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Schnelder*Sliwa, Rita Margarethe

RURAL NONFARM EMPLOYMENT AND MIGRATION: THE CASE OF COSTA RICA

The Ohio State University Ph.D. 1982

University Microfilms International300 N. 2eeb Road, Ann Arbor, Ml 4B106

Copyright 1982

by Schneider-Sliwa, Rita Margarethe All Rights Reserved

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University Microfilms International

RURAL NONFARM EMPLOYMENT AND MIGRATION:

THE CASE OF COSTA RICA

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio S tate U niversity

By

Rita Schneider-Sliwa *****

The Ohio S tate U niversity

1982

Reading Committee: Approved By

Lawrence A. Brown ^ t ' | j Douglas L. Graham \ ( \ Howard L. Gauthier ^ Richard L. Meyer Adviser Department of Geography TO

Ludvik, Linde, Monika, Lydia and Willi

i i ACKNOWLEDGEMENTS

A

I am grateful for the guidance provided by Dr. Lawrence A. Brown, who, along with other members of my committee, contributed so much to my intellectual development. I also want to acknowledge those who assisted me in various phases of the study, especially the Graduate School of The

Ohio State University, whose Alumni Research Award provided financial support, The National Science Foundation under Grant SES-8024565, and

Dr. Carolyn Hall de Saborio of the University of Costa Rica, Dr. Mario

Kaminsky of the Interamerican Institute for Cooperation on Agriculture, and Jorge Gonzales of the United States Agency for International

Development in Costa Rica. Mary Gorske's patient typing and re-typing of this dissertation is particularly appreciated.

My very special thanks to Ludwik Sliwa and all those who provided support and encouragement during this period.

i l l VITA

October 26, 1953...... Born - Oberhauaen, Heat Germany

1974 ...... B.A., Geography Rhelniach - Westfalische Techniache Hochaehule Aachen, Vest Germany

1977 ...... M.A., Geography The Ohio S tate U niversity Columbus, Ohio

1977-1981 ...... Teaching Associate, Department of Geography, The Ohio State U niversity, Columbus, Ohio

1981-1982 ...... Research Assiatant, Department of Geography, The Ohio State U niversity, Columbus, Ohio

PUBLICATIONS

"Innovation Diffusion and Development in a Third World Setting: The Case of the Cooperative Movement in Sierra Leone." (Co-authors: L.A. Brown, M.E. Harvey, and B.J. Riddell). Social Bcience Quarterly. Vol. 60, No. 2, September, 1979.

FIELDS OF STUDY

Major: Geography

iv TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... i i i

VITA...... iv

LIST OF TABLES......

LIST OF FIGURES...... x l

CHAPTER I . INTRODUCTION...... 1

Introduction ...... 1 Problem and Conceptual Framework ...... 3 Assumptions to be Examined ...... 8 Relevance of the Study ...... 8 Overview of the Following Chapters...... 11

I I . REVIEW OF PERTINENT LITERATURE...... 12 Rural Nonfarm Employment and Sm all-Scale E n te r p r is e ..... 13

The S h ift Toward Rural Nonfarm Employment S tra te g ie s. 13 Advantages of Rural Nonfarm Employment in Development Strategies ...... 17

Economic M u ltip lie r E ffe c ts ...... 18 Human Capital Effects...... 24

Summary of the Subsection..,., ...... 25

M igration ...... 26

The M igration D ecision P r o c e s s ...... 26 The Theory of the Household ...... 27

Individual Decision Making and Migration ...... 32 Macro Level Forces and Migration ...... 33

v Models Focusing on Economic F a c to rs .* ..* ...... *...... 36

The Labor Force Adjustment andDual Economy Model* 36 Cost-Benefit Models...- ...... 37

Models Focusing on Socio-C ultural F actors ...... 40

The Role of Extended Family and Acquaintances..... 41 Migrant Selectivity by Social Class ...... 42

A Development Paradigm of Migration.... 43 Summary of the Subsection ...... , 44

The Consequences of Migration ...... 46

The Disequilibrating Nature of Migration ...... 47 Summary of the Subsection...... 52

Summary of the Chapter ...... 53

I I I . RESEARCH DESIGN AND STUDY AREA...... 55

Research Design ...... 55

Identification of Basic Dimensions of the Rural Non­ farm, A g ricultural and Urban Economic S ectors 57 Classification of Rural Areas...... * ...... 61 Procedure for Addressing the Research Questions...... 62

Data...... 64 The Study Area ...... * ...... 67 Structural Characteristics ...... 70

Land D istrib u tio n ...... 70 Agricultural Orientation in Production and Exports ...... 71 Economic Dependence ...... 74 Terms of T r a d e ...... 75 Economic Trends 1967-1974...... 76 Exports...... 78 Imports ...... 82 The Growth of the Gross Domestic Product (G D P).... 83 Rural Poverty ...... 83 Summary of the Chapter ...... 91

v i IV. PRINCIPLE DIMENSIONS OF THE RURAL NONFARM, AGRICULTURAL AND URBAN SECTORS AND CLASSIFICATION OF RURAL CANTONS BASED ON THESE DIMENSIONS...... 95

Principle Dimensions of the Agricultural Sector ...... 96

Factor 1 ...... * . 99 Factor 2 ...... 101 Factor 3 ...... 103 Factor A...... 103

Principle Dimensions of the Rural Nonfarm and Urban Economic 8 ecto rs ...... 104

Factor 1«...... 107 Factor 2 ...... 109 Factor 3 ...... 109 Factor 4 ...... I l l

Classification of Rural Cantons Based on Dimensions of Their A gricultural and Rural Nonfarm S e c t o r s .... 112

Category A...... 114 Category B ...... 119 Category C...... 120

Summary of the Chapter...... 120

V. STATISTICAL ANALYSIS: THE RURAL NONFARM SECTOR AND AGGREGATE MIGRATION...... 122

The Dependent Variables ...... 123 Independent V ariables...... 125 Regression Analysis of Aggregate Level Migration Data... 128

Out M igration ...... 130 In Migration ...... 133 Net Migration ...... 135

Generalisation of Regression Results ...... 137

The A gricultural Sector and M igration ...... 138 The Rural Nonfarm Sector and M igration ...... 141 Urban Pull Effects and Migrations ...... 142

Analysis of Individual Level Migration Data...... 142 Summary of the Chapter ...... 149

v i i VI. RURAL NONFARM EMPLOYMENT AND MIGRANT SELECTIVITY...... 151

A g e ...... * ...... 159 Education ...... 161 Occupational Status ...... 163 General Observations on Migrant Selectivity and the Rural Nonfarn Sector...... 165 Summary of the Chapter ...... 167

V II. SUMMARY...... 169

Methods and R esults...... 171 Contributions and Policy Implications...... 175 Limitations and Suggestions for Further Research ...... 177

BIBLIOGRAPHY...... 182

v i i i LIST OF TABLES

Table Page

3.1 Migration Probabilities and Numbers of Migrants for the Canton Hierarchy of Costa Rica, based on Inter- cantonal Flows, 1973...... 68

3.2 Structure of the Value of Exports ...... 77

3.3 Indices of Exports of Goods (W O -lO O )...... 79

3.4 Evolution of Prices in Agricultural Exports ($ at current prices) ...... 80

3.5 Evolution of the Volume of Exports ...... 81

3.6 . Index of Imported Goods (1970*100)...... 84

3.7 Annual Growth of the Gross Domestic Product, 1965-1974, by Sector of Economic A ctiv ity (in p e rc e n ts) ...... 85

3.8 Fixed Investment by Sector (in millions of colones) 86

3.9 Costa Rica: Rural Poverty, by Sise of Farm s,..,...... , 88

3.10 Income Distribution of the Farming Population ...... 89

3.11 Comparison of the Proportion of Landless and Farming Population Classified as "Poor" ...... 90

3.12 Off-Farm Employment P a ttern s by Farm S i s e ...... 92

3.13 Percent of Total Active Labor Employed...... 93

4.1 Definitions, Means, and Standard Deviations for Agri­ cultural Resource Base Variables (n*62 rural cantons),... 97

4.2 Rotated Factor Loading on Agricultural Variables ...... 100

4.3 Definitions, Means, and Standard Deviations for Variables P ertain in g to the Rural Nonfarm and Urban S ectors ...... 106

ix Table Page 4.4 Rotated Factor Loadings fo r N onagricultural Economic Activity Variables in Rural Cantons (n*62) and in Rural Plus Urban Cantons (n*69) ...... 108

4.5 Principle Dimensions of the Agricultural and Rural Nonfarm S ectors ...... 113

4.6 Hean Factor Scores for Canton Groupings ...... 115

4.7 Classification of Rural Cantons...... 116

5.1 Dimensions of the Agricultural and Rural Nonfarm/Small- Scale Enterprise SectorB ...... 126

5.2 Variables Representing the Pull of the Labor Market in Major Urban Centers ...... 129

5.3 Regression Analysis of Out Migration ...... 131

5.4 Regression Analysis of In Migration ...... 134

5.5 Regression Analysis of Net Migration...... 136

5.6 Cross-Classification of Stayers, In Migrants, and Out Migrants by Type of Economic A ctiv ity , for Groups of Cantons...... 144

6.1 Mean Age, Educational Level, and Occupational Status of Out, In, and Non Migrants by Sector of Employment and Type of Area ...... 154

6.2 Mean D ifference in Age, Educational Level, and Occupa­ tio n a l Status Between Out, In, and Non Migrants by Sector of Employment and Type of A re a ...... 156

6.3 Mean D ifference in Age, Educational Level, and Occupa­ tio n a l Status Between Sectors of Employment by Type of Area and Migrant S tatu s ...... 157

x LIST OF FIGURES

Figure

1.1 A ltern ativ e Employment O pportunities and the Migration Decision ...... 5

2.1 Interconnectedness and M ultiplier Effects of Rural Nonfarm E n te rp rise ...... 19

2.2 Overview of the Literature on the Migration Decision Process ...... 28

3.1 Research Design ...... 56

3.2 Canton Map of Costa Rica ...... 65

3.3 Salient Rural Destination Cantons for Migrants 1968-73*.. 69

3 .A Supply and Demand Conditions for the World Market and Their Translation into the Small Nation Producer’s Market...... 73 t A .l Factor Score Maps Pertaining to Agricultural Activities (n*62 rural cantons) ...... 102

A .2 Factor Score Maps Pertaining to Rural Nonfann Activities (n-62 rural cantons) ...... 110

A.3 Typology of Costa Rican Cantons...... 117

5.1 Migration Rate Patterns ...... 139

x i CHAPTER I

INTRODUCTION

Research on development processes in less developed countries

(LDCs) has given a great deal o£ attention to rural development, particularly the creation of off-farm and rural nonfarm employment in small-scale enterprises. These are seen aa more effective in development strategies than large-scale, western-style urban based industries (Staley and Horse, 1965; Liedholm and Chuta, 1976; Heyer and

Larson, 1978)

In spite of the emphasis given to these economic activities by researchers, development planners, and international aid donors, tvo

*Rural nonfarm employment in small-scale rural enterprises will be discussed in greater detail in Chapter II. For the present, however, rural nonfarm employment refers to the very heterogeneous and highly labor-intensive economic activities (rural "industries") that are located in rural areas, villages, and towns. These economic activities produce virtually all kinds of goods such as traditional and ceremonial goods; crafts of artistic value; consumer goods such as furniture and household items, food products, farm implements, agricultural machinery and electric apparatus; as well as providing services like plumbing or vehicle repairs or marketing of agricultural products. Production is carried out at all levels of sophistication, ranging from traditional cottage industries, i.e., nonfarm activity within the rural farm household, to modern, small-scale operations with a higher degree of capital input, separation of management and workers, and division of labor among workers (Steele, 1975), However, while the term nonfarm/ off-farm employment is often used interchangeably with rural industrialisation, off-farm employment can also refer to an agricultural job outside one's own farm household.

1 2

issues have been neglected. First, there has been almost no examination of the effects of rural nonfarm employment/small-scale enterprises on rural migration in LDCs* Instead, migration research has been urban- oriented in that it relates rural migration primarily to vages and job opportunities in urban centers, thus ignoring the rural economic environment in vhich the decision to migrate is made.

This neglect of the rural environment is a serious shortcoming, because it is not only possible but very likely that factors in the rural environment are also critical determinants of permanent migration,

and may be responsible for accelerating or retarding migration. Rural- based earnings opportunities in the nonfazm/smal1-scale enterprise

sector are among such factors, and they are the focus of this research.

The second issue that has received little attention in the

literature is the question as to how rural nonfarm employment affects migrant selectivity in LDCs. To elaborate, the promotion of the rural

nonfarm sector has been shown to be beneficial for human capital

2 Indeed, even fo r more developed co u n tries (HDCs), th ere e x ists very little systematic research on the relationship between rural migration and rural industrialisation, although a relationship has been recognised (Brinkman, 1972; Hitshusen and Gray, 1977; Beale, 1976; Acquah and Hushak, 1978). 3 It should be noted, however, that urban migration models gave inadequate treatment to the rural sector because they were not designed to do so. Instead, they were urban oriented for good reasons. Drban- ward migration and labor absorption of migrants in the urban industrial sector have been theoretically regarded as instrumental in economic development. Empirically, however, migration has exceeded the rate of labor absorption and has caused for the cities severe problems of internal adjustment, auch as high open unemployment. Because of this problem, migration theory and research have been urban-oriented, focusing particularly on reasons for urban-ward migration despite high urban unemployment. 3

formation through the system of apprenticeship (Liedholm and Chuta,

1976)* Indeed, the base for the promotion of the rural nonfarm sector rests partially on its supposed improvement to human capital. Never­ theless, no systematic assessment has been made of how rural nonfarm employment affects in or outmigration of individuals with certain human capital levels. One may ask, for example: Does rural nonfarm enter­ prise attract or retain inmigrants with higher levels of human capital?

Alternatively, does the rural nonfarm sector absorb primarily individu­ als vith lover human capital characteristics while the younger, better educated/skilled individuals vith higher occupational status migrate avay? These considerations are also focused upon in this study.

A broader concern vith this issue is whether or not the non­ farm sector in a given area provides a basis for self-sustaining development and income growth through employment of manpower with higher human capital levels. If it does not, it may be necessary to enhance

the potential of the rural nonfarm sector in development strategies by attracting/retaining migrants with human capital levels deemed desirable

for productivity growth and development.

Problem and Conceptual Framework

In short, this study addresses two sets of questions:

1. How does the presence of rural nonfarm enterprises in an area

affect aggregate in, out, and net migration where in, out, and 4 net migration are considered as permanent moves. Do, for

4 Although the term migration can be understood to include seasonal and circular movements, this study limits itBelf to permanent migration. 4

example, wage labor opportunities provided by the^rural nonfarm

sector induce or stem rural outmigration compared with the opportunities provided by the agricultural and urban sectors?

2. How does rural nonfarm employment affect the selectivity

aspects of migration? That is, what human resource

characteristics are found concentrated in rural small-scale

enterprise compared to the farm sector or urban economic

activities?

Although the emphasis of this study is on the effect of rural nonfarm employment on migration, the impact on migration of the farm and urban sectors also is considered. This recognises that the migration decision is not determined by a single labor market. Rather, a rural household/individual may consider at least three alternatives: nonfarm employment, agriculture (or a combination thereof), or the urban labor market. This can be shown graphically in Figure 1.1, which shows that the potential migrant from a farm household may remain in an agricultural occupation, and/or commute or migrate to a nonfarm job in a nearby small town, or he/she may migrate to the city in search of employment.

Vith respect to the first question -- how rural nonfarm employment opportunities affect migration compared to the farm and urban sectors — one would expect that they stem and/or divert urban-oriented migration.

Regarding the second question — as to how migrant selectivity is affected — one may expect that the nonfarm sector, while not completely offsetting selective outmigration to urban areas, reduces it. 5

Figure 1.1. Alternative Eaployaent Opportunities

end the Hlgretlon Decision

The Individual nay conute or Migrate to a nonfan job in aeall or nediua towna

The individual Bay atay in aitu

FARM RURAL HONFARM EHPLOY HOUSEHOLD KENT IN SHALL AND MEDIUM TOWNS

The individual nay The individual nay nigrate to a najor nitrate "atepwiae," urban center in f ir a t to anall and eearch of eaploynent aediun tovna, then to a aajor urban canter in tearch of enploy- ■ent. /

URBAN EMPLOYMENT 6

The rationale for both these assumptions is well founded in the

Theory of the Household and migration models based thereon. To elaborate, the theory of the household — based on Cheyanov (1966),

Robbin (1930), Mellor, (1966), and particularly Gvenson (1967) and

Nakajima (1969) views the household sb an entity that allocates its resources so as to maximise a family utility function. As part of this,

labor time of household members is allocated according to the member's earnings power in the various labor markets. This, in turn, depends on

that member's human capital level. Members of greater education and skill levels, for example, will enter or migrate for off-farm/nonfarm activities and spend little time in agricultural pursuits on their own farm. Generally, the greater the level of human capital, the less

likely a household member will engage in agriculture (on one's own or someone else's farm), and the greater ia the propensity to engage in a nonagricultural job. These nonagricultural jobs may be found in the rural nonfarm/amall-acale enterprise sector or in the urban formal and informal sectors.

A great number of the migrants, however, may prefer rural nonfarm to urban jobs. Several reasons may account for thiB:

(a) A nonfarm job, particularly during the agricultural off­

season, represents a risk reducing diversification of income

for the household and individual. A household or individual

that pursues farming only is subject to risk each season due to climate, weather and pests and the absence of institutional

risk insurance (Stark, 1982: 67). By taking a nonfarm job — 7

all year round or just during the agricultural slack season ~

the individual or household is not subject to these periodic

r is k s .

(b) Nonfarm jobs in situ or in relatively short distance nay bear

less monetary and psychological costs for a potential rural

migrant, i.e., the cost of migrating to an urban area and

searching for a full-time job may exceed the cost of obtaining

a part-time job in a nearby nonfarm enterprise. In this

context, one may consider the very high risk for the newcomer

to the c ity of having no employment and income a t a l l for some

time. Although this risk in the urban area diminishes over

time, it may be altogether higher in the long run than any

risk associated vith a combination of farm and nonfarm

activities, or vith a rural nonfarm job alone (Stark,

1982: 67).

(c) Rural nonfarm enterprises may permit the individual to remain

in s itu and commute vhereas urban employment may require

relocation.

(d) The ru ra l migrant from a low income household may have more

information about jobs in the rural area than the better off

migrant who obtains this information and possibly an urban job

through kin that migrated earlier (Lipton, 1982).

Aa such, rural nonfarm sector jobs — albeit oftentimes lower paying

than the urban sector — may be an important determinant in accelerating

or retarding rural-urban migration. 8

Assumptions to be Examined

Given the above conceptualization, the following assumptions are examined:

1. Rural nonfarm employment is an important facto r in a c c e le ra t­

ing/retarding rural-urban migration. This would be manifest on

the aggregate and the individual level in the following ways:

out migration is low and/or in migration is high and net

m igration is positive in ru ra l locations which o ffe r employment

opportunities in the rural nonfarm sector.

2. Given that individuals with greater educational and skill

levels expect higher earnings in nonagricultural pursuits,

rural nonfarm activities — while not completely offsetting

selective urban-ward migration — retain people with relatively

high levels of human capital. Thus, the opportunity for rural

nonfarm employment reduces p o sitiv ely se le c tiv e ru ra l

out migration.

Attention now turns to the theoretical and policy implications of this study.

Relevance of the Study

As noted earlier, migration research has taken an urban emphasis, focusing on the factors which would explain the flow of migrants to the growing urban-induBtrial complexes, A reason for this is that economic development has commonly been associated with the transition from rural 9

to urban industrial activities (Lewis, 1954; Ranis and Fei, 1961;

Kusnetz, 1966, 1969) and rural to urban migration has been viewed as instrumental in this process.

Given the benefit of hindsight, however, it is obvious that rural- urban migration has exacerbated already existing problems of excessive urban growth and high open urban unemployment (Turnham, 1971; Todaro,

1976). Thus, the concern with the rural origin of these problems is of obvious importance, and particularly with rural based job and wage opportunities that could reduce the excessive and premature rural exodus. This would aid in designing more effective policies for the battle against the interrelated problems of rural poverty, and urban growth and unemployment.

While the rural nonfarm/small-scale enterprise sector may have a p o sitiv e impact on poverty, employment and in eq u ality in ru ra l areas in

LDCs, it is possible that some individuals/socioeconomic groups and areas w ithin a country may b en efit more from ru ra l nonfarm employment than others. A reason for this might be that the small-scale sector absorbs more strongly individuals with certain human capital charac­ teristics. For example, if the small-scale sector demands mostly better educated and skilled labor, the income levels of the majority of the unskilled would not be affected. If, on the other hand, mostly people w ith lower human c a p ita l lev els were absorbed, ru ra l nonfarm employment would not affect selective migration and its negative externalities.

The long-term concern with the latter is related to the role of human c a p ita l in development and growth of an economy. Rural indus- 10

trialization in LDCs has a positive role in human capital formation, through on-the-job training, the system of apprenticeship, and stimulation of entrepreneurship (Liedholm and Chuta, 1976). This may contribute to growth by (a) raising the quality of the labor force and increasing labor productivity, and (b) accelerating the rate at which society's state of knowledge advances, which in turn accelerates improvement in productivity and more efficient allocation of resources

(Denison, 1964: 13-55; Colclough, 1982).

An assessment of the human resources found in the rural nonfarm sector versuB the rural farm or urban sectors, then, may be valuable for policy questions such as the following: What kinds of labor skills or nonfarm employment opportunities are in short supply in an area? What are the alternatives for providing or developing the labor skills needed by rural industries? Has the area benefited from a rural nonfarm employment strateg y , i . e . , is the given ta rg e t group p o sitiv ely affected or not, which in turn has implications for migration. While these and similar questions have been raised in research on rural industrializa­ tion in more developed countries (MDCs) (Acquah, 1978), they have been neglected for LDCs.

The concern with these questions, however, is of obvious importance given that the nonfarm and farm sectors are the main sources of employ­ ment and incomes in rural areas and that their role can be enhanced by policy measures in several ways. One way, for example, is by attracting additional small-scale enterprises that require low skill levels; alternatively, one can encourage increasing educational levels (and per 11

capita income levels) through development of nonfarm employment that requires s k ille d labor (Acquah and Hushak, 1978).

Overview of the Following Chapters This dissertation proceeds with an elaboration of the themes presented above. The following chapter (Chapter II) summarizes relevant literature pertaining to (a) rural nonfarm/off-farm employment, and (b) migration. Chapter III presents the research design and discusses the study area, Costa Rica. Chapter IV depicts the rural nonfarm sector, the farm sector and pull effects emanating from the urban areas, using descriptive statistical methods. Chapter V examines the relationship between migration and the rural nonfarm/small-scale enterprise sector, the rural farm sector, and the urban-industrial Bector. Chapter VI is concerned with the human resource characteristics of the migrants and nonmigrants that are found concentrated in the various sectors. Chapter

VII provides a summary of the findings and implications for policy and future research. Attention now turns to a review of pertinent litera­ ture on (a) rural nonfara employment, and (b) migration. CHAPTER I I

REVIEW OF PERTINENT LITERATURE

This chapter reviews the literature relevant to the present study.

Two sets of literature will be discussed in particular. These concern:

(a) rural nonfarm/off-farm employment and small-scale industrialisation, and (b) migration.

Regarding the former, the increasing emphasis on rural nonfarm

employment and its advantages in economic development strategies are

summarised. This subsection serves to better understand the nature and

importance of rural nonfarm employment, with which this dissertation is

concerned.

Regarding migration, first the general economic theory of the

household is summarized which implicitly or explicitly underlies much of

the theoretical models and empirical research on migration. Second.

specific articulations of this general theory of the household, with

reference to migration, are presented. These two subsections provide

the theoretical basis for the assumptions stated earlier, that rural

nonfsrm employment is an important factor in accelerating/retarding

rural migration overall and selective migration in particular. Third.

migration literature focusing upon the negative human capital

12 13

consequences of migration for the origin areas is summarized. The observations made in this subsection highlight the importance of stemming excessive rural migration in general and selective migration in particular, such as through rural non-farm employment.

Attention nov turns to the literature on rural nonfarm employment.

Rural Nonfarm Employment and Small-Scale E nterprise

This subsection first summarizes the shift in the development arena from large-scale urban industrialization towards rural nonfarm employment strategies. Second, it reviews the advantages of rural nonfarm employment/small-scale enterprise over large-Bcale urban industrialization in development strategies.

The 8 h ift Toward Rural Nonfarm Employment S tra te g ie s

During the past two decades, many LDCs have pursued capital intensive industrialization strategies based on the classical two-sector growth model put forth by Lewis (1954) and modified by Ranis and Fei

(1961). This model envisages development to occur through the intersectoral transfer of labor from the subsistence to the urban industrial sector.^ Capital-intensive industrialization and inducement

^Economic growth in this model occurs through growth of the industrial sector which can draw on unlimited supplies of labor from the subsistence sector. When the surplus labor is exhausted and labor supply is less than perfectly elastic, wages begin to rise for both the subsistence and industrial sectors. Economic development then occurs through a smooth equilibrating process of continuous marginal adjustment in wage ra te s (Nugent, 1977). 14

to migrate, however, have exacerbated problems such as slow growth, unemployment, and inequalities because the transfer of labor out of the

subsistence sector was not matched by the rate of labor absorption in

the urban sector. This has been related to a policy bias toward capital

intensity (Oshima, 1971; Ho, 1972; Ho and Huddle, 1975; Meyer, et al.,

1978).6

Besides a more than unsatisfactory solution to the problem of unemployment, capital-intensive industrialization inevitably generates marginality.^ This means that growth and income distribution tend to

be concentrated, leaving certain regions and people outside of

development and at the margins of subsistence. Further, the centers of

growth are controlled for the benefit of people who are often external

Some, however, argue that capital-intensive industrialization is inherently not capable of absorbing labor on a large scale no matter whether it is in more or less developed countries. To this point Sweezey (1970: 83-90, 138-47) notes that the driving force of such development is the accumulation and reinvestment of capital which depends upon a disparity between the marginal product of labor and associated wages. This profit margin, however, will decrease over time when wages begin to rise. In response to this the entrepreneur will introduce labor-saving technology, the net effect of which is to create unemployment. The re-employment of these unemployed depends on further expansion of the industrial sector. The profit accumulation and reinvestment needed for this, however, is, in turn, contingent upon a rate of labor displacement that permits profits to be increased. This process goes on infinitely, repeating itself in a manner such that the rate of unemployment is always greater than the rate of labor absorption, leading eventually to a decline in effective demand and economic stag n a tio n . y This argument, developed by Gonzales Casanova and the Dependency School, is summarized in Kahl (1976: 74-128). 15

to the local community. Gilbert (1976) refers to thiB as a satellite relationship in which the "internal colonies" are linked in a dependency relationship to dynamic foreign centers* Thus, because of their o rie n ta tio n tovards other external economies and low employment multipliers, the urban industrial growth enclaves fail to establish domestic markets through backward and forward linkages with other regions and sectors in the economy*

In summary, capital-intensive, western-style industrialisation has been increasingly considered inappropriate and as a tour-de-force in development. It indiscriminately imposes aspects of modernisation from western countries without the processes leading to them, or giving due consideration to a country's resource endowments or comparative advantage. Each country and epoch, however, face unique conditions set by earlier historical trends and external circumstances which determine

the sequence and pace of the total internal transformation of society.

Problems of growth, poverty and inequalities, therefore, persist unless a country develops in phases which lead to functional integration of various parts of the economy, society and polity (Germani, in Kahl

1976: 123).

In response to the growing discontinuity between belief and fact about capital-intensive industrialization, development planners have

turned to strategies that are thought to achieve greater functional

integration of the economy and society. One such strategy places

greater emphasis on labor-intensive, small-scale rural nonfarm

activities and agricultural development. 16

The ru ra l nonfarm sector and nonfarn/off-farm employment were briefly described earlier. 8 To recall, rural nonfarm employment is the generic term covering a wide variety of nonagricultural economic activities that may be carried out within the rural farm household as well as in separate small-scale nonfarm enterprises. The distinguishing feature of these nonagricultural economic activities is their rural location and labor-intenaity (Anderson and Leiserson, 1978), rather than the kinds of goods and services produced, or the sophistication of production. Indeed, rural nonfarm activities produce all kinds of goods and serv ices, from tra d itio n a l goods of a r t i s t i c value to modern consumer goods and services. Also, production is carried out at all levels of sophistication, varying from traditional cottage industries to modern sm all-scale e n te rp rise s. The heterogeneity of a c tiv itie s and enterprises classified as rural nonfarm sector make definitions difficult. Existing definitions vary widely and have different implications in each country. The consensus, however, is that the rural nonfarm sector is characterised by four dimensions. These are:

(1) relatively small number of workers, (2) relatively small value of

Q See Chapter I, p. 1. 18

Economic M u ltip lier E ffe c ts. A ttention f i r s t turns to the economic multiplier effects, portrayed in Figure 2.1. Note that the elements in Figure 2.1 are numbered as they will be referred to in the te x t.

The rural nonfarm sector may be more efficient in the use of resources, particularly capital which is produced within the sector, thus reducing the drain on formal capital and foreign exchange marketB

(Oshima, 1971). One reason for this is the overwhelming amount of self- financing in the rural nonfarm sector. For example, Liedholm and Chuta (1976) found that 80% of the funds used for initial establishment came from personal, family, or friends' savings (Box A in Figure 2.1), and that up to 90% of the expansion capital in rural nonfarm firms was reinvested profits (Box 5 in Figure 2.1). There also is investment from agriculture (Boxes 15 and 5 in Figure 2.1), since many nonfarm enter­ prises are owned by farmers (1LO, 1972), and from the large-scale urban- industrial sector. In particular, Steele (1976) found that many of the rural nonfarm firms were 'putting out' industries, that is, subcontrac­ ted enterprises that use inputs put out by larger urban industries and produce goods with a high profit margin for the urban enterpreneur.

DeWilde (1976) shows that there is also investment from the urban service sector, since prior to entering the rural manufacturing sector, many entrepreneurs were traders or white collar workers.

A second reason for efficient resource use is that the rural nonfarm sector reliea strongly on available local technology and FIGURE 2 .1 . MTERCONNECTEONESS AND H U UV U B1 EFHEC1S OF RURAL NONfMM B ITU W ttb t

RURAL

or 20

resources (Box 2 in Figure 2.1) such as depreciated or 'secondhand' capital from the capitalist sector that would be of little or no use if not employed by small-scale firms (Oshima, 1971)* International Labor

Organization (1972) also reported that rural artisans re-use spare parts which constitutes an important saving relative to capital-intensive industries.

In addition, the production processes used by many nonfarm enterprises are less demanding in termVof sophistication of equipment, inputs, and technical skills. Thus, they have a lower import content I compared to more capital-intensive production and their expansion is not constrained by the scarcity of foreign exchange earnings (Johnston and

Mellor, 1975).

One can see, then, from the viewpoint of capital use, strategies promoting the rural nonfarm sector are more desirable in a capital- scarce developing economy than capital-intensive urban industriali­ zatio n .

Considering now the use of labor in the rural nonfarm sector, one finds that most small-scale enterprises are highly labor-intensive, with a particularly high demand for low or unskilled rural labor (Boxes 6 and

3 in Figure 2.1). Since people with little or no skills account for the largest share of the population, small rural firmB thus absorb a larger share of rural labor than the more capital-intensive enterprises, and offer greater opportunities for family and landless labor, for example, that is largely unemployable in capital-intensive industries.

Illustrative of thiB is the Iranian carpet weaving industry that 21

utilizes to a large extent women and children (Dhamija, 1976). Accordingly, nonfann activities generally provide greater income

earnings opportunities for a large segment of the population and

represent a substantial shore of total employment. For South Korea,

Taiwan, and the Philippines, for example, small rural industries with

less than 50 employees account for 51Z, 60Z, and 85Z, respectively, of

total manufacturing employment. Similarly, African surveys show that

small industries account for between 50Z and 95Z of the total employment

in Western Nigeria and Sierra Leone, respectively (Muller and Zevering,

1971; Liedholm and Chuta, 1976).

Furthermore, eamingB levels are higher in rural nonfann activities

than on the farm (Kuhnen, 1968; G eertz, 1972; Meyer and Larson, 1978).

These wages from nonfarm jobs, as well as remittances from farm members who have migrated to towns, can significantly stabilize rural incomes

that would otherwise be adversely affected by the seasonality of

agriculture (Norman, 1969; Geertz, 1972) (Boxes 7, 8, and 9 in Figure

2.1). Accordingly, rural nonfarm employment is the primary source of

income for approximately one-third of the labor force in rural areas and

towns in Africa, Latin America, and Asia, and a very important Bource of

secondary earnings for farm households.

More stable and improved rural incomes have an important effect on changing consumption patterns. With greater segments of the population

having more income, demand for consumer goods goes up, particularly for

the ru ra lly manufactured and processed commodities (Box 10 in Figure

2.1) (JohnBton and Mellor, 1975). Indicative of this are the high 22

income elasticities of demand for these products, suggesting that the demand for these goods would increase as rural per capita incomes rise

(Gibb, 1972; Child and Kaneda, 1975; Liedholm and Chuta, 1976). This, of course, would initiate a multiplier effect in which nonfarm activities and employment expand (Boxes 1 and 3 in Figure 2.1), incomes improve (Boxes 7, 8, and 9 in Figure 2.1), etc.

That small-scale nonfarm firms have close linkages to the agricultural sector also is important. This interdependence has been emphasized by Liedholm (1973) and Johnston and Mellor (1975), who point out that agricultural transformation and growth are indispensable requirements for the growth of the nonfarm sector. A viable nonfarm sector, in turn, is necessary for agricultural transformation. More specifically, if the agricultural sector is not able to produce a marketable surpluB, there will not be sufficient demand for the output of small industries and the services provided by nonfarm activities, such as processing of agricultural output. Thus, rural nonfarm sector growth dependB importantly on the growth of the agricultural sector.

This, in turn, is dependent upon the effective mechanization of small holdings with low cost hand tools, animal drawn implements, and manually operated machines that are locally produced (Steele, 1976). In other words, growth and autonomous advance in both the rural nonfarm and the agricultural sectors are stressed simultaneously and in reinforcing fashion through income from off-farm employment (Boxes 7, 8, and 9 in

Figure 2.1) and the demand for agricultural output from certain nonfarm firms (Box 11 in Figure 2.1). 23

The improved agricultural incomes may raise the demand for rurally produced consumer goods and services directly (Box 10 in Figure 2.1), or indirectly through investment into agricultur e (Box 12 in Figure 2 .1 ).

This may increase factor productivity (Box 13 in Figure 2 .1 ), the income of the remaining farm labor (Box 15 in Figure 2.1), and agricultural output (Box 14 in Figure 2.1). The growth in output further increases the cash income of farm households (Box 15 in Figure 2.1) which may be reinvested in agriculture (Box 12 in Figure ?.l) or uBed to purchase locally produced consumer goods (Box 10 in Figure 2.1). Furthermore, the increase in output in itself calls for morem i x rural nonfarm processing activities (Boxes 16, 1, and 11 in Figure 2.1), and this should give rise to an 'output effect* (Gibb, 1972; Liedholm, 1973) which increases the division of labor between farm and the various kinda of nonfarm work. Specifically, a growing share of the rural population should specialise in nonfarm activities (Hymer and Fesnick, 1969; Anderson,

1976),“ and those remaining in agriculture should be enabled to reduce subsistence cropping and to specialize in commercial crops instead, placing further demand on small-scale nonfax inti firm s.

Finally, the export potential of commodities produced in labor- intensive industries has been emphasized as a major advantage. Ho and

Huddle (1975) found that labor-intensive goods of artistic value have import demand elasticities far above unity and a high annual rate of expansion in trade. Thus, nonfarm firms that produce those goods can substantially increase labor absorption and fji oreign exchange earnings

(Boxes 19, 1, and 3 in Figure 2.1). 24

Human Capital Effects. As noted above* rural non£arm employment is seen to have two major advantages in development stra­ tegies* one being the economic multiplier effects described above, and secondly* the impact on human resources in an area. Regarding the lat­ ter* the rural nonfarm sector 1 b potentially able to stem out migration since i t has been observed th a t areas with more ru ra l nonfarm employment opportunities have lower out migration rates (Liedholm and Chuta, 1976) (Box 18 in Figure 2.1). As noted in Chapter 1* however, very little systematic evidence exists on the relationship between rural nonfarm work and migration* although a relationship can certainly be implied.

The rural small-scale enterprise sector also leads to upgrading of skill levels through the widespread system of apprenticeship (Box 17 in

Figure 2.1), the most significant employment category (Liedholm, 1973;

1LO* 1970). In Western Nigeria* for example, 56% of the nonfarm workers in the more modern c ra fts were ap p ren tices. The apprenticeship system in rural nonfarm activities is important in that for moBt low income countries it is the only or moBt widely used system of vocational training (ILO, 1972). Since not only the craft but also managerial skills are transmitted, a basis is provided for establishing new enterprises and operating them successfully. To this point, Liedholm and Chuta (1976) found that 90% of the proprietors in Sierra Leone had previously served as apprentices before branching out on their own.

Similarly, DeWilde (1976) reported that the managerial skills acquired in apprenticeship are the most important determinant of business 25

success. This gives rural nonfann activities another important advantage over capital intensive industries that usually grow without at the same time training people to manage their own enterprise.

D espite the ro le of the ru ral nonfaxm sector in human c a p ita l

formation, there exists very little research on the interrelationship between rural nonfarm employment and selective migration, i.e ., in and outmigration of individuals with certain human capital levels.

Summary of the 8ubsectlon

The advantages of rural nonfarm employment in development

strategies are in terms of economic multiplier effects and human

resource effects. Regarding the former, the greater labor-intensity and

ru ra l location of nonfarm employment allows an improvement in incomes

for a large number of the rural poor. This is seen to bring about long­

term shifts in the domestic demand schedule which, in turn, should

enlargen the sise of the domestic market in such a way that each

enterprise can advance along an expansion path determined by the

elasticity of demand for its output. Such growth further sustains the

absorption of rural labor, and this at the same time raises the food

demand. Thus, rural nonfarm activities lead to autonomous advance in

the agricultural sector as well, which is particularly important given the weight of the agricultural sector in the total (developing) economy

(Johnston and Kilby (1975). Regarding human resource effects, the rural nonfarm sector creates

an environment favorable to entrepreneurship, managerial ability and 26

upgrading of skill*. This might induce people to in migrate to areas with nonfarm employment opportunities or it might stem out migration.

tfhereas the aspects described above also have been the aim of previous development strategies, the advantage of rural nonfarm employment over previous strategies is that it generates bottom-up development. That is, the transformation of production and consumption structures and economic growth would be the result of broad participation of the rural population and would benefit greater masses of people.

Having reviewed the literature on rural nonfarm employment, attention now turns to the review of literature pertinent to migration.

M igration

The lite r a tu r e on m igration can be divided in to two p a rts, one. dealing with the migration decision process, and the other addressing the consequences of migration. Both of these are discussed in turn.

The M igration Decision Process

There are many alternative perspectives — indeed doctrines — of the migration decision process, which prompted one researcher to note

(Stark, 1982: 64),

....years of intensive research have left the field beset with loss of direction, grave confusion, and serious doubts as to; (I) Whether or not the academic profession has based its migration research effort...on an inappropriate set of presuppositions or, even worse, on invalid postulates; (2) the areas in which the marginal benefit of extra research amounts to zero; (3) those areas in which some solid consensus haB emerged, and a clear formulation of 27

this consensus; (4) which problem areas and specific issues in them merit additional intensive research....(5) whether research has really provided p ra c titio n e rs w ith fin e r, more sp e cific means of intervention—should it be deemed desirable, and (6) proper understanding of such intervention, its justification and results.

A way out of this quagmire may be to employ the theory of the household

as a framework for reconciling the diverse viewpoints on migration, and

for organising the review. The structure of this discussion is summar­

ised in Figure 2.2.

The Theory of the Household

Articulation of the linkage between the general theory of the

household and migration provides a conceptual framework that appears to

be singularly applicable to migration in traditional, developing or

advanced societies, and accounts for both microlevel considerations and

macrolevel forces that influence migration. The theory assumes that the

individual or household's behavior is rational, in that it tries to maximise a family welfare or utility function and to reach a "subjec­ tive equilibrium."^ This subjective equilibrium point is reached by

making household management decisions of two kinds. First, the

The rationality of households has been theoretically and empirically described by Schulte (1964), Lau and Yotopoulos (1971), and Wise and Yotopoulos (1969). 28

Ut*r* 1.1. WtMltW or THE LITEMTm Ok THE Htcurto* Mctiiod noctti.

n ttft Bf th« aonchou (tohhln, HJOj Hr Her, IHJl latter, IKT| HakaJUa, 1*1) iMitktW aeilatiae a (nit; ■llUt? (nttln, *hlck tntU li •( n n n l i n ( h i -in iih Ii Tirliblii, the rrlatlae lapottanca ol ■hick oariao with aaelo-ateaoalc an# natural toalltleai, ( i n kite* l» ;llli anl n i l t 1m laportaace *r |eo|taphlc localloa aa an erioaaat la anl coaaitainl •a I ha •tillt? lanct lull (ntelaaaa, IN I, 1174)

1 Ipttillc articulation ol Ihia aotioa with reared to al|talloo. 3 i flilU l'lU .111 1 (Wolfed, 1H)1 Itova aei Haora, 1170) (Caracal, 1M)| Hako|«a]o, 1170) o( aconoaie oak aoarconoaic (actora. aal Collwrat Variation,

Focui on Econoale an! Hon*tcnnoaK Palaralnanta al Hiiratiun

(a> Econualc (k) *on»tCrAoaic

laiioaat Intoaa anl Jok Ela-lalutuA Hlcratlon Irlartlrltjr QpporUnitr PlllartatlaU flirtation oi hr toclal Claaa Aaoa| Aroaa fllaratloa Chain UlMli V Lakor rcrta Atluafarnc Kuaaa laaaataaai. Hoaan Capital or CoaUlrnrf it (Lcmry, l h t | Creoawoot, nettle ol Nitration 1170) (Tolaca, IH tl Talaro ail Harrla, IV7t)

Paaalonaanl faralita o( flliTitioi (IcevH i ll SaaJara, If 10) Ealalltre kola ol nitration Oatacalaaata thlfta over tba Couraa o( Daaaloraaal 29

household decides on how to best utilize/conBuoe the earned income.

Second, the household attempts to maximize returns on its resources,

leading to decisions on the optimal allocation of land, capital and

labor. Typically, in an LDC with fixed land and limited capital

resources, it is the degree of labor input that determines whether

returns are maximized or not. Each household member's labor is

therefore allocated according to that member's comparative advantage in

farm or nonfarm/off-farm activities. Thus, family members of higher

earnings power participate in the labor force and pursue jobs outside

the farm or household.

Each household firm has itB own particular utility function, as

well as its own particular production function. The family achieves itB

particular subjective equilibrium when it has realized the maximization

of its utility, Bubject to its income and other constraints. It should

be noted th a t in bo characterizing the household's decision making behavior, no evaluation of the "rationality" of the particular utility

function or the labor allocation decisions is attempted (Nakajima,

1969). Rather, two points stand out as important.

The first is that households/individuals maximize a utility

function which is specific to them and rational from the viewpoint of

the decision maker, and which may differ in the various socio-cultural and economic contexts. Indeed, a utility function in LDCs may appear to be somewhat strange from the view of people in advanced so c ie tie s and vice versa, and even within a specific household it may differ over time 30

and the course o£ socioeconomic development (Mellor, 1965; Evenson,

1967; Tang, 1957; Krishna, 1969; Nakajima, 1969).

Second, this concept of utility maximization takes economic and non­

economic factors into account and is applicable to a vide variety of human behavior. Contemplating on the concept of utility, Samuelson

(1965, 79: 91-92), for example, notes that the household's/individual's

decision-making is explained in terms of preferences which, in turn, are defined only by behavior. The concept of utility, then, is somewhat

similar to saying,

people behave as they behave, a theorem which... contains no hypothesis and is consistent with all conceivable behavior while refutable by none.

Many other social science disciplines have used thiB theory and translated it into their respective terminology. Rogers (1969), reviewed in Nakajima (1969), for example, envisages the peasant and peasant household's behavior as guided by "farailisra." This refers to considerations of "one pocket" and "one pain", i.e., a family utility function, and a "limited aspiration level," i.e., the various micro and macrolevel constraints influencing the decision. Likewise, much of the geographic migration literature has adopted the theory of the household, for example, modeling the household/ individual migration decision in terms of a "place utility" that is influenced by individual-level characteristics and macro-level

"environmental stress factors" (Wolpert, 1965; Mabogunje, 1970; Brown and Moore, 1970). This is based on considerations such as expressed by

Friedman (1976: 46). 31

For a person considering where to settle, regional location can be considered as a good that can be represented on the axes of indifference curves. Regional location, however, is also a factor determining opportunities, summarised by the budget line in indifference curve analysis. Once a particular location is chosen, for example, it is an opportunity factor that affects incomes received and prices paid for various goods and services. Location is also a factor reflecting tastes (represented by indifference curves) since it may affect the importance a potential migrant attaches to certain goods and services. . . .The same factor, then, may for one purpose be treated as a good and measured along the axes of indifference curves for another as an opportunity factor for another as a taste factor.

Similar to Friedman's (1962, 1976) view of geographic location as a choice as well as a constraint set, the concept of place utility associates preferences and external economic and non-economic constraints with a given geographical location. TWo important articulations of the above concept are the models of

Brown and Moore (1970), emphasizing microlevel considerations, and of

Germani (1965) and Mabogunje (1970), who are concerned with the macrolevel forces controlling migration (Figure 2.1). Both concepts are concerned with general factors such as individual utility/disutility or environmental stress. Yet, they complement each other nicely and are a useful framework for understanding models and research dealing with specific factors, such as wage differentials (Brown and Sanders, 1981).

Therefore, these two models are presented next. 32

Individual Decision Making and Migration. Place utility is a

basic concept in the Brown and Moore model. This refers to an individu­

al's or household's overall level of satisfaction or dissatisfaction with a given location. If the place utility of the present residential

site is greatly at variance with the individual's immediate needs, that

person will consider a new location. Thus, migration ia viewed aB an adjustment whereby one residence or location is substituted for another

in order to better satisfy the needs and deBireB and to increase the place utility experienced at the residential site (Brown and Sanders,

1980).

Just as in the original household model, the decision (to migrate)

is seen as intendedly rational. It is based on subjectively rational utility maximising considerations within a constraint set given by a

limited informational basis. The individual or household as seen by

Brown and Moore is continually evaluating the congruence between needs/ expectations and the offerings associated with the present residential

site. These congruencies may pertain to such things as wage levels,

employment opportunities, climate, social and family relationships,

traditions and customs, or amenities such as education, social infra­

structural facilities or "bright lights" (Brown and Sanders, 1981;

150). Disparities between needs/expectations and actual environmental offerings put stress on the individual and when the stress threshold

level is reached, migration may occur as a coping mechanism. Alterna­

tively, the household may alter its need set and remain in situ (Brown and Moore, 1970). Whether or not and which one of the above factors may 33

cause the greatest stress, of course, depends on the individual's/ household's specific utility function.

The Brown and Moore model was originally proposed for intra­ urban migration, and this may be seen as a shortcoming. However, Brown and Sanders (1981) demonstrate its applicability to Third World interplace migration, such as the rural-to-urban. A more serious short­ coming, however, is Brown and Moore's focus upon the role of origin area characteristics in the decision to seek a new residence. This is contrary to one assumption of this study; that the individual also would take account of characteristics of the potential destination, say an urban area, and may not migrate because of its inadequacies. In other words, staying in the rural area may be a viable coping mechanism given the disparities between the expected urban and actual rural environmental offerings. Applied to LDC migration, then, the Brown and

Moore model does not recognize that an individual may have a "rational" preference for not migrating to an urban area.

Macro-Level Forces and Migration. One important aspect that tends to get overlooked in the plethora of factors addressed by Brown and Moore, is the socio-cultural context in which migration decisions are made, its influence upon economic and other motives of potential migrants, and its role in determining:

1) whether or not migration occurs, 2) if it does, what form this migration takes (permanent, circular, or other), 3) the destination of the migrant, and A) the nature of the migrant's experience at that destination" (Hugo, 1981: 188). 34

An earlier conceptualization in thia regard ia that by German! (1965).

He pointa out that individual decision takea place in a normative context. People evaluate and perceiv i the attractions or repulsions of particular locationa againat the fram •work of institutionalized roleB, expectations, and behavior patterna t lat, in a particular society, regulate migration, i.e., either facilitate or retard it. Subsequent to German! (1965), Mahojunge (1970) and Du Toit (1975a) advanced conceptualization of the 'mu ticausal nexus of the migration process' that attempts to take the ps; cho-social cultural, as well as economic environment, into account, labogunje, for example, divides the multitude of environmental stress fac :ors into rural push and urban pull factors. Drawing on African experien :e, he cites among the rural push factors the age at which a young person is expected to become economically independent of the paren ;a, the age at which marriage is encouraged, the system of inheritance (primogeniture versus partible), and the strength of community ties. Foremost among the urban pull fa c to rs a re wage ra te s and job opport inities, better housing conditions, health and educational facilities, an I the presence of friends, relatives and acquaintances that may sase the transition. Among the general environmental factors are also communication and transportation networks, general economic conditions, the level of technological progress and development, and governmji knt policies (as reviewed in Hugo,

1980). A strength of the Germani/Mabogubje conceptualizations is that they contain elements pertaining to the rural origin or system, the deatina- 35

tion or urban system, and the linkage between them. Brown and Sanders

(1980: 159-160) note, however, that the implementation of these models is beset with difficulties.

. . .many surrogates are employed and they tend to be crude. Thus, the linkage between the urban and rural systems is generally viewed solely in terms of the distance between them, and population sise is a common surrogate for the characteristics of the origin and destination ...... There is also a lack of co n sid eratio n of community or contextual ch aracter­ istics related to the level of technological pro­ gress and development, overall economic conditions, and government policies ......

With these broad conceptual frameworks in mind, attention now turns to theoretical and empirical work on migration that has limited itself to specific factors in the migrant's or household's utility function.

Such specific factors are wage and job opportunity differentials, the e ffe c t of so c ia l and community tie s , and the ro le of inform ation and interpersonal communication with family and friends who migrated earlier. In discussing these migration models, it should be borne in mind that they are mere variations of the utility maximising theme.

They emphasise certain migration determinants, which are really mechanisms through which some households/individuals seek to achieve their "subjective equilibrium." 36

Model* Focusing on Economic Factors

The Labor Force Adjustment and Dual Economy Model. The labor force adjustment model assumes that the migration decision is determined by vage rate or job opportunity differentials which, in turn, result from differentials in the supply and demand for labor across geographic space. Migration is seen as an instantaneous and rather mechanistic response to these differentials, the end result of which is a redistribution of the labor force so as to equilibrate the vage and unemployment rate differentials (Brown and Sanders, 1960: 157-58).

While Lowry (1966) and Greenwood (1979a) have been proponents of the labor force adjustment model in the Developed World context, this model has also been applied to the LDC context where sharp differences between the vage and job opportunities in the (rural) subsistence and urban-industrial sectors exist.** The model of the intersectoral labor transfer in a dual economy assumes, for example, that the decision to transfer from the subsistence to the industrial sector is based on economic incentives. 12 Such economic incentives are urban industrial

Examples of applications of the labor force adjustment/dual economy models to the study of internal migration in LDCs are Beals, et al. (1967) for Ghana, Polaris' (1979) for Peru, Levy and Wadyclci (1972) for Venesuela, and Greenwood fo r Egypt (1969b) and India (1971a, 1971b).

12It should be noted that the model of the intersectoral labor transfer in a dual economy has not been developed as a migration model. Since the two sectors in the dual economy have commonly been understood as the rural and urban sectors, however, the intersectoral reallocation of labor has obvious implications for migration. 37

wages that exceed the marginal product of labor by an incentive margin of 30-50Z. Subsistence sector incomes, in contrast, are based on the average product, which even though higher than the marginal product of labor in this sector, is considerably below the urban wage (Lewis, 1954;

Ranis and Fei, 1961).

A basic weakness of the labor force adjustment and dual economy models in explaining LDC migration lies in their urban focus and the way the migrant is portrayed. That is, the models contain the behavioral assumption that individuals respond to wage rate differentials in an instantaneous, mechanistic manner, without regard to inertia effects at the origin or to future conditions at either the origin or destination

(Brown and Sanders, 1981). F urther, as was pointed out in Chapter I , the individual is more likely to make a careful cost-benefit calculation of present and future conditions (wages, risk, cost) at both origin and destination, and this evaluation may cause him to stay in the rural area since this might maximize his utility function.

Cost-Benefit Models. Contrary to the labor force adjustment type of models which view the migrant's utility maximising behavior as having a short-term horizon, and neglect long-term conditions at either the origin or the destination, the cost-benefit, also termed human investment or human capital models, argue that individuals respond to present and future wage rate or employment opportunity differentials. Thus, the current income at a potential destination may be less than 38

that at the origin, yet, i£ the stream of expected incone over tine is greater, nigration will occur. Baaed on the work of Sjaastad (1962), the nost explicit formulation of this notion in LDCa is by Todaro (1969,

1976).

In this model, the individual's utility is based on a long-run permanent income calculation. The fundamental premise is that the decision maker considers the various labor market opportunities in the rural and urban centers available to him, then chooses the one which maximizes expected improvement to utility, measured by the difference in real incomes between rural and urban jobs and the probability of obtaining a modern sector urban job. Thus, unlike the labor force adjustment models that view migration as a mechanistic response to assumed higher wages, Todaro's focus on more complex cost-benefit calculations gives the individual the choice of staying in the rural area if that maximizes utility in the long run. Furthermore, implicit in this dynamic perspective of utility maximization is the view that migration can and w ill occur, even when the actual wage differentials no longer exist, or do not yet exist. Indeed, in Todaro's model it is sufficient that the potential migrant perceives that there may be wage differentials at a future time (Berry and Sabot, 1978), that is,

If the migrant anticipates a relatively low p ro b ab ility of finding re g u lar wage employment in the initial period but expects this probability to increase over time as he is able to broaden bis urban contacts, it would still be rational for him to migrate even though expected urban income during the initial period or periods might be lover than expected rural income. As long as the present value of net stream of expected urban income over the migrant's planning horizon exceeds that of the 39

expected rural income, the decision to migrate is economically justified (Todaro, 1976: 26).

Given a positive relationship between length of job search and

level of wages associated with expected jobs, then, unemployment may be

the most productive use of an individual's time, in view of the costs

and benefits of alternative activities (Berry and Sabot, 1978). Thus,

the individual/household is somewhat more "behavioristic" than the migrant in the labor force adjustment model (Stark, 1982). Further,

probabilistic considerations are at the core of the migration decision

process, i.e., the individual balances the probabilities and risks of

being employed or underemployed for a considerable period, against the

positive differential between urban and rural incomes, that is,

The fact that a migrant can expect to earn twice the annual real income in an urban area as in his rural environment may be of little consequence if his actual probability of securing the higher paying job within the one-year period is one chance in five (Todaro, 1976: 26).

While both the human capital and labor force adjustment models are variations on a basic theme, they put different emphasis on the role of

the urban sector. The labor force adjustment/dual economy model focuses on the modern economic sector in urban areas as the primary place for utility maximization. By contrast, the Todaro model does not exclude

that other economic sectors (e.g., urban informal and rural economy) may be equally relevant. Indeed, implicitly, Todaro does turn attention to the rural sector, which he sees as more promising in solving urban problems than the urban sector itself. In terms of the migration decision process, however, Todaro emphasizes more the urban than the AO

rural locus of utility maximization, and this is a weakness of the model.

It is Byerlee (1972), however, who turns attention specifically to the role of the rural economy, i.e., the jobs and earnings opporunities in rural farm and nonfarm sectors, noting that decisions to migrate are made in rural areas, and that a theory of migration should emphasize the rural environment in which that decision is made.

Of particular importance is not only the rural focus but a recognition that economic and non-economic factors in the migrant's cost-benefit calculations are inextricably intertwined, that is,

However, psychic coats and b e n e fits may a lso be important explanatory variables, particularly in •rationalizing the decision of rural people not to m igrate... In general, economic incentives may be a necessary but not sufficient condition for migration because of the presence of psychic costs and benefits (Byerlee, 1974: 556-57).

Models Focusing on Socio-cultural Factors

As noted earlier, much of the migration research has taken the view that economic factors (i.e., income) dominate the place utility

13 Examples of applications of the human capital approach to migration in the U.S. are Bowles (1970) and Wertheimer (1970). For modified approaches to LDCs see: House and Rempel (1980), Godfrey (1973), Speare (1971), King (1978), Sahota (1968), and Churi (1971). 41

function. There are, however, alternative views that focus on other determinants of migration, such as the socio-cultural system. The sociocultural context may influence: 1) the migrant's utility function,

2) whether or not migration occurs, 3) and if, of what form it is, 4) the destination, and 5) the nature of the migrant's experience at the destination (Hugo, 1981: 188).

To illustrate such influences of the sociocultural system, two factors are discussed next. One factor is the role that family, extended family, and acquaintances have on the migrant's choice of a destination and his experience at the destination. The other factor refers to the way social class and status affect who migrates.

The Role of Extended Family and Acquaintances. It has been argued that interpersonal communications, such as from family, extended family, or acquaintances, takes on a critical role in the migration decision leading to kin-induced "chain" migration (Levy and Wadycki,

1973; Greenwood, 1975; Findley, 1977). An important function of these communications is to provide information on employment opportunities and often directly opening up a job. Thus,

A number of stu d ies have shown the importance of kin in supplying potential migrants with information and thereby determining their choice of destination. ... This pattern of kin-induced migration can be particularly pertinent where employers rely on family members as the cheapest way of recruiting new labor (Connell, et al., 1976: 75).

Also, the presence of family, extended family or friendB in a 42

potential destination eases the assimilation of the migrants into that setting and generally reduces the social, psychic and economic costs of migration. Because of the informal nature of communication flows, the wage/job opportunity differentials in the migrant's awareness space are likely to be different from the actual ones. Indeed,, the wage/oppor­ tunity differentials about which the migrants have information may be limited to those places with relatives or friends (Brown and Sanders,

1981: 164). The migration chain concept, however, does not recognize how individual characteristics and the strength of community, kinship, or social class ties at the area of origin may impede or encourage migration* More specifically, it is the demographic, educational, and socioeconomic characteristics, personal background and psycho-social makeup of the m igrant th a t predispose him to evaluate and to re a c t to the information on available opportunities in a certain way. Most of, these variables are part of the "received wisdom" on migration and will not be discussed here. The socioeconomic background or class differentials, however, deserve special consideration, and this is discussed next.

Higrant Selectivity by Social Class. Class differences have been found to make a significant difference with respect to the amount of information a potential migrant receives. Generally, the better off have more access to job opportunities and education and, hence, better off villagers are more likely to migrate (Connell, et al., 1976: 28).

Brown and Sanders (1981) conclude from these variations by social c la s s, A3

Thus, the...labor force adjustment or human capital modela might be appropriate for migration by the better off villager, whereas...a model that gave more emphasis to chain aspects of migration might be more appropriate for the poor villager... Similarly, characteristics of the informal and rural nonfarm/small-scale enterpise sectors might be more significant for the poorer, more rural migrant, whereas the formal sector might be more significant fo r the b e tte r o ff, more urbanised m igrant (Brown and Sanders, 1981: 166).

This is another way of stating that people make migration decisions so as to maximise a utility function specific to them, subject to constraints imposed by the particular time, place, socio-cultural, and economic development context. In elaborating upon these contextual constraints, Brown and Sanders advance a "development paradigm of migration."

A Development Paradigm of Migration

A basic concept of this development paradigm of migration is that the conflicting findings in migration research, as to which factors predominate the migration utility function at any given time and place, could be reconciled if one views them in the context of an ongoing development process. In other words,

A way out of this dilemma, returning to the conceptual model of Habogunje, is to consider Third World migration in the broader context of an ongoing development process, which affects the environment of social and economic conditions, government policies, infrastructure characteristics, and the level of technological progress. From this perspective, migration can be seen as a process that is affected by different factors at different stages of development, and ambiguities in research findings are explained by reference to the development milieu 44

characterizing a given aituation ...or, more specifically, that there is a shift in the relative role of each of the migration factors as development progresses (Brown and Sanders, 1980: 169*175).

In taking this view, Brown and Sanders truly create a new paradigm in migration. They draw on the one hand on the well established theory of the household, which is a general decision making model. On the other hand, they have built from that a unifying concept of migration that reconciles the diverse findings of migration research and provides an agenda for future research.

Summary of the Subsection

This section has dealt with the migration decision process. The emphasis was on reconciling the conflicting postulates of the migration decision process by placing them in the broader framework of utility maximization. This concept states that individuals or households act so as to maximize a utility function, which is composed of economic and noneconomic variables and is subject to constraints imposed by the context of time, place and level of socio-cultural-economic development.

This utility function is specific to the individual/household, i.e., the arguments in this function, and the relative weight attached to them, are determined within each household and may differ among households. At any given locale or point in time, however, the subjective utility functions of households may be very similar since 45

they nay be influenced by the same let of constraints. Thin concept ia the moat widely applicable aodel of behavior aa it can be uaed to portray a variety of deciaiona (migration/nonmigration, consumption of goods, etc.) in a multitude of geographic contextaf i.e ., both MDCa and

LDCa.

Two baaic adaptationa of thia concept in the migration literature were diacuaaed next. Theae are the Brown and Moore model and that of

Germani/Mabogunje. Both are concerned with economic and noneconomic factora which together determine the degree of aatiafaction of the individual with hia location. The former, however, ia more micro­ oriented whereaa the Germani/Mabojunje model focuaea on macro-level forcea, particularly, the aocio-cultural-economic context.

While theae are fairly general modela, a number of writera have limited themaelvea to apecific variablea, auch aa economic (income and job opportunity differential) or noneconomic (migration chain effecta) variablea. The review preaented examples of modela focusing on economic factora such as the labor force adjustment/dual economy and human inveatment/cost-benefit modela of migration. Ac an example of a focus on noneconomic factors, the emphasis of kin-induced migration was discussed, and the role that social class plays in migrant selectivity.

As the moat comprehensive concept of migration in that it takes no axiomatic stand aa to which factor(a) or modela (economic or non­ economic) explain migration better, the Brown and Sanders development paradigm of migration was reviewed. Rather than emphasising one variable as sole explanation, this model states that the relative role 46

of economic and nonecononic variablea in the utility function nay shift

overtime* In other words, the constraints inposed on the individual/

household by tine, place, culture, and level of developaent determine vhich migration "cause" is relatively nore important at any moment in

time. As such, the development paradigm of migration is the closest

application of the original household model to migration*

Having reviewed the literature on the migration decision process, attention now turns to a review of the literature on the consequences of migration, particularly for the supplying areas.

The Consequences of Migration

In theory, migration is generally viewed aa instrumental for economic development and

...as a necessary condition for efficient allocation of resources in the context of changing demand and supply and for reasonably equitable distribution of income (because of i t s assumed ro le in reducing wage differentials among regions and occupations) (Schuh, 1982: 161; Kusnetx, 1966).

In other words:

Migration corrects localized poverty and locally low labor productivity because people with underprivi­ leged living standards will vote with their feet against regional biases (Lipton, 1982: 201).

There are, however, negative externalities associated with the migration of individuals with human resource characteristics that are deemed desirable for development. These externalities are discussed next. 47

The Disgguilibrating Mature of Migration: MigrantSeXectiyity1^

While migration may ultimately be equilibrating, the time period required may be long and its cost £ar above the social optimum

(Myrdal, 1957; Hirschman, 1957; Lipton, 1982). The first proponents of this argument were Myrdal and Hirschman. They contend that because of the selective nature of migration, e.g., migration of young males vho are better educated, the origin areas get drained of their most valuable human resources. Meanwhile, the receiving areas prosper because of the influence of individuals with attributes that contribute to economic growth and development. Thus, there eventually will be "polarization" between the "core" areas, to which migrants flow, and the "peripheral" areas, from where they come. In other words, while migration from the periphery to the core is a symptom of already existing development d if f e r e n tia ls , the se le c tiv e nature of m igration fu rth e r compounds these differentials. More recently, Lipton argues that rural out migration in LDCs originates in areas with relatively unequal distribution of land, a high proportion of landless laborers, high population pressure, with good urban contacts through information from previous migrants, and fairly good transportation links.

^This subsection draws heavily on Lipton (1982: 191-228). 48

Explaining why migration ia not equilibrating income differential, Lipton (1982: 202) groups migration into two types, ....th at in which the better-off country folk use their surplus (accumulated education, cash, or other assets) to buy into the urban scene with its prospects of further accumulation; and that in which the poorer (but seldom the poorest) villagers seek to make up for the land deprivation, high rents, and labor-replacing technologies associated with the concentration and use of surpluses by the better-off in their villages of origin.

The former tend to be pulled out by the better income opportunities to which earlier education has privileged them. The latter, on the other

hand, tend to be pushed out by the inequality in what often is the "unplanned migration of despair," involving lower skilled people and,

because the migrants fail to generate enough remittances, eventually forcing the entire household to wander off in search of work (Lipton, 1982: 201).

Migration, then, results from inequality and is itself the cause of further inequality. Within the origin area, it allows the already better-off to advance as a group, while the situation of the poor weakens further. Further, migration also worsens inequality among villages/areas because,

...inasmuch as it confers net benefits, a few villages...hook onto the chain of successive migration, contacts, and information through which migratory traditions are transmitted into a community (Lipton 1982: 196). Evidence on this point is provided in Connell, et al, (1976) for

India, Joshi (1973) for the Ivory Coast, Haney (1965) for Colombia, 49

Sahota (1968) for Brazil, Pretton (1979) £or Ecuador, and Riddell (1970)

for Sierra Leone. The last four examine specifically educational * d ifferen ces among m igrants, and fin d th a t ru ra l-ru ra l movements are

associated with the illiterate/less educated poor, and rural-urban

migration with the better educated. The latter is particularly serious

since it deprives rural areas of their potential for future

development. Low labor quality, for example, accounts for low income,

low productivity and low productivity growth (Griliches, 1960). Human

capital weakened through migration, therefore, may be a constraint on

the general development potential of these regions.

Other migrant characteristics are affected similarly. Since

migration dominates among the people aged 15-30 years, it takes away the

most significant agricultural innovators (Rogers and Svenning, 1969).

Also, the sex structure of migrants tends to perpetuate inequality. In

many areas, for example, it is primarily males that migrate, leaving

behind the women and children who are often the poorest and least

productive group of the population, particularly if they replace absent

young men, and indicating that not all underprivileged "vote with

their feet against inequality." Further, extra women's work can

endanger health, nourishment and care of babies before, as well as

after, childbirth, and extra children's work may stunt growth and

detract from schooling (Lipton, 1982: 208; Connell, et al., 1976).

Finally, prospects for productivity growth and incentives for heavy and

efficient hired labor are often low in households that are commanded by women (Lipton, 1982: 208). 50

Often tines an area of high out migration, then, suffers in several

ways. It has to shift from labor-intensive field crops to pasture or

tree crops which reduces caloric output per acre. Also, the remittances

from better educated migrants to the better-off farmers allows these to

buy out the poorer farmers. This further concentrates land and raises

the land/labor ratio which calls for labor saving technology (Lipton,

1982).

A further factor that explains why migration fails to accomplish

equilibrium are the costs associated with moving. High costs of urban

food and housing, costs of education required to get a job somewhere

else, information costs and psychic costs of social disruption are

likely to be highest for the poor and the illiterate, with few urban

contacts (Connell, et al., 1976). These problems tend to be lower for

the more educated and better-off. Levy and Wadycki (1974), for example,

find that the more educated migrants are less likely to be deterred by

distance — a proxy for the above costs — than the less educated.

While the cost of migration is high and can be better borne by the more well-to-do, these families also reap the greatest benefits from migration in the form of remittances. Although total migrant remit­

tances are generally found to be small, they tend to go disproportion­ ately to the better-off. Rural-rural migrants, who come from poorer households, are less likely to remit. The remittances that go to the

better-off families, however, often serve conspicuous consumption rather

than overall development. This may eventually change tastes and reduce

the demand for rurally-produced products (Lipton, 1982). In conclu­

sion, 51

The rich and poor families put their toea in vater in different ways. The richer family aenda one veil- prepared family member far afield to teat the prospects, perhapa aa a student, doctor, or engineer. Hie remittancea finance a similar distant move by younger brothers. Where the rich thus chain, the poor only step, and not so far; the poor fail to reach the metropolis (or, having reached it, to get paat its peripheral alums) because they cannot meet the coat of surmounting the intervening obstacles; or because they cannot afford to pass up intervening opportunities. This helps explain why complete household migration (while generally very low) ia usually higher among landless laborers, while individual migrants usually retain rural land or other assets (Lipton, 1982: 206).

The overwhelming evidence on the impact of out migration does not, however, mean that migration should be impeded, particularly since it is rational from an individual/household point of view and does, in most cases, improve th e ir incomes (Schuh, 1982; S tark, 1982). However, the excessive concentration of labor in urban areas, combined with the marginalisation of vide geographic areas, requires institutional intervention. In this context, rural nonfarm employment strategies are relevant. A policy of industrial decentralisation, through nonfarm employment, may be a solution to both the above problems. Particularly in the case of rural-urban migration, it would provide a means of retaining the human capital and skills in the remediate area — in effect, internalising the externalities (Schuh, 1982: 182). 52

Summary of the Subsection

This subsection considered the consequences of migration, .

particularly the negative externalities of selective migration.

Although some maintain that migration is equilibrating and in fact

instrumental in economic development, the emphasis in this section vas

that migration is both in the short-run and the long-run disequili-

brating. It widens already existing income and social class differ­

entials. The reason for this is the selective nature of migration which

involved primarily young, male, better-off, better informed and better

educated people.

Such migrant selectivity may be positive for the receiving areas,

except in cases of excessive influx of migrants causing problems of

internal adjustment. For the supplying regions, however, migrant

selectivity represents a considerable loss of the very human resources

that could be instrumental in development. Among other things, it vas

pointed out that because of the age and sex structure of the migrants,

an area is deprived of the most important labor resource, forcing eventually irreversible changes in production patterns. This affects mostly the poor households that have no means of substituting technology

for the labor power lost through migration. The better-off households,

on the other hand, can afford labor saving technology because of their

better initial financial situation, and because of remittances from

family members th a t m igrated e a r lie r . Thus, the b e tte r -o f f fam ilies

benefit from selective migration while the poor get poorer. As such,

existing economic inequalities within sending regions are perpetuated. 53

Summary of the Chapter

This chapter reviewed three bodies of literature, one dealing with rural nonfara employment and small-scale enterprise, and the others dealing with the migration decision process and migrant selectivity.

The review of the work on ru ra l nonfarm employment served to

b e tte r understand the nature and importance of the phenomenon th a t th is

study is concerned w ith. This showed th a t compared to large urban

industries the rural nonfarm sector has several advantages in development strategies. The rural nonfarm sector is shown, for example, to create economic multiplier effects. It also has a positive impact on human resources, creating an environment favorable for entrepreneurship and human capital development. Given theae advantages, this study assumes that rural nonfarm employment might affect migration such that the overall rate of out migration and selective out migration is reduced, or that in migrants might be attracted to areas with nonfarm employment.

In order to provide a theoretical basis for this assumption, the second body of literature was presented, dealing with the migration decision process. Based on the general household decision making model, several adaptations to the migration decision were presented. These modela posit in general that individuals/households make their migration decision so as to maximize a fam ily u t i l i t y fu n ctio n . This is composed of economic and noneconomic variables and is subject to constraints imposed by the context of time, place, and level of socio-cultural 54

development. This conceptualisation provided the rationale for the

assumption that potential migrants may maximize their utility function

in situ through rural nonfarm employment. If this is the case, this would be manifest in reduced out migration overall, and selective

out migration in particular.

To round off the discussion, the third body of literature on the

negative consequences of selective migration vas presented. This shoved

that because of the characteristics of migrants — being younger and

better educated, for example — origin areas are deprived of their most

important labor resources, which may retard development. The observations made in this third review underline the importance rural nonfarm employment might have in stessning selective migration.

The following chapter (Chapter III) presents the research design and discusses the study area. CHAPTER I I I

RESEARCH DESIGN AND STUDY AREA

The previous chapter reviewed literature pertinent to the present

study. From this literature two research questions were identified.

These are: (a) How does rural nonfarm employment affect in, out, and net migration compared to employment opportunities in the agricultural and urban sectors? (b) How does rural nonfarm employment affect

selective out migration, i.e., what human resource characteristics are found concentrated in rural small-scale enterprise compared to the farm sector or the urban economic activities?

Both (a) and (b) are important from a policy and knowledge perspec­ tive although very little research exists on these two relationships in

LDCs. This dissertation addresses these issues in a series of analyses as shown in Figure 3.1. The sequence of analyses is described first, followed by a discussion of the data, and a qualitative analysis of the study area.

Research Design

The initial step in this research is a qualitative examination of the economic characteristics and trends of the study area. Among these rigete J.l. atguitn otitcR

1. Orographic Inatat(gotten of Coeta tieaa Etenowy,

that-tar III

—^ — — 1. titalilUaliea ol title fttacteral Mnenalon al tha fatal Mealtrn latter, Aftitwlterc, ani Urban tcenoaU Aetlwitltr.

Matheii ratter Analyaia ot A ||ti|a ta Caatea Leeal Data,

Chart at l»

1, ClaaaifUatlea of fatal Araaa aa rraiooiiitoily Aftltaltaral et Agt Icaltatal with It rang fatal Mahler* Orientation,

Malheft Varf'a flararth ltal Creafla| Alferllha Ual*| fattor tcaraa,

Chaftar IV ______

A. taaalnatlaa ef falatleaahif falwaaa A ||ta|ata Migration an! faral Honiara, A ftltaltaral, an! Urban taployneat Orrertanltiaa,

halhoit laftaaalea al A||ra|ata la, Oat, ani Mat Mifiatlaa Vaiai ratter Itetea at lalapaalaat Variablea.

Creaatabalatlea el lailaliaal la, Oat, eai Rea Rlftaate bjr Eapleyneat Calaferiaa (Aftlcalture, Manufacturing, Coanerce, lerwica, a ll,) aal by Caegrephic Araaa el faaiieata (Urban, fatal, fatal bat freienlnaatly Aftltaltaral, fatal bat A ftitaltatal with a Iirony fatal Honiara Conpoeant). Chaftar V______

11,aLK41ion ol faletlenahlf batwaan Migrant falactiaity ani fatal Nonfat*, A ftltaltaral, ani Urban Eaployweat Ofpetlaaltlaa.

Hat hoi I Croat Tabulation by Maun faaourta Charactarlatita ol Iniiviiaal In, Oat, ani Man Mlftantt by Upleynrnt Cataforlaa (Agriculture, Manalattarinf, Coawerta, lattice, ate,) aal by Ceoftaphic Araaa el ietiiance (Urban, faral, fatal bat freioainently Agiicaltural, fare) bat Agrlcaltera! with a lltong faral Honiara Coaponent).

______Chapter ¥ 1 ______57

characteristics are land distribution* agricultural orientation in production and exports* and related to that* short-run export instability* dependence* deterioration o£ the terns of trade and rural poverty. This examination identifies characteristics that Costa Rica shares vith many LDCs* and provides an initial understanding of the importance of economic factors in rural migration. The latter is examined in greater detail in the statistical analyses.

As noted above* the aim of th is d isse rta tio n is to examine the effect on overall and selective rural migration of rural nonfarm employment opportunities versus those in agriculture or urban economic activities. In order to do this* however* it is necessary to first define the rural nonfarm* agricultural, and urban sectors in such a way that permits statistical analyses. This is done by characterising theBe three sectors in terms of a few basic structural characteristics. The method used for this is factor analysis of variables pertaining to the rural nonfarm* agricultural* and urban sectors. The procedure is as 15 follow s•

Identification of Basic Dimensions of the Rural Honfarm. A g ricu ltu ral and Urban Economic Sectors (Step 2, Figure 3.1)

Factor analysis is concerned vith the underlying structure or most salient features exhibited by a group of variables. To identify these

15 This description is based on Yeates (1974: 207-238). 58

feature*, the common and unique sources of variation in the data are partitioned. As a first step in this, the data matrix is transformed into a matrix of standard scores, and from this, a

correlation coefficient matrix is calculated. This matrix indicates the degree of intercorrelation between all the variables, where the total variation in the data matrix is shown on the diagonal. The elements of the diagonal are estimates of the proportion of the variance of that variable that is held in common with all the other variables. This

estimated proportion is the squared multiple correlation coefficient, derived from a regression of each variable against all others. The matrix which is factored in factor analysis is the correlation coefficient matrix. The number of factors obtained depends on the total common variance to be factored. This variance is the sum of the communalities, i.e., the squared variances of each variable due to common sources. The factoring procedure yields a matrix of factor

loadings. These can be interpreted as correlation coefficients between each respective variable and the respective extracted factor. The factoring procedure employed in this dissertation uses varimax rotation.

This yields loadings as large as possible relating to the fewest variables possible, which facilitates the interpretation of the factors. The squared factor loading indicates the proportion of variation in the variables that is associated with the variation in the factor, and the sum of squared factor loadings (the "eigenvalue") is the proportion of total common variation accounted for by this factor. 59

From the factor loading matrix a matrix of factor acorea can be

calculated. The factor acorea indicate how each obaervational unit rank

on each factor. High acorea for a particular obaervation indicate that

thia obaervation haa a high aaaociation vith a particular factor,

whereaa low acorea indicate the oppoaite. Where a factor ia deacribed

by poaitive and negative loadinga, the obaervationa with large poaitive

acorea are aaaociated vith the poaitive aide of the factor, whereaa

obaervationa with large negative acorea are aaaociated vith the negative

aide of the factor.

Factor acorea, then, are compoaite variablea that characterize each

obaervational unit in terma of a linear combination of the original variablea. Furthermore, aince the factora are orthogonal, the factor

acorea aatiafy the requirementa of independence among independent variablea. Thua, factor acorea are uaeful aa independent variablea in

regreBaion analyaea. Factor acorea are furthermore uaeful aa input variablea in grouping algorithma that group together obaervationa that are aimilar in terma of the characteriatica represented by the factora.

This dissertation uses factor acorea in both vaya, in regression and in

a grouping algorithm.

From the above description of factor analysis, it ia obvious that factor analysis ia not a hypothesis testing method, nor ia it used aa such in thia dissertation. Rather, this method is used here to set up the data for subsequent statistical analyses.

While there are several types of factorial analyses (for example, principal components and canonical factor analysis) that could be used 60

Co reduce a large data set to a few basic dimensions and to obtain new orthogonal variables (factor scores), this dissertation uses common factor analysis. This assumes that only part of the variation in the economic sectors is contained within the variables used to define the rural nonfarm, agricultural, and urban sectors.*** As such, the variables are chosen with a specific theoretical structure in mind. In the case of the agricultural sector, for example, variables are chosen that reflect crop specialization, subsistence or commercial agriculture, and population pressure on land. The variables pertaining to the rural nonfarm and urban sectors are chosen to reflect size, labor intensity, and earnings in industrial, commercial and service activities.

Two separate factor analyses were done, one for the agricultural and another for the rural nonfarm and urban sectors. Raving identified the basic dimension of these, the research proceeds by grouping the geographic units of observation as either predominantly agricultural with little nonfarm employment or as agricultural but with a strong rural nonfarm component. The method employed for this is sumnarized nex t.

In contrast to that, principal components analysis would assume that all the variation in the three economic sectors is contained within the variables used. Furthermore, factor analysis is chosen — versus canonical factor analysis, for example — because the data are available for all observations. If common factors would be determined from a sample of observations, canonical factor analysis would be more appropriate since it provides statistics which can be used to test whether the extracted factors are significant estimates of those that would result from an analysis of the entire population (Yeates, 1974: 235). 61

Clarification of Rural Areas (Step 3, Figure 3.1) »

The grouping of areal unite is bated on Ward's Hierarchical

Grouping Algorithm. Thia groups together observations which exhibit a

certain level of similarity across their representative variables, which

in this case are the factor scores representing the agricultural and

rural nonfarm sectors. The procedure begins by defining each original

observation, or canton in this case, as a "group." The n-observations or groups are then reduced in number by a series of steps until reaching

an optimum number of groups. This is done as follows.

The two observations "closest" together in n-dimensional space are paired and their mean values are used to characterize the new group. In

the next step a search is made for the next two closest pairs of groups, which may or may not include the already joined group, and a new average

ia computed as the centroid for the second group. The process con­

tinues, reducing the total number of groups by one on each iteration, until a specified number of groups is obtained. At each step, however,

the procedure is designed to minimize within group variance and to maximize between group variance.

The types of areas derived by this procedure are employed as a basis for cross tabulation as indicated below. 62

Procedure for Addressing the Research Questions (Steps 4 end 5, Figure 3*1)

With respect to how rural nonfarm activities af£ect overall nigration^ the first research question, the initial inquiry employs regression analysis. The dependent variables are gross out, in, and net migration and rates of out, in, and net migration. The independent variables are the factor scores derived from the analysis of the rural nonfarm, agricultural, and urban economic sectors. The geographic units of observation are 62 rural cantons. The existence of a relationship between the dependent variable(s) and the independent variables is evaluated by means of an F-test.

Then, to lend more credence to the general results obtained from the aggregate, area-level analysis, individual level data on migrants are used to see how migrant status (as in, out or non migrant) is differentially related to rural nonfarm, urban nonfarm, or agricultural employment in the urban and ru ra l secto rs of the economy, with the latter also broken down as either predominantly agricultural or agricultural with a strong rural nonfarm component. The unit of observation is 69,109 economically active individuals, drawn from the

1973 Census of Populations. The data is presented in the form of a. cross classification or contingency table that has employment categories

(agriculture, commerce, manufacturing, service, etc.) as one dimension, geographic areas (urban, rural, rural and predominantly agricultural, rural and agricultural with a strong rural nonfarm component) as the other, and the cells report the number of persons in each combination of employment category/geographic area who are in, out, or non migrants. 63

These aggregate and individual level analyses enable an assessment of the effect on migration of rural nonfaxm employment opportunities* compared to a g ric u ltu ra l or urban employment o p p o rtu n itie s. They do not* hovever* provide insight to the second research concern* migrant selectivity. To illustrate this, out migration may be reduced through rural nonfarm jobs* but positive selective out migration may not be reduced* that is* individuals with greater human capital levels may nevertheless leave. Therefore* it is necessary to explicitly examine how migrant selectivity is affected by rural nonfarm employment. The basis for this analysis is* again, a series of cross classifications or contingency tables with employment categories

(agriculture* manufacturing, commerce* service* etc.) on one dimension and the various geographic areas (urban, rural* rural and predominantly agricultural* rural and agricultural with a strong rural nonfarm component) on the other. In this case, however, separate tables are made for the human capital characteristics of age* educational level and occupational status* and the cell values represent the mean values of these characteristics for in* out, and non migrants as they vary by employment category and geographic area. The analysis also employs statistical tests to see if the differentials in mean age* education* and occupational status are significant. The unit of observation is, again* 69,109 economically active individuals drawn from the 1973 Costa

Rican Census of Population. This analysis enables seeing whether rural nonfarm employment* even if it doeB not completely offset urban-ward selective migration* does at least reduce it. If so, it would be manifest in the following way:

rural nonfara activities, as compared to agricultural.activities, and

rural areas with more nonfarm employment, as compared to rural areas

that are predominantly agricultural, would have a higher concentration

of younger individuals with greater educational levels and lesser

occupational status. Furthermore, one might find that rural nonfarm

e n terp rises or ru ra l areas with more ru ra l nonfarm employment might even

attract in migranta with higher levels of human capital.

Data

As noted, both aggregate areal level and individual level data are

used. The first, for the 79 cantons of Costa Rica, is derived from the

1973/74 Censes of Population, Agriculture, Industry, Commerce and

Service. The administrative division of the country into 7 provinces

and 79 cantons is shown in Figure 3.2. The second set of data are

69»109 economically active individuals, which represent a subset of an

approximately 10X sample of the Population Census, drawn by Centro

Demografia de Latino Americana (CELADE).

The use of this data harbora important limitations. For example,

the data ia cross-sectional, but it is used to explicate aspects of the migration process. The problem with this is elaborated upon by Golledge

(1982: 4-5)» who notes that by cross-sectional aggregation one stops a process, assuming that Figure 3.2, Canton Map of Costa Rica, 1974

finUitl t«UM , . fWlKM Ml CM IH U M dli (m U P MlI I CC*m i m C*m i *I« h M i t m i im BMW! MY M »|m M l Mpart* IBM in im lw . m U M m Ml Mm iI Altai 1 * 4 fwteul M4 ila M i *M MMH M Art in imM Mt MrrUlM M* Ml l*M IMtl M* AlaarM* Ml Malrta in Mf kMMM MY MIIU* IW kll»U4 M II M r u M l M i Ire* in ■*•** in Ml fwrlU ■in miNiiu Ilia M m l l a r ^ y e , .!, ••MU l ’. I n h u Ml Mil* m i Uni nn tin* fa*1 Iw n Cntult Um nw mhI< 4M Mm MtMti IH tm ci tin MW 4 * * 4 4 Ml lM MlMlCM in ll**lrt*i III Mtnten* Ml M* l»U n 1*4 blMM III MU Mf Mia* in ibi ku IIM bnUtW M l n*ra* in mmIm III hM UI*4h Ml M* Pall* IK t e n Omim 411 Mtapi**! gf ^ c i M ^ .i, U,wu a r ^ a rggin. Ml Ml *U*t« m MetU m M i Cm Mmh in til U lilla ■ u n i* I hlwH Mf Ml TIIm m •ntlN UUHMt Mi ia*M* CmImCmIm Ml M C m U| AtfmAlfara Ml* III Ml MlaatM Vafti Ml MU Ml M* CMIaa Ml M tai 9 M IiU m |M *a*ia** (Mt M t «f lM Mi Jaaa Mi«*v*Ut** Mm* 66

the state of being and the state of existing at that point of cutting space-time are invariant, repetitive, and difficult to extinguish**. It is* doubtful, however, whether one can justify the same set of assumptions over the entire course of the process and whether one can make generalisations concerning the invariance and continuity of things, events and relationships observed* Indeed, by using cross-sectional data one establishes the presence of relationships for only a fleeting period of time.

This is particularly significant in the study of migration, since the nature of the process and the determinants of migration themselves change over time (Brown and Sanders, 1981; Chapter II above)* Realizing this, the present study follows nevertheless the advice of philosophers from Plato to Wittgenstein, who suggested that the only way to grasp a continuous process is to stop it (as reported in Golledge, 1982: 3).

The knowledge so gained may help in coordinating policy at the present tim e.

Other methodological problems also are associated with this cross- sectional approach. For example, the level of aggregation chosen harbors the danger of making unwarranted generalizations, and if one were to change data aggregation, the findings could change as well.

Furthermore, the cross-sectional approach attempts to explain cumulative migration up to 1973. This neglects that many of these variables themselves may have been influenced by the migration process over time.

Thus, there is a possibility for misapecification and a simultaneous equation bias. To elaborate, rural outmigration may be high because of lacking rural nonfarm employment. On the other hand, the rural nonfarm sector and associated employment opportunities may be weak because high outmigration erodes the market for nonfarm goods and services. 67

While these problems are recognised, they will not be dealt with in the present study. Therefore, much criticism of cross-aectional'

* migration studies in other countries (House and Rempel, 1981; Levy and

Wadycki, 1974, 1972) applies here as veil. Attention now turns to a qualitative examination of the study area.

The Study Area

The empirical case used in this study is Costa Rica, which is interesting for several reasons.

First, although the total population of Costa Rica Is quite small

— 1.96 million in 1973 — 12Z are migrants, i.e., individuals that in

1973 lived in another place of residence than in 1968.

Second, a significant portion of the internal migration was di­ rected towards rural areas (Carvajal, 1981; Lawson, 1982), as is shown in Table 3.1. Further, not only did a siseable number and proportion of the population move from both urban and rural to rural areas, but there a lso was a s ig n ific a n t concentration and p attern in g of those movements as to indicate a spatial order and underlying rationality (Figure 3.3).

Indeed, the greater proportion of the migrants moved to predominantly rural areas that contained only small towns with a population of leas than 5,000. These figures attest to the importance of factors in the 17 rural environment in the decision to migrate.

should be noted, however, that government policy also may account for rural-rural migration. For example, during the period under study, massive investment was made in road construction to remote rural areas, and credit schemes were set up for settlement of remote rural areas (personal communication with Elena Teran, Directors, Division de Planificacion of Coordinacion Regional, Ministry of Planning). Totals 1.0 18,012 1.0 21,571 0.389 8,405 Urban Rural 3rd Order 0.418 19,603 1.698 76,299 186,021 0.109 12,140 0.402 45,049 1.0 111,861 0.106 3,652 0.392 13,569 1.0 34,577 0.154 2,772 0.515 9,276 Prob. Free. Prob. Frea. TO 5,118 0.049 1,039 5,964 5,473 Frea. 22,676 39,231 Urban 2nd Order 0.929 0.203 0.331 San Jose of Costa Rica, based on Intercantonal Flows, 1973.a Single Unit Prob. Free. Prob. 0.955 50,888 0.286 31,996 Table 3.1. Migration Probabilities and Kuabers of Migrants for the Canton Hierarchy Totals 2nd Order Rural San Jose Ho Flows 3rd Order Urban 0.325 7,009 0.237 Urban 0.344 11,883 0.158 asked of persons five years old or more. Source: Lawson (1982) *These calculations exclude intracanton flaws which represent stayers. Also, the migration question was only tk Pi O X Figure 3*3* Salient Rural Destination Cantons

for M igrants, 1968-73

Gillent CAntont

Souretj Ltvioo, 196 ? 70

Third, Costa Rica has generally been portrayed as a special case among LDCs.

The education and health indices are the highest in the region and income distribution does not show the same degree of disparity as the majority of Latin American countries, although in recent years land got increasingly consolidated into larger holdings leaving an ever growing number lan d less. The lack of aimed forces has not only affected the assigning of public funds but has generally also contributed to a fair measure of generosity in social develop­ ment (United Nations, 1974: 108).

Nevertheless, Costa Rica shares many characteristics generally

identified with LDCs, as shown next. As such, Costa Rica is quite

representative for the study of migration in an LDC context.

Structural Characteristics

Among the characteristics that Costa Rica shares with other LDCs

are a highly uneven land distribution, agricultural orientation in

production and exports, short-run export instability, economic dependence, long-run deterioration of the terms of trade, and rural poverty. These are discussed in turn.

Land Distribution. Costa Rica is characterised by a highly uneven land distribution. According to the agricultural census, 6Z of all farms, or 4,661 corporately owned farms own 1/3 of total farm land,

1,027,230 hectares. The remaining 94X of all farms, or 76,901 individually owned farms, occupy the remaining 2,095,226 hectares of farm land (Kreitmann, 1976: 66). 71

Agricultural Orientation in Production and Export. The major part of national income is derived from the production and export of traditional agricultural products, particularly coffee, bananas, meat, sugar, and cocoa. Such agricultural specialisation in production and exports is generally an obstacle to economic development for LDCs, and

Costa Rica is no exception as is explained next.

Instrumental in the process of economic development is the increased specialisation at the producer level, differentiation at the aectoral level, and integration of economic activities in markets

(Kusnets, 1966). An indication of hov veil economic development is proceeding and vhat potential it has, is the content and composition of the GDP,18

Considering Costa Rica's GDP over several years, one sees little differentiation on the producer and sectoral levels. Economic activity continues to be dominated by the above mentioned agricultural crops, although the manufacturing sector has increased over the past years to a share of 252 in total GDP by value of output.

The degree of market integration, i.e., intersectoral backward and forward linkages, development of markets for consumer goods and services, tools and machines, processed materials and components for

18 Economic development as referred to here is "an increase in the real per capita income of a country over long periods of time, provided that the number of people below an absolute poverty line does not increase and that the distribution of income doeB not become more unequal" (Meier, 1976: 7). 72

labor aervi.ee, land and capital funda, ia relatively modest. Rather, the Coata Rican caae — and thia ia very typical of other LDCa aa veil — ia characterized largely by one-way market relationships. There are no strong backward and forward linkages between the manufacturing and agricultural sectors in product and factor markets. Agricultural products finance the import of the essential inputs for the agricultural secto r i t s e l f , and fo r the re s t of the economy.

The export volume, however, is of relatively insignificant size in the in te rn a tio n a l commodity market (AID, 1976). Thus, Costa Rica is a

"small country producer," i.e., the country faces a perfectly elastic demand for its products as shown in Figure 3.A. This implies that Costa

Rica cannot influence the prices received for her exports. Prices are set on the world market by the total amount of the coomodity/commodities supplied (price p* and quantity q* in Figure 3.A) and demanded; Costa

Rican exports merely take that price thus determined.

Furthermore, the supply of many of Costa Rica's agricultural products is price inelastic, i.e., the quantity supplied does not readily respond to price changes because of weather or climatic conditions and the inflexibility of crops (like bananas, coffee, cocoa) which involve a long gestation period.

It is obvious, then, that Costa Rica, in pursuing agricultural export-led growth, subjects her economic development to short-run instability in export earnings. Figure 3.4. Supply and Denand Condition* for the Vorid Market and their Translation into the Snail Ration Producer's Market

upply price upply price

P

Oenand qu an tity

(a) World Market (b) Snail Ration Producer’s Market

'v l Cl 74

Economic Dependence. Compounding the s itu a tio n described above is the fact that a good share of the export crops is handled by foreign investors. Regarding the role of foreign interests, Cardoso

(1969) asserts that foreign influences almost always prevent domestic development from reaching its full potential. The presence of this influence is oftentimes reflected in the existence of growth enclaves created by the foreign investor.

In Costa Rica, for example, such enclaves are to be found in Limon and Puntarenas provinces, where foreign companies own much of the banana and cocoa plantations, port and railway facilities, and commercial activities.

Typically, these companies repatriate much of their profits and such income leakages may impede the domestic savings-investment-growth mechanism substantially. Furthermore, the economic activities that foreign investors pursue are often related to "shallow development" in the host country. The reason is that the companies often use only the least organised labor, thus limiting the possibilities for wage increases. If wages or other costs, however, should rise, foreign investors are inclined to shift their operations to other regions/ countries (Meier, 1976: 676).

Two cases in point are United Fruit and Standard Fruit Companies that until 1968 had repeatedly abandoned large tracts of land in Costa

Rica and cleared new land for cultivation in other parts of the country rather than incur costs through adoption of disease-free varieties, 75

upgrade soil quality, or improve atorage and shipment facilities to

prevent diseases from spreading (West and Augelli, 1976). Also, since

foreign investors generally handle the final marketing of the products,

there are minimal spillover effects in terms of technical or marketing

know-how (Heier, 1976: 676).

Terms of Trade. Considering now the long-run implications of

a strong agricultural export bias, one finds that Coata Rican

development has been suffering from deteriorating terms of trade, i.e.,

the value of primary exports relative to the value of imports steadily

declined over the intercensal period. The main reasons for

d e te rio ra tin g terms of trade are the low income e la s tic itie s of demand

for agricultural products, i.e., as incomes, particularly in developed

countries grow, the demand for food and agricultural products grows I cbb

than proportionately and a growing share of the income is devoted to

purchases of manufactured goods (Kusnetz, 1966).

Therefore, like other LDCs, Costa Rica, because of her dependence on the export of primary products and the import of capital goods, observed increases in the current account deficit, and only a slow rise

in domestic savings necessary for self-sustained growth and develop­ ment. Even import restrictions do not significantly change the

situation as the prices for these restricted imports are rising and gradually erode export earnings, thus indicating the onset of what has been termed "inmiserizing growth." 19

^See Rybczynski (1955) and Bhagwati (1958). 76

The folloving subsection elaborates on the above points by outlining the specific economic trends Costs Rica experienced during the period under study.

20 Economic Trends 1965-1974. Generally speaking, economic activity has shown considerable fluctuations over the perid 1965—1974.

These fluctuations, however, have not occurred in all parts of the economy simultaneously, partly because of government policy and partly as a r e s u lt of phenomena a ffe c tin g Costa R ica's ex tern al se c to r.

An overall analysis shows that the period 1965-1974 can be divided into two phases: Phase I from 1965-1969/70 shows an economic upswing which can.be attributed to such factors as the entry into the Central

American Common Market, the evolution of world markets in certain agricultural crops, import substitution policies, and the inflow of foreign capital.

The second phase from 1969/70-1974 is characterised by a loss of momentum that is related to the external sector. This phase will be described in more detail as it is relevant to migration addressed later on. Before going on to describe the evolution of exports, it should be recalled that three quarters of the value of exports originated in the agricultural sector (Table 3.2). Although industrial exports reveal an increasing share in the whole (Table 3.2), the predominance of agricul­ tural exports is a feature of the economic structure which is necessary

20 This subsection draws heavily on UNECLA (1974: 108-137). 77

Table 3.2. Structure of tbe Value of Eiporta.

~ . ■!»> ««»■ —j n ■ i Hllllaa el I tam t *1 MIIIm •( I htm t if Hllllaa at | fartaat .1 at mmt laid at carraat fatal at tarraat fatal f O it tl______t t l u i ______K u tlil______t t l i l l ______t> u rll______arltaa______laaatti Oaffat ii.» ».* n.i II.1 11.1 St.l lateact s i.I 11.1 M .l M .l 14.4 11.4 Ltaaatacfc aa4 Heat t i .i i.o 11.0 I .l 10,1 f.l la |a r « .| 4.1 10.1 *.* II .I S.l Cataa l.T t . t 0.1 I .l e .i Otkat A arltaltatal fralacta le.o S.l I .l ).* 0.1 i . i laOaatrlal fte*attlta *1.0 II.* M .l M .l 11.* 14.0 m u 111.* lM.e Ill.l 100.1 I l l . l 100.0

I III III! _111*_ Hllllaa at 1 fartaat at Mllliaa ar 1 fartaat at Hllllaa af 1 fartaat al at tarraat fatal at carraat fatal at carraat fatal IntKl arltaa Callaa 11.* *!.* *4.0 11.1 111.1 t l . l laaaaaa II .I 11.1 11.1 11.1 11.0 II.I Llaaatatk aa* Haat M .l 10.1 11.1 1.4 11.1 1.1 ■•tar II.I 4.1 II.I 4.0 14.0 1.1 Cataa 1.0 1.0 4.4 I .l 1.0 1.4 OtWar iaritaltarai fraO tm I .l 1.1 11.4 1.4 111.0 11.0 laOaatrlal fraOactlaa *4.1 11.0 • I .l t t . l IDT4L 110,1 100.0 111.1 100.0 411.0 100.0 taarcai MKU lllltt 101). 78

to keep in mind, both in the analysis of economic trends and in its relation to migration.

Exports. Although the current value of total exports increased overall in the period 1970-74. the export value of the different export items did not increase at equal rates. This results from the fact that neither prices nor volume exported increased at the same rate and at the same time for all export items. The differences in increases of prices and quantities for total exports are summarised in

Table 3.3. In 1971. for example, there was a drop in prices by SI whereas the volume of total exports increased by 3Z resulting altogether in a decline of the value exported, which was responsible for the general decline of economic activity of 1971. In 1972, volume of exports increased considerably, yet prices rose more slowly. When, in

1973, however, prices increased substantially, volume went up less rapidly (UNECLA, 1974: 110).

The discrepancies in price and quantities for the particular export items are shown in Tables 3.4 and 3*5. Since coffee and bananas are the moat important products, not only among exports but also among items of general production, their uneven price and quantity increases were fa c to rs which determined the evolution of the e n tire economy. The volume of coffee exported, for example, dropped by 7.4Z in 1971, increased by 25.7Z in 1972, and fell by 15.3Z in 1973 (Table 3.5). The price of coffee fell by 12.4Z in 1971, and despite the production and 79

Table 3.3, Indices of Exports of Goods (1970 ■ 100).

1971 1972 1973 1974

Price Index 95 100 120 143

Quantum Index 103 119 124 129

Index of Export Value 97 119 148 184

Source: UNECLA (1974: 110). 80

Table 3.4. Evolution of Prices in Agricultural Exports ($ at current prices).

Product 1970 1971 1972 1973 1974

Coffee (per quintal) 48.66 42.67 41.60 59.31 63.70

Banana (per stem) 2.27 1.95 2.15 2.22 2.41

Heat (per kg) 1.03 1.10 1.16 1.50 1.31

Sugar (per quintal) 6.93 7.15 7.78 5.59 9.39

Cocoa (p er q u in ta l) 27.30 19.93 22.67 42.44 62.20

Source: UNECLA (1974: 111). 81

Table 3*5. Evolution of the Volume of Exports.

Product 1970 1971 1972 1973 1974

Coffee (in 1000 q u in ta ls) 1500 1390 1871 1585 1965

Bananas (in 1000 stems) 29414 32859 38599 42112 38820 Livestock & Meat (in 1000 kgs) 17475 18617 26131 21763 23835

Sugar (in 1000 q u in tals) 1465 1807 1683 2438 1592

Cocoa (in 1000 q u in ta ls) 71 77 132 103 97

Source: UNECLA (1974: 110). 82

export maximum of 1972, the price fell to a minimum, brought about by a worldwide increase in coffee supply. The sudden price increase in 1973 coincided with a decline in coffee production. In summary, both price and quantity declined in 1971, and offset each other in the next two years, thereby reducing the variations in exports earnings (UNECLA,

1974: 111).

As regards bananas, a sustained growth of the volume exported was recorded at an annual average rate of around 13Z (Table 3.4). Banana prices, however, declined in 1971 and, despite the increase of the next two y ears, did not reach 1970 le v e ls.

As far as exports of meat and sugar are concerned, a persistent increase in prices was not always accompanied by a similar increase in the volume exported, and vice versa. The volume of livestock and meat exported declined by 17Z in 1973 despite price increases that year

(Tables 3.4 and 3.5). The prices of sugar cane fell by 7Z in 1972 and further by 22Z in 1973 in the face of failing export volumes in 1972 and rising exports in 1973.

Industrial exports rose by 150Z in value over the 1970-71 period

(Table 3.3), their share in total value of exports increasing from 23Z to 37Z in 1974. Despite this increase, however, the economy remains characterised by dependence on industrial imports.

Imports. The import coefficient, i.e., the share of value of imported inputs in total value of inputs, is very high, about 33Z during the 1970-74 period. The current value of imports showed sharp increases 83

in the early 1970s (Table 3.6). The table indicates that the increases in value are largely due to the increases in the prices of imported goods rather than to an increase in the quantities imported (UNECLA,

1974: 111). This implies that financial flows abroad increase by more than the flow of products into Costa Rica. In other words, export earnings get partially eroded by financial obligations abroad, and this happens increasingly the more the terms of trade deteriorate.

The growth of the Cross Domestic Product (GDP). The structure of the exports and imports has been very much to the detriment of GDP

(Table 3.7). The annual growth rate of GDP over 1970-74 lost momentum compared to a steady increase in average growth of GDP over 1965-1970. Associated with the declining growth of GDP has been a declining rate of investment into the economy overall. Noteworthy, however, is that learning from the dependence on agricultural exports, investment has been increasingly redirected towards industry and transport sectors

(Table 3.8). This is clearly an indication of the structural transformation from a typical agrarian export country towards a more industrialised one.

Given the above observations, it is not surprising that Costa Rica, like many other LDCs, has a high degree of poverty.

Rural Poverty. By the official definition of poverty, i.e., per capita income of less than U.S. $150, 42X of the farms and 513C of the farm operating population lives in poverty, as evident from Tables 84

Table 3*6. Index of Inported Goodi (1970 " 100)

1971 1972 1973 1974

Price Index 103 111 128 168

Quantum Index 107 106 109 125

Index of Import Value 110 118 140 210

Source: UNECLA (1974: 111). 85

Tibia 3 .7 . Annual Growth of C roii D o stitic Product, 1963-1974, by Sector of Econoale Activity (in parceoti).

1963 1966 1967 1966 1969 1970 1971 1972 1973 1974

Agriculture S.O 7.9 9.0 14.0 7.6 3.0 5.6 5.7 7.2 -3.0 Manufacturing and Mining 9.4 10.3 11.1 11.3 8.3 8.4 9.6 6.6 10.0 9.1

Conatruetion 15.3 -10.9 7.0 ----- 9.7 13.6 8.0 6.3 3.3 13.0 Otllitiea 9.6 5.6 7.0 ------7.9 10.1 13.4 10.3 4.6

Tranaport and Coaaunication 7.6 3.8 7.0 ----- — 6.7 7.5 8.0 6.7 4.6 Coantrce and rinanca 1.3 5.6 7.0 ----- — 13.3 6.1 9.8 S.O 4.3 Ovnerahip of Dwallinga 7.4 3.6 7.0 ----- — 3.1 4.6 6.2 3.6 4.6

Covaroaant 9.6 3.0 7.0 ----- — A .8 6.5 .6 4.1 4.6

Miae, Barvieei 7.4 5.0 7.0 4.5 — 6.3 3.6 6.4 4.5 4.5

Total Groai Doaaatic 7.5 6.5 6.3 6.1 7.6 5.9 6.6 6.8 6.2 4.1 Product

*Eaeh figure rapraaanta the percent irowth froa the beginning of the year to the end.

Source! ONECLA (1974: 119). 86

Table 3.8. Fixed Investment by Sector (in millions of colones).

Sector 1970 1971 1972

Agriculture 15.0 12.5 8.7

Manufacturing 19.3 15.9 17.1

Construction 6.4 7.4 8.0

U tilitie s 6.2 9.0 9.6

Transport 15.0 14.7 16.9

Commerce 6.8 5.9 4.9 Banking 2.0 1.0 1.5

Services 3.7 3.3 3.0

Government 12.0 14.6 18.0

Ownership of Dwellings 13.6 15.7 12.3

Source: UNECLA (1974: 118). 87

3.9 and 3.10. Furthermore, it appears that this poverty it self- perpetuating, i.e., the largest group of the poor farma, almost 40Z, are on holdings of less thsn 1 hectare. For these farms, however, there are

few prospects of finding crop combinations so intensive that this sice of holding can ever produce incomes above the poverty line (Baines,

1976: 2). Concomitantly with the high percent of poor farms, one finds a high percentage of the farming population in poverty, as shown in

Table 3.10. Further, the farming population and the rural landless in

Costa Rica are almost equally poor, as is evident from Table 3.11.

The above shows that Costa Rica has a considerable degree of rural poverty like other LDCs and, as such, one would expect that rural based wage labor opportunities should be a significant factor in the decision to migrate. Nonfarm/off-farm employment should be relevant for several reasons:

(1) The small-scale enterprise sector in Costa Rica is very

important in total employment with approximately 50Z of all

industrial employment occurring in small-scale establishments

(Daines, 1976: 67).

(2) The landless rural poor (those which would rely on nonfarm

income the moat) are the largest poverty group in the country.

They comprise 23.3Z of the national population, 37.SZ of the

rural population, 47Z of all the poor and 61.6Z of the rural

poor (Kreitman, 1976: 96).

(3) It has been suggested that the rural poor are poor because of

inability to obtain off-farm employment, as is evident from 88

Table 3.9. Coata Rica: Rural Poverty; by Size of Farms

Farm Number of Poor Farms Number of Percent of Farms Size (under U.S. $150) Non-Poor Farms Which are Poor

"Landless" farms 2,870 1,320 68.5 0-1 hectare 9.018 4,275 67.8

1 -2 hectares 4,336 2,498 63.5

2 -5 hectares 6,550 5,551 54.1

5-10 hectares 3,896 4,364 47.2

10 - 20 hectares 4,079 4,607 47.0 over 20 hectares 0 20,045 0.0

Total 30,739 42,660 41.9

Source: Dalnes (1976: 26). 89

Table 3.10 Income Distribution of the Farming Population

Income S tra ta Farming Percent of Population (per capita) Population by Income S tra ta

Less than 100 colones 42,943 9

100 - 300 colones 46,164 10

300 - 500 colones 42,531 9

500 - 800 colones 59,431 13

800 - 1100 (U.S. $150) 50,756 11

1100 - 1400 colones 41,369 9

1400 - 1700 colones 32,557 7

1700 - 2000 colones 26,374 6 over 2000 colones 129,501 27

Total 471,676 100

Source: Daines (1976: 27). Table 3.11 • Cooper iron of tha Proportion of Landleaa and Fa rains Population Claiaified aa "Poor."

Landleae Poverty Huaber Huaber of Percent of Percent of d e fin itio n of Poor Honooor Poor Honooor Total

Lea* than 1100 colonea 366,8)7 377,160 49.2 30.8 741,997 per cap ita

Feralnn Population Poverty Huaber Nuabrr of Percent of Percent of d e fin itio n of Poor Honooor Poor Honooor Total Leaa than 1100 colonae 241,823 229,831 31.0 49.0 471,676 per capita

Poverty -Total Poor Population in Coata Pica d efin itio n Huaber of Poor Percent of Poor

Leaa than 1100 colonea 606,662 30.0 per capita

Source! dalnea (1976! 17-28). 91

Table 3.12 (Daines, 1976: 17). The table shove a sizeable

difference in income for the poor with a low percent of the * economically activ e family labor employed o ff the farm, and

nonpoor farms vith a high percent of off-farm employment.

While off-farm employment is not the only significant factor

in explaining this difference in income, it appears to be a

major factor (Daines, 1976: 17). (4) Three-fourths to one-half of the available economically active

labor in poor fam ilies is without productive employment

altogether (Table 3.13). Given this, and point (3) above, one

would expect that the presence/absence of rural nonfarm

employment opportunities is an im portant determ inant of

m igration.

Summary of Chanter

This chapter presented the research design and study area. The research design uses both qualitative and quantitative statistical analyses to address the question as to (a) how migration is affected by ru ra l nonfarm employment compared to employment o pportunities provided by the agricultural and urban sectors, and (b) how selectivity is affected by rural nonfarm employment. The data and its limitations were then discussed. It was pointed out that, while both aggregate areal level and individual level data are used to address these questions, both harbor conceptual and, related to that, methodological problems.

These problems are related to the crosa-aectional nature of the data which are used to model an ongoing process. Table 3 .1 2 . Off-Farm Employment P attern s by Farm S ize.

Farm Size Percent of Total Active Family Labor Employed Outside the Farm ______Income Class ______In Agriculture ______Outside Agricnltnre ______A ll Off-Farm Employment

0 -1 hectare poor 2.1 11.7 13.8 nonpoor 24.8 133.3* 158.1

1 -2 hectares poor 1.0 5.6 6.6 nonpoor 15.7 89.4 105.1

2 -5 hectares poor 1.0 4.4 5.4 nonpoor 12.4 56.9 69.3

5-10 hectares poor 0.4 3.6 4.0 nonpoor 27.0 40.3 67.3

10 - 20 hectares poor 1.0 3.2 4.2 nonpoor 8.6 33.0 41.6 over 20 hectares , nonpoor 6.5 30.1 36.6

*Percentages over 100 indicate hired labor.

Source: Daines (1976: 17). 93

Table 3.13. Percent of Total Active Labor Employed,

Farm Sire______On the Farm ______Off the Farm Total Employment

0 -1 hectare 8.5 13.8 22.3

1-2 hectares 21.0 6.6 27.6

2-5 hectares 31.9 5.3 37.2

5-10 hectares 42.6 4.0 46.6

10 - 20 hectares 51.3 4.1 54.4

Source: Daines, Analysis of the Rural Poor (1976: 17). 9*

Following the research design the study area was discussed. It was shown that Costa Rica is particularly relevant for addressing the above research questions. The reason for this is that Costa Rica, despite its small size, experiences considerable migration; furthermore, the greater part of the migration stream is rural-directed which attests to the importance of factors in the rural environment in the decision to m igrate.

Attention then turned to the structural characteristics of the

Costa Rican economy, and th is showed th at as a sp ecial case among LDCs, it is not so special after all. In other words, it was shown that Costa

Rica exhibits characteristics and problems that are shared by many

LDCs. Among the characteristics discussed were uneven land distribution and a large degree of rural poverty, both of which have implications for the research questions addressed here. For one, the high degree of landlesness combined with rural poverty attests to the importance that rural nonfarm employment should have In migration. Second, the degree of poverty suggests that Costa Rica can ill afford the negative externalities associated with migrant selectivity. This underlines the importance of studying the relationship between migrant/nonmigrant c h a ra c te ris tic s and the various employment o p p o rtu n ities. With these considerations in mind, attention now turns to the quantitative analyses of the research questions. CHAPTER IV

PRINCIPAL DIMENSIONS OF THE RURAL NONFARM, AGRICULTURAL AND URBAN 8ECTORS AND CLASSIFICATION OF RURAL CANTONS BASED ON THESE DIMENSIONS

Ab noted in Chapter I, this dissertation addresses the question of hov rural nonfarm employment affects migration and migrant selectivity relative to the impact that agriculture or the pull of the urban sectors may have. Before examining this issue, however, it is necessary to define the rural nonfarm, agricultural and urban economic sectors in such a way that permits statistical analyses. This is the focus of this chapter. As a first step, three sets of variables are selected from the 1973 published census of Costa Rica. These variables pertain respectively to rural nonfarm, agricultural and urban economic activities in cantons, the only geographic unit for which these economic data are published.

Since none of these variables, when taken individually, tell the "whole story" of what the respective economic sector is like, they are taken together in factor analyses. This summarises the information contained in all the individual variables, characterises each sector in terms of a

95 96

few distinct, statistically independent dimensions, and yields new variables (factor scores) that are composites of the original variables.

This chapter proceeds by first presenting the variables relating to the agricultural sector and the results of their factor analysis. Then the variables and results from factor analysis pertaining to the rural nonfarm and urban economic sectors are reported. Having identified the important dimensions of the agricultural, rural nonfarm and urban sectors, the last section of this chapter groups cantons using these dimensions.

Principle Dimensions of the Agricultural Sector

Variables were selected to represent the following aspects of the agricultural sector in 62 rural cantons:

(1) Crop specialisation and degree of commercialism,

(2) Degree of mechanization,

(3) Ownership of land,

(A) Population pressure on land, and

(5) Information and education of the rural population.

The variable names, their definitions, means and standard deviations are shown in Table A .I. Examination of the means gives an i n i t i a l under­ standing of the characteristics of the agricultural sector.

The agricultural sector is highly commercialised. This is evident in the high percentages of the banana, coffee, and sugar crops that are exported, 851, 95Z and 90Z, respectively. Also, maize and beans, with

62Z and A9Z, respectively, are largely planted for sales on the market. 97

Tahla 4.1. hflilllM I, Matat, ill lln litl Davlatloae (at ARficallural latoarca Itia Vatlabloa (• * 41 ratal caaiaat).

Varlakta l l i i l i i l V ariable Rasa O efloltiea Deviation

Ctaa lotclaHaiUnn ant Deairt at CoaattrclaHin

Malta Protection Malta ptatactlai la taaa T40ilt.Pl tim p T .p p Malta Italaitlm far capita Malta prot,/rural population 4I.«1 *1.44 4*. salaa plait pat bi Malta prot,// ba 111.00 111.00 1 cewwattial aalia prat. Malta ceaaarciallp crepptt/oaira prat. .41 .14 lata Protectlaa laao pratartifo I 4 2 l l i . l l 111411.11 ■at* prot./rural populatloh 10.11 14,14 4a. b a it p l a it par ha laao prot.// ba 11.01 P1.41 1 t ■aattial kaaa prat. latoc cow rclallp croppat/kaaa prot. .4* .IP |«nann Protection laoana protact Ion (1000 atewa) Pita laaaaa prot./rural popalalloo 14.14 11.10 4a. kaaaaa plait par ha laaaaa prot,// ha (1.14 111.11 t aapultot iiu u i kinatat taper!et/banana protortloo .41 .11 fellaa Protection ColIra protactlon (1000 Rolotalt) 44PP140.11 41101P1.41 Calfaa protection par capita Coflta prot,/rural populatloo 110.PI 414.11 4a, c a lfa a p l a i t par ha Calfaa prot,// ha 11PP.00 1P11.00 1 calfaa aipartat Calfaa oaporlat/coffaa prot. .PI .11 iunnr Piiliclim luttt ptotuctlon (1000 tooa) 1I1PP.P1 14IP1.11 lupar protection par capita lupar prot,/rural populatloo 1.11 4.11 4a. aupar plait pat ha lo|tr prot.// ha 11.44 11.44 t taiar aapariat .P0 .11

1 Call l a Raachaa / cattlo raachaa// fonaa .11 .14 tpcpa Protection Cocoa p ro tu c tlo a U 1 0 l .l t 14)111.14 Cucoa p ro tect inn par ca p ita Cocoa p r o t,/r u r a l population 1.41 14.10 4a. Cocoa p l a i t par ha Cocoa p r o t ./ / ha 40.01 100,14 iM itttlilf Pi Itlll hactalra par forw / hactotta// fa rot P.1P .44 1 l.a prcaaoaatlp cvHivalat / ha pantaoaotlp Cultivate/// ha .11 .11 1 lataa uufttt I (atot totlvltuallj ownat// la tot .44 .04 l.a OaMat par c a p ita / ha intlvltuallp ounot/rural population 11.10 10.00 la m a aanapa* kp pcetacar p / (trot protucar*oaoa|ot/rural population 1.11 1 .01 ba aanapat kp p is ta c tr par ' pita / ha protucafoaaaiat/rural population 10.14 11.11

G uirt-kl. fteitrniJiiJLga T ittlvii 10.4] 100.PP S tr«ct«r (mi / tractor ualoi font// (ana .10 .40

£>»tLujpJt fiijju m .ti lilil Population prataura par ba P o p u latio n // ha 1.14 1.11 Papulatlau prataura par (a n Population// larna 11.10 1.10 Rural pvpulatfun Rural population 11011.44 0114,44 Dapaataucp ratiu (/ paoplo loan than or a/ual to 11 yaara t .10 .11 / paoplo o<|ual to or (ro a ta r than 4 )1 / / paoplo 1) to 44 JafpimJgiu-^miipaLtPi. AttidUn I paupla without atucatioa / of paoplo without anp otucatioa/pop. .11 ,01 t paupla with ttcoalarp atucatioa / of paoplo with aacontarp o/ucalion/pop. .01 .0) I beetot wilb TV / hautaa with Tv// of houaoa .It .14 I boutat with ratio 1 houaaa with ratio// of houata .11 .01 t haunt without loilat 1 houaaa without to ilet// of houata .11 .11 98

A not insignificant share of total maize and bean production, however, is cropped for subsistence.

The degree of mechanisation is generally low with only 10Z of the farms using tractors. Yet, the high standard deviation (.90) attests to the existence of some highly mechanized cantons.

Regarding land ownership, the variables employed here conceal the actual degree of land concentration in Costa Rica. Vhile generally a high proportion (94X) of a l l the farms are individually owned, and the average land holding is moderately large (23.20 ha), the statistics quoted in Chapter III indicated that one-third of the land under cultivation is corporately owned, i.e.,' thdre is a high concentration of land holdings in few hands.

Population pressure in the rural areas is high, which is best shown in the dependency ratio of .70. By and large, the rural population has received some basic education, with only a relatively small proportion

(18Z) having no formal education at all. On the other hand, there is also only a small percent of the people (7X) with higher education.

Host rural households have access to formal communication channels; 73X of all houses, for example, have radios.

For a more complete characterization of the agricultural sector, the above variables were factor analyzed for 62 rural cantons. Varimax rotation was used which yields orthogonal factors. The advantage of this method — factor analysis with varimax rotation — is that the informational content of all variables is utilized vhile multicol- linearity is avoided. This yielded four factors which together account 99

for 58% of the total common variance in the data. The factor loading

pattern ia shown in Table 4.2. The explanation of the dimensions thus

uncovered is as follows.

gactor_J_

High positive loadings on the first factor show the picture of

both subsistence and cosmercial agriculture specialising in aaise, beans

and cattle, with a high dependency ratio and a rural population with

little or no education. High negative loadings on the first factor

depict commercial coffee agriculture in densely populated areas that

have a high share of educated people and enjoy amenities like radio and

television. Looking at these factor loadings reveals a rather interesting fact.

They do not describe a general dimension such as "subsistence agricul­

ture." Rather, the factor loadings depict agricultural specialisations

that have very specific climatic-ecological requirements and, hence, are associated with certain areas of the country. Thus, although the factor analysis did not employ spatial input variables that would link the various dimensions with a certain region, such a spatial connection

21Only four factors were used because at the fifth the contribution of the factor to the explained variance drops to 6Z. 100

M lt 4.1, itlittl Paccar U rilt| m (iiitiltinl htliklti.'

Ceamaalltlaa VitliVU lane fatter 1 faetar 1 fatter 1 faetar 4

Ciea lire ill 1 tatiei_ied D rtntof Comet c lalita Malta ftalecIlea O.iJOII 0.4)14) .4) Malta tielatllm far taflta 0.4044) .71 4*. Malta flail far ha 0,7*411 .41 t cm trcial Malta ftaii -0.11474 .0)

£fa|i Production o .ta ii 0.11)14 .14 Iran product fen far capita 0,7101) .47 A*, baan f l a i l par fca 0.71411 0.14414 .44 t coaaerclal baan pred. 0.4)114 .14 banana Production -0.74*)) .4) ■iliana production fa r c a flla -0.7)107 .47 Av. banana field f i r ha -0.74)11 .44 X eiporltd banaaaa -0.1)410 .14 rollra Pradvttlan 0.41)7) .14 Cel I an pteducliea fa r tap lta -0.14)14 0.1)114 .41 Av. calfaa field par ba •0.17)0) 0.14)1) .40 1 cut tea aipurled .0) tucar Cant Prwducllon 0,44)10 .74 tuiar production par taplla 0.114)1 .7) A*, aaiar field par ba 0.411*0 .4) X tuaar tapertad 0.10111 .0) X Cat I la laacl>ra 0.41404 0.)40)7 .41

£Ht!l Production -0.71117 .40 Cacna praducliea far taflta -0.747)4 .4) A«. cocoa f l a i l par ba -0.74441 .41 SuiUtiiiiuiLliH bactataa fa r latM - f l.lllll 0.4)404 0.10441 .4) X ba prmanantlf colt Wat ad -0.11444 -0.40447 .44 X l a m waned 0.1411) 0.171)0 .4) ba waned pat capita 0.7444) .14 0.4)174 .41 0.7140) .4) Ptarar el Htchtnititle* 1 tractera 0.77*)) .4) I ttaciar ueiai larat 0.701*0 .14 Pooulai ion Pfnaura an tend -0,7444) 0.1114) .44 Population ftaaaora fa r (arm -0.4)117 0.41)11 .40 total Pnputatian 0.1)411 0.14174 0.4)411 .40 Daprndcncf la lie 0.1411) 0.1)474 .1) Infernal ion. flo ta tio n and Ambit i ti X paupla witkaet adacation 0.1)411 •0.1044* .11 I paupla el lb eataaderp education -0.717)0 0.111)4 .74 X boutaa nith TV -0,1)444 .47 I liouaaa e itb cadlo •0.11)1) 0.44)07 0.41110 .14 X bauata aitbeut ta ila t 0.14171 .41

C ilearelur 11.1) 1.11 ).)) ).)) CuMulatiaa Varlanca lip laiaad .17 .40 ,41 .1)

‘ru in lu iiiit bilmu

nevertheless emerges* To illustrate, the negative side of factor one, vhich is dominated by coffee, can be assumed to describe the coffee-

growing central highlands, vhile the positive side of factor one describes the mixed farming areas of the remainder of the country*

A map of the factor scores of factor 1 (Figure 4.1A) supports this observation. The cantons associated with the high negative side of the factor — coffee cultivation — are indeed located in the center, while the cantons correlated with the positive side of factor one are outside the central highlands. As will be seen, the other three factors also illustrate this geographic aspect of the agricultural economy.

Factor 2

The positive aide of the factor describes cantons in vhich there is a concentration of sugar cane and cattle, and some subsistence cultivation of beans. Farms are large, as is typical of sugar cane plantations, but crowded, i.e., having a high population pressure and dependency ratio. The negative side of the factor describes cantons with banana cultivation for export that also have some subsistence farming of maize.

The map of factor scores (Figure 4.IB) of factor two illustrates ita geographic manifestations. Cantons in the western part of the central highlands and in northwest Alajuela have the highest association with the positive side of the factor, indicating that these cantons are dominated by sugar cane cultivation and cattle. The cantons in the 102

Figure 4.1. Factor Score Maps Pertaining to Agricultural a A ctiv ities . (n*62 rural cantons)

A. Factor I

C. Factor III D. Factor IV

Factor Scores > 1 standard deviation between -.99 and .99 standard deviations

I_____ I <-1 standard deviation L I not included

See text for details of factor analyses and their interpretation. 103 provinces of Litaon and southern Puntarenas are shown by the facto r score nap to be doninated by banana cultivation. Both these resalts are 22 consistent with reality.

Factor 3

This shows only p o sitiv e loadings in d icatin g sugar cane cultivation with a high degree of mechanization. The factor score nap

(Figure 4.1C) shows that this sugar cane cultivation is not concentrated in the traditional sugar cane belt of southwest Alajuela but, rather, in the cattle belt of Northwest Guanacaste and Northern Alajuela, where 23 many large cattle ranchers have begun converting land to sugar cane.

Factor 4

Positive loadings here are associated with cantons that grow coffee in nore of a mixed agriculture setting that also includes sub­ sistence naize and beans; and have a high degree of educated people.

These cantons are found primarily in the southern part of the central highlands (Figure 4.ID). The negative loadings describe cocoa produc­ tion, geographically concentrated in the province of Liuon and Heredia, but also in some cantons of the Pacific lowlands (Figure 4.ID).

2ZSee also Vest and Augelli (1976: 455-461).

“ ib id . 104

Having determined the principle dimensions of the agricultural

sector, the focus now turns to the rural nonfarm and urban sectors*

P rin cip le Dimensions of the Rural Nonfarm and Urban Economic Sectors

At the onset of this subsection, the definition of the rural

nonfarm sector (Chapters I and II) should be recalled. Specifically,

this sector refers to the very heterogeneous, generally labor-intensive

nonagricultural economic activities in rural areas, villages and towns.

These activities may be manufacturing, commerce and service. Thus, the

difference between the rural nonfarm and urban sectors is not in terms

of the kind of economic activity or the type of goods and services

produced. Rather, the distinction between the two sectors has been made

in terms of:

(1) Rural versus urban location, (2) Scale, and

(3) Degree of labor versus capital intensity. This dissertation, however, distinguishes the rural nonfarm sector from the urban economic activities only in terms of location since available census data does not permit use of the other criteria. In this regard, urban areas are defined as those cantons comprising the San Jose Metropolitan Area, vhich is made up of 11 cantons (Figure 3.1), and the sis provincial capitals, which have a population of over 10,000 people and a high percentage of urban population relative to the remaining rural cantons. The remaining 62 cantons comprise the rural sector of the analysis. 105

Location being the only differentiating factor, both aectors vill be characterised by the sane set of variables, listed in Table A.3. The variables were selected to represent the following aspects of manufac­ turing, commerce and service activities in the rural nonfarm and urban se c to rs:

(1) Scale, in terms of average number of employees, capital

input, value of production,

(2) Efficiency in the use of resources, represented by capital-

output and capital-labor ratioB,

(3) Wages and earnings,

(A) Formal labor market arrangements, such aa insurance.

Examination of the mean values (Table A.3) c le a rly shows the difference in the characteristics of rural nonfarm and urban sectors.

Generally, rural manufacturing, commerce and service are smaller in scale than their urban counterparts, i.e., they have a smaller average number of employees,'lower average investment, and value of production; furthermore, rural nonfarm earnings and wages are generally lower than urban wages, and a smaller percentage of the labor force in the rural nonfarm sector has some form of insurance. More important aspects, however, are the efficiency in the use of resources. Relative to the urban sector, rural nonfarm activities use less capital per unit of labor input, and are thus more efficient in the use of scarce resources. This agreeB with findings made in other countries (see also Chapter II). Similarly, rural nonfara activities aeem to be more productive, using less capital input per unit of output Tablt 4,1. Dafititieni, Maim, an! Itanlarl 106 loll b itin lit llthi bititii

T illa b le T ariabla Otban Cantona fa * 1) Rural Cantata (n ■ XI) I a n D efinition _ Ptanlarl _ Ptanlarl t Deflation n Ota l i t lo t

J H a i t n I lib itiiil tatabliahwanta f of i*4*it(ial aatabl (alaatnta H I . 1J 441.21 IX .44 14.14 1 liliiiliil tapleytat 1 e( mplaytat 1* i*4aatlp IIS*.00 11044.11 101.40 241.41 4*. b fli|iii Ml lili Xatahl, ♦ 1*1. aop./l inlwitrial aaiabllalaatli 21.14 X.I4 11.31 14.11 4*. Iwlwt. Vi|*i fit to m Total l*4aa, war* bill/4 InlatltUI mpl. illlX.ll 1141.11 1040.40 1441.01 X I I I , ( < tib . Opa* 411 T*4l 4 Inlui, aaitb. opt* all paat/4 l*4aa, aaltb. .14 .04 144 .10 1 M il 1*4, toplwyaaa 4 Malt lilwa, aopl,/4 inlat, anplayaa* .11 .11 .14 .1) X Oaf*14 I n lu i. b f l i | l l i 4 wnpai4 (aiua. mpl./I i*4*a. anylayatt .01 .01 .12 .10 t Iiliii l*fl> «ilb Itm n ti 4 (* 1* 1 *1 i*4ui, aopl./l iniwa. top!. .11 .07 .44 .11 4*, Cifltal Input • Iniwatry (talwa of liaal aaaata plua *aiaaaaty 2 0 4 0 ttl.il 2141441.11 41)171.)4 1)114)0,14 puitbaaaal/T inlat, aatab, Capllal/lMlpul * l*4uiltf (talwa of flat! aaaatt plat attaiiatf 1.10 I.0X .S I .7) putchiittl/talua at inlwi, pralattlaa Capital/labur - tt/witty (talwa or fiaal aaaata plwa utttaaarp 11001,11 71100.1) 40470,44 3)4)1.7) purcbaaaa)// inlui. aoplopaaa I 1*4. Ullh Low Talwa el Prel. (4 ini, w/low talwaa ol ptolactlan, .10 .1 ) .30 .11 1,*., Itii than 30,000 calotte)/ 4 (Biuairlal *at*bll*hwe*li 1 lti, Vilb Mai. Talwa *1 h*4. (# {*4. w/oal. talwa *4 ptol. l.a,, op .10 .04 .0) .07 to 100,000 to l.)/l l*4uatrial aatab. X Int. With Rlph Talwa ol Tiei. (4 1*4, w/hiph talwa of prelect!**, .20 .04 .10 .11 1,*,, titatat than 100,000 col,)/ 4 ialw itrial aatabllahwaola I f ta lc a 1 taialia tatabllahwtala 4 ol lartitt tatablitlatati 3X1.11 (41.17 31.44 41.1) / itrvlia Cwployatt 4 *1 mplaytat 1* aartlt* 1011.00 1114.IX 1 )4 .SI 1)1.41 4 aartlta awyloyaat// a trtltt caiabl. 1.4X .40 2.3) .44 Total aart. wait blll/4 atrtict awpl. 1141.IX 114).07 1771.41 11X4.17 X iatwita (tlabl. Opt* 411 Ttar 4 aartlta aatabl, apt* all paar/ .41 .04 .40 .07 4 aartlta aatablfthwtaia X Malr f.rvlra Cwpluyaaa 4 nal* aartlta awpl,/I aartlta awpl. ,X1 .14 .70 .1) X Unpaid Iriw ll* Eaployaaa 4 wapall aartlta m pl,/4 aartit* awpl. .11 .01 .14 .11 4 Itnuial atrtict a*pl,/4 atrtict anpl. ,xo .44 .)! .44 4a, Capital tnpwl - lalwica (total talwa of (H al aaaata plot atcaa- I tS lS t.J I 14)42.2) 101074.41 444(1,1) aarp patch****)// aartlta aatabl, Capital/Ouiput - tarwlca (total talwa of (iiti aaaata plwa aacaa* .11 .04 .31 .11 aary patch,)/iota) i*cowa (row aataa aal othar aaurcta Capilal/Labul - laialca (total talua ol flaal aaaata plua tacta* 411)1.10 4104.iX 141X0.1) 1)040.40 aary puiclt,)/# aartlta taployaat

/ Cumurca Latablltliwwnta 4 el tomarc* aatabllahwanta 111,00 IHX.4I XI.14 44.41 / Cwmatta Cwplayaat 4 ol aaploytta 1* tomarc* 4111.71 7711.17 20).7) 1)3.41 4w, l a p U ] n i p al C um . C tttb . 4 comatca awpl./4 tomarta aatabl. 4.00 .47 l.X ) 1.01 4v. Cemrrcial Wapta pat Parao* Total com, **|t bill// com, awpl, 7014,20 1707.1) 1X01.>4 1147.00 X Cwmarr* Ealab, Opan 411 Ttar 4 comatca aatab), opan all yaar/ .41 .0) .41 .03 4 tomarta aatabl. 1 Mala C nam i Laplwyat* 4 mala tom. a*p!./4 tom, wwployatt .XT .04 .47 .01 1 Utpall lomtr.w bupleywaa 4 unpail com. m pl./I com, awpl. .17 .0) .14 .04 I laauiwl Lwaun Cwpleyuaa 4 (mural cam. mpl,/I cam. awpl. .11 .11 .04 .11 4v, Capital Inpwl • Comatca (total talwa ol flat! aaaata plwa tactt* 3X I4I.I) 1X441.OX 11X40.14 114)4.10 aary purchaata)/! tom arta aatabl. Capital/Output - Camarca (total talwa ol I tail aaaata plua trctt- 11,01 11.41 44.44 41.41 aary purchaaaa)/total Incowt (taw aalia aal athtr aawrta* Capital/Labor - Caaailta (lota) talua ol liaal aattlt plua atcai* 14441.40 XI47.IB 11417.)! 4)04,44 aary purchaiaa)/f comarca mpl. tmliSJvn. tulX ib'i.lrni X Tvpwlallua without InauiauCa 4 ol ptoplt without inaur,/total papulation ,10 .10 .70 .1) X Uotwpleytd bulb 4|t Tup. 4 uBmploytl/warbia| *|* populatia* .04 ,00 .0) .01 X to la Callal Labor Tore* 4 parte** 1* whit* collar occwp./werhini .11 .04 .0) .0) i |t population X 0(1 lea LaLut T una 4 paiaont In oil lea Joba/wurbinp a|a .04 .01 .01 .01 papulation I Labat Tail* 1* Ttaeapartatia* 4 partona la traBtpartalioa/workini .0 ) .01 .01 .01 apt populatia* t Labor Turca Ini tan*4 arilatBt/werhlai apt papulation .11 .04 .10 ,0b X Labor Tarta wllb Caaual 4 paopl* w/ctaual mpl,/worbi*| apt .01 .0) .04 .01 b p I lT M it papulatie* X Labor Tart* 0m 4 t t m l 4 aall-mpleyad/warbitp apt papulation .11 .0) .10 .01 I Labor Tarta ParlMlwa 4 part-tiw* mpl ay at/verb lap apt .01 ,01 .01 .01 twpluywaat population X Labor Tart* full-liw a 4 lull'll** mplayol/worbinp *|t .04 .04 .11 .11 Ewploywtnc population I Labor Tort* i« Low Waft Jeba 4 partona with low wap* )aba/worbtap .17 .07) .14 .01 apt population X labor Twit* 1* Mai. Ua|i Joba 4 paraana with wad. wapa joba/wurbinp .11 .0) .14 .10 apt population X labor Tarta i* lli|h blip* Jaba 4 partona with htph wap* jebt/verbiup .14 .0) .04 ,01 ap* population Vrba* Tepulalia* population raailinp In urban at*** 11)14.4) 14401.14 3070,47 1404.77 I Rural Papulation rural population/total population .4) .14 .11 .11 107

than do urban in d u s trie s . The one exception la commerce, which may be explained by the influx of foreign capital in commercial activities related to export crops, thua distorting the overall picture.

For a more in-depth description of the rural nonfarm and urban sectors, the variablea Hated in Table 4.3 were factor analysed using

the factor analytical routine with varimax rotation of the Statistical

Analysis System (SAS).

To describe the rural nonfarm sector, only the 62 rural cantons were factor analyzed. Factor analysis of the urban economic activitiea, however, proved somewhat problematic since there are only seven urban observations. Accordingly, to derive dimensions for the urban sector, and associated factor scores, the analysis employed all 69 cantons.

From this, then, the factor scores of only the seven urban areas were selected for further use in Chapter V, which relates migration to the agricultural, rural nonfarm, and urban sectors.

It should be noted that the factors extracted from the two analyses (n “ 62 and n “ 69) are essentially the same. Accordingly, the results from both analyses are discussed together; differences are pointed out only when significant. The following dimensions emerged from the analysis (Table 4.4).

Factor 1

On the positive side this reflects modern large-scale industry in more urbanized cantons that also have a relatively high degree of unemployment, casual, and part-time labor, as well as white collar trtli 4.4. b u iii futw Let I Inti ta t llni|tlrtiuril bMoalc Activity fatlablea ia 108 fatal CtilMi (a • 41) aai U lira! flue Data a Canteen (a * 44),

Cemaalttf of feeler t factor 2 factor 1 faetar 4 VatlaMta Callable Wane (a) (4) (a) (4) (a) (4) (a) (4) (a) (4) n*4) n-42 a*t) a-41 a-4) tH l •-*) a**l a s ) n-42 lB jfU tl f laiaattial Letabllalaeata 0.1 )2 1 1 0.1 2 1 )1 .11 .12 f teJuatrlal faplepeea 0.194M 0,1)421 0.M))2 0,4)11) • )4 .2 ) A*, bflafrca fat Inina. fatab. 0.42412 0 .4 4 4 1 ) 0.2141) 0.1)141 .11 .1 ) t i. taiaa, Wagee fat frtaoa 0.1)104 0.402)0 .1) .0) I Inina. tata4, Open All Uai .01 4 VD I Halt la in Laptvyeef •0.11141 •0 .1 )1 2 0 .1 ) .11 t Bapali taiaa, fnplnyeei 0 .2 2 2 )1 .14 .12 t laiaa. [apl.iyrre ulth laaaiaaca 0.41)41 0 ,) ) ) 4 4 0.41142 0,1401) .41 .4) Av» Capital lapul * Iniualty 0.241)1 0 .2 )4 4 ) 0.11)14 0,4111) .11 . ) ) CafItal/Oatfal • InJactty 0 .4 )0 1 ) 0,24411 .)) .10 Capilal/Lalwr • laiaatrf 0 .2 )4 0 2 0 .1 1 1 )0 0 .1 1 )4 ) .42 .10 1 Ini, With Ian falaa al frai. •0 .4)121 - O . l l l l l -0.1)2)) -0,211)0 -0,24)11 -0.22144 .4) .1 ) 1 lat, With Hci, falaa at frai, 0.24)4) 0 .1 1 4 )1 0.2 2 4 4 ) 0.21110 0.2121) 0 .2 )1 2 0 .41 .41 t tai. With Hl|h falaa al frai. 0.214)1 0.11411 .1 ) .11

Itnrlta I iaralca taiebllibnente 0 .1 )1 2 0 0 .4 2 1 1 ) . ) ! .1 ) f Iaralca Inpleyeae 0.)D)2) 0,41104 , ) l .14 ti. ta^la|aia frr Iaralca Iita4. 0.2)111 0.14104 0.44212 .)) .4 ) Aa. Irtaltt Vi|rl far Careen 0.14441 0,4))ll 0.114)) 0,4411) .)) .11 t letvlca Lalahl, Open All fear 0.4 )1 4 4 0.1 4 2 )1 .1 ) .11 t Mala loivlta iiaplayeea 0,)4lf) 0,11440 0,21011 0.1)441 .44 .44 I ttapali Iaralca Lnpleytae -0.10102 .1 ) .1 ) I lattice Waiter a ellh Ineuiaeca -0.1101) -0.1)14) .11 .41 Aa. Capital Input - Ittalia 0,1 4 )1 1 0,14441 .4 ) .24 Capllal/Dulpul - fervue 0.441)2 0,440)) .1 ) .24 Capita I/Labor • taraice 0.11411 0.11)14 .1 ) .1 ) i j n i m t Camaarca Catabllahaaola 0 .4 4 )1 ) 0.41240 .11 .10 I r-eaairce fnpluyeaa 0,1)141 0.11424 ,)S .40 ... Kaplsyeee fa t Cama, Caleb, 0.11)14 0.44141 .24 .10 ■>. C.«a«etclnl Waiaa far faiaoa 0.1)141 .12 .02 t :‘uuarca Caleb. Open All Year 0.41)42 0>))))l .14 .41 1 Mala Cona-erc* (apluyeee 0 .4 1 2 )0 0.4 4 1 1 ) .1 ) .24 X Unpaii C u u .iu hnpiuytte •0.41)01 • 0 ,1 1 2 )) -0 ,1 1 1 0 ) -0 .2 )2 2 ) .14 .22 1 laaarai C nattta Knpluyeea .04 .01 Aa. Capital Input * Coun.lta 0.41410 0 .1 4 1 )4 .14 Capilal/Outful • Cuaaarce -0.22)21 - 0 .2 ) ) ) 0 .1 ) ! l ) Capllal/Ubur * Coaaarce O .lflO ) .0 )

Im ItiieojUilitKr. Emu 1 Papulation aiihoui Inauranca -0.42)1) *0,1114) -0,4)))) -0.11)41 .4) .4 ) ) Untap I op ail Wait Ate fof, 0.41)24 0.1 )1 0 2 .11 .41 X While Collar Labor Corea 0 .2)100 0 .2 1 )4 ) 0 ,4 2 0 0 ) .10 .42 1 Office Labor Cotta 0,14)12 0.14011 0 .4 2 4 4 ) .04 .14 1 Labor Cuice lb liauaportalloa 0,10014 0 ,1 )4 1 4 .44 .41 X Labor Farce A itiaaaa 0.12)11 0.1 )2 4 1 0 . ) l ) ) l 0 .2 )1 0 1 .12 .1 ) 1 labor Ceita witb Caaoal 0,21112 0,422)1 0,22111 .)! .42 [nplayunht X Labor lorca (mu Account -0.10404 • 0 .1 )0 4 ) .21 .24 X Labor Corea P ert-lin e lapUjmaut 0.1)))) 0.1 1 1 )2 0,41144 0 .1 2 ) ) ) .2 ) .21 X lob«r forte tnll-liun hnpluyueet • 0 .4 1 2 )4 0.12424 0 ,1 2 2 )) ,)).)) X Labor futcv in Low Wage Jobe 0 .) l) 2 2 0.4 1 )1 4 .10 .)0 X labor Cur.a io Mai. Wape Joba 0 .1 )4 2 ) 0.12)22 0.2)410 .24 .*0 I Labor Cor.a In Hi|h Watt Joba 0 .2 )4 2 ) 0.211)1 0.41))) -0 .2 4 )0 1 ,10 .14 Urban Population 0,421)1 0.1)112 0.)2)|A .)4 .12 X lurel Populaliun -0.2H)) -0,1412) -0,11)01 •0 .1 )1 4 0 .11 .41

Iiganvetue 14,44 1 2 .4 ) 4 .1 ) ) . l l 1.21 4 .1 0 2.11 1 .4 4 emulative farianca faplaiaai .11 .24 .4 ) .1 1 .12 .42 .40 .14 a Loaiiage ol batueea *0,2) aai t0,2) have boon oailtai. 109

occupations with medium and high wages. The negative loadings

indicate small-scale service and commerce activitiea in more rural

cantons, with low capital-output ratio and value of production, and a

large proportion of unpaid, uninsured, own account, and overtime employed.

A'mapped version of th is facto r (Figure 4.2A), using only the 62

rural cantons, shows the modern large-scale industrial activities are

concentrated in the cantons adjacent to the San Jose Metropolitan area

and in Golfito, a major export center.

Factor 2

Loadinga on this are primarily positive and indicate general

industrial, commercial and service activities in more urbanised

cantons. A map of the factor scores shows that these concentrations of

non-agricultural activities are found in Limon cantons that specialise

in the export crop banana, and in areas north and vest of Alajuela that

specialise in cattle and sugar cane (Figure 4.2B).

Factor 3

This represents the degree of capital intensity of manufacturing,

commercial and service activities, as is evident from the high loadings

of the factor on average capital input, capital-output, capital/labor

ratios, value of output and average wage variables. Overall, the areas with high capital intensive industry, commerce and service are s I bo the

nodes of export agriculture, such as Limon and Puntarenas (Figure 110

Figure 4.2. Factor Score Maps Pertaining to Rural Nonfarn a Activities (n=62 rural cantons)

F actor IIFactor I Factor IIFactor

Factor III Factor IV

Factor Scores 1 ll >1 standard deviation between -.99 and .99 standard deviations I i <-1 standard deviation * I I not included a See text for details of factor analyses and their interpretation. I l l

4.2C). The high capital-intensity in theae areas nay be attributed to

either foreign investment (Limon) or increased investment activity on

behalf of the government in import-aubstitution and (traditional) export

promotion programs.

Areas that score significantly on the negative side of this factor,

i.e., have lover degree of capital intensity, are remotely located in

less densely populated areas. Furthermore, a lover degree of capital

intensity is also found in some banana groving areas (Limon) that have

traditionally suffered from plant disease end observed declining foreign

investment (Figure 4.2C).

Factor 4

This is largely unidimensional indicating, on the positive side,

informal industrial, commercial and service activity, vith a large

degree of casual, self-employed unpaid and lov-vage jobs. Note that

these activities are concentrated in areas of veil-established agricul­

ture (compare Figure 4.2D vith Figure 4.1) and possibly represent the marketing and processing associated vith stable commercial agriculture.

These activities are lacking in the southern and southeastern banana producing regions that experienced fluctuations in export demand and/or prices that adversely affected general entrepreneurship. Alternatively,

the lack of nonagricultural activity may be seen as a manifestation of shallow development by foreign companies that control the marketing and do not encourage lo cal commerce. 112

Classification of Rural Cantona Baaed on Dimensions of Their Agricultural and Rural Nonfarm 8ectora

The foregoing statiatical analyaea served to characterize the multi­

faceted nature of agricultural, rural nonfann and urban economic

activities in terms of a few complex dimensions that are summarized in

Table 4.5. These dimensions are used in the analyses of the research questions posed in Chapter I in the following ways:

(a) As independent variables in regressions designed to determine

the effects upon in, out, and net migration of the rural

nonfarm, agricultural, and urban economic sectors.

(b) As the basis of a typology of rural cantons to be used to

examine how ru ra l nonfarm employment a ffe c ts migrant

selectivity, that is, to identify the relationship between

human resource c h a ra c te ris tic s , employment in the various

economic sectors, place of residence, and in, out, and net m igration.

The typology of rural cantons is now addressed.

The grouping of rural cantons in terms of common characteristics of their farm and nonfarm sectors uses Ward's Hierarchical Grouping

Algorithm, as embodied in the program H-group (Veldman, 1967). The basic procedure was described in Chapter III. To recall, the aim is to group the observations so as to minimize within group variance while maximizing between group variance. The 62 rural cantons are the observations; the factor scores on each canton, representing the various 113

Table 4.5. Principal Dimensions of the Agricultural and Rural Nonfarm Sectora.

Agricultural Sector

Factor Poaitive Side of Factor Negative Side of Factor

Comnercial and aubaiatence Coffee cultivation agriculture, cattle, naise, population preaaure, beana, high dependency ratio, higher educational levels low educational levela

Cattle/dairy induatry, Banana c u ltiv a tio n and some augar cane, aubaiatence commercial maize farming of beana, high population preaaure

3 Sugar cane on mechanized farms

4 Coffee, aubaiatence maize, Cocoa c u ltiv a tio n commercial beans

Rural Nonfarm Sector

Modern la rg e r in d u strie s in Leas modern, informal small- more urbanized cantons scale industry, commerce and service in more ru ra l cantona

General industrial, com­ mercial and service activity in more urbanized cantons

Capital-intensive induatry, Leas capital-intensive commerce and service industry, commerce, and service Informal, casual, self- employed, full-tim e and part-tim e commerce and service activities 114

distensions summarized in Table 4.5, are the variables. At each step the tvo closest cantons, in terns of their eight factor score values, or the tvo closest groups of cantons, in terns of the nean of their eight intra group factor score values, are put together to form a (new) group; and this process, which begins with 62 groups in this case, i.e., each single canton, goes on until only one group remains, i.e., for 61 steps.

Accordingly, one has the choice of how nany groupings are desired, and by convention, this is done subjectively in a nanner that takes cogni­ zance of the substantive nature of the groups and the level of clarity of each.

Eight groups were selected in this case. The group members (rural cantons) and their characterization in terms of agricultural and rural nonfarm dimensions, indicated by the group's mean factor scores on each dimension, are shown in Table 4*6, The groups are verbally described in

Table 4.7. The latter also shows that, although eight groups were distinguished (marked 1 through 8 on the table), they conceptually represent three categories. The groups and the three categories to which they belong are as follows.

Category A

The cantons th a t make up category A are shown in Figure 4 .3 . These are characterized both by commercial agriculture and a well-developed secondary and tertiary sector, whereas the latter is almost totally ab­ sent in the cantons of category B. One may conjecture that this dicho­ tomy ia related to land tenure in the respective groups of cantons. Toll* 4.1, H*o* htttr Iton* f*r Cootoo CmM'l'/

Crony t*r*l OooforWlaill- R n b a ti AoritnUnril ricton Itol* totirorii* loctori Creoo (coRtoni) . ' 1 I 1 A 1114 1 Jlojiocho, Porrlttol, Koto, Torr*««, foot, Totrvbim , AUooi, .III 1.0} fu to n )* , Loo* C a r td , falnili h |i, JI m m i , I t Caorco

1 toot* lit, larbo, to Dot**, Oromoo, fieri*, toot* Doatnt*, I . ) i 1.11 1.11 loo M ful, loo foblo

1 firrtto, Acait*, Dot*, Orttioo, Coo**, O*otoo*, •1.11 -.10 l . i ) Cotltl*, Tonii*, A lairib, loot* lorbor*, Toloaoot*

« Tirol til**#* Cr*tlo, tin - l . l t 1.14 - l . i i - .♦ ) Cttloi, Torrlalbi

S 111 l i n o , 111 Hit**, h iu rii, -1 ,41 Atfir* toll, to* liHro, Iiyirt*

4 UytU, U* CMWi, linyU ni, *Uojri, loot* C m , Aboo|*rii, Titono, Rioliyon, L* Cr«l, Atuttri, 1.01 -.11 Ivioai A im , Moot** i i Oro, Cot* I r v i * J li|lt*l, On, Colfito, head, -.1 4 - I . l t 4.14 li|*irm , Kitloi C v itiM

1 l l l i o •1.11 -1.11

*T1>1 | i u ) m i u tfcovi ir* U law of fattor aiatai of tk* foctoi 4io«oilooi ioiicitil. Him a i l u i U lv ti* •-6.I0 o*4 *0.60 v*r« oot iaclafaf. Ctouf* f t ti a if by I-Creuy (la lfu a , IM f). loo loot for f a l ii li i Tabic 4.7. ClaaaificaticB of Sural Cantona,

Catfgory At Well KatahHahed Cowmcreialfreoort AtricuHun and Well Ptvitlgped Secondare and Tertiary Sector.

Croup It Kited commercial agriculture (coffee) and large number of inform al, aelf-employed Job a In aervice and toM trct in outlying areaa of the Keaeta C entral. Croup It Sugar cane, coffee and commercially cropped beana In tba center of the Heeeta Central and general induatrlel, commercial and aervice activity.

Croup Ji Coffee and banana with a one aubaiatence farming and larger modern induatry, coamerce and aervice in the Keaeta Central.

Croup A: Coffee, cattlefdalry vith aome augar cane and lree capltai-intenaive induetrial, commercial and aervice activitiea in the Keaeta Central.

Category It Coeaaercial/Eaoort Agriculture with few Job gBEimnLii«!*„gr£g.'»iiary tad Tc t iIt t AH-irMAr"- Croup 5t Cocoa monoculture with few, if any, nonagricultural activity in peripheral locationa (Limon, Puntarrnaa).

Croup 6t CoMcrcial cattle/dairy with aubaiatance farming of malae and very few, if any, kind of aervice and cowaerce activitiea.

Croup 7< Banana ronoculture with few, if any, kind of aervice and commerce a c tiv itie a .

Category Ct Hiehlv Canital-lntenaive Induatry and Some gaport Sericulture.

Croup I: Capital-intrnalve induatry and aome coffee cultivation. Figure H,. 1. Typology of Costa Rican fan tons

Rural with Vail EataMlahed Coaaaretu}/ Export ifrlcullura and Vail Daaalopad Secondary anl Tari lary factor

Rural with Coaarrrlal/Eipi>rt Aprlcullura vith Rou Job Oppnr*unitlaa to ftTtndnr) and Tertiary lett«ltl«i

Highly Capitel-lntanalae Induatry and toa* Riport igrlculture

L I

See taxt for aethodology uaad for deriving the . typology prevented here. 118

Specifically, in the cantoni of category A, the faroere have tradition­ ally owned their land (West and Augelli, 1976), farmed for their own benefit, and become relatively proaperoue. Aa a rem it, they are likely to have generated demand for food processing and marketing activities on the one hand, and for consumer goods and farm implements on the other.

Accordingly, the emergence/existence of rural nonfarm activities in the cantons of category A could be representative of the income and output effects described by Liedholm (1973: 14), who notes that the site of the rural nonfarm sector depends on the growth of the agricultural sector, i.e., that increased agricultural production would create not only an

indirect income effect that could increase the demand for rurally produced consumer goods, but also a direct output effect (associated with backward and forward agriculture linkages) that could increase the demand for rurally produced agricultural inputs and also provide opportunities for the local processing of agricultural outputs.

Indeed, many of the aervice and conaercial establishments found in cantona of Category A may have been set up by farmers/local entre­ preneurs themselves. Although hard data supporting this for Costa Rica are lacking, research in other countries has found that this is a common pattern (ILO, 1972; Liedholm and Chuta, 1976).

Alternatively, one may view the presence of rural area farm activities in terms of the proximity of category A cantons to the San

Jose Metropolitan Area (see Figure 4.3), in accordance vith Schulte's

(1953: Ch. 9) Urban Industrial Impact Hypothesis. This postulates that rural areas in the vicinity of a major urban industrial center experi- 119

ence positive spillover effects, i.e., they will have aore developed farm and nonfarm sectors than rural areas further away* One reason for this would be that such proximity allows commuting for cash earnings, which in turn can be used for investment in the farm and nonfarm sec­ tors, Also, proximity allows for subcontracting whereby urban based industries encourage rural farm households to pursue nonfarm, often cottage industry activities.

Category B

The lack of service and commercial enterprises in Category B cantons (Figure A.3) may be attributed to their landholding structure.

Specifically, in many of these cantons, such as in Cuanacaste province, land has traditionally been ownr.d by few or has been increasingly con­ solidated, As a result, there may be a growing number of landless, sharecroppers, and small subsistence farmers vith little money to spend on consumption goods, farm implements, or initiating a non farm enterprise. Accordingly, there would be few, if any, income and output effects to stimulate the growth of the rural nonfarm sector.

Alternatively, the lack of a strong rural nonfarm sector in

Category B cantons may be reflective of shallow development. To illustrate this, note that many of the Category B (Figure 4.3) cantons also happen to be centers of banana cultivation where land and produc­ tion are very much controlled by foreign companies which, as is commonly understood, do not encourage the growth of locally owned processing and marketing activities. 120

Cateeorv C

This includes only one canton that has highly capital-intensive

industry and some agriculture (Figure 4*3)* This industrial enclave nay be explained by this canton's proximity to the metropolitan area, and possibly represents an outlier of some urban industry. Since category C represents an aberration among the rural cantons of Costa Rica, it vill not be used in the following analyses and discussion.

Summary of the Chanter

This chapter is concerned with establishing a usable data set for the an aly sis of how ru ra l nonfarm employment a ffe c ts m igration and migrant selectivity. More specifically, a large number of variables pertaining to rural manufacturing, commerce and service, their urban counterparts, and agriculture were factor analysed. This summarises the information contained in the variables in terms of a few distinct dimensions. As such, a larger data aet is "reduced" to a few statistically independent composite variables.

From the analyses, four such composite variables were abstracted for the agricultural, rural and urban sectors. However, the same factors characterised both the rural nonfarm and urban sectors, which employed the same set of variables. These are sinmaarised in Table 4.5; maps of their spatial distribution are given in Figures 4.1 and 4.2.

The factors thus derived were used to group rural cantons as either predominantly agricultural or agricultural with rural nonfarm activities 121

(Table 4.7, Figure 4,3). This classification primarily is used in

Chapter VI in 'examining the question of how ru ra l nonfarm employment affects migrant selectivity, as it is manifest in the difference in human resource characteristics of migrants in rural areas with and without or rural nonfarm employment opportunities. First, however, attention turns to the use of the factor scores as independent variables in the regression analyses that relate aggregate migration to rural nonfarm employment, the agricultural sector, and the pull of urban economic activities. CHAPTER V

STATISTICAL ANALYSIS: THE RURAL NONFARM SECTOR AND AGGREGATE MIGRATION

This chapter seeks an understanding of hov the rural nonfann sector affects migration, relative to the impact that agriculture or the pull

of the urban sector may have. Before proceeding with this analysis, it

is useful to recall the problem. Theory generally models migration as a rational decision in

response to perceived differentials among labor markets. Empirical

atudiea, however, have been more narrow in focus, tending to view migration largely as a mechanistic response to urban sector wage and job

opportunities, while giving little consideration to the effect of other

labor markets. In particular, although some attention has been given to rural sector labor markets (Todaro, 1971; Byerlee, 1974), the effect of ru ra l nonfarm employment o pportunities on m igration remains a re la tiv e ly neglected issue. Given the emphasis on the rural nonfaxm sector in research and policy, this is surprising. Those promoting that sector,

for example, posit that its growth potential, combined vith its labor

intensity, promise a continued and burgeoning absorption of labor in

situ. Hence, the rural nonfarm sector ought to be a significant deterrent to rural-urban migration, particularly since, for a variety of reasons (see Chapter I), the potential rural migrant may prefer to stay

122 123

in the rural area. Nevertheless, the proposiion remains, for the most part, untested.

To examine the importance of the rural nonfarm sector in accelerate

ing/retarding migration relative to the agricultural or urban sectors,

attention now turns to the case of Costa Rica, for which the problem is examined in two ways:

1. In order to get an overview of the relationship between nonfarm

employment and m igration, aggregate canton lev el data and

regression analysis are used.

2. To support the findings made in the aggregate analyses,

individual level data arranged in contingency table form, is employed.

In elaborating the results of these analyses, attention first turns to

the dependent variables to be used in the regressions; then to the

independent variables, which are based on the factor scores pertaining

to the rural nonfarm, agricultural and urban sectors, discussed in the

last chapter. The results of the regression analysis of aggregate

canton level data are discussed next; followed by the results obtained

from the individual level data.

The Dependent V ariables

The dependent variables are out, in, and net migration, where migration status is determined by an inquiry aa to where a person lived five years before census enumeration, and net migration is defined aa in migration minus out migration. 124

The use of out, in, and net migration together is thought to

provide a complete picture from which to assess the relative importance

of rural, nonfarm employment (vis-a-vis the agricultural or urban sector)

in accelerating/retarding migration. Of the three dependent variables,

however, net migration is the most useful in thet it takes account of,

and cancels out, the effect of seemingly "random" migration, for

example, out migration from burgeoning areas and in migration into

economically depressed areas.

Out, in, and net migration are initially measured as gross amounts,

that is, O^ is the absolute number of persons who lived in canton i in

1968 and did not live there in 1973; for in migration is similarly defined. for net migration, then, is canton i's net change in popu­

lation due to migration during the same five year period.

However, out, in, and net migration are also defined as migration

rates, which corrects for the potential bias due to differences in

population sise between cantons. Migration rates are useful also for

another reason. The use of rates enables one to represent migration as

a stochastic process, i.e., rates can be interpreted as the probability

that individuals born in canton i w ill migrate to canton j, which makes

it appropriate to estimate parameters by regression analysis (Schulte,

1969; Levy and Wadycki, 1974).^

24 Note, however, that the migration rate also has deficiencies. In removing the effect of population size, for example, the population at the end of the intercensal period generally is used, rather than at the beginning. In fact, however, neither the initial nor the end population represent the actual population at risk, and approaches to get around the problem — e.g., using the means of initial and end populations — are not free of problems since this assumes a constant growth rate (Shryock and Siegel, 1976: 387). Of course, these problems also are present in previous m igration stu d ies such as by House and Rempel (1981); Levy and Wadycki (1974). 125

The standard approach to taking migration rates, also employed here, is to divide gross migration by total population (Shryock and

Siegel, 1976: 387), that is,

OR^ • 0 i / ? L x 100 - i 1/p jL x 100 **1 " V Pl X 100 vhere OR., IR^, and NR^ are the out, in, and net migration, respec­

tively, for canton i, and is its population size.

Independent Variables

This chapter seeks to explain the degree to which migration responds to job and wage opportunities in the rural nonfarm sectors, as compared to the agricultural and urban sectors. Accordingly, the independent variables consist in part of factor scores representing the four dimensions of agricultural activities, derived from the factor analysis of the 62 rural cantons, and factor scores reflecting the four dimensions of rural nonfarm/small-scale enterprise activities in the same 62 cantons. The derivation and interpretation of these factor scores are discussed in Chapter IV and summarized in Tables 5.1A and

5 .IB.

Variables indicating the influence on migration of the seven major urban centers of Costa Rica also were employed. These were constructed as follows. T tM t 5 .1 . DIm h Io m of the Agricultural and Rural Monf arm/final 1- Seala Enterpriae Sactora.

V ariable Kane Otad fa c to r factor Scora Definitione in Rcaraaaion Dlncnilon (♦) Obiervationa (-1 Observation* A. Rural Montana final1-Scala Enterpriae Rector

SBEl 1 Modern, large ru ra l Leaa modern am all- nonfarn induatry aeale Informal in­ in nore urbanlted duatry, conmerce, canton*. and aervice.

SSE2 2 Canaral induetrial, coamercial and aervice activity in nore urbaniaad cantona.

SSE3 3 Capital intenaive Leaa capital- induatry, commerce, inteneive aervice.

SBE4 4 Informal, caaual, eelf-employed, full- tina and part-time commerce and aervice activitiea. and coamercial beana. B. Afiricultural Sector

A| 1 1 Commercial and aub- Coffee, high popula- ture, cattle, maice, beana, blgh dependency ratio, low educa­ tional levela.

Afi 2 2 C a ttle /d a iry in - Banana a, aome com- duatry, aome augar mercial maixe. cane export, aubaia­ tence farming of beana, high population preaaure, aonevhat better educated population.

At 3 3 Sugar cane on mechaniaed fame.

A| 4 4 Coffee in a mined Cocoa c u ltiv a ­ agriculture aetting, tio n . with aubaiatence maice I and commercial beana.

*theee dimenaiona ara baaad upon tit analyeee of aaltctad variable* for 62 rural cantom. lit anelyaea ara deacribed in Chapter IV. 127

As noted in Chapter IV, the factor analysis of the nonagricultural

variables was done both for the 62 rural cantons and for all 69 can­

tons. Whereas the former was used to characterise the nonfara sector of « the 62 rural counties, the latter analysis yielded the variables used to

measure the effect of the seven major urban centers. Specifically, the

factor scores representing the San Jose Metropolitan Area and the six

province capitals, for each of the four dimensions, were divided by the

distance-squared between each of those seven urban places and the 62

rural counties. To illustrate, the pull of San Jose on dimension one

for rural canton i would be the factor score of San Jose on factor one 25 divided by the distance-squared between i and San Jose. This having

been done for each urban area, the variable was constructed by adding

together the respective quotients, that is,

Pull PU - F8n /

«

where Fj^ indicates the pull of urban factor dimension 1 on

canton i; FS^ indicates the factor score of San Jose (1) on

factor dimension 1; FS^ . •••FS^ indicates the factor score

of province capitals 2 to 7 on factor dimension 1; d£i»d£2"‘ d.j indicates the distance between canton i and San Jose or

province capitals 2...7.

25 The rationale for using distance-squared is given by Zipf (1949) who argues that the influence of an urben center on its rural hinterland decreases at an increasing rate, the greater the distance from that urban center. 128

The variables representing the pull effect of the wage and job opportunities in San Jose and the six province capitals combined are shown in Table 5 .2 .

Regression Analysis of Aggregate Level Migration Data

Reported here are the results of stepwise multiple regressions that relate gross amounts and rates of out, in, and net migration to the factor values representing the rural nonfarm, agricultural, and urban sectors, as defined above. Specifically,

Out migration ■ b^ + b.SSE, + b-SSEj 4 b.SSE. + b.SSE^ + (In migration, net migration) (Rural Nonfarm sector)

b5Agl + b6Ag2 + b7Ag3 * b8AgA + (Agricultural sector)

bjSJPCj + b1QJPC2 + bn SJPC3+ b12SJPC4+ e

(Urban sector) where:

b ■ constant term o bj....b^2 ■ parameters of regression

e •* stochastic disturbance term, and

the independent variables are as defined above.

Specific regression results are reported next. The discussion focuses on zero-order correlations, which give an initial understanding of relevant variables, as well as on the parameters of the stepwise Tabic 5.2. Variable* Repreaentiag the full of the Labor Market in Major Urban Center*.

V ariable Mane Deed Urban Center!*) tn Recreation Rrnreaented . D efinition s jr c i tan Joae Metro­ Pull of aodern. large-acale politan Area plu* induatry. the etc province cap ital*

SJPC2 tan Joae Metro­ Pull of general induatrial, politan Area plua coanercial and aervice the ais province activltiet. cap ital*

EJPCJ San Joae Metro­ Pull of cepital-intenaivc politan Area plua induatry. coentrce, and tb* ail province aervice. cap ital*

SJPCA San Joae Metro­ Pull of informal caaual, politan Area plua aelf-amployed couerce and tb* aia province aervice activitiea. cap ital*

*Tb**e variable* are baaed upon the facto r analyala of tolected variable* for 69 canton*. Tb* analyala la deacrlbad in ChapteT IV. 130 multiple regretsion*. These findings ere then generalised in view of

the broader question as to how rural nonfarm activities, the agricul­

tural sector, and the urban sector affect migration*

Out M igration

Examination of the sero-order correlations (Table 5.3) indicates

that gross out migration is positively related to general industrial

and service activities (S8E2), sugar cane production (Ag3) and coffee

production in mixed agricultural settings (Ag4). In the final multiple

regression model (Table 5.3), derived by stepwise procedures, these same

relationships appear, but also, there is a direct relationship of out migration with modern, formal industrial activities (SSEl) and specialised cattle production (Agl).

In part, theae relationships reflect population sise effects. For example, general industrial, commercial and aervice activitiea (SSE2),

the variable which has the largest aero order correlation and multiple regression F statistic, increase with increasing population sise.

Similarly, sugar cane (Ag3) and coffee (AgA) mainly are grown in the

Meseta Central, where Costa Rica's population is concentrated. Hence, on the basis of population sise alone, higher levels of out migration from theae areas is to be expected.

However, the nature of these variables suggests that they also represent structural characteristics that induce out migration.

Specialised cattle production (Agl), for example, is known to be labor- extensive and, during the period 1968-73, to have grown commerically Tabic 5.3. Kegreeeion Analyaia of (kit Migration.

lero-OrdtT Correlation* Multiple Starvation Croaa Oat lata of Oat Croaa Out lata of Oat Variable M igration M igration Migration M igration Standard Beta Standard Bata .CfiSilicitnS __ L_ Coefficient —I ------

s s u -.032 -.267** .194*** 4.77 SSE2 .775*** .016 .5*0*** 22.75 SSD -.010 -.15* SSB4 -.178 -.033

Agl .063 .32**** .283*** 9.26 .388*** 10.31 Agl -.105 .197 Ag3 .301*** .058 .182*** 3.85 .28**** 5.52 *8* •600*** -.068 .373*** 12.46 SJPC1 - . i n -.323*** SJPC2 -.233** -.358*** SJK3 -.171 -.3*8*** SJFC* -.236** -.407***

*2 - .723 »2 - .182 f - 29.21 7 - 6.55 a ■ 62 n - 62

***>igaifLeant at tha .05 level or batter. ** Significant at tba .10 1m l or battar. * Significant at tba .15 level or better.

Vor tba woltiple rogreaaloa, tba US backward etepwlae ragrcaaion routine vaa uacd which retained variablea with algaificance level of .15 or better. 132 through land consolidation, which both left snail faraers without land and eroded the basis of subsistence agriculture that had aupplenented earned incone (Place, 1982). Further, sugar cane (Ag3) experienced increasing nechanization and considerable fluctuations in export prices between 1968 and 1973 which, together with its usual seasonality, affected employment levels, and migration in search of more stable earnings is an expected response.

These observations are supported by the analyses of out migration rates. The zero order correlations of this show the rate of out migra­ tion to be directly related to specialized cattle production (Agl), which also is the most important variable in multiple regression.

Further, the only other variable entering into the multiple regression,

Ag2, shows out m igration to be p o sitiv e ly re la te d to the degree of mixed cattle and sugar cane commercial agriculture. The zero order correla­ tions with rate also show that out migration is directly related to the degree to which an area is characterized by less modern, small scale, informal industry, commerce, and service or, alternatively, inversely to the degree of modern large scale industry. Finally, these analyses show that the pull of San Jose and the province capitals operates inversely, that is, the higher the urban pull effecta, the lower the out migra­ tion. Primarily, this relationship reflects the fact that the higher rates of out migration occur in more remote and distant areas, where large scale cattle and sugar production have prospered, and where employment opportunities are fewer. 133

In Migration

Examination of the sero-order correlations (Table 5*4) related to * gross in migration indicates high levels in areas with more modern, lar­ ger scale industry (SSEl), general industrial, commercial, and service activity (SSE2), banana cultivation (Ag2), and commercial coffee produc­

tion in mixed agricultural settings (Ag4); and low levels in aress characterised by informal, casual, and part-time employment in commerce and service activities (SSE4), Similar relationships are obtained in

the multiple regression which show that especially important is the de­ terrent effect of an economic structure characterised by informal part- time employment (SSE4) and the attraction of commercial banana produc­ tion (Ag2).

Again, these relationships in fact reflect population sise effects, as in the positive relationship between total in migration and general industrial, commercial, and service activities (SSE2). There also is an indication, however, of structural characteristics that induce or deter in migration. An economic structure characterised by informal, casual and p art-tim e employment in commerce and serv ice a c tiv itie s (SSE4), for example, is not a likely inducement to in migration, and this observa­ tio n is supported by the m igration r a te a n aly sis which shows SSE4 as having the largest zero order correlation and being one of three vari­ ables to enter the stepwise multiple regression. Another important structural characteristic is commercial banana production (Ag2), into which there was a massive infusion of public and private capital during Tabic 3.4. Ittrtialoa lu ljiii of In Mitritios.

Zero-Order Correlatioaa Hnltinle Reerasaion" Croaa la Kate of la Croat la Rata of la ▼ triable N itratio n N itratio n H ieratioo N itratio n Standard Seta Standard Bata Coefficient T Coefficient F ssn .201* .148* SSE2 .333*** -.240** .186** 2.67 -.223*** 4.33 ssn -.084 -.088 -.156** 3.91 t t l i -.462*** -.433*** -.333*** 12.62 -.329*** 8.59

ASl .081 .224** Af2 -.300*** -.420*** -.446*** 21.62 -.323*** 8.29 4*3 .066 -.154 At« .358*** -.091 .266*** 3.46 SJPC1 .015 .067 SJFC2 .025 .117 SJFC3 .044 .069 SJFC4 .000 .060

R2 • .548 R2 - .33 F - 13.38 F - 9.59 n - 62 a • 62

♦♦♦Sljnif icent at tba .03 level or better. ** SLfDi.fieast at tba .10 level or batter. * Significant at tba .13 lcrcl or batter.

*For tba Multiple refreaaion, tba SAS backward etepwiae rasraaaioa rootiaa was aaad which rataiaad variables with significance level of .15 or batter. 135 the period 1968-73, at well aa an increasing export price (Table 3.4). Thus, a large number of new jobs opened up in banana growing areas.

Finally, it is noteworthy that while out migration was affected by the pull of wage and job opportunities in San Jose and in the six other major urban areas (SJPC), the rate of in migration in a given rural canton is not significantly affected.

Net Migration

The xero-order correlations (Table 5.5) indicate that gross and rate of net migration are inversely related to the presence of modern, more large scale industry (SSEl), general industrial, commercial, and service activity (SSE2), informal, casual, and part-time employment in commerce and service activity (SSE4), sugar cane production (Ag3), and coffee cultivation in a mixed agriculture setting (Ag4). Similarly, net migration is directly related to banana cultivation (Ag2) and to the pull of San Jose and the six province capitals (SJPC). In the stepwise multiple regression, the presence of general industrial, commercial, and service activity (SSE2) is inversely related to gross net migration, while the presence of banana (Ag2) and sugar (Ag3) c u ltiv a tio n are, respectively, directly and inversely related to both gross and rate of net migration.

In order to understand these relationships, one must recall that the net migration rate is a composite of in minus out migration. Renee, a negative relationship with a particular variable may be either because it deters in migration or encourages out migration. Similarly, a high Tibti 5.S. Begreasion A ulyili of Rot Migration.

rero-OrdetLCorrelations Hnltinlo 1secession Cross let late of let Cross let Bate of Rat Variable H irration N itratio n Mirra tio a R iaratlon Standard Beta Standard Beta Coefficient F Coefficient F

SSI1 .230** .253** SSE2 -.457*** -.182 -.362*** 10.65 sso -.038 .022 s s u -.240** -.193***

All .008 -.018 Ag2 -.344*** -.412*** -.392*** 13.73 -.437*** 13.22 Ag3 -.312*** -.143 -.259*** 5.25 -.232*** 3.93 AgA -.264*** -.028 MKt .202** .227** SJFC2 .248*** .283*** SJFC3 .126 .144 SJfCA .229** .269** I2 - .381 B2 - .22 r - 11.90 F - 8.39 n - 62 n - 62

***Slgnlfleant at tb* .OS level or hotter. ** Significant at the .10 level or hotter. * Significant at the .13 lerel or better. *For the multiple regression, the SAS backward atepwiae regreaaioo routine waa naed which retained variables with significance level of .15 or better. 137

positive relationship with that sane variable night indicate that it

encourages in nigration or deters out migration. Given this, the

interpretations for out and in nigration treated separately provide the key to understanding net nigration.

With this framework in mind, consider the relationships that are

strongest on both gross and rate of net migration and also appear in the stepwise equation. The inverse relationship between net migration and sugar cultivation (Ag3) appears to reflect the high out migration asso­ ciated with that activity. Similarly, the direct relationship between banana cultivation (Ag2) and net migration appears to reflect the high in migration associated with that activity. Moving now to relationships evident from the zero order correlations only, but on both gross and rate of net migration, the direct relationship with modern, more large scale industry (SSEl) appears to reflect an inducement to in migration; the inverse relationship with informal, casual, and partial commercial and service activity (SSE4) appears to reflect its deterrent effect on in migration; and the direct relationship with the urban effect vari­ ables (SJPC) appears to reflect their deterrence of out migration.

Generalization of Regression Results

The emphasis of this chapter is on the question of how wage labor opportunities in the rural nonfarm/small-scale enterprise sectors affect migration, compared to opportunities in the agricultural and urban sectors. In other words, do wages and job opportunities in the rural nonfarm sector stem or induce out migration, attract in migration, or 138 divert urban-oriented migration into other rural areas? In an effort to address this question, the discussion thus far has been concerned with

the specific findings from regression analyses of out, in, and net migration on variables reflecting the opportunities in the farm and rural nonfarm sectors and in urban areas. These specific findings will now be generalised in view of three questions:

1. Bov does the agricultural sector affect migration?

2. How doea the rural nonfarm employment/small-scale enterprise

sector affect migration?

3. How does the pull from the urban employment sector affect

m igration?

The A g ricu ltu ral Sector and M igration

With regard to the agricultural sector, migration is affected differently, depending on whether there is primarily (a) expanding export-monoculture, (b) export-monoculture on a cyclical and/or downward trend, or (c) mixed commercial (domestic and export oriented) and subsistence farming.

Generally, expanding export-monoculture tends to attract migrants from other rural areas. So, for example, the banana growing areaB ex­ perienced high in migration rates and, on balance, positive net migra­ tion (compare Figure 5.1B and 3.1C with Figure 4.IB). This was ex­ plained by the steadily increasing world market price for bananas (Table

3.4) and the infusion of public and private funds to revive and expand banana cultivation. On the other hand, out migration was observed in f

Figure 5«1* Migration Rate Patterns

A. Cat-flicrtlloe Rat«i S. iQ-Mlgratloa U tf t

u> to 140

sugar cane areas over the intercensal period (conpare Figure 5.1A with

Figure A.1C), as a result of cyclical trends in export prices and pro­

duction (Tables 3.A and 3.5). Finally, areas of nixed (multicrop) com­

mercial and subsistence farming experienced a relatively stable popula­

tion balance (compare Figure 5.1C with Figures A.1A and A.IB) due to the

relatively more dependable earnings and means of subsistence.

tfhile it is trivial whether migration is related to sugar cane or

banana cultivation per se. it is clear from the above that general

structural conditions of the agricultural sector have an effect upon its

role in stemming, diverting, or inducing migration. This is significant

both from a policy and theoretical perspective.

In terms of policies designed to reduce or attract migration or to

retain a relatively stable population, one must clearly focus attention

(and funds) on the agricultural sector, i.e., providing relatively

stable earnings opportunities by increasing incentives to agriculture and diversifying agricultural cultivation and agricultural sources of

income.

Regarding theory, migration models implicitly or explicitly assume

that people improve their own and family utility function only by urban- bound migration in response to the expected greater earnings there.

However, the validity of the assumption that people are urban oriented and motivated by the highest expected earnings must be questioned.

Instead, the findings of this study thusfar suggest an alternative explanation (as implied by Byerlee, 197A), i.e., people are very much

imbedded in their rural environment and may indeed prefer a rural to an urban setting, even if average earnings in the latter are higher. 141

The Rural Nonfarm Sector and Migration

Similar conclusions emerge when we consider the second major question this chapter addressed: How does the rural nonfarm sector affect migration?

In general, two things were found* First, that the presence of modern, more large scale (for the rural canton) industry (SSE1) and

general industrial, commercial, and service activity (SSE2) encourage in migration (compare Figure 5.IB with Figures 4.2A and 4.2B). Second,

that a preponderance of commercial and service activities offering only

informal and casual employment tends to discourage in migration (compare

Figure 5.IB with Figure 4.2D). These results, together with the fact that marked out migration was not associated with rural nonfarm activities, indicates that rural nonfarm activities do have an effect on stemming out migration and directing in migration. The results also indicate, however, the preference of the potential migrant for more permanent, formal work arrangements while remaining in the rural area.

These findings are consistent with expectations, although the marked preference for more permanent formal work arrangements and more modern types of industry is somewhat of a surprise. Accordingly, sup­ port is provided for the notion that policies designed to reduce prema­ ture rural-urban migration should promote nonagriculturai employment and small-scale enterprises in rural areas. 142 Urban Pull Bff ecta and Migration

Given the abov e, it is not surprising to find that the job and vage opportunities in ma jor urban centers did not significantly affect nigration. Thia an ggests a need for reevaluating nigration nodels that focua on urban-rura 1 labor market differentials as the moat important cauaea of out nigra tion* Furthermore, the relative role of rural vage labor opportunitiea in both the farm and nonfarn sectors, compared to those based in urbah areas, makes a case for rural development and employment generation rather than urban-biased development policies.

Analysis of Individ ual Level Migration Data

The above provides evidence that rural nonfarm employment is a significant factor in explaining aggregate rural migration. To further investigate the validity of thia finding, attention now turns to indivi­ dual level data. Sp ecifically, if migration is affected by employment opportunities in the rural nonfarm sector, one would find:

(1) Fever out migrants from cantons with more nonfarm activities

than from cantons without (or with fever) such activities;

this would attest to the nonfarm sector's importance in

re ta rd in g out migration.

(2) Hore in m igrants in cantons with nonfarm activity than cantona

w ithout ( or with fever) such activities; more in migrants than

non migrants in nonfarm employment; thia would attest to the

rural nonfarm sector's importance in attracting in migrants

and thus diverting urban-oriented migration streams. 143 The individual level data used for thia analysis are the 61,609 economically active individuals from the 10Z sample of the Costa Rican

Census. Of these individuals, the out, in, and non migrants were cross- tab u lated :

(a) with the economic sector they are employed in (agriculture,

m anufacturing, e tc ), and

(b) with the type of area as urban, rural, or rural that is pre­

dominantly agricultural, agricultural with a well-developed

nonfarm sector, or agricultural with capital intensive indus­

try, following the classification developed in Chapter IV

(Table 4.7; Figure 4.3),

Table 5.6 presents this cross tabulation. For the purpose of this analysis, rural nonfarm activities are the nonagricultural types of economic activity shown in that table, except mining. Non migrants and in migrants are enumerated at their canton of present residence while out migrants are enumerated at their previous place of residence. For all migrant groups, however, the present type of economic activity is used in the cross-tabulation. Finally, although the category

'Agricultural canton, oriented towards highly capital-intensive industry' appears on Table 5.6, it is comprised of only one canton and, therefore, will not be discussed.

To first determine whether or not the patterning of values in the contingency table is non-random, that is, whether there is a relationship among the variables comprising the rows and columns of that table, a chi-square statistic is used: M i l ) . t . CnN i|tuti, cat M R ittsa ts Vj Tjft sf IcoMic Activity, tut Ctuufu at C c s ts u N 5 I i 4 | Bt«t m •j 3 t tf • : = an 5 5 U i I U 3 5 HSB EH EH ft H * 1 SB JB l E _■» 1 5 a a It M It m EH 9 * ? l g *

M a i * 1 = •“B BB SB i " =1 1 " SB SB "B • • • • • • A 1 2 1 2 ? >1 ?* I ! i Z SB 5 5 1 1 3 s * 8 3 * 5 5 5 5 B “ 1 * 1 2 1 « S • *1 •as ih *» B i a BB 8 9 BSB SB •H 3 5 1 « SB 1 = R2 BB m m SB* M * *m BB 1 " m • • 4* « at a 1 5 * 2 1 1 m " 5 3 ; * 3 * 3 S *3 3 2 -B 1 " 3 S • • 5 1 Bii 2 as m a* • 3 2 «*2 i s 5 * J • 8B 2 5 "ti ! * f r " SB 9 2 1 " I B "W CH 5 J -.4 s a •B SB th S 1 = ■J* BB 3 3 5 5 2 * 5 5 BB •» * 1 5 r - 1 " I S 1 5 1 5 I S "1 m Bb at *4 at at • at a* 1 * 5 * 1 5 1 * 1 * "V SB BB 1 5 • • *• BB XB **•« 1 5 9 - • • « « •* 4 a* • 4

at at at 1 5 ! * «a«t BB 3 * 1 5 -1 3 a at « ".J SB H It 1 5 3 5 at at «a a.5 »B 4 H 4 4 M • • at ^ a *4 1 1 1 5 • s a -•c -as s a .- n a | M ! l „*sa;|& 1 S Z233B 3 3 2 -Z 1 1 1 = j • a 1 s -•a i l E * l a l l l a 1 t f 1 1 5 3 3 ! 1 l l a S a S n a a nv** “ 1 £ * * * ' • 2 8 9 2 3 1 1 2 s i 5 aa j a sa w S JtHHR s s a JRB 'W^a's **• « « B i A S s 8 3 1 m r 1

ii !! - ijii - . iK 1 i M M UU **• ix : s I: X 144 145

r s ( 0 - e ) ij 1J II B------• r p-ixs-i)] 1=1 j=l i j

where: r - number of types of economic activity,

s ■ number of migrant status categories (in, out, non

m igrant),

Ojj ■ number of in, out, and non migrants (j) in a given

canton group (rural, urban, etc.) observed in a given

type of economic activity (i),

0 £j ■ number of in, out, and non m igrants ( j) in a given

canton group (rural, urban, etc.) expected in a

given type of economic activity (i).

The assumption that out, in or non migration of individuals in specific

groups of cantons is not related to their employment in certain types of

activities is rejected if the computed chi-square is greater than the

tabulated chi-square for a given degree of freedom (df ■ 24 in this

case) and at a predetermined significance level (.05 here). Expected

frequencies in this case are derived separately for each group of cantons as:

EA MS lk J*. TS i j k 5^ EA £ MS <1=1 ik jTL jk , where k represents the canton group (rural, urban, etc.), EA the 146 economic activity (agriculture, manufacturing, etc.), MS the migrant statua (in, out, or non), TS the number of persona sampled for group k, and other symbols are defined as above. Hence, the expected number of persons in economic activity i and of migrant status j is the joint probability of being in that economic activity and that migrant status multiplied by the total sample for group k. Accordingly, the expected values reflect both the employment and migration structure of places k.

Examining the resulting chi-square statistics for each group of cantons indicated that the distribution of values is, in all cases, significantly different from random. Furthermore, judging by the level of the chi-square statistics, this is particularly so for the rural cantons, especially those vith a veil developed secondary and tertiary sector, and relatively less so for the urban cantons.

In order to more specifically assess the interrelationships between economic structure and migration, however, attention must be given to other details of Table 5.7. In particular, first consider the percent­ ages of rows A, C, and D under 'Total1. By comparing the percentage of, say, in migrants that come from rural cantons (row A), 43.9Z, vith the percent of the total sample that is rural (row C), 52.5X, it is clear that rural cantons attract significantly fewer in migrants than would be expected on the basis of their population size. Similarly, by comparing the percentage of in migrants that enter rural cantons of claBS 2, 25.31 of all in migrants (row A) or 57.6X of rural in migrants (row D), with the percent of the total sample that is rural-class 2 (Row C), 23.3X, or the percent of the rural sample that is rural-class 2 (Row C), 147 44.5X, it it clear that rural-class 2 cantont attract more or lets vhat ia expected of them in terns of their share of the national population, hut considerably nore than expected in terns of their share of the rural population.

Applying this procedure to the rural cantons indicates the following. Rural cantons vith both a well-developed agricultural and nonfare sector perforn slightly better than expected in retaining their population, whereas the rural cantons lacking a well-developed nonfarn sector perforn slightly less well than expected. In terns of in nigra­ tion, those cantons without a well-developed nonfarn sector perforn much better than expected, as indicated above, whereas the rural cantons with a well developed nonfarm sector perform much worse. Finally, in terns of out nigration, there is little difference between the two types of cantons; both perforn nore or less as expected.

In general terms, then, it appears that the presence of rural nonfare activities acts to increase the retention of an area's population, whereas the lack of such activities acts to decrease retention. In terns of in nigration, however, the presence of rural nonfara activities is inconsequential, and in fact, migrants respond equally as well to agricultural activities if they represent employment opportunities. If we compare the rural vith the urban sectors, however, the lack of attractions for in migrants appears to be in the sise of the rural nonfarm economic base, so that if thia were increased, migrants would be attracted. 148 In drawing general conclusions, however, it also is instructive to look at the relationships between migration and particular types of eco­ nomic activity. Considering rural cantons with both strong agricultural and nonfarm sectors, one can see that proportionately more in migrants than stayers are found in the nonagricultural occupations, that is,

A3.IX of stayers versus 65.1Z of in migrants. This suggests that the rural nonfarm sector in these cantons attracted migrants, particularly when one considers that the comparable statistics for rural cantons lacking development in that sector are 31.8Z for stayers versus 35.3X for in migrants. Said another way, even in those cantons lacking rural nonfarm development, there are proportionately more in migrants to, than stayers in, rural nonfarm occupations. To further underscore this point, out migrants from these cantons are considerably more attracted to nonfarm occupations, 68.9Z of those leaving rural cantons with a well- developed nonfarm sector and 5AZ of those leaving rural cantons lacking such development.

In a more general context, this indicates several conclusions.

First, (in) migrants respond to economic opportunity in general, but even where much of that opportunity is in the agricultural sector, rural nonfarm employment appears to be a greater attraction. Accordingly, out migrants from cantons with few nonfarm activities pursue to a large ex­ tent nonfarm jobs in their new destinations. This implies that more rural nonfarm employment at the origin might have stemmed out migration.

Alternatively, however, out migrants from cantons vith strong nonfarm sectors also choose non-agricultural occupations at their new destina­ 149 tions. This suggests that while the origin canton did provide nonfarn employment, there nay not have been enough, aa a reiult of which people nigrated to another canton in aearch of a nonfarn job. It aleo is pos­ sible, however, that theae people left because their skill/educational level did not neet the requirements of the nonagricultural jobs in their origin cantons. This would suggest that the "right kind" of nonfarm employment, geared to the specific human capital levels of the canton's population, could have retarded out nigration. Note, however, that contrary to expectations, the percentage of out nigration is only alightly less for the cantons with nore nonfarn activity than those lacking such development (16.5Z versus 17.IX). This suggests that non­ farm employment nay be an intermediate step in rural-urban migration, to some extent preparing the migrants with skills that are necessary to find urban employment, or alternatively, that the presence of nonfarm activity spurs persona not employed in that sector to seek it elsewhere.

Summary of the Chapter

This chapter examined how rural nonfarm employment opportunities affect migration compared to employment opportunitiea in the agricultural and urban sectors. TVo sets of analyses were done: (1) regression analyses of out, in, and net migration using

aggregate canton level data, where the independent variables

were the scores derived from the factor analysis of the rural

nonfarm, farm, and urban economic sectors. 150

(2) contingency table analyaes of individual level data cross"

classifying migrant status (out, in, non) vith sector of

employment (agriculture, manufacturing, etc,) for each canton

type (urban, rural, rural vith significant nonfarm employment,

and rural vith little nonfarm activity).

The aggregate analysis vas designed to get an overall understanding of the relative importance of rural nonfarm employment opportunities in stemming/diverting rural-urban migration. It vas found that variables pertaining to the rural nonfarm and agricultural sectors vere more significant in explaining out and in migration than variables reflecting the pull from urban economic activities.

The individual level analysis also shoved a clear association betveen out and in migration of vorking age individuals and their employment in nonfarm activities. This association suggested that individuals in migrate into rural cantons in search of both rural nonfarm and farm jobs. Furthermore, it appeared that individuals out migrated because of a lack of nonfarm employment.

These findings are important in terms of policy. They suggest that rural nonfarm employment tends to retain people in an area. Thus, policies designed to reduce rural-urban migration should promote nonfarm employment in small-scale enterprises in rural areas. It is also important for policy purposes, however, to know how migrant selectivity is affected by nonfarm employment, which is addressed in the following ch ap ter. CHAPTER VI

RURAL NONFARM EMPLOYMENT AND MIGRANT SELECTIVITY

The findings from the aggregate and individual level analyses of the previous chapter suggest that rural nonfarm employment may significantly reduce rural out migration and divert urban-oriented migration atreams. It is possible, however, that while aggregate out migration ia reduced, critical human resources from an area might be removed, leading to sizeable negative externalities (Schuh, 1982;

Lipton, 1982). An examination of the impact of rural nonfarm activity on migration, therefore, cannot end without asking the question of how the export end import of human capital from/to rural areas might be

* affected by nonfarm activities.

G enerally, ru ra l nonfarm employment ought to reduce the human capital loaa from out migration and stimulate human capital gains from in migration. This expectation is implicit, if not explicit, in much of the research done on the rural nonfarm sector, but a more concrete rationale is provided by the theory of the household, discussed in

Chapters I and II. By this theory, individuals who are younger and better educated, for example, would have greater earning power in the nonfarm labor market and would, therefore, prefer a nonfarm job. Given thia, nonfarm activities ought to reduce selective out migration by

151 152

providing in situ employment opportunitiea for individuals with higher

human capital levels. Similarly, rural nonfarm enterprises ought to

attract in migrants vith higher human capital levels. This would be desirable for growth and development. Particularly if selective out migration was reduced, communities and areas would be

able to capture the returns on their human capital investment. Also, a

higher human capital base ought to lead to more monetary investment in

an area and/or reduce the flight of monetary capital. Thus, a higher

rate of productivity and of aggregate economic growth should eventually

be achieved (Schultz, 1982). If, in fact, rural nonfarm employment reduces selective out migra­

tion and/or attracts in migrants with human resource characteristics

that are relatively desirable for development, that ought to be mani­ fested in several ways. To illustrate, when comparing rural areas vith and without a well developed nonfarm sector, the areas with nonfarm enterprise should have consistently "better" in migrants, even though these areas may s till be deficient when compared to the urban areas.

Accordingly, in areas with rural nonfarm employment, human capital gain from in migration should offset the loss from out migration to a greater degree. But the human capital balance also would benefit in that per­ sons with, more education, skills, achievement motivation, etc., might be absorbed into the nonfarm sector, rather than out migrating. Hence, out migrants from areas vith a well articulated nonfarm sector ought to have less desirable human capital characteristics than do out migrants from areas that are more committed to agriculture. 153

Differences in human resource characteristics also should be evident when comparing the economic sectors directly. Generally, persons employed in the rural nonfarm sector have consistently higher human capital levels than those in the agricultural sector.

Accordingly, rural nonfarm activities should also gain more than the agricultural sector from in migration and lose less human capital through out migration.

To further examine the validity of such expectations, individual level data from the CBLADE sample are employed. This provides information on 61,609 economically active persons as enumerated in the

1973 census. Three human resource characteristics represent the human capital dimension: age, measured as number of years since birth; education, measured as number of years of school attendance; and occupational status, calibrated from the match between a person's occupational category and the weight given to that category by the

Traiman (1977) international occupational scale.

These characteristics are used in three kinds of comparisons.

First, for each area type (urban, rural, etc.) and economic sector

(agriculture, manufacturing, commerce, service) within that, the mean age, education, aned occupational status of in, out, and non migrants is 26 presented (Table 6.1A, 6.IB, 6.1C). Second, for each area type

26 To ease interpretation, only the major economic sectors are employed in the analyses of this chapter, i.e., agriculture, manufacturing, commerce, and se rv ic e . 154

Table 6.1. Mean Age, Educational Level, and Occupational Status of Out. In, and Hon Migrants by Sector of Employment and Type of Area.®

4. K t

Init •111 ti|alfl* Mfm *OT«t fill Ulllt total t il l toriltl tttoi total ■Miin htlMMi Vi HmIi IM ti to* h t I* tol u 0*4 0*1 1ft toft tot IB tea ftttltt Mia Nit. Nil* ..HU, via. nu. ■it. Hit. ■It. ■la. lliln ltit* )I.M 11.91 11.11 11.44 11.14 14. M 11.11 II.M 1*. W 11.11 11.11 14.4* 11,11 11,4* M.tl IN •M Mil »?* *41* 1441* 1*11 Ilf ■ III 1*41 1141 Till » 1 II M.M IMJ II,It 11.44 11.44 14.1l M.ll M.tl li.r* 11.41 11.11 11.41 *•.11 I4.M ii.to m 1*11 4111 4*4 111 III* 111 III 1**1 III *1 111 4 1 il ii.n M.tl 14.11 11.11 1**44 II. tl ll.l* lt.41 II.*4 *1. *4 11.4* ll.tl 41. M M,« 41.11 HI *1? iin 444 Ilf 1*11 111 141 11*4 111 IM 411 1 4 11 Im lii M.ll 11.4* M.ll 14.41 *1.41 11.V 14,11 ir.il 11.41 *4.41 14.U 11.4* II.M It.O It.*4 tiff 1141 4W 14*1 41* >4*1 *14 414 *111 4M 444 II** t 4 11

t. rMittnui u?n, *r tmi nr inoit canttri

total oil* llfftlfl* o n ImIm« l«ttl klO Ultlt tot 11 till Ctflltl i*ii 1 h iliw tt iilMAit^Alflli£l_ 0*| t* 4m 0*1 1* toft tot Is M la — tot 1* Pm lMt»l . _ Rli. . . RU, Nil. m i. HI. mi. ■it. ■it. Hit. ■it. MMtklim 1,11 4,44 1.11 i.if I.ll i.M I.ll l.*t I.ll I.ll 1.1* I.M *.** t.M I.M IM IN Mil irif 144* mi* l**4 411 •It* mi 1*44 IIW 4 1 1* M u i i r i i i *.n 4,11 4.11 1.14 1.14 i . ii 1.1* 4.11 1.1* I.M 4.t1 4.91 I.M I.ll I.M 4*1 IM* 4i«r 4*4 >4* in* 111 IM 1111 *41 *4 HI 4 * 11 t a u n t 4.11 4.44 4.41 1.11 4,44 i.n 1**1 I.M l.tt 1.14 1.11 I.ll I.M 4.M 1.11 444 449 4414 44* 111 1*41 >41 141 u n 1M 14* 411 1 4 II *«r»lt« r.it *.#• 1.11 i.ir 1.41 4.14 4.11 I.ll 4.1! 9.14 1,11 4.11 14,M 4,11 M l till 11*1 4114 11*4 VV4 14*4 *14 41% Till 4*4 4*1 11*1 t 4 II

r. acrvnripui

total •Ml ca*t to f to •■TBl *114 UltlB total • lit toflU l Itkii t o l t n t tl l o t o i t tol IB to* tol IB to* tot IB to* tot Ib to* tot u too ■it. ■ it. ■ it. ■i«. ■it. ■il. ■it. Nil, ■11. Nil.

HllllllHI ll.l* 11,41 11.11 M.M 11.4* 11.11 M.4* M.44 i i . i ? M.4* 11.11 11.1* ll.U M.M M.ll 4M IN Mil III* 1*44 |M>| 111* 411 ir ti IMI 1944 1141 4 1 S* R M fH lltM 11,11 M .ll M .ll M .ll M .tl M .tl 11.41 11.11 M .tl 14.14 11.14 11.11 41.11 M.1* M .ll IM 111! *11* 4*4 >W 11*4 111 111 114* 141 H 111 4 t II C m tf i M .ll 11.41 M.4I I4.il M .ll )>.*! l«.«l I9.M II. M >4.44 11.4* M .ll M.M M .ll H.4I 441 tir 11*1 44* Iff IMI HI It) 1194 >11 IM 411 1 4 II I m J o 4t.1T 14.41 41.44 IM* 41.14 14,14 >1.1* 41.49 14.11 )!•** *4,4* 11.11 ll.U 14.11 41,1? 1111 m i till 11*1 41* 1444 414 494 m i 4M 4*4 114* 1 4 11

*TW In H tw il M l «.ll t. IM MM HIM »M II* U lla .M ill M U llH III ,

kkMM

(urban, rural, etc.) and economic aector (agriculture, manufacturing, etc.) within that, the differences in mean age, education, and occupational status between migrant status types (in, out, net) are presented (Table 6.2A, 6.2B, 6.2C). Thus, one can see from Table 6.2A that in migrants to the urban area who are employed in agriculture are, on the average, 3.46 years younger than non migrants in the same area and economic sector. Third, then, for each area type (urban, rural, etc.) and migrant status (in, out, net) within that, the differences in mean age, education, and occupational status between economic sectors

(agriculture, manufacturing, etc.) are presented (Table 6.3A, 6.3B,

6.3C). Thus, one can see from Table 6.3A that among non migrants in the urban area there is, on the average, a 3.90 year's age difference between those in agriculture and those in manufacturing. Finally, in order to guage the significance of the differences in mean values reported in Tables 6.2 and 6.3, statistical tests for comparing means 27 were employed.

27 The significance for the differences between means uses the following test statistic:

z - *1 ~ *2 ______

2 2_ 2 where the sample variances S. 2 and S. replace2 the respective unknown population variances, and the e-score is computed since the samples are large enough for the central limit theorem to be invoked. The hypo­ thesis R : there is no significant difference between the sample means in tested versus the alternative hypothesis H^: the mean differences are not due to chance. 156 T a b le 6 . 2 . Mean Difference in A g o, Educational Level, and Occupational Statue Between Out, In, and Non Migrants by Sector of Employment and Type of Area.

to il aitfc lipalfl- M at vttk UUW M a t with teH U t ta tl| I# M| la Rla la l l | , p m ni|, la M§« ila s a M om Rlaw d i m Ml M|* Ml M |, PM M|* Ml M |, Ml 1

1.4) e.M I.M •I.U I.PP I.M M i - •1.44 •.1 9 M r •I.M •M 1.99 •1.14 i . n •9 ,1 ) M llM I *1.41 •••* 1.19 •1.41 •1) I . l l •1.99 M l ).P) •P. CP • * tl M l ••91 • M 1.44 M t *** t t t aee * a aea aea

M aafaa- •MP -*•» 1*4) •.H I . l l l . f l •1.11 I . l l I.PP 1.44 4,91 I.PP *4.94 • I . l l 1.19 Pap lap • i . n •1.99 1.1) •*4P 1.91 I.P4 •1 .1 ) I.PP 4.11 1.14 M l M l -4 .4 ) ••II .41 aa* M t «e ae a ae* aea ae*

-4.11 •1.14 1.41 •I.M M t 1.99 • I . l l M l 4.PP •).« i .n l . f t •I.PP • I . l l ••II •IP.II •1,11 P.Pi •4.49 1.19 P .l t •I.PP l . f t 11.14 •I.PP I.)) 1.14 - , t i •.PI * .t l aea aea aea aea aae aa aea

I.M •1.14 M l I.M •I.IP I . l l 4,99 •t.4 4 1.99 I . l l •9.91 •4,9 9.1 P a rtita • M l •I.M 1.41 M l . l l 4 , 1 ) II.M - I . l l P .14 14.11 -9.19 I.PI II.P I •I.M I.M • . I t • t.n • i.M *** aee aae aee aee aea aa aea aee aea aea ae a

I , PPRUfMRPL L i n t , M T U II Of ICPOOL OQMtfTlI

M i a I * | | l IlftlM * ■..llbl- tu t Mai atm Meal tillI U iita laral aitb Capital la M |, la « l |, IM M |« I* Rl|, la Mg, Pm l i | , la Mil, la ail, MS Mp, I# M |. la Hip* Mm al|, la Rip, (a ll|, Ma Ma, Mpm I t M Rlae* •l*M Rlae* M aaa Riaaa Miaa* M m # Hlaaa Rt*a* RIm HI aea HI*m Rati** M b M f. M t: M|. M l Ma M l* M l M |, M l M ||, Ma M||, ftH M||. Ml M |, PM M |. Mt «l|, Mt Rip. Ma M |. Ml MJ|. Ml M|,

4p< U I .l l • It ••94 .91 •*PI •,49 ••IP ••44 • IP ,» 9.99 • t l i t f * ••11 - M l •I.U • I . l l 9*19 •PI *9.19 .91 •.I) • l , t ) •I.P) - .4 ) I . l l 1.14 9.99 - I . l l •1 .1 ) •I.TP •,P1 aea aee a aaa ae* ee a

M a f M - •IP .9 ) •.II ,4) 9,9 .9) ,41 .1 ) •9P .11 -.11 *.)P • I . l l •4 .1 ) •I.M t w i n . n ,M *.14 .19 P .t • II • 11 •94 .19 •4f *.41 •I.P) •I.IP • I . l l ••M aee a*a M n i i -.M -.1 1 .11 • *1 ,11 •.M • It .44 • a t .94 .91 .91 •.11 1.99 1.1) • i .p i • l . i t • I t t.S ) I.TI • . I f 1.41 1.41 •* t) .14 • IP .91 • • f t ,91 .VI aee eea * ee* *** P a rtita •.4 4 •,P4 ••14 I.PI 1,44 •*PI I . l l I.M • 19 I.M 1,14 ••14 •1.14 •4.1V •1 .4 ) -4.11 •),9T •I.PI II.M 9.94 •♦IIP P .19 I.M 1.9) t . l l 4.9P • I . l l • ♦ n •1,49 • l . t ) aaa eea a ae* eea ae* aee ** aee aaa aee aae

c . o c o m t im u m m r

Petal *i|tk llpatfl- Mata I tilt Ulila M*«l tilt Capital - ■ . * 9 — ■ , . fcRln— l , , ■ f c l f w ■■k— L» 1*1—alti liMart______la M |, la Hip, Pm Rl|, la Rig, la Ri|. in alp, ta M |, la Nip, Pm R l|, la Mip* la Mip, Pm a lp , la M p, la R i|, Raa Mip, ■lava M om Mlaaa Maaa Mla«* MlaM Maaa Miami ■Mat k |M Maaa Patter Pat Rip, Pat Rip, Pat Rip, 9ae Rip, •a t Rip. Pal Rip, Rea Rip, Pat Rip, Pat Rip. Re* R ||* Pat R li. Pat Rip. Raa Rip . Rat Rip. 9*1 Rip.

Ppri* • n *.U -.9 9 -.1 1 •11 •11 ••IP ,11 .1) *.»r .19 .49 -.11 •I.P) • I . l l aa liw * I.M •.44 •I.PI • i.M 1.41 1.14 •1.14 1,49 I . l l •I.PP •IMI.M -.9 9 * •1 ,9 ) •1.41 a aee a aaa a aae a* *ee

M i t . •N-•VI •M I.PI 1.4 -.1 1 1,99 I . l l I.M I.M 1.4 ••)■ -4.M •4.49 t w i n .94 - I . l l - l . t ) •1) I.M 4.4 f -1.91 I.M l . t l I.M t.P I I.4T - .1 ) •I.P I • I . l l ae •a* eea e eea » eea a a

Mm h * -.4 9 -.14 •M *14 I.PP .94 I.M I.M ,U ••41 I.M I . l l 1.4) •1) •1.9P •1.11 •1 .9 ) • I t i .4 ) 1.9) ».14 1.1 ) t . l l I.M ••M 1.41 1.44 m i .91 - l . t l ee aa* a* ee ae* aa a*

PaeelM •I.M •1,94 ••99 1.9 4,11 •)) 4.1 ) ) . ) ) ,M t . l l 4,P» *.M •1.99 •11.11 •9 .4 ) •).)! • M l •I.IP 19.99 14.11 •M 4.9) 4 .1 ) I . l l I.TP I.U ••49 • a t • I . l l - I . l l aae aa* ee aaa ee* aea aaa aaa **

I t a la a a •ipaifiaaaaa. aMtai • alpaifiaaal al IP* , i l I m l , •• ilpailicpai i l i t t .11 I***!, M l **• *lf*ltU *at al I t i ' Pi I m l , Satapaltaaal Ctaiaa aaataa a it bated e a aa i(* H « iiM at Tietaa*1* tltlll i t u m i i M i i i c ii i , Table 6.3* Mean Difference in Age, Educational Level and 157 Occupational Status Between Sectors of Employment by Type of Area and Migrant Status.a

I, AC*

MM *■* H 9-9 IK M c-i m i-C 9-1 IK M t-9 4-41 '• ( *H M M C-4

9rM« I.M .11 1.19 •1.99 -.41 .91 I .n 1.41 1*11 -1.99 .1* 1.44 1.99 .44 1.14 -1.94 -I.H .11 I.M •99 1.91 •I.M **M 1.94 4.19 1.41 9.44 -t.99 I.ll 1.49 11.41 t.ll 1.99 -19.99 *19.11 I.N «H aaa aa aaa aa aaa aa aa* aa* aaa aa* aa* a* M il 1.99 l.tl ».1> *1.19 I.H 1.99 I.t4 1.99 l.tt *.M 1.91 l.tl l.t l .14 l.tl -1.49 •! .N 1,14 f.»! 9,99 11.41 •1.9) V.M 4.19 1.14 1.99 l.tt *,41 I.U 1.94 l.tl I.9T 1,44 - l.tl *1 .M 1.19 m aaa aaa a aa aaa aaa aa* aaa aa* M il *ll4 9,99 I.M I.M •1.19 .11 l.tt 4,19 1.41 9.11 -.IF .99 l.tl 4.41 1.14 l.tl -I.M *1.11 1.91 9l|tlflttM 9*99 9.ft 9.11 •1.49 .99 i .n 4.44 1.94 T.ll -.49 l.tt I.TI 19.19 1.99 9,11 *1.91 -I.H 9.11 M im M4 aaa aa aaa aaa aaa a aaa aa a*a aaa aa* *** H*l »*•**' Mtil t il l 1.99 1.91 9.99 •1.11 1.14 l .t r -1.14 -.49 1.41 l.«f 4,11 1.49 l.tl -,9| t.94 -I.M .41 1.41 u n it >,91 I.ll 1.91 •1*19 I.M i .ti •1.19 •.49 I.H 1.99 1.19 f .14 4.91 • 1,11 4,41 -4,11 •14 4.11 Mai am aa* a aaa aa aaa a aaa aa* aa aa* aa* aa* aaa

Mral all! I.M •II.If •|.4f • 11.19 -4.19 9.99 l.ff ••1.14 1.94 •11.99 > 1.91 9.99 l . t l •l.tl •! .11 •11.49 •I.ti 4,11 M id i- .11 •1*99 ••tl •4.14 •1.49 1.99 l.tt *.4F 1.41 *1.44 -.19 1.11 I.TI •*91 • •IF -t.ll -1.99 .IT taiaatln aaa aaa aaa • aa *• liM d i

I . INUTtML UTtL. If VUII or tcaoot ( v t im i

CaaarapfeM Mat *-C P-l C-I

tihaa •1.91 •1.99 • i.n -.11 •1.41 •1.41 • i.n •I.ll •t.94 a t *.n -.91 •1.99 •M l -4.11 -.19 •M l *1.14 •14*14 *11.19 •M.ll -.If -M l •9.91 • ii.ti • 1,44 •19.ft 1.99 •i.ti •9.94 •41.49 •49.99 •44.91 *4,11 • •M.tl *11,94 •at aaa aaa aaa aaa aaa aaa aaa aa* aa* aa* aaa aaa aa* aa* M il •M l •1.19 - i.n -.14 •1,91 •1.19 •1.91 •1.49 •9.11 *.99 •t.ll • l.tl •M l •M t •1.91 *,11 -I.ll -M l •11.11 •11.91 -11.99 •l.tl •19.91 •19.91 -11.11 •19.99 •19.44 *1.11 •14.11 •11.99 •11.44 •11,44 •91.91 -I.ll •19,14 -14.91 Mt aaa aaa aaa aaa aaa aaa aaa aa aaa aaa aaa aaa aaa *** aaa 4at*l all* •1.91 •l.tt -1.19 •.!« • i.n •I.ll •1.14 ■1,49 •4.94 -*F| •1,19 •1.91 •I.ll •M l •M l -.91 -I.M *1.91 IlialfliMt •II.M -19.91 -11.91 •1.19 •T.ll •4,94 •11,41 •19.19 •14.91 *1.14 •II.M •T.M •It.91 •14,41 •4M I -.19 -IM 9 -IM 9 M lirt Ml aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa MaUyaaal Mttl all* •I.M •1.99 •I.It ••II •1.91 •1.99 • 1.41 •I.M •9.14 -,H •4.9) • 1,4t •I.ll •t.ll •1,94 *.41 •1.94 •1.41 Mllla •9.11 •19.19 ■11.41 ••91 •9,41 •l.tl •9.IF •1.19 •11.94 -.41 •11.19 •It.91 -14.F9 •19.91 •11,91 -I.M ' ‘11,44 •9.91 Mafaiv mm aaa aaa aaa aaa aaa aa* aaa aa* aaa aaa aa* aa* aaa M»l*TM* Batal t il l •1,91 •.It •>.4f I.M •9.99 •9.99 • t.ll •9.99 •9.11 -I.ll •I.M •*ll •1.99 • t.ll •4,91 -.11 •I.IF -I.T4 c**iul« •1*91 *.91 •I.M 1.14 •1.99 • 1,49 • 1.44 •4,44 •l.tt -I.ll -1.99 -.14 •I.ll •1.91 • i.n *.n •M l -.91 IllMllM aa aaa a aaa aa aa* aa* aa* laMtitt

c. OCCmTIAJ* IT4T44*

. la Pintail Mat 9* 4-e 4*9 a-t ■*4 C-I 4*41 4< 4-1 P-f M>l C-I AH' i< 4*9 IK H C-I

9rMa •I.ll •>.9* •19.14 I.ll •9.99 -4.41 •4,19 •).%t •1*94 I.H •t.ll -4,91 •9,11 •1,99 -19.H .14 *9,14 *1.11 •9.91 •I.ll •11.11 1.14 •1.49 • ll.lt •I.M -4.99 •I9.9F t.ll -9.11 • 9,9* - n .n -H.94 •41.11 .19 •19,14 -11.11 aaa aaa aa #aa aaa aaa aaa aaa aa a*a aaa aaa aa* aaa aa* feral •I.U •9.91 •9.14 -.11 -9.11 -9.4T •I.ll -9.49 •14,91 -.44 -1,99 •1,14 •4. FI -4,49 •M l .11 •l.tl -1,49 •9.19 •19.91 •11*11 •1.14 •9.99 •9,19 •9.11 •11.19 *11,11 -,49 •lt.4 | •11.41 •M .ll •tl.14 •11.41 .11 •9.99 •11.11 •aa aaa aaa aaa aaa aaa aaa aa* aaa aa* aa* aaa aaa aa* aaa feral vlit •I.M •4**4 •I.T9 •1,11 •4.91 •>.M •4.91 •4.99 •11.41 -1.41 •9.19 •4*11 •9.91 •i.ti •t.ll .19 -t.99 -1,44 ilfelfU*** •9.91 •9.11 •14.91 •1,99 •4.44 •9.91 •4.11 •9.49 •it.91 •i.n •9.44 •4.11 •11,41 •19.14 •H.9F i .n •l,IF *4.94 Malt— aaa aaa aaa aaa aaa aa* aaa aaa aaa aaa aaa a** fe»liy*ai Mttl *M •I.M •)*?! •9.94 ••I* •9.41 •l.tt •4.11 •4.91 •19.94 1.19 •9.19 •M .tl -4.91 *4,9T -9.14 -.99 •4,11 -l.lt Uttla •1.99 •1.91 •14.91 *.« •4.19 -4.99 H.4I •4.99 •11.41 |,|9 •9.99 •19.91 •9.1) *14,94 -1M9 *1.99 - i . n h .i i Maltta aaa aaa aaa aaa aaa aaa aaa aaa aa* aa* aa* aaa as* aaa M » * |m Mral til l •9.91 •11.41 i.n •9.19 •11,99 •4.91 •19.91 •9,91 *1,91 •*.« 1.99 •4.41 *11,11 *11,44 1.94 -4.11 -*,H C*»iul- • i . n •t:U •4.99 1.91 •1.49 •1.19 •1.94 •t.49 • I.ll ••94 •.49 • It •1.19 •1.91 •1.19 .M -I.ll -l.tl tat Malta aaa aaa aaa a aaa aaa aa aa aaa ** a** a Ia9aatry

*19* I*# MftM U Mtfe Mil It |l* HM m >»Ul MIMMMI lU Ml lM lllll —*lf* if M fl U41**l*# 14# l«l*t ll «**««l • tlfetlflUM *' IU ,|| |*»*|, N II lit* .91 IlMl, Ml •** llHiritM l tl IM ,91 l«Ml.

IUIM m m art Wtai a* m i Ii h i H h if IriltM 'i (l*MJ Im iim IIm iI k i Ii , 158

The procedure in using these tables is as follows. Recall that the purpose of this chapter is to assess whether or not there are signifi­ cant differences in age, education and occupational status of in, out and non migrants between cantons with a well developed nonfarm sector and those lacking such development. The first step, then, is to compare human resource characteristics among migrant status type, as they vary by type of area (Tables 1 and 2); that is, the emphasis is on human capital differences between in, out, and non migrants. In this context, to the extent that rural areas with more nonfarm employment experience an overall gain in human capital through migration, one generally would attribute this to the nonfarm sector. Likewise, if areas without, or vith little , nonfarm enterprise lose human capital through migration, one generally would conclude that this is related to the relative lack of nonfarm employment. In the present case, however, it is possible to actually see the source of benefits or losses in human capital by con­ sidering the difference between in and out migrants for specific economic sectors.

To get a more systematic view of economic sector effects, however, differences in human capital characteristics among the sectors them­ selves are examined in the second step of this analysis (Table 3). By this, one can see whether or not there are significant differences in human resource characteristics among the various economic sectors and, in particular, which economic activities are instrumental in channel­ ing/draining human resources into and from areas. 159

To farther elaborate theae themes, attention nov turns to each of

the hunan resource characteristics.

Inspection of Table 6.1A indicates that, whatever the area or the

economic sector, non migrants tend to be older than either in or out

migrants. With regard to the absolute age of non migrants, there is

little variation between areas, but the variation between economic

sectors is noteworthy. In particular, agriculture tends to have the

older non migrants, averaging between 34 and 35 years of age, while

manufacturing has the younger non migrants, averaging between 30 and 31

years of age* With regard to the migrant population, in migrants to

rural cantons lacking nonfarm sector development tend to be older than

in migrants to other areas, whereas for out migrants, urban areas hold

th is d is tin c tio n . Economic sector e ffe c ts do not ex h ib it a p a rtic u la r

pattern with regard to the age of migrants.

As one might anticipate from the above, the comparison of age as it

varies by migrant status (Table 6.2A) indicates that in all cases, non

migrants are older than out migrants and, in all cases but one (which is not significantly different from zero), non migrants are older than in migrants. Thus, whether an area is urban or rural, migration is age se­

lective to the detriment of the sending area and betterment of the

receiving area. Within this generalization, however, an interesting observation is evident from Table 6.2A. In agriculture, there is less of a gap between out and non migrants in the rural areas than in the 160

urban, whereas in the nonfarm activities, there ia more of a gap. By comparison, the gap between in and non migrants is always greater in the urban areas, whatever the economic sector of employment. In terms of nonfarm activities, then, there is in fact a steady drain of younger persons out of the rural areas. This is further evident from the

comparison of in and out migrants for each ares. In the urban areas, in migrants are younger than outs, although the difference is very small and significant in only one of the four cases; in the rural areas, in migrants are, in all cases but one (which is not significantly different from zero), older than out migrants. However, it also is important to note that the gap between in and out migrants is much narrower in the rural areas with a well-developed nonfarm sector (about 1.2 years), compared to rural areas lacking such development (about 3.0 years). It appears, therefore, that the nonfarm sector is having the anticipated effect upon age selectivity in migration.

This latter observation is confirmed by examining age as it varies by economic sector (Table 6.3A). In almost all cases, the age of those in the agricultural work force is significantly greater than the age of those in the nonfarm sectors of the economy, for in, out, and net mi­ grants in all areas. In comparing rural cantons with a meaningful non­ farm sector and those lacking such a sector, another interesting differ­ ence emerges. In general, the age gap between the agriculture and nonfarm economic sectors is much greater where there is a developed nonfara sector, whether one is considering in, out, or non migrants.

Further, this difference is especially marked for in migrants. The 161

picture that emerges, then, ii a gradual increaae in human capital, as judged by age, for the rural sector with significant nonfara develop­ ment.

Education

Inspection of Table 6.1B indicates that there are noticeable differences in educational level among economic sectors. In particular, persons in agriculture average about 3.5 years of education whereas persons in nonfara activities average from about 5 to as high as 8 years of school. Table 6.IB also shows higher levels of education in urban, than in rural, areas. Within those constraints, however, there appears to be no systematic variation with migrant status. That is, given the range of education levels for a particular economic activity, there is no noticeable difference, or systematic variation, between in, out, and non migrants. There is, however, one important exception to this.

Migrants to rural cantons with significant development of the nonfara sector, who themselves are working in the nonfara sector, exhibit mean levels of education that are higher than those for either out or non migrants, an occurrence that is not even found in the urban area.

As one might anticipate from the above, the comparison of education as it varies by migrant status (Table 6.2B) indicates only occasionally significant differences between migrant groups. In the urban area, no clear pattern emerges. In the rural area, however, in migrants do tend to be better educated than the non migrant population. Also, although not always significant, in the rural areas lacking rural nonfara 162

development there tends to be higher educetionel levels tmong out migrants than among non migrants, a tendency not found in rural areas

with significant rural nonfarm development. Also, in areas with significant rural nonfarm development, the educational characteristics of in migrants tend to be stronger than for out migrants.

Now, turning to the comparison of education as it varies by

economic sector (Table 6.3B), a much clearer picture emerges. As noted above, persons in the agricultural sector are systematically less well

educated than persons in any of the nonfarm sectors. Renee, whether the

sample is in, out, or non migrants, the educational level of persons in agriculture is significantly less than that of persons in any other sector, and this is true of urban or rural areas.

In summary, then, higher educational levels are systematically

associated with nonfarm, as opposed to farm, employment. Further, among

rural cantons, areas with nonfarm employment have, on balance, in mi­

grants that are more educated than out migrants, and are thus experi­ encing a net gain in this human resource. Predominantly agrarian can­ tons with little nonfarm employment, on the other hand, show a slipping

away or, at best, status quo of well educated people. Finally, special note should be made of the clearly significant differences when educa­

tio n a l level by economic sector was the focal p o in t, and the more ambi­ guous results when migrant status was the focal point. The lesson to be drawn from this, it seems, is that those areas, with nonfarm employ­ ment are improving in educational human capital, but more by an exit of lesser educated persons in the agricultural sector and an entrance of 163

better educated people in the nonfarm sectors of the economy. Hence, one can only conclude that fostering more development of the nonfarm

sector in rural areas vould further upgrade their educational human capital base.

Occupational Status

Inspection of Table 6.1C indicates noticeable differences in occu­ pational status among economic sectors. In particular, persons in agri­ culture average a scale value of approximately 30 to 32, persons in m anufacturing and commerce average a scale value of approximately 35 to

36, and persons in service average approximately 39 to 42. Also, for each of these economic sectors, the occupational status tends to be, on the average, one or two points higher in urban than in rural areas.

Finally, within these constraints, there is no systematic variation with migrant status. That is, given the range of occupational status levels for a particular economic activity, there is no noticeable difference, or systematic variation, between in, out, and non migrants. There are, however, variations in sections of the table which are noteworthy. In the rural areas with significant nonfarm development, in migrants to the nonfarm sector, in all of its aspects, are of higher occupational status than either out or non migrants. Furthermore, with the exception of economic activity in commerce, this observation also holds for the rural areas lacking nonfarm development. The implication seems to be, then, that even in lesser developed areas, nonfarm activity gives rise to an increase in human capital as measured by occupational status. 164

As one might anticipate from the above, the comparison of occupa­ tional itatus aa it varies by migrant status (Table 6.2C) indicates only occasionally significant differences between migrant groups. There are, however, some interesting patterns, parts of which show significant dif­ ferences. In the urban areas, out migrants are systematically of higher occupational status than in migrants. By contrast, in rural areas, in migrants are systematically of higher occupational status than are out migrants, and this is especially marked for those economic activities of the rural nonfarm sector. There also is a tendency in rural areas for out migrants to be of lesser economic status than non migrants.

Now, turning to the comparison of occupational status as it varies by economic sector (Table 6.3C), a much clearer picture again emerges.

As noted above, persons in the agricultural sector systemstically have lesser occupational status than persons in any of the nonfarm sectors.

Hence, whether the sample is in, out, or non migrants, the occupational status of persons in agriculture is significantly less than that of per­ sons in any other sector, and this is true of both rural and urban areas.

In summary, then, higher occupational sta tu s lev els are systema­ tically associated with nonfarm, rather than farm, employment. Further, among urban cantons, in migrants tend to be of lesser occupational status than out migrants, whereas the reverse is true of rural cantons, whether or not those cantons have a well developed nonfarm sector.

Nevertheless, the greater changes in occupational status, as the result of in and out migration in rural areas, occur in the nonfarm sector of 165

the economy. It seems, then, that human capital, aa measured by occupa**

tional status, may be shifting from urban to rural areas, in a manner

consistent vith development theory of the core-periphery variety.

General Observations on Migrant Selectivity and the Rural Wonfarm Sector

The above analyses indicate definite differences between areas and

economic sectors in terms of human capital. In particular, nonfarm sec­

tors of the economy are associated with higher levela of human capital

than are agricultural sectors, and urban areas are generally better off

in this regard than rural areas. Furthermore, there does not seem to be

a systematic difference between those rural areas with a significant

level of nonfarm enterprise development and those rural areas lacking

such development. What's important, however, is not the present state

so much as the trend of human capital shifts, and by this criteria,

rural areas vith a developed nonfarm sector should be better off in the

fu tu re .

This apparent preference of individuals with higher human capital

for nonfaxm employment is, of course, quite consistent vith theory. To

recall, the theory of the household holds that an individual's labor

time to farm and nonfarm activities is allocated in view of that indi­ vidual's comparative advantage in the various labor markets. Individu­

als with higher earnings power which, in turn, depends on human capital

levels, will obviously engage in nonfarm pursuits rather than agricul­

tu re . 166

Nevertheless, this finding is significant. It supports the notion

that rural nonfarm employment, while not completely offsetting selective out migration to the urban areas, does stem the exodus of people with characteristics that are deemed desirable for development. Furthermore, the rural nonfarm sector appears to also attract individuals with more desirable human capital attributes. As such, rural nonfaxm employment indeed seems to provide a means of retaining/fostering the growth of human capital and skills in an area, and this eventually contributes to its overall growth and development.

Looking at this issue from another perspective, an interesting aspect of the above observations is that they make relatively little mention of migration processes, particularly in that the literature leads us to believe that migration is a major mechanism of human.capital shifts in a development context. The fact is, though, that except for age, human capital differences between migrants and non migrants gener­ ally were not in evidence. Instead, the major mechanism of human capital shifts appears to be the employment opportunities set, which might be bridged by a lateral in situ shift of job as well as by mi­ gration. Hence, selectivity of migration, and its consequences to sending and receiving areas, seems to be very much a function of the type of economic activity ongoing in an area or the area's economic structure, and clearly, positive selectivity is strongly associated with rural nonfarm enterprise. Hence, the findings of this chapter are con­ sistent with the human capital assumptions of promoting this activity for rural areas. 167

Summary of the Chapter

This chapter examined how rural nonfarm employment opportunities may affect the export of human capital from rural areas. To represent the human capital dimension* three attributes were chosen: age* edu­ cational level* and occupational status. To assess whether nonfarm employment in a rural area reduces "selective" out migration (of younger individuals with higher educational level and occupational status) or attracts selective in migrants, cross-tabulation and means tests were used. Individual out* in* and non migrants were cross-classified by sector of employment and type of area (urban, rural* rural with nonfarm employment* ru ra l w ith l i t t l e nonfarm employment)* and the mean values for age* education* and occupational status were calculated for each group. Based on this* two kinds of comparisons were made:

1) Between areas, focusing particularly on rural cantons with

nonfarm activities and those without or few. For this* first

the differences in age* education and occupational status were

calculated between (a) out migrants and stayers, (b) in

migrants and stayers* and (c) in and out migrants within an

area. This showed whether or not an area generally gained or

lost human capital through migration.

Comparing areas with each other* then* it was found that

rural areas generally lose from migration relative to urban

areas. Among the rural areas* however* it was the cantons with

nonfarm employment that recorded an overall gain from the in 168

migration of individuals vith high human capital levels. The

cantons vith little nonfana employment, on the other hand, ex­

perienced overall a loss of human capital.

2) Between economic sectors. For the differences in age, educa­

tion, and occupational status of out migrants, in migrants, and

stayers (taken separately) betveen sectors vere calculated.

These mean differences shov vhich sector has the "better" out migrants, in migrants or stayers (comparing each migrant group

separately).

It vas found that the nonfarm activities (manufacturing,

commerce, and service) consistently gain in terms of human

capital, relative to the agricultural sector. Not only do

these activities have the "better" stayers, but the nonfarm

sectors also gain from the in migration of younger persons vith

greater educational levels and occupational status.

This indicates that rural nonfarm employment in developing

countries may provide a means of retaining human capital and

skills in an area, thus promoting growth and development. CHAPTER VII

SUMMARY

This chapter summarises the nain points of the dissertation,

Firat, the problem and the conceptual framework are recalled. This is followed by a summary of the methodological approach and the results from the various analyses. The chapter concludes with a discussion of the implications of the study for theory and policy, its limitations, and suggestions for future research.

This study has addressed two concerns:

(1) How rural nonfarm employment/amall-scale enterprise affects

aggregate migration, and

(2) How rural nonfarm employment/smal1-scale enterprise affects

migrant selectivity, i.e., the export of human capital from a

rural area.

Civen the high ratea of rural-urban migration in LDCs and related problems of excessive urban growth and unemployment, the concern with the rural origin of these problems — addressed in question 1 — is of obvious importance. The second question is relevant because migration removes human resources from an area that are critical for its economic growth and development, and rural nonfarm employment may retard this exodus of human capital. 170

The basic argument of the study vas that migration represents a decision within a constraint set which is partially determined by the availability of employment opportunities in the rural origin area. While this is not precluded by classical migration theory (Stouffer, 1940; Bright and Thomas, 1941; Levy and Wadycki, 1974; Todaro, 1971),

migration research has given little consideration to the effects of rural labor markets on migration. The present study, however, followed the suggestion by Byerlee (1974) that in order to understand migration,

one has to focus on the rural economic environment in which the decision

to migrate is made. Hore specifically, it was assumed that employment opportunities in the nonfarm sectors may be a prime factor in explaining/triggering off migration. Furthermore, it was hypothesized that nonfarm employment may also significantly affect positively selective migration, i.e., reducing

out migration and/or attracting migrants that are younger,*better educa­

ted, and have higher occupational status. The rationale for these assumptions are given by the theory of the household. It postulates that households allocate their resources so as to maximize a family utility function. As part of this, labor is allo­ cated between farm and nonfarm activities according to the family member's earnings power in the various labor markets. This, in turn, is contingent upon that member's human capital characteristics. The higher the member's educational level, for example, the more likely he/she will pursue a nonfarm job in either a rural or urban location. 171

A rural location, however, nay be preferred in view of the high

riak of unenployment in the urban area, and becauae rural nonfara em-

ploynent in aitu nay allow one to inprove/atabilixe inconea without

incurring the monetary or peychological coata aaaociated with a perman­ ent nove to the city. Alao, the average rural migrant nay have more

inform ation about the nonfara job narket than urban employment and therefore chooae a rural nonfara job. In examining the queationa — how the amall-acale nonfarm rector affecta aggregate and aelective migration — the following approachea were taken and reaulta achieved.

Methoda and Reaulta

Firat, the economic atructure of the etudy area waa examined quali­ tatively in order to gain an underatanding of economic factora and trenda that underlie the data and that may affect the interpretation of the reaulta (Chapter III). Then, the aalient featurea of the amall- acale aector in rural areaa, ita urban counterpart, and the agricultural aector were depicted by factor-analytical methoda (Chapter IV). From this factor analyaia, acorea were derived that measured how each geo­ graphic unit of observation ranked in terms of these dimensions.

These dimensions were used in two ways. For one, they provided the basis for a classification of rural cantons into those with significant nonfarm sectors, those without or fewer nonfara activities, and those with capital intensive industry (Chapter V). (The latter category, how­ ever, represented an a b erratio n in the CoBta Rican case, and waa th ere- 172

fore not included in the discussions.) Secondly, factor dimensions were

used as independent variables in regression analyses of out, in, and net migration in order to get an overall understanding of the relative im­

portance of rural nonfarm employment in stemming/diverting rural-urban m ig ratio n .

It was found that aggregate net out migration was high in areas with few, if any, nonfarm enterprises, particularly of the modern vari­

ety. Also, net out migration was high in areas whose agriculture was very extensive (cattle ranching) or that experiences great fluctuations

in prices (sugar cane cultivation). Out migration was inversely related

to the pull of urban areas. That is, the highest out migration would

occur in the more remote areas, where the urban pull is lowest. This suggests that structural characteristics of the rural areas related to the farm and nonfarm sectors were relatively more important for out migration than the urban pull effects.

Among the structural characteristics that deterred in migration were nonfarm activities of a more informal, casual, part-time nature. By implication, more formal, permanent nonfarm jobs might have attracted in migrants. Burgeoning agriculture, on the other hand, acted as an inducement to in migration. Overall, the pull from urban areas did not significantly influence in migration into rural cantons.

In summary, the aggregate analyses suggested th a t:

(a) rural nonfarm activities do have an effect on steaming out

migration and directing in migration, with migrants apparently

p re fe rrin g more permanent, formal work arrangements and more modern type of nonfarm activities, 173

(b) structural conditions of the agricultural sector have an

effect upon its role in stemming, diverting, or inducing m igration.

To lend more credence to the general results obtained from the ag­

gregate analysis, individual level data and contingency table analysis

were used to see how migrant status (as out, in, or non migrant) related

to employment in rural nonfarm (versus farm or urban economic) activi­

ties. If, indeed, migration was retarded/induced by nonfarm employment,

one would find fever outmigrants from cantons vith more nonfarm activi­

ties than from cantons without. Also, there would be more in migration

into cantons vith more small-scale nonfarm enterprise than into cantons

with few such enterprises.

Overall, the results were in accord vith the aggregate findings.

Within a given rural area, the observed number of in migrants into can­

tons vith significant nonfarm sector and the the observed number of out migrants from cantons with few nonfarm activities were not random.

Comparing among rural areas, it was found that the presence of rural nonfarm enterprise acted to increase the retention of an area's popula­

tion, whereas the lack of such activity tended to decrease retention.

Even in areas where much of the employment opportunity lie s in the a g ri­

cultural sector, rural nonfarm employment seems of great attraction as

shown by the fact that out migrants from these areas largely pursued nonfarm jobs at their new destinations. From this, one could conclude that more nonfarm activity at the origin might have stemmed out migra­ tio n . 174

Generally, in migrants tended to prefer urban to rural areas which could imply that if nonfarm activity were increased in the rural area, in migration to these cantons would be increased and urban-ward migra­ tion might be diverted. These findings, however, did not address the issue of how the ex­ port of human capital from rural areas may be affected. While aggregate out migration may be reduced through nonfarm jobs, for example, indivi­ duals with greater human capital levels may nevertheless leave. There­ fore, in Chapter VI, this dissertation turned to the question of whether or n o t:

(a) rural areas with more nonfarm employment retain/attract

migrants with higher human capital levels than areas without

or few nonfarm jobs, and (b) rural manufacturing, commerce and service retain/attract

migrants with more desirable human capital levels than the

agricultural or urban economic activities.

The findings showed definite differences between areas and economic sectors in terms of human capital. In particular, the nonfarm sectors of the economy are associated with higher human capital levels than is agriculture, and urban areas are generally better off in this regard than rural areas.

Among the rural areas, the cantons with significant nonfarm sectors experienced clearly a net gain in human capital as judged by age and educational levels. Regarding the trend in human capital shifts, these areas appear to be better off in the future. The human capital differ- 175 encea among areas vere indeed attributable to the various economic sec­ to rs : ru ra l manufacturing, commerce, and service not only have a non migrant stock vith greater human capital levels than agriculture, but they also attracted in migrants vith more desirable attributes* While both farm and nonfarm sectors lost out migrants vith relatively high human capital levels, the net loss from the agricultural sector vas greater than from the nonfarm sector.

The major conclusion, then, is that selective migration and its impact on sending and receiving areas seems to be very much a function of the type of economic activity and an area's economic structure. As such, ru ra l nonfarm employment seems to provide a means of fo sterin g the grovth of human capital and skills in an area, thus contributing to o v e ra ll grovth and development.

Contributions and Policy Implications

This study adds to existing knowledge on the rural nonfarm sector and to the theory of migration and related policies. Regarding the former, the results attest to the importance small-scale nonfarm enter­ prise may have in retarding aggregate migration. This is shown more explicitly than haB been done before.

Further, while empirical evidence suggests that small-scale nonfarm enterprise has a positive role in human capital formation through the system of apprenticeship, the findings show that the nonfarm sector also attracts/retains individuala vith already higher human capital levels.

This suggests that nonfarm employment strategies have potential to make 176

the rural areas attractive to school leavers or more educated manpower and, generally, may reduce the negative externalities associated with selective out migration*

More generally, the findings support the urgency of a fundamental change in the allocation of resources between rural and urban areas and economic sectors, suggested by Liedholm and Chuta (1976: 121). Among these changes ought to be increased agricultural investment and promo­ tion of the rural nonfarm sector through more equitable distribution of physical and social infrastructure between rural and urban areas, loans to small-scale firms and removal of tariffs protecting large-scale industries.

Regarding migration theory, the results suggest that migrants do respond to their perception of market forces and changing economic opportunities in the rural areas as well as in the urban areaB. Thus, migration theory ought to model the migration decision process with due regard to the individual's evaluation of the rural labor markets in the nonfarm sector, and possibly the farm sector as well.

Given that migrants respond to the conditions in the rural and urban labor markets allows one to view migration more broadly as an economic resource allocation mechanism which may have policy implica­ tions. That is, an empirically observed migration pattern may repre­ sent to some extent optimal national resource allocation. For example, migration withdraws labor from areas where economic opportunities contract and supplies to those regions where economic opportunities 177

emerge but local labor supply is inadequate* 28 This interpretation

allows one to make a link between migration research and policy

(Carvajal and Geithman, 1974: 121).

A theoretical framework such aa the one developed here can be a valuable tool in providing policy makers with some insight into the nature, scope, and elements of economicaoly rational migration policy. With population movements performing an important function in facilitating structural changes associ­ ated with the development process,' policies could be designed to increase the flow of information about existing economic opportunities in the different geographic regions and reduce uncertainties associ­ ated with the exploitation of these opportunities... If massive migration into cities were to become strongly dysfunctional and productive of high unem­ ployment rates, more and better information on economic conditions might help reverse the flow.

Limitations and Suggestions for Further Research

Any claim to have made a contribution to existing knowledge muBt inevitably be seen in view of the limitations of the study. The limi­ tations of this study caution one to understand the results as sugges­ tive observations rather than as definitive statements. Some of the limitations and suggestions for further research are briefly summarised next.

Conceptually, the study draws a link between the individual or household decision making process and observed aggregate migration pat­

28 This was first pointed out in the Costa Rican case by Carvajal and Geithman (1974: 120). 178

tern, as reflected in census data. The rationale for this is that the observed migration pattern on the aggregate level represents the summa­ tion of the decisions made on the individual level. While this approach is widely applied in migration research (Carvajal and Geithman, 1974;

Levy and Wadycki, 1974; Feder, 1980), it nevertheless harbors the danger of an ecological fallacy. That is, it assumes that the observed relationships between the variables are the same at all levels of aggre­ gation. Data availability, unfortunately, did not permit a more rigorous test of the correspondence of the results on the aggregate and individual levels.

As such, the levels of aggregation and the analytical methods used here — while appropriate for the particular data set — conceal much of the economic or noneconomic rationale of the migrant. -

The p a rtic u la r data se t may lim it somewhat the usefulness of the conceptual framework employed. There is evidence, for example, that a considerable proportion of the migrants in the data set were displaced from their land by credit policies in the 1966-72 period. Such policies favored cattle ranching and induced many small farmers to convert their land to cattle ranching. These farmers failed to achieve the competi­ tive edge in the market that the big ranchers had. As a result, many small farm households had to eventually sell out and migrate. 29 Their

29 Conversation with Elena Teran, Directors Division de Planificacion y Coordinacio Regional, OFI LAN, based on unpublished data from the C entral Bank of Costa Rica. 179

migration nay have to be considered 'forced* migration. Aa such, the

conceptualisation of the migration decision process employed as a

careful cost-benefit consideration of various labor markets may not be

appropriate.

Future research efforts relating migration to rural and urban wage labor opportunities would therefore have to give due regard to

contextual — economic-political — factors. To examine the influence

of such factors, of course, the use of survey data asking specifically why people migrated would be more appropriate.

Further limitations resulting from the data set used are associated with the definition of rural nonfarm employment or rural small-scale sector. As pointed out in Chapter IV, the rural small-scale sector was identified from census data as small-scale manufacturing, commerce, and service in rural cantons. This definition gives little indication of the true extent, nature and composition of nonfarm employment. Surveys in other countries, for example, have shown that only a portion of total nonfarm employment is made up of sm all-scale industry, commerce, and service establishments. The larger proportion of nonfarm employment (95 in Sierra Leone, for example) is nonfarm work pursued within the farm household. In limiting itself to the census enumerated rural nonfarm establishments, this dissertation leaves unaddressed a significant share of nonfarm employment and its effect on migration and migrant selectivi­ ty. A comprehensive study of this question would have to use household survey data v ith households broken down by income sources and income classes, since migration propensities may very well vary with incomes. 180

Another shortcoming may result from some variables, for example,

average incomes in the various sectors. Average income in urban

economic activities and the rural nonfarm sectors as defined in this

study (total wage b ill in sector/total number of employees in that

sector) are not only crude but also may or may not be comparable. Urban

sector wages may be higher than rural wages, but real incomes may

actually be the same or lower; or average income in the rural areas may

be lower because rural people tend to work less hours per year than

urban people (Liedholm and Chuta, 1976: 121).

Thus, a comparison or inclusion of these variables may be difficult

for theoretical and empirical reasons. Future research might model the

migration decision process in terms of a perception of real incomes, or

how well off the migrant expects to be in alternative locations.

Methodologically, the OLS approach used in the first part of

analyses (examination of aggregate in, out, and net migration) may con­

ceal a dual causality. It was found, for example, that in migration is

positively related to rural nonfarm enterprise, while out migration was

related negatively. Rather than assuming that in or out migration is

'caused' or explained by the rural nonfarm sector, one may assume the

opposite. Rural nonfarm activities may be numerous because high in migration increases the demand for nonfarm goods and services. Like­ wise, high out migration and a small market may be the cause for few

small-scale enterprises. A methodologically more satisfying approach

to the study of migration and employment opportunities would have to

account for this possible simultaneity bias. 181

Despite its many shortcomings, the approach employed in this study broadly corroborates the view that:

(a) People respond to their perception of market forces and

changing economic opportunities in rural areas, and that

(b) Factors in the rural economic environment may be equally or

relatively more important in the decision to migrate than the

pull of urban economic sectors.

As such, this study sees the task for designing and testing migration in relation to the rural economic environment as a challenge and important avenue for future research. BIBLIOGRAPHY

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