950. He got some thirteen hundred stories, which were all translated into English. Code was used on proper names, so that the scorers would not know the national origins of the stories. The tales were then mixed together, and coded for n Achievement. McClelland (1961: 74) described how the scoring of the stories was done. That was done on the basis of desires for success, anticipations of success and failure, and various types of instrumental ability (or blocks to achievement). To get a total score for a country, each achievement-related story was given a score of +2, each further subtype of imagery in such a story a score of +1 (with the limitation that each subtype can be scored only once per story), each possibly achievement-related story a score of +1, and each unrelated story a score of 0 (see also McClelland et al, 1953).
The next problem he faced was to find a measure of economic development which is dependent variable. The problem was to insure comparability. Economists consider national income figures in the per capita terms to be the best measure available; but they are difficult to obtain for all countries, and it is hard to translate them into equal purchasing power. Ultimately, McClelland came to rely chiefly on the measure of electricity produced: the units of measurement are same all over the world; the figures are available from the 1920s on; and electricity is the form of energy (regardless of how it is produced) that is essential to modern economic development. In fact, electricity produced per capita correlates with estimates of income per capita in the 1950s around .90 anyway.
The result he found is interesting. The correlation between the n Achievement level in the children’s readers in 1925 and the growth in electrical output between 1925 and 1950, as compared with expectation, is a quite substantial .53, which is highly statistically significant. It is especially interesting that n Achievement level in 1950 is not correlated either with previous economic growth between 1925 and 1950, or with the level of prosperity in 1950. McClelland argues that this result strongly suggests that n Achievement is a causative factor—a change in the minds of men which produces economic growth rather than being produced by it.
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In another psycho-social theory, Hagen (1962) relegates economic variables to a relatively minor role and has put an emphasis on certain aspects of the personality. He used the term “innovational personality.” Innovation involves two steps: arriving at a new mental conception, and converting it into action or into material form. Hagen (1962: 50) raised a point: “…why is technological creativity taken for granted in the economic theories? One reason no doubt is ethnocentricity. In Western societies we see technological creativity at work around us and assume that it is a fact of human nature rather than a culturally acquired trait. Therefore we assume that if technological progress is not going forward it must be because there are barriers to the capital formation in which it will be embodied. But there is another and perhaps more important reason. For reasons which were valid in the context in which they were first applied, economic thought since the second quarter of the nineteenth century, in which present-day economists are steeped, has ignored not only creativity but also the process of technological change itself.” In explaining the term “innovational personality” Hagen adds: “When it is stated that innovation requires creativity, the reader should not assume that the term ‘creativity’ refers to genius. Creativity exists in varying degrees; the man who conceives of an improvement in a can opener as well as the man who conceives of the theory of relativity is creative. Technological progress results from the actions of men characterised by varying degrees of creativity. The discussion of creativity refers, therefore, not merely to the limiting case of genius but to the quality of creativity in general, in whatever degree it may be found in a given individual” (p. 88).
According to Hagen, creative individuals are those who primarily respond productively. The unproductive, uncreative individual who responds with unacceptable fantasies may shut them out from his conscious mind, but he senses dimly the emotional surges within him and fears what is going on in his unconscious. Finding impulses in himself, which he regards as evil or foul or dangerous, he is afraid of letting his unconscious processes come to the surface for fear that dangerous or evil or vile urges will appear. Hence his unconscious processes are not only primarily unproductive; even insofar as they are productive, they are unavailable to him. The results do not appear in his conscious mind. The creative individual, on the other hand, is not afraid of his unconscious processes, and
158 Chapter 4 their results appear in his conscious mind. In the technical terms of psychoanalysis, he can “regress in the service of the ego.”7
This psycho-social theory of Hagen provides an alternative way of thinking to bye pass the building blocks of economic development or to break the vicious circle of poverty. But the problem is that the poor people in poor countries have remained poor for generations. In consequence, a culture of poverty might have developed amongst them. Hence, it is quite natural that they may suppress their fantasies in their unconscious minds and may not try to bring them out to their conscious surface as well. This could happen because of fear. Despite this limitation, Hagen’s approach is interesting for presenting a unique idea in the field of economic development and social change. We should not stick to the economic line of thinking; rather we should seek other avenues with a holistic view to find a path of economic development of the poor nations.
4.5 Family Environment and Entrepreneurship
Is achievement motivation a crucial factor for generating entrepreneurship? Or, is it family background which plays a greater role in reproducing entrepreneurs? Many argue in favour of the second. Family environment often really matters and many examples may support this view. Examples will come later. Let’s first start with a theoretical model. A mathematical model of entrepreneurship has been developed by Ghosh (1989) and it is shown that ‘supply of enterprise’ is crucially dependent on environment. Ghosh, in his article, has not defined what enterprise is. Let us pick up the definition from Schumpeter (1911) who wrote that the carrying out of new combinations (innovations) is to be called ‘enterprise’ and the person who carries out such thing is to be called the ‘entrepreneur.’ Although the model has been developed in relation with entrepreneurship in agriculture-based subsistence economy (not industry-based entrepreneurship), it is found interesting as well as relevant for the present chapter. Ghosh (1989) has presented the model by writing
7 Hagen has borrowed these words from Kris (1952).
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∂S ∂S S = S(E,Ω); > 0, > 0 (1) ∂E ∂Ω
Equation (1) states that production of surplus S is an increasing function of first, enterprise E, supplied in the subsistence economy. This surplus can be viewed as various types of goods are produced. For example, a subsistence economy, which usually produces foodgrains, may branch out into production of commercial crops or industrial goods. Secondly, supply of surplus S also depends on endowment Ω . The endowment variable can be looked at as combinations of available land, capital, technology and other assets. One can, following the subsistence economy models, assume Ω to be constant. However, in a model where entrepreneurship is considered, perhaps one should let endowment ( Ω ) vary. This is because, with accumulation, land and technology can be improved, new capital can be bought, and investment in human capital can be made. With this in mind, one can make endowment an increasing function of enterprise (E). This gives us
Ω = Ω (E) d Ω /dE > 0 (2)
It is now asked what determines supply of enterprise. Following the logic of the subsistence literature one can say that it is the limited needs of the members of the subsistence sector which restricts the supply of enterprise. Then it can be written as
E = E (N) dE/dN > 0 (3)
Equation (3) states that supply of enterprise (or entrepreneurship) is an increasing function of needs. The extent of needs in a subsistence economy is limited. Needs, in these societies, are limited not because the members therein are contended with their existing circumstances. They are limited because the existing circumstances have forced them to accept some sort of low level of living from which it is difficult to break out. This last observation is represented by the following equation
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N = N(V) dN/dV > 0 (4)
This can be stated as needs (N is a function of environment V). The meaning of the term environment will emerge from the following discussion.
Now there is a system of four equations with four endogenous variables S, Ω , E, and N and one exogenous variable V. Combining equations (1) and (2) one can write
⎛ ∂S ∂S ∂Ω ⎞ dS = ⎜ + ⋅ ⎟dE (5) ⎝ ∂E ∂Ω ∂E ⎠
From (5) one can write
dS ⎛ ∂S E ⎛ ∂S Ω ⎞⎛ ∂Ω E ⎞⎞ dE = ⎜ ⋅ + ⎜ ⋅ ⎟⎜ ⋅ ⎟⎟ (6) S ⎝ ∂E S ⎝ ∂Ω S ⎠⎝ ∂E Ω ⎠⎠ E
Expression (6) states that the rate of change of surplus (dS/S) is determined by enterprise elasticity of surplus ( ∂ S/ ∂ E . E/S), endowment elasticity of surplus, enterprise elasticity of endowment, and the rate of change of enterprise (dE/E).
From equations (3) and (4) one can have
dE ⎛ ∂E N ⎞⎛ ∂N V ⎞ dV = ⎜ ⋅ ⎟⎜ ⋅ ⎟ (7) E ⎝ ∂N E ⎠⎝ ∂V N ⎠ V which says that the rate of change of enterprise is determined by needs elasticity of enterprise, environment elasticity of needs and rate of change of environment (dV/V).
Looked at from this point of view the importance of environment becomes obvious. If environment is stagnant, i.e., if dV/V=0, then from (7) dE/E=0 and hence dS/S in (6) is
161 Entrepreneurship and Culture also equal to zero. In other words, supply of enterprise which is crucial to accumulation is itself crucially dependent upon environment.
Let’s now go into cases based in India. For a large proportion of firms in India, the basic unit of entrepreneurship is the extended family. India’s industrialists are usually members of old trading families, which frequently exercise control of a number of firms through the managing agency firm. The successful entrepreneurs and top executives, interviewed by Gupta (1991), were either born into families with business interests or were drawn into business either by choice or, as they themselves liked to describe it, by some quirk of fate. Included in the group interviewed were Marwari businessmen, as well as entrepreneurs from West Bengal, Uttar Pradesh, Northern India, and Assam.
The Marwaris who were interviewed made no secret of their burning desire, from childhood, to be in business. It was not simply a desire to participate in business activities, but to outshine all others. This was radically different from the orientation of most Bengali entrepreneurs whose driving ambition, as they were growing into manhood, seemed to be to compete and excel in examinations, to be brilliant if possible in academic fields. “We Bengalis believe and indoctrinate our children into believing,” said one, “that business is bad, that the guy running around in a Mercedes-Benz is unquestionably evil.” Perhaps, here, the meaning of evil is a manifestation of the image of a blackmarketeer. Gupta also met Bengali businessmen who said, “Business is in my blood,” or words to that effect. Other saw their fathers in business and admitted that the thought of going to work for someone else had never occurred to them. This group does not constitute an innumerable number of Bengali populations, of course. In all societies, there are always some people who move opposite to the mainstream. Consequently, the above mentioned observation of Gupta does not show a remarkable diverse nature of Bengali entrepreneurial culture. Clearly, the family environment proved to be the most critical in developing entrepreneurial instincts.
Just as Bengal idealised the intellectual life, other parts of India held up other career ideals. A highly successful entrepreneur from northern India admitted that, within his
162 Chapter 4 community, it is taken for granted that business is worthwhile and rewarding occupation. Another respondent of northern India said, “Most of my family members are either in military or government service. My father discouraged me from government service. Instead, my aunt, a doctor, persuaded me to join medical college. I left after only ten days of classes.” Others had dreams to realize. An engineering graduate from prestigious Indian Institute of Technology said, “I had dreams of a decent life, a car, a house. After a year working for a major managing industry house in Kolkata, I had a new car, a bungalow, a club membership, but a boring job.” A trip to Japan was all he needed to make up his mind about starting his own business. There is an interesting finding of the study of Gupta (1991: 110) which is as follows:
Among those we interviewed, the general consensus was that, compared with the rest of the country, there was less and less entrepreneurship as one moved eastward. “There’s a lot of family education in Marwari and Gujarati families,” said one. “They train their children for business, instil discipline, whereas Bengalis and Assamese don’t.”
The respondents, as Gupta wrote, tended to agree that life was easy in Bengal once upon a time. “Making a living, providing for two meals for one’s family, were comparatively easier in Bengal than in the rest of the country, especially arid spaces like Rajasthan and Gujarat,” said one Bengali businessman. And the most successful entrepreneurs in India are from these two states. During British period the zamindar (landlord) class emerged which later, by extension, created a bhadralok (so called gentleman) or babu (white- collar worker) culture in West Bengal and this is hardly compatible with entrepreneurship. Middleclass Bengali mothers discourage their sons from joining businesses. It is not uncommon for generations of Bengalis to find in their family circles lawyers, doctors, and teachers, but no businessmen. Currently, for economic security, government jobs are the priority. In business, one may gain or lose huge. This unforeseen future is not welcomed by the Bengali average middleclass families. Families still prefer to marry their daughters to government servants or to those holding even clerical jobs in the public sector rather than to businessmen (Gupta, 1991). Therefore, family
163 Entrepreneurship and Culture environment plays a crucial role in promoting entrepreneurship in a society. So far as business profession is concerned, Bengali entrepreneurs generally receive moral support from their own people only when they turn around as successful entrepreneurs, not before that. But an entrepreneur may not be successful from the very first day of his career and, therefore, his initial journey may happen to be very lonely. Appreciation at the initial stage should be seemed to be more important than that after the turning around towards success. So, family environment, along with McClelland’s achievement motivation and Hagen’s personality traits, is said to be another factor that has great influence on entrepreneurial rise/fall of a society. In this section, we have discussed how family environment holds responsibility in generating entrepreneurship. In the next section we will turn to the discussion of cultural root of India in influencing entrepreneurship.
4.6 Culture of Development (in Indian Perspective)
This section of our study provides a debate on whether certain aspects of Hindu culture and religious values embedded in the social system acted as a major barrier to economic development. Max Weber (1958) believed that Hinduism adversely affected economic development. Other-worldly and fatalistic orientation of the Indians had an adverse influence on economic development. Mishra (1962) indicated that belief in karma and rebirth retarded economic development of India. Hindu concept of karma implies that a person’s previous life’s good/bad acts result in the good/bad conditions of the present life. That means nothing but surrendering to fate. Common belief in fatalism indicates that every action of a person is regulated by fate. Kapp (1963) has also stressed the negative role of the doctrine of karma and social institutions (like caste, joint family and kinship) in economic development. Tilman (1963) pointed out that the caste system stood in the way of economic development as it restricted social mobility. On the other hand, there are scientists like Singer (1956),8 Srinivas (1958), Dube (1963; 1976), Rao (1969)
8 Singer (1956) wrote: In my study of the Madras industrialists, as well as from my observation of other Hindus, I found that Hinduism also generates in its believers a “salvation anxiety” about how to escape from the effects of one’s own past actions and the endless cycles of rebirths. The anxiety is not an intolerable one, however, that leads to an overwhelming pessimism and
164 Chapter 4 and Tinberg (1978) who argued that traditional Indian culture has several elements that could encourage and stimulate economic growth. Especially, Rao pointed out that the Veerasaivas of Karnataka were puritanical, propagating that work was heaven, and tried to stimulate entrepreneurial spirit among its followers. Tinberg pointed out that Marwaris, in spite of their conservatism, were highly entrepreneurial businessmen.
Gupta (1991) interviewed some entrepreneurs. One from several respondents said: “Religion has played a great part in my life. Faith in God has given me strength. My religion has helped me tide over numerous crises.” Another said, “I have a little temple in my office. I have Lord Krishna in my office, and he has played a big part in my life.” A third person said, “Religion is a great corrective. I don’t go to temples, but I have a temple in my house. I spend some time in it every morning. Religion pulls you back when you’re on the verge of doing something wrong.” Gupta describes that most entrepreneurs kept religion out of their business on a day-to-day level, but there were some expectations. In some companies, a priest visits every day to offer prayers and flowers to Goddess Lakshmi, Lord Ganesh, or Krishna. The practice is much more widespread around special festivals when new images of the gods are installed in many organisations. A Marwari entrepreneur remarked, “Religion motivates you to remain honest. After all, I have to go to God. How do I answer him? A man wouldn’t want to adulterate if he’s conscious of his religion. I can’t adulterate medicines that might kill a person. Ultimately, God is there to take the final account.” This is one side of the story. The other side is as follows as it is seen in Gupta’s study. One of the younger Marwari respondents said, “Personally, I couldn’t care less. The elders of the family are more traditional. So we never start anything new, never hire anyone, on a Friday. I see no logic in it.” Religious belief strongly influences a businessman to stay out of major actions on a particular day of a week. The detrimental part of this kind of exercise is that religion often determines what businesses one stays out of. The mighty Birlas chose to stay out of
defeatism or to a despairing burden of sin and guilt. That one becomes good by good deeds and bad by bad deeds is taken as an inexorable law of fate (karma) but not necessarily as a denial of freedom of choice and action in the present or as a reason for not exercising effort, intelligence, foresight, and resourcefulness in taking advantage of opportunities to improve one’s condition in this life and the next.
165 Entrepreneurship and Culture the hotel business because of the necessity to consider serving non-vegetarian food. “My family are all vegetarians,” said one Marwari entrepreneur. “They didn’t want me to make potato chips with artificial chicken flavour. They felt if the public had the slightest suspicion that the same oil is used to fry vegetarian and non-vegetarian products (even if artificially ‘flavoured’), they wouldn’t buy the chips.” Another Marwari said, “My wife wouldn’t want to earn a single penny from the death of a single bird.”
In Gupta’s study several of the Eastern Indian respondents were categorical in denying that religion played any part in their business. They admitted, however, that most successful Indian businessmen are probably quite religious, even if religion had no part in their business. “He sticks to his religion,” suggested one entrepreneur by way of explanation, “because he is afraid to upset the mental equanimity, the balance, which he derived from religion and which was a part of him when [he] became successful. He will not part with religion, then, for fear it will upset the overall configuration that he knew as present when he found success.”
The emerging picture is very mixed: for some, religion plays a role, and for some it does not. In this context of discussion, Alexander and Kumaran (1992) suggested that while there were elements of traditional values and institutions which could adversely affect the process of socio-economic development, there were also elements in the culture which could stimulate and sustain development. In such a context, according to them, the speed of development is dependent upon the elements, which are embedded into the culture and society and which are responsible for strengthening and weakening the development process. But the discussion cannot end up with this simplified conclusion. Let us try to go into further expansion.
The corrosive impact of colonialism and the negative aspects of traditional cultures of India have, since Rammohun Roy’s time, been chosen by turn, and sometimes together— depending upon who the commentator is—to explain the socio-economic backwardness of the country. We have talked about Max Weber’s view above. India had failed to develop capitalism and rational attitudes to life generally because, Weber argued, her
166 Chapter 4 religions were ‘other-worldly’. The cosmos was seen by Indians as being essentially moral—the rich deserved their richness and the poor their poverty—and, therefore, the question of changing it did not arise. Human beings were tied by a stern determinism to reaping the fruits of their actions, one incarnation after another, and the only way out of human bondage and suffering was renunciation and ultimately release from the never- ending cycle of birth, death and rebirth. This was then the ideology, the dharma, which legitimized the caste system. The effects of the notion of ritual purity/impurity as attached to occupations, and of the caste system generally, or development were judged by Weber as being ‘negative’—incapable of “giving birth to economic and technical revolutions from within or even of facilitating the first germination of capitalism” (Weber, 1958: 112). Moreover, since ideologies are far more difficult to change than modes of production, Weber could hardly have been optimistic of the chances of capitalism and industrial development even under colonial rule. But, writing after the railway system and modern industry had actually arrived in India, Weber perceived loopholes in the rigid framework of the caste system, because caste system did not allow free mobility of people from one occupation to another, especially from an odd profession to a noble one. Perhaps, free occupational mobility was mainly seen in the urban industrial areas. In fact, Habib (1969) commented that occupational changes had not been all that unknown in pre-British India as Weber erroneously believed.
Morris’s (1960) studies have shown that not only has caste not proven to be an impediment to the formation of an industrial working class, but Indian entrepreneurs also have shown themselves to be ‘aggressive, rational and creative.’ He concludes:
There is no precise definition of a ‘Hindu value system’ that can be identified as a significant obstacle to economic growth or change. Nor does the value system working through the caste system exhibit any decisive impact on the process of change. While jati seems to be the operative unit it behaves historically as extended kin groups have done elsewhere. For example, the description of entrepreneurial behaviour in nineteenth- and early twentieth-century India resemble similar activities in Renaissance Europe and seem to reflect primarily
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the limited scope of economic opportunities rather than any specific form of social structure (p. 607).
Gunnar Myrdal’s Asian Drama was published in 1968. The whole work is devoted to showing how material poverty of Asian nations in general and of India in particular is the result of a deadly combination of, on the one hand a social structure—and its associated cultural ideology—which is rigid, non-innovative, inequalitarian and indifferent (if not inimical) to development, and on the other a development strategy which mistakenly puts its faith almost exclusively in physical investment, ignoring the overall institutional setting which includes a corrupt and ‘soft’ state (Vol. 1, p. 57ff). Myrdal also discussed the productivity consequences of poor health and irrelevant education. His conclusion was that the nations of Asia were caught in a vicious circle of underdevelopment and a wide range of drastic changes in existing institutional structures, as also in development strategies, was urgently needed. The narrow notion of standard of living, he argued, should be replaced by a broad-based concept of levels of living—of social systems defined not only by output and incomes and conditions of production but also by levels of living, attitudes to life and work, institutions, and appropriate policies (Vol. 3, p. 1860ff).
Another element is consumerism which should be mentioned in the light of present discussion, because consumerism comes in the wake of consumption and consumption leads to production and production leads to income. Consumerism, in our opinion, is a state of mind which provokes a person to buy things. Madan (1983) has made a point in this regard. “There are, of course, other elements also which have contributed to the making of the socio-economic impasse in India, but among these I would like to focus attention on the phenomenon of runaway consumerism in the midst of widespread poverty and deep socio-economic inequalities—without meaning to imply that this explains everything—as a matter of the gravest concern” (p. 35). The inevitability of the emergence of consumerism in the economies, particularly those characterized by capitalist enterprise, has been recognized for long. But, in a developing economy, a social frustration may arise out of consumerism. Madan (1970) helps us to identify this problem:
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Another major development of the sixties has been what may be called the legitimization of Consumption as a major value in Indian society. The peasant’s demand for finer varieties of foodgrain or mill-made cloth; his desire to give his children school education; the ubiquitous bicycle and transistor; the acceptance of the refrigerator as a ‘necessity’ of upper middle-class life; the seemingly insatiable demand for motor cars, air-conditioners and such other luxuries among richer people—these are a few of the many manifestations of the new attitude to consumption. Consumerism is coming in the wake of consumption. This, of course, also gives a keener edge to income disparities and to one’s sense of disappointment over the inability to buy the many consumers’ goods and services that have become available. Here then is major source of social frustration (p. i).
This problem of frustration remains at the side of the have-nots. So far as the Indian ‘uprising’ middle-class people are concerned, another kind of mental complex may remain in their minds. With the increase in their incomes they may be hesitant or resist themselves to buy more consumer as well as luxury goods keeping the thing in mind that many around them do not have food even. A social value works behind this motive which can only be identified as emotional attitude rather than rational attitude. Not the affluent class, only the uprising middle class bears this suffering because they have come across a very struggling life for survival. It may happen to be the case that they could be in fears of unknown evils of consumerism or what may be explained in the way that they are not psychologically prepared to consume in bulk amount after their long fight against poverty. Of course, this attitude changes but it changes gradually, not overnight. This view about Indian middle-class consumers is simply a hypothesis and has never been tested. Another argument can go like the way that the middle-class people, having been afraid of unsecured future (since, in India, there is no social security like the developed countries), may put main thrust on savings, not on consumption.
What lies at the core of the received/borrowed models of development from the West is the notion of growth as an unlimited process—the more we have, the better off we are.
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But the thinkers at the other pole criticize the view of mindless consumerism and the loss of autonomy. According to them, consumerism infantalizes people by turning them ‘into a herd of compulsive consumers’ (Roszak, 1969: 16). Consumerism will ultimately destroy everything, values as well as resources: for as Gandhi once warned, the earth produces enough for everybody’s need but not for everybody’s greed (see Madan, 1983: 37).9
Hence the core of the economic development of a nation is not only due to the several economic factors but also embedded in the culture of the people. Economics may no longer continue to be separated from culture. Some argue that if a traditional culture stands in the way of economic development, then modern Western culture should be introduced for the sake of a speedy course of economic development. Professor D.N. Majumdar, one of the founding fathers of Indian anthropology, conceived such kind of concept.10 In a memorial lecture T.N. Madan has explained Majumdar’s view in detail (see Madan, 1983). Modern Western culture, based on science and technology, was the model for Majumdar and he advocated interventionist policy, backed by applied social science, to achieve modernisation, but with due regard for the specific character of India’s society. Modernisation or social development and economic growth were seen by Majumdar as generally desirable and interrelated goals—as an unquestioned ‘good’— with the clear implication that the country needed to develop in its villages and cities, in its home and offices, i.e. a culture which would initiate, promote and sustain development. One was not to wait and watch for the ‘unfolding’ of the potentialities of a culture but to take charge of the process of innovation, not only to speed it up but also to give it a particular content and direction. Whatever stood in its way was to be eradicated. Since development had not been generated from within the traditional cultures, it had to be introduced into them from without (see Madan, 1983).
9 Perhaps this is the reason that most of the western people consider Gandhi a great politician and social reformer but not a good economist. 10 Majumdar was not very happy with the role of economists. Noting the long-lasting interest of economists in rural studies, but castigating them for a preoccupation with the perfection of techniques of quantitative analysis and a neglect of ‘the cultural background’ and ‘the interrelation that exists between different sets of social phenomena,’ he called for a holistic approach to the problems of development (see Madan 1983).
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In contrary to the notion of imposing Western model, there is another notion which is called endogenous path of development. Endogenous development supports the idea of self-reliance; it points the way to self-directed development—to original styles of development—which alone can ensure authenticity of the effort. It underscores respect for cultural identity and, as a corollary, future options directed towards a wide range of types of development that are based on the fundamental socio-cultural features and relations of various societies. At the same time it also grounds itself in the ideal of a redemptive world community. In short, what the notion of endogenous paths of development seeks to highlight is that the issue today is not how to alter cultural traditions and reshape social institutions to bring about one particular kind of development of all nations; the real issue is how to envisage the goals and strategies of development so as to bring out the best potential of each culture in the realisation of universally valued life-styles (Madan, 1983). Even, despite criticizing the negative aspects of Hindu culture, Max Weber did not ask India to see its future in the image of the West (see Madan, 1983: 28). The Japanese experience should be instructive in this regard. One of the makers of the Japanese miracle, the engineer-planner-bureaucrat Saburo Okita, has written:
….economic growth of a country is essentially a result of economic efforts made by the people who live there, and nothing else….there is no cure-all medicine for economic development; each country has its own background history, religion and tradition. Any economic development scheme cannot be planned without paying due attention to these factors…. Japanese society of today maintains many elements that are far from being Western, and still Japan is the only non-Western country that has, more or less, succeeded in modernizing its economy. Many traditional elements played a vital role for the success of this process (Okita, 1979: 12f).
According to Madan (1983), the concept of modern culture which seeks to make the whole world look alike, live alike, think alike, and feel alike is a dull concept.
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Modernization of culture should not mean sameness because sameness is dullness of the mind and it deadens creative faculties. The peoples of a uniformly modernized world would have nothing to tell one another: they would become speechless. A crucial point which Alatas (1977: 28) makes is that “although modern societies share many features in common, they need not belong to the same type.” The affirmation of the positive value of cultural pluralism is the cornerstone of the new thinking on alternative visions or models of development. Such rethinking calls for a high degree of self-awareness on the part of intellectuals: neither mere imitation nor cultural arrogance will promote this process (Madan, 1983).
While talking about entrepreneurship and economic development, the cultural issues should be incorporated into that discussion. Why they should be added into the discussion has been well-argued by two scholars. Hoselitz (1960: 155) states that industrial “entrepreneurship can develop only in a society in which cultural norms permit variability in the choice of paths of life, and in which the relevant processes of socialization of the individual are not so completely standardized and demanding conformity to a prescribed pattern that the bases for appropriate personality development leading to productive orientation are absent.” Leff (1979: 46) has been more assertive in commenting (based on some scholars’ previous researches) that the socio-cultural conditions associated with the emergence of entrepreneurship indicates pessimism concerning the prospects for less-developed countries’ generating sufficient entrepreneurship to achieve high rates of economic development.
Now, in such a context, the question which inevitably arises is: Does culture change? Yes, of course, it does. If not, then a traditional society would have remained so for ever. The world history does not legitimize the notion of unchanging culture. “The period of the combined Dark and Middle Ages in Europe is notorious for its ignorance of natural phenomena and for its lack of original thinking in science. The ignorance and the folkloristic character of the natural history of the period are not to be wondered at, for Europe did not descend into the Dark Ages….” (Barnett, 1953: 68-69). Culture which is generally considered to be a stable component of a society has been proved a changing
172 Chapter 4 process in the study of Lerner and Pevsner (1958). They examined the process of modernisation in the Middle East and indicated that modernisation was associated with an increase in literacy, increase in media exposure, increase in per capita income, and political participation. Literacy and education became the basic components in the transformation of the mental outlook of the members of a traditional society. Literacy became the sociological pivot in the activation of psychic mobility and education was found to the most powerful force shaping people’s modernity. Geographic mobility, urban contact and participation in mass media also were found to be associated with individual modernisation.
Regarding change in culture Barnett (1953) states that the more a man knows about a given set of data, or about diverse sets of data, the more likely he is to develop something new. Among some people there is a tradition of learning, a stimulation of curiosity, and a nurturing of the quest for new and diverse knowledge. In a few parts of the world, libraries, publication centres, schools, and apprentice systems have been developed for the wide dissemination and perpetuation of ideas. On the other hand, in many societies the acquisition of knowledge is a privilege of the few who are selected either formally or indirectly by economic or inheritance preferences. According to Barnett, knowledge is a prerequisite for innovation and innovation is the basis of cultural change. The ideal of breadth and depth of knowledge does not mean that innovation is confined to specialists and encyclopaedists. It means only that added resources multiply the chances that new thoughts will occur, and that resources may be increased by intensive and extensive explorations. It means, too, that innovation is impossible for an individual beyond the limits of his understanding of his experiences. Breadth and depth of ideas increase the frequency with which anyone is likely to conceive of something new. By the same token it is an auspicious condition for acceleration in cultural change.
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4.7 Darwinian Principles of Evolutionary Change and Technical Culture
It seems to be interesting to see how changes in economy can be explained by the Darwinian theory of evolutionary change and how the entrepreneur generated from an environment of technical culture plays a significant role in evolutionary economics. How can we use biological phenomena as social phenomena? In reply, Greenfield and Strickon (1981: 486-487) tell us: “Although we recognize quite clearly that biological and social phenomena are not the same, we propose to think of the social as if it were like the biological, so that we may use the model of evolutionary process Darwin gave us as the metaphor for thinking about the ways in which social phenomena are produced.” So “we are using the Darwinian imagery metaphorically, not literally” (p. 488). Greenfield and Strickon explained that among the varied individuals that constitute any breeding population, Darwin noted, some are better able to utilize the resources of their environment than others (principle of variation). Those better able to utilize resources produce more offspring relative to the other members of the population (principle of cumulation). Over time this differential reproduction results in a shift in the average characteristics of the population (principle of selection). This is the process of evolution (see Greenfield and Strickon, 1981).
Let us go into more detail. Evolutionary change refers to a specific kind of dynamic process which is performed by the simultaneous interplay of the three functional elements of variation, cumulation and selection. These three elements are found in Darwinian theory. The phenomenon of the complex interdependencies that work for the emergence of new species has been termed by Darwin “correlation of growth”: “I mean by this expression that the whole organisation is so tied together during its growth and development, that when slight variations in any one part occur, and are accumulated through natural selection, other parts become modified” (Darwin, 1859: 182).11 Darwin talked the emergence of new species, whereas, using Darwin’s frame as a metaphor, Peneder (2001) spoke of the evolution of an economy. According to Peneder, the above mentioned three functional elements form the building blocks which are essential to any
11 Cited in Peneder (2001).
174 Chapter 4 evolutionary explanation of economic change, including the dynamics of the market process. In simple and general terms, Peneder’s (2001) explanation is as follows:
“First of all, in a system lacking any source of variation there can be no change at all. Secondly, in any system, which does not include the element of cumulation, single variations lack the force necessary for the generation of different paths of development. …Finally, without the mechanism of selection, the system lacks any meaningful direction or guidance of motion; i.e. any arbitrary historical variations are preserved and might be indefinitely amplified via cumulative effects” (p. 18).
How do we relate the above mentioned principles to our perception of free market processes, characterized by perpetual motion, rivalry, and entrepreneurial alertness to new profit opportunities? Peneder continues:
“We should however attempt a positive formulation, which more directly relates to our concern for the process of entrepreneurial competition. In this particular context, the principle of variation can be applied synonymously with the entrepreneurial function of creative response, i.e. the realisation of new competitive practices, such as the introduction of new products or processes, new marketing methods, organisational reform, or refined pricing strategies. Similarly, the principle of selection is tantamount to competition. It constitutes the essential negative feedback or control device that keeps the system within its proper boundaries, as defined by the external constraints of economic scarcity.12
12 About principle of selection, Peneder (2001: 21) has given further explanation: “Selection is the principle source of guidance in the system, imposing a certain direction on its movements towards the alleviation of relative scarcities, which influence the system as a whole.”
Regarding the theory of natural selection, there is existence of a catchy but misleading notion of “survival of the fittest” in Darwin’s work. Peneder (2001) informs us that Darwin unwisely adopted it in his later editions, which did not appear in his first edition (and generally considered best) of the ‘Origins.’ It is hard to say any one is fitter or more advanced than another. Huxley (1873: 397), one of Darwin’s most effective defenders, suspected that the “fallacy has arisen out of the unfortunate ambiguity of the phrase ‘survival of the fittest.’ ‘Fittest’ has a connotation of
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Finally, the principle of cumulation allows successful competitive practices to continue and expand over time. Only the simultaneous interplay of all three functional elements can establish the principle capacity of a social system to discover and learn about its opportunities to generate profits by alleviating the impact of external constraints on the system” (p. 19).
Figure 4.1: Evolutionary economic change
Economic development through technological change
cumulation selection
variation
Evolutionary change through variation, cumulation and selection
Education system that pays due respect to technical culture
‘best’; and about ‘best’ there hangs a moral flavour. In cosmic nature, however, what is ‘fittest’ depends upon the conditions.”
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Following the Darwinian framework, variation is essential to bring about changes in the economy. Actually, by variation, the entrepreneurial function of creative response gets recognition, because the entrepreneur initiates change in the economy through innovation. But, from the same framework, it is not clear at all as to when/where such creative entrepreneurial function generates. What is the conducive atmosphere which helps the entrepreneur to go for innovative action? Sweeney (1987) offers an argument that there are certain components that are conducive for the creation of an innovation- friendly environment. They are:
1. the education and training system in a region; 2. the quality and intensiveness of the information flows between firms and entrepreneurs and their friends; 3. the cycle time between need and delivery of information for decision-making; 4. the technical culture and progressiveness which determine the innovative potential of a region.
Of the four components, the first one and the last one—which raise the issues like ‘education’, ‘training’ (let us say vocational training or technical training) and ‘technical culture’—are very crucial for initiating variation in a traditional rural society where static attitude of people has become inhibitive to industrialisation and modernisation of the region. It is almost known to all that education and training are key components of social as well as economic change but very less has been discussed about ‘technical culture and progressiveness which determine the innovative potential of a region.’ According to Sweeney (1987), the division between culture and technology are becoming a barrier to innovation. The author argues that we should not single them out since “[i]ndustrial action has to reconcile technical, economic and aesthetic needs” (Lang, 1983).13 As a strong protagonist of technical culture, Sweeney (1987: 31) argues: “The great cathedrals of Europe, for example, were not the brainchild of an artist without technology or a technologist without art.” In a disappointed voice, Sweeney states: “Culture came to be
13 Cited in Sweeney (1987).
177 Entrepreneurship and Culture associated solely with the humanities, literature, music, art—and pure science. It was and is snobbish culture. Technology and industry are considered to be somehow at a lower level, tedious, dirty and certainly not creative activities” (p. 32). “The alienation between technology and culture has been reinforced by an education system which has organised itself to associate culture and creativity with the humanities and ‘pure’ sciences. Technology, the creation and application of ‘know-how’ has been increasingly associated with all that is anti-social or at best a ‘vulgar’ art” (p. 33). Of course, Sweeney’s arguments are too strong to gain support. But, on the other hand, we cannot escape from the argument that Sweeney further extends: “Society, influenced in attitude by the education system, including its lack of understanding of technology, has in effect ostracised technology, industry, and industrial culture from its culture” (p. 33). Let us now try to see the problem from another angle.
Referring to the developing world, except a few pockets where the large industrialists and the businessmen live, the city and the suburbs are mainly dominated by the middleclass people who usually were/are very likely to show technical culture a path of departure from the mainstream culture towards marginal culture. And, consequently, when this class, due to its majority, represents the policy making body or formulates educational policy, introduction of technical culture gets support of lower priority. At the same time, the rural society is so traditional and so heavily dependent on agriculture that technical culture or industrial culture is not likely to enjoy considerable indulgence over there too; rather folk culture and folklore are still very popular in remoter villages. In a society of such typical dual characteristics, people lack technical culture which is the driving force of generating innovation and progressiveness that further leads to variation in production system and finally guides or directs the society towards evolutionary economic change. Therefore, technical culture should be treated as a dynamic element of evolutionary economics, in absence of which an economy may get stagnant. For a schematic presentation of the above discussion, see Figure 4.1.
4.8 Some Culturally-Sensitive Models
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Is there really any relationship between entrepreneurship and culture? Dana (1998) states that the most important environmental variable that explains entrepreneurship phenomenon is the cultural context. In the following section the role of cultural context in entrepreneurship is discussed by examining entrepreneurship in different cultural groups. These models are developed by Dana (1998). The role of these models is important to signify as to why the study of culture is crucial while undergoing entrepreneurial study of a particular set of people. Different cultural groups maintain their own cultural values. One culture may encourage the members of its group to promote entrepreneurship, while the other culture may not. Entrepreneurship is not just a function of opportunity, but rather is a function of cultural perceptions of opportunity. This is the hypothesis that Dana tries to test by the following models.
4.8.1 Entrepreneurship among the Amish
The Amish form a religious group which evolved in Europe, but which spread to America, with concentrations in Ohio, and in Pennsylvania. The culture of these people values asceticism, frugality, thrift and work as well as humility. Adults teach their young that work is pleasurable. In order to maintain their cultural values, the Amish try to avoid close contact with people who do not hold the same traditions. Furthermore, due to religious discrimination in the past, the Amish often exhibit a mistrust of outsiders. Consequently, given the choice, the Amish prefer not to work for companies in mainstream society. Hence, the Amish prefer to be self-employed or to work amongst themselves, as it is their belief that a community of believers is the context for life. It is a different model of Western kind of entrepreneurship. Unlike Western entrepreneurship which often implies technological innovation, entrepreneurs among the Amish do not even use electricity.
The primary motive of such self-employment among the Amish is neither profit nor prestige, but rather the maintenance of cultural values, separately from mainstream society such as to emphasise humility over pride. Self-employment is perceived as much
179 Entrepreneurship and Culture a social activity as an economic activity. Although the outsider world is penetrating Amish territory, the Amish have managed to hold on to their cultural values. They continue to exhibit a propensity for entrepreneurial behaviour. Their society is self- sustaining, with virtually no unemployment, and their membership is growing.
4.8.2 Entrepreneurship among the Indigenous People in Alaska
The results of an exploratory study by Dana in Nome, Alaska reveal that Eskimos in Nome have a lower tendency to become entrepreneurs than do non-natives. Although the Eskimos make up the majority of the population, only 21.9 per cent of the entrepreneurs are Eskimos. Furthermore, only 7.1 per cent of the Eskimo respondents claimed to have actively sought opportunity for profit. Among those who actively sought profit opportunities, 94.4 per cent are non-natives. Only 1.6 per cent of the sample consisted of opportunity-seeking Eskimos, while 98.4 per cent were either non-native or Eskimos who passively identified an existing opportunity for entrepreneurship. This study suggests that entrepreneurship should not be viewed as a function of opportunity, but rather as a function of cultural perceptions of opportunity.
4.8.3 Entrepreneurship in the Canadian Sub-Arctic
A field study using ethnographic methodology compared self-employment among different ethnic groups in the Canadian north. Significant differences were found between aboriginals and non-aboriginals. Aboriginals tended to express entrepreneurship in the form of informal self-employment, reflecting traditional activities, such as hunting and fishing. In contrast, entrepreneurship among non-aboriginals was found to be the result of opportunity identification. Again this study supports the hypothesis (of Dana) that entrepreneurship is not merely a function of opportunity but rather a function of one’s cultural perception of opportunity.
4.8.4 Entrepreneurship in Laos (Asia)
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Dana gives an account of entrepreneurship in Laos, explaining the impact of religion and culture on enterprise. The national religion of the Laos Kingdom (dating back to 1353) is Theravada Buddhism. Associated cultural beliefs include high social status being given to monks, and entrepreneurship being relegated to outsiders with inferior social standing.
Central to Theravada values is the ultimate goal of extinguishing unsatisfied desires on the premise that unsatisfied desires are believed to cause suffering and that suffering can be eliminated if its cause (desire) is eliminated. A respectable man, therefore, should not work towards the satisfaction of materialistic desires, but should rather strive to eliminate desire itself. Given that entrepreneurship may be perceived as a means to satisfy desire (e.g., for power or materialistic goods), entrepreneurial behaviour is shunned. Lao men are thus excluded from entrepreneurship. Yet, the same Lao folk tales (which reinforce the belief that a religious man should not be self-employed), encourages women to accept a heavy burden in exchange for home, protection and security. Even the Lao currency portrays agricultural work being done exclusively by women.
The economy in Laos is growing. There are several hundreds (licensed) foreign entrepreneurs in this country. Most of the 2,500 ethnic Chinese in Vientiane are entrepreneurs. Many Moslems, Australians and Thais own enterprises in Laos. Numerous Lao women have stands at local markets, but for Lao men, cultural values make entrepreneurial work taboo.
4.8.5 Entrepreneurship in the Kingdom of Lesotho (Africa)
In general, different cultures have different frames of reference. Culture dictates that certain property cannot be bought or sold in exchange of cash. In every society there exists some restriction on economic exchange. Democracies have imposed a cultural ban on treating people as a commodity. Laws prohibit slavery, baby-selling, trade of human organs etc. Along the same lines, Basuto (people of Lesotho are known as Basuto) culture dictates what is morally acceptable in that society, and what may not be treated as a commodity. For example, a cow is not treated as a commodity by the people of Lesotho.
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For this reason to essential to differentiate between two types of property in Lesotho: (1) property for personal consumption; and (2) property with social value. This distinction is necessary because, according to the Basuto culture, there is a lack of convertibility between these categories of property. However, cultural values have created a one-way barrier inhibiting a Basuto from giving up property with social value in exchange of cash or in exchange of property for personal consumption, although the norm dictates to trade cattle in exchange of a bride. Thus, a Basuto might not have money for food but, yet, culture prevents him from selling his house, which falls under the category of property with social value.
In this way, due to the lack of convertibility between types of property, it is possible to be wealthy in one type of property while poor in the other. Western societies measure degree of wealth; in Lesotho, in contrast, it is possible to be very poor and wealthy simultaneously. Therefore, it is quite evident, according to Dana, that Western models are not necessarily applicable in such a context.
Not in that strict manner, but some kind of social value is attached with some properties like land, gold etc. in rural India. Properties like land and gold are saleable in India but still some rural people try to keep them for not losing social prestige. This is the characteristic of traditional cultures (characteristics of stationary societies and changing societies which are given in the APPENDIX 4.2 are nicely furnished by Ogburn (1964)). Often this kind of trait inhibits economic growth. For instance, a rural landlord, despite incurring continuous losses in his agricultural activities, may not try to gradually diversify or shift in industrial activities by selling his land properties.
4.9 Conclusions
This chapter has a unique feature in conceiving the idea of seeing entrepreneurship from a cultural point of view. It first presents a brief analytical history regarding the disappearance of entrepreneurship from the mainstream economics. It combines the discussion with arguments offered by the Austrian school of thought in favour of
182 Chapter 4 entrepreneurship. Entrepreneurship in connection with cultural values has been discussed with due significance. The main emphasis has been given to examine, with the context of the existing literature, whether cultural values play any role in influencing entrepreneurship. It is very difficult to close the chapter with a clear conclusion, because culture has multifaceted role to play so far as entrepreneurship is concerned. Some community may find encouragement for entrepreneurship from within its own particular religious culture, like Hindu culture, while some other community from same religious culture may not find encouragement. In the empirical chapter, we will further examine the issues, amongst others, relating to whether or not the cultural factors influence entrepreneurship in our study area, Bardhaman district of West Bengal state.
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APPENDIX 4.1
Plato, Aristotle and McClelland
One comment should be made in connection with McClelland’s findings. In McClelland’s study, Aristotle’s time has been marked as the period of economic decline. But n Achievement should have been at maximum level during Aristotle’s time due to the strong influence on society of his philosophy of materialism which is supportive to capitalism. And accordingly, in this connection, his time should have been considered as the period of prosperity or economic growth and the subsequent period as the climax. We explain it in more detail.
All through the brilliant work Aristotle comes forth as a fundamental materialist and teaches that matter is the principle of the living reality of the world existing around us (Losev and Takho-Godi, 1990a). Earlier, Plato had an idealistic world view; he is acknowledged to be the founder of European idealism. He was the first to supply an idealistic basis for the primacy ideas over matter. The idea of an object is something essentially, vitally and reasonably necessary for us to understand the object, to make relation with it, to use it. The idea of an object is necessary for us to reconstruct it and determine its uses. In this sense, every object and in general everything that exists in the world has its own idea, its own meaning. Without ideas it would be impossible to distinguish one thing from another, and thus all reality would be transformed into formless and incomprehensible chaos. The idea of an object indicates the aggregate of an object’s essential characteristics, its composition and structure, its purpose, and its meaning in general. We have then an integral unit that contains within it something that is not contained in its individual components. A table can be of different colours, it can be big or small, it can be decorated or repaired, it can be broken into pieces, burned and turned to ash. But can this be done to the idea of the table? Can the idea of the table be pale or dark, red or brown, heavy or light? Can the idea of a table be smelled or touched? The table itself can be smelled and touched, but what about the idea? Can the idea of the
184 Chapter 4 table be broken into pieces and turned to ash? Of course, water can freeze and boil, but can the idea of water freeze and boil (Losev and Takho-Godi, 1990b: 86-87)?
On the contrary, Aristotle starts the definition of a thing with matter. A thing is, first, matter, second, form (idea), third, operative cause, and fourth, a certain expediency or utility. Idea or eidos (as it is pronounced in Greek) does not exist separately, but is always embodied in matter. Aristotle conceives the idea of the thing to be not separate from the thing and off somewhere else, but within the thing itself. Aristotle at one point was in full agreement with Plato, i.e., he (Aristotle) did not himself deny the existence of ideas. Nevertheless Aristotelianism was a revolution with regard to Platonism which recognised the existence of a separate, heavenly world of ideas. Aristotle admitted that the idea of the thing could be anywhere, even outside the thing. However, whatever functions of the idea of the thing were involved, the most important for Aristotle was precisely the presence of the idea within the thing itself, the functioning of the idea of the thing within the thing itself, the complete absence of any gap between the two and of any dualism. For example, we breathe air, but we don’t breathe the idea of air. If a person were to be put in an airless place, no idea of air in its pure form would save him from suffocation (Losev and Takho-Godi, 1990a).
Although there may not be any relationship between Aristotle’s materialism and McClelland’s n Achievement, the question which inevitably arises here is that if n Achievement of a section of population can be identified from the contemporary literatures and be correlated with contemporary economic situation, then how come the impact of Aristotle’s revolutionary materialistic world view has a positive correlation with the declining period of the Greek economy? Not only this materialistic view, there are more in Aristotle in connection with the present discussion.
Aristotle conceptualised Mind as prime mover. The fact is that everything in the cosmos moves, and every movement depends on another movement. For Plato, the World Soul governs the cosmos. For Aristotle, it is Mind which moves absolutely everything and consequently is life as eternal energy, but which is itself immobile, because its mobility
185 Entrepreneurship and Culture would require some other cause, and there is nothing above it. If Mind governs the cosmos then there must be a positive influence of Mind on a civilisation’s entrepreneurial energy.14 But McClelland’s study gives us a different result. Also, according to Aristotle, anger is necessary, and no battle can be won without it—unless it fills mind and fires the soul; it must serve, however, not as a leader, but as the common soldier; and Aristotle stands forth as the defender of anger, and forbids us to cut it out (Losev and Takho-Godi, 1990a: 174-75). Although anger is not synonymous to dream or fantasy, it could have been treated as one of the “inner concerns”.
Our main confusion toward McClelland’s consideration is how the time of Aristotle’s revolutionary materialistic world view can be positively correlated with the declining economic growth. Otherwise, McClelland deserves overwhelming praise for his extraordinary imagination and innovative research direction in the area of economic development in general and entrepreneurial energy in particular. Lipset (2000: 111) said: “Perhaps the most impressive comparative evidence bearing on the significance of value orientations for economic development may be found in the work of David McClelland and his colleagues….”
14 A somewhat supportive view is seen in the works of some Austrian theorists who have brought the discussion of the act of entrepreneurship more into the realm of mental processes. For example, Mises saw entrepreneurship as action, which was by nature always speculative, since the future cannot be known. Entrepreneurs made decisions, which could bring profit or loss—but
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APPENDIX 4.2
Characteristics of Stationary Societies
In stationary societies what is done is good because it has always been done. Experimentation or new methods would not be looked upon with favour if they should be presented. The prestige of the past is exceptionally great, and the elders who know it best are greatly respected. Fate and inevitability are accepted, and since there is little thought of changing conditions, efforts toward adjustment and modification face inhibitions and control. Those who succeed have character. Authority of the past and of elders counts. Law is majestic; rules of moral conduct are set forth in detail and are followed. The mores are strictly obeyed, and deviation from them not permitted. The people have excellent manners. There is likely to be good deal of sentiment attached to institutions. Ritual and ceremonialism are prominent. The conditions are favourable for art, religion, and for class lines. Especially is a stationary society a well-balanced, harmonious society.
Characteristics of Changing Societies
In a changing society the attitude is one of seeking improvement. There is always a better way. The new tends to be favoured somewhat. Progress is a feature of the social mind. Optimism tends to prevail, and the social philosophy may favour pragmatism. The past is like a dead hand, something to get away from. The position of youth is strong, and young men often rise to influence. Authority as power yields to reason and evidence, but in crises dictators arise. There is no great respect for law, and crime is more frequent. Moral codes are ineffective, and good conduct rests upon intelligence in problem-solving. Mores are of slight significance. Manners are bad, and the egos of others become very annoying. Behaviour is more in accordance with biological nature and animal tendencies. Sentiment about institutions does not flourish while the ceremonial tends to decline.
it was in the mental act of decision making by the entrepreneur that profit originated: “Profit is a product of the mind, of success in anticipating the future state of the market” (Mises, 1951: 21).
187 Entrepreneurship and Culture
Conditions do not favour rigid barriers between the classes, and the milieu is somewhat difficult for art. Traditional religion finds a more hostile environment. There is no great harmony in culture. The times seem out of joint, and there is much maladjustment between the different parts of culture, due to the lagging of some changes behind others. The different parts of culture are moving at unequal rates of speed.
188 Chapter 4
189 Entrepreneurship and Culture
190 Chapter 5
Data Collection
5.1 Introduction
Jan Tinbergen (1965), the renowned Dutch economist, makes us clearly understand the need of operational development policy oriented research: “It is the requirement that research undertaken in direct or indirect support of development policies should be as ‘operational’ as possible. In a general way we may say that there are two types designing scientific activity. One type is directed at satisfying our curiosity and the other at reaching certain aims of policy. As a rule scientific activity starts in the first way. When it is discovered that scientific knowledge can be used to further certain concrete aims part of the activity takes the second form. Of course there is no fundamental difference between the two aims; upon closer thought, both in some way are forms of reaching higher levels of satisfaction. The differences are that the first type of scientific activity satisfies a few people very intensively and large parts of the community only in a weak way; while the second type of scientific activity is directed at the satisfaction of more pressing needs of large sections of society and undertaken in a more systematic way.”
Ours is the second type of research and, of course, is an empirical research. Data collection through field survey is the most important part of an operational, development- policy-oriented, empirical work if the study is solely dependent on primary data. Whether the study requires secondary data or primary data is subject to the objective of the study. The objective helps setting hypotheses or research questions which are reflected through the theoretical considerations/model of the study. For testing hypotheses or answering research questions the researcher may need data. If the data available in government or non-government offices, which are mostly secondary data, can serve the purpose of the researcher concerned then primary data may not be needed. But if the secondary data Chapter 5 does not fulfil the demand of the study then the researcher may have to go to the field for collecting primary data in search of the information required for the purpose. Especially, for micro level study the researcher finds almost no option other than going to the field survey because often secondary data available are found to be of macro nature. Even if secondary micro data are available, the researcher may not find them sufficient or up-to- date to perform at least a satisfactory research. Honestly speaking, primary data collection is such a job which is interesting but, however, often realised as a time- consuming as well as pain-staking work. So far as data collection in rural areas of developing country is concerned, it is not an easy task at all. Any way, the job is the part of the game and the researcher needs to play the game very carefully right from the very beginning for the sake of yielding interesting findings (or discoveries) concerning the subject under study.
Keeping the theoretical considerations (or theoretical model) in mind, the researcher is to formulate the questionnaires which usually require very careful attention because a silly mistake in this work may lead the whole study towards a wrong direction or may make the study incomplete. In the present study, as we have said in chapter 1, we are to identify the determinants of non-farm entrepreneurship among farmers. Data collection has been based on two types of questionnaires. In the first type of questionnaire, we focused on the farmers who had been diversifying into non-farm manufacturing sector. In the second type of questionnaire, we focused on farmers who had been retaining farming only. The first category includes both the firm- and farm-level data-collection from a sample of heads of farm households who are also engaged in non-farm manufacturing business. And the second category includes mainly the farm-level data-collection, including some non-farm related perceptions, from a sample of heads of farm households who are not engaged in non-farm manufacturing business.
In this chapter, we present a detailed description of the data collection. Section 5.2 introduces us with the secondary data that were collected. Section 5.3 describes the sampling procedure for collecting primary data. In section 5.4, we find the structure of the questionnaires. Section 5.5 tells us the story of the fieldworkers’ selection and
192 Data Collection training. Section 5.6 discusses the pilot survey, whereas the story of organising the main survey is discussed in section 5.7. Section 5.8 mentions the timing of the survey. Section 5.9 gives us the statistics of number of observation. Section 5.10 describes the problems occurred during field survey. Checking of the filled-out questionnaires has been discussed in section 5.11. Section 5.12 tells us how the reliability of the data has been tried to be maintained. Section 5.13 presents the conclusion of this chapter.
5.2 Secondary data
Although the core of the present work is dependent on primary data, the study, like any other research work, uses some secondary data for better understanding of the study area. The secondary information we used include historical background of entrepreneurship in the study area, agricultural as well as industrial performance of West Bengal and of Bardhaman district, urbanisation and the condition of rural infrastructure in the study area etc. Various publications and reports were reviewed.
5.3 Primary data: sampling procedure
We have already given the rationale in chapter 2 as to why we have selected Bardhaman district as our study area. So, selection of the district involves no sampling; the district is chosen purposely. Sampling played an important role in next part of the operational work. Sampling involved three stages:
1. selection of the administrative blocks in Bardhaman district; 2. selection of the panchayats (cluster of some villages) in the blocks; 3. selection of the households.
The important considerations in selecting the administrative blocks were to avoid the region of the district where the heavy industries are located and to concentrate in the
193 Chapter 5 region where the farmers are found in number. If we broadly divide the district into two parts, then we find that the western part (Durgapur-Asansol area) of the district is dominated by the heavy industry sector, whereas the eastern part can be regarded as the agricultural area. Consequently, our survey has been based on the eastern part of the district, i.e. the blocks have been randomly selected from the eastern part of the district.
Table 5.1: Administrative blocks and panchayats that have been randomly selected for fieldwork
Administrative Block Panchayat
Raina-II Uchalan Painta-II Gotan Pahalanpur Kaiti Arui
Galsi-II Sati Nandi Galsi Adrahati Sanko Khano Gohogram
Kalna-II Baidyapur Pindira Kalyanpur Badla Borodhamas Purba Satgachhia
Mangalkote Kaichor-I Mangalkote Godista Maghigram Simuliya-I Simuliya-II
Bardhaman-II Barsul-I Barsul-II Baikunthapur-I Baikunthapur-II Nabasta-I Hatgobindapur
194 Data Collection
Bardhaman district is divided into 31 administrative blocks. Roughly 20 blocks constitute the district’s eastern part. Of 20 blocks, five have been randomly selected for this study (see Table 5.1). Thereafter, from each block, six panchayats have been selected randomly (see Table 5.1). And, then lastly, 10 farmers have been randomly selected from each panchayat. For selecting the farm households, we faced trouble in getting the name-list of the farmers. At last, we found a solution. We met the chief of the pachayats for getting the name list. From that list, we randomly selected the names of the respondent-farmers. It is important to note here that we interviewed two kinds of farmer—one, who were engaged in farming and non-farm manufacturing activities; two, who were engaged in farming only. So, accordingly, we made two lists of the names in each panchayat and then we randomly selected the respondents from those lists.
5.4 The structure of the questionnaires
As we said earlier that we have visited two kinds of farmer for interviews—the farmer engaged in farming and non-farm manufacturing activities and the farmer not engaged in non-farm manufacturing activity. We constructed two questionnaires for the purpose— one questionnaire was for the first kind of farmer (questionnaire 1) and the other was for the second kind of farmer (questionnaire 2). The purposes of both the questionnaires were same, i.e. identification of the determinants of non-farm entrepreneurship among the farmers. The two questionnaires differed only regarding one issue: we deleted the questions relating to non-farm industrial unit from the questionnaire which was meant for the farmers who were not engaged in non-farm activities. For both the questionnaires (i.e. questionnaire 1 and questionnaire 2), see APPENDICES 5.1 and 5.2.
At the beginning of the questionnaires, we presented a brief introduction for why this research is conducted. The panchayat authorities, the educated respondents, and any government officials might want to know the purpose of this research. For that purpose, we included such an introduction. For the fieldworker, there are number of instructions in the questionnaires. They include general instructions and instructions relating to
195 Chapter 5 particular questions. These instructions were incorporated into the questionnaires in order to help the fieldworker to conduct interviews smoothly. These instructions included some definitions too. For example, the differences among the locations like village, rural growth centre and small town have been described in the questionnaires; the difference between primitive technology and modern technology has also been described. They have been very useful tips for the fieldworkers.
In questionnaire 1 (i.e. the questionnaire which was meant for the farmer who was engaged in non-farm activity), the questions were grouped into fourteen sections dealing with different types of information. The question sections are as outlined below:1
1. Household composition: family size, sex, age, marital status, education level, occupation.
2. Economic status of the household: farm size, size of homestead, kind of irrigation system used, kind of puddle system used, kind of harvesting method used, number of livestock.
3. Farm related question: primary identification of the farmer (big land owner or sharecropper?), crops produced in a year.
4. Occupational background in non-farm activities: number of non-farm enterprises owned, primary motivation for starting non-farm business, year of establishment, reasons for selecting the product line, type of ownership, type of business.
5. Locational factors: location of the non-farm enterprise, reasons for selecting the location, obstacles faced in starting up non-farm unit.
6. Technology/machinery: kind of technology used in the firm, procurement of machine.
196 Data Collection
7. Financial aspects of the non-farm enterprise: capital, sources of capital, expansion of firm.
8. Employees/workers: number of workers, wage.
9. Raw materials: kind of raw materials used (agricultural or non-agricultural), procurement of raw material, availability of raw material.
10. Consumption linkages: consumer’s demand for the farmer’s non-farm products, competition.
11. Forward production linkages: producer’s demand for the farmer’s non-farm products.
12. Performance data of the non-farm unit: production capacity (per month), total output (per month), costs, profit, increase/decrease in demand for the products, future plans, government policies for the small-scale industries.
13. Risk/psychological factor/sociological factor/cultural factor: marriage relationship with business family, faith in fate or work effort, risk, innovativeness.
14. Political factor: political affiliation of the farmer.
In questionnaire 2 (i.e. the questionnaire which was meant for the farmer who was not engaged in non-farm activity), the questions were grouped into six sections. Sections 1, 2, 3, 13 and 14 of questionnaire 1 hold same in the questionnaire 2. In addition, questionnaire 2 incorporates a section relating to the perception of the farmer about the non-farm manufacturing businesses.
1 Not all information gathered is used in this study whose main focus is on the determinants of NFE. The information relating to the performance of non-farm entrepreneurship will be used in a follow up study.
197 Chapter 5
5.5 Selection and training of fieldworkers
In selecting fieldworkers, we took their educational level and experience into consideration. In such a study, a fieldworker’s role is very important because he/she determines the success of interviews and the quality of data collected. The questionnaires were drafted in English and it was the duty of the fieldworkers to translate it into Bengali, the local language. Literal translation of a question was not always expected to bring out actual information from the respondent during interview. It was expected that sometimes fieldworker might be needed to gossip with respondent to get to the actual information. So, key considerations for recruitment of fieldworkers were their education and experience. Fortunately, we got five trained and experienced fieldworkers from the economics department of Bardhaman University (located in the study area). They were all Masters in economics and were engaged in different projects of economics department. All of them were from the same district, i.e. our study area. We did not consider the fieldworkers from districts other than Bardhaman because an overall familiarisation with the district’s geography and with the district’s transportation system was important.
Just before fieldwork we sat together with the fieldworkers to convey them a brief story and the objective of the study, to tell them what we want, to make them familiarize with each question of the questionnaires, and to train them how to approach to interviews with villagers.
5.6 The pilot survey
A pilot survey was first organised and conducted in order to test our questionnaires and to familiarize the fieldworkers with the procedure of interview. In the pilot survey, we faced little trouble with some questions of the questionnaires. For example, during pilot survey we observed that the respondent mixed up his cultivable land and homestead when he
198 Data Collection was asked question about the size of his landholding. So, after pilot survey, we split the question into two subparts—one is about the respondent’s cultivable land size and the other is about his homestead. In total, ten trial interviews were conducted in the pilot survey. In addition, the pilot survey helped us to determine the time needed for an interview.
5.7 The survey
Organisation
In a meeting, we divided and distributed the geographical areas to the fieldworkers for conducting the field survey. We sat with the transportation details and designed how to reach the destination areas. In remotest villages, fieldwork was expected to be very time- consuming due to unavailability of transport vehicles. We were well aware of the fact that we might have to walk kilometre after kilometre due to lack of sufficient intervillage public or private transport facility. So, we organized our field-visits accordingly.
Timing
The fieldwork was conducted between November 2002 and March 2003. We chose this time period because this period covers two seasons like winter and spring. In West Bengal (except the Himalayan area) winter is not that severe; so winter is the right time for fieldwork. Summer and monsoon are not suitable seasons in West Bengal to carry out fieldwork. We now go into detail about the collection of data for the present study. Only the head of the household was interviewed.
Number of observations
In this chapter, the procedure used for data collection is discussed, including the sampling procedure and household interviews. The plan of the fieldwork involved the interviewing
199 Chapter 5 of 300 farmers. But finally we could interview 290 farmers. Ten farm households among our samples remained non-respondents. Of 290 farm households, we interviewed 169 farmers who were found to have been engaged in non-farm manufacturing activities. And we talked to 121 farmers who were engaged in farming only.
Problems faced during fieldworks
Each of the sample households was visited. Some problems were encountered during the field implementation of household interviews. As we said earlier, 10 farmers were found to be non-respondents. They were not found at home and at work as well. Instead, the female members of these households could not answer the questions. So we had to reject them.
The availability of the farmer was a real problem. We could not make any appointment prior to the interviews. So, we directly visited the household on the day of interview. In some occasions, the household heads were not found at home. In that case, we had to rush either to the farm or to the crop-field to meet the farmer. We conducted many interviews on the crop-field. For this reason, in the remotest places time management was a problem.
Checking of the completed questionnaires
Everyday, at the end of interviews the questionnaires were collected from the fieldworkers and checked for completeness, mistakes, omissions, and irrelevant responses. Identified mistakes and problems were discussed with the fieldworkers. As regards mistakes, arrangements were made to go back to the respondents concerned and to correct the mistakes on the next day. In a very few cases, respondents had to be reinterviewed.
200 Data Collection
Reliability of the data
Accuracy of collected information is very important aspect of an empirical research. If the interviewer does not correctly perceive information provided by the respondent then inaccuracy may exist in data. On the other hand, if the respondent is unable to provide accurate information then also data may produce errors.
In the fieldwork of our study, some respondents could not correctly answer the questions relating to financial aspects of their firm like production capacity, fixed capital, working capital etc. In such cases, we talked to the respondent’s educated elder children or other relatives around (if found any) and clarify things. Also, we ourselves tried to bring out the actual information not by asking specific questions, but by holding a relevant chat.
5.8 Conclusions
This chapter intended to offer an overview of the data collection procedure including the descriptions of the questionnaires and the fieldwork details. Data collection included gathering secondary and primary data. Secondary data were required for a better understanding of the study area and other issues, whereas primary data were required for the empirical model of this study. Primary data collection was divided into parts. The first part was meant for testing the questionnaire, which is called as pilot survey, and the second part constitutes the main survey of the study. During the interviews some problems were encountered. Through innovative means the problems were tackled in order to reduce data inaccuracy. Information collected from the household interviews was organised and processed into a form that can be utilized to address the study questions.
201 Chapter 5
APPENDIX 5.1
QUESTIONNAIRE 1
SURVEY ON RURAL INDUSTRIES IN BARDHAMAN DISTRICT OF WEST BENGAL (Questionnaire for farmers who are engaged in non-farm manufacturing activities)
Location of survey (To be filled out by the interviewer immediately before interview): Block: Panchayat: Village / rural town: Post office:
INTRODUCTION: We are carrying out a research project on various aspects of rural industry in Bardhaman district. Our objective is to study the problems and prospects of rural industrialisation in the district. This project will be trying to raise the issues relating to the interest of your village as well as yourself. More specifically, we are studying the problems of investments in non-farm activities and, accordingly, our study is concerned with, first, identification of the obstacles of the rural industrial sector at micro as well as macro level and, second, formulation of eradicative measures of those obstacles. So, we are sure that the findings of this research will be directly or indirectly beneficial for you through some specific policy recommendations for industrial development of your district. This research is being done by a team comprising one researcher and supervisors from Wageningen University, the Netherlands and Indian Statistical Institute, Kolkata, India. We have come here to talk to you about agricultural and industrial development of your village, infrastructural condition in general and some aspects of your own household, your farm, your non-farm activities (if any) in particular. We are not from any government department or tax office, so please don’t worry about the questions. You can trust us and freely exchange your dialogues with us. We promise that we will not use this data for anything except our academic research purpose. We invite your wholehearted cooperation in this work.
INSTRUCTIONS FOR THE FIELD INVESTIGATOR: In this questionnaire we have used the terms ‘your household’ and ‘your household member’ frequently. Here household consists of family-head (usually male), his spouse, his sons and daughters including those (children) who are not living in the same house any more and have started families of their own. For the present study this definition of household is formulated in the light of the Indian tradition. For the interview the family-head should usually be our respondent and household would be considered to be the unit of observation. As a field investigator of this project you should be careful about where to use the term ‘you’ and where to use ‘your household member’ (or you can directly use a term like ‘your son / your daughter / your wife’ instead of ‘your household member’ wherever applicable). For example, if the respondent himself is the owner of the business, then you can use the term ‘you’, otherwise ‘your household member’ (to be more precise, use ‘your son/daughter/wife’ as instructed above).
202 Data Collection
A. General Background
Name of the family head:
1. Respondent’s household composition
Sl. Household Sex Marital Age Education Occupation No. members (M/F)* status** (see note below General*** Technical/ Primary Secondary for vocational (1) (2) abbreviations) (Respondent first) 1. H1
2. H2
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
*M = 1, F = 2; **Married = 1, Never married = 2, Divorced = 3. ***Never went to school = 1, Incomplete primary = 2, Primary = 3, Incomplete secondary = 4, Secondary = 5, Higher Secondary = 6, Bachelors = 7, Masters = 8.
Note: family head = H1, head’s spouse = H2, their children = C1, C2, and so on; children of C1 = C11, C12, and so on; children of C2 = C21, C22, and so on. (Hence C11, C12, etc. are grandchildren to H1 and H2). B. Economic Status of the Household
203 Chapter 5
INSTRUCTION FOR THE FIELD INVESTIGATOR: From this section the properties such as land, farm, livestock, non-farm businesses etc. owned by family- head’s children (who are not living in the same house any more) will be counted together with that of the household directly selected by us.
2. (a) Your household is engaged in: □ only agriculture [1] □ both agriculture and non-agriculture [2]
(b) How many number of farm units do you (your household) have?: □ one [1] □ more than one (specify) [2]: ………………….
(c) Who is the owner of the largest unit?: □ myself [1] □ spouse [2] □ son [3] □ daughter [4]
3. If your household is engaged in both agriculture and non-agriculture, which one is the main source of income? □ agriculture [1] □ non-agriculture [2]
4. What is the size of your household’s landholding?:
(i) homestead (in katah/bigha2): …………………………….. (ii) cultivable land (in katah/bigha): …………………………
5. Does your household irrigate the land?: □ yes [1] □ no [2]
(a) If yes, what kind of irrigation system does your household have?: □ manual: from canal (traditional system)[1] □ tube well [2] □ shallow pump or diesel pump [3] □ other (specify) [4]: …………………….
6. What kind of puddle system does your household have? □ only traditional system: bullock drawn plough [1] □ power tiller [2] □ tractor [3]
2 Indian measures.
204 Data Collection
7. What kind of harvesting method does your household follow? □ manual [1] □ mechanical [2]
8. Does your household have livestock? □ yes [1] □ no [2]
(a) If yes, how many (fill-out the following table)?:
Bullocks: Cows:
He buffaloes: She buffaloes:
Calves: Goats:
C. Some Farm Level Data
9. How is your household involved in agriculture? □ primarily as a land-owning farmer [1] □ primarily as a share-cropper [2]
10. Now I am going to ask you some questions about the crops your household produces. Please inform me which crops you produce.
Season Plot Name of crop Area (kottah/bigha) Average no. of workers no. 1
2
Kharif 3
4
5
1
2
Rabi 3
4
5
205 Chapter 5
1
2
Summer 3
4
5
D. Occupational Background in Non-agricultural Activities
INTRODUCTION: In the present section, we will ask you some questions about your household’s non-farm business activities. These questions will be asked about the enterprise established personally by you or any of your household members.
11. (a) If your household owns non-farm enterprise/s, then how many?: □ one [1] □ more than one (specify) [2]: ……………
(b) Who started the enterprise/s?:
□ I myself [1] □ I and other member/s of the household (specify the other member/s by spouse, son, daughter, etc.) [2]: 1) ……………………………………………. 2) ……………………………………………. 3) ……………………………………………. 4) ……………………………………………. □ other member/s of the household (specify by spouse, son, daughter, etc.) [3]: 1) ……………………………………………. 2) ……………………………………………. 3) ……………………………………………. 4) …………………………………………….
(c) In case your household owns multiple non-farm enterprises then which one is the most important (mention the name of the enterprise)?: …………………………………………………………………………………….
(d) Who started the most important one (specify by spouse, son, daughter, etc.)?: 1) ……………………………………………. 2) ……………………………………………. 3) ……………………………………………. 4) …………………………………………….
206 Data Collection
[INSTRUCTION FOR THE FIELD INVESTIGATOR: By enterprise we mean the most important one identified in 11(c)]
12. What was your (your household member’s) “primary” motivation for establishing the enterprise?: □ farming is not that much profitable anymore [1] □ household (member) desired to invest surplus from agricultural activities [2] □ household (member) did not like agricultural activities[3] □ other (specify) [4]: ……………………………………..
13. What is the year of establishment?: ………………
14. What are the reasons for selecting this product line?: □ I (household member) wanted to offer a product which is somewhat new in the market [1] □ the product has a good market demand [2] □ I (household member) have some skill in this line [3] □ this product line needs very little skill [4] □ this product line requires low capital [5] □ raw material is locally available3 for this product [6] □ other (specify) [7]: …………………………………… ………………………………………………………….
15. Type of ownership of the non-farm enterprise: □ proprietorship [1] □ partnership [2] ◊ with household member/s [2.1] ◊ other than household member/s [2.2] □ cooperative [3] □ other (specify) [4]: …………………………….
16. Type of business: □ only manufacturing [1] □ only assembling [2] □ only repair [3] □ both manufacturing and assembling [4] □ both manufacturing and repair [5] □ both assembling and repair [6]
3 In this questionnaire, the term ‘local’ indicates the concerned administrative block (where we are going to carry out survey) of the district. ‘Non-local’ would mean the places outside the concerned administrative block.
207 Chapter 5
□ manufacturing, assembling, and repair [7] □ job-work [8] □ service [9] □ other (specify) [10]: ………………………………………….
E. Locational Factors
INSTRUCTIONS FOR THE FIELD INVESTIGATOR: 1) Village is an area which falls under gram panchayat, but does not look like a bazaar or a trading centre. Village is considered to be a residential place. 2) Rural growth centre falls also under gram panchayat, but typically looks like a bazaar. It is mainly a trading centre of village. 3) Small town is an area which falls under municipality. It is mainly an urban area in a rural setting.
17. (a) What is the location of the non-farm enterprise? □ village [1] □ rural growth centre [2] □ small town [3]
(b) (i) Is the non-farm enterprise situated at the household’s home land? □ yes [1] □ no [2]
(ii) If no, how far is it from the home?: ………………………………….
18. What were the reasons for selecting this location?: □ space available [1] □ it is close to home [2] □ land is relatively cheap [3] □ labour is cheap [4] □ its proximity to market [5] □ its proximity to the source of major raw material [6] □ it has necessary infrastructure [7] □ other (specify) [8]: ……………………………………
19. (a) Did you (your household member) experience obstacles in starting up / expanding the non-farm business? □ yes [1] □ no [2]
(b) If yes, then what were the obstacles you (your household member) experienced?
□ poor roads [1] □ power cut [2] □ power with low voltage [3]
208 Data Collection
□ lack of water supply [4] □ lack of skilled labour [5] □ poor consumer (lack of purchasing power) [6] □ bazaar is situated at a distant location [7] □ other (specify) [8]: …………………………………….
F. Technology / Machinery
INTRODUCTION: Technology here mainly indicates the production techniques used in the non-farm enterprise. It is broadly divided into two categories — one is primitive and the other is modern. (1) Manually produced goods as well as products produced by hand- made-machines are called to be the products of primitive technology. (2) Things produced by machine-made-machines are called to be the products of modern technology.
20. (a) What kind of technology is used in the firm?: □ primitive □ modern
(b) If primitive technology, then it is: □ manual [1] □ traditional machines and equipments [2]
(c) If modern technology, then it is: □ new machines [1] □ second hand [2]
(i) If second hand, give the reasons: □ second hand machines are cheaper [1] □ new machines are not available [2] □ other (specify) [3]: ……………………..
[INSTRUCTION FOR THE FIELD INVESTIGATOR: If the production technique is manual (where no machines or equipments are used), then go to question 23.]
21. Where did you (your household member) procure your (his/her) machines? □ own village [1] □ within block [2] □ within district [3] □ nearest town (specify) [4]: □ any other town/city (specify) [5]: □ other parts of the state (specify) [6]: □ outside state (specify) [7]:
22. How did you (your household member) procure this equipment? □ directly from small industrial producer [1] □ directly from large industrial producer [2] □ small shop (e.g. retail shop) [3] □ big shop (e.g. area distributor) [4]
209 Chapter 5
G. Financial Aspects of the Non-farm Enterprise
23. (a) How much was the total capital employed (in Rupees)?:
(b) How much was the fixed capital for (i) Land & building: ……………………… (ii) Machinery and equipments: ………………...
(c) How much was the working capital?: …………………………………………….
24. What was the source/s of capital for initial investment (fill out the following table):
Source Amount Location of source 1. Bequest Rs.
2. Own savings Rs. □ From □ Past job (specify agriculture what/where)
3. Borrowed from Rs. □ From □ Other occupation relations agriculture (what/where)
4. Banks Rs. Location
5. Government Rs. Specify the scheme 6. Wholesalers Rs. Location
7. Individual money Rs. Location lender 8. NGOs Rs. Location
9. Other (specify) Rs. Location
25. Have you (your household member) been expanding the non-farm business? □ yes [1] □ no [2]
[INSTRUCTION FOR THE FIELD INVESTIGATOR: If the respondent/entrepreneur has not been expanding the non-farm business, then go to question 27.]
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26. (a) Have you (your household member) experienced problems in financing expansion?: □ yes [1] □ no [2]
(b) If yes, then what are the problems you (your household member) have been facing?: □ do not have own funds [1] □ high cost (interest) of borrowing [2] □ large security requirement [3] □ high transaction cost (if any) [4] □ other (specify) [5]: …………………………………………… …………………………………………………………………
(c) If no, then how were funds generated?
Source Amount Location of source
1. Bequest Rs.
2. Own savings Rs. □ From □ Past job (specify agriculture what/where)
3. Borrowed from Rs. □ From □ Other occupation relations agriculture (what/where)
4. Banks Rs. Location
5. Government Rs. Specify scheme
6. Wholesalers Rs. Location
7. Individual money Rs. Location lender
8. NGOs Rs. Location
9. Other (specify): Rs. Location
211 Chapter 5
H. Employees / Workers
27. (a) Total number of workers employed (including proprietor/partners) Now: Initially:
28. What is the average wage/day : highest wage/day : lowest wage/day : local casual wage/day : local agricultural wage/day :
I. Raw Materials
29. Now we need some information about raw materials which are used in the firm. Please give us following information.
Do you use: Procured from:
(a) Agricultural raw materials: □ yes [1] □ Local [1] / □ outside [2] □ no [2]
(b) Non-agricultural raw materials: □ Local [1] / □ outside [2] □ yes [1] □ no [2]
30. Raw material is available: □ throughout the year [1] □ only for some months (mention) [2]: ………………….
J. Consumption Linkages
31. Demand for your (your household member’s) non-farm goods/services comes from:
□ rural Bengal [1] □ urban Bengal [2] □ both rural and urban Bengal [3] □ outside West Bengal [4]
32. Locally who are the consumers of your (your household member’s) business? □ no local consumers [1] □ local rich farm households [2] □ local poor farm households [3] □ local rich non-farm households [4]
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□ local poor non-farm households [5] □ almost all local consumers [6]
33. (a) Does the product of your (your household member’s) business face competition?
□ yes [1] □ no [2]
(b) If yes, then it is from □ other local small units [1] □ local medium or large units [2] □ non-local small units [3] □ non-local medium or large units [4]
(c) What kind of competition is it? □ it is in terms of price [1] □ it is in terms of quality [2] □ it is in terms of both [3]
K. Forward Production Linkages (producer goods)
34. (a) Are there producers who use your (your household member’s) business products?
□ yes [1] □ no [2]
(b) If yes, then who uses the products? □ agricultural sector [1] □ industry [2] □ construction [3] □ both agriculture and industry [4] □ both agriculture and construction [5] □ both industry and construction [6] □ agriculture, industry and construction [7] □ other sectors (specify) [8]: ………………
(i) Within agriculture the users of your (your household member’s) business products are: □ local small farmers [1] □ local large farmers [2] □ non-local small farmers [3], Specify location: ……………………… □ non-local large farmers [4], Specify location: ………………………
(ii) Within industry the users of your (your household member’s) business products are:
213 Chapter 5
□ local small units [1] □ local large units [2] □ non-local small units [3], Specify location: ………………………. □ non-local large units [4], Specify location: ……………………….
(iii) Within construction your (your household member’s) business product is purchased by □ local users [1] □ non-local users [2]
35. Does your (your household member’s) business have sub-contracting arrangements? □ yes, local medium and/or large units [1] □ yes, non-local medium and/or large units [2] □ no [3]
L. Performance Data
36. Firm performance:
1. Production capacity In a month (value in Rs.):
2. Total output (actual) In a month (value in Rs.):
3. Total costs (Rs.) Raw materials per month:
Power per month:
Packaging per month:
Other costs (including per month: sales tax) Interest per month:
Capital service cost per month:
4. Total working No. of hours per day in hours (including peak agricultural season own and hired labour) No. of hours per day in slack agricultural season 5. Per month how much is your profit after meeting all costs?
37. (a) Do you think demand for the product of your (your household member’s) business □ has grown in the last year [1] □ has remained steady [2] □ has declined [3]
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(b) Give reasons for your answer:
(c) In case demand has increased, it originates from: □ mainly local rural market [1] □ mainly non-local rural market [2] □ mainly local urban market [3] □ mainly non-local urban market [4] □ all of the above [5]
38. (a) (i) Do you (your household member) intend to shut down the business?: □ yes[1] □ no [2]
(ii) If yes, why?:
(iii) If no, what are the future plans?:
□ to maintain status quo [1] □ to expand production [2]: (a) with existing capital and labour[2.1], (b) with more labour [2.2], (c) with more capital [2.3]; □ to diversify into new products [3] □ to start another unit [4]
39. (a) In expanding/maintaining the business do you (your household member) face problems other than financial ones? □ yes [1] □ no [2]
(b) If yes, then what are those problems? □ uncertainty with raw materials [1] □ inadequate rural infrastructure [2] □ lack of adequate distribution channels [3] □ not enough training available for entrepreneurs [4] □ business information not available [5] □ other (specify) [6]: …………………….. …………………………………………...
40. (a) Are you (your household member) aware of government policies for SSI?4 □ yes [1] □ no [2]
4 SSI stands for small-scale industries.
215 Chapter 5
(b) If yes, then (i) mention policies which are favourable to promote and protect your (your household member’s) business:
(ii) mention policies which need modifications to promote and protect your (your household member’s) business more effectively:
(iii) mention policies which need to be introduced to promote or protect your (your household member’s) business:
M. Risk Factor / Psychological Factor (Achievement Motive) / Sociological Factors / Cultural Factors
41. Will you establish a marriage relationship with a business family? □ yes [1] □ no [2]
42. Do you believe that □ your fate will determine the future of your life? [1] □ your efforts will determine the future of your life? [2]
43. What would you do if you were given a large amount of money, say Rs. 50 lakhs?: □ start a business [1]: ◊ One of the businesses that are currently being run by others[1.1], ◊ An innovative/new business that is not being run by others [1.2], Specify the kind of new business: ………………………. □ travel to different places [3] □ save in bank and enjoy interest [4] □ donate [5] □ implement any kind of fantasy/imagination (specify) [6]: ……………..
……………………………………………………………………………...
N. Political Factors
44. (a) Do you (or any of your household members) hold any portfolio in local Panchayat/Municipality? □ yes [1] □ no [2]
(b) If yes, then specify the person: ………………………………………………….
(c) If yes, then specify the position: □ chief of the local government body [1]
216 Data Collection
□ deputy chief of the local government body [2] □ ordinary member of the local government body (belonging to ruling party) [3] □ ordinary member of the local government body (belonging to opposition party) [4]
INSTRUCTION TO THE FIELD INVESTIGATOR FOR THE FOLLOWING QUESTIONS: The following questions are not to be asked to the respondent, rather to be filled out by the field investigator.
45. (a) Who is the MLA (member of legislative assembly) of this constituency?: ……………………………………………………………………………………..
(b) Which party does he/she belong to? □ CPI(M)5 (largest member of the ruling coalition) [1] □ Any of the other members of ruling coalition (specify) [2]: ...... □ Opposition (specify) [3]: ……………………………….
5 CPI(M) stands for Communist Party of India (Marxist).
217 Chapter 5
APPENDIX 5.2
QUESTIONNAIRE 2
SURVEY ON RURAL INDUSTRIES IN BARDHAMAN DISTRICT OF WEST BENGAL (Questionnaire for farmers who are NOT engaged in non-farm manufacturing activities)
Location of survey (To be filled out by the interviewer immediately before interview): Block: Panchayat: Village / rural town: Post office:
INTRODUCTION: We are carrying out a research project on various aspects of rural industry in Bardhaman district. Our objective is to study the problems and prospects of rural industrialisation in the district. This project will be trying to raise the issues relating to the interest of your village as well as yourself. More specifically, we are studying the problems of investments in non-farm activities and, accordingly, our study is concerned with, first, identification of the obstacles of the rural industrial sector at micro as well as macro level and, second, formulation of eradicative measures of those obstacles. So, we are sure that the findings of this research will be directly or indirectly beneficial for you through some specific policy recommendations for industrial development of your district. This research is being done by a team comprising one researcher and supervisors from Wageningen University, the Netherlands and Indian Statistical Institute, Kolkata, India. We have come here to talk to you about agricultural and industrial development of your village, infrastructural condition in general and some aspects of your own household, your farm, your non-farm activities (if any) in particular. We are not from any government department or tax office, so please don’t worry about the questions. You can trust us and freely exchange your dialogues with us. We promise that we will not use this data for anything except our academic research purpose. We invite your wholehearted cooperation in this work.
INSTRUCTIONS FOR THE FIELD INVESTIGATOR: In this questionnaire we have used the terms ‘your household’ and ‘your household member’ frequently. Here household consists of family-head (usually male), his spouse, his sons and daughters including those (children) who are not living in the same house any more and have started families of their own. For the present study this definition of household is formulated in the light of the Indian tradition. For the interview the family-head should usually be our respondent and household would be considered to be the unit of observation. As a field investigator of this project you should be careful about where to use the term ‘you’ and where to use ‘your household member’ (or you can directly use a term like ‘your son / your daughter / your wife’ instead of ‘your household member’ wherever applicable). For example, if the respondent himself is the owner of the business, then you can use the term ‘you’, otherwise ‘your household member’ (to be more precise, use ‘your son/daughter/wife’ as instructed above).
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A. General Background
Name of the family head:
1. Respondent’s household composition
Sl. Household Sex Marital Age Education Occupation No. members (M/F)* status** (see note below General*** Technical/ Primary Secondary for vocational (1) (2) abbreviations) (Respondent first) 1. H1
2. H2
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
*M = 1, F = 2; **Married = 1, Never married = 2, Divorced = 3. ***Never went to school = 1, Incomplete primary = 2, Primary = 3, Incomplete secondary = 4, Secondary = 5, Higher Secondary = 6, Bachelors = 7, Masters = 8.
Note: family head = H1, head’s spouse = H2, their children = C1, C2, and so on; children of C1 = C11, C12, and so on; children of C2 = C21, C22, and so on. (Hence C11, C12, etc. are grandchildren to H1 and H2). B. Economic Status of the Household
219 Chapter 5
INSTRUCTION FOR THE FIELD INVESTIGATOR: From this section the properties such as land, farm, livestock, non-farm businesses etc. owned by family- head’s children (who are not living in the same house any more) will be counted together with that of the household directly selected by us.
2. What is the size of your household’s landholding?:
(i) homestead (in katah/bigha6): …………………………….. (ii) cultivable land (in katah/bigha): ………………………
3. Does your household irrigate the land?: □ yes [1] □ no [2]
(a) If yes, what kind of irrigation system does your household have?: □ manual: from canal (traditional system)[1] □ tube well [2] □ shallow pump or diesel pump [3] □ other (specify) [4]: …………………….
4. What kind of puddle system does your household have? □ Only traditional system: bullock drawn plough [1] □ Power tiller [2] □ Tractor [3]
5. What kind of harvesting method does your household follow? □ Manual [1] □ Mechanical [2]
6. Does your household have livestock? □ yes [1] □ no [2]
(a) If yes, how many (fill-out the following table)?:
Bullocks: Cows:
He buffaloes: She buffaloes:
Calves: Goats:
6 Indian measures.
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C. Some Farm Level Data
7. Your household is engaged in: □ only agriculture [1] □ both agriculture and non-agriculture [2]
8. How is your household involved in agriculture? □ primarily as a land-owning farmer [1] □ primarily as a share-cropper [2]
9. Now I am going to ask you some questions about the crops your household produces. Please inform me which crops you produce.
Season Plot Name of crop Area (kottah/bigha) Average no. of workers no. 1
2
Kharif 3
4
5
1
2
Rabi 3
4
5
1
2
Summer 3
4
5
221 Chapter 5
D. Non-farm Business Related Data
10. (a) Had any of your household members started a business which went bankrupt?: □ yes □ no
(b) If yes, why did it go bankrupt?: □ uncertainty with raw materials [1] □ inadequate rural infrastructure [2] □ lack of adequate distribution channel [3] □ not enough training available for entrepreneurs [4] □ business information not available [5] □ other (specify) [6]: …………………….. …………………………………………...
11. (a) Has/have any/some of the household members thought of starting a non-farm business? □ yes □ no
(b) If yes, who (specify them by name/s)?1) …………………………………….. 2) …………………………………….. 3) …………………………………….. 4) ……………………………………..
(c) If yes, why didn’t he/she/they start a business?: □ uncertainty with raw materials [1] □ inadequate rural infrastructure [2] □ lack of adequate distribution channel [3] □ not enough training available for entrepreneurs [4] □ business information not available [5] □ other (specify) [6]: …………………….. …………………………………………...
12. (a) In your opinion, which non-farm businesses of your locality have flourished most in the last two years (mention two)?:(1) ………………………………………………… (2) …………………………………………………
(b) Give reasons for your answer:
For business (1): □ it supplies a new kind of product/service (innovative product/service)[1] □ it supplies a better quality product/service [2] □ other (specify) [3]: ………………………………………………………
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For business (2): □ it supplies a new kind of product/service (innovative product/service)[1] □ it supplies a better quality product/service [2] □ other (specify) [3]: ………………………………………………………
(c) You think that these businesses are going well, but then why didn’t you (or any of your household members) start any of them?:
□ we have never been in non-farm business [1]; □ we don’t have the special skill needed [2]; □ number of competitors is rising rapidly, so demand will fall soon [3]; □ other (specify) [4]: ……………………………………….....
………………………………………………………………………….
13. (a) Does your household think that government policies for SSI7 are responsible for your household’s apathy in non-farm business?: □ yes [1] □ no [2] □ not aware of government policies [3]
(b) If yes, then mention policies which are responsible for your household’s apathy in non-farm business:
E. Risk Factor / Psychological Factor (Achievement Motive) / Sociological Factors / Cultural Factors
14. Will you establish a marriage relationship with a business family? □ yes [1] □ no [2] 15. Do you believe that □ your fate will determine the future of your life? [1] □ your efforts will determine the future of your life? [2]
16. (a) Until now you have been just engaged in farming. But if you are now interested in a non-farm business, will you get any support from your family?
□ yes [1] □ no [2] (b) If yes, it is □ only verbal support [1] □ only financial support [2] □ only labour support [3] □ all of the above [4] □ other (specify) [5]: ……………………..
7 SSI stands for small-scale industries.
223 Chapter 5
17. What would you do if you were given a large amount of money, say Rs. 50 lakhs?: □ start a business [1]: ◊ One of the businesses that are currently being run by others[1.1], ◊ An innovative/new business that is not being run by others [1.2], Specify the kind of new business: …………………………. □ travel to different places [3] □ save in bank and enjoy interest [4] □ donate [5] □ implement any kind of fantasy/imagination (specify) [6]: ……..
……………………………………………………………………………...
F. Political Factors
18. (a) Do you (or any of your household members) hold any portfolio in local Panchayat/Municipality? □ yes [1] □ no [2]
19. If yes, then specify the person: ………………………………………………….
20. If yes, then specify the position: □ chief of the local government body [1] □ deputy chief of the local government body [2] □ ordinary member of the local government body (belonging to ruling party) [3] □ ordinary member of the local government body (belonging to opposition party) [4]
INSTRUCTION TO THE INVESTIGATOR FOR THE FOLLOWING QUESTIONS: The following questions are not to be asked to the respondent, rather to be filled out by the field investigator.
21. (a) Who is the MLA (member of legislative assembly) of this constituency?
……………………………………………………………………………………..
(b) Which party does he/she belong to? □ CPI(M)8 (largest member of the ruling coalition) [1] □ Any of the other members of ruling coalition (specify) [2]: …. □ Opposition (specify) [3]: ……………………………………...
8 CPI(M) stands for Communist Party of India (Marxist).
224 Chapter 6
Determinants of Non-farm Manufacturing Entrepre- neurship of Farmers: Theoretical Considerations and Empirical Findings
6.1 Introduction
In this chapter, first we present some theoretical considerations (supported by some empirical evidences) to account for the differences of involvement of farmers in non-farm manufacturing activities. The success or failure of rural industrialisation programme in a predominantly agrarian economy is due to the section of the farmers. From our study, we exclude the probability of involvement of landless labourers in non-farm manufacturing entrepreneurial activities in manufacturing sector with an assumption that this section cannot afford to invest money in a manufacturing business. Even if a few people are involved in some repair works or so, we exclude them from our study if they do not possess any cultivable land. Since the large part of the rural capital belongs to the farming group, diversification of rural economic activities from agriculture to non-agriculture largely depends on the farmers’ willingness of being non-farm entrepreneurs—i.e. whether or not an individual farmer opts for non-agricultural manufacturing entrepreneurship. The distinction of the farmers’ involvement in non-farm entrepreneurship provides us with an opportunity to determine empirically whether or not different factors are important. Hence, the second part of this chapter deals with the empirical analysis of several factors responsible for determining non-agricultural entrepreneurship of farmers. In other words, in the second part of this chapter, we have tried to test the hypotheses of the theoretical considerations through the LISREL estimation results. We have described the LISREL model later in this chapter.
The model integrates the personal characteristics of the farmers (age, education, marital status and number of children); the factors relating to the farmers’ agricultural Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
background (whether the farmer is principally a owner or sharecropper,1 and whether or not the farmer produces three crops viz. aman rice, boro rice and potato in a year); the financial factors (wealth of the farmer, and financial support from the farmer’s family); the psycho-socio-cultural factors (whether the farmer has faith in work-effort or fate, and whether the farmer is willing to arrange his daughter’s or sister’s marriage with a non- farm businessman); the factors relating to risk and innovation; and the political factor (whether the farmer is holding an office in panchayat, the local government).
In chapter 4, we have extensively discussed the different theoretical concepts relating to entrepreneurship along with an emphasis on psycho-socio-cultural issues which have assumed much negligence until now in the literature of economics. In the present chapter we emphasize the determinants of non-farm entrepreneurship of farmers.
In the model, the individuals are assumed to be rational decision makers who relate their existing resources that they control to the requirements of starting their non-farm manufacturing business. These resources include land, labour, and financial capital. In addition to these, the opportunities—that a potential non-farm entrepreneur may have to find out from his own personal characteristics and his milieu as well as to make use of them—include the person’s education, political affiliation, psychological state of mind i.e. achievement motive, and cultural environment.
6.2 Basic concepts and assumptions of the theoretical considerations
6.2.1 Rationality and Decision Making
The concept of rational behaviour is frequently used in economic theory. What is meant by rational behaviour? Maurice Allais provides us with a clear definition, generally accepted by economists (see Godelier, 1972: 12):
“We have to have recourse to the definition which seems to emerge from scientific logic, by which a man is considered rational when:
1 Sharecroppers are not categorized as landless labourers. Usually sharecroppers own some amount of land which is not considerably big.
226 Chapter 6
(a) he pursues ends that are mutually coherent, and (b) he employs means that are appropriate to the ends pursued.”
Analysis of rational behaviour is thus seen as a theoretical investigation aimed at discovering the conditions under which it is possible to attain a certain objective, taking into account a certain set of constraints. J. Bénard put it in the following way (see footnote in Godelier, 1972: 12). The quest for an economic optimum consists in choosing the best means of arriving at the ends that are regarded as the best. If we speak of an optimum, this means, therefore, that we agree that choices are possible, that is, that several procedures exist for arriving at the same end, and that these can be arranged in order of preference. In other words, at a single point of time there must be alternatives that can be substituted one for another, and there must also be criteria of choice. Determination of the optimum will result from combining these two series of elements.
In a review, Sen (1987) has found two quite distinct motivations in the development of the concept of rational behaviour. 1. It is interesting to know how one could behave rationally in a given situation; 2. The second concern is the possible use of models of rational behaviour in explaining and predicting actual behaviour. The exercise is done in two steps. First, we characterize rational behaviour and second, following that, we assume that actual behaviour is rational because, in normal case, through actual behaviour an individual, according to his/her own preference, picks up the best means among alternatives in order to arrive at the ends that are considered by that individual as the best. In this way, the characterization of rational behaviour may end up specifying the predicted actual behaviour as well. In the following discussion, the primary concern is with the way rational behaviour has been characterized.
Rational behaviour under uncertainty will be taken up here, but before that the more elementary case when there is no uncertainty has to be dealt with. According to Sen (1987), although there are many different approaches to rational behaviour under certainty, it is fair to say that there are two main approaches to this question. The first emphasizes internal consistency: rationality of behaviour is identified with a requirement that choices (in making a decision) from different subsets should correspond to each other in a cogent and systematic way. The condition of internal consistency, which seems to command most attention in formal economic theory among others, is binaries which
227 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
require that the choices from different subsets can be seen as maximizing solutions from the respective subsets according to some binary relation R (xRy standing for ‘x being preferred or indifferent to y’). The second common approach to rational behaviour under certainty sees human beings as tirelessly fostering their respective self-interests. Many other motivations are important in human behaviour in general, but it is certainly true that the assumption of the ‘economic man’ relentlessly pursuing self-interest in a fairly narrowly defined form has played a major part in the characterization of individual behaviour in economics for a very long time (Sen, 1987).
So far as the present study is concerned, rational behaviour under uncertainty is to be regarded as an important phenomenon since an entrepreneur lives in an uncertain world. Future does not always move along the predicted line, so the proposition of present investment for future benefit holds considerable amount of uncertainty. The extension of the modelling of rational behaviour from certainty to uncertainty involves both (1) the characterization of uncertainty, and (2) taking note of uncertainty in making actual decisions over alternative courses of actions.
The model that has been most extensively used in the context of uncertainty is that of ‘expected utility.’ This takes the form of weighing the value of each of the outcomes by the respective probabilities of the different outcomes. The probability-weighted overall ‘expected value,’ thus derived, is then maximized in this approach to rational choice under uncertainty.
6.2.2 The Rational Entrepreneur
The behaviour of entrepreneurs, that is, the totality and succession of acts of decision- making and management by which they direct the activity of the enterprises, makes up the essential aspect of economic practice under a system. And this practice is dominated by the problem of investment-choice, that is, the problem of measuring the efficiency of investments. The theory of rational behaviour for entrepreneurs thus undertakes to break down its elements into the series of strategic acts that are the entrepreneur’s prerogative—determination of investment possibilities, forecasting of the consequences bound up with each of these, choice between alternatives, ways of carrying out
228 Chapter 6
investment—and to determine for each of these the optimum conditions for its accomplishment. Knowledge of these conditions therefore supplies the norms, principles or recipes for maximizing profit of enterprise. These norms determine the forms of behaviour and forms of organisation (institutions, structures) that are best adapted to the end that is aimed at. The conditions are not merely economic but also psychological, sociological, legal, etc., and in order to analyze them the help will have to be taken from psychologists, sociologists, lawyers, and, above all, mathematicians (Godelier, 1972: 31). Taking these into consideration our study in this chapter includes a few variables which relate to the psychological and sociological factors, along with the economic ones and personal characteristics of the entrepreneur.
From a psychological point of view, the difference comes out when Godelier emphasizes ‘appetite for power’ and, also, when our concern centres ‘need for achievement’ introduced by McClelland (discussed in chapter 4). In Godelier’s (1972: 32) version, the psychologists and sociologists have begun to study the motivations and aptitudes of the head of an enterprise, and shown that desire for ‘gain’ is not the only motive behind his actions, this often being combined with appetite for power. In a traditional society where culture of non-agricultural entrepreneurship is not commonly present, the problem is more related with initiating ‘need for achievement’ than with emphasizing ‘appetite for power.’
As we have said earlier, decision making about the firm is prerogative of the entrepreneur. Determination of investment possibilities is a crucial part of starting an enterprise. Investment is considered as present sacrifice for future benefit (Hirshleifer, 1987). Individuals, firms, and governments all are regularly in the position of deciding whether or not to invest, and how to choose among the options available. An individual might have to decide whether to buy a bond, plant a seed, or undertake a training course; a firm whether to purchase a machine or construct a building; a government whether or not to erect a dam. In the theory of intertemporal choice, the object of investment is taken to be to optimize one’s pattern of consumption over time. According to Hirshleifer (1987), the elements needed to determine an individual’s investment decisions are: (a) his endowment, in the form of a given existing income stream over time; (b) his preference function (i.e. time preferences), which orders in desirability all possible time-patterns of consumption; and (c) his transformation set, which specifies the possibilities (productive
229 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
opportunities) for transforming the original endowment into other time-combinations of consumption.
Again, whether or not a person is willing to be an entrepreneur is a question of decision making. Decision making, particularly with respect to the business world, had traditionally been considered to be an art, although it has recently been evolved into a science. Scientific decision making tends to hold the similar meaning of rational decision making. Any individual who is faced with a choice among possible actions obviously needs to devise some method of selection in order to reach a decision. The consequences of possible actions must be evaluated in terms of some criteria of desirability. In the real world, this problem of decision making among alternatives is complicated by the fact that an individual is rarely sure that his predictions regarding the future consequences of various actions are correct (Brownlee and Buttrick 1968).
In any event, the general problem of making decisions may still usefully be broken into three parts. First, the environment in which one finds himself typically sets limits to the alternative actions which are available. At one extreme is the man who can choose only the frying pan or the fire; at the other is the possessor of Aladdin’s lamp, for whom anything is possible. Second, there is the problem of predicting the consequences of each set of actions that might be chosen—how much profit will be made if this rather than that is done, for example. Such predictions are usually subject to error, the size of the error being determined by unpredictable and, in part, unknown factors in the environment as well as by the means used to make the predictions—the model, the data employed, and the person doing the predicting. How to predict can be considered an economic problem in itself. Third, there is the task of constructing the criteria by which the consequences of alternative actions may be compared and ranked in order from best to worst (Brownlee and Buttrick 1968).
The setting in which the decision maker is placed differs from model to model, the assumptions made about the decision maker’s objectives differ from model to model, but the models are tied together by a common thread wherein efficient decision making is sought. Irrespective of his problems or motivations, it has been assumed throughout that the decision maker will make an effort to utilize the best of his ability and knowledge to resolve these problems, in light of his recognized alternatives and their perceived
230 Chapter 6 consequences. He will want his decisions to be based on reason, the decision maker is presumed to want to behave rationally (Horowitz, 1969).
Lastly and cautiously, we all expect other people to have rational motives, though we know very well that our own acts are half of them due to impulse. Moreover, individuals differ, and they differ as decision makers. In a world immersed in uncertainty one can be irrational and survive. But, of course, there is no place for impulse in economics.
Given the above background, we now propose to employ the variables that are likely to be the systematic factors determining whether or not a farmer will be a non-farm entrepreneur. The influence of the different variables on the tendency of the farmer’s non-farm entrepreneurship is presented in a schematic diagram in Figure 6.12 and the theoretical justification (along with some empirical evidences) in favour of employing the variables are furnished below. A short description of the variables is given in Table 6.1 (see section 6.4.15). Table 6.2 presents the descriptive statistics of all the variables (see section 6.4.15). In this chapter, we will present the LISREL model which is actually the simultaneous equations model and which deals with latent variables. This model will be estimated in order to control simultaneity bias, i.e. to explain interdependence among dependent and explanatory variables.
6.3 Non-farm entrepreneurship (NFE)
Non-farm entrepreneurship (NFE) of the farmer is the dependent variable of the model. It’s a binary variable indicating that the farmer who is a non-farm entrepreneur belongs to category 1 and the farmer who is not a non-farm entrepreneur belongs to category 0. In section 6.4, we are going to discuss the explanatory variables in detail.
6.4 Explanatory variables
6.4.1 Sex
2 Reverse dependency, i.e. impacts from non-farm entrepreneurship on explanatory variables is not represented in Figure 6.1.
231 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
In the present study, initially sex/gender of the farmer had been considered a variable in construction of the theoretical as well as empirical model, but, during the present literature survey it became clear that we might not be able to find women entrepreneurs engaged in non-farm manufacturing activities in the agricultural regions. The arguments drawn from existing literature are as follows.
Figure 6.1: The conceptual framework
Agricultural/occupational factors Financial factor and family support
● Farmer’s involvement in ● Wealth/socioeconomic status agriculture
● Financial family support ● Types of crops produced Psycho-socio-cultural factors
● Age ● Marriage relationship
● Marital ● Work-effort or fate? Non-farm status entrepreneurship
● No. of children
Personal characteristics ● Innovation ● Education
● Risk
● Political position of the farmer
Innovation and risk Political factor
In a country like India, rural elites (even the rural middle class too) are generally very orthodox and conservative in nature. In such a society, women find their place at home and men are responsible for outside work. In urban areas the trend is somewhat different, of course. But, in general, women—except a very few—are not found to play an entrepreneurial role in manufacturing business in a developing economy like India. In a study on large farmers and rural industrialists in central Gujarat, Rutten (1995) found that, within all the entrepreneurial families, only male members were responsible for the daily running of the farm or industrial enterprise. That is why he comments that the sociological study he carried out is not only primarily about entrepreneurs but also
232 Chapter 6 primarily about men. Participation of women in manufacturing business activities in the other Asian developing economies is not remarkable as well. However, in some cases, married women appear to be helping-hand in their husband’s manufacturing business. Let us give an example here. In a study on the Philippines, Carroll (1965) observed that out of 92 entrepreneurs (in manufacturing business) 87 were found to be men and only five women. There is a long tradition of business activity by women in the Philippines, and belief that they make particularly shrewd and energetic traders. Then, why is the number of women entrepreneurs (in manufacturing sector) so poor in the Philippines? Actually Carroll (1965) found plentiful evidence of the important roles played by women in the establishment of many of the enterprises studied; but they did not appear to have played the entrepreneurial role. More frequently their role was that of treasurer, corresponding to their traditional role at home. In this regard, Rutten’s observation based on his study in central Gujarat in India can be mentioned. Rutten (1995: 65) observed that the women who were co-owners [in official papers] were never present in the industrial unit. This, he continued, did not mean that the owners’ spouses and female kinsmen played no role. But Rutten remained superficial by mentioning only the ‘role’ of women in this regard and, at the same time, by not mentioning which particular ‘role’ women played. Within the area of economics, not so many research-works have been conducted in different regions of the world in order to study women’s (who do not appear to be entrepreneurs, but join hands of their husbands for performing some duties in business activity) role, if any, played in family business.
In all societies—either traditional or modern—some social or human values might have stood on the way of development in general and women’s active participation in different economic activities in particular. The degree of retaining traditional values or maintaining traditional human occupations in the static pre-industrialised countries is much higher than that in the dynamic industrialised countries. Therefore, bringing about a change in values bears great significance in this regard. It will be clearer if an example is given. Let us take up the case of the USA. Fry (1993) has observed that dramatic change in values among women in the USA led them (who were potential entrepreneurs) to dive into the field of entrepreneurship leaving their traditional occupations. According to Fry, in the USA the number of women-owned business grew from 2,612,621 in 1982 to 4,114,787 in 1987, a 57.5 per cent increase. The primary reason for growth in the number of women- owned ventures can be traced ultimately to the 1970s. During this decade, an unprecedented number of women moved out of the traditional roles of housewife,
233 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
teacher, nurse, and secretary. Some became entrepreneurs immediately after high school or college. Some went the corporate route and then moved into entrepreneurship. Some raised families and then started their own ventures. Some took over ventures left by husband or fathers. Others started their own from scratch. This is the picture of a highly industrialised society where changes in social values played an important role in changing women’s attitude towards their occupation. In this context, let us now take a look at a developing country for a comparison. In a study on women entrepreneurs of NGOs in India, Handy et al (2002) found that all the women entrepreneurs they studied used to share a feminist ideology. It is to be noted here that feminism is very much an urban phenomenon and has not yet diffused in rural traditional society even in the smallest possible volume. This view gains support from Handy et al (2002) when they write that their study was conducted in and around the city of Pune in Maharashtra, not in the rural base. They observe that most of the Indian female NGO runners are the products of the so called urban society, not the products of traditional rural (agricultural) society. In such a country, one can hardly expect the existence of women entrepreneurs in non- farm manufacturing sector in the agriculture-based rural areas. As regards women’s role in rural agricultural society, Rutten (1995) has observed the changes in Patidar community in central Gujarat in India and remarked that as a result of their early economic rise and their aspiration to higher status, the Patidar farmers were among the first of the present-day entrepreneurial families to remove their women from direct involvement in field labour. For the wealthier Leva Patidars, this happened as early as the 1930s, while many members of the lower sub-castes of Kanbi and Kadva Patidars did not make this change until the end of the 1950s. They employed hired labourers instead of using their female family members. According to Rutten (1995), the overall outcome of these recent changes in working pattern for the women of the entrepreneurial families is that housekeeping has become their main daytime activity. The major part of the day’s work of these women consists of preparing and serving the food to other members of the household. Lastly, to conclude this section, we informally hypothesize that no female non-farm manufacturing entrepreneurs will be found in the study area. Based on the field survey, we will informally observe this variable in the section of data analysis later in this chapter.
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6.4.2 Age and age squared (AGE)
Hypothesis 1: The combined impact of age and age2 of the farmer on his entry into non- farm manufacturing entrepreneurship follows an inverted U-curve.
The argument is as follows.
In terms of age, which group of people is most likely to enter the field of non-farm entrepreneurship? Younger or older? Younger persons do possess a spirit of doing something in life and have strong urge to get established in the professional field. On the other hand, people of older ages may possess less ambition and accordingly may not find stimulus to start a new business. But, at the same time, we have to keep in mind that older people have greater experiences, which may be regarded as one of the positive factors for entry into non-farm entrepreneurship. Older people can make use of their experiences earned from different fields to combat/avoid uncertainties. The combined effect leads to the hypothesis that the impact of age and age2 of the farmer on his entry into non-farm manufacturing entrepreneurship follows an inverted U-curve (see Figure 6.2).
Figure 6.2
Probability of entry
age of entry
We now review the literature. Age of entry of an entrepreneur has a pivotal role to play in the performance of enterprise. Studies of Sharma (1980), Deshpande (1982), and Rao (1986) have shown that the age at which entrepreneurs enter industry has much to do with the growth of enterprises. Young persons are generally more energetic, change prone, progressive, and innovative than the older ones are. In this context, Joseph (2003)
235 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
has hypothesized that the entrepreneur-manager’s performance is affected by his age of entry, because an entrepreneur has to play many roles in a small scale industrial unit. For example, he is the owner, worker, financier, technical expert, and manager. Over a period of time an entrepreneur acquires additional skills, technical as well as managerial, which would help him perform better3.
Bird’s (1989) study shows that there are three age groups that yield large numbers of entrepreneurs. The first group is made up of those in their early twenties who start a business soon after graduation from high school or college. A second group consists of those in their late twenties to early thirties who work for someone else for a while, but have the intention of starting a venture as soon as they have amassed the necessary capital and experience. A third group consists of those who had no intention of starting a non-farm business but who encounter a compelling reason to do so later in life. Bird observes that a significant subgroup in this latter category is those who start a venture after retiring. A supporting observation is found in another study too. Singh (1986) observes that once middle age sets in, there is a tendency not to take any risks and to postpone the idea of entrepreneurship till after retirement.
According to Ramamurthy and Kumar (1990), the best age for entry into innovative establishments was observed to be between 20 and 40 years. Young persons have greater attraction towards entrepreneurial ventures. The same phenomenon was found in Singh’s (1986) study of entrepreneurs in Mumbai. In his study, the maximum concentration was between the ages of 20 and 30 years. The average age at which entrepreneurs actually started business was found to be 28 years. However, in the same study for entrepreneurs of Tamilnadu it was found that the respondents started the enterprises in their later age. But, in general, it is observed that younger entrepreneurs tend to take risks whereas the older ones have less risk-taking capacity.
Entrepreneurship at different ages may be influenced by maturity as well as experience of an entrepreneur on the one hand and lengthy pay-back period on the other. First we
3 There is also a relationship between age and quality of performance. Mishra (1991), while discussing performance, points out that the successful entrepreneurs were relatively younger in age. Unsuccessful entrepreneurs mostly belonged to the older age group. The mean age of successful entrepreneurs was 41 years whereas unsuccessful entrepreneurs had the mean age of
236 Chapter 6 discuss maturity and experience. In normal cases, maturity and experience increase when age goes up. Fry (1993) observed that almost 65 per cent of entrepreneurs launched ventures during their twenties or thirties. According to Fry, this time period is referred to as an entrepreneurial window. He says that prior to this period most entrepreneurs do not have sufficient experience or capital to launch a successful venture. An entrepreneur, who starts a business at an early age, gets time to get more matured and earn wider experience in his profession. This further broadens the scope of his success in the long run. Hence, after staring a business a young entrepreneur gains a wider range of maturity and experience through the process of learning by doing and this gain may play a role of causative factor for his better performance. On the other hand, starting a business at an older age may face certain disadvantage. As a man ages, he may become more risk averse and may not want to risk his savings on a venture that might fail. Consequently, the person may not leave his existing work, even though the work may not be highly satisfying.
Pay-back period is another important aspect of the decision of starting up a business. Usually a business, especially a manufacturing business, does not pay the entrepreneur back a substantial amount of investment immediately after starting a business. So, it is natural to guess that an older entrepreneur is not likely to bear a lengthy pay-back period, because he has to get financially established in his profession and prove himself worthy in his society at least earlier than a relatively younger entrepreneur.
The study by Sekaran (1986) shows that an individual may prioritize different facets of his life at different times depending on his needs of the moment and the stage he is in. For instance, most individuals assign top priority to their careers until they acquire a firm foothold in them. Raising a family will be paramount to individuals in their late twenties and thirties. Consolidating career gains will attain significance when people reach their forties and fifties. Later, they may give more of their time to preparing for relaxation, and they may become more involved in temple, church, community, or other activities. Thus, entry will vary in the different stages of the life cycle of the people. Depending on how they prioritize the different aspects of their lives at different stages, individuals will assign different criteria for success. At the early stages of an adult life, the propensity to build a good career is higher than that in later stages. In the field of entrepreneurial
48 years. In a study, Joseph (2003) found that 72% of the entrepreneur-managers belonged to the age group of 21-40. Of this more than 50% belonged to the age group of 21-30.
237 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
career, it can be assumed that fresh spirit and enthusiasm at young age may lead an individual to take high risk. As the age increases, risk taking propensity decreases, i.e. people become more risk averse as they age. At older age, shift of an individual from one organisation to another, or from one company to another, or even from one nature of job to another is not that uncommon. But shift from non-entrepreneurial field to entrepreneurship is unlikely unless the person concerned is forced to shift by the intervention of circumstances, e.g. loss of job, reduction in salary or wage, etc. Similarly, in the rural economy of a developing country, older people generally stick to agricultural profession (since agriculture is their traditional occupation) and are not likely to shift from farming to non-farm entrepreneurship unless they are forced by circumstances like fall in production in successive years, tremendous fall in agricultural prices due to rising production in successive years, etc. We will go to these issues further in the analysis of our data in the later part of this chapter.
Let us turn to the issue of ‘success of life’ again. Sekaran (1986) tried to look at the success of life from another angle as well. According to her, for one individual, success might connote a peaceful, happy home that resonates with joy and laughter. To another individual, success might mean not only involving himself in career and family activities but also making a significant contribution to the community’s advancement. In the first place, for both the cases, success means economic success, ignoring which a person can make neither a ‘happy home’ nor a ‘significant contribution to the community’s advancement.’ Whether it is home or community, man’s most coveted goal is the enhancement of his own personal power and influence (Burgess and Locke, 1960), which is highly related with economic success achieved through competition and struggle. Because competition and struggle play such a large part in all life, some scholars (see, for example, Lehman 1953) have advanced the theory that the desire for power is the most universal and the most fundamental of all human motives. If we consider these statements to be true then probability of being entrepreneur among younger age groups is higher, because people in older age groups are likely to realize that an entrepreneur can only enhance his personal power and influence after he becomes successful and that success requires a long walk of hard-work with dedication starting from the entrepreneur’s younger age. Accordingly, people among older age groups are likely to show comparatively less interest in entering into the field of non-farm entrepreneurship leaving the occupations they are engaged in. This view is supported by an empirical finding in the
238 Chapter 6 work of Joseph (2003). She observed that as the age increases, the percentage of entrepreneurs entering manufacturing is decreasing. She found that only 28% of the enterprises were started by entrepreneur-managers who were of 41 years and above.
6.4.3 Marital Status (MARS)
Hypothesis 2: The probability of being non-farm entrepreneurs among the married farmers is higher than that among the unmarried farmers.
Usually the farm family is an economic partnership with all members having an interest and a stake in the success of the occupational enterprise. Work brings the farm family together. Burgess and Locke (1960) studied six communities in the USA viz. Old Amish, El Cerrito, Irwin, Sublette, Harmony, and Landaff and depicted that in a farm family, the father, mother, and children work together in making the living, with the father doing the outside work, the mother taking care of the house, and the children being given and accepting responsibilities.
This occupational cohesion between two partners of a married couple in a farm family is quite common in nature almost all over the world. The wife of a farmer not only works at home but also helps in farming when urgently needed. It is a universal culture of the farm families. Hence, the picture is same in India. In this sense, a farmer, while entering into the field of non-farm entrepreneurship, finds an added advantage from his marital status if he is married, since he has got an opportunity to avail the service of his wife as a free labour. In this regard, we can cite from Burgess and Locke (1960: 70-71):
“In all six communities the family is a working unit with the members having specialized responsibilities. The father is responsible for operating the farm, specifically the field work. The mother manages the house, generally takes care of the garden and chickens, and in emergencies may help in the field. Her housework includes cooking, washing, ironing, mending, and making house dresses, …, and frequently children’s clothing. She also does a great deal of canning and preserving of foods, and some baking.”
239 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
In the housework, the contribution of the housewife is enormous. At the same time, it is not expected that in the traditional societies the wife will go to provide the husband with some help at their own firm. In the low income group of rural society, husbands may allow their wives to work outside home for earning money as wages, but farmers of the middle income group and capitalist group will not be in a position to lose their social status by allowing their wives to work outside home, even in their own farm (or firm). It has been discussed earlier with an example of the Patidar community in Gujarat. According to Ross (1976), in agricultural societies the division of labour between the sexes is clearly defined, and remains so until industrialisation upsets the pattern. Even in early stages of industrial development women are not considered to be capable of doing male-type jobs, particularly at the higher levels of administration (see also Boserup, 1970). Once the process of industrialisation has reached a certain point, the demand for labour increases; and this directly and indirectly produces factors which encourage women to move out of the home into the former male domain of work. Some of these factors are growing economic insecurity of women as industrialisation progresses, the spread of education that gives women wider visions and skills, and the gradual disappearance of the original social taboos on women working outside the home (Kara, 1972). From sociological viewpoint, two ideologies are related with the women’s role in industrialisation. One is the ideal of housewife and the other is the ideal of employed woman. Both of these ideals were formulated publicly in the nineteenth century. The ideal of housewife was expressed in the statement: Family is the woman’s natural place. This was originally the ideal of the middle class, from whom it spread out to the working class (Jallinoja, 1989). This tended to distinguish between the role of husband and wife more sharply than earlier. Husbands became the sole breadwinners of the family, while wives devoted most of their time to childcare and household management (Tilly and Scott, 1978). The ideal of the employed woman was presented publicly mainly by those women who struggled for their liberation and independence (Jallinoja, 1989). These kinds of conflicting views do not exist in traditional societies (mainly in rural areas which are far from urban influence). In the rural areas of a country like India, only one rule applies, i.e., housewife mainly contributes to housework. In a pre-industrialised society this role of women may positively impact on the productivity of their husbands at farm/firm by their contribution to daily work at home.
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The other thing is that marriage may be viewed as an incentive for the farmer to go for non-farm entrepreneurship in order to support his wife. How? Wives are not paid for their works at home. So, the reciprocal part of the story is that, in exchange of the free service of the wife in the family and occasionally in the work, the husband often gets morally obliged to provide his wife with financial support. In the Indian society like many other societies, it is a cultural tradition that husband should be responsible for his wife’s well- being. It is a Hindu custom that the husband is liable to take a pledge during the marriage ceremony that he would continue to provide his wife with food and clothing for whole life. In this regard, we may conclude that the male partner of a married couple in rural India can find himself in a better social position or upper social status in their own community, if he can provide his wife (or family) with better financial conditions than the others do.
There is a counter argument too. The probability of being non-farm entrepreneurs among the unmarried farmers may be higher than that among the married farmers. In such case, the above hypothesis will be rejected. The (younger) farmers may not have been interested to get married before they are well established in their occupation, especially when they are planning or taking initiatives to start a non-farm manufacturing enterprise which is not their traditional occupation. A new business generally involves tremendous risks which in turn demands substantial amount of physical labour and almost 24-hour mental involvement from the entrepreneur. At this situation, a prospective (younger) entrepreneur may not like to take extra responsibilities through his marriage (which involves psychological factor), because uncertainty in business does not leave the entrepreneur with tension-free mind unless and until he finds his firm feet on the professional ground (which involves economic factor). Thus such a phenomenon involves both psychological and economic factors and therefore the likelihood of young manufacturer’s being unmarried finds justification in psycho-economic analysis.
6.4.4 Children (CHIL)
Hypothesis 3: The probability of being non-farm entrepreneurs among the farmers who have children is higher than that among the farmers who do not have children.
241 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Since children in farm families help their parents at home as well as at work, they can be treated as positive factor if their parents tend to perform non-farm (manufacturing) entrepreneurial venture. We have already discussed the culture of farm family in a nutshell in the previous section. Here we specifically emphasize the role of children. Children in farm families are to spend a considerable amount of time either at home (in case of girls) or at farm (in case of boys). Burgess and Locke (1960) said that the children assist their parents, the degree of responsibility depending on their age. They attain economic importance and status at a much earlier age than in cities. For example, the Amish assign youngsters definite tasks at an early age. The following excerpt from a personal document illustrates the roles played by the children in the collective occupation of farming:
On the farm we children had duties to perform. My sister helped mother prepare meals and do the laundry, cleaning, and other things about the house. My brothers and I had tasks to do when we were five or six years old. At first it was carrying in fuel, later caring for stock and working in the garden, and by the age of ten or twelve, working in the field. We all worked together to make the farm a go (Burgess and Locke, 1960: 61).
Thus, children’s contribution in farm families bears importance. It seems that a farmer who is going to diversify his activity by starting-up a non-farm manufacturing enterprise may also capitalize his children’s contribution in the family and firm. In the traditional societies, family appears to be an important phenomenon so far as starting-up of a new business venture is concerned. The reason is as follows. It is known to everybody that in a dual economy like India where urban large-industrial world has got western corporate influence, the rural economy still exists in its traditional form. Therefore, in a society where professional ethics are not yet developed as such, the idea that only family members are reliable in business bears some truth. Besides, the trust the entrepreneur feels toward his children enables him to delegate part of his functions to the children corresponding to their ages. For this reason, it is the family enterprise which appears at the very beginning of any industrialisation process. Corporations make their appearance at more advanced levels of development (Derossi, 1971).
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Responsibility of being a father can be considered to be an incentive for a farmer in becoming a non-farm entrepreneur for earning higher income. A child need to be fed, clothed, kept in good health, looked after, and schooled (Ray, 1998). All these require a good income of father, because in most of the societies including India a father is mainly responsible for providing his children with all the basic needs mentioned above. Therefore, it is hypothesized that a farmer who has child/children has an incentive to be a non-farm entrepreneur than a farmer who does not have a child. In the similar vein, probability of being a non-farm entrepreneur increases with the number of children of the farmer.
6.4.5 Education (EDU)
Hypothesis 4: The probability of being non-farm entrepreneurs among the farmers increases with their educational achievements.
Earlier a common stereotyped belief about entrepreneurs was that they were at the low level of education. To be sure, many of the outstanding entrepreneurs of the last century have had little formal schooling.
Occasionally, however, some studies come up, which go against the common assumptions (see, for example, Hornaday and Aboud, 1971; Cooper, 1975; and Mancuso, 1975). In a study on the relation between education and success of entrepreneurs based in Atlanta metropolitan area in the USA, Douglass (1976) shows that entrepreneurs are more educated than the general population, although their educational advantage may not contribute directly to their success in business. As for developing nations, Derossi (1971: 163) confirms that “wherever studies have been carried out in the developing countries, the entrepreneur emerges as one of the most highly educated members of his community.” The writer continues: “In developing countries, where illiteracy is still widespread and where the percentage of highly educated people is extremely low, the majority of entrepreneurs are the men with higher education.” An example is given based in Mexico. In the country, 38% of the population is illiterate and less than 1% receives higher education. In contrast, 68% of the businessmen in the sample had a university degree. Only three people out of 143 were found to have received primary education.
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Education may reduce the informational costs associated with starting a new manufacturing enterprise. Education may also augment skills in the most profitable manner, particularly if the technology is complex (Schultz, 1975). Technical entrepreneurs seem to have at least Bachelors Degree, often in engineering, and frequently hold Masters Degrees. Therefore, in the high-tech field, the education level is higher (Douglass, 1976; and Fry, 1993). In a broad sense, entrepreneurship not only requires the support of physical capital, but also the support of human capital. The second one can be augmented by investing time and money in education. Objective oriented proper education can make such a person who is skilled in production, a person who can operate sophisticated machinery, a person who can create new ideas and new methods in economic activity (Ray, 1998). And all these are qualities of a person, which resemble with the qualities of an entrepreneur. Thus education has a positive impact on entrepreneurship. Now let us turn the discussion with a basic question what education is.
What is education? The question was raised by Schumacher (1973). His own answer is important here. Education is the transmission of ideas which enable man to choose between one thing and another. We know how to do many things, but do we know what to do? Let us make it more specific in the context of our discussion. Education may influence input choice of a production and thus selection of improved or better inputs may have a positive impact on output and income. For example, in agriculture, farmers experience how seed choice affects quality and volume of output. Moreover, farmers, who have been diversifying from agriculture to non-agricultural manufacturing activities, or at least thinking of diversification, presently realize the need for improved education. An example is due to Rutten (1995). Much earlier, almost all the village families in his study area relied on the availability of primary and secondary education in the village. But, during his visits in the villages, Rutten observes that there is a tendency among the families to send their children to primary and secondary schools outside the village. This is done particularly by those families who have economic interests outside agriculture. As their situation improved in terms of wealth and mobility as a result of their economic activities in trade and industry, the families have started to consider sending their children to be educated at expensive private institutions outside the village. The purpose of this is to provide their children with not only an educational but also a socio-cultural background which will help them “set up industrial enterprises in the future” (Rutten, 1995: 265).
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Also, for this variable we may find counter argument, approval of which may reject the above mentioned hypothesis. The probability of being non-farm entrepreneurs among the farmers may decrease with their educational achievements. The farmer may be more interested in getting a government or semi-government job (where the gain is not conditional upon any risk factor) with the increase in his education, rather than starting a non-farm business (where the gain is fully conditional upon financial risk). More explicitly, security in getting monthly salary is assured in government jobs, so educated farmer may opt for secure earning.
Small non-farm manufacturing venture is not a lucrative or prestigious profession for an educated member of farm family. The educated member does neither show his interest in farm activities of his family, nor in starting a non-farm business. All these are last options whereas the first option is to secure a permanent income. Moreover, social prestige is augmented if a family member is attached with the public sector, be it a job of clerk or whatever. Mainly financial security is the root cause of such a tendency of the farmer. Even the rich farmers are not exceptions. This may result in the consequence where we may find the farmers who are engaged in non-farm manufacturing activities are relatively less educated.
6.4.6 Political position of the farmer (POLIT)
Hypothesis 5: The farmer has a higher likelihood to become a non-farm entrepreneur if he is occupying a position in the local government i.e. panchayat.
It is assumed that a farmer who is occupying a position in local government body may have a higher chance of availing different financial or allied packages of the government, which are allocated for individual development of rural population and this may create an extra opportunity for the farmer to become a non-farm entrepreneur in order to earn a higher income from outside agriculture. Before we proceed with this discussion, the issue of political affiliation of the farmer needs to present a background. In the traditional system of India, the rural elites (usually belonging to the upper castes), because of their superior socio-economic status, enjoyed dominance in all sectors of activities and had virtually monopolized the socio-economic institutions in the society. As they enjoyed a
245 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
monopoly of wealth, political power, and education, they have naturally been put in an advantageous position to avail the inflow of resources and the additional technical knowledge in the village to further enhance their socio-economic status in the society. This had, thus, led to the emergence of a group of progressive farmers mainly from the upper echelons of the society most favourably endowed with material resources and wealth (Singh, 1985). Saith (1992: 62) has argued that “the local elites have almost invariably been in a stronger economic position to divert the resource inflows in their favour and to take better advantage of the new opportunities opened up by the package intervention.” Chambers (1983: 132) has also argued: “Local elites stand as nets between the poorer people and outside world, in the sense that they catch and trap resources and benefits.”
The above mentioned socio-political structure of rural society has taken a different shape by the intervention of the democratic institution of Panchayati Raj. The impact of the intervention of the Panchayati Raj institution in the rural society requires an attention towards the macro observation. Although we are here concerned about micro-study, it seems to be highly interesting if we start the present discussion from macro point of view and thereafter narrow it down to the micro focus. Such a presentation may help us understand the scenario better. The introduction of Panchayati Raj and the subsequent rural development programmes had the effect of establishing a separate infrastructure for economic development to initiate a process of transformation of the social and economic life of villagers (Desai, 1969). The objective was to decentralize and transfer the political as well as economic power from the upper caste rural elite (mainly large farmers) to the representative body elected by the local people. It is true that previously the categories of large farmers and small-scale industrialists were increasingly merging into one class of rural capitalists. It is this class of rural capitalists which had become socially and politically the most powerful group in the Indian countryside. They dominated the various social and political organisations at the local level. In the 1950s and 1960s in Gujarat, as Rutten (1995) described, being in the position of political dominance, members of the rural entrepreneurial families and many of the large farming families were responsible for establishing public facilities. They were actively involved in getting public funds to connect the locality with electricity, to put in water pipes and to construct school buildings. In most cases, they themselves were the main beneficiaries: the connections to the water and electricity mains were confined to the residential areas in the
246 Chapter 6 centre of the village inhabited by the middle and high castes and the schools were mostly attended by children of these same communities. Nowadays, this picture has changed in several parts of the country through the intervention of the democratic institution. The gradual shift in power in favour of the lower castes (or lower class) affected the balance of power in the village panchayats. Following the decline in power of the upper caste landlords and entrepreneurs, most upper caste people “began to distance themselves from local politics, which had become for them a low status profession, dominated by members of the lower castes” (or lower economic class elsewhere) (Rutten, 1995: 307). In a general sociological discussion on Indian villages, caste factor inevitably comes in talks. But when the discussion is about the West Bengal villages, caste factor loses its prime importance. Caste in the West Bengal villages has lost its stigma and discriminatory meaning. In this respect, a revolutionary change has been brought about (Lieten, 1992). Although this is not true for cent per cent case, caste factor plays much less role in West Bengal than in other Indian states. That is why, for West Bengal, class based study finds relevance instead of caste based sociology. It is worth mentioning here that people of upper castes may also belong to lower economic class. Seen in such perspective, at the level of elected village bodies in West Bengal, one would expect a communist party to have a high percentage of agricultural labourers and poor peasants (Lieten, 1992), i.e. the people of lower economic class (whereas, as we have said earlier, the local bodies are dominated by the people of lower castes in some other Indian states).4 It is said that the Left Front government has earned huge popularity from the rural people of lower economic profile due to the massive implementation of land reforms or land- ceiling act (through the programme of ‘Operation Barga’) by which lands of the landlords over and above the land ceiling have been redistributed to the marginal peasants and landless labourers. Rudra (1981), Khasnabis (1981) and others present different view about the story of land reforms made by the Left Front government. Rudra (1981) argued that the CPI(M) has been rather slow on land reforms, and has suggested that the process of land distribution had been going on since 1953. The Left Front government continued the line of the previous Congress governments to confirm that if a political party aims at majority support among the agricultural population, it cannot achieve that by not betraying the most exploited and the most oppressed sections of the rural masses (Rudra, 1981: A-61). The interests of the weaker sections, the argument continues, will automatically be sacrificed in trying to maintain an alliance with the rich peasants.
4 Since 1977, a coalition, called as the Left Front Government, led by the Communist Party of India (Marxist) (CPIM or CPM), has been ruling the state of West Bengal.
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Khasnabis (1981: A44-A45) appreciates the political will of the CPI(M) to implement reforms, but squarely challenges ‘the shameless compromise’ with the state structure: “This reduces an erstwhile revolutionary programme to an ordinary reformist one. Thus [the political will] was conditioned and constrained by the will to serve the institutions of the class society where they run the government.” This simply depicts that the upper class has a strong influence on the Left Front government. Lieten (1992) criticizes Rudra, Khasnabis and others for they have not correctly understood the programmatic understanding of the agrarian question by the CPI(M) and finally concludes that “the type of economic restructuring which has taken place has delivered many of the goods” (p. 289). If we believe Lieten and go on further in believing that the poor people are benefited in the Left Front rule, still it is evident from the study of Harriss (1993) on a small West Bengal village, called as Jungul in Birbhum district, that there is a positive relationship between the holding of important political position by a person even if he is proved to be a erstwhile poor man and the mobilisation of resources in favour of the person concerned. Here we link up the above written macro discussion with the micro incident or individual evidence. Harriss (1993: 1243) describes: “Politics are important….and it is surely significant in the Jungul story that the first elected member of the panchayat, after the Left Front government came to power, was a CPI(M) supporter from the low ranking mal community. He was subsequently the panchayat prodhan (chief of the local government), in a panchayat which has always been dominated by the CPI(M). It cannot be coincidental that his family has acquired a comparatively large amount of land, a shallow tube-well and an oil engine, or that he himself should now have secured a position in government service.
In this perspective, it is hypothesized that the farmers who have direct political affiliation, especially who are holding offices in panchayats, are powerful people in the rural society and can benefit by mobilizing different kinds of resources in their favour. Therefore, this section of the rural population has a higher likelihood to become non-farm entrepreneurs.
There may be some argument in favour of the alternative hypothesis, i.e. the farmer may have higher likelihood to become a non-farm entrepreneur if he is not occupying a position in the local government i.e. panchayat. The farmer who is holding office in panchayat may find no time left for non-farm manufacturing activities. Both his farm occupation and political occupation together may take all his time and energy. Similarly,
248 Chapter 6 running a manufacturing enterprise demands substantial amount of time of a day from the entrepreneur. Consequently, a farmer is forced to select one activity out of the two. Due to lack of time, the farmer who is occupying a position in panchayat has no scope to establish or run a non-farm business even if he has the opportunity to channel some public facilities/resources, distributed and disbursed through government machinery, in his favour. Therefore, the farmer has a higher likelihood to become a non-farm entrepreneur if he is not occupying a position in the local government i.e. gram panchayat.
6.4.7 Financial family support (FSUP)
Hypothesis 6: The farmer has a higher probability of becoming a non-farm entrepreneur if he enjoys financial support from his family.
In traditional societies, the family is often the only locus where a concentration of capital can be found. It can, therefore, provide the basic elements which are necessary in setting up a business: capital and organisation (Derossi, 1971). According to Joseph (2003), main role of the family in the entrepreneurial activity is the financial support. Kinship links play an important part at every stage and in every aspect of entrepreneurial activity. They represent one of the most important sources of financial aid in starting an industry (see Madan, 1993). In a study, Singh (1986) concludes that the willingness to take risks is not very high in the low income groups but is fairly high in the middle class which perhaps is inspired by a desire to get into the higher income groups (the study mainly included middle and low income groups, not the high income group). For people coming from lower income group, such movement towards entrepreneurship would mean relatively higher risk. What is implicit in Singh’s observation is that the low income group may have suffered from higher risk due to their weak strength/support of family finance. This is because of the fact that once an individual of low income group loses his invested money he may be left with bankruptcy. This fear might have led the low income group to be risk-averse. The important role of financial family support in starting small manufacturing enterprise has also been observed in the Philippines. Carroll (1965: 158) found that “a major share of the original capital for the enterprise was provided in most cases by either the entrepreneur himself or his family.”
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Let us discuss the theoretical importance of family support in rural economy. Family acts as a unit in rural areas of developing countries. Derossi (1971) depicts that it is not only the natural focus of individual emotion, but it also represents the strongest social unit. Hence, family as a total unit has the power of going beyond an individual member of the unit so far as the decision making is concerned. An example can be taken from Rutten (1995). In rural Gujarat, Rutten finds that the surplus accumulated by the families of large farmers and by the families of small-scale industrialists is substantial. “Part of it is reinvested in the farm or factory, resulting in an increase in capital-intensity and in scale of agricultural or industrial operation. Part of the accumulated surplus is invested outside the agricultural or industrial sector, resulting in a move towards diversification of economic activities by these families” (p. 233). It should be noted here that Rutten talks of families, not of the individuals. Hence, family matters so far as the decision making of the business is concerned. Moreover, in rural India joint family or extended family still exists.5 The highly developed sense of jointness and family feeling is very much there in the rural families. Rutten found, while studying the Patidar community, that agricultural land, for a long time, has played a great emotional role in tying the family members of the community together, as a result of which family-centrism has become an important characteristic of the behaviour and attitude of the members of this community. But, in such a family structure, who does take the decision of investment? We may assume that a despotic head of the family takes all decisions and others just obey. The family behaviour would then be just a reflection of the head’s choice function. Family welfare—in terms of revealed preference—would then have to be seen as the maximum implicit in the head’s choice function. But it is difficult to assume that in actual societies family heads do typically have such complete command over all economic actions of everyone in the family (Sen, 1983). However, it is a controversial issue and bypassing the controversial part it can be concluded that investment decision of a non-farm manufacturing business idea conceived by a member of an agrarian family would require support from the family since a single member does not have sole control over financial aspects of the whole family.
5 An extended joint family includes parents, married sons and their wives and children, and often also other relatives along the male line of descent, such as the family of the father’s brother and father’s sister.
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6.4.8 Work-effort or fate? (FATE)
Hypothesis 7: The farmer has a higher likelihood to become a non-farm entrepreneur if he believes in work-effort, not in fate.
Entrepreneurship demands a number of specific personal qualities. Foremost among these is an optimistic outlook (Derossi, 1971). The man who does not have an optimistic attitude cannot become an entrepreneur because an entrepreneur deals with substantial amount of financial risk. And this risk factor, which is of course calculated risk, has some relationship with optimism. A pessimist will never take entrepreneurial risk. Hard-work constructs the road of optimism. It’s not fate which directs the line of the future. In a traditional society like India, most of the people have faiths on supernatural power and fate is considered to be something which is linked to the will of god. The traditional belief is: it’s not human’s work-effort, rather it’s god who makes the future—either prosperity or destitution—of a human; and, therefore, the future of a human is dependent on his/her fate, not on his/her work-effort. Moreover, in Hindu ideology, one’s karma (action) of the previous life has the total impact on his/her karma of the present life. Whatever karma one is performing now had been programmed in his/her fate before he/she was born. Thus fate is given by god, and human cannot change it. Hence, believers of fate are not expected to bring change in their lives either through entrepreneurship or through other initiatives/activities which require a substantial amount of work-effort. Even if someone has been on the line of sharp prosperity in life by hard-work and entrepreneurial zeal, then it may be said that he/she is directed by his/her karma. According to Derossi (1971: 169), the entrepreneur must believe in the possibility of change, that the environment can be mastered and that he himself can introduce the required change. On the contrary, the environment cannot be mastered by human being in the traditional line of thinking. Of course, there are some successful entrepreneurs who strongly believe in fate. Naturally, as we have indicated earlier, such kind of people may argue that their work-efforts have obviously made important contribution in achieving the goal, but finally their successes have appeared as god’s blessings.
Another quality among many others should belong to a prospective entrepreneur, which, according to Derossi (1971), is that an entrepreneur should be able to turn an unfavourable situation to his own advantage. Alternatively, as Derossi (1971: 170) suggests, “it may be said that the entrepreneur has a Machiavellian approach, believing
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that obstacles can be manipulated and, far from being frustrated by difficulties, finding them, on the contrary, challenging and stimulating.” Here one thing needs further explanation. First, for some people, an unfavourable situation may be considered an inevitable reflection of their karmas, whereas, for some others, an unfavourable situation may be considered a challenge which can be overcome by hard-work. In business, there are very few difficulties which hard work, thrift, and common sense cannot overcome. Naturally, the second category of people, who believe in work-efforts, has the quality of becoming entrepreneurs. In the present chapter, our discussion is about the determinants of non-farm entrepreneurship in farmers and in doing so we must not forget that the farmers who are not non-farm entrepreneurs are of course regarded as farm entrepreneurs by profession. How did they become entrepreneur (i.e. farm entrepreneur), if we assume they all are fate-believers? How did they take risk? The answer is simple. Most of the farmers inherited the agricultural profession from their previous generation. It was not a new occupational entry for them. For entering into a new field, one’s self-confidence has a crucial role to play and he has to understand that he is not at the mercy of conditions and events beyond his control and, also, he has to tend to underestimate obstacles.
In this perspective, we may conclude that the farmer who believes in work-efforts, not in karma or fate, has higher likelihood of inviting challenging career in life through non- farm manufacturing business.
The above arguments were presented based on common logic but the reality in India differs from the logical arguments. A counter argument may support the alternative hypothesis. That is, the farmer might have a higher likelihood to become a non-farm entrepreneur if he believes in fate, not in work-effort. Let us first try to understand what the reality is. Most of the businessmen are religious in this part of the world. In the reality, one can find that almost all Hindu businessmen in India—starting from the biggest to the smallest one—has placed the images of the god, Ganesha, and the goddess, Lakshmi in their factories/shops/offices and often they start daily operation of their business activities after worshipping those images. This indicates that the industrialists and businessmen are very much faithful to the gods or goddesses, or, in other words, to their fates, because they usually worship gods with an expectation to have a good fortune in their business profession.
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One may argue that fate itself is not a scientific concept, so it cannot play any scientific role in generating entrepreneurship. This is not true, because fate may be an unscientific concept but faith-in-fate is a psychological phenomenon which may involve achievement motivation and determination in a particular activity. There are many people who actually win a game or cross a hurdle due to their hard-work and merit but still they believe that fate favours them from behind the screen and helps them achieve the goal. In such cases, faith of a person in good fate may play positive role, i.e. his belief may provoke him towards an effort. Therefore, we may hypothesize that the farmer has a higher likelihood to become a non-farm entrepreneur if he believes in fate.
6.4.9 Marriage relationship (MAREL)
Hypothesis 8: The farmer has a higher likelihood to become a non-farm entrepreneur if he is willing to arrange his daughter’s or sister’s marriage with a non-farm businessman.
In India, unlike the West, marriage partners of the young boys and girls are selected by the parents (exceptions are seen in some families). Usually, parents of a girl give preference to a marriage arrangement with a boy of a well-to-do family. To an individual (who is family head), the marriage of the daughter/sister is associated with social and economic status. In this regard, the meaning of social and economic status varies from region to region or from community to community within the country. For some community, bridegrooms of high-paid government servants may be preferred for the prospective brides in order to reach higher status, and guardians/fathers belonging to some other community may want to make marital relationships for their daughters with well-established self-employed bridegrooms.
Profession and occupation greatly influence the matrimonial alliances in every country. Settled occupation and stability in life provide confidence to youthful talents. Commonly, for all practical purposes a male is supposed to be an earning member whereas a female is a dependent counterpart. The traditional Hindu society favours this kind of relationship.
In general population, as we have indicated earlier, two categories of people are found in India. Some people have ideas of constancy and stability of a profession. On the other hand, there are some people who would like to choose prospective male mates who are
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settled in private firms and business. This is a sort of adventure in social living. Matrimonial alliances based on such links are less secure but more prosperous in nature (Reddy, 1978). In case of matrimonial alliance in West Bengal, preference is given to the people who are in stable profession, especially to the government servants. In a study on two communities, Malmaddi and Haveripeth, in Dharwar city in north Karnataka in India, Kadetotad (1979) finds that one’s social position is counted on the matrimonial relationship of his daughter and, therefore, many people like to arrange their daughters’ marriage with the persons who are occupationally in a better position. But the meaning of “occupationally better position” differs from community to community. For example, the entrepreneurial community, such as the Marwari community, may prefer self-employed bridegrooms for their daughters, whereas the Bengali people may prefer bridegrooms who are in secure jobs, like government jobs. Before we go into more detail about this, let us present the observations of Reddy (1978): In Indian society, the social status of a qualified medical doctor or a full-fledged engineer or a technological expert sometimes excels or supersedes the usual family status and its history. The British imperialism and its legacy have given the Indians altogether new patterns of social values with respect to services. For instance, IAS and IPS job holders are placed at the top of the hierarchy of the social values with respect to marriage alliances in the Indian society.6 Next to these are class-I gazetted officers, executive heads of the departments, services of educational status, and last in the scale comes the category of the private sector employees. Private sector job holders (except for rare cases) enjoy low status in the marriage market mainly due to its insecure nature. Especially Bengali people love security in occupation. While searching for daughter’s bridegroom, a father first looks for a boy who is in government service, corresponding to his own status.
The farmers of West Bengal are not the exceptional ones. Some well-to-do, educated, elderly farmers themselves have been in the profession of school-teaching (mostly semi- government jobs) simultaneously with their traditional profession of farming. Also, most of the young farmers of the present generation, who have finished university courses, primarily like to be engaged in government sector. On the other hand, the farmer, who is the father of an adult unmarried daughter, may like to give her daughter to a bridegroom
6 IAS and IPS stand for Indian Administrative Service and Indian Police Service respectively. In the government sector, both these positions enjoy highest facilities as well as respect since these two positions are at the top of the administrative hierarchy.
254 Chapter 6 who is in secure job. In the context of above discussion, it may be concluded that a farmer’s desire for making a marital relationship (for his daughter/sister) with a non-farm business family is positively related with his likelihood of being non-farm entrepreneur.
6.4.10 Involvement in agriculture (AGRI)
Hypothesis 9: The farmer has a higher probability of becoming a non-farm entrepreneur if he is an independent (wealthier) farmer, not a sharecropper.
Let us first divide the farmers into three categories: a) large landowner-cum-tenant, b) fixed-rent tenant (medium sized landowner-cum-tenant), and c) sharecropper (only tenant or little landowner-cum-tenant). Large land-owners are those who either cultivate the land on their own or lease out the land to the tenant or do both. In some cases, some amount of ‘reverse tenancy’ may be found in the agrarian sector. This concept suggests that large farmers lease in land from the small farmers, particularly if the former possess indivisible irrigation assets which can provide water to the holdings in question. The fixed-rent tenants are those who pay a fixed sum of money to the landlord in return for the right to cultivate the land.
Those are characterized as sharecroppers—a kind of tenants—who yield to the landlords an agreed-upon share of the crop. For instance, fraction of tenanted output under share tenancy is usually 50% in India.
In the present study, we divide the farmers into two categories. First category includes both the large landowner-cum-tenant and fixed-rent tenant together, i.e. fixed-rent tenant merges with the large landowner-cum-tenant. And the second category includes only the sharecropper. The reasons we have made two divisions are as follows. First, fixed-rent form is largely a variety of Latin American tenancy (Ray, 1998). Very limited fixed-rent tenancy cases are present in Asia. Primarily, sharecropping has received considerable attention in South Asian literature (Boyce, 1987; Ray, 1998). That is why we have ignored separate entity of this group in our study. Second, richer tenants engage in fixed- rent tenancy and, that is the reason why we have put them in the group of the large landowners-cum-tenants, who are also richer. Both these groups are richer than the sharecropper, who is identified as relatively poor tenant. Why richer tenants go for fixed-
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rent tenancy is described in the following. In the case of fixed-rent tenancy, risk is borne by the tenant. The landlord is relieved of all risk. The rent is the same whether the crop does well or not. Thus in this sense, fixed-rent tenancy requires that the tenant be willing and able to bear the risks of agricultural production. This is generally so if the tenant has substantial wealth of his own (Ray, 1998). In this perspective, our study broadly considers two groups of farmers—wealthier farmer and sharecropper. We have already initiated the discussion that the sharecropper is relatively poor, which means that the sharecropper has very limited financial capacity that may not support him to be a non-farm manufacturing entrepreneur, because a non-farm business may require a lump-sum initial investment. Ray (1998) argues that in the case of sharecropping arrangement the tenant is small and averse to risk: if a given fraction of output is paid as rent, then the tenant is, to some extent, insulated against output fluctuations, because he can share some of these fluctuations with the landlord. The weak economic strength of the sharecropper suggests that sharecropping probably reflects, on the whole, land-leases from relatively large landowners to relatively small landowners. Consequently, so far as farming is concerned, families that own relatively large land may have better access to working capital than families that sharecrop. This again clearly indicates that the relatively large landowners are financially capable of becoming non-farm entrepreneur whereas the sharecroppers do not possess that capacity.
Also, from another point of view, the sharecropper does not qualify for non-farm entrepreneurship. Let us explain it in brief. The sharecropper has an incentive to undersupply his effort due to economic reason. If the effort of the sharecropper cannot be monitored and controlled by the landlord, the tenant has an incentive to undersupply his effort, because, under the sharecropping contract, part of the output produced by him gets siphoned off to the landlord. It is true that he may give full effort if he starts a non-farm manufacturing business where there is no such tenancy agreement, i.e. where he himself is the sole owner of the business. But, at the same time, one should also consider the psychological setting of the sharecropper who has usually been undersupplying his effort (when there has been no monitoring and control from the landowner) with an expectation that he will never enjoy full amount of output in exchange of his hard effort. This psychological setting of the sharecropper-farmer may affect his effort negatively in non- farm business since at the initial stage of a non-farm manufacturing business one should not expect full return against the effort given. The start-up cost cannot be recovered
256 Chapter 6 overnight. In the competitive market, a new product may require a time period to substantially capture the market. And, so, the sharecropper may find an incentive to undersupply his effort in his non-farm manufacturing business. Given such consideration, the sharecropper does not have the quality to become an entrepreneur. On the contrary, the landowner, unlike the sharecropper, has not got any scope to develop such psychological setting (in himself) which may negatively affect the supply of his effort.
Another important aspect is that the large or medium landowner (or independent farmer) may make use of a piece of his big landholding for the purpose of establishing his non- farm manufacturing enterprise, if necessary. The sharecropper mainly earns his living by sharecropping on his landlord’s land, so he lacks such opportunity. Even if the sharecropper owns a small amount of land, he is not expected to start a non-farm unit on his small land by stopping farming substantially, because such action may be recognised as a big gamble for him. To be more specific, in such a case, the sharecropper is not expected to have been a prospective hopper from farming to non-farm activities, i.e. from at least little certainty to complete uncertainty, at the cost of the gamble with his own land.
For this variable too, we may find some counter argument which may support the alternative hypothesis. The farmer may have a greater probability of becoming a non- farm entrepreneur if he is a sharecropper, not an independent (wealthier) farmer.
The sharecropper has the higher likelihood to become a non-farm manufacturing entrepreneur. The rationale for this statement implicitly lies in the fact of incorporating more family labour by the sharecropper in the cultivation process. Let us go into more detail. The large owner uses hired labour whereas the sharecropper uses family labour as far as possible. In a study on Bangladesh, Boyce (1987) observes that family size is larger for those households that lease in. Boyce argues: “For both landowner and tenant, sharecropping may well represent a ‘rational’ response to a situation where one has more land than can be cultivated by family labour, and the other less” (p. 220). The rationale for why the sharecropper has the higher probability of becoming a non-farm entrepreneur lies in the same argument.
Let us expose it more explicitly. We can see the possibility of the sharecroppers to be engaged in non-farm production at least from two angles. (1) Since the sharecropper has
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a larger family size (than the wealthier landowner) which exceeds the number of workers needed to cultivate their own land, he occupies an extra force of free manpower that can be initially used in some labour-intensive non-farm business enterprise (in which relatively less amount of capital is required) if he partially ventures so at the expense of a part of sharecropping. At the initial stage, such partial engagement may give the sharecropping family the opportunity to operate both the activities, i.e. farming and non- farm business. At the later stage, if the non-farm activity flourishes greater than farm business then the sharecropper may finally lean towards full devotion in his non-farm manufacturing unit. (2) To meet the economic needs of his large family, the sharecropper leases in and cultivate other’s land based on agreement of sharing output. This indicates that the economic needs of the sharecropping families may insist them to start a non-farm business for earning a better living. It is true that their investment strength is much weaker than the landowning farmers, but at the same time it is also true they are not as distressed as landless labourers. Moreover, the sharecroppers are expected to make use of their farm business experiences in non-farm business.
Seen in this perspective, we hypothesize that the sharecropper is likely to turn into non- farm entrepreneurship for earning a greater income, since he has an incentive to support his family members.
6.4.11 Types of crops (CROP)
Hypothesis 10: The farmer has a higher likelihood to be a non-farm entrepreneur if he produces three crops a year such as aman rice, boro rice, and potato, than the other who produces less than these three.
There are three principal crop seasons in West Bengal: (1) the spring season, roughly from April to July, during which the main crop is aus rice; (2) the rainy season, during which aman rice is grown almost exclusively, beginning with the commencement of the monsoon in June and ending with the aman harvest in October and November; and (3) the winter or rabi season, roughly from November to March, during which a variety of crops is grown, including boro rice, wheat, potato, pulses, oilseeds, etc.
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Aman is common rice which is produced by the most farmers all over Bengal. One of the main reasons of producing this crop in enormous volume is availability of sufficient rain water. In the spring and winter seasons, shortage of water is the key problem. The farmer faces great difficulty in rabi (winter) season. Boyce (1987) observes that, owing to insufficient soil moisture and lack of irrigation, much land remain fallow in the winter season. This means that producing crops in winter heavily depends on the farmer’s private irrigation system. So, the farmer, who can afford to irrigate land in winter on his own and produce crop, is financially stronger than the other who cannot. Naturally, the former has a stronger capital base and deserves to be a non-farm entrepreneur. Therefore, in the formulation of hypothesis we stress on the production of boro rice and potato which are produced in winter (rabi) season and accordingly we hypothesize that the farmer, who is producing three crops a year such as aman rice (which is a common one), boro rice, and potato, has a higher likelihood to be a non-farm entrepreneur.
The counterargument may support the alternative hypothesis, i.e. the farmer has a higher likelihood to be a non-farm entrepreneur if he produces less than three crops such as aman rice, boro rice, and potato, than the other who produces these three crops a year.
The producer of three crops may be financially stronger than the other producer who is not engaged in the production of the above mentioned three crops in a year. It may be assumed that the rich farmers are engaged in farming throughout the year. And since they are rich and happy with their traditional occupation they do not have urge to occupationally deviate. At the same time, it is also true that the producer of the three crops may have lack of time to be engaged in some activities other than farming. Producing three crops, along with the other minor crops, in different seasons requires much time from the farmer who may not find considerable amount of extra time to think of diversification in non-agriculture. Hence, we hypothesize that the farmer has a higher likelihood to be a non-farm entrepreneur if he produces less than three crops mentioned above.
6.4.12 Wealth (WEALT)
Hypothesis 11: The farmer’s probability of being a non-farm entrepreneur increases with the increase in his wealth.
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Income could be an important variable for determining non-agricultural entrepreneurship of the farmer. But, with anticipation that the farmer may hide information relating to his actual income during interview, which is a common phenomenon experienced in such kinds of field studies, the farmer’s wealth (which is verifiable) has been selected as a substitute. That means, instead of income, wealth of the farmer has been taken as a variable for this model. The wealth of the farmer may further be assessed by four indicators mentioned below:7
• Size of farm (Landc) • Homestead (Landh) • Whether or not the farmer has access to irrigation (Irri) • Harvesting method used in farming (Harvest)
The above (four) indicators are considered as observable variables in the model. They are important to explain the farmer’s economic position in the society. Especially, land and gold are two prestigious items which can indicate the farmer’s status in the village. The farmer feels pride in holding them in enormous volume. But the information of gold, which the farmer may have provided us, is not verifiable. So we have refrained ourselves from obtaining such information. Rather the farmers’ assets can be compared by their size of landholdings which are verifiable. Landholding—both homestead and farm-size— is a crucial factor to indicate the farmer’s financial strength. Other two indicators mentioned above are also similarly important in this regard. The farmer who has access to irrigation system is expected to have been better off than the other who doesn’t have access to irrigation. The farmer who uses mechanical harvesting method is expected to have been better off than the other who harvests manually. Thus the four indicators directly or indirectly reflect the farmer’s income which is further accumulated as his wealth.
7 Please note that, in the present study, the variables ‘involvement in agriculture’ (AGRI) and ‘types of crops’ (CROP) are treated separately rather than as indicators of wealth. This is because of the fact that although these two variables indicate wealth, they involve the entrepreneurial qualities of the farmer needed to run their agricultural activities, whereas the actual four indicators of ‘wealth’ (WEALT) indicate only the assets of the farmer.
260 Chapter 6
What is the significance of wealth of the farmer in determining non-farm entrepreneurship? The farmer who is wealthier has greater probability of becoming a non-farm entrepreneur, because the wealthier section can afford the start-up investment cost required for a non-farm manufacturing unit. The start-up cost of a non-farm manufacturing business includes the purchase of land, plant and equipments, and raw materials. Hired human capital, such as skilled labourers and other staff, may also be required (Ray, 1998). In order to invest money for employing such physical and human capitals in the non-farm business the farmer may need to be wealthier. That is, the wealthier the farmer is, the greater is the possibility of being a non-farm entrepreneur. In this perspective, we may conclude that wealthier farmer has a higher likelihood to become a non-farm entrepreneur.
Some counter argument may be furnished here. In such case, the farmer’s probability of being a non-farm entrepreneur may decrease with the increase in his wealth which is reflected by four indicators. The farmers of the higher socioeconomic status, or the wealthier farmers, have always typically been recognised as agriculturists and, moreover, their economic condition has never forced them to put a parallel footstep in non- agriculture. Temporary slumps in agri-business may not affect them too much. On the other hand, farmers in the lower level of wealth may be driven by their economic condition to move towards an alternative source of income, as a result of which this section of farmers may try to find out an avenue in the non-agricultural sector. Thus, the farmer of relatively lower socioeconomic status has the probability of being a non-farm entrepreneur for his survival.
6.4.13 Innovation (INNOV)
Hypothesis 12: The likelihood of the farmer’s being a non-farm entrepreneur is higher if he has an innovative bent of mind with regard to non-farm business.
Schumpeter has convinced us that entrepreneurship was essential to development, but hadn’t told us how to create entrepreneurship where it didn’t exist, except to make sure that the “climate” was appropriate for entrepreneurial endeavour (see Higgins, 1997: 1- 2). What “climate” had Schumpeter referred to? Can we assume it to be a situation where innovations gain momentum to emerge? According to Peter Drucker (1986),
261 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
entrepreneurs are those who can create new satisfaction for the customers or new consumer demand. This “new” thing in the business world can only occur through innovation. Therefore, entrepreneurship of a person is related with his innovativeness. But, again a question arises: how innovation takes birth. Let us start with a remark of Theodore W. Schultz as to how innovation evolves. Innovation requires a particular type of ability of a person. In analyzing the equilibrating activities of people, according to Schultz (1975: 833-834), it is postulated that “there are economic incentives to reallocate resources, that people respond to these incentives to the best of their ability, and that the difference in their performance is a measure of the difference among people with respect to the particular type of ability that is required. In accordance with this postulate, there is a type of ability that is useful and whose value is some function of the demand for and the supply of that ability. This particular ability, as noted at the outset, represents the competence of people to perceive a given disequilibrium and to evaluate its attributes properly in determining whether it is worthwhile to act, and if it is worthwhile, people respond by reallocating their resources. The realized gains from such reallocations are the observable rewards.” Thus, the expected gains are the economic incentives to enter the equilibrating activities and the gains that are realized represent an improvement in income. Examples can illustrate it better. In agriculture, these gains are exemplified by the profitability of the adoption of hybrid corn; in industry, these gains may be exemplified by the profitability of the adoption of capital intensive technology rather than labour intensive technology. Innovation not only means bringing changes in the existing products or services but also indicates bringing changes in techniques of production. Developing a new packaging design of a product is also a kind of innovation. It could relate to the changes in organisation too. We are not going into detail with regard to the definition and kinds of innovation. Our discussion centres the phenomena of innovativeness and non-farm entrepreneurship of the farmer.
However, entrepreneurs innovate. Innovation is the specific instrument of entrepreneurship (Drucker, 1986). Can an innovative farm entrepreneur be an innovative non-farm entrepreneur? We need to distinguish between innovativeness of a farmer in the farm activities and that in non-farm activities. An innovative farm entrepreneur does not necessarily prove himself to be a potential innovative non-farm entrepreneur. The reason is as follows. To the most farmers, farming is a traditional business. That is, most of the farmers are born farm entrepreneurs. Whether or not they were willing to be farmers does
262 Chapter 6 not matter, they have become farmers by birth. Naturally, we may postulate that many (or at least some) of them are inherent innovators. Can most of those inherent innovators in farming be interested in non-farm manufacturing activities? Let us take an example from Punjab where the agricultural sector is very rich and the farmers are known to have been very innovative. In a research paper, Dunham (1989: 86) concludes that the ‘new capitalist farmers’ are unlikely to become involved in the industrial sector to any significant degree. He suggests instead that they tend to invest in economic activities which are closely related to agriculture—putting them at a comparative advantage and making monitoring easier—such as trading, seed distribution, dealerships, local transport businesses and repair shops. This is empirically confirmed by Bhalla and Chadha (1983: 161), who in their study on the farming-community in Punjab conclude that:
“large and very large farmers are recording substantial savings in the rest of Punjab. Strangely enough, only a fraction of these are now being used in capital formation in agriculture or even in house construction. Having reached a plateau in farm investment, the rich farmers seem to be squandering their surpluses on conspicuous consumption, including purchase of jeeps, cars and television sets, excessive indulgence in alcoholism and demonstrative expenditure on social ceremonies, etc. A microscopic fraction of them have also invested on houses or shops in urban areas and mandis and on goods transport and cold storages.”
In Punjab, due to rise in rural incomes through agricultural development, an effective consumer demand for the non-farm goods was present in the economy. But the farmers were not interested in investing money in the production of non-farm goods due to the fact that perhaps they might have lacked the ability “to perceive a given disequilibrium” in the non-farm economy and to “respond by reallocating their resources.” Thus, the farmer who is innovative in the farm sector may not come up as a non-farm entrepreneur. The Punjab experience showed us that the capitalist farmers were interested in investing money in economic activities which were “closely related to agriculture” and the amount of innovation required in running these trading businesses (mentioned above) is generally very nominal. Also, trading businesses can be imitated very easily. Some argue against the compatibility of imitation with entrepreneurship. If the manufacturing businesses are imitated in a greater volume by the new-comers then the growth of the business may be inhibited by early market saturation which may result in a very nominal profit margin
263 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
earned by the owners. So, in the next phase, an industry requires innovative ventures for its growth by creating new consumer demand. In this context, entrepreneurship is often judged by the person’s innovativeness. Real or healthy competitiveness grow in a particular industry when the entrepreneurs exhibit their innovativeness through launching their products. Therefore, the prospect of rural industrialisation depends on whether or not the farmers are interested in innovative diversification in manufacturing business, not in imitative diversification, of course. Imitation is ruled out as a quality of an entrepreneur. Drucker (1986: 21) maintains strict view regarding this: “…not every small business is entrepreneurial or represents entrepreneurship,” even if it is bearing some risk. He emphasizes innovative ventures so far as small entrepreneurship is concerned. But the question is: how can we measure innovativeness of a person? It is difficult to measure indeed. However, we can get some indication about one’s innovativeness if the person is simply interested to do something new or something different in his own field, irrespective of his age, education, or anything else.
Seen in such perspective, we may hypothesize that if the farmer has an innovative (not imitative) bent of mind with regard to the non-farm manufacturing business, i.e. if the farmer has an intension to create new consumer demand in the non-farm manufacturing sector then he has higher likelihood of becoming a non-farm entrepreneur. Please note that, for this study, we have measured innovativeness of the farmer by asking the question: if you start a business, would that be one that is being run by the others, or would it be one which is new/innovative? (See APPENDICES 5.1 and 5.2 for the questionnaires). If the farmer likes to start a new kind of business then he is innovative, otherwise not.
6.4.14 Risk (RISK)
Hypothesis 13: The farmer’s likelihood of being a non-farm entrepreneur is higher if he is a risk loving person so far as diversification is concerned.
Risk is an important part of entrepreneurship. What determines a person’s attitude toward risk? Partly, it is a matter of taste. Some people simply tend to be more cautious than others. To a large extent, however, it is also shaped by the economic circumstances of a
264 Chapter 6
person (Ray, 1998). Therefore, personal taste and the economic condition determine a person’s risk taking attitude.
How is risk measured? We can try to understand it by an example. Suppose that a farmer tosses a coin and the farmer wins Rs. 100 if it shows a head and the farmer loses Rs. 100 if it shows a tail. That is, the farmer expects to win Rs. 100 with probability ½ and the farmer expects to lose Rs. 100 with probability ½. The farmer’s expected return is:
½(100) + ½(-100) = 0.
This is called a fair gamble. A fair gamble is a gamble whose expected return is 0. A risk lover will pay some positive price to play this game or gamble. A risk-neutral individual will pay a zero price; that is, he will play if it is free. An individual afraid of taking any risks will demand some money to play this game. We now have the following definitions:
• Individuals are called to be risk averse if they are not willing to undertake a fair gamble; • Individuals are called to be risk neutral if they are indifferent between accepting and rejecting a fair gamble; • Individuals are called to be risk loving if they are eager to undertake a fair gamble.
Now let us come back to the discussion on our variable. Starting a business involves financial risks. Similarly, diversification of economic activities is a matter which is very much embedded in a person’s risk taking propensity. The farmer interested in starting a new non-farm manufacturing enterprise must have to be a risk loving person. Those farmers who are not risk loving may find it difficult to diversify in non-farm manufacturing business. Please note that, for this study, we have measured risk attitude of the farmer by asking the question: if you are offered a large amount of money, would you start a business, or do other things? (See APPENDIXES 5.1 and 5.2 for the questionnaires). If the farmer likes to start a business then he is a risk loving person, otherwise not. 6.4.15 Summary of the explanatory variables and descriptive statistics
265 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
The discussion presented in the preceding sections is summarized in Table 6.1. We present an overview of the explanatory variables, their definitions, measurement scales, and the labels used to represent them in subsequent tables. Moreover, in Table 6.2 we present descriptive statistics for NFE and its explanatory variables.
6.4.16 Simultaneity bias
Above we have described the expected impacts of the explanatory variables. However, for several explanatory variables a reverse impact is likely to hold. For instance, non- farm entrepreneurship (NFE) of a farmer is not only supposed to be influenced by his children and wealth but, on the other hand, a farmer’s non-farm entrepreneurship may influence his number of children and wealth. The other explanatory variables that are likely to be influenced by NFE are marital status of the farmer, types of crops produced by the farmer, political position of the farmer, financial family support, marriage relation, farmer’s risk taking propensity, and farmer’s innovativeness. In order to avoid simultaneity bias remedial action has to be taken. Well-known procedures are limited information estimators such as 2SLS or full information estimators such as simultaneous equations method. We opt for the latter, particularly the LISREL approach which does not only make it possible to deal with simultaneity bias but simultaneously with latent and observable variables as well. A brief summary of the LISREL approach is presented in section 6.5.
Table 6.1: Independent variables of the empirical model and their hypothesized influence on the dependent variable
Independent Description Measurement Variable Hypothesized
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Variables Names Influence on the Farmer’s Non- farm Manufac- turing Entrepre- neurship (NFE) Personal characteristics
Age and age2 Age and age2 Years AGE, +/- of the farmer AGE2 - Marital status Marital status Married/otherwise: 1/0 MARS +/- of the farmer Children Children of the Number CHIL + farmer Education Education level Years EDU +/- of the farmer Political factor
Political position Whether or not Yes/no: 1/0 POLIT +/- of the farmer the farmer holds office in panchayat Financial factor
Wealth Wealth of the A latent variable measured WEALT +/- farmer by four indicators: Landc (size of the farmer’s cultivable land), Landh (size of the farmer’s homestead land), Irri (whether or not the farmer has access to irrigation), and Harvest (harvesting method the farmer uses) Financial family Household’s Yes/no: 1/0 FSUP + support financial support to the non-farm venture
267 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Agricultural factors
Involvement in Whether the Independent farmer / AGRI +/- agriculture farmer is sharecropper: 1/0 independent owner or sharecropper Types of crops Whether the three/less than three: 1/0 CROP +/- produced farmer produces three crops or less a year Psycho-socio- cultural factor Marriage Whether or not Yes/no: 1/0 MAREL + relationship the farmer is willing to arrange marriage for his daughter/sister with a non- farm business family Work-effort or Does the Work-effort/fate: 1/0 FATE +/- fate? farmer believe in work-effort or fate?
Innovation and risk factors Innovation Whether or not Yes/no: 1/0 INNOV + the farmer is interested in innovative business8 Risk Whether or not Yes/no: 1/0 RISK + the farmer is willing to take risk9
8 We have measured innovativeness of the farmer by asking the question: if you start a business, would that be one that is being run by the others (0), or one which is new/innovative (1)? (See APPENDICES 5.1 and 5.2 for the questionnaires). 9 We have measured risk attitude of the farmer by asking the question: if you are offered a large amount of money, would you start a business (1), or do other things (0)? (See APPENDICES 5.1 and 5.2 for the questionnaires).
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Table 6.2: Descriptive Statistics for NFE and Explanatory Variables (Sample Size: 290)
Variable Mean St. Dev. Skewn Kurtosis Minimum Freq. Maximum Freq. ess NFE 0.583 0.494 -0.337 -1.899 0.000 121 1.000 169 AGE 45.234 15.583 0.295 -0.841 17.000 1 90.000 1 AGE2 2288.138 1501.955 0.834 0.058 289.000 1 8100.000 1 MARS 0.859 0.349 -2.069 2.298 0.000 41 1.000 249 EDU 9.131 4.147 -0.162 -0.370 0.000 14 17.000 11 CHIL 1.828 1.485 1.110 2.227 0.000 58 9.000 1 AGRI 0.955 0.207 -4.422 17.679 0.000 13 1.000 277 CROP 0.431 0.496 0.280 -1.935 0.000 165 1.000 125 POLIT 0.021 0.143 6.770 44.132 0.000 284 1.000 6 FSUP 0.634 0.482 -0.561 -1.697 0.000 106 1.000 184 MAREL 0.666 0.473 -0.705 -1.513 0.000 97 1.000 193 FATE 0.831 0.375 -1.776 1.162 0.000 49 1.000 241 RISK 0.710 0.454 -0.932 -1.139 0.000 84 1.000 206 INNOV 0.207 0.406 1.455 0.117 0.000 230 1.000 60 WEALT* 0.000 1.000 2.859 18.220 -1.138 1 8.525 1 Landc 17.121 14.277 3.551 26.142 1.000 1 150.000 1 Landh 7.169 6.540 2.348 7.190 1.000 12 40.000 3 Irri 0.862 0.345 -2.111 2.473 0.000 40 1.000 250 Harvest 0.293 0.456 0.914 -1.173 0.000 205 1.000 85 *Four indicators have been used for the latent variable wealth (WEALT). The last four rows of this table show the descriptive statistics about them.
6.5 The LISREL model10
As indicated above, the LISREL approach will be applied to estimate the model outlined above. In this section, we present a brief summary of the LISREL model. In section 6.5.1 we discuss the measurement model and in section 6.5.2 the structural model. Some submodels are dealt with in section 6.5.3. Section 6.5.4 discusses the theoretical and the sample covariance matrices. Sections 6.5.5 and 6.5.6 deal with identification and estimation of the model, respectively. Model judgement and model modification are discussed in section 6.5.7.
In order to deal simultaneously with both the measurement and the main theory a LISREL model is made up of two related submodels:
- a latent variables measurement model, which represents the relationships between the latent variables and their observable indicators.
10 The full form of LISREL is LInear Structural RELations.
269 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
- a structural model, representing the relationships between the latent variables.
6.5.1 The measurement model
T T Let y = (y1 , y2 ,..., y p ) and x = (x1 , x2 ,..., xq ) be vectors of observable endogenous and
11 T exogenous variables, respectively. Furthermore, let η = (η1 ,η2 ,...,ηm ) be a vector of
T latent endogenous variables and ξ = (ξ1 ,ξ 2 ,...,ξ n ) a vector of latent exogenous
T T variables. Finally, ε = (ε1 ,ε 2 ,...,ε p ) and δ = (δ 1 ,δ 2 ,...,δ q ) are defined as vectors of measurement errors of y and x, respectively. The relationships between the observed and latent variables are given in the latent variables measurement models (1) and (2):
y = Λ yη + ε (1) and x = Λ xξ + δ (2) where Λ y and Λ x are (p x m) and (q x n) matrices of regression coefficients (also called factor loadings).
6.5.2 The structural model
The structural model consists of a set of relationships among the latent variables: ~ η = Βη + Γξ + ς (3) or Βη = Γξ + ς (4) ~ where Β is an m x m coefficient matrix with β ij representing the effect of the j-th endogenous variable on the i-th endogenous variables; Γ is a m x n coefficient matrix with γ ij representing the effect of the j-th exogenous variable on the i-th endogenous ~ variable; ς is a random vector of residuals; Β = Ι − Β , where I is the identity matrix.
270 Chapter 6
In connection with model (1) – (4), the following notation is introduced. The covariance matrices of ε and δ , which need not be diagonal in LISREL, will be denoted by
Θε ( pxp) and Θδ (qxq ) and the covariance matrices of ξ and ς by Φ(nxn) and Ψ(mxm) .
The following remarks are in order here. First, for reasons of simplicity but without loss of generality, it is assumed that B is non-singular. Thus, dependent equations are assumed to have been removed from the system of equations. Secondly, it is possible to estimate intercept terms of the equations (1) – (4). Such parameters may be of interest in the comparison of different, mutually exclusive, sets of observations. In the present kind of study, however, attention will only be paid to the analyses of a single sample. In such analyses, the intercept terms hardly provide any information. Therefore, the assumption is made here, that both the observed and the latent variables are centralized. Formally:
E(y) = 0; E(x) = 0; E(η ) = 0; E(ξ ) = 0 (5)
Thirdly, the following standard assumptions are made:
E(ε ) = 0; E(δ ) = 0; E(ς ) = 0
E(ηε T ) = 0; E(ξδ T ) = 0; E(ηδ T ) = 0; E(ξε T ) = 0; E(εδ T ) = 0 (6)
E(ςξ T ) = 0; E(ςδ T ) = 0; E(ςε T ) = 0
In (5) and (6), “0” denotes a vector or matrix of appropriate order.
Fourthly, multiple observable variables for a latent variable are often preferable and necessary so as to provide a tool for identification (see, among others, Goldberger 1972, 1973). Besides, one single observable variable may be an indicator of more than one latent variable. Finally, as described by, among others, Theil (1971), the problem of multicollinearity arises as a consequence of the occurrence of (highly) correlated explanatory variables. It usually leads to the increase of the estimated variances of the estimators of the coefficients of the collinear explanatory variables, so that one may be
11 The superscript ‘T’ denotes the transposed vector of matrix.
271 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
led to drop variables incorrectly from an equation. By means of the possibility to handle observable and latent variables simultaneously within one model framework, as in the LISREL case, the consequences of multicollinearity can be mitigated. This can be seen as follows. Collinear explanatory variables, which are indicators of a given latent variable, are dependent variables in one of the latent variables measurement models (1) and (2) and therefore are not removed from one of these models because of their collinear nature. Furthermore, in the structural model the latent variables appear instead of their corresponding observable variables. So, collinear variables are neither removed from the structural model in spite of the fact that they are collinear.
6.5.3 Submodels
Model (1) to (4) is a general framework in which several specific models are contained. The most common of these models are first- and second- order factor analysis models, structural equation models for directly observable variables, and various types of regression models. The specifications for the various models mentioned above are given below.
- Suppose the following specifications are made: Β = 0 , Γ = 0 , Λ y = 0 , Θε = 0 , Ψ = 0, where “0” denotes a zero matrix of appropriate order. Then the well known factor analysis model is obtained:
x = Λ xξ + δ (7)
- If only the x-variables are removed from the model, i.e. Λ x = 0 , Θδ = 0 , we have:
y = Λ yη + ε (8) ~ (Ι − Β)η = Γξ + ς (9) or ~ η = (Ι − Β) −1 (Γξ + ς ) (10) ~ Putting Β = 0 and substituting (10) in (8) gives:
y = Λ y (Γξ + ς ) + ε (11) Model (11) is a second-order factor analysis model.
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- Remove all latent variables from the model by specifying identity relationships between y and η and between x and ξ . This is done by defining Λ x and Λ y as identity matrices and Θε and Θδ as zero matrices. This results in: Βy = Γx + ς (12) which is a simultaneous equation model with observables only.
- If (12) is written as ~ y = Βy + Γx + ς (12a) ~ and Β is specified as a zero matrix then the “classical” linear model is obtained: y = Γx + ς (13)
If (13) consists of one equation only, we have the standard linear model.
- If only the latent exogenous variables are removed from the general model i.e., ξ ≡ x , so that Λ x = Ι , the identity matrix, and θ δ = 0 , we get: y = Λ yη + ε (14) Βη = Γx + ς (15)
A special case of model (14), (15) is the so-called fixed-x model. In that case the conditional distribution of the y variables for given x is studied. This type of model is frequently met in traditional econometrics (see, for instance, Johnston, 1972). It should be noted that in both the fixed-x and the random-x cases Φ = S xx , where S xx is the sample covariance matrix of the x-variables.
We are now in a position to present the model we are going to estimate. The measurement model reads as follows:12
12 It should be observed that, for the latent variables η1 - η9 , only one indicator for each is available. Hence, they are identical to the corresponding y1 - y 9 variables.
273 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
⎡y10 ⎤ ⎡λ10 ⎤ ⎡ε10 ⎤ ⎢y ⎥ ⎢λ ⎥ ⎢ε ⎥ y = ⎢ 11 ⎥ , Λ = ⎢ 11 ⎥ , η = [η ], ε = ⎢ 11 ⎥ ⎢y ⎥ y ⎢λ ⎥ 10 ⎢ε ⎥ ⎢ 12 ⎥ ⎢ 12 ⎥ ⎢ 12 ⎥ ⎣y13 ⎦ ⎣λ13 ⎦ ⎣ε13 ⎦
The structural equation matrices are:
⎛ 0 β β β β β β β β β ⎞ ⎜ 12 13 14 15 16 17 18 19 110 ⎟ ⎜ β 21 0 0 0 0 0 0 0 0 β 210 ⎟ ⎜ β 0 0 0 0 0 0 0 0 β ⎟ ⎜ 31 310 ⎟ ⎜ β 41 0 β 43 0 0 β 46 0 0 0 β 410 ⎟ ⎜ β 0 0 0 0 0 0 0 0 β ⎟ Β = ⎜ 51 510 ⎟ ⎜ β 61 0 β 63 β 64 0 0 β 67 β 68 0 β 610 ⎟ ⎜ ⎟ ⎜ β 71 0 0 0 0 β 76 0 β 78 0 β 710 ⎟ ⎜ β 0 0 0 β β β 0 β β ⎟ ⎜ 81 85 86 87 89 810 ⎟ ⎜ β 91 0 0 0 0 0 0 β 98 0 0 ⎟ ⎜ ⎟ ⎝ β101 0 0 β104 β105 0 0 0 0 0 ⎠
⎛ γ γ ⎞ ⎜ 11 12 ⎟ ⎜ γ 21 0 ⎟ ⎜ γ γ ⎟ ⎜ 31 32 ⎟ ⎜ 0 0γ 42 ⎟ ⎜ γ γ ⎟ Γ = ⎜ 51 52 ⎟ ⎜ γ 61 0 ⎟ ⎜ ⎟ ⎜ 0 γ 72 ⎟ ⎜ γ γ ⎟ ⎜ 81 82 ⎟ ⎜ 0 γ 92 ⎟ ⎜ ⎟ ⎝γ 101 0 ⎠
6.5.4 The theoretical and the sample covariance matrices
When topics of identification estimation and judgement of LISREL models are discussed, the theoretical covariance matrix and the corresponding sample matrix play essential roles. As will be explained below, the sample covariance matrix should preferably be
274 Chapter 6 analyzed. The sample covariance matrix of z = (yT , X T )T will be denoted as S and the theoretical covariance matrix as Σ .
Let us first pay attention to the structure of Σ . The matrix Σ can be expressed in terms of the eight model matrices Λ y ,Λ x ,Β,Γ,Φ,Ψ,Θε and Θδ . This can be seen as follows.
Because Β −1 exists, equation (4.4) can be written as: η = Β −1Γξ + Β −1ς (16) Substitution of (16) in (1) gives
−1 −1 y = Λ y (Β Γξ + Β ς ) + ε (17)
Calculation of the covariance matrix of y , i.e. E(yyT ) , using (17) and the assumptions (6) gives:
T −1 −1 −1 −1 T E(yy ) = E(Λ y (Β Γξ + Β ς ) + ε)(Λ y (Β Γξ + Β ς ) + ε)
−1 T −1 T −1 −1 T T = Λ y (Β ΓΦΓ (Β ) + Β Ψ(Β ) )Λ y + Θε (18)
In similar ways E(xxT ) and E(yxT ) are calculated. This gives:
−1 T −1 T T −1 T ⎡Λ y Β (ΓΦΓ + Ψ)(Β ) Λ y + Θε Λ y Β ΓΦΛ x ⎤ Σ = ⎢ T −1 T T T ⎥ (19) ⎣⎢ Λ x ΦΓ (Β ) Λ y Λ xΦΛ x + Θδ ⎦⎥
On the basis of prior information (expectations, theoretical considerations, etc.), the elements in the parameter matrices, and thus in Σ , may be regarded either as free, fixed or constrained (see among others, Johnston, 1972). A constrained parameter is unknown but assumed to be equal to one or more other parameters.13 All independent, free and constrained parameters contained in the matrices Λ x , Λ y , Β , Γ , Φ , Ψ , Θε and Θδ will be denoted by the vector π . It is obvious that a specific structure of π , i.e. a specific configuration of free, fixed and constrained parameters, determines a specific structure of Σ .14 Moreover, the determination of the value of π forms the core of the estimation problem.
13 In addition to these constraints various other equality and inequality constraints on various parameters, such as variances, correlations, factor loadings and structural coefficients, as well as ordered inequalities can be imposed. 14 It should be noted that when Σ must explicitly be expressed as a function of π , we will write Σ(π ) ; otherwise the argument will be omitted.
275 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Let us now turn to the sample covariance matrix S. Let Z be a M x (p+q) matrix of M observations of the y and x vectors and z = (y T , x T )T the sample mean vector. Then:
1 S = (Z T Z − Mzz T ) (20) M −1
When there are ordinal or nominal variables among the observable variables, (20) cannot be used in general. When there are ordinal variables among the y-variables or among the x-variables, which may not be considered as fixed, the LISREL program can estimate and analyze the matrices or polychoric, tetrachoric and polyserial correlation coefficients. In all three cases the ordinal z variables are regarded as crude measurements of an underlying unobservable continuous variable, say z*, which is assumed to be standard normally distributed. The polychoric correlation coefficient is the correlation between two underlying z* variables.15 The tetrachoric correlation coefficient is a special case when both observables are dichotomous. The correlation between a z* variable and a normally distributed observed variable is called the polyserial correlation coefficient. By way of the observable variables the various correlations mentioned above are estimated (for details see, among others, Olsson 1979, Olsson et al 1981 and Muthén 1978, 1979, 1981).
It should be noted that not only the sample covariance matrix can be analyzed by the LISREL program, the sample matrix of moments about zero and the sample correlation matrix can also be used to estimate their theoretical counterparts. The sample matrix of moments about zero, defined as:
1 Z T Z (21) M has to be used when intercept terms and means of the latent variables are required.
15 In the case of a large number of distinct categories the ordinal variable is treated as a continuous variable.
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When the measurement scales are very different the correlation matrix could be analyzed for numerical expediency. Then each variable is expressed in units of its standard deviation. The correlation matrix is defined as:
D −1SD −1 (22) with
D = (diag S)1/ 2 , (23) i.e. a diagonal matrix of standard deviations.
Now that the most important features and assumptions of LISREL models have been described, we can pay attention to the identification problem in connection with this type of models.
6.5.5 Identification
In order to be able to draw inferences for the vector η from the variance-covariance matrix of the observable variables, the structure of Σ has to be such as to allow a unique solution of η from Σ . Thus, the vector η has to be uniquely determined by Σ ; in other words, the model has to be identified.
A necessary condition for identification is that the number of distinct elements in Σ is at least as large as the number of independent parameters to be estimated. A second necessary condition for identification is that each individual parameter can be separated from the other parameters. This condition is often difficult to test. Furthermore, it is not a sufficient condition. However, the LISREL program gives hints about identification problems. It calculates an estimate of the matrix of second-order derivatives of the fitting function used to estimate the model. Rothenberg (1971) has shown that under quite weak regularity conditions local identifiability is equivalent to non-singularity of the information matrix. Furthermore, the rank of the matrix indicates which parameters are not identified.
277 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
In the case of models with latent variables, the model is not identified if the latent variables have not been assigned measurement scales. The easiest way to fix the measurement scales of the latent variables is to set one λ -coefficient equal to 1 for each latent variable. It is usually possible to fix or to constrain unidentified parameters on the basis of theoretical knowledge or ad hoc reasoning so as to render the model identified.
6.5.6 Estimation of the model
As mentioned above, estimation of LISREL models consists of fitting the theoretical matrix to the sample matrix of a set of M observations on z = (yT, xT)T by minimizing the distance between both matrices in some metric. The exposition below will be in terms of the covariance matrices Σ and S but it has already been remarked that the matrix of moments about zero and the sample correlation matrix can also be analyzed. However, the estimates based on each of these matrices are usually not the same.
The LISREL program can provide any of seven estimators:
• Instrumental variables (IV) • Two-stage least squares (TSLS) • Unweighted least squares (ULS) • Generalised least squares (GLS) • Maximum likelihood (ML) • Generally weighted least squares (WLS) • Diagonally weighted least squares (DWLS)
The maximum likelihood estimator is described below in detail.
Maximum likelihood has been the “traditional” estimator of LISREL models in the sense that the other estimators were introduced at a later stage. The maximum likelihood procedure is based on minimization with respect to the unknown parameters of the non- negative function:
278 Chapter 6
1 F = []log Σ + tr(SΣ −1 )− log S − ()p + q (24) 2 by means of a modification of the Fletcher-Powell algorithm. In (24) | . | stands for the determinant and tr ( . ) for the trace of the matrix concerned. When ξ,ς,ε,δ are multinormally distributed, and thus z, then: 1 F′ = − M [()p + q log 2π + log Σ + tr(SΣ −1 )] (25) 2 is the log-likelihood function of the sample in the case of independent observations.
From (24) and (25) it follows that under the assumptions of normality of z and independence of the observations, minimization of F (which gives the same parameter estimates as maximization of F′ ), results in ‘genuine’ maximum-likelihood estimators. Under the usual regularity conditions, which are satisfied in the case of normality, the maximum likelihood estimator of π is asymptotically normally distributed with mean π 1 and covariance matrix []J (π ) −1 .16 Furthermore, this estimator is consistent and M asymptotically efficient.
It is obvious from (24) that S has to be positive definite. This condition is satisfied when there exists no exact linear relationship between any of the z variables, and if M ≥ p+q. Furthermore, the starting values needed for minimization algorithm, say π ′ , should be such that Σ(π ′), is also positive definite. The initial estimates provided by LISREL program usually satisfy this condition (see below).
The maximum likelihood procedure also produces an estimate of the covariance or correlation matrix of the estimators, which can be used for model judgement purposes. However, it should be noted that although an estimate of the covariance or correlation matrix of the estimator is produced whatever sample matrix has been analyzed, the covariance or correlation matrix of the estimators is only valid when a sample covariance matrix has been analyzed.
⎡ ⎤ 16 δ δ J ()π is defined as J ij ()π = Eπ ⎢ log p()z;π . log p()z;π ⎥ with F′ substituted for ⎣⎢δπ i δπ j ⎦⎥ log p()z;π , where p(z;π ) is the likelihood function.
279 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
As mentioned above, a necessary condition for the maximum likelihood procedure to give “genuine” maximum likelihood estimates is the normal distribution of the observable variables. However, the distribution of the observables is seldomly known in practice. It also rarely happens that the theory at hand suggests a definite distribution.17 However, when the range of the variables is in principle (-∞,∞ ) and second order moments exist, the assumption of multivariate normality can be justified as a first working hypothesis on the basis of a central limit theorem or maximum entropy. The latter means that the normal distribution reflects the lack of knowledge about the distribution more completely than other distributions (see, among others, Rao, 1965).
Maximum likelihood under normality (i.e. application of maximum likelihood under the assumption of normality whereas the distribution actually deviates from normality) may also be defended on the basis of the fact that it usually leads to a reasonable fitting function and to estimators with acceptable properties for a rather wide class of distributions. Under quite weak distributional assumptions, maximum likelihood under normality is consistent and asymptotically normal. However, in the case of deviation from normality the standard errors produced by the LISREL program should be interpreted very cautiously. The same applies to various other judgement statistics to be described below.
The maximum likelihood fitting function can also be used without the assumption of normality. In that case it is similar to the unweighted least squares estimator. Under these circumstances the resulting estimator is still consistent. However, the judgement statistics are no longer valid.
6.5.7 Model judgement and model modification
The purpose of model judgement is to judge how well an estimated model fits to the sample data. Various aspects of a LISREL model may be considered in this connection, such as the model as a whole, the various submodels and the individual parameters.
Individual parameters
17 For an exception, see the theory of rational consumer behaviour (cf. Theil, 1975, 1976).
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The statistics which relate to the individual parameters are parameter estimates and, when maximum likelihood has been used, standard errors and correlations of the estimators of the individual parameters.
Separate equations
For the equation of each observed variable in each latent variables measurement model the squared multiple correlation is given.
The latent variables measurement models
The coefficient of determination for the latent variables measurement model (i.e. for the endogenous and exogenous latent variables jointly) shows how well the observed variables serve jointly as indicators of the endogenous and exogenous latent variables.
The structural model
The coefficient of determination for all structural equations jointly shows what proportion of the variation in the endogenous variables is accounted for by the variation in the systematic part of the model.
The overall fit
For the model as a whole several statistics are provided. First, there is the χ 2 -measure which is given if maximum likelihood is used. Another measure for the overall fit, when maximum likelihood is used, is the goodness of fit index (GFM) defined as:
tr(Σˆ −1S − I) 2 GFM = 1− (26) tr(Σˆ −1S) 2
This measure, adjusted for degrees of freedom (AFGM), is defined as:
( p + q)( p + q +1) AGFM = 1− (1− GFM ) (27) 2h
281 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Measures similar to (26) and (27) are given for unweighted least squares. Then GFM is replaced by GFU defined as:
tr(S − Σˆ ) 2 GFU = 1− (28) tr(S 2 )
All measures (26) – (28) are expressions of the relative share of variances and covariances accounted for by the model. They usually fall between zero and one. A good fit corresponds to values close to one.
A measure of the average of the residual variances and covariances is the root mean square residual. A small value of this statistic in relation to the sizes of the elements in S is an indication of a good fit. Finally, the LISREL program gives normalized residuals which are approximately standard normal variates. As a rule of thumb, a normalized residual larger than 2, is an indication of specification errors.
For further details, the reader is referred to LISREL manual (see Jöreskog and Sörbom, 2001).
6.6 The Results
6.6.1 The measurement model
The estimation results of the measurement model are presented in Table 6.3 (see APPENDIX 6.1 for the detailed results). The coefficient of the variable Landc (i.e. the farmer’s farm size) has been fixed to 1 in order to fix the measurement scale of the latent variable η10 . From Table 6.3 it follows that Landc and Landh (farmer’s homestead land) are the main determinants followed by Harvest (harvesting method the farmer follows) and Irri (whether or not the farmer has access to irrigation). The R2 values of the first two indicators are low whereas for the latter two indicators they are very low. This implies that improvement of the model is needed.
282 Chapter 6
Table 6.3: The results of the measurement model
Observable Coefficient Standard error t-value R2 variable Landc 1.00000 - - - - 0.30729 Landh 0.30224 0.05287 5.71667 0.13380 Irri 0.00812 0.00264 3.07509 0.03459 Harvest 0.01539 0.00356 4.32247 0.07136
6.6.2 The structural model
Goodness of Fit Statistics
The goodness of fit statistics in the box below show that the model fits the data reasonably well. Minimum fit function chi-square value is 167.05504 and normal theory weighted least squares chi-square value is 164.43435. Both of them are nearly double of the value of degrees of freedom, which is 84. This is usually interpreted as a reasonable fit. Also, goodness of fit index (GFI) is found to be very high, which is 0.92949. This also indicates that the model fits the data well.
Interpretation of the results
We shall discuss the main results of the structural model in the following. See Table 6.4 for the results and for the detailed results see APPENDIX 6.1. Our main emphasis is on equation 1 (see Table 6.4) since that equation relates to the main objectives of this study, i.e. identification of determinants of non-farm entrepreneurship among farmers. We have incorporated the other equations in the structural model in order to control for simultaneity bias as well as to examine the interdependence between some variables. We find from the results presented in Table 6.4 that the values of R2 for a few dependent variables (see, for example, equations 4 and 8) are very low, which implies that further development of these equations is needed.
As indicated earlier, during data collection we did not find any female entrepreneur in rural non-farm manufacturing sector in our study area and, consequently, the variable ‘sex’ has not been incorporated into the model. So, no further discussion is presented here.
283 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Goodness of Fit Statistics
Degrees of Freedom = 84 Minimum Fit Function Chi-Square = 167.05504 (P = 0.00000) Normal Theory Weighted Least Squares Chi-Square = 164.43435 (P = 0.00000) Estimated Non-centrality Parameter (NCP) = 80.43435 90 Percent Confidence Interval for NCP = (47.87108 ; 120.79258)
Minimum Fit Function Value = 0.57805 Population Discrepancy Function Value (F0) = 0.27832 90 Percent Confidence Interval for F0 = (0.16564 ; 0.41797) Root Mean Square Error of Approximation (RMSEA) = 0.057562 90 Percent Confidence Interval for RMSEA = (0.044407 ; 0.070539) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.16397
Expected Cross-Validation Index (ECVI) = 0.81811 90 Percent Confidence Interval for ECVI = (0.70544 ; 0.95776) ECVI for Saturated Model = 0.83045 ECVI for Independence Model = 5.39569
Chi-Square for Independence Model with 105 Degrees of Freedom = 1529.35478 Independence AIC = 1559.35478 Model AIC = 236.43435 Saturated AIC = 240.00000 Independence CAIC = 1629.40300 Model CAIC = 404.55007 Saturated CAIC = 800.38571
Normed Fit Index (NFI) = 0.89077 Non-Normed Fit Index (NNFI) = 0.92711 Parsimony Normed Fit Index (PNFI) = 0.71261 Comparative Fit Index (CFI) = 0.94169 Incremental Fit Index (IFI) = 0.94254 Relative Fit Index (RFI) = 0.86346
Critical N (CN) = 203.51118
Root Mean Square Residual (RMR) = 1.51949 Standardized RMR = 0.065117 Goodness of Fit Index (GFI) = 0.92949 Adjusted Goodness of Fit Index (AGFI) = 0.89927 Parsimony Goodness of Fit Index (PGFI) = 0.65064
284 Chapter 6
- -
2 R 0.90966 0.22868 0.88578 0 .07588 0.65739 0.09458 0.14990 0.56638 ------0.01994 (0.0053) 3.75221 EDU -0.00803 (0.0028) -2.83695 ------0.01071 (0.0011) 9.25647 -0.00897 (0.0016) -5.49441 0.38224 (0.0403) 9.46365 AGE ------0.50441 (0.1177) 4.28502 WEALT -0.06190 (0.0056) -10.8792 ------INNOV ------0.29794 (0.0485) 6.14300 RISK 0.06804 (0.0267) 2.54367 ------0.12417 (0.0356) -3.48123 MAREL ------FSUP ------POLIT ------CROP 0.06002 (0.0250) 2.39336 ------CHIL ------al model (beta and gamma results) MARS 0.18676 (0.0473) 3.94576 ------Explanatory variables NFE 6.78127 (1.694) 4.00165 -.33629 (0.061) -5.5055 0.79354 (0.034) 23.2898 NFE MARS CHIL CROP POLIT FSUP MAREL RISK INNOV WEALT Eq. 1 Eq. 2 Eq. 3 Eq. 4 Eq. 5 Eq. 6 Eq. 7 Eq. 8 Eq. 9 Eq. 10 Endo-
genous Table 6.4: The results of the structur variables
285 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
From the results it is found that the sign of the coefficient of the explanatory variable MARS in equation 1 is positive. It depicts that the probability of a farmer to be a non- farm entrepreneur (NFE) is higher if he is married. Marriage may be an incentive for the farmer to go for non-farm entrepreneurship in order to support his wife. In Indian society, like many other societies, marriage is such a tie that the wife usually takes care of home and the husband finds moral obligation to provide his wife with financial security. The housework that the wife provides bears high value to the family. In turn, this may ease the burden of the farmer-husband and stimulate him to devote greater attention into work, e.g. into some risky non-farm venture, for the betterment of the family. In addition, the farmer can find himself in a better social position in his own community if he can offer his wife financial affluence. Therefore, the married farmer has higher likelihood of becoming a non-farm entrepreneur than the unmarried farmer.
In contrast to expectation, the number of children of the farmer (CHIL) does not have an impact on starting up a non-farm enterprise (NFE). This indicates that the farmer who has children does not have an incentive to diversify in non-farm manufacturing activity. This may be because of the fact that children heavily bring the sense of responsibility to the mind of the farmer. This is such a responsibility which may obstruct the farmer to enter into a new risky venture. Thinking of the future of the children, the farmer may not want to gamble in a new business. But it is interesting to observe that the inverse effect holds, i.e. NFE has a positive impact on the farmer’s number of children. This indicates that the number of children of the farmer increases as the farmer has diversified into non-farm entrepreneurship. Let us go into little detail. Once the farmer has started a non-farm manufacturing activity he is likely to have been stimulated in having children through gaining additional income as well as ensuring higher financial security in the family. Perhaps, this farmer is not frightened of losing money for raising children since he has already diversified and has become familiar with his new non-farm business.
We find that the variable CROP has a positive impact on NFE. This indicates that as the farmer, who is engaged in the production of three crops—aman rice, boro rice, and potato—in a year, does have higher probability of being a non-farm manufacturing entrepreneur and the farmer who produces less than three crops in a year is less likely to be a non-farm entrepreneur. The farmer who grows three crops a year is more active than the one who produces less than the major three. Growing three crops a year implies that
286 Chapter 6
the farmer likes engagement and activity in his profession. He does not want to keep himself idle and may further like to have more engagements and may want to diversify into non-farm businesses. Equation 4 shows us that there is a negative impact of NFE on CROP. Non-farm activities are time consuming which depresses his farming activities.
Political position of the farmer (POLIT) is a variable which is not found to have any impact on NFE. Involvement in politics leaves little time for other activity except farming (which is his primary occupation). Doing politics is an activity which demands substantial amount of time from the person concerned. Along with doing politics, the farmer may not find time to run non-farm activity.
The variable FSUP (financial family support) has also been found to have no significant impact on NFE. A possible explanation is that the family considers NFE as risky. Moreover, the traditional farm family may not support the farmer to put a footstep outside agriculture. This may be viewed as a traditional cultural blockage on the road to rural industrialisation. The culture of modern thinking through building industrial unit (since agriculture is considered as traditional sector) is probably absent in the soul of farm family. Farm family as a unit is perhaps not motivated to break the tradition. On the other hand, in equation 6 we find that NFE has a positive impact on FSUP. This implies that the farmer who diversifies into non-farm manufacturing entrepreneurship is likely to support the family financially.
The variable MAREL does also have no impact on NFE. This implies that the farmer who is willing to arrange his daughter’s or sister’s marriage with a non-farm businessman does not increase the possibility of becoming a non-farm entrepreneur.
The positive coefficient of the variable RISK suggests that the farmer’s probability of being a non-farm entrepreneur is higher if the farmer is prepared to take risk for non-farm manufacturing activities. The farmer who is prepared to bear risk may not keep his surplus idle (either at home or in bank) and may look for challenging, non-traditional business projects. The farmer of this category is more likely to be a non-farm entrepreneur. This clearly supports our hypothesis.
287 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
INNOV is another variable which has been found to have no impact on NFE. The fact that whether or not the farmer has an innovative bent of mind does not influence the probability of his being a non-farm entrepreneur.
The coefficient of the variable WEALT (wealth of the farmer) has a negative sign, the implication of which is that there is a negative relationship between wealth of the farmer and the probability of the farmer to be a non-farm entrepreneur. That is, the more the farmer has wealth, the lower the possibility of the farmer to be a non-farm entrepreneur. This simply does not support our hypothesis. A possible explanation is the following. The greater the farmer has wealth, the happier the farmer is. The wealthy farmer does not have an urge to look for an alternative source of income possibly because he is leading a smooth life with his existing properties. On the contrary, the farmer who has relatively little wealth may want to diversify in non-farm activity in search of greater earning.
The variable AGE has no direct impact on NFE, but it has several indirect impacts on NFE. First, we observe that AGE has a positive impact on NFE via MARS. To be explicit, in the second equation AGE has a positive impact on MARS and then in the first equation MARS has a positive impact on NFE. As age of the farmer increases, the probability of the farmer to be married is higher, and as the farmer gets married, the probability of being a non-farm entrepreneur is higher. Similarly, we observe an indirect effect of AGE on NFE via RISK and this effect is negative. Let us take a closer look at it. AGE has a negative effect on RISK and RISK has a positive effect on NFE. As age of the farmer increases, his attitude to risk decreases, and as attitude to risk decreases, the probability of the farmer’s being a non-farm entrepreneur decreases. In the similar way, we observe that AGE has a negative impact on NFE via WEALT. To be more explicit, AGE has a positive impact on WEALT, whereas WEALT has a negative impact on NFE. As age of the farmer increases, his wealth increases, and as wealth of the farmer increases, his probability of being a non-farm entrepreneur decreases.
The variable EDU, educational achievement of the farmer, has a direct, negative impact on NFE. This indicates that the more the farmer is educated, the lower the probability of the farmer’s being a non-farm entrepreneur. With increase in educational achievement, the farmer may prefer more secure job in government for side income. Additionally, if some one has achieved higher education and then starts a non-farm business, he may not
288 Chapter 6
be able to enjoy social dignity as, for instance, a school teacher (employed in a government aided school) or other government employee enjoys. So, achieving higher education may disqualify the farmer to be a non-farm entrepreneur. On the other hand, the farmer with relatively less education may weigh his social position in terms of money and, accordingly, he may drive for non-farm entrepreneurship in search of a better income.
As regards the other three exogenous variables, i.e. age squared (AGE2), farmer’s involvement in agriculture (AGRI), and farmer’s faith in fate or work-effort (FATE) have been found to be insignificant and consequently have been removed from the structural model.
6.7 Conclusions
This chapter is the most crucial chapter of this book since it contains theoretical considerations and empirical findings of our model. Our objective was to identify the determinants of non-farm entrepreneurship among the farmers. In the theoretical considerations, we have formulated the following hypotheses:
1. The combined impact of age and age2 of the farmer on his entry into non-farm manufacturing entrepreneurship follows an inverted U-curve.
2. The probability of being a non-farm entrepreneur among married farmers is higher than that among unmarried farmers.
3. The probability of being a non-farm entrepreneur among farmers who have children is higher than that among farmers who do not have children.
4. The probability of being a non-farm entrepreneur among farmers increases with their educational achievements.
5. The farmer has a higher likelihood to become a non-farm entrepreneur if he is occupying a position in the local government body i.e. panchayat.
289 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
6. The farmer has a higher probability of becoming a non-farm entrepreneur if he enjoys financial support from his family.
7. The farmer has a higher likelihood to become a non-farm entrepreneur if he believes in work-effort, not in fate.
8. The farmer has a higher likelihood to become a non-farm entrepreneur if he is willing to arrange his daughter’s or sister’s marriage with a non-farm businessman.
9. The farmer has a higher probability of becoming a non-farm entrepreneur if he is an independent (wealthier) farmer, not a sharecropper.
10. The farmer has a higher likelihood to be a non-farm entrepreneur if he produces three crops a year such as aman rice, boro rice, and potato, than the other who produces less than these three.
11. The farmer’s probability of being a non-farm entrepreneur increases with the increase in his wealth.
12. The likelihood of the farmer’s being a non-farm entrepreneur is higher if he has an innovative bent of mind with regard to non-farm business.
13. The farmer’s likelihood of being a non-farm entrepreneur is higher if he is a risk loving person so far as diversification is concerned.
The LISREL approach has been applied to estimate the model including the above mentioned hypotheses. The LISREL approach makes it possible to deal with simultaneity bias. Another important feature is that it allows the simultaneous estimation of a latent variables measurement model and a structural model. More specifically, a LISREL model is made up of two related submodels:
i) a latent variables measurement model, which represents the relationships between the latent variables and their observable indicators. ii) a structural model, representing the relationships between the latent variables.
290 Chapter 6
The LISREL programmme can provide any of the following seven estimators:
Instrumental variables (IV) Two-stage least squares (TSLS) Unweighted least squares (ULS) Generalized least squares (GLS) Maximum likelihood (ML) Generally weighted least squares (WLS) Diagonally weighted least squares (DWLS)
In our case, the full information maximum-likelihood-estimator has been used.
Let us now turn to the results of the model. The measurement model is made up of one endogenous variable, i.e. wealth of the farmer (WEALT), and four exogenous variables, i.e. farmer’s farm size (Landc), farmer’s homestead land (Landh), farmer’s access to irrigation (Irri), and the harvesting method farmer uses (Harvest). These four indicators indicate the wealth of the farmer. Each of the indicators was found to be significant. The most important indicators turned out to be farmer’s farm size (Landc) followed by farmer’s homestead land (Landh), the harvesting method farmer uses (Harvest), and farmer’s access to irrigation (Irri). We observe that the overall fit of the measurement model is rather low which means that more indicators would be desirable. The structural model consists of 10 equations, amongst which the first equation is the most important one. The first equation is formulated to examine the influence of various factors, as indicated in the above mentioned hypotheses, on farmer’s non-farm manufacturing entrepreneurship. To control for simultaneity bias (that is, interdependencies between dependent and explanatory variables), we have formulated a simultaneous equations system of 10 equations. During estimation, we had to remove three exogenous variables (AGE2, AGRI, and FATE) from our structural model since they were found to be highly insignificant.
A farmer’s marital status (MARS), types of crops produced by a farmer in a year (CROP), a farmer’s risk taking propensity (RISK), a farmer’s wealth (WEALT) and a farmer’s education (EDU) have been found to have direct impacts on a farmer’s non-farm entrepreneurship (NFE). But, among these variables, WEALT and EDU have been found to have negative impacts on NFE, whereas the other variables (MARS, CROP and RISK) have been found to have positive impacts on NFE. The positive sign of the coefficient of
291 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
the variable MARS suggests that the probability of a farmer to be a non-farm entrepreneur (NFE) is higher if he is married. The positive sign of the variable CROP suggests that the farmer, who is engaged in the production of three crops (aman rice, boro rice, and potato) in a year, does have higher probability of being a non-farm manufacturing entrepreneur and the farmer, who produces less than three crops in a year, is less likely to be a non-farm entrepreneur. The positive coefficient of the variable RISK indicates that the probability of being a non-farm entrepreneur is higher if the farmer is prepared to take risk. The negative sign of the coefficient of the variable WEALT depicts that more the farmer has wealth the lower the possibility to be a non-farm entrepreneur. Similarly, the negative sign of the coefficient of the variable EDU indicates that the more the farmer is educated the lower the probability of the farmer’s being a non-farm entrepreneur. Age of a farmer (AGE) has been found to have indirect effect on non-farm entrepreneurship via marital status, risk attitude and wealth. The explanatory variables political affiliation (POLIT), financial family support (FSUP), marriage relation (MAREL), and innovativeness (INNOV) have been found to have insignificant impacts on NFE. The variable -- number of children (CHIL) -- has also been found to have an insignificant effect on NFE but interestingly NFE has been found to have a positive impact on the number of children.
The results we have obtained in the present study can hardly be compared with those of other studies because the present study manifests itself in the field of rural industrialisation as a very different kind. Several studies pertaining to rural industrialisation have been carried out in order to examine the linkage between agriculture and non-agriculture among other issues (see, for example, Chandrasekhar 1993, Eapen 1999, among others). Some are non-empirical, qualitative, policy oriented, studies (for example, Saith 1992). Unlike them, our emphasis was on non-farm entrepreneurship of farmer, which does not resemble with other studies. Yet, at least from one point of view, we can relate our result with Mellor’s (1976) thesis. Mellor argued that additional income of farmers caused by increased agricultural based on cost decreasing technology can generate demand for rural non-agricultural goods and consequently promote rural industries. If we have good faith in Mellor’s argument then we can imagine that the wealthy farmers will come forward with their investible surplus to produce and supply the non-farm goods in order to meet the new demand. During 1980s and 1990s West Bengal experienced high growth in agricultural production. But our result shows
292 Chapter 6 that wealthy farmers are not likely to be non-farm entrepreneurs. This simply contrasts with Mellor’s arguments. Entrepreneurship is such an issue which is often tied with cultural tradition. For example, orthodox, rich, farmers of West Bengal might not have liked to break their professional tradition by investing their money out of agriculture and thus might have inhibited growth in rural industrial sector.
To summarize the results of this study we may state that farmers who are married, engaged in producing three crops a year, and risk takers have a relatively high probability to become non-farm entrepreneurs. The reverse holds for wealthy farmers and those who have higher levels of education. Finally, age has indirect positive impact on farmer’s non- farm entrepreneurship via marriage, and indirect negative impact on farmer’s non-farm entrepreneurship via risk attitude and wealth.
293 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
APPENDIX 6.1: The Full Results
DATE: 3/18/2004 TIME: 10:21
L I S R E L 8.54
BY
Karl G. Jöreskog & Dag Sörbom
This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2002 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com
The following lines were read from file C:\han3\lisrel850\Subroto\LISRELL13.lpj:
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294 Chapter 6
DA NI=23 NO=290
Number of Input Variables 23 Number of Y - Variables 13 Number of X - Variables 2 Number of ETA - Variables 10 Number of KSI - Variables 2 Number of Observations 290
DA NI=23 NO=290
Covariance Matrix
NFE MARS CHIL CROP POLIT FSUP ------NFE 0.24399 MARS -0.04881 0.12181 CHIL -0.20367 0.23852 2.20546 CROP -0.06867 0.01617 0.17838 0.24609 POLIT -0.00518 -0.00399 0.00704 0.00143 0.02033 FSUP 0.18952 -0.02763 -0.13936 -0.06336 -0.00625 0.23272 MAREL 0.03297 -0.01285 -0.03711 -0.02142 -0.00344 -0.00157 RISK 0.06904 -0.02725 -0.01205 -0.00966 -0.00437 0.04255 INNOV 0.01742 -0.01563 -0.00919 -0.00298 0.00262 0.00668 Landc -3.77819 0.95311 5.73887 2.23327 0.00441 -3.07857 Landh -1.09535 0.35788 2.08806 0.50823 0.09857 -0.78405 Irri -0.03007 0.02195 0.04534 0.01814 -0.00406 -0.02983 Harvest -0.05721 0.01736 0.09569 0.04624 0.00084 -0.04820 AGE -5.28591 2.60074 11.07171 1.32765 0.00205 -3.70292 EDU -0.30500 0.09471 -0.60709 -0.09128 -0.00964 -0.23222
Covariance Matrix
MAREL RISK INNOV Landc Landh Irri ------MAREL 0.22337 RISK 0.00313 0.20647 INNOV 0.01408 0.06014 0.16466 Landc -0.84184 -1.35592 -0.13751 203.84525 Landh -0.51941 -0.03912 0.02547 30.32296 42.76720 Irri 0.01945 -0.01587 -0.03019 1.02189 0.28117 0.11932 Harvest -0.02273 -0.03245 0.01181 2.15481 0.53335 0.02327 AGE -0.91784 -2.17752 -0.63692 101.10482 29.05367 0.87675 EDU -0.22938 -0.06918 0.32228 8.74019 4.78747 -0.04761
Covariance Matrix
Harvest AGE EDU ------Harvest 0.20791 AGE 1.00716 242.81680 EDU 0.21406 3.98647 17.19384
DA NI=23 NO=290
Parameter Specifications
LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE 0 0 0 0 0 0 MARS 0 0 0 0 0 0 CHIL 0 0 0 0 0 0 CROP 0 0 0 0 0 0 POLIT 0 0 0 0 0 0
295 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
FSUP 0 0 0 0 0 0 MAREL 0 0 0 0 0 0 RISK 0 0 0 0 0 0 INNOV 0 0 0 0 0 0 Landc 0 0 0 0 0 0 Landh 0 0 0 0 0 0 Irri 0 0 0 0 0 0 Harvest 0 0 0 0 0 0
LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE 0 0 0 0 MARS 0 0 0 0 CHIL 0 0 0 0 CROP 0 0 0 0 POLIT 0 0 0 0 FSUP 0 0 0 0 MAREL 0 0 0 0 RISK 0 0 0 0 INNOV 0 0 0 0 Landc 0 0 0 0 Landh 0 0 0 1 Irri 0 0 0 2 Harvest 0 0 0 3
BETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE 0 4 0 5 0 0 2-MARS 0 0 0 0 0 0 3-CHIL 9 0 0 0 0 0 4-CROP 11 0 0 0 0 0 5-POLIT 0 0 0 0 0 0 6-FSUP 12 0 0 0 0 0 7-MAREL 0 0 0 0 0 0 8-RISK 0 0 0 0 0 0 9-INNOV 0 0 0 0 0 0 10-WEALT 0 0 0 0 0 0
BETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE 0 6 7 8 2-MARS 0 0 0 0 3-CHIL 0 0 0 10 4-CROP 0 0 0 0 5-POLIT 0 0 0 0 6-FSUP 13 0 0 0 7-MAREL 0 0 0 0 8-RISK 0 0 0 0 9-INNOV 0 14 0 0 10-WEALT 0 0 0 0
GAMMA
1-AGE 2-EDU
296 Chapter 6
------1-NFE 0 15 2-MARS 16 0 3-CHIL 0 0 4-CROP 0 0 5-POLIT 0 0 6-FSUP 0 0 7-MAREL 0 0 8-RISK 17 0 9-INNOV 0 18 10-WEALT 19 0
PHI
1-AGE 2-EDU ------1-AGE 20 2-EDU 21 22
PSI
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------23 24 25 26 27 28
PSI
7-MAREL 8-RISK 9-INNOV 10-WEALT ------29 30 31 32
THETA-EPS
NFE MARS CHIL CROP POLIT FSUP ------0 0 0 0 0 0
THETA-EPS
MAREL RISK INNOV Landc Landh Irri ------0 0 0 33 34 35
THETA-EPS
Harvest ------36
DA NI=23 NO=290
Number of Iterations = 27
LISREL Estimates (Maximum Likelihood)
LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE 1.00000 ------
MARS - - 1.00000 ------
CHIL - - - - 1.00000 ------
CROP ------1.00000 - - - -
POLIT ------1.00000 - -
297 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
FSUP ------1.00000
MAREL ------
RISK ------
INNOV ------
Landc ------
Landh ------
Irri ------
Harvest ------
LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE ------
MARS ------
CHIL ------
CROP ------
POLIT ------
FSUP ------
MAREL 1.00000 ------
RISK - - 1.00000 - - - -
INNOV - - - - 1.00000 - -
Landc ------1.00000
Landh ------0.30224 (0.05287) 5.71667
Irri ------0.00812 (0.00264) 3.07509
Harvest ------0.01539 (0.00356) 4.32247
LAMBDA-X
1-AGE 2-EDU ------AGE 1.00000 - -
EDU - - 1.00000
BETA
298 Chapter 6
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - 0.18676 - - 0.06002 - - - - (0.04733) (0.02508) 3.94576 2.39336
2-MARS ------
3-CHIL 6.78127 ------(1.69462) 4.00165
4-CROP -0.33629 ------(0.06108) -5.50550
5-POLIT ------
6-FSUP 0.79354 ------(0.03407) 23.28980
7-MAREL ------
8-RISK ------
9-INNOV ------
10-WEALT ------
BETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE - - 0.06804 0.02104 -0.06190 (0.02675) (0.02553) (0.00569) 2.54367 0.82410 -10.87925
2-MARS ------
3-CHIL ------0.50441 (0.11771) 4.28502
4-CROP ------
5-POLIT ------
6-FSUP -0.12417 ------(0.03567) -3.48123
7-MAREL ------
8-RISK ------
9-INNOV - - 0.29794 - - - - (0.04850) 6.14300
10-WEALT ------
GAMMA
1-AGE 2-EDU ------1-NFE - - -0.00803
299 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
(0.00283) -2.83695
2-MARS 0.01071 - - (0.00116) 9.25647
3-CHIL - - - -
4-CROP - - - -
5-POLIT - - - -
6-FSUP - - - -
7-MAREL - - - -
8-RISK -0.00897 - - (0.00163) -5.49441
9-INNOV - - 0.01994 (0.00531) 3.75221
10-WEALT 0.38224 - - (0.04039) 9.46365
Covariance Matrix of ETA and KSI
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE 0.24478 2-MARS -0.04003 0.12181 3-CHIL -0.20162 0.22995 2.19691 4-CROP -0.06893 0.01346 0.15856 0.24618 5-POLIT ------0.02033 6-FSUP 0.19425 -0.03177 -0.16000 -0.05470 - - 0.23971 7-MAREL ------0.02774 8-RISK 0.06154 -0.02332 -0.00253 -0.02069 - - 0.04883 9-INNOV 0.01693 -0.00610 0.00504 -0.00569 - - 0.01343 10-WEALT -3.69060 0.99410 6.56858 1.24110 - - -2.92863 1-AGE -5.34370 2.60074 10.57899 1.79701 - - -4.24044 2-EDU -0.21559 0.04270 -0.69335 0.07250 - - -0.17108
Covariance Matrix of ETA and KSI
7-MAREL 8-RISK 9-INNOV 10-WEALT 1-AGE 2-EDU ------7-MAREL 0.22337 8-RISK - - 0.20647 9-INNOV - - 0.06080 0.16506 10-WEALT - - -0.83233 -0.21760 62.63881 1-AGE - - -2.17752 -0.56928 92.81395 242.81680 2-EDU - - -0.03575 0.33224 1.52378 3.98647 17.19384
PHI
1-AGE 2-EDU ------1-AGE 242.81680 (20.19969) 12.02082
300 Chapter 6
2-EDU 3.98647 17.19384 (3.80804) (1.43034) 1.04685 12.02082
PSI Note: This matrix is diagonal.
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------0.02211 0.09396 0.25093 0.22750 0.02033 0.08213 (0.00631) (0.00782) (0.36215) (0.01900) (0.00169) (0.00683) 3.50593 12.02082 0.69289 11.97620 12.02082 12.02082
PSI Note: This matrix is diagonal.
7-MAREL 8-RISK 9-INNOV 10-WEALT ------0.22337 0.18694 0.14031 27.16174 (0.01858) (0.01555) (0.01167) (5.37150) 12.02082 12.02082 12.02082 5.05664
Squared Multiple Correlations for Structural Equations
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------0.90966 0.22868 0.88578 0.07588 - - 0.65739
Squared Multiple Correlations for Structural Equations
7-MAREL 8-RISK 9-INNOV 10-WEALT ------0.09458 0.14990 0.56638
Squared Multiple Correlations for Reduced Form
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------0.48432 0.22868 0.22977 0.05446 - - 0.31143
Squared Multiple Correlations for Reduced Form
7-MAREL 8-RISK 9-INNOV 10-WEALT ------0.09458 0.04936 0.56638
Reduced Form
1-AGE 2-EDU ------1-NFE -0.02188 -0.00746 (0.00133) (0.00267) -16.50978 -2.79896
2-MARS 0.01071 - - (0.00116) 9.25647
3-CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201
4-CROP 0.00736 0.00251 (0.00138) (0.00104)
301 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
5.32880 2.40335
5-POLIT - - - -
6-FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896
7-MAREL - - - -
8-RISK -0.00897 - - (0.00163) -5.49441
9-INNOV -0.00267 0.01994 (0.00065) (0.00531) -4.09531 3.75221
10-WEALT 0.38224 - - (0.04039) 9.46365
THETA-EPS
NFE MARS CHIL CROP POLIT FSUP ------
THETA-EPS
MAREL RISK INNOV Landc Landh Irri ------141.20643 37.04507 0.11519 (11.87944) (3.09001) (0.00959) 11.88663 11.98867 12.01476
THETA-EPS
Harvest ------0.19307 (0.01608) 12.00678
Squared Multiple Correlations for Y - Variables
NFE MARS CHIL CROP POLIT FSUP ------1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
Squared Multiple Correlations for Y - Variables
MAREL RISK INNOV Landc Landh Irri ------1.00000 1.00000 1.00000 0.30729 0.13380 0.03459
Squared Multiple Correlations for Y - Variables
Harvest ------0.07136
BE was written to file C:\han3\lisrel850\Subroto\BE1
302 Chapter 6
GA was written to file C:\han3\lisrel850\Subroto\GA1
PS was written to file C:\han3\lisrel850\Subroto\PS1
TE was written to file C:\han3\lisrel850\Subroto\TE1
TD was written to file C:\han3\lisrel850\Subroto\TD1
Goodness of Fit Statistics
Degrees of Freedom = 84 Minimum Fit Function Chi-Square = 167.05504 (P = 0.00000) Normal Theory Weighted Least Squares Chi-Square = 164.43435 (P = 0.00000) Estimated Non-centrality Parameter (NCP) = 80.43435 90 Percent Confidence Interval for NCP = (47.87108 ; 120.79258)
Minimum Fit Function Value = 0.57805 Population Discrepancy Function Value (F0) = 0.27832 90 Percent Confidence Interval for F0 = (0.16564 ; 0.41797) Root Mean Square Error of Approximation (RMSEA) = 0.057562 90 Percent Confidence Interval for RMSEA = (0.044407 ; 0.070539) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.16397
Expected Cross-Validation Index (ECVI) = 0.81811 90 Percent Confidence Interval for ECVI = (0.70544 ; 0.95776) ECVI for Saturated Model = 0.83045 ECVI for Independence Model = 5.39569
Chi-Square for Independence Model with 105 Degrees of Freedom = 1529.35478 Independence AIC = 1559.35478 Model AIC = 236.43435 Saturated AIC = 240.00000 Independence CAIC = 1629.40300 Model CAIC = 404.55007 Saturated CAIC = 800.38571
Normed Fit Index (NFI) = 0.89077 Non-Normed Fit Index (NNFI) = 0.92711 Parsimony Normed Fit Index (PNFI) = 0.71261 Comparative Fit Index (CFI) = 0.94169 Incremental Fit Index (IFI) = 0.94254 Relative Fit Index (RFI) = 0.86346
Critical N (CN) = 203.51118
Root Mean Square Residual (RMR) = 1.51949 Standardized RMR = 0.065117 Goodness of Fit Index (GFI) = 0.92949 Adjusted Goodness of Fit Index (AGFI) = 0.89927 Parsimony Goodness of Fit Index (PGFI) = 0.65064
DA NI=23 NO=290
Fitted Covariance Matrix
303 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
NFE MARS CHIL CROP POLIT FSUP ------NFE 0.24478 MARS -0.04003 0.12181 CHIL -0.20162 0.22995 2.19691 CROP -0.06893 0.01346 0.15856 0.24618 POLIT ------0.02033 FSUP 0.19425 -0.03177 -0.16000 -0.05470 - - 0.23971 MAREL ------0.02774 RISK 0.06154 -0.02332 -0.00253 -0.02069 - - 0.04883 INNOV 0.01693 -0.00610 0.00504 -0.00569 - - 0.01343 Landc -3.69060 0.99410 6.56858 1.24110 - - -2.92863 Landh -1.11546 0.30046 1.98531 0.37511 - - -0.88516 Irri -0.02996 0.00807 0.05332 0.01007 - - -0.02377 Harvest -0.05680 0.01530 0.10109 0.01910 - - -0.04507 AGE -5.34370 2.60074 10.57899 1.79701 - - -4.24044 EDU -0.21559 0.04270 -0.69335 0.07250 - - -0.17108
Fitted Covariance Matrix
MAREL RISK INNOV Landc Landh Irri ------MAREL 0.22337 RISK - - 0.20647 INNOV - - 0.06080 0.16506 Landc - - -0.83233 -0.21760 203.84525 Landh - - -0.25157 -0.06577 18.93219 42.76720 Irri - - -0.00676 -0.00177 0.50843 0.15367 0.11932 Harvest - - -0.01281 -0.00335 0.96403 0.29137 0.00782 AGE - - -2.17752 -0.56928 92.81395 28.05243 0.75336 EDU - - -0.03575 0.33224 1.52378 0.46055 0.01237
Fitted Covariance Matrix
Harvest AGE EDU ------Harvest 0.20791 AGE 1.42844 242.81680 EDU 0.02345 3.98647 17.19384
Fitted Residuals
NFE MARS CHIL CROP POLIT FSUP ------NFE -0.00079 MARS -0.00878 - - CHIL -0.00205 0.00857 0.00856 CROP 0.00027 0.00270 0.01982 -0.00009 POLIT -0.00518 -0.00399 0.00704 0.00143 - - FSUP -0.00472 0.00414 0.02063 -0.00866 -0.00625 -0.00699 MAREL 0.03297 -0.01285 -0.03711 -0.02142 -0.00344 0.02616 RISK 0.00750 -0.00393 -0.00952 0.01103 -0.00437 -0.00628 INNOV 0.00049 -0.00953 -0.01423 0.00271 0.00262 -0.00675 Landc -0.08759 -0.04099 -0.82971 0.99217 0.00441 -0.14994 Landh 0.02011 0.05742 0.10274 0.13312 0.09857 0.10111 Irri -0.00011 0.01389 -0.00798 0.00806 -0.00406 -0.00606 Harvest -0.00041 0.00206 -0.00540 0.02713 0.00084 -0.00313 AGE 0.05780 0.00000 0.49272 -0.46937 0.00205 0.53751 EDU -0.08941 0.05202 0.08626 -0.16378 -0.00964 -0.06114
Fitted Residuals
MAREL RISK INNOV Landc Landh Irri ------MAREL - - RISK 0.00313 - - INNOV 0.01408 -0.00067 -0.00040
304 Chapter 6
Landc -0.84184 -0.52359 0.08009 0.00000 Landh -0.51941 0.21244 0.09124 11.39078 0.00000 Irri 0.01945 -0.00911 -0.02842 0.51346 0.12750 0.00000 Harvest -0.02273 -0.01964 0.01516 1.19078 0.24198 0.01544 AGE -0.91784 0.00000 -0.06764 8.29087 1.00124 0.12338 EDU -0.22938 -0.03343 -0.00996 7.21640 4.32692 -0.05998
Fitted Residuals
Harvest AGE EDU ------Harvest 0.00000 AGE -0.42128 0.00000 EDU 0.19060 0.00000 - -
Summary Statistics for Fitted Residuals
Smallest Fitted Residual = -0.91784 Median Fitted Residual = 0.00000 Largest Fitted Residual = 11.39078
Stemleaf Plot
- 0|988555422111111000000000000000000000000000000000000000000000000000000000+23 0|111111111111222555 1|002 2| 3| 4|3 5| 6| 7|2 8|3 9| 10| 11|4
Standardized Residuals
NFE MARS CHIL CROP POLIT FSUP ------NFE -0.45277 MARS -1.91454 - - CHIL -0.40406 0.81623 0.31542 CROP 0.45277 0.28539 1.16083 -0.45276 POLIT -1.24786 -1.36135 0.56626 0.34405 - - FSUP -2.14628 0.60036 0.82900 -1.05290 -1.52248 -2.39112 MAREL 2.39677 -1.32437 -0.90052 -1.55265 -0.86685 2.39677 RISK 0.97816 -0.50403 -0.52505 0.90701 -1.14583 -0.64898 INNOV 0.04812 -1.17856 -0.61329 0.23289 0.77032 -0.63206 Landc -0.85792 -0.16746 -1.05764 3.03720 0.03687 -0.66323 Landh 0.36726 0.47988 0.24120 0.79221 1.79698 0.88319 Irri -0.03597 2.12434 -0.32842 0.85895 -1.40021 -0.95344
305 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Harvest -0.10328 0.24181 -0.17306 2.23426 0.21839 -0.38007 AGE 1.12400 - - 1.20558 -1.64154 0.01570 2.67388 EDU -1.21669 0.69706 0.49501 -1.41478 -0.27720 -0.66559
Standardized Residuals
MAREL RISK INNOV Landc Landh Irri ------MAREL - - RISK 0.24746 - - INNOV 1.24653 -0.31760 -0.31760 Landc -2.12087 -1.56070 0.24020 - - Landh -2.85686 1.30267 0.59042 2.69830 - - Irri 2.02522 -1.02559 -3.45882 2.17307 1.05090 - - Harvest -1.79305 -1.69379 1.40121 3.89767 1.54148 1.76184 AGE -2.11865 - - -0.20738 1.23214 0.28001 0.61006 EDU -1.98973 -0.31760 -0.31760 2.28305 2.82492 -0.71973
Standardized Residuals
Harvest AGE EDU ------Harvest - - AGE -1.61678 - - EDU 1.75149 - - - -
Summary Statistics for Standardized Residuals
Smallest Standardized Residual = -3.45882 Median Standardized Residual = 0.00000 Largest Standardized Residual = 3.89767
Stemleaf Plot - 3|5 - 3| - 2|9 - 2|41110 - 1|98766665 - 1|444322211100 - 0|9997776665555 - 0|44333333222100000000000000000 0|22222233334 0|555666678888999 1|011222234 1|5888 2|0122344 2|778 3|0 3|9 Largest Negative Standardized Residuals Residual for Landh and MAREL -2.85686 Residual for Irri and INNOV -3.45882 Largest Positive Standardized Residuals Residual for Landc and CROP 3.03720 Residual for Landh and Landc 2.69830 Residual for Harvest and Landc 3.89767 Residual for AGE and FSUP 2.67388 Residual for EDU and Landh 2.82492
DA NI=23 NO=290 Qplot of Standardized Residuals
3.5......
306 Chapter 6
...... x . . x . . . x . . . x . . . x . . . * . . . x* . N . . * xx . o . . xx x x . r . . x* . m . .*** . a . .*xx . l . x**x . . ** . Q . x * . u . *xx . a . *. . n . x*x . t . xx*. . i . x*x . . l . **x . . e . xx . . s . xx . . . xxx . . . x x . . . x . . . x . . . x . . . x . . x ...... -3.5...... -3.5 3.5 Standardized Residuals
DA NI=23 NO=290
Modification Indices and Expected Change
Modification Indices for LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE - - 5.64299 0.48291 2.05724 0.15489 0.18601 MARS 4.02985 - - 0.11346 0.83024 2.63414 0.16798 CHIL - - 3.49897 - - 0.25982 1.69311 0.09522 CROP - - 0.00068 1.24485 - - 0.02473 1.78070 POLIT 1.55715 1.85327 0.32065 0.11837 - - 2.31796 FSUP - - 2.75837 0.54001 2.06182 1.14287 - - MAREL 5.74451 0.94943 0.54996 3.24149 1.13144 5.74451 RISK 0.84979 0.00056 0.00031 0.19962 2.61183 0.00621 INNOV 0.00002 1.52814 0.34176 0.06188 1.65529 0.14491 Landc 0.95147 0.64576 1.34855 8.58982 0.56300 1.33957 Landh 0.11342 0.02521 0.04503 0.52502 2.14509 0.38631 Irri 0.00284 3.94102 0.11850 0.68269 2.72470 1.18199 Harvest 0.01680 0.00005 0.03849 4.78432 0.01066 0.33012
Modification Indices for LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE 0.05424 2.34439 0.56376 0.38113 MARS 0.26414 0.25405 1.45781 3.07968
307 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
CHIL 0.12673 0.08634 0.05479 - - CROP 0.74158 1.23747 0.06416 1.38292 POLIT 0.75142 1.31294 0.59340 1.43427 FSUP - - 2.58579 0.62467 0.59161 MAREL - - 0.00462 1.13071 5.42100 RISK 1.06625 - - 0.12508 0.58588 INNOV 2.80034 - - - - 0.02472 Landc 0.94897 1.74801 0.16241 - - Landh 4.58486 2.47322 0.48969 - - Irri 6.29955 0.85525 12.10714 - - Harvest 1.45193 2.44190 2.24286 - -
Expected Change for LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE - - -0.12303 -0.01029 0.04954 -0.03814 0.03394 MARS -0.13942 - - 0.01012 0.03491 -0.19329 -0.01983 CHIL - - 1.23469 - - -0.18862 0.63514 0.07342 CROP - - -0.00217 0.05051 - - -0.03046 -0.12600 POLIT -0.02115 -0.03272 0.00320 0.00582 - - -0.02608 FSUP - - 0.08247 0.00869 -0.05083 -0.12639 - - MAREL 0.13468 -0.07612 -0.01365 -0.09899 -0.20318 0.16972 RISK 0.06869 -0.00183 0.00055 0.02245 -0.26858 -0.00484 INNOV 0.00020 -0.07893 -0.01271 0.01117 0.19858 -0.01827 Landc -4.93723 -1.84391 -0.75368 4.39897 -3.68888 -2.59642 Landh 0.80200 0.17859 0.06465 0.55340 3.68118 0.70499 Irri -0.00686 0.12244 -0.00567 0.03510 -0.23117 -0.06841 Harvest -0.02183 0.00056 -0.00423 0.12039 -0.01873 -0.04689
Expected Change for LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE 0.00897 0.05240 0.02698 -0.00523 MARS -0.01847 -0.02102 -0.05380 0.00769 CHIL 0.05242 -0.09978 -0.08062 - - CROP -0.05033 0.07188 0.01756 0.02702 POLIT -0.01538 -0.02115 0.01590 0.00128 FSUP - - -0.06203 -0.03291 0.00521 MAREL - - -0.00408 0.07130 -0.00821 RISK -0.05177 - - -0.06354 -0.00352 INNOV 0.07792 - - - - -0.00045 Landc -1.44489 -2.10684 0.69774 - - Landh -1.62367 1.27746 0.61896 - - Irri 0.10605 -0.04181 -0.17144 - - Harvest -0.06593 -0.09152 0.09557 - -
Standardized Expected Change for LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE - - -0.04294 -0.01525 0.02458 -0.00544 0.01662 MARS -0.06898 - - 0.01501 0.01732 -0.02756 -0.00971 CHIL - - 0.43092 - - -0.09359 0.09056 0.03595 CROP - - -0.00076 0.07487 - - -0.00434 -0.06169 POLIT -0.01047 -0.01142 0.00475 0.00289 - - -0.01277 FSUP - - 0.02878 0.01288 -0.02522 -0.01802 - - MAREL 0.06663 -0.02657 -0.02023 -0.04911 -0.02897 0.08310 RISK 0.03399 -0.00064 0.00082 0.01114 -0.03830 -0.00237 INNOV 0.00010 -0.02755 -0.01884 0.00554 0.02831 -0.00894 Landc -2.44273 -0.64355 -1.11711 2.18263 -0.52599 -1.27121 Landh 0.39679 0.06233 0.09583 0.27458 0.52490 0.34517 Irri -0.00340 0.04273 -0.00840 0.01741 -0.03296 -0.03349
308 Chapter 6
Harvest -0.01080 0.00020 -0.00626 0.05974 -0.00267 -0.02296
Standardized Expected Change for LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE 0.00424 0.02381 0.01096 -0.04141 MARS -0.00873 -0.00955 -0.02186 0.06090 CHIL 0.02478 -0.04534 -0.03276 - - CROP -0.02379 0.03266 0.00714 0.21386 POLIT -0.00727 -0.00961 0.00646 0.01011 FSUP - - -0.02818 -0.01337 0.04126 MAREL - - -0.00185 0.02897 -0.06502 RISK -0.02447 - - -0.02582 -0.02784 INNOV 0.03683 - - - - -0.00360 Landc -0.68289 -0.95732 0.28347 - - Landh -0.76739 0.58046 0.25147 - - Irri 0.05012 -0.01900 -0.06965 - - Harvest -0.03116 -0.04159 0.03883 - -
Completely Standardized Expected Change for LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE - - -0.08679 -0.03083 0.04968 -0.01099 0.03359 MARS -0.19764 - - 0.04299 0.04963 -0.07897 -0.02781 CHIL - - 0.29073 - - -0.06314 0.06110 0.02425 CROP - - -0.00152 0.15090 - - -0.00875 -0.12433 POLIT -0.07340 -0.08008 0.03331 0.02024 - - -0.08956 FSUP - - 0.05879 0.02632 -0.05151 -0.03681 - - MAREL 0.14099 -0.05621 -0.04280 -0.10392 -0.06130 0.17582 RISK 0.07480 -0.00141 0.00180 0.02451 -0.08428 -0.00521 INNOV 0.00024 -0.06781 -0.04637 0.01364 0.06969 -0.02202 Landc -0.17109 -0.04507 -0.07824 0.15287 -0.03684 -0.08904 Landh 0.06067 0.00953 0.01465 0.04199 0.08026 0.05278 Irri -0.00983 0.12371 -0.02431 0.05041 -0.09543 -0.09696 Harvest -0.02369 0.00043 -0.01373 0.13101 -0.00586 -0.05035
Completely Standardized Expected Change for LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE 0.00857 0.04813 0.02215 -0.08370 MARS -0.02501 -0.02737 -0.06263 0.17449 CHIL 0.01672 -0.03059 -0.02210 - - CROP -0.04794 0.06583 0.01438 0.43102 POLIT -0.05099 -0.06740 0.04531 0.07091 FSUP - - -0.05756 -0.02731 0.08427 MAREL - - -0.00392 0.06129 -0.13756 RISK -0.05385 - - -0.05682 -0.06127 INNOV 0.09065 - - - - -0.00886 Landc -0.04783 -0.06705 0.01985 - - Landh -0.11734 0.08876 0.03845 - - Irri 0.14510 -0.05500 -0.20164 - - Harvest -0.06834 -0.09120 0.08515 - -
Modification Indices for LAMBDA-X
1-AGE 2-EDU ------AGE - - 1.99757 EDU 0.69261 - -
309 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Expected Change for LAMBDA-X
1-AGE 2-EDU ------AGE - - -0.25346 EDU 0.05794 - -
Standardized Expected Change for LAMBDA-X
1-AGE 2-EDU ------AGE - - -1.05100 EDU 0.90283 - -
Completely Standardized Expected Change for LAMBDA-X
1-AGE 2-EDU ------AGE - - -0.06745 EDU 0.21773 - -
Modification Indices for BETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - - - 8.29762 - - 0.21284 0.40065 2-MARS 4.02985 - - 0.09294 0.83024 2.42930 0.06373 3-CHIL - - 3.49897 - - 0.25982 1.69311 0.09522 4-CROP - - 0.00068 1.21017 - - 0.00599 1.56271 5-POLIT 1.55715 1.85327 0.32065 0.11837 - - 2.31796 6-FSUP - - 2.75837 0.54001 2.06182 1.14287 - - 7-MAREL 5.74451 1.75395 0.81093 2.41072 0.75142 5.74451 8-RISK 1.09645 0.25405 0.07541 0.31642 1.43789 0.02327 9-INNOV 0.00002 1.52814 0.34488 0.06188 1.71821 0.13107 10-WEALT 0.52543 3.07968 0.73604 4.34347 3.29866 2.16132
Modification Indices for BETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE 0.28735 ------2-MARS 0.12557 0.25405 1.45781 3.07968 3-CHIL 0.12673 0.08634 0.05479 - - 4-CROP 0.60697 1.23747 0.06416 1.38292 5-POLIT 0.75142 1.31294 0.59340 1.43427 6-FSUP - - 2.58579 0.62467 0.59161 7-MAREL - - 0.06124 1.55384 5.67872 8-RISK 0.18035 - - 0.12508 0.92873 9-INNOV 2.89606 - - - - 0.02472 10-WEALT 1.42071 0.92873 0.00414 - -
Expected Change for BETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - - - -1.43186 - - 0.03052 0.02148 2-MARS -0.13942 - - 0.00918 0.03491 -0.19709 -0.01278 3-CHIL - - 1.23469 - - -0.18862 0.63514 0.07342 4-CROP - - -0.00217 0.04981 - - -0.01523 -0.12009 5-POLIT -0.02115 -0.03272 0.00320 0.00582 - - -0.02608 6-FSUP - - 0.08247 0.00869 -0.05083 -0.12639 - - 7-MAREL 0.13468 -0.10550 -0.01689 -0.08700 -0.16901 0.16972 8-RISK 0.08846 -0.04182 -0.00956 0.03001 -0.21388 -0.01028 9-INNOV 0.00020 -0.07893 -0.01277 0.01117 0.20256 -0.01738 10-WEALT -3.18102 2.22442 -0.48106 1.87696 3.96444 -1.96618
Expected Change for BETA
310 Chapter 6
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE 0.01070 ------2-MARS -0.01352 -0.02102 -0.05380 0.00769 3-CHIL 0.05242 -0.09978 -0.08062 - - 4-CROP -0.04625 0.07188 0.01756 0.02702 5-POLIT -0.01538 -0.02115 0.01590 0.00128 6-FSUP - - -0.06203 -0.03291 0.00521 7-MAREL - - 0.01514 0.08530 -0.00843 8-RISK -0.02285 - - -0.06354 -0.00508 9-INNOV 0.07934 - - - - -0.00045 10-WEALT -0.78494 -0.73843 -0.04996 - -
Standardized Expected Change for BETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - - - -1.95256 - - 0.43268 0.08868 2-MARS -0.80740 - - 0.01775 0.20160 -3.96036 -0.07476 3-CHIL - - 2.38675 - - -0.25648 3.00524 0.10117 4-CROP - - -0.01252 0.06773 - - -0.21524 -0.49433 5-POLIT -0.29987 -0.65740 0.01516 0.08221 - - -0.37361 6-FSUP - - 0.48260 0.01198 -0.20924 -1.81039 - - 7-MAREL 0.57596 -0.63955 -0.02411 -0.37100 -2.50795 0.73346 8-RISK 0.39348 -0.26371 -0.01420 0.13311 -3.30115 -0.04623 9-INNOV 0.00099 -0.55666 -0.02120 0.05540 3.49664 -0.08739 10-WEALT -0.81237 0.80529 -0.04101 0.47798 3.51296 -0.50741
Standardized Expected Change for BETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE 0.04576 ------2-MARS -0.08196 -0.13254 -0.37945 0.00279 3-CHIL 0.07484 -0.14815 -0.13389 - - 4-CROP -0.19723 0.31884 0.08714 0.00688 5-POLIT -0.22828 -0.32645 0.27453 0.00113 6-FSUP - - -0.27881 -0.16546 0.00135 7-MAREL - - 0.07050 0.44425 -0.00225 8-RISK -0.10642 - - -0.34422 -0.00141 9-INNOV 0.41320 - - - - -0.00014 10-WEALT -0.20984 -0.20534 -0.01554 - -
Modification Indices for GAMMA
1-AGE 2-EDU ------1-NFE 3.13493 - - 2-MARS - - 0.48589 3-CHIL 2.20024 0.37852 4-CROP 2.39270 2.81222 5-POLIT 0.00025 0.07684 6-FSUP 3.97872 0.07212 7-MAREL 4.48867 3.95902 8-RISK - - 0.10087 9-INNOV 0.04301 - - 10-WEALT - - 1.41949
Expected Change for GAMMA
1-AGE 2-EDU ------1-NFE 0.00275 - - 2-MARS - - 0.00304 3-CHIL 0.01895 -0.02215 4-CROP -0.00419 -0.01142 5-POLIT 0.00001 -0.00056 6-FSUP 0.00299 -0.00110
311 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
7-MAREL -0.00378 -0.01334 8-RISK - - -0.00195 9-INNOV -0.00031 - - 10-WEALT - - 0.09512
Standardized Expected Change for GAMMA
1-AGE 2-EDU ------1-NFE 0.08646 - - 2-MARS - - 0.03608 3-CHIL 0.19927 -0.06196 4-CROP -0.13147 -0.09544 5-POLIT 0.00092 -0.01631 6-FSUP 0.09528 -0.00930 7-MAREL -0.12463 -0.11704 8-RISK - - -0.01781 9-INNOV -0.01184 - - 10-WEALT - - 0.04984
No Non-Zero Modification Indices for PHI
Modification Indices for PSI
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - 2-MARS 3.13493 - - 3-CHIL 8.29762 0.84106 - - 4-CROP - - 0.28685 0.25982 - - 5-POLIT 0.21284 2.42930 1.69311 0.00599 - - 6-FSUP 0.58268 1.13111 0.15045 2.06182 1.14287 - - 7-MAREL 0.28735 0.12557 0.12673 0.60697 0.75142 - - 8-RISK 3.13494 0.25405 0.00346 0.61834 1.43789 1.35270 9-INNOV - - 1.64988 0.00090 0.06619 1.71821 0.05582 10-WEALT 3.13493 3.07968 2.20024 4.10474 3.29866 2.89926
Modification Indices for PSI
7-MAREL 8-RISK 9-INNOV 10-WEALT ------7-MAREL - - 8-RISK 0.18035 - - 9-INNOV 2.89606 0.04301 - - 10-WEALT 1.42071 0.92873 0.00002 - -
Expected Change for PSI
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - 2-MARS -0.02408 - - 3-CHIL -0.35930 0.04968 - - 4-CROP - - 0.00464 -0.04291 - - 5-POLIT 0.00062 -0.00401 0.01291 -0.00031 - - 6-FSUP 0.00218 0.00553 0.00774 -0.01156 -0.00257 - - 7-MAREL 0.00239 -0.00302 0.01171 -0.01033 -0.00344 - - 8-RISK 0.05722 -0.00393 0.00368 0.00956 -0.00435 -0.00849 9-INNOV - - -0.00868 -0.00156 0.00270 0.00412 -0.00149 10-WEALT -0.19506 0.20900 -1.34686 0.41347 0.08060 -0.19960
312 Chapter 6
Expected Change for PSI
7-MAREL 8-RISK 9-INNOV 10-WEALT ------7-MAREL - - 8-RISK -0.00510 - - 9-INNOV 0.01772 -0.00644 - - 10-WEALT -0.17534 -0.13804 -0.00056 - -
Standardized Expected Change for PSI
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - 2-MARS -0.13945 - - 3-CHIL -0.48995 0.09604 - - 4-CROP - - 0.02680 -0.05835 - - 5-POLIT 0.00880 -0.08052 0.06110 -0.00438 - - 6-FSUP 0.00899 0.03237 0.01066 -0.04760 -0.03681 - - 7-MAREL 0.01022 -0.01831 0.01672 -0.04406 -0.05099 - - 8-RISK 0.25454 -0.02478 0.00546 0.04241 -0.06712 -0.03818 9-INNOV - - -0.06118 -0.00259 0.01342 0.07109 -0.00750 10-WEALT -0.04982 0.07566 -0.11481 0.10529 0.07142 -0.05151
Standardized Expected Change for PSI
7-MAREL 8-RISK 9-INNOV 10-WEALT ------7-MAREL - - 8-RISK -0.02377 - - 9-INNOV 0.09230 -0.03486 - - 10-WEALT -0.04687 -0.03839 -0.00018 - -
Modification Indices for THETA-EPS
NFE MARS CHIL CROP POLIT FSUP ------NFE 0.69656 MARS 3.25588 3.13493 CHIL 0.99397 1.26482 - - CROP 2.08001 0.36336 0.15273 - - POLIT 0.15489 2.63414 1.69311 0.02473 - - FSUP 0.25704 0.56531 0.15045 2.36687 1.14287 - - MAREL 0.09609 0.12437 0.18196 1.32776 1.13144 - - RISK 1.28724 0.00477 0.02409 0.38620 2.61183 1.27567 INNOV 0.05466 1.60252 0.00002 0.06619 1.65529 0.07370 Landc 9.13000 1.04116 3.74235 8.12394 0.56300 1.18255 Landh 1.91680 0.00527 0.23247 0.44755 2.14509 0.01133 Irri 2.91721 4.31713 3.16711 0.90907 2.72470 0.44508 Harvest 3.04388 0.28699 0.25193 4.43187 0.01066 0.68386
Modification Indices for THETA-EPS
MAREL RISK INNOV Landc Landh Irri ------MAREL - - RISK 1.53311 0.03121 INNOV 2.51203 0.05910 - - Landc 1.37477 1.19984 0.22896 - - Landh 4.31120 2.58160 0.15630 7.28083 - - Irri 5.40574 0.18751 10.25394 4.72223 1.10440 - - Harvest 1.81269 5.73810 3.30238 15.19180 2.37616 3.10408
Modification Indices for THETA-EPS
Harvest ------Harvest - -
313 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Expected Change for THETA-EPS
NFE MARS CHIL CROP POLIT FSUP ------NFE 0.00632 MARS -0.00866 0.12894 CHIL -0.02451 0.05954 - - CROP 0.01119 0.00522 -0.03271 - - POLIT -0.00078 -0.00393 0.01291 -0.00062 - - FSUP 0.00339 0.00366 0.00774 -0.01218 -0.00257 - - MAREL 0.00263 -0.00277 0.01375 -0.01474 -0.00413 - - RISK 0.00685 -0.00051 0.00898 0.00714 -0.00546 -0.00767 INNOV 0.00127 -0.00855 -0.00025 0.00270 0.00404 -0.00171 Landc 0.58071 -0.21513 -1.94651 0.94878 -0.07500 -0.21846 Landh 0.13017 -0.00762 -0.22224 0.11301 0.07484 0.01093 Irri 0.00878 0.01202 -0.04389 0.00895 -0.00470 -0.00382 Harvest 0.01169 0.00403 -0.01625 0.02561 -0.00038 -0.00613
Expected Change for THETA-EPS
MAREL RISK INNOV Landc Landh Irri ------MAREL - - RISK -0.01359 -0.01797 INNOV 0.01615 -0.00754 - - Landc -0.38057 -0.31246 0.12566 - - Landh -0.34454 0.23277 -0.05303 11.56950 - - Irri 0.02150 0.00349 -0.02393 0.51762 0.12790 - - Harvest -0.01612 -0.02500 0.01759 1.20353 0.24303 0.01547
Expected Change for THETA-EPS
Harvest ------Harvest - -
Completely Standardized Expected Change for THETA-EPS
NFE MARS CHIL CROP POLIT FSUP ------NFE 0.02582 MARS -0.05014 1.05848 CHIL -0.03342 0.11510 - - CROP 0.04558 0.03014 -0.04448 - - POLIT -0.01099 -0.07897 0.06110 -0.00875 - - FSUP 0.01399 0.02141 0.01066 -0.05013 -0.03681 - - MAREL 0.01126 -0.01681 0.01962 -0.06284 -0.06130 - - RISK 0.03048 -0.00321 0.01333 0.03167 -0.08428 -0.03448 INNOV 0.00632 -0.06030 -0.00041 0.01342 0.06969 -0.00861 Landc 0.08221 -0.04317 -0.09198 0.13393 -0.03684 -0.03125 Landh 0.04023 -0.00334 -0.02293 0.03483 0.08026 0.00341 Irri 0.05139 0.09968 -0.08572 0.05221 -0.09543 -0.02257 Harvest 0.05183 0.02530 -0.02405 0.11321 -0.00586 -0.02745
Completely Standardized Expected Change for THETA-EPS
MAREL RISK INNOV Landc Landh Irri ------MAREL - - RISK -0.06326 -0.08705 INNOV 0.08411 -0.04086 - - Landc -0.05640 -0.04816 0.02166 - - Landh -0.11147 0.07833 -0.01996 0.12391 - - Irri 0.13168 0.02221 -0.17049 0.10496 0.05662 - -
314 Chapter 6
Harvest -0.07481 -0.12065 0.09493 0.18487 0.08150 0.09822
Completely Standardized Expected Change for THETA-EPS
Harvest ------Harvest - -
Modification Indices for THETA-DELTA-EPS
NFE MARS CHIL CROP POLIT FSUP ------AGE 0.07071 5.36483 1.09116 3.37250 0.89972 1.44918 EDU 0.07470 0.80147 0.59430 2.62418 0.20398 0.02182
Modification Indices for THETA-DELTA-EPS
MAREL RISK INNOV Landc Landh Irri ------AGE 0.01482 0.35675 0.05844 1.50654 0.21654 0.03811 EDU 4.09467 0.06410 0.05910 2.93580 7.20751 0.11713
Modification Indices for THETA-DELTA-EPS
Harvest ------AGE 4.51233 EDU 1.44160
Expected Change for THETA-DELTA-EPS
NFE MARS CHIL CROP POLIT FSUP ------AGE -0.04310 -0.61334 1.15125 -0.49824 -0.07584 0.19345 EDU 0.01643 0.06520 -0.46319 -0.18414 -0.01501 -0.00986
Expected Change for THETA-DELTA-EPS
MAREL RISK INNOV Landc Landh Irri ------AGE -0.03160 0.17626 0.05089 8.45611 1.60635 -0.03725 EDU -0.21832 -0.02661 0.37748 4.82405 3.82945 -0.02710
Expected Change for THETA-DELTA-EPS
Harvest ------AGE -0.52637 EDU 0.12329
Completely Standardized Expected Change for THETA-DELTA-EPS
NFE MARS CHIL CROP POLIT FSUP ------AGE -0.00559 -0.11278 0.04985 -0.06444 -0.03413 0.02536 EDU 0.00801 0.04505 -0.07536 -0.08950 -0.02538 -0.00486
Completely Standardized Expected Change for THETA-DELTA-EPS
MAREL RISK INNOV Landc Landh Irri ------AGE -0.00429 0.02489 0.00804 0.03801 0.01576 -0.00692 EDU -0.11140 -0.01412 0.22407 0.08148 0.14122 -0.01892
Completely Standardized Expected Change for THETA-DELTA-EPS
Harvest ------
315 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
AGE -0.07408 EDU 0.06521
Maximum Modification Index is 15.19 for Element (13,10) of THETA-EPS
DA NI=23 NO=290
Standardized Solution
LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE 0.49476 ------MARS - - 0.34901 ------CHIL - - - - 1.48220 ------CROP ------0.49617 - - - - POLIT ------0.14259 - - FSUP ------0.48960 MAREL ------RISK ------INNOV ------Landc ------Landh ------Irri ------Harvest ------
LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE ------MARS ------CHIL ------CROP ------POLIT ------FSUP ------MAREL 0.47262 ------RISK - - 0.45439 - - - - INNOV - - - - 0.40627 - - Landc ------7.91447 Landh ------2.39210 Irri ------0.06424 Harvest ------0.12181
LAMBDA-X
1-AGE 2-EDU ------AGE 15.58258 - - EDU - - 4.14655
BETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - 0.13175 - - 0.06019 - - - - 2-MARS ------3-CHIL 2.26358 ------4-CROP -0.33533 ------5-POLIT ------6-FSUP 0.80189 ------7-MAREL ------8-RISK ------9-INNOV ------10-WEALT ------
BETA
316 Chapter 6
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE - - 0.06249 0.01728 -0.99018 2-MARS ------3-CHIL ------2.69338 4-CROP ------5-POLIT ------6-FSUP -0.11986 ------7-MAREL ------8-RISK ------9-INNOV - - 0.33323 - - - - 10-WEALT ------
GAMMA
1-AGE 2-EDU ------1-NFE - - -0.06734 2-MARS 0.47820 - - 3-CHIL - - - - 4-CROP - - - - 5-POLIT - - - - 6-FSUP - - - - 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV - - 0.20354 10-WEALT 0.75258 - -
Correlation Matrix of ETA and KSI
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE 1.00000 2-MARS -0.23185 1.00000 3-CHIL -0.27495 0.44451 1.00000 4-CROP -0.28081 0.07775 0.21561 1.00000 5-POLIT ------1.00000 6-FSUP 0.80189 -0.18592 -0.22048 -0.22518 - - 1.00000 7-MAREL ------0.11986 8-RISK 0.27373 -0.14707 -0.00376 -0.09179 - - 0.21950 9-INNOV 0.08422 -0.04300 0.00837 -0.02824 - - 0.06754 10-WEALT -0.94251 0.35989 0.55994 0.31605 - - -0.75579 1-AGE -0.69313 0.47820 0.45804 0.23243 - - -0.55581 2-EDU -0.10509 0.02950 -0.11281 0.03524 - - -0.08427
Correlation Matrix of ETA and KSI
7-MAREL 8-RISK 9-INNOV 10-WEALT 1-AGE 2-EDU ------7-MAREL 1.00000 8-RISK - - 1.00000 9-INNOV - - 0.32937 1.00000 10-WEALT - - -0.23145 -0.06767 1.00000 1-AGE - - -0.30754 -0.08992 0.75258 1.00000 2-EDU - - -0.01897 0.19722 0.04643 0.06170 1.00000
PSI Note: This matrix is diagonal.
317 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------0.09034 0.77132 0.11422 0.92412 1.00000 0.34261
PSI Note: This matrix is diagonal.
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1.00000 0.90542 0.85010 0.43362
Regression Matrix ETA on KSI (Standardized)
1-AGE 2-EDU ------1-NFE -0.68927 -0.06256 2-MARS 0.47820 - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV -0.10248 0.20354 10-WEALT 0.75258 - -
DA NI=23 NO=290
Completely Standardized Solution
LAMBDA-Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE 1.00000 ------MARS - - 1.00000 ------CHIL - - - - 1.00000 ------CROP ------1.00000 - - - - POLIT ------1.00000 - - FSUP ------1.00000 MAREL ------RISK ------INNOV ------Landc ------Landh ------Irri ------Harvest ------
LAMBDA-Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE ------MARS ------CHIL ------CROP ------POLIT ------FSUP ------MAREL 1.00000 ------RISK - - 1.00000 - - - - INNOV - - - - 1.00000 - - Landc ------0.55433
318 Chapter 6
Landh ------0.36578 Irri ------0.18598 Harvest ------0.26714
LAMBDA-X
1-AGE 2-EDU ------AGE 1.00000 - - EDU - - 1.00000
BETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE - - 0.13175 - - 0.06019 - - - - 2-MARS ------3-CHIL 2.26358 ------4-CROP -0.33533 ------5-POLIT ------6-FSUP 0.80189 ------7-MAREL ------8-RISK ------9-INNOV ------10-WEALT ------
BETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE - - 0.06249 0.01728 -0.99018 2-MARS ------3-CHIL ------2.69338 4-CROP ------5-POLIT ------6-FSUP -0.11986 ------7-MAREL ------8-RISK ------9-INNOV - - 0.33323 - - - - 10-WEALT ------
GAMMA
1-AGE 2-EDU ------1-NFE - - -0.06734 2-MARS 0.47820 - - 3-CHIL - - - - 4-CROP - - - - 5-POLIT - - - - 6-FSUP - - - - 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV - - 0.20354 10-WEALT 0.75258 - -
Correlation Matrix of ETA and KSI
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE 1.00000 2-MARS -0.23185 1.00000 3-CHIL -0.27495 0.44451 1.00000 4-CROP -0.28081 0.07775 0.21561 1.00000 5-POLIT ------1.00000 6-FSUP 0.80189 -0.18592 -0.22048 -0.22518 - - 1.00000 7-MAREL ------0.11986 8-RISK 0.27373 -0.14707 -0.00376 -0.09179 - - 0.21950 9-INNOV 0.08422 -0.04300 0.00837 -0.02824 - - 0.06754 10-WEALT -0.94251 0.35989 0.55994 0.31605 - - -0.75579
319 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
1-AGE -0.69313 0.47820 0.45804 0.23243 - - -0.55581 2-EDU -0.10509 0.02950 -0.11281 0.03524 - - -0.08427
Correlation Matrix of ETA and KSI
7-MAREL 8-RISK 9-INNOV 10-WEALT 1-AGE 2-EDU ------7-MAREL 1.00000 8-RISK - - 1.00000 9-INNOV - - 0.32937 1.00000 10-WEALT - - -0.23145 -0.06767 1.00000 1-AGE - - -0.30754 -0.08992 0.75258 1.00000 2-EDU - - -0.01897 0.19722 0.04643 0.06170 1.00000
PSI Note: This matrix is diagonal.
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------0.09034 0.77132 0.11422 0.92412 1.00000 0.34261
PSI Note: This matrix is diagonal.
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1.00000 0.90542 0.85010 0.43362
THETA-EPS
NFE MARS CHIL CROP POLIT FSUP ------
THETA-EPS
MAREL RISK INNOV Landc Landh Irri ------0.69271 0.86620 0.96541
THETA-EPS
Harvest ------0.92864
Regression Matrix ETA on KSI (Standardized)
1-AGE 2-EDU ------1-NFE -0.68927 -0.06256 2-MARS 0.47820 - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV -0.10248 0.20354 10-WEALT 0.75258 - -
DA NI=23 NO=290
320 Chapter 6
Total and Indirect Effects
Total Effects of KSI on ETA
1-AGE 2-EDU ------1-NFE -0.02188 -0.00746 (0.00133) (0.00267) -16.50978 -2.79896
2-MARS 0.01071 - - (0.00116) 9.25647
3-CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201
4-CROP 0.00736 0.00251 (0.00138) (0.00104) 5.32880 2.40335
5-POLIT - - - -
6-FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896
7-MAREL - - - -
8-RISK -0.00897 - - (0.00163) -5.49441
9-INNOV -0.00267 0.01994 (0.00065) (0.00531) -4.09531 3.75221
10-WEALT 0.38224 - - (0.04039) 9.46365
Indirect Effects of KSI on ETA
1-AGE 2-EDU ------1-NFE -0.02188 0.00057 (0.00133) (0.00054) -16.50978 1.05162
2-MARS - - - -
3-CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201
4-CROP 0.00736 0.00251 (0.00138) (0.00104) 5.32880 2.40335
5-POLIT - - - -
6-FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896
7-MAREL - - - -
8-RISK - - - -
321 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
9-INNOV -0.00267 - - (0.00065) -4.09531
10-WEALT - - - -
Total Effects of ETA on ETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE -0.01978 0.18307 - - 0.05883 - - - - (0.00995) (0.04567) (0.02402) -1.98782 4.00848 2.44949
2-MARS ------
3-CHIL 6.64711 1.24142 - - 0.39895 - - - - (1.69580) (0.21306) (0.13891) 3.91976 5.82655 2.87192
4-CROP -0.32963 -0.06156 - - -0.01978 - - - - (0.05774) (0.02018) (0.00995) -5.70869 -3.05095 -1.98782
5-POLIT ------
6-FSUP 0.77784 0.14527 - - 0.04668 - - - - (0.03432) (0.03677) (0.01916) 22.66473 3.95039 2.43606
7-MAREL ------
8-RISK ------
9-INNOV ------
10-WEALT ------
Total Effects of ETA on ETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE - - 0.07284 0.02062 -0.06067 (0.02565) (0.02501) (0.00560) 2.83949 0.82467 -10.83005
2-MARS ------
3-CHIL - - 0.49394 0.13986 0.09296 (0.14635) (0.16760) (0.01465) 3.37509 0.83451 6.34472
4-CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497
5-POLIT ------
6-FSUP -0.12417 0.05780 0.01637 -0.04815 (0.03567) (0.02051) (0.01986) (0.00490) -3.48123 2.81861 0.82415 -9.82023
7-MAREL ------
322 Chapter 6
8-RISK ------
9-INNOV - - 0.29794 - - - - (0.04850) 6.14300
10-WEALT ------
Largest Eigenvalue of B*B' (Stability Index) is 46.979
Indirect Effects of ETA on ETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE -0.01978 -0.00369 - - -0.00119 - - - - (0.00995) (0.00240) (0.00108) -1.98782 -1.54081 -1.10122
2-MARS ------
3-CHIL -0.13416 1.24142 - - 0.39895 - - - - (0.05848) (0.21306) (0.13891) -2.29418 5.82655 2.87192
4-CROP 0.00665 -0.06156 - - -0.01978 - - - - (0.00424) (0.02018) (0.00995) 1.57045 -3.05095 -1.98782
5-POLIT ------
6-FSUP -0.01570 0.14527 - - 0.04668 - - - - (0.00793) (0.03677) (0.01916) -1.98062 3.95039 2.43606
7-MAREL ------
8-RISK ------
9-INNOV ------
10-WEALT ------
Indirect Effects of ETA on ETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE - - 0.00480 -0.00042 0.00122 (0.00763) (0.00056) (0.00063) 0.62902 -0.73899 1.95062
2-MARS ------
3-CHIL - - 0.49394 0.13986 -0.41145 (0.14635) (0.16760) (0.11480) 3.37509 0.83451 -3.58406
4-CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497
5-POLIT ------
6-FSUP - - 0.05780 0.01637 -0.04815 (0.02051) (0.01986) (0.00490) 2.81861 0.82415 -9.82023
323 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
7-MAREL ------
8-RISK ------
9-INNOV ------
10-WEALT ------
Total Effects of ETA on Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE 0.98022 0.18307 - - 0.05883 - - - - (0.00995) (0.04567) (0.02402) 98.48886 4.00848 2.44949
MARS - - 1.00000 ------
CHIL 6.64711 1.24142 1.00000 0.39895 - - - - (1.69580) (0.21306) (0.13891) 3.91976 5.82655 2.87192
CROP -0.32963 -0.06156 - - 0.98022 - - - - (0.05774) (0.02018) (0.00995) -5.70869 -3.05095 98.48886
POLIT ------1.00000 - -
FSUP 0.77784 0.14527 - - 0.04668 - - 1.00000 (0.03432) (0.03677) (0.01916) 22.66473 3.95039 2.43606
MAREL ------
RISK ------
INNOV ------
Landc ------
Landh ------
Irri ------
Harvest ------
Total Effects of ETA on Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE - - 0.07284 0.02062 -0.06067 (0.02565) (0.02501) (0.00560) 2.83949 0.82467 -10.83005
MARS ------
CHIL - - 0.49394 0.13986 0.09296 (0.14635) (0.16760) (0.01465)
324 Chapter 6
3.37509 0.83451 6.34472
CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497
POLIT ------
FSUP -0.12417 0.05780 0.01637 -0.04815 (0.03567) (0.02051) (0.01986) (0.00490) -3.48123 2.81861 0.82415 -9.82023
MAREL 1.00000 ------
RISK - - 1.00000 - - - -
INNOV - - 0.29794 1.00000 - - (0.04850) 6.14300
Landc ------1.00000
Landh ------0.30224 (0.05287) 5.71667
Irri ------0.00812 (0.00264) 3.07509
Harvest ------0.01539 (0.00356) 4.32247
Indirect Effects of ETA on Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE -0.01978 0.18307 - - 0.05883 - - - - (0.00995) (0.04567) (0.02402) -1.98782 4.00848 2.44949
MARS ------
CHIL 6.64711 1.24142 - - 0.39895 - - - - (1.69580) (0.21306) (0.13891) 3.91976 5.82655 2.87192
CROP -0.32963 -0.06156 - - -0.01978 - - - - (0.05774) (0.02018) (0.00995) -5.70869 -3.05095 -1.98782
POLIT ------
FSUP 0.77784 0.14527 - - 0.04668 - - - - (0.03432) (0.03677) (0.01916) 22.66473 3.95039 2.43606
MAREL ------
RISK ------
INNOV ------
Landc ------
Landh ------
325 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Irri ------
Harvest ------
Indirect Effects of ETA on Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE - - 0.07284 0.02062 -0.06067 (0.02565) (0.02501) (0.00560) 2.83949 0.82467 -10.83005
MARS ------
CHIL - - 0.49394 0.13986 0.09296 (0.14635) (0.16760) (0.01465) 3.37509 0.83451 6.34472
CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497
POLIT ------
FSUP -0.12417 0.05780 0.01637 -0.04815 (0.03567) (0.02051) (0.01986) (0.00490) -3.48123 2.81861 0.82415 -9.82023
MAREL ------
RISK ------
INNOV - - 0.29794 - - - - (0.04850) 6.14300
Landc ------
Landh ------
Irri ------
Harvest ------
Total Effects of KSI on Y
1-AGE 2-EDU ------NFE -0.02188 -0.00746 (0.00133) (0.00267) -16.50978 -2.79896
MARS 0.01071 - - (0.00116) 9.25647
CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201
CROP 0.00736 0.00251 (0.00138) (0.00104) 5.32880 2.40335
326 Chapter 6
POLIT - - - -
FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896
MAREL - - - -
RISK -0.00897 - - (0.00163) -5.49441
INNOV -0.00267 0.01994 (0.00065) (0.00531) -4.09531 3.75221
Landc 0.38224 - - (0.04039) 9.46365
Landh 0.11553 - - (0.01861) 6.20720
Irri 0.00310 - - (0.00099) 3.14496
Harvest 0.00588 - - (0.00130) 4.52325
DA NI=23 NO=290
Standardized Total and Indirect Effects
Standardized Total Effects of KSI on ETA
1-AGE 2-EDU ------1-NFE -0.68927 -0.06256 2-MARS 0.47820 - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV -0.10248 0.20354 10-WEALT 0.75258 - -
Standardized Indirect Effects of KSI on ETA
1-AGE 2-EDU ------1-NFE -0.68927 0.00478 2-MARS - - - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK - - - - 9-INNOV -0.10248 - - 10-WEALT - - - -
327 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
Standardized Total Effects of ETA on ETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE -0.01978 0.12914 - - 0.05900 - - - - 2-MARS ------3-CHIL 2.21880 0.29232 - - 0.13355 - - - - 4-CROP -0.32870 -0.04330 - - -0.01978 - - - - 5-POLIT ------6-FSUP 0.78603 0.10356 - - 0.04731 - - - - 7-MAREL ------8-RISK ------9-INNOV ------10-WEALT ------
Standardized Total Effects of ETA on ETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE - - 0.06690 0.01694 -0.97059 2-MARS ------3-CHIL - - 0.15142 0.03834 0.49636 4-CROP - - -0.02243 -0.00568 0.32547 5-POLIT ------6-FSUP -0.11986 0.05364 0.01358 -0.77831 7-MAREL ------8-RISK ------9-INNOV - - 0.33323 - - - - 10-WEALT ------
Standardized Indirect Effects of ETA on ETA
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------1-NFE -0.01978 -0.00261 - - -0.00119 - - - - 2-MARS ------3-CHIL -0.04478 0.29232 - - 0.13355 - - - - 4-CROP 0.00663 -0.04330 - - -0.01978 - - - - 5-POLIT ------6-FSUP -0.01586 0.10356 - - 0.04731 - - - - 7-MAREL ------8-RISK ------9-INNOV ------10-WEALT ------
Standardized Indirect Effects of ETA on ETA
7-MAREL 8-RISK 9-INNOV 10-WEALT ------1-NFE - - 0.00441 -0.00034 0.01959 2-MARS ------3-CHIL - - 0.15142 0.03834 -2.19702 4-CROP - - -0.02243 -0.00568 0.32547 5-POLIT ------6-FSUP - - 0.05364 0.01358 -0.77831 7-MAREL ------8-RISK ------9-INNOV ------10-WEALT ------
Standardized Total Effects of ETA on Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE 0.48497 0.06389 - - 0.02919 - - - - MARS - - 0.34901 ------CHIL 3.28870 0.43327 1.48220 0.19794 - - - - CROP -0.16309 -0.02149 - - 0.48635 - - - -
328 Chapter 6
POLIT ------0.14259 - - FSUP 0.38484 0.05070 - - 0.02316 - - 0.48960 MAREL ------RISK ------INNOV ------Landc ------Landh ------Irri ------Harvest ------
Standardized Total Effects of ETA on Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE - - 0.03310 0.00838 -0.48021 MARS ------CHIL - - 0.22444 0.05682 0.73571 CROP - - -0.01113 -0.00282 0.16149 POLIT ------FSUP -0.05868 0.02626 0.00665 -0.38106 MAREL 0.47262 ------RISK - - 0.45439 - - - - INNOV - - 0.13538 0.40627 - - Landc ------7.91447 Landh ------2.39210 Irri ------0.06424 Harvest ------0.12181
Completely Standardized Total Effects of ETA on Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE 0.98022 0.12914 - - 0.05900 - - - - MARS - - 1.00000 ------CHIL 2.21880 0.29232 1.00000 0.13355 - - - - CROP -0.32870 -0.04330 - - 0.98022 - - - - POLIT ------1.00000 - - FSUP 0.78603 0.10356 - - 0.04731 - - 1.00000 MAREL ------RISK ------INNOV ------Landc ------Landh ------Irri ------Harvest ------
Completely Standardized Total Effects of ETA on Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE - - 0.06690 0.01694 -0.97059 MARS ------CHIL - - 0.15142 0.03834 0.49636 CROP - - -0.02243 -0.00568 0.32547 POLIT ------FSUP -0.11986 0.05364 0.01358 -0.77831 MAREL 1.00000 ------RISK - - 1.00000 - - - - INNOV - - 0.33323 1.00000 - - Landc ------0.55433 Landh ------0.36578 Irri ------0.18598 Harvest ------0.26714
Standardized Indirect Effects of ETA on Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE -0.00979 0.06389 - - 0.02919 - - - - MARS ------CHIL 3.28870 0.43327 - - 0.19794 - - - -
329 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
CROP -0.16309 -0.02149 - - -0.00982 - - - - POLIT ------FSUP 0.38484 0.05070 - - 0.02316 - - - - MAREL ------RISK ------INNOV ------Landc ------Landh ------Irri ------Harvest ------
Standardized Indirect Effects of ETA on Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE - - 0.03310 0.00838 -0.48021 MARS ------CHIL - - 0.22444 0.05682 0.73571 CROP - - -0.01113 -0.00282 0.16149 POLIT ------FSUP -0.05868 0.02626 0.00665 -0.38106 MAREL ------RISK ------INNOV - - 0.13538 - - - - Landc ------Landh ------Irri ------Harvest ------
Completely Standardized Indirect Effects of ETA on Y
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP ------NFE -0.01978 0.12914 - - 0.05900 - - - - MARS ------CHIL 2.21880 0.29232 - - 0.13355 - - - - CROP -0.32870 -0.04330 - - -0.01978 - - - - POLIT ------FSUP 0.78603 0.10356 - - 0.04731 - - - - MAREL ------RISK ------INNOV ------Landc ------Landh ------Irri ------Harvest ------
Completely Standardized Indirect Effects of ETA on Y
7-MAREL 8-RISK 9-INNOV 10-WEALT ------NFE - - 0.06690 0.01694 -0.97059 MARS ------CHIL - - 0.15142 0.03834 0.49636 CROP - - -0.02243 -0.00568 0.32547 POLIT ------FSUP -0.11986 0.05364 0.01358 -0.77831 MAREL ------RISK ------INNOV - - 0.33323 - - - - Landc ------Landh ------Irri ------
330 Chapter 6
Harvest ------
Standardized Total Effects of KSI on Y
1-AGE 2-EDU ------NFE -0.34102 -0.03095 MARS 0.16690 - - CHIL 0.69185 -0.20990 CROP 0.11468 0.01041 POLIT - - - - FSUP -0.27061 -0.02456 MAREL - - - - RISK -0.13974 - - INNOV -0.04163 0.08269 Landc 5.95626 - - Landh 1.80024 - - Irri 0.04835 - - Harvest 0.09167 - -
Completely Standardized Total Effects of KSI on Y
1-AGE 2-EDU ------NFE -0.68927 -0.06256 MARS 0.47820 - - CHIL 0.46677 -0.14161 CROP 0.23113 0.02098 POLIT - - - - FSUP -0.55272 -0.05017 MAREL - - - - RISK -0.30754 - - INNOV -0.10248 0.20354 Landc 0.41718 - - Landh 0.27528 - - Irri 0.13996 - - Harvest 0.20104 - -
Time used: 0.300 Seconds
331 Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
332 Chapter 7
Conclusions
7.1 Summary of the study
In the introductory chapter of this book, we have discussed rural poverty as a problem area. Rural poverty is the real threat to a country’s development. Rural deprivation is usually described as of two types: the core poverty of the landless or near landless in accessible areas with flat, fertile, and often irrigated lands; and the peripheral poverty of those in remoter hinterlands, usually with undulating and less fertile lands and rainfed agriculture. In India, core poverty is concentrated especially in the Eastern Gangetic basin and peripheral poverty especially in Madhya Pradesh and neighbouring regions. Unless and until rural poverty is eradicated through possible policy measures, the country will not be able to prosper rapidly. This study gives importance to rural development through rural industrialisation. The issue of rural industrialisation has been studied from entrepreneurial point of view. The study attempts to identify the factors that influence non-farm entrepreneurship among farmers. We focus on farmers (not the agricultural labourers) because in a country like India the rural economy is dominated by agriculture and land-owning farmers do have investible surplus generated from agriculture.
This study holds special importance at least from two angles among others. First, although several studies have been conducted in the field of rural industry, one can hardly find studies that emphasize rural diversification from entrepreneurial point of view. In other words, most of the existing literature in the field of rural non-farm economy have shown little interest in studying farmer’s attitude towards non-farm entrepreneurship. In this context, the present study contributes additional dimension to the literature of rural industrialisation. Secondly, This study has a unique feature, since it analyzes data through the LISREL approach which helps control for simultaneity bias in the model. Moreover, Conclusions it can handle observable and unobservable latent variables within one framework. We will return to LISREL when we summarize chapter 6 later in this section.
Chapter 2 has presented an overall description of the study area. It has described the state of West Bengal and one of its districts called Bardhaman. The first part of the chapter is devoted to West Bengal whereas the second part is devoted to Bardhaman. We started the chapter with description of a place (situated in West Bengal) called Murshidabad where the colonial regime had commenced from. The chapter points out the fact that the town of Murshidabad which was comparable to London in the past has gradually turned into a rural village during the British rule. A similar discussion on account of whole Bengal, i.e. Bengal in retrospect, has also been presented. The chapter has given a brief history of entrepreneurial background of the Bengalis. The Bengali businessmen were not favoured by the British rulers in getting business contracts. So, the uneven competition ruined the strength of the Bengali business class. Moreover, it is true that the Bengalis put low esteem on business occupations—especially on the profession of traditional businessman such as petty trader. This lack of participation in traditional business virtually excluded the Bengalis from the upward mobility to modern business. As a result of which, the Marwaris and other non-Bengali business castes have gradually occupied the dominating positions in modern manufacturing and trades in Bengal.
The chapter has provided description of the agricultural and industrial background of the study area. In agriculture, West Bengal experienced a slow growth between 1950s and end of 1970s. Since early 1980s the state agriculture started experiencing high growth. Although there is much debate with regard to the methods of measuring actual growth rate and reasons for higher growth, there is a consensus among scholars that since early 1980s West Bengal agriculture has experienced higher output growth than what it has witnessed in the first three decades of the post-independence period. In industry, Bengal was the leading state in British India. After independence, the industrial performance of West Bengal was also good. The share of income earned from registered industry in West Bengal to that of India started going down after 1963 – initially at slow pace, but afterwards rapidly.
334 Chapter 7
In addition to the above discussions, chapter 2 has emphasized the linkage between urbanisation and rural industrialisation in West Bengal. It showed that significant diffusion of urban centres has not occurred in West Bengal. Strengthening the major primate city Kolkata and neglecting the rural sector in terms of building infrastructure hampered rural industrial sector grow in the state. Not so many small industries have flourished in those districts of the state which are less urbanised. With respect to this issue, the chapter suggests that the investment projects in rural infrastructure can be nothing but worthwhile in order to promote rural industry.
Chapter 2 also paid attention to Bardhaman district in particular. Bardhaman is mainly identified as an agricultural district of West Bengal, although a part of the district is covered with heavy industries. Especially in rice production, Bardhaman is traced as an important district in the state. Selecting such a district for the present study is justified since we have examined farmers' participation in non-agricultural industrial activities through investment of their surplus generated from agriculture.
Chapter 3 has discussed several issues relating to rural industries. It has focused on the linkage between agriculture and rural industry. The chapter starts discussion from the Physiocrats and includes the views of Adam Smith and Karl Marx while presenting the classical political economy framework with regard to agriculture/non-agriculture linkage. The basic difference between the Physiocrats and Adam Smith is that the Physiocrats put emphasis on agriculture while Adam Smith gives high importance to industry. But the similarity between them is that both of them recognized interlinkage between agriculture and industry. From Marx’s point of view, agriculture and domestic rural industry live together in the sense that the peasant family’s demand for manufacturing goods is met by their own community as they themselves often supply different raw materials required for the domestic industrial production and produce goods. But Marx traces out capitalistic system of production as the destructive force of such rural domestic industry since capitalists look for a big market and consequently intend to destroy the small production units.
335 Conclusions
Next, the chapter turns towards the neo-classical views pertaining to agriculture/non- agriculture linkage and presents Kuznets’s growth theory. Kuznets recognizes agricultural growth as an engine of non-agricultural growth and shows that with the increase in non-agricultural income over time the agriculture’s share to gross domestic product goes down. But Kuznets’s theory remains silent about the separate existence of rural non-agricultural sector as a subset of the whole industrial sector.
Chapter 3 has also presented an econometric model based on the linkage between agricultural and industrial growth and identified the shortfalls of the model from econometric modelling point of view. This model also reserved no place for rural industrial sector as a vital player in the process of development at the initial stage. Further, the chapter presents the theories as well as the theoretical debates on the linkage issue.
The theoretical discussions first start with Hirschman’s general concepts of backward and forward production linkages and then turn towards specific areas of agriculture/rural industry linkage. It was John W. Mellor who first empirically specified the linkages between agriculture and rural, small-scale, industry. Mellor’s argument is that increased food production based on cost decreasing technology can give large farmers an additional income which may generate/increase demand for rural non-agricultural goods. Following him many literature supported the argument that agricultural growth is a precondition for non-agricultural growth in rural areas, i.e. for the development of rural non-agricultural sector self-sufficiency in agriculture has to be earned first. Since people in poor countries are for the most part agrarian and pastoral folk (Dasgupta, 1993), agricultural growth can play a crucial role to break the vicious circle of poverty by raising the rural income level in the first place. But, at the same time, it has also been argued that agricultural growth is necessary but certainly not a sufficient condition for rural non-agricultural growth. There is a chance that demand for non-farm goods, generated from agricultural growth, may shift from rural choice to urban choice. Also, from the supply side, the larger volume of agricultural produce available for processing may tend to shift the processing industries
336 Chapter 7 to town in order to tap larger urban demand (since size of rural market is relatively small due to remarkable uneven income distribution). So, there may be other factors as well, which are responsible for the growth of rural industrial sector. Agriculture, of course, may be placed at the centre, but the other factors should be placed at the immediate periphery. The other factors like urbanisation and development of rural infrastructure, which require a good amount of government expenditure, can help the rural economy.
Chapter 3 theoretically analyzed how urbanisation or development of rural infrastructure can play important role in rural industrialisation. This apart, the chapter presents Hymer- Resnick model which takes up rural non-farm economy into consideration but fails to define the particular developmental role of this sector; rather it gives importance to the linkage between rural agricultural sector and urban industrial sector. The shortcomings of Hymer-Resnick have later been covered in a series of new models developed by Ranis- Stewart who showed us the important transitional role (gradual shift of traditional household/village products to modernizing non-agricultural products) of rural non-farm sector during transformation of a developing economy from an unfavourable low income situation to a favourable high income situation.
Chapter 4 has discussed the very important issue of culture with regard to development of a country in general and entrepreneurship development in particular. It has emphasized the role of psychological, sociological and cultural factors in the development of a region. It first presents a brief analytical history regarding the disappearance of entrepreneurship from the mainstream economics. This part of the chapter includes the discussion with arguments offered by the Austrian school of thought in favour of entrepreneurship. In this chapter, the main emphasis has been given to examine, within the context of the existing literature, whether or not cultural values play any role in influencing entrepreneurship. We observe that entrepreneurship which has virtually deserved an important place in economic development occurred through technological change has always been neglected by mainstream economic studies. The other important matter is that the scholars who talked in favour of the significant role of the entrepreneurs in economic development kept themselves silent in talking about culture or environment or value in the society. The
337 Conclusions non-economic factors are entirely ignored in the concerned literature. But it is interesting to note that entrepreneurship is such an economic issue which somehow maintains links with the non-economic factors.
Chapter 4 presented David McClelland’s theory of achievement motive that tried to understand economic growth from psychological point of view. Rapid economic growth is usually explained in terms of “external” factors like favourable opportunities for trade, unusual natural resources, or conquests that opens up new markets or produced internal political stability etc. On the contrary, McClelland was interested in looking at economic prosperity through the “internal” factors, i.e. people’s values and motives that lead them to extract opportunities or to take advantage of favourable trade conditions. McClelland examined correlation between achievement motive of people (manifested in literature) and economic development (measured in terms of electricity produced, which is the form of energy and which is essential to modern economic development) for several modern nations and found the results which confirmed the hypothesis of his thesis. In this connection, Hagen’s concept of “innovational personality” has also been discussed in the chapter. Family environment is another important issue. In the same chapter, Ghosh’s (1989) model of entrepreneurship has been presented in which Ghosh has shown how entrepreneurship is crucially dependent on environment.
We show how Bengali family culture differs from that of Marwari and Gujarati families who are known as business castes in India. Although one may find some dedicated Bengali businessmen, Bengalis are generally risk averse. This may be the result of the fact that Bengali children grow up in an environment where the parents teach them to put low esteem to business profession and go for other profession, whereas the environment is very different in Marwari or Gujarati families. The chapter elaborately discussed culture of development in Indian perspective. We picked up religious and sociological issues and analyzed how culture influences development of a region.
In chapter 4, we discussed Darwinian principles of evolutionary change and technical culture and explained how an entrepreneur generated from an environment of technical
338 Chapter 7 culture plays a crucial role in economic evolution. For instance, a society where technical culture is regarded as marginal culture and is not accepted into mainstream culture may lack industrial culture and consequently fall far behind the advanced nations. Finally, the chapter ends up with presentation of some interesting models of entrepreneurship which are culturally sensitive.
Chapter 5 has discussed the procedure and other facts relating to data collection. It has described sampling procedures of administrative blocks, panchayats (cluster of villages) and farm households. It has described obstacles faced during data collection and reducing of errors during interviews, among many other things. Bardhaman district is divided into 31 administrative blocks. The western part of the district is dominated by heavy industries whereas the eastern part is known as agricultural area. We have eliminated the western part from our study and confined ourselves to the eastern part, i.e. the agrarian area that roughly counts 20 administrative blocks. Of 20 blocks, five have been randomly selected for this study. From each block, six panchayats have been randomly selected. For the names of the selected blocks and panchayats, see Table 5.1 in chapter 5. Finally, ten farmers have been randomly selected from each panchayat. So, 300 farmers were selected for interviews. But, ultimately, we could reach 290 farm households, since ten households remained non-respondents. Of 290 respondents, we interviewed 169 farmers who were found to have been engaged in non-farm manufacturing activities and 121 were found to be engaged in farming only. For two kinds of farmers we formulated two types of questionnaires – one was for the former and the other was for the latter. Some practical problems were encountered during fieldwork, but they were handled carefully so as to make the fieldwork to be least hampered.
Chapter 6 is the crucial chapter of this book which contains the theoretical considerations and the empirical model of the study. Totally 13 hypotheses were formulated to determine the factors that may influence non-farm entrepreneurship of farmers. The model that included the thirteen hypotheses has been estimated by the LISREL approach (LInear Structural RELations approach). By using the LISREL approach, one can control for simultaneity bias in the model. Also, LISREL can simultaneously deal with latent and
339 Conclusions observable variables of a model. To be more explicit, a LISREL model is made up of two related submodels. One is a latent variables measurement model, which represents the relationships between the latent variables and their observable indicators. The other is a structural model that represents the relationships between the latent variables.
The LISREL programme can provide several estimators. We have used the full information maximum-likelihood-estimator for estimating our model. In the same chapter, we have presented the results of the estimation. The construction of the measurement model holds one endogenous latent variable, i.e. wealth of the farmer (WEALT), and four exogenous observable variables, i.e. farmer’s farm size (Landc), farmer’s homestead land (Landh), farmer’s access to irrigation (Irri), and the harvesting method farmer uses (Harvest). Farmer’s wealth is indicated by these four indicators which are all found to be statistically significant in the estimation results. The overall fit of the measurement model was found to be rather low which indicates that some other indicators need to be included in the model.
In order to control for simultaneity bias in the structural model (i.e., interdependencies between endogenous and explanatory variables), a structural simultaneous-equations- system of 10 equations has been formulated. During estimation, we removed three exogenous variables – age squared (AGE2), farmer’s primary involvement in agriculture either as a landowning farmer or as a sharecropper (AGRI), and farmer’s faith in work- effort or fate (FATE) – from our structural model since they were found to be highly insignificant. The variables marital status of farmer (MARS), types of crops farmer produces in a year (CROP), farmer’s risk attitude (RISK), farmer’s wealth (WEALT), and education of farmer (EDU) have been found to have direct impacts on farmer’s non- farm entrepreneurship (NFE). Of these, marital status, number of crop, and risk attitude have been found to have positive impacts on non-farm entrepreneurship, whereas farmer’s wealth and education have been found to have negative impacts. This means that farmers who are married, engaged in producing three crops a year, and risk takers have a relatively high probability to become non-farm entrepreneurs. On the other hand, farmers who are relatively wealthy and have higher levels of education are less likely to become
340 Chapter 7 non-farm entrepreneurs. Lastly, age of farmer (AGE) has been found to have indirect positive impact on his non-farm entrepreneurship via marriage, and indirect negative impact on his non-farm entrepreneurship via risk attitude and wealth. The policy implications of these findings are presented in the following section.
7.2 Policy recommendations
Most of the explanatory variables of this study do not involve direct policy implications. Still there is scope of government intervention which may be required to make the farmers less risk averse by creating facilities and influencing the culture.
The results show that the married farmer has a higher probability of being a non-farm entrepreneur. It can be imagined that usually the unmarried farmer is younger than the married farmer. Since success in non-farm manufacturing business is often subject to lengthy pay-back period (i.e. considerable instant monetary return at the initial stage may not be expected), the younger farmer may not be psychologically motivated in non-farm entrepreneurship at the cost of prolonged patience; rather they are interested in an alternative source of income. For instance, they may look for permanent job in government in order to ensure instant income and security in professional life. So, the unmarried, younger farmer needs some incentives to become interested in non-farm manufacturing businesses.
The government policy makers should think of launching some incentive measures with regard to the promotion of small-scale industries in the rural areas in order to attract the unmarried young farmers into the field of manufacturing business. For example, the government may think of building industrial estates in the rural and remote areas for the young generation. In the industrial estate, the young farmer may be offered free space and other free infrastructural facilities at the initial stage, say, for a 2-year period. Publicity is also needed to attract the young unmarried generation. The approach of publicity should be in such a way that it emphasizes the theme that self employment is much better than employment in public and wage jobs and that the degree of self dignity in self
341 Conclusions employment is higher than that in other employment. Publicity programme should focus on the fact that in self employment financial gain is possible whereas wage employment provides limited salary to the employee.
Wealth has been found to have a negative impact on non-farm entrepreneurship. The government policy should incorporate some measures to attract the wealthier section of the farmers towards non-farm manufacturing entrepreneurship. This section could play a vital role in rural industrialisation once they are interested, because they are the affluent class. At least they do not have dearth of money to invest. What is lacking in the policy now is that there is no programme from the side of the government to convince this section of farmer to start a business in the industrial sector. The existing rural industrial sector in the villages is suffering from lack of capital investment. A part of the rural economy is belonging to the vicious circle of poverty. It is rolling between poor producer and poor consumer. To break this tradition, heavy investment is required. If we look at the problem from such an angle, then we observe that only the rich farmer can bring about change in the rural economy with his investible surplus. But the unfortunate thing is this that the richer section of the farmers shows relatively little interest in non-farm sector. The policy makers should take steps to tap this section which can be playing a pioneering role in rural industrialisation in the economy of rural India since financial resource transfer from agriculture to non-agriculture is often considered one of the pre- conditions of the development and modernisation of rural non-farm economy. The government has never thought of improving the condition of villages through this possible line. As soon as they take steps in order to channel rural agricultural surplus towards rural non-farm economy, it is better for the rural society.
The results also show that education of the farmer has a negative impact on non-farm entrepreneurship. Here the concept of education mainly reflects general education. General education hardly stimulates people to take risky business ventures. The education policy in the state of West Bengal has not given serious attention towards entrepreneurship. It should have incorporated entrepreneurship as a major subject into the syllabi right from early stage to higher education. A major section of rural people is risk
342 Chapter 7 averse. They are afraid of taking risk so far as starting up a new business is concerned. If the government is to influence such culture in order to generate awareness about positive risk attitude among people then intervention in the education policy is needed and entrepreneurial education should start from early stage. Training programmes like entrepreneurship development programme and creativity generation programme may also be useful. Currently, such programmes are mainly concentrated in the core urban areas and in the peripheries, but these programmes should reach the remotest places of the region.
7.3 Further research recommendations
Most part of the rural areas of the developing countries is suffering from acute poverty. Rural policy oriented development research needs to be substantially undertaken by the future researchers. The variables we have taken up in our study may be applied to the same kind of work based on other geographical regions to examine any interesting difference in the results.
Several of the operational definitions of the variables could be improved upon by using a set of questions. For instance, we have measured innovativeness of a farmer by asking a question: if you start a business, would that be one that is being run by the others, or would it be one which is new/innovative? Future researchers could think of improving such operational definitions.
The variable wealth has been taken in this study as a substitute of income, predicting that the farmer may hide his actual income during interviews. The wealth of the farmer has been assessed by four indicators, viz. size of farm, homestead, whether or not the farmer has access to irrigation, and harvesting method used in farming. This operational definition of wealth can also be improved upon in further study by taking a more perfect set of indictors of wealth.
343 Conclusions
Moreover, the cultural and sociological factors that may affect economic situation have not been fully explored yet. To what extent and under what conditions is development related with the cultural factors? Answer to this question has not been intensively sought yet. Since the cultural issues differ from region to region, intensive studies are recommended to be undertaken based on various parts of the globe. In our study, the cultural factors covered a part only. Broader thoughts with regard to cultural issues may be recommended in further rural development oriented research.
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364 Samenvatting en Conclusies (Summary and Conclusions in Dutch)
Samenvatting van het onderzoek
In het inleidende hoofdstuk hebben we armoede op het platteland besproken als een probleem. Rurale armoede is een reële bedreiging voor de ontwikkeling van een land. Men onderscheidt gewoonlijk twee types in de plattelandsachterstelling: de kernarmoede van de (bijna) landlozen in toegankelijke gebieden met vlak, vruchtbaar en vaak beregende grond; en de perifere armoede van hen in het afgelegen achterland, gewoonlijk bergachtig en minder vruchtbaar terrein en regengevoede landbouw. In India is de kernarmoede voornamelijk gelegen in het Oostelijk stroomgebied van de Ganges en de perifere armoede voornamelijk in MadPradesh en omgeving. Tenzij, en tot wanneer, armoede op het platteland zal verdwijnen door politieke maatregelen, zal het land economisch gezien niet snel kunnen bloeien. Deze studie hecht belang aan ontwikkeling op het platteland door middel van rurale industrialisatie. De kwestie van rurale industrialisatie is bestudeerd vanuit het standpunt van ondernemerschap. De studie tracht de factoren vast te stellen die het niet-agrarisch ondernemerschap van boeren beïnvloeden. We richten ons op boeren (niet de landarbeiders) omdat in een land als India de rurale economie wordt gedomineerd door landbouw en landbezittende boeren met een beleggingsoverschot, gegenereerd uit de landbouw.
In hoofdstuk 2 is een algemene beschrijving gepresenteerd van het onderzoeksgebied. De situatie van West-Bengalen en een der districten, Bardhaman, is beschreven. Het eerste deel van het hoofdstuk is gewijd aan West-Bengalen en het tweede aan Bardhaman. Er is een korte beschrijving gegeven van de geschiedenis van het ondernemerschap in het onderzoeksgebied, alsmede een beschrijving van de landbouw- en industriële achtergrond. De nadruk is gelegd op het verband tussen urbanisatie en rurale industrialisatie in het onderzoeksgebied.
365 Hoofdstuk 3 beschrijft verschillende kwesties gerelateerd aan rurale industrieën. Veel literatuur steunt de redenering dat groei van de landbouw een voorwaarde is voor niet- agrarisch gerichte groei in rurale gebieden. Tegelijkertijd wordt er ook gesuggereerd dat groei van de landbouw nodig is, maar het zeker niet genoeg is. Er zijn ook ‘andere factoren’ die verantwoordelijk zijn voor de groei van de rurale industriële sector. Er is ook benadrukt dat de landbouw in het centrum moet worden geplaatst en andere factoren in de periferie. Aangezien mensen in arme landen voornamelijk agrarisch en pastorale mensen zijn (Dasgupta, 1993), kan agrarische groei een cruciale rol spelen in het doorbreken van de vicieuze cirkel van armoede door in de eerste plaats het rurale inkomensniveau te verhogen. De ‘andere factoren’ zoals urbanisatie en ontwikkeling van de rurale infrastructuur, die een behoorlijk bedrag aan overheidsuitgaven vereisen, kunnen de rurale economie helpen.
Hoofdstuk 4 behandelt het zeer belangrijke aspect van cultuur met betrekking tot de ontwikkeling van een land in het algemeen en van het ondernemerschap in het bijzonder. Het legt de nadruk op de rol van psychologische, sociologische en culturele factoren in de ontwikkeling van een regio. Allereerst wordt er een korte analytische historie gepresenteerd betreffende het verdwijnen van het ondernemerschap in de hoofdstroom economie. Het hoofdstuk combineert de discussie met argumenten vanuit de Oostenrijkse school, die ondernemerschap prefereert. In dit hoofdstuk wordt de meeste nadruk gelegd op het onderzoeken, binnen de context van de bestaande literatuur, of culturele waarden een rol spelen in de beïnvloeding van het ondernemerschap.
Hoofdstuk 5 behandelt de procedure en andere feiten met betrekking tot het verzamelen van gegevens. Het beschrijft onder anderen de belemmeringen die zich voordeden tijdens de verzameling van gegevens en het verminderen van fouten tijdens de interviews.
Hoofdstuk 6 is het cruciale hoofdstuk en bevat de theoretische overwegingen en het empirisch onderzoeksmodel. Een analyse is gemaakt van de gegevens door middel van het LISREL model (LInear Structural RELations model). De LISREL benadering maakt het niet alleen mogelijk om simultaneïteit bias te behandelen, maar tegelijkertijd latente
366 en waarneembare variabelen. In hoofdstuk 6 worden de resultaten van het onderzoek gepresenteerd. De variabelen huwelijkse staat van de boer, type gewassen dat de boer produceert, risicogeneigdheid, bezit en opleiding van de boer blijken een directe invloed te hebben op niet-agrarisch ondernemerschap. Van deze hebben huwelijk, aantal gewassen en een positieve houding t.o.v. risico een positieve invloed op het niet- agrarisch ondernemerschap, terwijl bezit en opleiding een negatieve invloed hebben. Leeftijd van de boer blijkt een indirect effect te hebben op niet-agrarisch ondernemerschap via huwelijkse staat, risicohouding en bezit. De beleidsimplicaties van deze resultaten worden gepresenteerd in de volgende paragraaf.
Aanbevelingen voor het beleid
De meeste verklarende variabelen van dit onderzoek brengen geen directe beleids- implicaties met zich mee. Daarom moet overheidsinterventie gericht zijn op het verminderen van risicomijdend gedrag van boeren door het creëren van voorzieningen en beïnvloeden van de cultuur.
De resultaten geven aan dat getrouwde boeren een grotere kans hebben op niet agrarisch ondernemerschap. Men mag aannemen dat de ongetrouwde boer jonger is dan de getrouwde. Aangezien succes in de niet-agrarische productie-industrie vaak onderworpen is aan terugbetaalperioden die lang op zich laten wachten, zijn de jongere boeren mogelijk niet gemotiveerd tot niet-agrarisch ondernemerschap waarbij hun geduld te veel op de proef wordt gesteld. Als ze al geïnteresseerd zijn in een alternatieve bron van inkomsten, zoeken ze eerder een vaste baan bij de overheid om verzekerd te zijn van onmiddellijk inkomen en de zekerheid van een professioneel leven. Dus de jongere ongetrouwde boer heeft enige prikkels nodig om geïnteresseerd te raken in niet-agrarisch ondernemen. De beleidsmakers moeten denken aan het lanceren van prikkelende maatregelen met betrekking tot het bevorderen van kleinschalige industrieën in the rurale gebieden om jonge ongetrouwde boeren aan te sporen op het gebied van de productie- industrie. Bijvoorbeeld: de overheid kan industrieterreinen bouwen in rurale en afgelegen gebieden voor de jonge generatie. Op deze industrieterreinen kan de jonge boer in de beginperiode (bijvoorbeeld voor 2 jaar) gratis ruimte en andere infrastructurele
367 faciliteiten geboden worden. Ook is publiciteit nodig om de jonge ongetrouwde generatie aan te trekken. De publiciteitsaanpak moet zo zijn dat de nadruk komt te liggen op het feit dat zelfstandig ondernemen veel beter is dan overheids- en particuliere functies en dat de eigen waardigheid in zelfstandige beroepen hoger is dan in een ander beroep. De publiciteitsprogramma’s dienen gericht te zijn op het feit dat in zelfstandige beroepen financieel gewin mogelijk is, terwijl loonwerk slechts een beperkt salaris oplevert.
Gebleken is dat bezit een negatief effect heeft op niet-agrarisch ondernemerschap. Overheidsbeleid moet maatregelen bevatten om het meer welvarende deel van de boeren aan te trekken tot de niet-agrarische productie-industrie. Deze boeren, eenmaal geïnteresseerd, kunnen een vitale rol spelen in rurale industrialisatie, omdat zij de welvarende klasse vertegenwoordigen. Zij hebben tenminste geen gebrek aan investeringsgeld. Waarin het beleid nu tekortschiet is dat er geen programma is vanuit de overheid om deze boeren te overtuigen een bedrijf te starten in de industriële sector. De bestaande rurale industriële sector in de dorpen lijdt aan het gebrek aan investeringskapitaal. Een deel van de rurale economie behoort tot de vicieuze cirkel van armoede. Het beweegt zich van arme producent naar arme consument. Om deze traditie te doorbreken, is een stevige investering nodig. Als we het probleem zo benaderen, dan zien we dat alleen de rijke boer verandering te weeg kan brengen vanwege zijn investeringsoverschot. Maar jammer genoeg heeft het rijkere deel van de boeren relatief weinig interesse voor de niet-agrarische sector. De beleidsmakers moeten stappen zetten om dat deel aan te spreken, welke een pioniersrol kan vervullen in de rurale industrialisatie in de economie van ruraal India, aangezien de transfer van financiële bronnen van agrarisch naar niet-agrarisch vaak wordt beschouwd als één van de voorwaarden voor de ontwikkeling en modernisering van de rurale niet-agrarische economie. De overheid heeft nooit overwogen via deze lijn de situatie van de dorpen te verbeteren. Zodra ze stappen zetten om het rurale agrarische overschot te kanaliseren richting de niet-agrarische economie, zal het beter worden voor de rurale samenleving.
De resultaten tonen ook aan dat opleiding van de boer een negatief effect heeft op niet- agrarisch ondernemerschap. Met opleiding wordt hier algemene opleiding bedoeld. Algemene opleiding stimuleert mensen nauwelijks om iets te wagen. Opleidingen in de
368 staat West Bengalen besteden geen serieuze aandacht aan ondernemerschap. Het zou een belangrijk onderwerp moeten zijn in het leerprogramma vanaf het allereerste begin tot en met de hogere opleidingen.
Een belangrijk deel van de rurale bevolking is risicomijdend. Ze zijn bang om risico’s te lopen wat betreft het starten van een onderneming. Als de overheid zo’n cultuur moet beïnvloeden om bewustwording ten aanzien van een positieve risicohouding onder de mensen te creëren, dan is wederom interventie in het onderwijsbeleid nodig, zodat onderwijs gericht op ondernemerschap op een jonge leeftijd kan beginnen. Trainingsprogramma’s gericht op het ontwikkelen van ondernemerschap en creativiteit zijn tevens nuttig. Op dit moment zijn deze programma’s hoofdzakelijk geconcentreerd in de kern stedelijke gebieden en omgeving, maar deze programma’s zouden de meest afgelegen plaatsen van de regio moeten bereiken.
Aanbevelingen voor vervolgonderzoek
In het grootste deel van de rurale gebieden van ontwikkelingslanden heerst sterke armoede. Toekomstige onderzoekers moeten zich met name bezighouden met ruraal beleidsgericht ontwikkelingsonderzoek. De variabelen gebruikt in dit onderzoek kunnen toegepast worden voor eenzelfde soort studie gebaseerd op andere geografische regio’s, om zo mogelijke verschillen in resultaten te bestuderen.
Verscheidene operationele definities van de variabelen zijn voor verbeteringen vatbaar door middel van vragen. Bijvoorbeeld, we hebben vernieuwingsgezindheid van de boer gemeten door de vraag te stellen: als u een onderneming zou starten, zou dat één zijn die ook door anderen wordt gedreven of zou het iets nieuws/innovatiefs zijn? Toekomstige onderzoekers zouden kunnen denken aan het verbeteren van dit soort vragen.
De variabele bezit is in dit onderzoek gebruikt als een vervanger voor inkomen, omdat de boer mogelijk zijn eigenlijke inkomen niet zou prijsgeven tijdens de interviews. Het bezit van de boer is gemeten met behulp van vier indicatoren: bedrijfsgrootte, boerderij, wel of geen toegang tot irrigatie, en toegepaste oogstmethode. Deze operationele definitie van
369 bezit kan ook verbeterd worden in een vervolgstudie, door betere indicatoren voor bezit te creëren.
Bovendien zijn de culturele en sociologische factoren die van invloed kunnen zijn op de economische situatie nog niet onderzocht. Staat ontwikkeling in verband met culturele factoren? Naar een antwoord op deze vraag is nog niet uitputtend gezocht. Aangezien de culturele kwesties verschillen van regio tot regio, moeten er intensieve studies worden gedaan, gebaseerd op verschillende delen van de wereld. In onze studie besloegen de culturele factoren slechts een deel. Ruimere aandacht voor culturele kwesties is aanbevolen voor volgend ruraal ontwikkelingsonderzoek.
370 About the Author
Subrata Dutta was born on 12 June 1967 at Barasat, West Bengal, India. He followed his secondary education at Duttapukur Mahesh Vidyapith and passed Madhyamik Examination conducted by the West Bengal Board of Secondary Education (WBBSE) in 1983. After finishing his higher secondary education, conducted by the West Bengal Council for Higher Secondary Education (WBCHSE), in 1985 from Nebadhai High School, Duttapukur, North 24-parganas, West Bengal, he completed his Bachelors and Masters in economics at Rabindra Bharati University, Kolkata, in 1988 and 1990 respectively. From 1991 to 1994 he worked for the Human Resource Development Research Institute, Kolkata. From 1994 to 1996 he worked for the Indian Council of Small Industries, Kolkata. In the mean time, in1995, he attended a short course on Small Entrepreneurship Promotion and Industrial Assistance at the Maastrciht School of Management, the Netherlands, and earned a post-graduate diploma. From 1996 to 1999 he worked as a University Junior Research Fellow at the department of economics at Rabindra Bharati University, Kolkata. In 1999, he started his Ph.D. study at the Maastricht School of Management, the Netherlands. In 2000, he moved to Wageningen University in the same country for his Ph.D. study.
He has published an academic paper in Economic and Political Weekly (EPW) in 2002 on linkage between urbanisation and development of rural small enterprises.
His interest includes directorial work in theatre and film.
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