PhD Dissertation

An Assessment of Magnitude and Correlates of Poverty in District

Submitted by:

Muhammad Mehboob Alam

Rag # 1234102

Ph.D. Economics

Supervisor:

Dr. Muhammad Kashif

SZABIST, Karachi Campus

i

2 | P a g e

3 | P a g e

4 | P a g e

Acknowledgement

First and foremost, I am extremely grateful to my supervisor Dr. Muhammad Kashif, whose encouragement, supervision, suggestions, ideas, creativity and patience helped me to complete this dissertation in a timely manner. His insight and wisdom throughout the dissertation process is invaluable. In addition, I thank SZABIST, Karachi for providing me the opportunity and platform to represent my skills. I would like to express gratitude to all teachers and administrative staffs of SZABIST, Karachi especially Dr. Riaz Ahmed Shaikh (Dean, Faculty of Social Science & Education, SZABIST Karachi) who helped me a lot whenever needed. I am particularly pleased about the superb advice by Dr. Syed Irshad Hussain. Dr. Irshad has introduced me to interesting techniques that allowed me to strengthen the analysis. Population Welfare Department of has also provided their valuable support in providing the complete detail of District data, Sub Districts, Union Councils, Villages and number of total households in each village as per the latest census conducted by the Govt. of . It was not even possible to complete this dissertation without their support. The biggest word of appreciation has to go to the members/heads of the households participating in the survey across the four sub districts of Jhang districts of rural Punjab (Pakistan). Without their assistance and cooperation, this study would also not have been possible. I am also deeply thankful to all anonymous referees for helpful comments and suggestions. With The research contributes to competency building, development, knowledge creation, and innovations. I am also thankful to all my family members and friends for their support and encouragement. Finally, I want to say my special thanks to my parents who provided me with all kinds of financial and moral support during my studies.

5 | P a g e

Non-Technical Summary

Poverty is a global issue and remains a subject of much concern to observers and researchers alike. More importantly, how people perceive the causes of poverty in the context of culture and value system in which they live, have a great impact on poverty. The core objectives of the study are to find the causes/factors of the poverty level in the target area, which are found to be socio- economic, demographic and social factors. In Social factors person per room, house structure, education, and Health were the main correlates and, In Socioeconomics factors total assets, Landholding , Livestock and in demographic factors age of household head, person per room, family size, family type, education of head of household and occupation of the head of the household are the main correlates of poverty in the Jhang Districts.

A cross-sectional survey using multi-stage random sampling procedure was conducted among the poor and non-poor of Jhang District. According to the survey conducted in 2016-17, 54% are below the Poverty Line and remaining 46% people are above the poverty line in Jhang District. Depth and severity of poverty are 36% and 13% respectively. It is also noted that 16% of people are extremely poor.

In all sub-districts poverty level is also on the higher side which is 51.3 %, 57.6 %, 56.0% and 53.5% in Jhang Sub District, Sub District, Attahara Hazari Sub District and Ahmed Pur Sial District respectively. In Shorkot Sub District Poverty level is highest as compared with other sub-districts.

The need of the hour is that the people of the area may be introduced to new patterns of thinking to change their lives for the better. Awareness programmes may be launched to prepare the new generation for the changes and challenges ahead. In this regard, the people especially women of the area may be educated and empowered to enable them to actively participate in income- generating activities on sustainable grounds.

6 | P a g e

Abstract of Dissertation

Poverty means different things to different people. There are many different approaches to defining poverty but the basic needs approach is commonly applied, particularly in developing countries where a bigger majority of the people struggle to attain a predetermined minimum level of income to satisfy their basic needs. The research study on “An assessment of magnitude and correlates of Poverty in Jhang District” was carried out with the objective to document the status and trend analysis of poverty situation in all four sub-districts of Jhang District, Punjab. It is an attempt at estimating the incidence, intensity and severity of poverty and identified the determinants of poverty as well the poverty coping strategies among the households in Jhang district. (A medium size city of Punjab Province, Pakistan.). The analysis of the study carried out on the basis of primary data and all data have been collected through a specifically designed questionnaire with a sample of 1000 households in its background in the selected villages of all four sub districts (Tehsil) of Jhang District, Punjab. This study has used bivariate and Multivariate analysis (income regression model and logistic model) to determine how various indicators of the poverty such as socioeconomic, demographic and social characteristics of households affect the poverty incidence in Jhang District. According to the survey conducted in 2016-17, 54% are below the Poverty Line in Jhang District. Depth and severity of poverty are 36% and 13% respectively. It is also noted that 16% of people are extremely poor whose income is Rs. 1515. The people of the area are found poorer compared to that of 33% in Punjab Province and 36% in Pakistan and 31% in the world. Education is the most significant factor that distinguishes the poor from the non-poor. Results of the logistic regression suggest that an increase in the landholding, livestock and total assets of the household have considerably decreasing the probability of being poor in the Jhang district. In the income regression analysis that size of household is the prime determinant of the per capita income of the household as a 1 % positive increase in the size of the household would decrease more than 1% per capita income of the household. It implies that the size of the household can add well to the level of poverty incidence in the Jhang district, Punjab. The results of logistic model shows that age of the households head, household size, household head is illiterate, household head is farmer, household head is daily wager or labor, residence in kacha(made of mud) house was positively and significantly correlated with the probability being poor while households satisfaction with

7 | P a g e

education facilities and household have members in abroad for income purpose are negatively and significantly correlated with the probability of being poor. The results of the logistic regression suggest that an increase in the landholding, livestock and total assets of the household have considerably decreased the probability of being poor in the Jhang district. Moreover, the results show that an increase in the earners per household significantly reduce the probability of being poor in the Jhang district. Education plays an important role in the reduction of poverty and improving the socio-economic status of households. The results signify that educational attainment is significantly related to the likelihood of being poor in the Jhang district, Punjab. The ownership of the agriculture land and livestock is considered good for the reduction of the rural poverty in Pakistan. The estimated results of the logistic regression revealed that the coefficients of the landholding and livestock are negative and statistically significant in all four models. This indicates that an increase in the landholding and livestock of the households will reduce the probability of being poor in the Jhang district. The ownership of farming land and livestock have a significant impact on the reducing the probability that a household is poor. Similarly, the total assets of the households have also a negative and significant effect on the probability of being poor as the estimated coefficients of the total assets in all three models are negative and highly significant. Finally the dissertations conclude that it is important to examine these issues and address them on a long-term sustainable basis for all households at macro and micro level. Private individuals, industrialists, philanthropists, landlords, businessmen, and farmers all stakeholders collectively have to play a positive role in this regard. The primary and secondary data used in this study would help policymakers to design result-oriented programmes that would address poverty (Its magnitude and Correlates) in the study area.

8 | P a g e

List of Tables

Table 1.1: Latest Official Poverty indices, 2015-16……………………………….… Page 34 Table 1.2: Trends in the Poverty incidence of Pakistan…………….……….……….. Page 35 Table 3.1 Poverty Related Millennium Development Gaols.….………………….. Page 72 Table 3.2 Variable Formation……….……….………….………….………….……..Page 75

Table 3.3 Sampling of Questionnaire in Jhang District ……….………….……….. Page 85

Table 3.4 Sampling of Questionnaires in Jhang District (Rural and Urban Wise) …. Page 86 Table 3.5 Sampling of Questionnaires in Jhang District (Union Council wise)…..… Page 86 Table 3.5.1 Sampled Union Council in Jhang Sub District……….………….…….…. Page 88 Table 3.5.2 Sampled Union Council in Sub District………………..…. Page 88 Table 3.5.3 Sampled Union Council in Shorkot Sub District……….…………..…..… Page 88 Table 3.5.4 Sampled Union Council in Ahmed Pur Sial Sub District……….….…..… Page 88 Table 3.6.1 Sampled villages Jhang Sub District……….…………..………………… Page 89 Table 3.6.2 Sampled Villages/areas in Attahara Hazari Sub District……….………… Page 89 Table 3.6.3 Sampled villages Shorkot Sub District……….………….………….….… Page 91

Table 3.6.4 Sampled villages Ahmed Pur Sial Sub District……….…………..……… Page 92

Table 3.7 Categorization of Poor w.r.t income……….………….…….…….……… Page 93 Table 3.8 Poverty Categories……….………….………….…….…….…….…….… Page 99

Table 4.1 Gender, Marital Status, and Age of the Head of Household……….…... Page 102

Table 4.2 Education Level and Occupation of the Household Heads……….……. Page 103 Table 4.3 Type of Family and Household Size ……….……….………….……..… Page 105

Table 4.4: Age Structure of the Sampled Population ……….………...………….… Page 106

Table 4.5 Average Household Size and Dependency Ratio ……….…….………… Page 107

Table 4.6 Ownership and Structure of the Houses……….………….………….……Page 108

Table 4.7 Indices of Congestion ……….………….………….……………………. Page 109 Table 4.8 Household Sanitation ……….………….….…….………….…………. Page 110

9 | P a g e

Table 4.9 Kitchen System and Source of Cooking ………….………….………… Page 111

Table 4.10 Water Supply and Electricity Provision……….…..……….………….… Page 112

Table 4.11 Source of Information and Communication ……….….……….…………Page 113

Table 4.12 State of Health Facilities ……….………….………..………….………...Page 115

Table 4.13 State of Education Facilities……….………….………….…….…………Page 115

Table 4.14 Children in School and Household’s Members Literacy Rate ………….. Page 116

Table 4.15 Cultivated Land per Housing Unit……….………….………….………...Page 119

Table 4.16 Livestock Population per Housing Unit……….……………………..… Page 120

Table 4.17 Total Assets of the Household……….………….………….………….…Page 121

Table 4.18 Sources of Income ……….………….………….………….………….… Page 122

Table 4.19 Earning Members per Household ……….………….………….…………Page 123 Table 4.20 Income from Farming and Livestock ……….………….……………...…Page 124

Table 4.21 Income from Business……….………….………….…………………..…Page 124

Table 4.22 Income from Services ……….………….………….…………….………Page 125

Table 4.23 Total Monthly Income of the Household……….………….………….…Page 126

Table 4.24 Total Expenditure of the Household……….………….………….………Page 126

Table 4.25 Food Expenditure of the Household ……….………….…………………Page 127

Table 4.26 Balance of Income and Expenditure ……….………….…………………Page 128

Table 4.27 Categorization of Poverty with respect to Income Level in Pakistan…..…Page 129

Table 4.28 Poverty Estimates of Households of Jhang District at Glance ………..… Page 130

Table 4.29 Magnitude of Poverty in Jhang District with respect to the Poverty Bands (Income Level) ……….………….………….…………………………….….… Page 132

10 | P a g e

Table 4.30 Poverty Estimates of Households of in Jhang Sub District ……….…… Page 133

Table 4.31 Magnitude of Poverty in Jhang Sub District……….………….………… Page 134 Table 4.32 Poverty Estimates of Households of in ShorKot Sub District..…………. Page 134

Table 4.33 Magnitude of Poverty in Shor Kot Sub District……….………………… Page 134

Table 4.34 Poverty Estimates of Households in Athara Hazari Sub District ...... …Page 135

Table 4.35 Magnitude of Poverty in Atthara Hazari Sub District…………………… Page 135

Table 4.36 Poverty Estimates of Households of in Ahmed Pur Sub District…………Page 136

Table 4.37 Magnitude of Poverty in Ahmed Pur Sub District ……….…….………...Page 137

Table 4.38 Decomposition of Poverty by Educational Attainment………………… Page 138

Table 4.39 Decomposition of Poverty by Edu. Attainment in all Sub-districts……..Page 138

Table 4.40 Decomposition of Poverty by Job Structure……….…………….……… Page 140

Table 4.41 Decomposition of Poverty by Job Structure in all sub-districts ……….…Page 141

Table 4.42 Decomposition of Poverty by Family Type……….…………………….. Page 142

Table 4.43 Decomposition of Poverty by Family Type in all four sub-districts …… Page 143

Table 4.44 Decomposition of Poverty by Age of the Head of House Hold……….… Page 144

Table 4.45 Decomposition of Poverty by the age of Head of the Household in all sub-

districts…………………………………………………………………....Page 145

Table 4.46 Decomposition of Poverty by House Structure ……….…….…………. Page 146

Table 4.47 Decomposition of Poverty by House Structure in all four sub-districts… Page 146

Table 4.48 Decomposition of Poverty by House Roof Type……….……………..… Page 147

Table 4.49 Decomposition of Poverty by House Roof Type in all sub-districts……….Page 148

Table 4.50 Decomposition of Poverty by No. of Room per House……………….….Page 149

11 | P a g e

Table 4.51 Decomposition of Poverty by No. of Room/House in all sub-districts……Page 150

Table 4.52 Decomposition of Poverty by Bathroom Situation in the House……….…Page 151

Table 4.53 Decomposition of Poverty by Bath Situation in all sub-Districts………... Page 152

Table 4.54 Decomposition of Poverty by Land Holding……….………….………… Page 153

Table 4.55 Decomposition of Poverty by Land Holding in all sub-districts………… Page 155

Table 4.56 Decomposition of Poverty by Household Size ……….……………..…... Page 156

Table 4.57 Decomposition of Poverty by Household Size in all fours sub-districts…. Page 157

Table 4.58 Decomposition of Poverty by Dependency Ratio……….………….…… Page 159

Table 4.59 Decomposition of Poverty by Dependency Ratio in all sub-districts. …. Page 160

Table 4.60 Decomposition of poverty by Male-Female Ratio ……….………..….… Page 161

Table 4.61 Decomposition of poverty by Male-Female Ratio in all sub-districts…… Page 162

Table 4.62 Decomposition of poverty by Female-Male Ratio ……….………….… Page 163

Table 4.63 Decomposition of poverty by Female-Male Ratio in all sub-districts…... Page 164 Table 4.64 Decomposition of Poverty by No. of Adults in Household. ..…………. Page 166

Table 4.65 Decomposition of Poverty by No. of Adults in Household in all sub-districts

Of Jhang Districts……….……………..………….………….…………. Page 167

Table 4.66 Decomposition of Poverty by total Assets in the Household………….… Page 169

Table 4.67 Decomposition of Poverty by total Amount of Assets in the Household of all

Sub-districts………………………………………………………….……Page 170

Table 4.68 Decomposition of Poverty by No. of Children in the Household…….…. Page 170

Table 4.69 Decomposition of Poverty by No. of Children in the Household in all sub-Districts of Jhang District………………………………………………....…….…Page 171

12 | P a g e

Table 4.70 Decomposition of Poverty by Household’s Livestock Population. ….… Page 173

Table 4.71 Decomposition of Poverty by Household’s Livestock Population in all sub-

Districts …………………………………………………….…...…….… Page 174

Table 4.72 Regression Analysis (04 model) ……….……………….….….…………Page 176

Table 4.73 Results of the Logistic Regression ……….………….……………..…… Page 181

13 | P a g e

ACRONYMS AND ABBREVIATIONS

ADB Asian Development Bank. BISP (Benazir Income support Programme CPI Consumer price index CPRC Chronic Poverty Research Center DCO District Coordination Officer. EDO Executive District Officer. FAO Food and Agricultural Organization of United Nations. FEI Food Energy Intake GDI Gender Development Index GDP Gross Domestic Product. GOP Government of Pakistan. GOS Government of Sindh HDI Human Development Index HIES Household Income and Expenditure Surveys HIICS Household Integrated Income and Consumption Survey MDG Millennium Development Goals MPI Multidimensional poverty index MPI Multidimensional Poverty Index NCHD National Commission for Human Development. NGO’s Non-Governmental Organizations. NHDR National Human Development Report NRSP National Rural Support Programme PIDE Pakistan Institute of Development Economics PIP price index for poor PLSM Pakistan Living Standard Measurement PPAF Pakistan Poverty Alleviation Fund SPDC Social policy development center TMA Tehsil Municipal Administration. UCs Union Councils UNDP United Nation Development Programme

14 | P a g e

UNESCO United Nation Educational , Scientific and Cultural Organization VDO’s Village Development Organizations

15 | P a g e

Contents Chapter 1 - Introduction ...... 20 1.1 Introduction ...... 20 1.2 Background of the study: ...... 22 1.3 Understanding the Poverty Line ...... 25 1.4 Broad Problem Area ...... 26 1.5 Specific problem statement...... 29 1.5.1 Research Questions ...... 29 1.5.2 Hypothesis of the study ...... 29 1.5.3 Research Objectives ...... 30 1.6 Justification for conducting the research...... 30 1.7 Significance of the study ...... 32 1.8 Poverty Situation in Pakistan ...... 32 1.10 Limitation of the study ...... 36 1.11 Overview of Jhang District ...... 37 1.11.1 Introduction ...... 37 1.11.2 Administrative System ...... 37 1.11.3 Sanitation System ...... 38 1.11.4 Land Distribution ...... 39 1.11.5 Current Economic situation of Jhang ...... 39 1.11.6 Geographic Condition ...... 40 1.11.7 Demographics of Jhang District...... 40 1.11.8 Communication Linkages ...... 40 1.11.9 Topography ...... 41 1.11.10 Source of Livelihood ...... 41 1.11.11 Area ...... 41 1.11.12 Agriculture...... 41 1.11.13 Environmental Conditions ...... 42 1.11.14 Map of the District ...... 43 Chapter-2 Literature review ...... 44 2.1 Definition and Concept ...... 44 2.1.1 Deprivation from Basic Human Needs ...... 49 2.1.2 Severe Deprivation of Basic Human Needs ...... 50

16 | P a g e

2.2 A review of Poverty prospective studies ...... 50 2.2.1 Poverty Prospective studies in Pakistan ...... 50 2.2.2 A Brief look at Some Poverty Prospective studies in South Asia ...... 55 2.2.3 A Brief look at some poverty Prospective studies in the world ...... 56 2.3 Measuring Poverty ...... 59 2.4 Monetary indicators of poverty ...... 61 2.5 Absolute Poverty Line ...... 62 2.6 Relative Poverty Line ...... 63 2.7 Multidimensional Poverty ...... 65 2.8 Dimensions of Poverty ...... 69 Chapter 3 Research Methodology ...... 72 3.1 Poverty Variables ...... 72 3.1.1 Education (MDG's Goal 2 (achieve universal primary education)) ...... 73 3.1.2 Health and Nutrition ...... 73 3.1.3 Living Standards/ Housing ...... 75 3.2 Variable Formation ...... 75 3.3 Explanation of the variables ...... 78 3.3.1 Demographic factors ...... 78 3.3.2 Social Factors ...... 80 Social factors are the determining factors of a person’s lifestyle, personality, and attitudes...... 81 3.3.3 Socio-Economic Factors ...... 81 3.3.4 Component of Household ...... 82 3.4 Data collection procedures ...... 83 3.5 Universe/Population and sampling ...... 84 3.6 Sampling Procedure (Multi Stage Sampling) ...... 85 3.6.1 Stage 01: ...... 85 3.6.2 Stage 02: ...... 86 3.6.3 Stage 03 and Stage 04: ...... 89 3.7 Data analysis techniques ...... 92 3.7.1 Construction of poverty bands ...... 93 3.7.2 Econometric Models ...... 94 3.7.3 Income regression model ...... 94 3.7.4 Logistic Model ...... 96

17 | P a g e

3.8: Poverty Line...... 97 3.9 Adult Equivalence Scale ...... 99 3.10 The Head-count Index ...... 99 3.11 Poverty Gap ...... 100 Chapter 04 Result and Discussion ...... 102 4.1: Demographic, Social and Economics Characteristic Heads of the Household ...... 102 4.1.1: Gender, Marital Status, and Age of Household Heads ...... 102 4.1.2 Education Level and Occupation of the Household Heads ...... 103 4.2 Demographic Characteristics of the Households...... 105 4.2.1 Type of family ...... 105 4.2.2 Age Structure ...... 106 4.2.3 Household Size ...... 107 4.3 Social Characteristics of Household ...... 108 4.3.1 Ownership and Structure of the Houses ...... 108 4.3.2 Level of Congestion ...... 109 4.3.3 Household Sanitation ...... 110 4.3.4 Location of Kitchen and Fuel of Cooking ...... 111 4.3.5 Source of Drinking Water and Electricity Provision ...... 112 4.3.6 Source of Information and Communication ...... 113 4.3.7 Health Facilities and Household’s Health ...... 114 4.3.8 Education Facilities and Household’s Members Literacy ...... 115 4.4 Socio-economic Characteristic of Households ...... 118 4.4.1: Ownership of Assets ...... 118 4.4.5 Employment Categories and Earning Members per Housing Unit ...... 121 4.4.6 Income Dynamics in Jhang District ...... 123 4.4.7 Household’s Expenditure ...... 126 4.4.8 Income and Expenditure ...... 127 4.5 Poverty Level in Jhang District ...... 128 Source: National Poverty Report 2015-16 (Ministry of Planning and Reform) ...... 129 4.5.1 The Head-count Index ...... 129 4.5.2 Poverty Gap ...... 129 4.5.3 Decomposition of Poverty in Jhang District ...... 130 4.5.5 Comparison of Poverty numbers in all four Sub-districts in Jhang District ...... 137

18 | P a g e

4.6 Correlates of Poverty in Jhang District ...... 138 4.7 Decomposition of Poverty in Jhang District by Household Characteristics ...... 138 4.7.1 Decomposition of Poverty by Educational Attainment ...... 139 4.7.2 Decomposition of Poverty by Job Structure ...... 142 4.7.3 Decomposition of Poverty by Family Type ...... 144 4.7.4 Decomposition of Poverty by Age of the Head of House Hold ...... 145 4.7.5 Decomposition of Poverty by House Structure ...... 147 4.7.6 Decomposition of Poverty by House Roof Type ...... 149 4.7.8 Decomposition of Poverty by Bathroom Situation in the House ...... 153 4.7.9 Decomposition of Poverty by Land Holding ...... 154 4.7.10 Decomposition of Poverty by Household Size ...... 157 4.7.11 Decomposition of Poverty by Dependency Ratio ...... 160 4.7.12 Decomposition of poverty by Male-Female Ratio ...... 163 4.7.13 Decomposition of poverty by Female-Male Ratio ...... 165 4.7.14 Decomposition of Poverty by No. of Adults in Household ...... 167 4.7.15 Decomposition of Poverty by total Amount of Assets in the Household ...... 168 4.7.16 Decomposition of Poverty by No. of Children in the Household...... 171 4.7.17 Decomposition of Poverty by Household’s Livestock Population...... 173 4.8 Regression analysis of Level of Poverty and its determinants in Jhang District ...... 175 4.9 Logistic regression Results and Discussion ...... 180 Chapter 05- Conclusion and Policy Recommendation ...... 185 5.1 Conclusions ...... 190 5.2 Recommendations ...... 190 5.3 Follow up Studies ...... 192 References ...... 1925 Questionnaires ...... 208

19 | P a g e

Chapter 1 - Introduction

1.1 Introduction

What is the meaning of the terms “poverty” and “poor”? Generally speaking, these terms are used in different ways and contexts. They are primarily used to indicate the economic and social status of people. People who earn a low income are poor and live in poor areas. When one says: “That poor person”, poverty has the added connotation of pity, inferiority and subservience. For This reason, according to Adcock (1997:208), less affluent people dislike being referred to as “poor”. Poverty is furthermore the direct opposite of wealth. Wealth is generally linked to concepts such as abundance, status and high quality.

According to the World Bank, “Poverty is hunger. Poverty is lack of shelter. Poverty is being sick and not being able to see a doctor. Poverty is not having access to school and not knowing how to read. Poverty is not having a job, is fear for the future, living one day at a time. Poverty is losing a child to illness brought about by unclean water. Poverty is powerlessness, lack of representation and freedom”. (World Bank: 2005)

Poverty is defined as the absence of the minimum standard of living which possibly is enough food, water, and shelter for survival. However, the concept of poverty has different forms, i.e. low income, low expenditure, no access to the resources. Poverty has many faces, such as hunger, lack of shelter, being sick and not being able to see a doctor and go to school, not having a job, fear of the future and lack of basic needs such as food, etc. Poverty has many features; changing from place to place and across time, and, has been described in many ways (World Bank, 2006). Precisely “poverty is defined as a state in which a person or community lacks the financial resources to enjoy a minimum standard of life and wellbeing that is considered acceptable in society” (Government of Pakistan, 2014).

Poverty is a complex phenomenon based on a network of interlocking economic, environmental, social, political, and demographic factors. There has been and continues to be – much debate about how poverty should be defined and measured, as it has been perceived differently by

20 | P a g e

economists, sociologists, politicians and development thinkers alike. The conventionally poverty measurement method have focused mainly on income/consumption expenditure as the criteria to measure poverty In economic term poverty has defined as a person living under one dollar per day or very low (for country per capita income under one dollar per day) is considered to be poor or living under the poverty line (UNDP 2006). In human terms, poverty means being poor living in pathetic conditions, in which people die of extreme hunger, malnutrition, and starvation (Arif, 2006). In social terms Poverty means when a country, region or household breeds all types of socially unacceptable behaviors like drug addiction, crime, prostitution, violence in a family or in a community and terrorism, all of which degrade human self-respect, moral and social values of the society as a whole, when more and more people in the community become intolerant of each other and are rude towards each other in their day to day life (Aslam, 2004). In political terms, a country, a region or a group of people is poor, which are dependent on more powerful groups or individuals in order to express their own rights or choices. In environmental terms, poverty destroys the living environment not only of those who live in poverty, but of all other human and non-human species that depend on the same resources and the ecosystem on which those living poverty depend and survive upon. People living in poverty cannot change their behavior easily not only because of lack of resources, but also because of the lack of knowledge about their own surroundings and survival techniques, lack of education and illiteracy. More importantly, if they do not change their already marginalized living behaviors they might die. Thus, by destroying their own living environment, the poor in reality are destroying their own resources on which they survive in the long run (Amjad and Kemal, 1997).

In general terms poverty is defined as living below the standards determined by the norms of a society, i.e., lack of sufficient resources necessary to maintain a minimally adequate standard of living and/or lack of money to purchase the essentials of life; especially, food, shelter, and clothing. Theorists were not uniform in approaching poverty and its parameters; hence its definition varies from model to model. For example, Ravallion (1992) thinks that poverty exists in a society where a person (s) does not attain a certain level of material well-being required to constitute a reasonable minimum by the standards of the society, and this definition is sociological in origin. Another universal definition comes from the World Health Organization, which measures poverty by the nutritional needs and fulfillment of a person. However, the

21 | P a g e

World Bank, an internal donor Agency, relates poverty to the issue of income, i.e., a person earning less than one dollar a day is declared as poor. In short, Poverty has many dimensions, among them the most important dimension is “consumption poverty” extent to which the consumption of a family or household falls below the poverty line. The poverty line is the minimum acceptable standard of private consumption in society. There is a considerable difference in the incidence of consumption poverty between urban and rural areas of Pakistan and it differs by region. Rural areas comprise 74 percent of the country's poor, though they have about 70 percent of the total population (World Bank, 2005).

1.2 Background of the study: Poverty is the incapability of individuals to satisfy their basic needs. It is a global problem and based on the definition of 1.90$ a day [for details see Ferreira F.H.G et.al (2016)] about 13 percent population of the world was unable to meet basic needs of life [World Development Indicators (2016)] Empirical analysis of poverty has always been an area of interest for economists. Initial work on the measurement of poverty merely focused on shortfall of consumption1 as a measure of poverty. Poverty is one of the greatest issues in the world, and Pakistan has been encountering it since the inception of its birth. If we check the latest estimates related with the poverty situation in Pakistan it is concluded that More than one-third of the population is living below the poverty line, it is close to 38 percent of the population (74 million approximately) was poor during the year 2015/16 which means that these people in Pakistan are those who do not get 2350 calories a day (UNDP, 2016). The pace of poverty reduction and human development has been much slower than the pace of economic growth in Pakistan. Despite impressive economic growth, around one fourth of the population is living below the poverty line. There are two major factors that restrict the poor in Pakistan to benefit from rising economic growth. First, lack of human capital in terms of education, training and health. Second, the poor are not provided with enough income earning opportunities in terms of jobs. The government approach has been welfare oriented rather than empowerment of the poor. No doubt, the long term solution to poverty in Pakistan lies only in accelerated human development along with adequate employment opportunities. Realizing the significance of poverty alleviation as not an end in itself but also as a critical factor for sustaining future economic growth, the government

1 Consumption requirements mainly comprises of food, shelter and clothing. 22 | P a g e

of Pakistan has been showing an increasing commitment to reduce poverty. There are many factors and forces responsible for this social evil. It can be attributed to multiple causes, such as poor natural and human resources, weak infrastructures, lack of opportunities, and non- availability of basic services such as education, health, lending and borrowing facilities, etc. Poverty is an important, universal human problem. This is evident from its position on the world development agenda (for instance, the Millennium Development Goals). To ensure credible, effective action in addressing in poverty and inequality, information on the poverty situation is required on a regular basis. Information of this type is key to the policy development cycle when evidence -based decision-making is practised in setting poverty reduction strategies. Poverty reduction is one of the fundamental challenges faced by Pakistan and they refer to many factors and forces which have been instrumental in deteriorating the situation in the country.(Haq and Bhutti , 2002). The government of Pakistan has introduced many poverty alleviation programs to reduce poverty. However, the level of poverty is still very high among rural households in Pakistan.

Many theorists believed that the success and failure of any poverty alleviation program depend to a large extent on a better understanding of the policies-focused questions. For example, (i) what proportion of the people are poor? (ii) How far are the poor from the poverty line? (iii) What is the gap between the average poor and the core poor and (iv) what are the main determinants of poverty in the given society? The possible answers to these questions are required for several significant reasons. First, it would help to identify who the poor are, where they live, and why they are poor. Therefore, the answers to these questions can help the policymakers in alleviation poverty incidence among rural households.

To minimize poverty is always a matter of serious concern for all the countries of the world. There are a lot of reasons and elements which decides as to which things the poverty belongs. Poverty can be assigned by many factors like weak position of infrastructure, lack of natural resources, lack of opportunities, poor human resources, lack of easy borrowing and lending facilities and unavailability of very basic services of human capital development e.g. education. However, there are no such substantial data available regarding the poverty of Jhang District. In most of the surveys, Jhang District is considered as sectarianism based area. The analysis of

23 | P a g e

poverty can help policy makers to formulate and design policies more effectively. In addition, the findings may be used as a “launching pad” for developmental activities in the area on sustainable grounds.

The concept of poverty is not easy to address, but there are certain factors on the basis of which the different theorists have defined poverty. Several scholars have defined poverty on the basis of the standard poverty line i.e 1.25 dollar per day,(for 2005) and 1.90 dollar per day (for 2011) called absolute poverty; some philosophers have defined it on the basis of calorie intake per day, called food poverty, and many others have defined poverty as having no freedom, no rights. However, almost all the scholars agreed to the statement that absolute poverty (consumption approach2) is a more appropriate measure of poverty in the developing nations, therefore it is necessary to estimate and analyze the absolute poverty in developing nations where the majority of the population lives in rural areas and have limited access to resources. Ahmad and Sikander (2008), has estimated and expressed the poverty on the basis of food, income, assets, consumption, capability, and well-being in Bangladesh. They reported that the capability and well-poverty are greater than food poverty in Bangladesh. As Pakistan is a purely agricultural country and the majority of the population (60.78%) are living in the rural areas of the country. Therefore, the present study used the absolute poverty (consumption approach) to estimate the level of poverty and correlates in the Jhang District. These estimates are based on the survey results from the different areas of Jhang District.

Jhang district is located in the Sahiwal division, which produced a lot of notable people. The first and only noble laureate of Pakistan was brought up in the Jhang district. Dr. Abdus Salam was buried in the Rabwa. Globally known Sufi saint, Hazrat Sultan Bahu was from Jhang and presently the wide religion figure, Dr. Qadri is also from Jhang. Nation’s two most leading political entities are from this region, namely Dr. Abida Hussain and Mukhdoom Faysal Saleh Hayat. World leading elite umpire of present Aleem Dar is also from Jhang; The Jhang district is geographically small but is very well known for the sectarian movements all over Pakistan. Many sectarian and terrorist organizations are headquartered in the district. Lashkar-e-Jhangvi is a well- known terrorist organization that was founded and operated by Riaz Basra inspired by the

2 Consumption poverty is the degree to which the consumptions of a family or household fall below the poverty line.

24 | P a g e

ideologies of Haq Nawaz Jhangvi the founder of Sipah-e- Sahaba, a sectarian organization (Roul, Animesh, 2005).

1.3 Understanding the Poverty Line To measure poverty in the world, the World Bank has used a standard of US $ 1 per day per adult equivalent as a reference. Therefore, people who have a daily income of less than US $ 1 are considered poor. (World Bank, 1990) By using this standard definition to measure poverty and by ignoring various factors, comparisons can be made between several regions or cities, as well as between several countries. In 1990, this standard measure of a dollar per day appeared for the first time by the World Bank and until now, this standard measure of poverty persisted as a poverty line to differentiate between poor and non-poor. In mid-2008, the World Bank issued a new standard for the poverty line after the adjusted inflation from 1980 (The Economist, 2008). To adjust inflation, in 2005, a poverty line would reach almost $ 1.45. This line of poverty again evaluated by (Ravallion et al, 2009), applying the same method, but with better data, instead of making the idea of the poverty line excessive. For the national poverty line the data was taken from 75 of the national analysis and from that 15 lowest were selected which includes 13 Sub- Sahara African countries, Nepal and Tajikistan. On average, they found an estimate of approximately $ 1.25 per day per adult equivalent as the poverty line using the purchasing power parity terms in 2005. After this estimation world bank again estimated for getting Purchasing power parity of 2011.( Jolliffe et al. ,2014) The World Bank’s international poverty line of $1.90/day, at 2011 purchasing power parity, is based on a collection of national poverty lines, which were originally used to set the international poverty line of $1.25/day at 2005 purchasing power parity. (Ferreira et al. 2016)

In 1990, the research wing of World Bank introduced the dollar-a-day international poverty line which reflects the standards of absolute poverty in the poorest countries of world and was based on the purchasing power parity exchange rates (PPPs) of 1985. The purchasing power parity exchange rates are periodically revised and with the revision of these rates, international poverty line is also adjusted. For instance, a new set of PPPs was published in 1993, the line changed to $1.08 per day. PPPs were revised again in 2005, and the poverty line was accordingly up scaled

25 | P a g e

to $1.25. Latest revision of these rates took place in 2011 which yielded and international poverty line of 1.90 $ a day. [For details see Ferreira F.H.G et.al (2016)]

1.4 Broad Problem Area Poverty is a global issue and remains a subject of much concern to observers and researchers alike. More importantly, how people perceive the causes of poverty in the context of culture and value system in which they live, have a great impact on poverty. Poverty research goes back similarly as late 1800s. Poverty is a complex phenomenon impacted by an expansive number of components which can be examined from a wide range of points of view. Till the word poverty is not extinct from the planet, there would be endeavors to clarify it. Poverty has got tight links with income crisis. The fundamental reason is that one is said to be poor, if he does not have the earning or the other financial assets expected to keep up a decent quality of life. The valuation of poverty can be segregated into three classifications - construction of poverty profile (who the poor are), reasons for poverty (why individuals/Households are poor) and poverty alleviation strategies (what to do about poverty). Absolutely financial methodologies have neglected to precisely catch the level of poverty experienced. This explained the definition of poverty in expressions of complete scarcity that actually give the impression in order to make respectable intellect. In such cases, when people do not want to see basic needs such as food, shelter and clothing, which they most want to, lead a respectable life so that they can escape from the poverty line. It is not wrong to say that scarceness everywhere is a danger to affluence all over the place. Around 60% of the present inhabitants of Pakistan, that actually lives in villages. Similarly on the other hand, with respect to analysis and as well as exploration regarding this topic, the ratio of poverty has been increased approximately from 30 percent to around 40 percent for the duration of the last few years. Some interesting can be observed with this figure, for an illustration, it means that 40 percent of the population of the country is receiving their source of revenue lower than the ratio of poverty. Moreover, such type of situation usually societies are disheartened, and the main reason is that they are not getting the sufficient amount of their basic necessities regarding their personal life. In order to spend a happy and successful life, correct education and prescription or medications are much-desired factors. They make them self-able in order to support their family in such sort of cases. As well as use those as receiving influences form the duration of their early ages. Although, poverty is a difficult situation and

26 | P a g e

similarly has a durable interrelated connection with economics based factors. Particularly in regards to the population and its inhabitants, Pakistan is considered as the sixth largest country in the world. The quality of life can be measured with the help of its ongoing development because it shows that in what ways their inhabitants are living their lives. A successful life actually shows its meaning towards the improved education, well-being and public health amenities and most important as well as enhanced future accompanying for fresh inhabitants. The rustic areas are comparatively poor in the comparison of urban zones and roughly 33% of the inhabitants are spending their lives in the range of poverty. The degree and amount of poverty in any type of country actually depend upon two sorts of factors, these two major factors are, the usual range of domestic returns, and the second factor which is the amount of dissimilarity in its range of spreading. At this point, revenue means the least possible revenue that is actually essential in order to buy those substances that the social order deliberates indispensable to preserve sensible existing, the smallest range that is mandatory in order to escape existing environments to spend a successful and healthier life. In some other way, it is generally supposed that lesser the normal ratio of income or further the inadequate circulation of the prosperity and as well as the larger the prevalence of poverty. Pakistan is the traditional challenging place for the measurement purpose of the singularity where actually the range of domestic revenue is not enough for its people, be existent serious level of dissimilarity in financial incomes of existing, promptly accumulative inhabitants along with negligible level of methodological and specialized sort of education, frugality with extraordinary rate of price increases and joblessness.

The inadequacy of the quality of education in our country is not able to deal with the challenges of the 21st century (Ivanic & Martin, 2008). Because of the poverty, the quality of education is not affordable for the people for their children. Moreover, the negligence of government is more spoiling the condition. Nevertheless, in order to enhance the literacy rate, the various government are taking several steps, the literacy rate is at 56% for more than a decade. On account of low investment, the schools which are run by government lacks very basic facilities like water, proper classrooms, sanitation facilities, and electricity. The job done by the private sector is quite admirable in this regard. The issue which arises in this regard is the money making the objective of the sector. This approach makes education far from the reach of the poor. The completion rate of primary in Pakistan is given by UNESCO which is 47% males and 33.8%

27 | P a g e

in females, which depicts that in the sixth largest nation of the world the people, is not able to get the basic education.

The rate of growth in Pakistan is quite high and is one of the highest among other countries. Since the independence of the country, the population of Pakistan has increased more than three times. The country has almost 180 million populations. The increase in population has been a major cause of the governments of the country. The governments are facing the troubles in controlling the increasing population due to limited resources of the nation. The economic disparity among the people of Pakistan is very high. This economic disparity is increasing the rate of suicide in the country of the poor, while the rich of the nation are piling up more and more money. Political unpredictability is a circumstance whenever the hesitation surrounded by the administration organization increase because of certain elementary reasons and it in due course which actually ends up the government of the present state. Frequent interventions of the army have not ever assumed consensus a fair unintended factor in order to embellishment in a country. The leaders who are associated with the political activities are also answerable for this dilemma and as well as issues which are faced by the people of that country. As well as, on the other hand regarding this particular situation, these active politicians have always strained in order to accomplish and realize as a significant factor their conferred benefits in the apparel of policymaking parties as well. Every so often, it has only to some extent happened or has been terminated completely which is actually as a result of the conditions adjoining the instance whenever the statement effort was completed. On the other hand, frequently, it has only in some measure happened or similarly as well as further it has been terminated exclusively as a result of the situations adjoining the instance as soon as the statement endeavor was completed by that particular organization. This study can help improve the design of poverty alleviation programs and determine the ways in which resources can be distributed so as to maximize poverty reduction. Similarly, this study can help with information for targeting programs within communities in view of the fact that the poorest of the poor need to be identified and specifically supported. Future research can be made focusing on the severity of poverty by looking at household structures of poor households by comparing male and female-headed households.

28 | P a g e

1.5 Specific problem statement.

The purpose of the doctoral research is to assess the level of poverty in Jhang District (Punjab). The assessment provides an overview of the poverty situation in the target areas as well as the determinants or correlates of the poverty level in the Jhang district. The main concern of the study is to empirically investigate the determinants of poverty using consumption poverty approach in Jhang District.

1.5.1 Research Questions • How many households are below the poverty line?

• How many of the households or people are extremely poor?

• How many of the people or households are radically poor?

• How the demographic characteristics, for example occupation, education of the household head, age, the ratio of male/female, size of the household, dependency ratio etc. of the household affect poverty?

• How the social characteristics (house structure, person per room, etc.) of the household affect poverty?

• How socio-economic characteristics (Total assets, earner per household, agriculture land, livestock etc.) of the household affect poverty?

1.5.2 Hypothesis of the study The hypotheses of the study are: 1. Poverty is significantly depended on household characteristics such as household size, household type, dependency ratio, and household assets. 2. The poverty level is considerably depended on respondent characteristics such as educational status, gender, age, and tenancy and marital status. 3. The poverty level largely depends on agro-economic indicators: land Possession and low productivity.

29 | P a g e

4. Poverty significantly depends on the female-male ratio: the higher the ratio of female- male, the higher the poverty level.

1.5.3 Research Objectives ➢ To estimate the magnitude (incidence, depth, and severity) of Poverty across the Jhang district. Also in all four sub-districts of Jhang district separately. ➢ To analyze and document the poverty correlates of poor households on the basis of various socio-economic, demographic and social variables ➢ To recommend some policy lessons on the basis of finding for the poverty reduction in the Jhang district.

1.6 Justification for conducting the research. The above discussions have clearly indicated that poverty is in its very complete meanings, and while understanding what poverty means we are recognized by its existence. It is the first step in the fight against poverty to combat the hardships it creates. It should be a matter of great concern for the relief of something to be first identified then, after proper identification, a goal or target should be set in order to properly and efficiently combat it. The same is with the case of poverty, that it should be fully recognized as a first step and after it is identified in a specific or target area of concern. The setting of target intervention should be made after a clear understanding and identification of the poor. This understanding and identification bring great awareness of the consequences of poverty, and this awareness of poverty inspires activism.

Second, the world always needs new ways to fight poverty, which will be sustainable and effective. Therefore, being a student of social science, it is my priority to search and collects the related facts of poverty in the country that will be the basis for combating this evil. The study is not a new idea; however, it identifies the major determinants of rural poverty which can help in the poverty reduction in Pakistan. For any successful poverty alleviation program, the information is needed: (i) what proportion of the people are poor? (ii) How far are the poor from the poverty line? (iii) What is the gap between the average poor and the core poor and (iv) what are the determinants of poverty in the given society? Once these questions are answered correctly then one will be able to know who the poor are, where they live, and why they are poor. By

30 | P a g e

examining the incidence, depth, severity, and correlates of poverty in Jhang District, this study will provide answers to these questions in the context of the Jhang District.

Several researchers assessed the incidence and determinants of poverty in the province of FATA, Punjab, and Sindh, but they did not use any moderate methodology to analyze factors and levels of poverty at a district level. Few studies have analyzed the poverty at a district level; however they have used a small sample frame covering only a village or sub-tehsil. For example, Lawal (2009) investigated the correlates and level of poverty in Badin & Sanghar districts using an income regression model on a small sample size 320 households. However, the current study is different from the already existing literature in many ways. First, this study used a large sample size (1000 households) covering all 4 sub-districts of Jhang district which is likely to give a real picture of poverty in the country and specially in Punjab Province because of its unique geographical and demographic characteristics. Secondly, the study used both income regression and logistic models to investigate the correlates of poverty in the Jhang district. The justification for conducting this research in the Jhang District is that, it is ranked 76 in MPI Index of district ranking in Pakistan and include in those districts who are lacking social amenities in Pakistan, fail to implement poverty reduction programmes properly and the issues of empirical analysis of the determinants of rural poverty has not been done properly.

Jhang district is one of the poorest districts in Punjab province and according to the ranking of those districts lacking social amenities in Pakistan the incidence of rural poverty is very high. More than half of the population lives below the poverty line. Apart from humanitarian considerations, the high incidence of poverty becomes a crucial social factor for the governance of civil society. The average literacy rate is around 20 percent of the total population. Poor facilities about roads, power, health and education add up to lack of access to a range of essential services needed to conduct life beyond survival. The situation requires emergency rather than development, what is being seen in the area is the collapse of communities, a situation that requires mitigation before any serious development strategy can be implemented. The failure to adequately implement poverty reduction programmes designed by the government bilateral, multilateral agencies and private sector initiatives can be seen as the precursor to most of the present causes of poverty in both rural and urban areas of Pakistan. In view of the above

31 | P a g e

scenario, following are the justification for conducting the present research. Apart from this good number of economists had conducted research on poverty in different areas of Pakistan but using a small sample but in my own case I am using a bigger sample of 1000 covering all 04 sub districts of Jhang.

1.7 Significance of the study Poverty alleviation is always one of the keys and urgent challenges faced by Pakistan, and the numerous forces and factors are responsible for determining the incidence of poverty in the rural areas of the country. The identification of the poor and factors determining the poverty level is essential for successful intervention with the aim of reducing or alleviating the poverty. For alleviation of poverty in any society or region, identification of poor and how poor they are is essential. In this context, this study will help government, non-governmental organizations and other donor agencies which are working for alleviating poverty. The study also useful for the international donor agencies, who are working on the poverty reduction in the various . It will provide sufficient material and powerful background of socioeconomic to various government and non-government organizations and policymakers before initiating any developmental projects and schemes. Specifically, the information discussed in this dissertation will use and provide enough material and strong socioeconomic background in making policies and plans for the reduction of poverty in the Jhnag district. Planers and policymakers from any agency, whether government or non-government, but it will be useful to everyone. The work will give them enough material and strong socio-economic background that will help them in starting any development scheme and policies for reducing poverty in the specified district. More importantly, this study will be an additive benefit for researchers in the field of social sciences. It would provide them with an active source of information and motivate them to begin research on subjects related to poverty in Pakistan. Finally, the piece of writing will help me enormously in the addition of information, and will enhance my knowledge of the developmental sector of the economy.

1.8 Poverty Situation in Pakistan

32 | P a g e

According to United Nations Development Program, the one-third of the population is living below the poverty line which means that about 60 million people in Pakistan are those who do not get 2350 calories a day (UNDP, 2013). During the period of 2000 to 2007, the Government of Pakistan proposed one trillion rupees for poverty alleviation measures, which reduced about 24% of poverty in 2006. Since the beginning of 2009, poverty has again reached 37% of the population. The HDI value of Pakistan is 0.504 as compared to the average human development index value of 0.548 and against the world’s average HDI value of 0.682 (UNDP, 2011). The percentage of the population which is living below the line of poverty has decreased from 34.46% in the year of 2001 to 23.9% in 2005, which shows a decrease in 10.6 percent of points. This creates a decrease accordingly to the ratio of headcount. In sheer numbers, the number of poor people has fallen from 49.23 million to 36.45 million from 2001 to 2005. The population living in rural areas of the country below the line of poverty has decreased from 39.26% to 28.10% whereas the people in the urban area living below the poverty line have decreased from 22.69% to 14.9%. It clearly shows that poverty in rural regions has fallen 11.6% points and 7.79% points in urban regions (UNDP, 2000). A close look at data on poverty levels and trends in Pakistan over the past five decades leads to two broad conclusions: firstly, poverty reduction has not been sustainable rather than it has fluctuated remarkably; and secondly, a large proportion of the population has been found under the poverty line, and any micro/ macro shock (positive or negative) would likely lead them into poverty or pull them out of poverty.

The inhabitants in rural areas of Pakistan are suffering from poverty to much more extent then their urban counterparts the reasons for this can easily be seen in agricultural census such as this one below.

The HDI (Human Development Index) value of Pakistan in the year 2012 is 0.515 which is in the category of low human development index. It positions Pakistan at the rank of 146th among 187 (UNDP, 2013). The same rank was of Bangladesh in the year 1980. It is a matter of fact that Pakistan and Bangladesh shared the same position the years of 2012 and 1980. With the start of 2009, the poverty line once again merged 37% of the population. Before the year of 2008, the HDI’s value of Pakistan increased 53% i.e. 0.337-0.515 or an increment of 1.3 percent annually as average (UNDP, 2013). During the period of 2000 to 2007 Pakistan government has proposed

33 | P a g e

one trillion rupees for the poverty reduction activities which reduced about 24 percent poverty in 2006. The latest estimate of poverty which is based on the one dollar a day approach indicate that 17% of people live under the line of poverty in 2005. On the other hand on the basis of two dollar a day approach the numbers goes to 73.6%, which is the two third of the total population According to United Nations Development Program the one third of the population is living below the poverty line which means that about 60 million people in Pakistan are those they do not get 2350 calories a day (UNDP, 2013). A survey by FAO in 2005 says that about 20% of the population i.e. 29.3 million are undernourished and 32.6% of the people are below the poverty line during 2000 to 2002 (FAO, 2005).

The poverty incidence in Pakistan differs from province to province (International Monetary Fund, 2010). Poverty is considered widely in mountainous areas, where small communities are living, isolated and scattered. The low rainfall and coastal belt areas also try for having a high poverty’s incidence. The fragile ecosystem and rough land in these areas are creating difficulty in farming, whereas deficiency to access services and markets takes part to further poverty in the local population. The main reason for the rural poverty in Pakistan is the unequal distribution of resources, mainly means of approach to water and land. As an outcome, the direct benefits of the income from the production of crops have a propensity to originate in a small part of the population (Weiss & Haider, 2006). The spending by the state is comparatively more than earning.

The government of Pakistan conducts a household integrated economic survey on annual basis through Pakistan Bureau of Statistics and their latest survey reported that According to estimates (Government of Pakistan, 2016) it is close to 38 percent of the population (74 million approximately ) was poor during the year 2015 16.

Table No. 1.1: Latest Official Poverty indices, 2015-16 Estimates of Poverty Indices 2015-16 [Percentage of Population] Pakistan Urban Rural Head Count Index 37.9 31.9 41.2 (Incidence) Poverty Gap Index 8.2% 6.7% 9% (Severity)

34 | P a g e

FGT2 Index 2.5% 2.1% 2.8% (Depth) Ref: Government of Pakistan (2016), “Pakistan Economic Survey (2015-16)”, Ministry of Finance As per the above table related with the officially disclosed poverty estimates it is concluded that overall, 38 percent of the population was poor during the year 2015-16. The incidence, depth and severity of urban poverty are relatively lower as compared to rural areas.

Table 1.2: Trends in the Poverty incidence of Pakistan Trends in Poverty Incidence [Percentage of Population living below the Poverty Line] 1987-88 1996-97 1998-99 2001-02 2004-05 2010-11 2015-16 28 30 23 33 30 38 38 Pakistan [2.4] [3.6] [3.3] [-3.0] [4.4] [0.0] 25 25 19 30 28 34 32 Urban [3.5] [0.0] [6.7] [-2.2] [3.6] [-1.2] 30 32 26 35 31 39 41 Rural [1.7] [3.3] [3.1] [-3.8] [4.3] [1.0] Ref: Government of Pakistan (2016), “Pakistan Economic Survey (2015-16)”, Ministry of Finance

The above table reveals a relatively higher incidence in rural poverty during the period 1987-88 and 2015-16. Comparison of 2001-02 and 2004-05, shows a decline of 3 percentage point in poverty incidence. Moreover, the decline in urban poverty is relatively less than rural poverty. Rural poverty in this period has dropped with an annual growth rate of 4 percent, while the decline is about 2 percent in the case of urban poverty incidence. On the contrary, during 2004- 05 and 2010-11, estimated poverty incidences are showing again an upward trend. Further, the rate of growth in rural poverty in this period is relatively higher (4.3 percent) than the increase in urban poverty incidence (3.6 percent).

1.9 Reports of Different Organizations on Pakistan’s Poverty Situation UNDP reported that the latest estimate of poverty which is based on the one dollar a day approach indicate that 17% of people live under the line of poverty in 2011. On the hand, on the basis of two dollars, a day approach the numbers goes to 73.6%, which is the two third of the total population (UNDP, 2013).

35 | P a g e

The World Bank estimates in 2012 that about 40% of the one hundred countries are highly affected by poverty. Amongst the worst poverty stricken forty-three countries of the world, Pakistan is included. It is far-flung in Pakistan and exists much in the rural parts of the country. In Pakistan, about two-thirds of the population lives in the rural region. A large proportion of the population of rural areas depends on agriculture for earning. A lot of them do not even have basic necessities like health care, pure drinking water, education (World Bank Group, 2012).

A report of the Asian Development Bank in 2014 describes that poverty is increasing in Pakistan because of Pakistan’s internal situation, unequal distribution of income, rise in population, traditional mode of income generation, backwardness in agriculture, defense expenditure, involvement in unproductive activities and increase in the charges of utility, uncontrolled increase in population and lack of power sources.

Poverty position was discussed by SPDC with regard to provinces. According to a report, Baluchistan is the poorest province of the country of which 52 percent of people live below the line of poverty. In Sindh 33 percent of people are living below the poverty line. Then Khyber Pakhtunkhwa comes with 32 percent population below the poverty line. Punjab has 19 percent of the population below the poverty line. The report clearly states that three of the provinces of Pakistan are hugely affected by poverty and especially the rural areas and remote regions of the country. On the other side, Punjab has a much better position but it is also affected to some extent (SPDC, 2012).

According to the report of the Pakistan planning commission, in Pakistan, the poverty level has increased from 23.9 percent to 37.5 percent during the last three years i.e. from 2007 to 2011. The commission reported against the prime minister of Pakistan that 35.5 million people were living below the poverty line in 2005 as compared to the 64 million people in 2008. This increase in poverty would lead to an increase in the unemployment ratio in the country.

1.10 Limitation of the study The study was limited only to the one district which is Jhang District however here all four Sub district were covered. Due to Financial Constraint and time limitation Data were collected on the sampled basis. A sample size of 1000 was used for data collection.

36 | P a g e

1.11 Overview of Jhang District 3

1.11.1 Introduction The most prominent and well-known among all the province of Pakistan is Punjab, in which urban areas composed of 40% population i.e. approximately 38 million population. About 20 million which is the majority of the urban population lives in five of the large cities of Punjab, approximately 6.3 million of the urban population lives in the intermediate cities which have the population between the ranges of 250,000 to 1 million (Nazli et al 2008).

Jhang District is from one of the oldest subcontinent’s district and has been populated around 2000 BC when Jhang Sial was the name for its identification. Sargodha from the north, Gujranwala from the northeast, Toaba Tek and Faisalabad from the east, Muzaffargarh, and Khanewal from the south, Bhakkar and Layyah from the west, and Khushab from the northwest are bordering the Jhang District. (Government of Punjab, 2008).

The Jhang district is in a well-known province of Pakistan, Punjab. The Jhang is one of the districts of Pakistan which are stricken by the poverty and most of them are the rural residents of the district. The district lacks the agriculture facilities needed for prosperity and to meet the needs of the locals of the district. Jhang is situated beside the chiniot which has the headquarter of the Ahmeddiyah minority. The district is also known for its architecture on the wooden furniture and carved wooden products.

1.11.2 Administrative System Jhang became a city in 1292 AD as shown by its demographical record. During British rule, this city of Jhang has become tehsil. Fourteen UCs are present over there. The total area occupied by Jhang city is 28.27 Sq. Km. as researched by the urban unit, the total population of this city of Jhang was found to be 342,285 in 1998. The literacy rate of Jhang was 60.8% and 6.9 is the

3 More detail regarding History, Culture, demographics and administrative system can also be viewed through following URLs. http://uu.urbanunit.gov.pk/Documents/Publications/0/111.pdf https://www.punjab.gov.pk/jhang

37 | P a g e

average household size found in Jhang city. During 1981-98, the growth rate of Jhang was 2.41% reported by the district census.

There are 4 tehsils (administrative units) Athara Hazari, Jhang, Ahmad Pur Sial, and Shorkot in the district. The area mentioned i.e. Jhang constitute of the fauna and flora such as Kikar or acacia Arbica, Beri i.e. Zizyphusjajaba, Aak i.e. Calotropoishamiltonit, Jand trees i.e. Prosopisspicigera, van or Salvadoraabeoides, Shisham or Dalbergia, Karir or Capparisaphylla. All of these fauna and flora are found within the area. This is a very wide variety of fauna and flora present in this area. (Govt. of Punjab, 2009).

1.11.3 Sanitation System The management of solid waste and liquid waste services coverage are very poor in Jhang i.e. almost the same as in the other cities like Sargodha, Bahawalpur, Sahiwal and Rahim Yar Khan. There is a lack of basic disposal of both the wastes without any difference among them i.e. solid waste and the liquid waste and these wastes are being discarded as untreated to the nearby passages and there is dumping of solid wastes to the near city areas takes place. This creates pollution to a very high extent which leads to health problems in the city. This situation of solid waste will be become better whenever the three will be a construction of the landfill take place over there, as identification of the landfill site has done by the District and TMA Jhang at the Gojra Road which is about 12 Km away from City Centre (Anwar, Qureshi, Ali & Ahmad, 2004). This would definitely be a great step for a pollution free environment.

The supply of drinking water in this area is better even network coverage of water supply is less with a comparison to the other cities (35% city area). The Municipal area generally has fully sufficient groundwater. As like in other cities, Jhang city also has a lack of metered connections. This is another problem faced by the city and this matter should also look forward in future. The major issue of Jhang city is a lack of transport and this is because of its fast growth regarding urban and economics. This issue requires immediate concentration and there should be planning for present and future needs regarding urban transport services.

At present services of urban transport in the city of Jhang are same as having in other cities. For instance, Auto-rickshaws and the motorcycle rickshaws are in use as urban transport. As like other TMAs facing issues same as the case in TMA Jhang is there, issues like lack of

38 | P a g e

management skills, lack of technical skills, lack of commercial skills, capacity issues of both human and financial and on the whole overall in all matters about O and M which is having a low level of recognition and low priority. Location of Jhang provides good economic conditions within the national corridor. Jhang is very popular because of its so many arts and craft.

1.11.4 Land Distribution Entire zone of the whole district is around 8,809 square kilometers and its present inhabitant’s ratio is approximately more than three million. Similarly, there are around 10 urban localities and approximately 1,040 communities (Moazas) in the region, same as around 80 percent of the over- all inhabitants are regularly spending their lives in old-fashioned leading rustic zones. The external area of the region offers two types of well-defined topographic backgrounds. These topographical landscapes contain the sand ridges of Thal, which are a desert in the west of the river of Jhelum and an amusing productive plain in the east of the Chenab and as well as the Jhelum. Similarly, overflows of water are a collective occurrence in the district of Jhang as state’s two enormous rivers Jhelum and Chenab flow of water from side to side its whole length. The land beside the series of the waterways is swamped very nearly each and every year and lands away from the rivers also targeted by high floods in the past years. Numerous flood damages have ensued in last few decades disturbing expiry fatalities, harm of unindustrialized crop, land as well as heifers, household obliteration, compensations to foundations corresponding to public buildings, roads, waterway and proper water system and interrupted transference and indispensable civic facilities such as blockage of road due to the floodwater.

1.11.5 Current Economic situation of Jhang The economic activities associated with agriculture are most prominent in this district along with some developments in the small service industry and textile industry. There have been a controlled by native feudal over other resources and on the local population of rural areas because of possessing the large holdings of agricultural land where the people of village work as labor during sowing and harvesting season. The large numbers of houses in the village are in the possession of local people of the district, which had contributed further in complete dependence of villagers on feudal for earning their living even at expense of long life exploitations. The highest ratio of lack of opportunities, unemployment, and social stratification had increased the level of poverty and immigration of people of rural areas towards cities. Gender inequality and

39 | P a g e

issues are reflected truly in the old statistics and reflected the women’s low status and other problems of rural areas in District Jhang. The occurrences of damages because of the consistent flood in the rural areas had raised the vulnerability’s elements in the pattern of existing living. The majority area of Jhang is cultivated except some areas of Rabwah and Chenab Nagar where some of the hill regions are present. Some portion of is located in western areas of district Jhang, which runs from to the bank of River Jhelum in the Bhakkar and Khushab district. (The Urban Unit P & D Department, Punjab, 2016 ).

1.11.6 Geographic Condition In the sub-continent, Jhang is the oldest District and have been developed around 2000 BC at that time it was called “Jhangi Sial”. Jhang is surrounded by Toba Tek Singh district and Faisalabad district to the East, Sargodha district to the North, Bhakkar district and Layyah district to the North West, , and Khanewal district to the South (Cheema, Khalid & Patnam, 2008). The flora and fauna of the area comprise of Karir i.e. Capparisaphylla, Kikar i.e. Acacia arbica, Jand trees i.e. Prosopisspicigera, Beri i.e. Zizyphusjajaba, Aak i.e. Calotropoishamiltonit, Van i.e. Salvadoraabeoides, and Shisham i.e. Dalbergia are present within the area. (Disaster Risk Management Authority, 2009)

1.11.7 Demographics of Jhang District. Jhang city is located on the river Chenab on its left bank which is at a distance of around 11 kilometers from its bed. It is situated between East longitude 72 degrees 18’ and 72 degrees 22’ while North latitude 31 degrees 15’ and 31 degrees 17’. The railway line which passes through the town linking Sargodha with Shorkot Cantt. Toba Tek Singh and Gojra are the cities which are situated about a distance of 40km from the Jhang city while Faisalabad city is situated about 76km and Chiniot is situated about 86km distance from the Jhang city (Disaster Risk Management Authority, 2009)

1.11.8 Communication Linkages District Headquarters Hospital Jhang is adjoined with the metalled road along with the every Tehsil Headquarter Hospital. Eighty percent BHUs and all RHCs have good access and link road. District Jhang is adjoined with Rawalpindi, Sargodha, Karachi, & Multan through Shorkot Cantt 40 | P a g e

& Shaeenabad railway lines. Jhang is linked by railway or road to some of the main cities of Pakistan but still lacks the air services.

1.11.9 Topography Jhang District has three different kinds of topographies. THALL, on the River Jhelum which contains a semi-desert condition and holds sand dunes run in a nearly uniform direction and is changed with quite good soil. Jhang is also linked by Thal desert to other more districts like Khushab, Bhakkar, Layyah, Muzaffargarh. Those are the fertile plain and are found in the east of Chenab and river Jhelum. It’s a Sandal Bar’s part. Usually, the soil is very fertile. The areas that are lying low with the River Chenab and Jhelum are confronted with flood every year where the cultivation is nearly completely Sailabi, (Government of Punjab, 2008).

1.11.10 Source of Livelihood The commonest livelihood source of there is agriculture which is around seventy percent; whereas rest of thirty percent of livelihood is of different resources.

1.11.11 Area The total area of Jhang District is six thousand one hundred and seventy-nine square kilometers.

1.11.12 Agriculture Nearly all the area of a district is cultivable except the area of north near Chenab Nagar and Rabwah where the land has been turned rocky and approached the Kirana Hills. The large area of cultivable land has been contributed a lot to make agriculture a dominant occupation of the people Jhang. Twice a year, their fields are cultivated. Nevertheless, due to the subsistence holding of land people aren’t self-adequate in agriculture. Majority of crops are utilized for the use of domestic purpose, except seasonally sold vegetables. The main crops are Bare, Wheat, Maize, Mustard, Sugarcane, and Jowar. Vegetables like onion and tomato are also cultivated in the area. Jhang is based on purely agricultural feudalistic society, in comparison to other areas of Punjab agriculture is the main source of the income and the employment. Regarding the total area of land, 85% of the area is well irrigated. The main crops are Wheat and Cotton and the rest of the crops are oilseeds, corn, sugarcane, fruits, vegetables, and rice. Nevertheless, few peoples are transforming their resources toward livestock and poultry.(Government of Punjab ,2008),

41 | P a g e

1.11.13 Environmental Conditions The climate of the city is hot and dry in summer and cold in winter. The summer season is lengthy which begins in April and continues till October for about seven months. The hottest months are May, June and July. Maximum mean temperature during summer is 46-degree centigrade. Whereas it is 26 degrees centigrade in winter. Mean temperature during summer and winter seasons are 41degree centigrade and 21.9degree centigrade respectively. Whereas the minimum mean temperature is 23degree centigrade and 2-degree centigrade during summer and winter seasons respectively. Dust storms are quite common in summer carrying to think clouds of dust with them. But on the whole they are not much destructive. They begin in April and continue until the Mon-soon set in. Winds direction in Jhang keeps changing. Still the predominant wind direction during the winter season is North-West to South –East and during summer it is South-East to North – West. Major issues related to Environment include; Improper collection & Disposal of Solid Waste, Choking of Sewerage system , Mixing of Untreated Waste Water with Fresh Surface Water , Encroachment / Illegal Constructions (plazas, Commercial Markets, Housing Schemes, Industries etc.), Use of wastewater for irrigation purpose without treatment , Solid Waste Heaps and Waste Water Ponds , Smoke & Noise of Vehicles , Unplanned Urbanization, Commercialization and Industrialization without Zoning (The Urban Unit P & D Department, Punjab,2016 ).

42 | P a g e

1.11.14 Map of the District

43 | P a g e

Chapter-2 Literature review

Overall poverty specially in the developing countries has discussed earlier in literature and many authors have discussed this issue in detail in their studies like for Pakistan Khan, et al, (2013); (Khan et al., 2014) ; Arif and Farooq (2012); Alam and Hussain (2013); Akhtar et al., (2007); Sikandra and Ahmad, (2008); Cheema and Naseer (2013); Awan et. al. (2008) , Lawal et al. (2009) , Chaudhry & Rehman (2009) , Ali & Nishat (2010) , in South Asia , Susheela et al., (2000) , Mehta & Shah, (2001) , Bourguignon and Chakravarty, (2002) and for some other countries Lawal et al. (2008) , Minasyan & Mkrtchyan, ( 2005) , Kaplinsky, 2013 , Pogge, (2008) , Ivanic & Martin, (2008). They all discussed the poverty issue in detail and find the relationship of poverty with different correlates in their different studies. All of the studies have used different poverty definitions for poverty and its measurement and finding the determinants of it by using different approaches. Apart from these researches many other agencies has also highlighted this issued in detail and provided different estimates in almost all regions. In Pakistan The HDI (Human Development Index) value of Pakistan in the year 2012 is 0.515 which is in the category of low human development index. It positions Pakistan at the rank of 146th among 187 (UNDP, 2013). The same rank was of Bangladesh in the year of 1980. It is a matter of fact that Pakistan and Bangladesh shared the same position the years of 2012 and 1980. With the start of 2009, the poverty line once again merged 37% of the population. Before the year of 2008, the HDI’s value of Pakistan increased 53% i.e. 0.337-0.515 or an increment of 1.3 percent annually as average (UNDP, 2013). The detail of Poverty that included its definition, concept , its different estimates in previous years , and its correlates are discussed in detail as below.

2.1 Definition and Concept Poverty is derived from the Latin. In Latin, there is a word called “Pauper” which means poor. In Latin, the word means a position of lacking basic human necessities such as clothing, availability of health care, shelter and clean water. A different definition of poverty says it as a condition of fewer resources and income as compared to the other members of the society, city or even a country. According to the World Bank, an individual is regarded to be poor if the household

44 | P a g e

extent of the income is lower than the bottom level which is required to attain the basic needs. The bottom level is regarded as the line of poverty (World Bank, 1990). In a global scenario, it can be compared to the whole world.

There has often been a debate regarding what constitutes poverty and, as a result, there are many definitions of poverty. One of the most widely accepted definitions of poverty comes from the United Nations (UN). According to the UN (1998), “Fundamentally, poverty is a denial of choices and opportunities, a violation of human dignity. It means lack of basic capacity to participate effectively in society. It means not having enough to feed and clothe a family, not having a school or clinic to go to; not having the land on which to grow one’s food or a job to earn one’s living, not having access to credit. It means insecurity, powerlessness, and exclusion of individuals, households, and communities. It means susceptibility to violence, and it often implies living in marginal or fragile environments, without access to clean water or sanitation” (UN Statement, June 1998 – signed by the heads of all UN agencies). This is a multidimensional definition of poverty which covers nearly all areas of concern to poverty. The World Bank also offers another comprehensive definition of poverty. According to the World Bank (2000), "Poverty is pronounced deprivation in well-being and comprises many dimensions. It includes low incomes and the inability to acquire the basic goods and services necessary for survival with dignity. Poverty also encompasses low levels of health and education, poor access to clean water and sanitation, inadequate physical security, lack of voice, and insufficient capacity and opportunity to better one’s life." As most of the definitions of poverty are quite broad, there are many approaches to measure poverty. For the ease of measurement and comparison, I shall consider the income poverty and adopt the absolute approach of measuring poverty. The absolute approach sets a threshold level of income or consumption expenditure. This threshold is widely known as the poverty line in development literature. An individual or a family living with an income or consumption expenditure below the set poverty line is designated as poor with reference to the poverty line (Ravallion, 1992). Different countries have different national poverty lines. These differences in the national poverty lines make it difficult to compare and analyze the poverty statistics across countries. Hence, in order to be able to objectively calculate and compare the poverty rates across countries, the World Bank has introduced the international poverty line. In 2008, the World Bank came out with a revised figure of $1.25 per day adjusted for 2005 purchasing-power- parity (PPP) as the international poverty line, which was previously

45 | P a g e

set at $1.00 per day (Ravallion, 2009). This means a person with a daily income or consumption less than $1.25 lives below the international poverty line. The percentage of population living below the poverty line is referred to as the poverty headcount ratio or simply the headcount ratio (UNDP, 2003). The same poverty line was re adjusted with PPP of 2011 and it became 1.90 USD. (World Bank, 2016). This approach is very popular because of its objectivity and simplicity. The concept of poverty has different forms i.e. low income, low expenditure, no access to the resources, no access to the justice, the absence of education, weak health conditions and lack of essential needs such as food style and refuge. Precisely poverty is defined as a state in which a person or community lacks the financial resources to enjoy a minimum standard of life and wellbeing that is considered acceptable in society (Government of Pakistan, 2014). Poverty has been categorized by many agencies differently. International organizations such as (World Bank, 1990,1995), (CRPR, 2002), (ADB, 2006), (UNDP, 2004), (UN, 1995) and researchers like (Oppenheim and Harker , 1996), (Haq, 2004), (Anand and Sen 1997 ) because of different measurements and a lot of them are of the view that poverty is regarded as a situation in which a person has a shortage of the main resources required by the person or household for consumption.

An individual is regarded to be poor if the income he earns is less than the minimum level to suffice the very basic needs of him. The minimum level of income as compared to society is termed as a line of poverty. There are a lot of reasons and elements which decides as to which things the poverty belongs. Poverty may be a status under which the individual lives as defined by the cultural and social norms and practice. The poverty is related in two ways. Number one is connected to the equal income and relative price levels. For example, 5 dollars can buy different kinds of goods. Number two is that the different countries caliber poverty in different ways. Many societies consider a person poor who lacks access to education or medical care. There are a lot of differences exists regarding the living standards of an individual but it is also essential to acquire an estimate of the exact poverty line before moving on to estimate it (CRPR, 2002).

Generally, poverty is defined as a living condition which is below the standards as determined by the society. It means as the deprivation of an individual of insufficient resources from maintaining the living standards and of having insufficient money for the purchase of the essentials needed for living especially housing facility, clothes, and food. Many of the theorists

46 | P a g e

have not come to a consistent approach regarding poverty and its measures. If we take the example of Martin Ravallion (1992) the poverty is in the society in which an individual does not have a sure amount of income or things needed to fulfill an adequate minimum standard as described by the society from which he belongs. The definition of Ravallion is sociological in its existence.

What is the right definition of poverty? No, there is not, but current thinking does not allow us to have simple explanations. There is no ethical difference of the statement poverty that has to be understood first as an individual instead of the domestic level, although it is important to understand the individual’s place within the family to understand the heights of the problem that has occurred. Most of the eyewitnesses involve their income that they get from corporate assets and states that provide supplies, particularly payment that contains collective wellbeing and it does not always include education and health endowment. There is a slight difference that how people see the state of poverty but the most important thing is periodic and cyclic shocks. When we write about poverty then we come to know the division between long-lasting and temporary food security. Theory considered both relative poverty and relative deficiency as the relevant terms. Poverty is defined as the price of the basket that is required for basic needs and for the distribution of data in most of the developing countries.

There are different views about the belongings that include public rights and are added up in a poverty matrix, weaknesses importance, ranking of economic and non-economic factors beyond these four factors Objective measures of poverty are denied by the supporters of the sharing approach either it is based on income or wealth. But chambers consider these approaches as a phenomenon. But this can be problematic as Ravallion (1999) mentioned that there is a central compromise between the distinction of the poor and the combining relevant, international and national integers, actual estimates of the poverty which are existing in the issues for distinguishing poor but this may cause the statistics strategy impulse.

Another definition which is universally determined is given by the World Health Organization (World Bank, 1995). It calculates the nutritional value needed by the individual and the fulfillment of the value in a person. While the definition given by the World Bank, as it is a donor agency, matches the poverty with the problem of insufficient income, which is an individual who earns less than 1 dollar daily is stated as poor. As a result of it, this simple

47 | P a g e

definition started a series of problems to be resolved. Considering it an imperative definition arise, which is poverty of consumption. The poverty of consumption is that in which the power of a family or individual falls below the line of poverty. For private consumption, it is the poverty line which determines a minimal accepted measure as described by the society.

The line of poverty is the standard which is set out by various organizations and it varies from country wise and time wise. Each definition of poverty explains a different meaning of it. Normally it is considered as the minimum living standard of an individual. It can be estimated in parameters such as Number of minimum calories a person uses daily, education, minimum income, and the consumption he made more often (CRPR, 2002). The poverty line is the defined parameter in which a household or a person is living. It defines the finalized status of the individual as determined by a government (CRPR, 2002).

Poverty is defined in many ways varying from different times and from different places. Poverty cannot be defined in a single definition which can completely tell the actual panorama and it can neither be explained in only one sentence. Poverty has many dimensions in it and it can be discussed in different views. Poverty can be explained socially, economically, historically, and politically.

Money or Earnings: Poverty may be a difference in the level of income, which puts a line differentiating poor the rich. The World Bank reported income-based approach which is 1.25$ (World Bank, 2005).

Basic Needs: Poverty is the no fulfillment of the basic needs of an individual. The basic needs include things like housing, clothing, and food. If considering more, then we can also add education, health facilities, and access to information (CPRC, 2004). Protection of food supply has a direct relation to poverty as poverty depends on purchased food items in rural and urban regions both. The poverty elevation causes the increment in the prices of food, which creates further insecurity towards the availability of food. The rate of growth has a direct relation to equity, poverty and food security (UNDP, 2004).

Standards of living: The standards of living are also explained in poverty and these standards differentiate from culture to culture and country to country. And it is dependent on the level of income of a country for a proper standard of living (CRPR, 2002).

48 | P a g e

Economic Deprivation: Poverty is characterized by the inadequacy of material resources which provide the necessary services and goods. When the individual cannot satisfy the very basic needs this non-sufficient situation creates economic deprivation (CRPR, 2002).

Social Context: The deprivation of a person socially, materially and emotionally is included in the social context. When a person is socially deprived it means that the person will not be capable of having better health, not able to acquire a proper education, and cannot have a better home to live. (CRPR, 2002).

An agreement was made between the governments of 117 countries in which a formal line of poverty and the anti-poverty plan was determined in all of the countries, counting the categorical and the entire poverty. The specific condition was characterized as poverty which states poverty as “the severe deprivation on very basic human needs including safe water for drinking, availability of food, facilities of sanitation, health care availability, shelter facilities, information and good education. It does not just base on the income level but also on the easy availability of the social services (UN, 1995).”

On the basis of previous studies a poverty definition which is consisted of deprivation from basic human needs and severe deprivation from basic human needs can be formulated which has the following components:

2.1.1 Deprivation from Basic Human Needs Shelter: When a number of persons living in only one room with three or more than those children.

Sanitation: When there are poor facilities of sanitation.

Drinking water: When the water is acquired from open springs and wells which are undeveloped sources.

Information: When there is no access to broadcasting media such as radio and TV.

Food: When food is only to keep the weight in terms of height and weight.

49 | P a g e

Education: When there is current enrollment or do not have primary education.

Health: When there is no vaccination from two years of age. In addition to it, the child has not got the vaccination and there is no consultant for sickness.

2.1.2 Severe Deprivation of Basic Human Needs Shelter: When four or more persons live in only one room with five or more than five children.

Sanitation: When there is no access to any toilet facility of any kind

Drinking water: When there is no access to drinking water or water from outside or surface water of rivers or dams is used and also when it takes thirty minutes for a person to have access to fresh water.

Information: When there is no access to any form of info such as TV, radio, paper or internet.

Food: When there are less than 2300 of calories of food daily.

Education: When the individual has never attended or currently not receiving an education.

Health: When there is no access to assistance related to sickness.

Therefore, deprivation and severe deprivation in relation to human basic needs with poverty have been determined by the same measures but in the case of severe deprivation, there is more neediness. The existence of poverty is all around the world and due to its consequences, it must be fought with everywhere. Many people have been living for many times under the poverty but it has achieved a lot of attention at the end of the century due to the growth of rapid population and our ability to fight against the poverty. Organizations which are international, academies and governments have been making tremendous efforts to reduce and evacuate the poverty.

2.2 A review of Poverty prospective studies

2.2.1 Poverty Prospective studies in Pakistan The investigation of poverty in Pakistan was examined by Haq (2004). He used the data as set by the socioeconomic survey of Pakistan during the last two periods. He resolved that poor in

50 | P a g e

Pakistan are categorized into two kinds, namely transitory poor and absolute poor. The individuals who have less than $0.75 expenditure per capita of the poverty line are considered as absolute poor. And the individuals who have more than $0.75 dollar expenditure per capita but less than the 1.25 dollar of the line of poverty are transitory poor. The outcomes of the study suggest that the severity of poverty, depth, and incidence has increased in the country over time. The classification and investigation of the situation of poverty in Pakistan in the urban and rural regions were stated by Arif (2000). He said that there is more poverty in rural areas as compared to urban areas in Pakistan. Poverty in Pakistan inclines to minimize according to the index of headcount. During the year 2001, the population living below the line of poverty was about 34.46 percent which can be minimized to 23.9 percent and 22 percent in the years of 2005 and 2006. He said that a decline of 10.6 percent points occurred during 2001 and 2005. As a sheer term, the 49.23 million people of Pakistan were under the line of poverty in the year 2001, and a declined has occurred and made it 36.45 million in the year of 2005. Roughly 19.63 percent people in urban regions while 11.34 percent of the people in the rural regions were living lower than the poverty line in the year 2001. This percentage was declined to 28.10 percent in urban and 14.9 percent in rural areas of the country in 2005. Therefore overall poverty in the urban region has declined to 7.79 percent and 11.16 percent in the rural areas (UNDP, 2005). Human Development Index says that 31 percent of the poor population is living on the extreme margin of life and they are earning less than a dollar a day in the year 1998. The expectancy rate of birth is 64.4 years and the literacy rate in adults is about 44 percent in the year 1998. A report of the Asian Development Bank (ADB, 2002) reported that poverty in the provinces demonstrated a trend of increase during the years of 1993 and 1999. The report demonstrated that in the province of Punjab the poverty has increased from 25.5 percent to 33 percent, in the province of Sind it has raised from 24.1 percent to 26.6 percent and the province of Khyber Pakhtunkhwa showed an increase of 24.6 percent to 45.5 percent.

The presence of chronic poverty is in many regions of Pakistan and it is sore in the areas where the social and environmental deprivation is not a parted from one other. The northern areas mountainous parts and the Baluchistan and Sind’s infertile regions in the south and west are stricken by poverty because of the rough conditions of the environment. Secondly, the areas which are administrated by the feudal or tribal agrarian are disadvantaged socially. The same

51 | P a g e

situation is in the majority of the areas of Khyber Pakhtunkhwa, Sind, and Baluchistan (CPRC, 2004).

Malik, S., Imran, S.C. and Hassan, A., in 2006 have made an analysis in the village of Southern Punjab in Pakistan about the overall impact of various economic, social and the demographic features of the occupant on the poverty. For the estimation of the relationship between social, economic and demographic features of the occupants and the poverty, they have made use of income regression model. The per capita income of an occupant external variables with all of the other variables i.e. female-male ratio, household size, dependency ratio, the value of the total assets, households’ landholding, the age of the head of the household, population of the livestock have been used as independent variables. The output of the studies pointed out that age of the head of the household, household size, female-male ratio, dependency ratio and persons per room of the workers have mainly inverse relationship with the per capita income of the occupant and have a positive relationship to the poverty. In contrast, income with the incidence of poverty is positively related to the education level of the household members, total assets of the household, population of the livestock, participation rate and the household landholdings. The logistic model gives the result of the presence of the female head of the household and resides in Kaccha (mud) house is positively related with the probability of being poor, whereas, farmer as a head of the household and the head of the household is a literate one than they have an inverse relationship with the probability of being poor. They also made an analysis on the impact of economics, demographics and the social features of the household on the poverty by the income regression model for the estimation of the connection between poverty and social, economic and demographic features variables of the household. The results of the study show that age of the head of the household, dependency ratio, female-male ratio of the workers, household size, and persons per room are mainly inversely related with per capita of the household but positively related with the level of poverty. While participation rate, total assets of the household, household’s land hold, education level of the household members and a population of the livestock are mainly positively related with the income but negatively related with the poverty. The results also indicated that being a female head of the family or a household and live in a Kaccha (mud) house have a positive relationship with the poverty whereas being a literate head of the household or a farmer shows an inverse relationship with the probability of being poor.

52 | P a g e

Khalid Bhatti (2008) analysis the impact of prices of food on the poverty and concluded that due to increases in food prices 27 million Pakistani have one under the poverty line in one year. Almost 87 million people are living below the poverty line out of 160 million people in Pakistan. Quality of life is falling and round 77 % people are suffering in food insecurity. Lawal et al. (2008) present the findings of research on rural household security among agro- pastoralists in Nigeria. The study was based on a sample of 87 agro-pastoralist food households shows that majority of the agro-pastoralist households in the study area are food secured. The probability that an agro-pastoralist household will be food secured increases as crop enterprises are further diversified and decreases as household size increases. Food insecurity among the agro-pastoralists is poverty triggered and not as a result of low crop output. For the estimation of nature and determinants of socioeconomic variables and their impact on rural poverty (Chaudhry, Malik, & Hassan, 2009), conducted a study on “Impact of Socioeconomic and demographic variables on poverty”. According to the researchers, “Poverty is a complex phenomenon based on a network of interlocking economic, social, political and demographic factors”. The empirical analysis suggested that increase with the house holding, depth, and incidence of poverty increases. As there is a positive relation between householding and incidence of poverty. With the increase in the participation rate, poverty tends to fall. In the analysis, the female-male ratio has a negative relation with poverty.

Haq & Zia (2009) aimed to analyze the effects of socio-demographic variables of household on poverty, by using primary data obtained from southern Punjab. In the study, household per capita income had a positive relationship between household size, participation rate, the ratio of workers, age, dependency ratio, livestock population, and landholding and negatively related to poverty incidence, positively related to income level.

To analyze the features of rural poverty and agrarian economy (Chaudry, Malik, & Ashraf, 2006) conducted a study on “Rural poverty in Pakistan”, the rural poverty in Pakistan is due to rapidly growing population growth rates, poor sanitation, sky-scraping dependency rates, low literacy rates mainly among females and dreadful infrastructural facilities. Pakistan is an agricultural country, people rely more on the agriculture sector. A majority of people are living in rural areas depending on agriculture. The study claimed that rural poor are the main source of poverty.

53 | P a g e

In the study, secondary data for the year 1963 to 1999 were analyzed using multivariate regression analysis. Rural poverty as the dependent variable and the explanatory variables were agriculture growth rate, Gross domestic product (GDP) growth rate, consumer price index (CPI), Trade openness, unemployment rate, remittances, per capita income and Gini coefficient. The researchers hypothesized that agriculture growth rate, GDP growth rate, trade openness index, Gini coefficient and per capita income has a negative relationship and CPI (Consumer price index) and unemployment has a positive relationship between rural poverty. For analyses, the OLS method of regression was used to figure out the results. The results concluded that inflation, growth rates, and unemployment have a significant effect on the reduction of rural poverty and the rest of the variables in the study were positively related to rural poverty but statistically insignificant.

To determine and analyze the determinants of rural poverty on the provincial level in Pakistan (Sikander & Ahmad, 2008) conducted research on “Household determinants of poverty in Punjab”, explains reasons of poverty of household is due to age, gender, and level of education of household which leads to poverty. The more the dependency ratio, the more the large family size leads to poverty. The region of Punjab was divided into three groups, rural area, and urban area in major cities of Punjab. Per capita monthly expenditure and per capita monthly calorie intake was taken as dependent variables.

In the study the cross-sectional data were collected from Bureau of statistic and from governmental administered agencies of one year or more, 30,932 households were selected and 34 districts were selected including rural, urban and major cities of Punjab. A binomial logit model was used in the study to determine the probability of being poor. The dependent variables were spending on household and calories intake. The variables used in the study were head of household working in government sector , head of family working as self-employed , head working in private sector , head working as laborer , head working in agriculture sector , head of household working in livestock, disabled members , age , years of education (schooling ), family size ,dependency ratio ,household assets value ,number of earners in household . Rural residents are receiving more remittances as compared to urban and other main cities.

54 | P a g e

Chaudhry et. al. (2010) explored the role of education in reducing poverty in Pakistan. The results of their study confirm that primary and middle education is positively and insignificantly related to poverty. University education is negatively and significantly related to poverty.

2.2.2 A Brief look at Some Poverty Prospective studies in South Asia

(Ahmad, 2004) presented a paper based on a sample of 5180 households of poorest regions of Bangladesh i.e. Gaibandha Sadar and Tanore. The study was conducted in April-May 2006 which defined and measured poverty indicators on the base of food, income, assets, consumption, capability, and well-being. The paper examined the association between poverty and eight socio- demographic variables i.e. location, age, gender, marital status, size of household, occupation, house ownership and land ownership in the poorest areas of Bangladesh. Furthermore, Chi- square technique of association and Pearson’s correlation technique was used to examine the level of poverty and its correlation with socio-demographic variables. The results indicated that the occurrence of rural poverty in Bangladesh is from 46 to 67 percent of income; capacity and well-being poverty is relatively greater than food poverty. However, the crucial correlates of poverty found to be the occupation and land ownership followed by gender, marital status, age, and location.

(Mehta & Shah, 2001), the paper examines the nature and degree of persistent poverty traps in rural areas of India. The study targeted two data sets i.e. one was the “dry land” areas in which there was lack of crop production and lack of opportunities and other was the “forest-based” areas which mainly consists tribal population which had limited natural resources and no regular information about markets. The paper estimated the severity and depth of poverty, for this study the data was collected from household’s consumer expenditure surveys from the year 1973 to 1974. The study was based on six states of India i.e. West Bengal, Orissa, Maharashtra, Uttar Pradesh, Bihar and Madhya Pradesh.

The results obtained from the study argued that major factor which results in the chronic poverty is the low production yield in the agricultural economy, in this way the rural laborers get low wage rates from agrarian sector. That was a high upshot of demographic strain accompanied by slighter economic growth and partial labor force diversification in the states like Bihar, M.P., and

55 | P a g e

Orissa.” Casual agricultural farmers were the leading groups and cultivators were second leading among the persistently poor, mainly poor laborers are dependent on earnings”.

A study was conducted on the poverty level in rural families of the Dharwad district of Karnataka province in India (Susheela et al., 2002). The income was considered by him from all the possible sources which include supplementary occupation, agriculture, and income yielded by all the members who earn by the means of wages as the household’s annual income. The household which has an annual income of fewer than 6400 rupees would be considered poor. And the families which earn less than 4800 rupees per annum are considered to be the poorest. The Shibaragatti village was found to have the highest poverty which was 34.6 percent. The ratio of poor families differentiates on the basis of the types and property of families. 54.8 percent of the landless households were living below the line of poverty. Whereas the ratio of small landholders was 34.2 percent, of medium landholders was 29.8 percent and of large landholders was 2.1 percent. In nuclear families, 12.2 percent and in joint families 39.5 percent of families were living below the line of poverty (Susheela et al., 2002). (Ravallion , 1994) made classification of the concept of poverty like subjective poverty and the objective poverty. On the basis of the data taken in Indonesia, he also examines the method of the relationship between food-energy intake i.e. FEI and cost of basic need i.e. CBN advance towards objective poverty lines. He had not found almost any link between these two of the mentioned methods. He further explained the aspect of poverty as the elementary needs of the occupants or the individuals as per the other main elements in the viewpoint of health required over the materials at an individual or the occupant's extent. It is considered as truth that when an individual’s income rose, the occupants or the individuals would be enabled to enhance some of the financial or non-financial features. As a result, in the estimate, some of the other factors also be included like housing facilities, education, the provision of public services and the life anticipation, however, income is an insufficient estimate being the only indicator

2.2.3 A Brief look at some poverty Prospective studies in the world

56 | P a g e

(Minasyan & Mkrtchyan, 2005), attempted to develop research on “Factors behind the persistent rural poverty in Armenia”. The study monitored the crucial and important factors behind the rural poverty in Armenia. The data used in the study was taken from Household surveys from 1996 to 2003 and data from national statistics of Armenia obtained from UNDP.

The study argued that in Armenia the rural household had failed to take a large part of the growth in the agrarian sector from 1996 to 2003. Due to the brisk commercialization, there was an increase in the involvement of urban household in agricultural farming. Furthermore, agriculture sector also experienced the shock in domestic terms of trade and growth in production was not considerable enough to alleviate its harmful impact. The study also highlighted the role of non- farm’s income for the rural household and resulted that to drop the economic growth to countryside areas throughout incomes from self-employment and hired job was significant. It was found that poverty risk was negatively related to the expansion of agriculture production.

To analyze the effect of socio-demographic variables on Rural Poverty (Thompson, Traub, & White 2011) conducted a study on “Socio-Demographic predictors of rural poverty” , the study focused to examine the nature and the degree of variables of race, gender , sex , occupation , number of children , education and willingness to travel for employment of rural individuals level of poverty. “For the precision of variables in discriminating among poor and non-poor families, a random sample of thirty low-income rural states of U.S. was selected”. By using the discriminant analysis, the results suggested that at five percent of the level of significance the predictor variables are highly significant among non-poor and poor individuals of rural states. Furthermore, the study concluded that household’s head was poor, semi-skilled; residents of the farm that tend to be old have a large number of children and pay very less on the traveling for employment purposes outside the country’s side.

According to (Minasyan & Mkrtchyan, 2005) in Armenia, the rural household had failed to take a large part of the growth in the agrarian sector from 1996 to 2003. Due to commercialization, there was an increase in the involvement of urban household in agricultural farming. Furthermore, agriculture sector also experienced the shock in domestic terms of trade and growth in production was not considerable enough to alleviate its harmful impact.

57 | P a g e

To highlight the relationship between rural poverty and natural resources (Cavendish, 2000) conducted a study on “Empirical regularities in the Poverty-Environment relation of rural households: Evidence from Zimbabwe”, examined that there exists a negative relationship between total income of rural household and total environment income share. The data for this study was taken from two different sets of years from 1993-1994 and 1996-1997 from a group of a random sample of 197-panel households in 29 villages of Zimbabwe. The results examined that environmental resources contribute more to rural incomes, as poor population depends highly on natural resources than rich population. The reason for this is, the average income came from free an environmental resource that is why poor rely more on environmental resources for the source of their income. “It was also observed that Aggregate total resource demands still increased the demand that’s why better-off rural households are highly significant users of environmental resources”.

(Sugema et al, 2010) examined a study on “Impact of inflation on rural poverty in Indonesia”, the rural poor household is more exposed to economics shocks like inflation. Fluctuation in the prices of food and products has a larger impact on poverty relative to the non-food products. Rural poor will face more poverty due to the rapid changes in the rise of food prices. Inflation has a greater impact on poor household both on rural and urban than on non-poor household. The data used in the study was collected from 277 households in all the provinces of Indonesia. Data related to income and expenditure was collected from the households. Consumer price index (CPI), household consumption data was also taken for the analysis. The main focus was on to find the elasticity for Indonesian households and to find price index for poor (PIP). Consumer price index was used for seven groups of commodity goods like foods, processed foods, drinks tobacco, water, gas, housing, fuel, cloth, health, education, sport and recreational activities. To estimate the indirect and direct impacts of different types of government expenditure on rural poverty and productivity level (Fan et al , 2009) conducted a study on “Government spending , growth and poverty in rural India” , lowering down the poverty trap, government should invest more on rural development and roads of rural areas. The government should do investments more in irrigation, education, the physical condition of citizens and community development of the rural side of the country. In fact, the government should work on rural development.

58 | P a g e

The data from the year 1970 -1993 from 14 states were selected for the study, in order to find out the direct and indirect impacts of diverse types of government spending on rural poverty and production level in India. Simultaneous equation model was used in the study. The exogenous variables were the one-year lag value of the rural residents, the lag value of GDP, five-year lag values of trade variable, agriculture cost index, rainfall annually and total factor productivity growth at nationalized level. The endogenous variables were government spending on( irrigation , revenue and capital accounts ), government spending both on (revenue and capital ) on agriculture research and development ,expenditure on rural roads, rural power ,spending on public health , people lying under the poverty line ,%age of cropped area that is irrigated ,%age of rural households that are not having lands, wage rate of rural labor and %age of the specific rural villages that are electrified.

A study by Akanbi (2015:132), which sought to establish the structural and institutional determinants of poverty in sub-Saharan Africa, found that gross domestic product (GDP) and human capital – which are often used as proxies for employment and education in the literature – were statistically significant determinants of poverty.

In Kenya, Mwabu et al. (2000) used regression analysis for determining the major determinants for Poverty over there and found that level of education/schooling in the household, places of residence(urban and rural) and a population of live stocks etc are the major determinants of poverty in Kenya.

By taking household survey data of the year 1993 and 1994 (Kelly and Jefferies, 1999), have done the analysis about the link or connection of poverty, which show results that poverty was in a high extent among the occupants or households that are located in rural areas. Moreover, they made a conclusion of their findings as the female head of the household has a positive relationship with the frequency of poverty while so much schooling of the household head has given an inverse relationship with the level of poverty.

2.3 Measuring Poverty Mostly poverty is measured by an income-based approach which contains the per capita income. So that if it below than the standard which also differs area to area and time to time as well as 59 | P a g e

often remains the same for everyone. Moreover, the sometimes calorie-based approach is favored that how many calories are being taken by the person so that if that person is taking fewer calories than he likely to be poor. Furthermore, approaches regarding measurement of poverty is a monetary approach which is one of the common approaches being used. It includes the calculations of household income including both the own production and expenses per capita in order to point out a shortfall in consumption or an income from a particular poverty line. The World Bank portrays the poverty line for the well-developed countries and according to the per capita income should be US$370 i.e. approx. dollar a day and the bank also indicates that basic needs differ by the place and time. Hence, as the poverty line varies from country to country with each country measuring two or more than two poverty lines. These points out the differences in the purchasing power parity, for example, urban or rural, societal norms and values and local levels of developments. With the present situations, this measurement technique even not functions which leads to the appearance of the concept regarding multidimensional poverty. 4

Since the last thirty years, poverty has expanded a lot (Carter & Barrett, 2006). There are certain faults including the importance of economic factors, on the impartial or personal estimates, and in the relationship between real income and broader works in the society. Many people believed that prosperity is because of the defective estimate of the income and also the change if felt by them at the time. The idea of the relative deficiency has been approved by the hypothesis (Kemal, 2003). Nevertheless, a different point of views is there regarding the significance of non- financial factors, such as dignity and regarding the weight that must be included in the ideas which are expressed by the poor ones by themselves. The intellectual argument is accepted leading to the estimation. For acknowledgment to explain the goals at the international level conferences and to estimate the development against it, a small technical industry has been developed in order to measure the poverty and shortage. Several of the patterns of poverty has been proposed. Particularly, the supporters of the involved pattern are awarded of the corrections and the measurements of the quantities.

To measure poverty, three components are highly required (UNDP, 2004). Firstly, proportions and the indicators regarding the prosperity are selected. Secondly, poverty is selected which is a

4 This approach is based upon the following major steps; 1) Identification: identifying th poor among the total population 2) Aggregation: constructing a numerical measure of poverty giving ratio of poor in the population. 60 | P a g e

doorstep below that a given occupant or an individual would be called as poor. Last, poverty estimate is needed to be selected for describing the population as a group or an individual. This portion emphasized the financial measurements of welfare, expenditure and the income. In relation to the non-financial measurements like education, health, and resources; substantial and qualitative estimates are perceived by poverty.

2.4 Monetary indicators of poverty When measuring the poverty by using financial estimates, individual may have to do selection between the income or expenditure for the identification of the welfare. The domestic survey is entirely providing the information regarding the expenditure which is described very clearly.

According to (Zaman et al, 2012) Expenditure is regarded as an enhanced factor of poverty because of the following reasons:

As compared to the income, consumption is considered a good indicator. There is a close relationship between the actual consumption and prosperity of a person which means to have a lot to satisfy the present main requirements. In contrast, utilization of the goods has a better source i.e. income; measurement of consumption is better than the income. In accordance with the harvest cycle, income is continued to be changed during the year regarding the poor agricultural economies. Having the large informal sections in urban economies, the developments of the income also be considered as unrealistic. This indicates a strong difficulty in orderly recollecting their income due to which income is obtained from low quality. The additional difficulty which is encountered in the estimation of the cost is considered as the exception of the particulars bought for the production of the agriculture from the incomes of the farmers. In the end, it would be hard for those occupants who have to utilize production and exchange of other materials if larger incomes are legalized as money. If consumption module would be more stable if it is well arranged but it is loaded with some difficulties also and the actual status of living and capability to satisfy the fundamental requirements of the occupant can be indicated. The expenditures of the consumption also demonstrate that household can communicate its savings or the credit markets at the time of low-income availability, it cannot just show materials and the facilities that a house have dependents on its present income; due to the seasonal differences, or failure of the harvest or due to the other reasons, an income may vary consistently.

61 | P a g e

Nevertheless, for estimating poverty, one should not be so determined regarding the use of the consumption data. If income is used for estimating the poverty one can achieve some advantages. For instance, this estimate can create differences between the derivations of the income. When some differences are present, like wages, income can then be easily differentiated with data obtained from the other sources, therefore, quality of the data used in the household survey can be examined. In the end, because the researches consumption is being gathered. The person who analyzes is comparing the outcomes when consumption and income both are accessible. By computing, the transition matrix is the simplest way in order to examine the outcomes for the variety of consumption or income. In order to measure the transition matrix divide the population into groups, for instance, 10 characteristics, each showing 10 percent of the population, ranging from the respectful 10 percent to the doubtful 10 percent. Some of the occupants may be associated with one accommodating for the consumption and another for income but each of the house related to only one group of ten for each indicator, in this situation many households will not be associated to the crossways of the matrix. Various features of the poverty can be attained by the income and consumption for this reason.

Conventionally economic conditions are measured by poverty, but it has many scopes. Unsatisfactory revenue is not only linked with poverty but it also includes unsatisfactory consequences in detail with well-being, nourishment, and knowledge and it lacks poor relationships, anxiety and self-respect and inability. It will be possible to put on the equipment to economic signs of comfort. Relating equipment of shortage extent to economic signs requires the possibility of equating the conditions of economic signs to an available individual or a family to a beginning or deficiency beneath which it can be believed that the single or family is unable to meet elementary requirements.

2.5 Absolute Poverty Line Poverty lines can be described as either absolute or relative thresholds for distinguishing the poor from the non-poor. The definition of the absolute deficiency line is essentially the least informally acceptable degree of the intake and expenditure that is consumed to make a difference in population between the poor and non-poor. Consequently, this is the line that differentiates the singles below the deficiency line as poor and non-poor and it will remain same over time and in between the regions. Cheema (2005) defines that differences in absolute deficiency are by

62 | P a g e

pleasure two persons that are not relevant to the tie and place at the same actual usage degree of both that can be poor or non-poor. Ravallian (1994) describes the discrepancy of deficiency profile that if one or two families believed to have the similar living standard that is situated in different parts are considered to be poor on the other side it is not. According to Cheema (2005), The standard of living that is forced by the poverty line in Pakistan changes in between the regions and over the time that leads to the increase a rare condition that rich regions have more deficiency than poorer have. The attendant of the condition is to provide non-poor great benefits than a poor which leads to the misallocation of the resources provided by the government so there is consistency that is needed to get poverty line to be the same in between the regions to make differences at different degrees

The foremost feature that is answerable is the result of the favorable sharing of the land size, an educational accomplishment the ratio of dependency rates of participating male and female and the age of the head of the family. The family has no land and deficiency are therefore endured least revenue grouping. Whereas our review points out the significance of established location for healthier sharing of property and lots of resources, at the same time it highlights the details that there are few non-farm events are also able to allow a family to generate revenue and they can be avoided by deficiency. The study concludes by answering questions that are related to the agricultural growth and rural deficiency movements and track sources out and into the deficiency used by family information and secondary information to analyze revenue changes effect in four regions of Pakistan from the late 1980s to 2002.

2.6 Relative Poverty Line Relative poverty lines measure poverty in relation to the wellbeing of the society. Relative poverty is used to determine the poverty level in the context of the social environment. The arbitrary proportion of poverty line is often around 50% of the median or man of living standards and differs with the dominant trend of the sharing of the living standards in the society. In this method, it will not be the same poverty/deficiency line across units and over time. In this way, it will not be the same poverty line across regions and over time. The problem that creates the poverty line to increase but respectively there is more of those who are richer. On the other side of this review, a methodology that hurts unconditionally everyone will lead to decrease the poverty if the rich is also hurt respectively. Disobediently, Cheema (2005) suggests that a

63 | P a g e

poverty measure is based on comparative methodology and are in fact a degree of unequal and it is a useful degree to observe poverty over time or space. In the same way, the comparative methodology is not relatively the useful approach to measure poverty which does not permit evaluations in between regions and countries. Consequently, a relative deficiency method is not able to provide a changing under attack measure to observe the government programs as they change every year in order to raise or reduce in actual intake heights as compared to remain the same in actual form. He defines that relative methodology is not a useful approach for measuring poverty in evolving countries. Consequently, in such countries, there is a suitable and more related degree is the complete standard of living that guarantees a minimum living standard living that is necessary for bodily productivity in the society. The key part of the analysis is the official deficiency rate that is used by the family where the family is defined every member is living in the same family that is linked to birth, acceptance or marriage. If we look into the official definition then it argues that partners that live together are not the part of the household. So in a family that involves a mother, her two children and her unmarried people that live together that household eliminates the person that live together.

Knowledge of the poverty that does not change provides information on well-being that is altering the amount of Americans that whose key features are not being met. Therefore altering deficiency rates do not include a complete presentation of the deficiency. They do not disclose i- e the poverty that occurred last year was needed or whether a newly single person falls below the poverty line that is mentioned above. Neither have they disclosed how long a single remain in deficiency. Inspecting deficiency dynamics or changes that provides information on how, why, and when households expenditures that fall below or increase above the poverty rate that fills the difference when someone looks back to the same poverty rates unaccompanied. The most unit that needs consideration is, therefore, the enhancements to the official deficiency rate in the deficiency changing aspects and that is relatively small. Whereas there is few alterations in the deficiency degree is utilized between the studies, our analysis of the experiential studies display that in spite of these alterations there is much a statement between the solutions. There can be a chance that upcoming deficiency changes effect examines both official deficiency and changing deficiency degree (e.g., including taxes and in-kind transfers) can be found in significant lacks.

64 | P a g e

This study concludes several questions that are: Who are the poor in the United States and is this population changing? Are those below the poverty threshold at any one point in time chronically poor or just experiencing a short poverty spell? What events lead people to poverty and what events lead them out?

2.7 Multidimensional Poverty Arguments regarding the single dimension poverty lines have been arisen by most of the organizations and the researchers which is based on different indexes and they think that it is acceptable in the present situation i.e. 21st century. In the twenty-first century, poverty is far apart regarded and accepted as one of the most complex economic and social problems in the developing world. So that it was constantly related to the income which is only one dimensional, nevertheless, it is still representing the position of the poverty but not inclusively. For this reason, many researchers, for example, Tsui, (2002), Alkire and foster (2008), Alkire and foster (2015), Anand and Sen (1997),(UNDP, 2014) have given advice for the use of multidimensional poverty. They had made an argument on the usage of the uni-dimensional poverty line and said that it is the shortage of needs of material in actual, but also the refusal of the chances for living a bearable life. Similarly, Rojas (2008), also pointed out the income-based approach and he stated that income-based estimation of poverty is not enough parameter for an individuals’ economic and social health and the personalized welfare idea have brought that shows the accomplished poverty as well as it shows the enhanced and clear picture as compared to the factors which are dependent economically. Multidimensional poverty has also been compared recently at the district-level by Haq and Zia (2013) and Naveed and Ali (2012). Multidimensional poverty approach is also explained widely in the millennium development objectives which contain the use of goods, assets, and services in type which is same as income, in order to get the conditions of life such as amenities, materials, and services, standard that can be included in poverty. These things tend to make them able to participate in relationships, play the roles, follow the customary behavior as well as express the opinion which is expected by them in behalf of their membership in a society. Ratio regarding poverty is dependent upon the consumption or an income, actually, poverty is said to be a degree of unconditional poverty, that are originated in the poverty lines equivalent to $1.25 (then revised to $ 1.90 )and $2.00 per day for the persons in the undeveloped country.

65 | P a g e

Same on the other side comparative deficiency develops those persons whose income is very less in the area i.e. half the median of all government employees, it is just that if someone is living with the lacking and by recycling struck in the developed country.

Same as another main point or a factor i.e. multidimensional poverty index (MPI), which is required to be deliberate it is dependent upon the three kinds of dimensions and ten signs that decrease under the dimensions. According to Alkire & Foster (2015), these dimensions can be classified into few factors such as;

1) Learning, with two signs i.e. institute appearance and duration of training.

2) Existing values, by six signs i.e. hygiene, assets, power, cooking fuels, consumption of water and flooring.

3) Condition by two signs i.e. sustenance and the other one child mortality. On the other hand, it does not exist in the degree of deficiencies like inequality of gender and partisan authorization.

How poverty may possibly be practiced by a family can support procedures in the proposal and estimating the poverty actions and incomes that create packages by integrating a view. A single can practice different types of deficiencies and it can be domestic multidimensional examinations, it’s not sufficient for interferences which will be appropriate on the basis of gender etc. The domestic level examination does not include documentation about the individual i.e. men and women who practice deficiencies within a family is the most important thing. We determine the appreciated material by relating individual and domestic deficiency approximations on who is poor when the family is consumed as an entity (Alkire & Foster, 2015).

In the individual analysis, outcomes specify that single in a home is dependent on charity. It also includes the gender deficiency that does not exist in a family and it becomes very important. The lack of men and women dependent on as a poor is 1 percent as a domestic practice over the 39 percent in the individual analysis. Deficiency in poverty includes the usage of gender head of a family in domestic level. These are the major outcomes that lead to the taxonomy of a single person as nonpoor in a domestic level practice. In conclusion, we determine that the practice of poverty is not similar for everyone. Important lacks in the types of depreciation can be

66 | P a g e

determined that donates to the diverse collections of poverty. Significant strategic suggestions include depreciation that does not originate at the domestic level.

Amartya Sen (1981) declared that decently currency centered deficiencies have theoretical difficulties elsewhere in dimension matters. Income or money is declared to be a good source of improved living circumstances but in it, they are not. People without poverty can gain enhancement in existing surroundings. Whereas revenue symbolizes the skill to buy cargos which helps in the performance but a change in the cargos is not detailed. The factors that change an individual’s skill of cargos include age, gender or mental skill and rank within the family, which also influence the method in which the cargos are circulated among a family.

Unrestricted products are being frequently reinforced by the cargos in demand of performance where there is a lot of schooling is required for studies (Alkire, 2002; Sen, 1999). Furthermore, cargos are not originated from the performance. For example, in Sen’s approach one of the significant human performances is to select liberty or workout for individual’s action. Individual’s action and treasure are frequently limited by gender, age, marital status etc.

Yet, existing different aspects of the events are being capable of completely participate in the sex scopes of deficiency. Classically, different aspects of the events have reflected Gross Domestic Product (GDP) scopes in that they are founded on country means. The important pains were the Human Development Index by the United Nations Development Program. HDI is dedicated to durability, instructive achievement, and typical existing conditions. The Gender Development Index (GDI) and the Gender Empowerment Measure (GEM) brought the sex viewpoint although they do not involve any particular sex aspect of deficiency that is the usage of time and experience of fierceness and etc. (Bessell, 2010). By donating means of statistical attainment for the country as an entire, such directories distract the attention of the poor (Pogge, 2010).

The better and freshly production of a family data is acceptable multidimensional procedures to emphasize on the depreciation faced by a family. The most determined energy for appliance a multidimensional degree of deficiency is defined in the2010 Human Development Report (Alkire and Santos, 2010) for multidimensional Poverty Index (MPI) introduced in the 2010 Human Development Report (Alkire and Santos, 2010). MPI calculates depreciation created on a family includes knowledge, well-being and existing conditions. There are several symbols for

67 | P a g e

calculating each of these scopes and them a mixture of the catalog and exact performance. These scopes are likewise subjective and these can lead to the family depreciation rank to a recognized depreciation endpoint. Meanwhile, the MPI emphases on evidence from each family as a difference to country mean, it is likely to consider the multiple and interconnected depreciations for the family to enable documentation of not only counting the number of people but it also refers to the strength of the depreciation.

Women headed families were increasingly getting poorer than any other families in the analysis of the gender issues of poverty and this is limited to the hormonally induced development of female sexual characteristics of deficiency. It has not been tested with the help of knowledge that comes from observation. Induced development of female sexual characteristics of deficiency has happened in the eight Latin American countries they learned. The conclusion of the sexual characteristics of the deficiency does not change in diverse scopes of deficiency. Generally, the research concludes about the conclusion that established and establishing countries do not take chance to accept the induced sexual characteristics of the deficiency as a formalized detail. Rare training concludes that the existing information for Pakistan also does not discover families having women are being inferior to the male ones. Nationally representative data for Pakistan declares that families having women are not poorer in rural India .

Marital status is the major part of producing poverty in the addition of the family .Their findings show that poverty does not come with existing married women it is because of the lack of knowledge that leads towards the deficiency if it is compared to the male compliments. Induced levels of sexual characteristics of the deficiency include certain other matters too. Poverty is concerned with the shortage of the outdated behavior of the revenue and depletion. It has been analyzed for the broader behavior of revenue scopes that ignores other domains where deficiency is practiced as major importance to the women. It occurs due to the lower performance of women in the department of well-being knowledge, nourishment and decision making. Another example that is relevant to women is that the income measures are not successful to identify the difference between the accessibility of revenue and the original governor and payment of revenue in between the members of the family (Bessell, 2010).

The importance lists a family level instead of the actual reflection within the changing aspects of the family as explained by San (2010), which also lessens the sex examination to slim attention

68 | P a g e

of women deficiency. Sex examination is more certainly vast and it must involve a point that how not just women or women-headed or poor family is affected but it also affects all family and its members? In circumstances, the mistaken figure of deficiency can be explained by the examination of the family head. Information from Latin America and the Caribbean display that for particular categories of property, sex and variations are being overvalued as a headship based examination that ignores women in families where male are more prominent. Additionally, leadership examination usage standardizes all women in these two groups. There are other parameters that unload women and are also reflecting or involving gender differences that are age, marital status, caste and the most important is religion.

2.8 Dimensions of Poverty

Poverty has many dimensions, among them the most important measurement is “consumption poverty”- “extent to which when the consumption of a family or household falls below the poverty line”. The poverty line is the minimum acceptable standard of private consumption in society. There is a significant difference in the level of consumption poverty between rural and urban area. Rural areas contain 74 percent of the country's poor, though they have about 70 percent of the total population (World Bank, 1995).

The multidimensional deficiency calculates four dimensions that are knowledge, well-being, the leadership of productive property and authorization. There is universal approval of the education relevant to key standards of wellbeing in understanding and grouping poverty. These dimensions are the part of the Millennium Development Goals (MDG) accepted by the United Nations. These dimensions when are known to the family or an individual can lead to the existing poverty band and coming future health. Meanwhile, these dimensions are being discussed in the literature where we focus on concluding dimensions.

The most important and significant dimension that is ignored is the wellbeing. Health dimension is not involved in the article as the information was incomplete. The main issue was that in health status there was a lot of deficiency between genders that is men and women that is always underestimated in the article that the family faces inequality attribute among the individual differences.

69 | P a g e

There are both conceptual and empirical problems faced when comparing family and individual depreciation. Particular dimensions are not local in nature as they are not included. For an instance, the toilet is the major necessity of an individual but that is not available to the household. Furthermore, the leadership of the toilet cannot be determined. In such cases, the poverty differences will be the same.

The stock of the property holding the family documents the longer term view of the financial security in such a way that there is no such revenue and consumption information. Property profiles reflect both past and coming revenue chances through their role to select choices among livelihood and the ability for taking part in economic markets, generating rents generation. Profiles of the property show both past and future opportunities regarding income-propagation by their contribution to income selections and the ability for getting involved in the financial markets, profits of saving and business. Property of insufficiency can be implemented in various structures. The security through the trade and sale can be achieved in the income lacking situations when there are some financial issues occurring on the properties or the resources. In order to judge the exposure to the shortage, there is an increasing awareness that the presentation of the strength can be a great force. No or with little bit fertile property possessing families are weaker for longer term or severe deficiency than families having some level of the property but they face revenue variations (Carter and Barrett, 2006).

It has been declared that property management does not discuss the present condition of living of an individual and family as some of the indicators and standard appear to be. In actual, it is not appropriate to be included in the property measurement incorporating with the other necessary measurements in calculating the multidimensional shortages, as well as it does not develop the living status but it is a source of the living standard. Therefore, we can persist in opposition of the security that is against the barrier for the shock that can develop a sense of welfare and manage the disturbed life and the life which is in difficulties in order to enhance the living standards of someone. Moreover, the well-developed part of the directions regarding the estimation of poverty in Pakistan is the control area of fertile property levels. The information or the evidence of the poverty line are incorporated with the size of the land and the type of housing since the year 1997 (BPL). Therefore, the BPL methodology is having much analysis, interchanged methods recommended continually to admit the relevance of control of the house

70 | P a g e

and the land for the investigation of the poverty in the Indian frame of reference (Khera and Dreze 2010).

The World Development Report (2000) claims that poorest fifth of the population is due to the rise in the usage of the intake but it is only when certain local procedures and recognized preparations are taken place. Consequently, development strategies should include the optimistic results of knowledge and well-being, suitable financial procedures and robust, unspoiled institutions whereas it has negative results as well such as natural disasters and complex emergencies, macroeconomic impulsiveness, poor improvements, circulation of environmental poverty and inequality (WDR 2000). Labor comprehensive programs and policies of investments are specifically appropriate in giving both of the economic growth and poverty depletion, in the meantime establishing the human resources as stated by the investigations of the accessible evidence attained from many countries. This assessment can assure that fertile ability of the poor is perceived in the progress of the growth cycle and therefore can assist to eliminate the depletion of poverty from the unpredictability of processes of trickle-down (Amis 1999). An expert and healthy workers required for the consistent economic growth which is economically and fruitfully variable. Therefore, it is debatable that human development is the tool for the depletion of poverty and economic growth.

Loughhead and others in 2000 recommend that in order to assure that the enhancing poor persist to enhance, the diminishing poor have an opportunity to change their condition and the strived poor get out of their baseless state, a combination of estimates of social establishment and safety is required for the sustainable assessment to poverty depletion. In order to have the opportunities, the poor should have the fundamental survival requirements like food, health and shelter satisfied and the extent of risk suffered by them depleted. The kind of social security can be explained with the example of an adequate regulatory structure which can deplete poverty. In contrast, the extension of imperfect structures would lead to the consistent divulgement of poor with the risk of insecure employment and the basis on the sponsors who save them from expulsion and jobs being offered at a price.

71 | P a g e

Chapter 3 Research Methodology

Though academically poverty is measured on the basis of income and the consumption level, but in real terms it includes much more; hunger, poor state of basic civic provisions, lack of medical treatment, non-accessibility to electricity, water supply, children education, and deprivation of other essential needs and opportunities which any human being is supposed to be entitled to make its living leads to the development of poverty phenomenon.

3.1 Poverty Variables According to (Alkire and Santos, 2010), human overall functioning is based upon the three major things (domains) which are standard of living, Health, and Education e. And Multidimensional poverty is also based upon these three parameters. These three major domains will again be divided into sub-domains and each has been given a number (weight). As per Multi-dimensional poverty measurement Poverty variables are usually the sub-dimensions of the dimension of the poverty used in the following mentioned chart used by the Alkire and Santos (2013).

Table 3.1 Poverty Related Millennium Development Gaols Dimensions Related Relative Indicator Deprived if… of poverty to… No household affiliate has accomplished MDG2 1/6 Years of the duration of schooling that is basically Schooling Education about 45 years. Child School Any school-aged teenager is not appearing MDG2 1/6 Attendance in an educational institute up to class 8. Child Mortality Any child has died in the family. MDG4 1/6 Health Any adult or teenager for who there is MDG1 1/6 Nutrition nourishing info is starving. Electricity The household has no electricity. 1/18 Living Improved The sanitation competence of household is MDG7 1/18 Standard Sanitation not enhanced (conferring to MDG

72 | P a g e

procedures), or it is developed but united by means of additional households.

The household does not have access to MDG7 1/18 Upgraded improved consumption water or nontoxic Drinking Water water for drinking purpose is further a 30- minute walking distance from home. The household has a mud, strand or as well 1/18 Flooring as fertilizer flooring. The house organizes by means of MDG7 1/18 Catering Fuel fertilizer, woodland or charcoal. Resources Not having, Motorbike, car, TV, MDG7 1/18 ownership refrigerator. Source: (Alkire and Santos, 2010, 2013)

3.1.1 Education (MDG's Goal 2 (achieve universal primary education)) One of the most discussed goals in MDGs is the education. By getting the education one will give the potential to participate in economic political and social activities of their life sphere by him/her. Education is further been divided into two sub-indicator which is related to the primary education and present child status. How developed countries become prosper. There are many reasons and one is more important is education and investment in human development. This implies that the ability of the adult males to read and write can enable them to make rational decisions on issues that affect their households, especially their standard of living. In addition, it can equally make them obtain jobs but in the lower paying sector. a. Years of Schooling: (Basu and Foster 1998) describe that its every person right to get access to at least getting the primary education and Primary education also have a positive relationship with reducing the poverty, also been well described in MDGs. b. Child status: To achieve the universal primary education set by the MDGs, also contain what is the present child status so by that anyone should know that is our future involved in getting an education or they are the case of child labor. 3.1.2 Health and Nutrition (MDG's Goal 4, 5, & 6)

73 | P a g e

Pertain to various aspects of health. Just like education and more specifically primary education health is also very important instrumental and intrinsic. Ariana and Naveed (2009) stated that to achieve different valuable capabilities one should have the great health status and fully controlled body Mass Index and nutrition status. Thus the following two indicators were selected under health and nutrition. Nutritional status of women: For having the nutritional status of women usually Body Mass Index is been used which usually represent the age group 20-60: Body Mass Index (BMI), which is considered as one of the standard measures of health and nutrition, this directly corresponds to MDG Goals. b. Under-five mortality rates: In Pakistan, under-five mortality rates are very high and number of incidence of child mortality has been witnessed from age (0-5)

To determine and analyze the determinants of rural poverty on the provincial level in Pakistan (Sikander & Ahmad, 2008) conducted research on “Household determinants of poverty in Punjab”, explains reasons of poverty of household is due to age, gender, and level of education of household which leads to poverty. The more the dependency ratio, the more the large family size leads to poverty. The region of Punjab was divided into three groups, rural area, and urban area in major cities of Punjab. Per capita monthly expenditure and per capita monthly calorie intake was taken as dependent variables. In the study the cross-sectional data were collected from Bureau of statistic and from governmental administered agencies of one year or more, 30,932 households were selected and 34 districts were selected including rural, urban and major cities of Punjab. A binomial logit model was used in the study to determine the probability of being poor. The dependent variables were spending on household and calories intake. The variables used in the study were head of household working in government sector , head of family working as self- employed , head working in private sector , head working as laborer , head working in agriculture sector , head of household working in livestock, disabled members , age , years of education (schooling ), family size ,dependency ratio ,household assets value ,number of earners in household . Rural residents are receiving more remittances as compared to urban and other main cities.

74 | P a g e

3.1.3 Living Standards/ Housing For judging the poverty their living standards and housing facilities are more considerable and become a very important factor after know about the Multi-dimensional poverty index. a. Quality of housing: this will depend upon that how the house is being made is it Kaccha (mud) (made of mud) house or made of concrete means Pakka house. b. Electrification: Electric connectivity has also become another important factor as because of this people will be connected to rest of the world as the majority of information resources are based on electrification and even proper light will be provided through simple electrification c. Safe drinking water: To be wellbeing one must have access to clean and safe drinking water because so many diseases are because of poor water like & Diarrhea, and many of child death has been because of due to unsafe drinking water. d. Sanitation: Human health is very much dependent upon the improved sanitation facilities e. Fuel used for cooking/air quality: To be wellbeing one should have better fuel for their cooking as wood, coal or cow dung etc. are so harmful especially in the congested environment under roof.

3.2 Variable Formation

Table 3.2 Variable Formation (Referencing previous studies)

Research Demographic Social Factors Socio - Economic Factors Factors.

World Bank MDGs Years of Schooling Electricity Resources ownership Improved Sanitation Sadeghi et al (2001) Education Income Arif (2000) House Hold Size Dependency Ratio GOP (2008-09) Income Asif (2007) Farming (Live Stock Population). Lerman, 2002 Male-Female Ratio, Female –

75 | P a g e

Male Ratio

Keister, 2007 No. of Children ,literacy Employment(Occu pation) Arif (2004) Live Stock Land Holding Zaman and Aman (2004) Family Type Employment Sharif (2003) Land Holding Hina Nazli et al (2000) Land Holding Murtazulhaq and Aziz (2006) Live Stock Pakistan Participatory Poverty Land Holding Assessment (PPPA) Sindh (2006) Veloram (2005) Education of Head of House Hold households with fewer numbers of earners Age of the Head of House Hold Susheela et al., 2002 Land Holding household’s annual income female male ratio persons per population of the room livestock household size dependency ratio age of the head of the household Education of Head of Hous Hold Family Type Kelly and Jefferies, 1999 the female head of the household schooling of the household head Asif (2007) Total Assets Malik, S., Imran, S.C. and Female-male ratio Value of Total Hassan, A., in 2006 Assets household size households’ landholding dependency ratio The population of the livestock age of the head of House Structure

76 | P a g e

the household Kelly and Jefferies, 1999 Education of Head of House Hold Awanet.al (2015) Education Assets Awanet.al. (2011) Awanet.al. (2012)

Arif and Iqbal (2008) Land Holding Munawar et al. (2006) Land Holding Mallick and Ghani (2005) Education of Head of House Hold Sharif (2003) Land Holding Malik et al. (2002) Micro Credit Saqib (1998) Land Holding Malik (1996) Land Holding Shirazi (1994) Education of Head of House Hold age of the head of the household household size Murtazulhaq and Aziz (2006) Land Holding Zaman, Khan, Ahmed, Ikram Income (2004) Alkire & Foster (2011) Quality of assets House

Alkire and Santos (2014) House Services Vijahaet.al. (2014) Housing structures Naveed and Islam (2012) Access to electricity Alkireet.al.(2015) No. of Rooms Roof Quality Gangopadhyay and Wadhwa, Education of Head (2004) of House Hold Female and Male Ratio Cheema (2005) assets Alkire and Santos, 2010, 2013 Years of Schooling Health facility Electricity Improved Sanitation House Hold Structure

77 | P a g e

Person Per Room Ariana and Naveed (2009) Health facility Davis, 2011 Assets Livestock Ownership Basu and Foster 1998 Education, Years of Schooling Appleton, 2002 Family Size, Dependency Ratio Bartram et al. 2005 Improved Sanitation Naveed & Islam (2012) Education Health Land Holding

Salahuddin&Zaman (2012) Education Health

Jamal (2009) Education Land Holding

Atta ullahet.al. (2016) Education Health Assets , Land Holding

3.3 Explanation of the variables.

The determinants of poverty can be macroeconomic or microeconomic. Our study is concerned with microeconomic variables and characteristics. On the basis of the Variables defined in Millennium development goals, a household survey of Pakistan and also as defined in the previous researchers, three major variables are deployed and these are demographic, social and Socio-economic and every variable has sub-factor which is impacting the Per capita Income of the household and poverty line. All are defined below briefly.

3.3.1 Demographic factors

78 | P a g e

Poverty dynamics are closely linked with demographic characteristics of the household especially family size, dependency ration, sex of the head of the household, age composition and literacy of the head of the household. Household size is a prime demographic factor and it is generally positively related to the poverty status [Qureshi and Arif (2001); Chaudhry (2009)]. Large family size is likely to put an extra burden on a household’s assets and resource [McKay and Lawson (2002)]. Education of household head is the significant determinant of household poverty [Qureshi and Arif (2001)] and the literate head of household reduces the probability of being poor [see Chaudhry (2009)]. Jamal (2005) showed that in urban areas dependency ratio is also positively related to the poverty status of the household. The demographic information affords an understanding of the household structures of the sample population. The classification of the population from different angles could be a reflective measure of the area’s resources and of the availability and distribution of such resources. These demographics form an important part of the government’s development mandate since households provide the labor for the production of goods and services, and also consume the final output of production. In addition, the size of a particular population is an important determinant of the socio-economic needs of the population.

The demographic factors are the personal characteristics of a population. The demographic factors are collected in a census. Following heads will be discussed in the framework: a. Age of Household Head The head of a household is the person who is running and looking after dependent in a household. The age of the household head matters as it measures the working capacity of the head. If the age of the head is less he/she can earn more of its living which would increase the per capita income as a whole. b. Occupation of Household Head The occupation of the household determines the earning of the household. It varies from white-collar occupations to blue-collar ones. The wage and salary differentiation also affect the per capita income of the household. c. Education of Household Head The household’s income is also decided by the education of the head of household. Unskilled labor earns less than the skilled labors in every part of the world. The highly skilled

79 | P a g e

labor is appreciated in every economy and their earnings are also handsome. The education of the head of the household plays a vital role in the increase of the per capita income. d. Size of Household Size of a household is the number of family members who are living in a house. It is further classified into adult and children, and male and female. If the size of the household is more the per capita income will be affected and the poverty finds a way to arise. Family size is important because as we increase the family size the burden upon the pool of resources of any family will increase and practically we have lesser and lesser resources for the welfare of individuals. Large families are more prone to poverty. e. Family Type A family is a social group which shares accommodation, reproduction, and economic cooperation. The type of family varies i.e. it may be a nuclear family, a single parent, an extended family, childless family, step-family, grandparent family. The type of family has a direct influence on the per capita income of the household, as it determines the cost of living also. f. Dependency Rate The dependency rate of a population indicates the number of nonworking individuals to the number of working ones. If the ratio is high it depicts the working age and the economy as a whole is facing a burden of the supporting of the non-working population of the country. It retards the growth in any economy and leads to poverty. g. Female to Male Ratio The female-male ratio or the sex ratio determines the number of females to the number of males in a population. In many economies, the females do not take part in the economic activities of the society, and if the female to male ratio is high and this condition prevails then the per capita income of the individuals reduces and leads to poverty.

3.3.2 Social Factors

80 | P a g e

Social factors are the determining factors of a person’s lifestyle, personality, and attitudes. a. Number of Room per household The room allows a person to accommodate ease and comfort. The shelter is a basic necessity and it is needed by every individual and deprivation of it is a basic characteristic of poverty. b. Housing Structure The housing facilities are the prime need of every individual. This facility is a basic factor for the development of human capital and it is the human capital an increase in which, leads to economic prosperity and relives household from the curse of poverty. c. Health Facility Health facilities are the basic factor of human capital development. It enables the individual to work efficiently to earn and helps him during the inevitable problems of nature that is a sickness. d. Education facility The education facilities provided to an individual helps him to meet the standards set by society at large. The individuals possessing a high level of education earn more than those who have a less level of education or skill.

3.3.3 Socio-Economic Factors The socio-economic factors are the combination of social as well as economic factors of a household or economy. a. Total Assets The assets possessed by an individual determine the resources which can be used for future. The total assets of a person give a chance to earn more for a living and enable him to keep himself safe from poverty. b. Land Holding Landholding is a kind of asset which a person possesses. In agriculture economies, the landholding is a very major factor because it is the land from which all the cultivation is obtained, and the landholder has more right in the crop. Landholding also gives the possessor a social status in the society.

81 | P a g e

c. Livestock population/production The livestock is the major contributor to the increase in per capita income as a whole. The production of it increases the overall national output of the country and by it, several goods and services are further produced. d. Household’s members The household is the unit consisting of family members who live together. It is the unit which. Mostly the household gets one earning of income, but now a day’s multiple earners in a household are present.

3.3.4 Component of Household Despite these major variable following are the main component of defining the household as these all are as per the Household survey of Pakistan.

a. Household: A household can be comprised of one person or more than one person. (A single person household or a multi-person household). As per the household survey of Pakistan a household in which one person/individual can make provision for his/her own food and other essentials of living, without combining with any other person. A multi-person household is a group of two or more persons who make some common provision for food or other essentials of living and who are without a usual place of residence elsewhere.

b. Head of the household: The head of the household is that person who is considered as the head of the household members. If a person lives alone, that person is considered as the head of the household. If a group of persons lives and eat together as defined above. In practice, when the husband, wife, married and unmarried children from a single household, the husband is generally reported as the "head". When parents, brothers, and sisters comprise a household, either a parent or the eldest brother or sister is generally reported as the head of the household. When a household consists of several unrelated persons either the respondent or the eldest household member is selected as the "head".

82 | P a g e

c. Household Members: Household members are all such persons or group of persons in a household who normally live and eat together and consider the living quarter/space occupied by them as their usual place of residence. Such persons may be related or unrelated to each other. All such persons who normally live and eat in the household and are present at the time of enumeration and those who are temporarily absent for reasons such as, visiting, traveling in connection with business, attending schools/ colleges/ universities/ polytechnics/ other educational institutions, admitted in hospitals, outside tours etc., are treated as household members. Visitors, purely temporary borders, and lodgers, transients, servants, and guests, etc. who consider their usual place of residence to be elsewhere but are found staying with the household included in the sample are not household members. d. Earners Earners are those persons aged 10 years and above who provide the household with the material return, in cash or in kind. Earners are divided into two categories, economically active and not economically active. All employed persons are included amongst the economically active. Pensioners and those who receive incomes from renting buildings and land (i.e. landlords) are classed as not economically active.

e. Household Income Household income in cash includes all money receipts such as wages, salaries, rent from land and property, income from self-employment, gifts, and assistance.

3.4 Data collection procedures The data which is generally used in case of analysis purpose is termed as primary data, produced by means of an accurate questionnaire. The information including in the questionnaire regarding household size, the income level of the household, expenditures of the household and other social and demographic characteristics of the household. Every possible effort was prepared in order to confirm the dependability and precision of the info. Questionnaires section has completed with the help of the individual interviews that have been accompanied.

83 | P a g e

Questionnaire was formd after revieiwng and consulting different questionnaires already used for collecting the data of same nature. (1) Questionnaire from Social audit of governance and delivery of public services ,Pakistan (UNDP , 2012) (2) Multidimensional Poverty Index Questionnaire used by UNDP in different years (Alkire and Foster ,2011) (3) Questionnaire from BISP (Benazir Income support Programme) has also used collected the poverty data from Pakistan in 2009. After reviewing and combinging some changes as suggested by different researcher according to region/district specific done and finalized. By the questionnaire, we find the magnitude of poverty and then it will be compared with the poverty numbers has been quoted by different government resources. On the other hand, secondary data was also recycled and obtained as of the inhabitant's survey intelligence of Punjab (Specifically in Jhang) in the year of 1961, 1998 and as well as 1972. Data related to the terrestrial consumption, foremost produces, ordinary remedies, healthiness etc. were attained from the Population welfare department of Jhang District and various website such as Federal Bureau of Statistic etc. Other relevant data related to poverty will derive from available literature. The study is based on primary data collected through the household survey in selected villages of Jhang District. The data was collected from 1000 respondents through questioning the household head and other members of household & direct interviewing the selected respondents.

3.5 Universe/Population and sampling

As discussed earlier people and household of the whole Jhang District is considered as our population in this study. The consequences would have been further precise, uncertainty the entire of the inhabitants of the all the municipalities/communities will be interviewed. For a collection of illustration defendants, sampling approach consist of multiple stages (Cochran, 1977) was adopted. In the first phase, the numbers of the respondents/households have been selected in all four sub-districts of the Jhang district on the origin of inhabitants/ amount of household in respectively sub-districts. In second stage villages/Union Councils would be selected through probability sampling technique in each sub-district. In the third phase, we have employed non-probability convenience sampling throughout the Jhang district and select

84 | P a g e

households from each village/Union Council based on opportuneness and preparedness of the accused persons in order to response our demand.

3.6 Sampling Procedure (Multi Stage Sampling)

With the confidence level of 95% and error margin of 0.05, Total 1000 Questionnaire has been filled from the overall population on the following sampling criteria. We have selected the sample size of 1000 households from all four sub-districts of Jhang District. Multistage sampling has been used to select households. The primary data collected from household survey was analyzed. Our universe has already been defined. In the first stage, all four Sub Districts (Jhang Sub District, Shorkot Sub District, Attahra Hazari Sub District and Ahmed Pur Sial Sub District) has been selected and then from each Sub-district on the basis of percentage of no. of household’s questionnaire has been distributed and filled. In the second stage, Different No. of UCs has been selected from each sub-district. And in the third stage, different No. of villages located in sampled UCs has been selected; and after that in the fourth stage, different no. of household selected from each village. So by that, total 1000 households have been selected to get the final Data. The sample size proportion in each sub-district obtains by the following formula:

Nk = n Nk  Σ Nk

3.6.1 Stage 01: (a) From the population, all four sub-districts will be selected. And from each sub-district following number of questionnaires on the basis of percentage of no. of households in each sub district are selected.

Table 3.3 Sampling of Questionnaire in Jhang District District Name No. of Questionnaires Jhang District 480 Atthara Hazari 160 ShorKot 150

85 | P a g e

Ahmedpur Sial 210 Grand Total 1000 Source: Government of Pakistan, Census 1998

(b) From the population, Rural and Urban UCs / Areas will be selected on the basis of their percentage to total no. of Households.

Table 3.4 Distribution of Questionnaires in Jhang District (Rural and Urban Wise) Rural and Urban Distribution No. of Questionnaires Rural 830 Urban 170 Source: Government of Pakistan, Census 1998

Attahar Hazari is completely rural sub-district in all four sub-districts and apart from it there is the huge population in the other three sub-districts also living in rural areas. 83% of total populations in Jhang district are living in rural areas however very few people (only 17%) are living in urban areas of Jhang District.

3.6.2 Stage 02:

(a) From each sub-district, No. of UCs have been selected on the basis of their percentage to total no. of Union Councils in that sub-district (proportionate basis). And also it has been ensured that at least 25% of UCs will be selected and if No. of UCs is lesser then 25% then the whole population will be selected. We have selected our sample (of Union Council) on the basis of probability sampling and in Probability sampling, we have selected Simple Random sampling. The total number of Union council which was selected from each sub-district is shown in Table 3.5 The sample size proportion in each sub-district obtains by the following formula:

Nk = n Nk  Σ Nk

Where nk is the proportion of the sample in the kth sub-district, n is the size of the sample and Nk is the population of the kth sub-district (Jamal, 2005).

86 | P a g e

Table 3.5 Distribution of Questionnaires in Jhang District (Union Council wise) Sub District Name Total No. of UCs Sampled No. of UCs. Jhang Sub District 45 11 Rural 32 8 Urban 13 3

Athara Hazari Sub District 10 3 Rural 10 3 Urban 0 0

ShorKot Sub District 16 4 Rural 15 4 Urban 1 1

Ahmedpur Sial Sub District 13 3 Rural 11 3 Urban 2 1 Total 84 22 Source: Government of Pakistan, Census 1998

(b) The name of the Union Council and number of Forms (Questionnaires) which were selected on the basis of random sampling are shown in Table 3.5.1, Table 3.5.2, Table 3.5.3 and Table 3.5.4.

Table 3.5.1 Sampled Union Council in Jhang Sub District S. No. Sampled UC Name Forms to be filled 1 Civil Station 44 2 Havali Lal 44 3 Sultan Pur 44 4 Chak No. 159 44

87 | P a g e

5 Chak No. 220 44 6 Havali Sheikh Rajo 44 7 Kari Wala 44 8 Dhoori Wala 44 9 Civil Line (Urban) 44 10 Sultan Wala (Urban) 44 11 Satellite Town I (Urban) 44 Source: Government of Pakistan, Census 1998

Table 3.5.2 Sampled Union Council in Athara Hazari Sub District S. No. Sampled UC Name Forms to be filled 1 Rashid Pur 44 2 44 3 Kot Shakir 44 Source: Government of Pakistan, Census 1998

Table 3.5.3 Sampled Union Council in Shorkot Sub District S. No. Sampled UC Name Forms to be filled 1 Allah Yar Juta 44 2 Chak No. 497 44 3 Bhango 44 4 Binda Surbana 44 5 Shorkot City 44 Source: Government of Pakistan, Census 1998

Table 3.5.4 Sampled Union Council in Ahmed Pur Sial Sub District S. No. Sampled UC Name Forms to be filled 1 Ranjeet Kot 44 2 Hasu Balal 44 3 Garh Mahraja 44 4 Ahmad Pur Sial 44

88 | P a g e

Source: Government of Pakistan, Census 1998

3.6.3 Stage 03 and Stage 04: From each Union council, villages will be selected on a random basis and following villages have been selected. And from those villages following a number of the questionnaire will be selected for our survey. In Jhang Sub District Total 11 Union Councils has been selected and from each Union Council villages are also selected and from each selected village Number of Questionnaire has been distributed on the basis of their proportionate population. The complete detail of each village name and Number of forms/questionnaires in the mentioned village is shown for Jhang Sub District in Table 3.7.3.1 and for the sampled Villages/areas in Attahara Hazari Sub District 3.7.3.2 and for the sampled Villages/areas in Shorkot Sub-district is in 3.7.3.3 and for Ahmedpur sial sub-district is shown in Table 3.7.3.4.

Table 3.6.1 Sampled villages Jhang Sub District S. No. Names of UCs Sampled villages/areas No. of Household No. of forms

1 Civil Station Jhang Chak Janubi, 424 11 Jhang Chak Shumali. 1280 33 2 Havali Lal Gilmala 1297 40 Jalalpur 146 04 3 Sultan Pur Kalu Sehmal 07 03 Kaccha (mud) Shamira 36 13 Chah Kamir 76 28 4 Chak No. 159 Chak No. 179 192 03 Chak No. 162 300 05 Chak No. 232 663 11 5 Chak No. 220 Chak No.267 665 11 Chak No.266 536 9 Chak No.174 346 6 6 Havali Sheikh Cheela 1175 22

89 | P a g e

Rajo Sai Sarwar 287 5 Talwara 248 5 Kot Sahib 51 1 Kutiana 154 3 Borana, 175 3 Bali Ahmad Khan 127 2 Kot Sukha. 103 2 7 Kari Wala Bhavna Veran 113 4 Doshera 758 26 Latif Pur 414 14 8 Dhoori Wala Boori 313 14 Kot Khushal 141 7 Bindi Haidan 203 9 Aqilpur Sargana 274 13 Bhutta 22 1 9 Civil Line Circle No. 7 Block 2162 44 No.1,2,3,4,5,6 10 Sultan Wala Circle No. 20 Block No. 1106 44 1,2,3,4 11 Satellite Town I Circle No. 32 Block 2544 44 No.1,2,3,4,5,6,7, Circle No.33 Block 2676 44 No.1,2,3,4,5,6, 7,8 Source: Government of Pakistan, Census 1998

Table 3.6.2 Sampled Villages/areas in Attahara Hazari Sub District

1 Rashid Pur Kot Bahadur Janubi 41 3 Chokan Jan Pur 484 41 2 Rodu Sultan Rodu Sultan 977 34

90 | P a g e

Lashari 272 10 3 Kot Shakir Chakuana 73 2 Aliana 52 1 Sajhar 90 2 Thathi Gul 25 1 Thathi Mandrani 40 1 Aura 167 4 Umrana Shumali 1420 33 Source: Government of Pakistan, Census 1998

3.6.3 Sampled Villages/areas in Shorkot Sub District Table 3.6.3 Sampled villages Shorkot Sub District 1 Allah Yar Juta Allah Yar Juta 665 41 Kulachi Fauja Dhera 3 1 Hasuwali 49 3 2 Chak No. 497 Chak No. 495 405 10 Chak No. 499 356 9 Chak No. 412 347 9 Chak No. 498 4 1 Baseera-II 634 16 3 Bhango Shorkot Shumali 1028 12 Rakh Bhango 306 4 Chak No. 504 2381 28 4 Binda Surbana Binda Surbana 430 8 Khaki Lakhi 1444 26 Thatti Ailchi 346 6 Dhalna Kamlana 256 5 5 Shorkot City Circle No.2 Block 310 20 No.1,2,3,4,5

91 | P a g e

Circle No. 3 Block 365 24 No.1,2,3,4,5 Source: Government of Pakistan, Census 1998

3.6.4 Sampled Villages/areas in Ahmed Pur Sial Sub District

Table 3.6.4 Sampled villages Ahmed Pur Sial Sub District 1 Ranjeet Kot Chak No. 5/3L 398 23 Chak No. 7/3L 302 18 Chak No. 1/4L 55 3 2 Hasu Balail Binda Goli 247 12 Fatehpur Parati 654 32 3 Garh Mahraja Circle No.2 Block 293 5 No.1,2,3,4,5,6,7 Alla-ud-Din-Wal. 2563 39 4 Ahmad Pur Sial Circle No.2 Block No.1, 2, 2563 44 3,4,5,6, 7. Source: Government of Pakistan, Census 1998

3.7 Data analysis techniques Finding the magnitude of poverty in the Jhang District was performed through the Head Count ratio and after that, we have also found and analyze the Depth and Severity of Poverty as well.

1. Headcount of the poor is the proportion of those below the poverty line in the total population: H = q/n,

Where q is the number of the poor (with income below the poverty line) and n is the total population (poor + non-poor).

92 | P a g e

2. Poverty gap ratio is the sum of income gap ratios of the population below the poverty line divided by the population of the poor: PGR = 1/n Σ [(z – yi)/z] Where z is the poverty line income, yi is the income of each poor person and n is the population of the poor. PGR is an index of the income transfer required to get every poor person out of poverty.

3. The severity of poverty takes into account the distribution of income among the poor and is measured by the squared proportionate poverty gap ratio:

SP = 1/n Σ [(z – y1/z)2 + (z – y2/z) 2 + (z – y3/z) 2 + …. + (z – yq/z) 2] Where z is the poverty line income level, y1 to yq is the income level of the poor and n is the population of the poor.

3.7.1 Construction of poverty bands

The availability of data with rich information is a crucial point for the inquiry of poverty and its determinants. The study is based on the standard poverty line as declared by the Planning Commission (Government of Pakistan), that is, Rs 3030 per month per adult equivalence for the years 2016. And for better clarification of poverty it is more categorized as six more different bands which are as follow. The same was also used by Sadiq (2016).

Table 3.7 Categorization of Poor w.r.t income

Poverty Category Income Level

Extremely Poor 50 % of the poverty line or less 1515

Ultra Poor 50%-75% 2273

Poor 75%-100% 3030

Vulnerable 100%- 125% 3788

93 | P a g e

Quasi Non-poor 125%-200% 4545

Non-poor 200% of the poverty line and above 6060

Source: Economic survey of Pakistan

3.7.2 Econometric Models The determinants of poverty can be macroeconomic or microeconomic. Our study is concerned with microeconomic variables and characteristics.

3.7.3 Income regression model We used the Income regression model to identify the linkage between determinants/correlates of poverty and Per capita Income. The same model has also been used in the research on economic growth and its determinants in Pakistan conducted by Shahbaz et al. (2009) and they conclude that Income regression model gives better results than simple or multiple linear regression models. The use of Income regression model to determine poverty correlates of households has wider conduct among researchers (Chaudhry et al 2001, Achia et al 2010, and Ahmad 2011). This learning can similarly custom an revenue reversion exemplary in order to regulate by what means numerous presentations of the poverty like demographic, socio-economic and collective types of homes disturb the deficiency and every profit of the household. In the model per capita income is used as the dependent variable, and socioeconomic, social and demographic characteristics are used as explanatory variables. The regression model of revenue has been recycled comprehensively by way of examiners (Ravallion, 1996) attempting to explain poverty. Following four models have developed for better understanding of the relationship of per capita income with the determinants of poverty.

LnPCIi = β0 + β1LnHSi + β2LnEDRi + β3LnEANPHi + β4LnTDRi + β5LnCHILRi + β6LnAGEDRi

+ β7LnFMRi + β8LnTASTi + β9LnLIVSTi + β10LnLANDHi + µi …..(1)

94 | P a g e

LnPCIi = β0 + β1LnHSi + β2LnHHAGEi + β3LnEDRi + β4LnEANPHi + β5LnTDRi + β6LnCHILRi

+ β7LnAGEDRi + β8LnFMRi + β9LnLIVSTi + β10LnLANDHi + β11D2 + µi …..(2)

LnPCIi = β0 + β1LnHSi + β2LnHHAGEi + β3LnEDRi + β4LnEANPHi + β5LnTDRi + β6LnCHILRi

+ β7LnAGEDRi + β8LnFMRi + β9LnTASTi + β10LnLANDHi + β11D1 + β12D2 + β13D3 + µi …..(3)

LnPCIi = β0 + β2LnHHAGEi + β1LnEDRi + β3LnEANPHi + β4LnTDRi + β5LnCHILRi +

β8LnFMRi + β6LnTASTi + β7LnLANDHi + β8D1 + β9D2 + β10D3 + µi …..(4)

Where Ln PCI = Per capita Income alienated by means of poverty link

HS = House Hold Size EDR = Earner Dependency Ratio

HHAGE = household head’s age

EANPH = Earner per Household TDR = Total ratio of dependency CHILR = Child to dependent Ratio AGEDR = Ratio of male and female members FMR = Female to Male Ratio LNTAST= Value of Total Assets. LNLIVST = Number of Live Stocks. LANDH= Total land holding by the Household D1= Dummy Variable: (Agriculture Land): 1 if the house has no farming land. (0 otherwise) D2 = Dummy variable (Education Level): 1 in such case when head of household is Matric or less then Matric. (0 otherwise) D3 = Dummy Variable (No Livestock): 1 in such case when Household has no livestock.

(0 otherwise

µi = Error term. Β0 is a constant. β2 to β13 are the elasticities with respect to corresponding variables.

95 | P a g e

3.7.4 Logistic Model A Logistic regression model was constructed to identify the linkages. The Logistic regression model gives better results than simple or multiple linear regression models (Landwehr, Hall & Frank, 2005). This learning can also expenditure a logistic typical in order to regulate in what way demographic and socio-economic influences distressing scarcity in the district of Jhang. In the logistic model, we have to use dummy variables. In the model we have used poverty is dependent variable and all other quantities and qualitative variables such as household size, Joint family system, house structure, dependency ratio, education level, the age of head of the household, and occupation of head of the household etc. are used independent variables. Mathematically, the model is expressed as follow:

Povi = β0 + β1HSi + β2HHAGEi + β3EDRi + β4EANPHi + β5TDRi + β6CHILRi + β7FMRi +

β8LnTASTi + β9LnLIVSTi + β10LANDHi + β11D1 + β12D2 + β13D3 + β14D4 + β15D5 + β16D6 +

β17D7 + β18D8 + β19D9 + µi …..(5)

Povi = β0 + β1HSi + β2HHAGEi + β3EANPHi + β4TDRi + β5CHILRi + β6FMRi + β7LnTASTi +

β8LnLIVSTi + β9LANDHi + β10D1 + β11D2 + β12D3 + β13D4 + β14D5 + β15D6 + β16D7 + β17D8 +

β18D9 + µi …..(6)

Povi = β0 + β1HSi + β2HHAGEi + β3EDRi + β4EANPHi + β5AGEDRi + β6LnTASTi + β7LnLIVSTi

+ β8LANDHi + β9D2 + β10D3 + β11D4 + β12D5 + β13D6 + β14D7 + β15D8 + β16D9 + µi …..(7)

Povi = β0 + β1HSi + β2HHAGEi + β4EANPHi + β4AGEDRi + β5LnTASTi + β6LnLIVSTi +

β7LANDHi + β8D2 + β9D3 + β10D5 + β11D6 + β12D7 + β13D8 + β14D9 + µi …..(8)

Where Pov = Poverty; 1 if the household is Poor; 0 otherwise

HS = House Hold Size EDR = Earner Dependency Ratio

HHAGE = household head’s age

EANPH = Earner per Household TDR = Total ratio of dependency

96 | P a g e

CHILR = Child to dependent Ratio AGEDR = Ratio of male and female members FMR = Female to Male Ratio LNTAST= Value of Total Assets. LNLIVST = Number of Live Stocks. LANDH= Total land holding by the Household D1= 1 If head of household is farmer (0 otherwise) D2= 1 If the household’s is agriculturalist (0 otherwise) D3= 1 If the head of the household is daily wager (0 otherwise) D4= 1 If the house has no farming land (0 otherwise) D5 = 1 If structure of household is Kaccha (mud) (0 otherwise) D6= 1 in case of joint family system (0 otherwise) D7 = 1 in such case when head of household is Matric or less then Matric. (0 otherwise) D8 = 1 in such case when House Hold Size is more than 08. (0 otherwise) D9 = 1 in such case when House Hold have no Live stocks (0 otherwise)

µi = Error term. Β0 is a constant. β2 to β19 are the coefficients with respect to corresponding variables.

3.8: Poverty Line. In the present world, economic unfairness, as well as poverty, are the siblings the underdevelopment”. Economic inequality and poverty are closely related to each other and it appears to have been on ascending worldwide in recent decades at both national and global levels”. In Pakistan, the rising velocity of poverty in rural areas has prompted debate on growth and production. About 80% of the population lives in the countries where incomes differ are more. Up to the present day, “the poorest 40% world’s population account for only 5% global income and richest accounts for 75% of world income (UNDP 2010). According to the estimates of Food and agriculture organization (FAO 2006-2008) 850 million people are undernourished in the world, in 1969-1971 the number of undernourished people was 26% but the value of undernourished people has declined to 13% in 2006-2008. In the present day, 25% of individuals among the population are underfed and malnourished (FAO 2006-2008).

97 | P a g e

In the World Summit which was held in Copenhagen 1995, the absolute poverty was defined as “a condition characterized by severe deprivation of basic human needs, including food, safe drinking water, sanitation facilities, health, shelter, education, and information. It depends not only on income but also on access to services” (Gordon, 2005). World Bank has come up with several statistical definitions of poverty that use ratios, percentages, and figures to define poverty. The statistical example of defining poverty is that a person earning less than the US $1.25 per day (then revised as $ 1.90 per day for 2011(World Bank, 2015)) will be considered as poor. This value is assumed for the poor countries. And the individual earning less than US $2 per day will be considered as poor. This value will be taken for the fewer developing countries (Yerevan, 2010). In this definition, the main focus is given on the income level of individuals and depending on the nature of countries the certain level of earning is decided which shows the how many individuals are laying below the poverty line. Not only poor or less developing countries use poverty line for measuring poverty but many superpower nations also use poverty line for measuring poverty like personal income below US$14 or $ 26 per day are considered as poor (Townsend, Sep 2006). The World Bank defines absolute poverty line as 1.25 dollar per adult equivalent per day in 2005, and then using Purchasing power parity of 2011 they again update the poverty line and that is 1.90 dollar per adult. Pakistan has a strong tradition of poverty estimation since early 1960s, mostly by independent researchers using the secondary published data of the household income and expenditure surveys (HIES). The estimation of poverty based on the micro-data of HIES was started in 1980s, and the Pakistan Bureau of Statistics (PBS) was the first public sector institution which estimated the poverty number from HIES in 1990s by applying Food Energy Intake (FEI) method on the micro datasets. The official poverty line was notified for the first time in 2002 by the Planning Commission after getting mandate of estimating poverty statistics in 2001. The first official poverty line was based on the threshold level of 2350 kcal per day per adult equivalent and applied FEI method on the micro-data of 1998-99 HIES. For consistency, this line, after adjusting it with CPI, was used for poverty estimation in subsequent years when the HIES was carried out, notably in 2001-02, 2004-05, 2006-07, 2007-08, 2010-11 and 2011-12. However, for poverty estimates of 2016, the Planning Commission adopted the cost of basic needs (CBN) methodology and estimated the poverty line for this period as Rs. 3030 per adult

98 | P a g e

equivalent per month. After the release of Household Integrated Income and Consumption Survey (HIICS) 2016, the Ministry of Planning, Development & Reform constituted a Poverty Estimation Committee, representing academia, statistical agencies and practitioners. In Pakistan economic survey a new poverty line has also estimated using cost of basic need approved and it comes to 3030 for year 2016 per adult equivalent per month using the latest HIES. (GOP, 2016). The same latest poverty line is used to estimation of magnitude of poverty is used in our study.

It is also mentioned that for the year 2016 the poor people are categorized as (1) Poor (whose income are less than the poverty line) , (2) Ultra Poor (who are earning between Rs. 1515 to Rs. 2275.5) and (3) the extremely Poor people who are earning less than 1515. These all are mentioned in Table 3.16

Table 3.8: Poverty Categories Poverty Category Extremely Poor 50 % of the poverty line or less 1515 Ultra Poor 50%-75% 2275.5 Poor 75%-100% 3030 Source: Economic Survey FY 2016, Government of Pakistan

3.9 Adult Equivalence Scale Adult equivalent scales are used for transforming the number of household members to adult equivalents for ready comparisons and easy understanding. The estimates are based on a simple equivalent scale that weighs 0.8 to individual younger than 18 years old and 1 for all other individuals (GOP, 2008)

3.10 The Head-count Index The most commonly used measure of poverty, the head-count index, and share of poor households based on this measure. The head-count index Po can be defined as follows:

99 | P a g e

H Po = 100 N

Po measures the percentage of households that fall below the specified poverty line. H is the total number of households below the poverty line. N is the total number of households.

3.11 Poverty Gap This indicates the aggregate poverty depth of the poor relative to the poverty line. This is a good indication of the depth of poverty in that it depends on the distance of the poor below poverty line i.e., the average consumption gap between the actual expenditure of the poor and the poverty line. Poverty gap also represents the total amount of income necessary to raise everyone, who is below the poverty line up to that line. Estimating Procedure for this indicator as follow:

n 1 Z−Yi P = ∑ ( ) n i=1 Z

Where

P = Poverty Gap (Distance of the poor below the poverty line).

Z = Poverty line determining expenditure.

Yi = Consumption Expenditure of the ith poor household.

Poverty gap measures (P1) is insensitive to the numbers of poor people under the poverty line (Sen, 1976). Squared Poverty Gap (P2) measure the difference within the poor population i.e. inequality among the poor. P2 relatively gives more weight to the very poor as compared to the less poor. It is somewhat difficult to understand its application. The headcount index (H) is the percentage of the population living in households with income per person below the poverty line. The poverty gap index (PG) gives the mean distance below the poverty line as a proportion of that line (the mean is taken over the whole population, counting the nonpoor as having zero gaps.) For the squared poverty gap index (SPG) the individual poverty gaps are weighted by the gaps themselves, so as to reflect inequality amongst the poor (Foster et al., 1984). For all three, the aggregate measure is the population-weighted mean of the measures across any complete partition of the population into subgroups. Datt and Ravallion

100 | P a g e

(1992) describe our methods for estimating the Lorenz curves and calculating these poverty measures from the grouped data provided by the NBS tabulations.

101 | P a g e

Chapter 04 Result and Discussion

This chapter presents the results and discussion of the study. First, we explain demographic, social and economic characteristics of the household. Secondly, we determine the poverty level of the household by using the consumption approach of poverty and analyze the household’s poverty status according to the various demographic, social and economic characteristics of the households. Thirdly, using the appropriate econometric techniques, we identify the correlates/determinants of the incidence of poverty in the Jhang District, Punjab, Pakistan. Lastly, we discuss and interpret the results in accordance with economic theories, policy application and hypotheses of the study.

4.1: Demographic, Social and Economics Characteristic Heads of the Household

4.1.1: Gender, Marital Status, and Age of Household Heads

In Table 4.1, we would briefly analyze the profile of the head of the households. The outcome in Table revealed that all the households are headed by a male in the Jhang district during the survey period. Moreover, the material status of the head of the household was investigated, it can be seen from the Table 4.1 that out of the 1000 respondents, the 99.9 percent were married and .1 percent head of the household was single. Table 4.1 further indicates the age group of the head of households in Jhang district during August 2016 till January 2017). The total respondents were 1000 persons with the age-wise break-up as follows: persons of 19-35 years make 18.4 %; 35-50 years 33.0%; more than 50 years 48.6 % of the sample size. This signifies that almost 50% of households are head by the aged person with age more than 50 years.

Table 4.1 Gender, Marital Status, and Age of the Head of Household

Variables Frequency Percentage

Gender of the Household Heads

Male 1000 100.0

102 | P a g e

Female - -

Marital Status of the Household Heads

Married 999 99.9

Never Married/Engaged 1 .1

Age of the Household Heads in Years

19-35 184 18.4

36-50 330 33.0

More than 50 486 48.6

Source: Calculated from Primary Data Collected in 2016-17

4.1.2 Education Level and Occupation of the Household Heads

Nguyen (2007) reported that education has a very strong effect on the living standards of households and more the education of household head, higher will be the households’ living standards. The education level of household heads in Jhang District is not very high with regard to the education level of the head of household, it can be observed that majority of the households are headed by illiterate and a person who has matric education. Table 4.2 presents the education level and occupation of the household head in the study area. The findings in table 4.2 explicate that 69.3 percent of household heads are educated. Out of the 69.3 percent, 40.8 percent had matric education/secondary school education. 6.7 percent have completed their intermediate. The results further indicate that only 21 and 0.8 percent heads of the household have graduation/equivalent and post-graduation degree respectively. Asif (2007) investigates factors affecting occupational choices in rural North West Pakistan. Census data of 2825 households in 6

103 | P a g e

villages were used for the analysis. The author compares six district occupations with non-farm informal activities. It was revealed that due to the lack of natural, financial and human capital many people in the study area had to look for employment in the formal sector. Munawar et al. (2006) assess the impact of small-scale irrigation on agricultural production and poverty in marginal areas of Punjab. They computed headcount index, it was found to be 33 percent in the area. Poverty headcount index was computed to be 50, 34, 20, 37, 4, 42, 33 and 19 percent respectively for Jang, Ghraib, Fateh Jang, Attock, Gujar Khan, Kahuta, Rawalpindi, and Chakwal districts. The analysis of farm size with access to irrigation was carried out the study revealed that headcount was 44% small farmers, 40% of medium and 12% large-scale farmers. When the distribution of the heads of the household related to their occupation is examined, it can be observed that approximately half that is 48.7 percent of household heads are engaged in the farming sector. However, a small percentage of the heads of the household that is only 7.9 percent are involved in the business activities. Another occupational sector is services sector; 15.2 and 28.2 percent people were working as government and private sector employee, respectively. These results imply that the majority of the households generate their income from the farming sector in the Jhang district of Punjab.

Table 4.2 Education Level and Occupation of the Household Heads

Variables Frequency Percentage

Level of Education

Illiterate 307 30.7

Matric (SSC) 408 40.8

Intermediate/College (No degree) 67 6.7

Graduate/ equivalent 210 21.0

Post Graduate 8 0.8

104 | P a g e

Occupation

Government employee 152 15.2

Private Employee 282 28.2

Farmer 487 48.7 own business 79 7.9

Source: Calculated from Primary Data Collected in 2016-17

4.2 Demographic Characteristics of the Households

4.2.1 Type of family In the rural area of Pakistan, most of the people are living in the joint family system. According to the survey results reported in Table 4.3, a joint family system is dominant in Jhang District. The results indicate that only 19.3 percent of people are living in the nuclear family system while remaining 80.7 percent of people are in a joint family system. The results in Table 3 further indicate the household size in the Jhang district. Generally, household size has been defined as a group of people including family members, relatives, servant, and friend etc., are living and eating together or using the common cooking arrangement. The results in Table 4.3 explicate that several families are very large while a small percentage of families have small household size. 6.2 percent families consist of 1-4 members, 33.7 percent families are such, which have 5-7 members in their household, 41.6 percent families consist of 6-10 members and 18.5 percent people have household size more than 10. To sum up, the survey results reveal that the majority of the people in the Jhang district are living in the joint family system and average household size is approximately 8.

Table 4.3 Type of Family and Household Size

Variables Frequency Percent

105 | P a g e

Type of Family

Nuclear family 193 19.3

Joint family 807 80.7

Household Size

1-4 62 6.2

5-7 337 33.7

8-10 416 41.6

More than 10 185 18.5

Source: Calculated from Primary Data Collected in 2016-17

4.2.2 Age Structure

Table 4.4 indicates the age structure of the sample population in the Jhang District of Punjab. The sample population was divided into four groups including children (age 0 to 14), children (age 15 to 17), adult (18-64), aged 65 and above. According to the results of the field survey, the total population is 7983 persons in the sample area. Out of total population, 32.22 percent are children (below the age of 14 years), 16.54 percent are the children (above 14 years and below 18 years), 38.22 percent are the adults and 13.02 percent are the aged in the Jhang district. This implies that 45.24 percent people are in the nonworking age while 54.76 percent people are in the working age. This is not a good sign for the people in the Jhang district because the percentage of the nonworking age (children plus aged) people relatively high compared to developed area. It is not appropriate economically for the people of Jhang district because the working age population faces a huge burden of supporting the nonworking age population.

Table 4.4: Age Structure of the Sampled Population

106 | P a g e

Frequency Percent

Total Population 7983

Children (0-14) 2572 32.22

Children (15–17) 1320 16.54

Adult (18-64) 3052 38.22

Aged (65 and Above) 1039 13.02

Source: Calculated from Primary Data Collected in 2016-17

4.2.3 Household Size

Table 4.5 shows the average household size and the dependency ratio in the Jhang district. The results indicate that on average the household has 7.98 members. In addition, the findings in Table 4.5 shows that average male and female are 3.83 and 4.15, respectively. Female in the sampled area is more than 50 percent of the whole sample population. This is economically not a good sign because in the remote area of Pakistan female are mostly restricted to their household and they are not allowed to participate in the income generation activities, hence, it will increase the number of dependent on male working age population, especially on the earner members of the household. Table 4.5: Average Household Size

Variables

Household Size

Average Household Size 7.98

Average Male per Household 3.83

Average Female Per Household 4.15

107 | P a g e

Average Earner Per Household

Source: Calculated from Primary Data Collected in 2016-17

Family size/number of members in the household is important because as we increase the family size the burden upon the pool of resources of any family will increase and practically we have lesser and lesser resources for the welfare of individuals. Large families are more prone to poverty.

4.3 Social Characteristics of Household

4.3.1 Ownership and Structure of the Houses Generally, the majority of the people in the rural area have their own house, However, the structure of the houses are the below the standard of the normal construction. According to the primary survey Conducted in 2016-17, 94.5 percent people have their own houses, 1.8 percent people live in rented ones and 3.7 percent people live as tenants, those who work on the farms on a contractual basis. In addition, the finding in Table 6 indicates that 65.9 percent people have Pucca (cemented) houses made of concrete and iron bar, 25.4 percent people have lived in the Kaccha (mud) houses made of mud brick etc., and 8.7 % people have mixed housing structure, i.e., some portions are Pucca (cemented) and others Kaccha (mud).

Table 4.6 Ownership and Structure of the Houses

Variables Frequency Percent

Ownership of House

Own House 945 94.5

Rented 18 1.8

Tenant 37 3.7

108 | P a g e

Housing Structure

Pucca (cemented) 659 65.9

Kaccha (mud) 254 25.4

Mix (Pucca (cemented) & Kaccha (mud)) 87 8.7

Source: Calculated from Primary Data Collected in 2016-17

4.3.2 Level of Congestion

The people in the rural are facing the serious congestion problem. In the majority of the household two or more than two members of the household share the common room. The results of the survey show that the households are highly congested in the Jhang district. On average each household has 8 members. However, the average rooms per housing unit are only 2.74. Almost 3 persons in each household are share one room in the Jhang district. In term of the percentage housing unit with 1-2 rooms is very high it is 50.8 percent while housing units with 3- 5 and more than 5 rooms are 48.4 and 0.8 percent, respectively. To sum up, more the 50 percent household’s members are lived in one or two rooms and the level of congestion is very high in the Jhang district.

Table 4.7 Indices of Congestion Average Household Size 7.98

Average Rooms per Housing Unit 2.74

Average Person Per Room 2.91

Rooms per Housing Unit

Housing units with 1-2 Rooms (Percentage) 50.8

Housing units with 1-5 Rooms (Percentage) 48.4

109 | P a g e

Housing units with 5 Rooms (Percentage) 0.8

Source: Calculated from Primary Data Collected in 2016-17

4.3.3 Household Sanitation

In the case of the rural area of Pakistan, many people have not toilet and have a very poor sanitation system in their houses. The estimates based on the primary data indicate that in the Jhang district almost all people have latrines and bathrooms in their houses. However, the location and structure in the term of materials are changed across the houses. The findings of the field survey in 2016-17, 75.5 percent of households have latrine inside their houses while 24.5 percent households have latrine outside the houses. Similarly, 53 percent household have bathroom inside their houses whereas the 43 percent of households have a bathroom outside the houses. In term of the structure of the latrine and bathroom in the Jhang district, the results in Table 8 explicate that 78.4 percent households have Pucca (cemented) latrine in their houses while 21.6 percent households have Kaccha (mud) latrine in their houses. Likewise, 83.3 percent houses have Pucca (cemented) Bathroom whereas 16.7 percent households have Kaccha (mud) Bathroom in their houses.

Table 4.8 Household Sanitation

Variables Frequency Percent

Latrine System and Location

Inside 755 75.5

Outside 245 24.5

No Latrine

Latrine Structure

Pucca (cemented) 834 78.4

110 | P a g e

Kaccha (mud) 166 21.6

Bathroom System and Location

Inside 530 53.0

Outside 470 47.0

No Bathroom

Bathroom Structure

Pucca (cemented) 833 83.3

Kaccha (mud) 167 16.7

Source: Calculated from Primary Data Collected in 2016-17

4.3.4 Location of Kitchen and Fuel of Cooking

Table 4.9 presents the kitchen system and source of cooking in the Jhang district. The results indicate that almost all housing units have a kitchen in their houses. However, 71.3 percent of houses have a kitchen in the covered area and 28.7 percent of households have a kitchen in the open area. This signifies that the majority of the households are faced with a huge problem in term of cooking food etc., during the bad climate in the sampled area. In addition, the results indicate that more than half percent of families have no gas connection in their houses and rely on the other source of cooking including wood, Cylinder (LPG/Gas) etc. According to survey results, 48.3 percent households have used piped gas as a source of cooking, 31.0 percent families used Cylinder LPG/gas for the source of cooking and 20.7 percent housing units used wood for cooking purposes.

Table 4.9 Kitchen System and Source of Cooking

Variables Frequency Percent

Kitchen System

111 | P a g e

covered area 713 71.3

Open area 287 28.7

Source of Cooking

Piped Gas(SNGPL) 483 48.3

wood 207 20.7

Cylinder(LPG/Gas) 310 31.0

Source: Calculated from Primary Data Collected in 2016-17

4.3.5 Source of Drinking Water and Electricity Provision

Clean water is the basic need of life. However, most of the households in the rural area of Pakistan do not have access to clean water. Table 10 presents the source of the drinking water and lighting in the Jhang District. The estimates show that the tap water facility is not available in most of the households in the Jhang district. According to the survey results, 55 percent of households have tap water facility in their houses. Out of which 44.7 percent household have tap inside in the houses and 10.3 percent have a tap outside of houses. In addition, 35.8 houses depended on hand pumps. The remaining 9.2 percent households are relying on the lower water sources like dug well etc. Moreover, the results in Table 9 indicate that 100 percent of households are using the electricity as a source of lighting in the Jhang district. It implies that all the households in the Jhang district have access to electricity.

Table 4.10 Water Supply and Electricity Provision

Variables Frequency Percent

Water Supply

112 | P a g e

Tap inside the house 447 44.7

Tap outside the house 103 10.3

Hand Pumps 358 35.8

Dug Well and Other 92 9.2

Source of Lighting

Electricity 1000 100

Source: Calculated from Primary Data Collected in 2016-17

4.3.6 Source of Information and Communication

Table 4.11 reports the sources of information in the Jhang district of Punjab. Due to cultural norms or tradition, most of the households do not have access to television. According to field survey results, 62.2 percent have television facility in their houses and have access to electronic media, 32.5 percent households have newspapers facility in their houses and reading newspaper. In addition, 73.4 housing units are listing radio in the Jhang district. This signifies that radio channels are the main source of the information in the Jhang district. Moreover, the results show that the majority of the housing units have a landline phone and mobile facilities in their houses for communication. According to filed survey results, 39.7 percent household have a landline phone and 94.3 percent housing units have a cellular/mobile facility in their houses in Jhang district while 3.7 percent of households have internet facility in their houses.

Table 4.11 Source of Information and Communication

Yes No

Sources Frequency Percent Frequency Percent

113 | P a g e

Landline Phone 397 39.7 603 60.3

Cellular / Mobile 943 94.3 57 5.7

Internet 37 3.7 963 96.3

TV/Cable 622 62.2 378 37.8

Radio 734 73.4 266 26.6

Newspaper 325 32.5 675 67.5

Source: Calculated from Primary Data Collected in 2016-17

4.3.7 Health Facilities and Household’s Health

In the current state, the health facilities in the rural areas of Pakistan are not good enough. In most areas, the proper doctors and other medical staff are not available. The majority of the doctors in Pakistan do not want to work in rural areas especially in the remote areas due to lack of proper residential and other infrastructure. Table 12 present the health facilities and other health indicators in the Jhang district of Punjab. The finding indicates that around 98 percent of households have access to health facilities and there are at least one hospital, basic health unit, rural dispensaries available in the sampled areas. However, the children immunization facility were not reachable to all the people in the Jhang district and 87.7 percent household have access to vaccination centers and immunization facility and 11.3 % housing units do not have access to them. In rural Pakistan, some segment of the population is avoiding the vaccination to their children, especially pregnant women due to various reasons. However, in the Jhang district, 83.7 percent of families have immunized their children against basic diseases while 44.2 percent households have vaccinated their women against basic diseases during the pregnancy. Due to the

114 | P a g e

poor health facilities in the target areas, the majority of the population was dissatisfied with the health services in the Jhang district. The results further explicate that 63.3 percent of households are not satisfied with the health facilities while the remaining 36.7 percent are satisfied with the health facilities in the Jhang district.

Table 4.12 State of Health Facilities

Variables Yes No

Frequency Percent Frequency Percent

Hospital, BHU etc. 997 99.7 3 0.3

Immunization and Vaccination Facilities 871 87.1 129 12.9

Immunize Children against Basic Diseases 837 83.7 163 16.3

Vaccinate pregnant Female 442 44.2 558 55.8

Satisfaction with Health Facility 367 36.7 633 63.3

Source: Calculated from Primary Data Collected in 2016-17

4.3.8 Education Facilities and Household’s Members Literacy Education can add to the value of production in the economy and also to the income of the person who has been educated. In this way, education is played the critical role in the development of skill labors that eventually improve the standard of living of the population as well as reduce the poverty. However, the majority segment of the population, especially in the rural areas, is not literate due to inadequate education facilities in most areas of Pakistan. The estimates in Table 13 show that around 99 percent of households have access to primary, middle and high schools and the mentioned schools are available in the sampled areas. In addition, the results of field survey indicate that there is no boys and girls degree college in the Jhang district while technical college available in the sampled area and reachable to all the households included in the sample. Moreover, the field survey estimates reveal that madrasah for both boys and girls

115 | P a g e

are accessible to all the households included in the survey. Furthermore, the results in Table 13 show that more than half population i.e. 55.3 percent of housing units in the target area were dissatisfied with the educational facilities available in the Jhang district.

Table 4.13 State of Education Facilities

Yes No

Availability of Education Facility Frequency Percent Frequency Percent

Primary School 999 99.9 1 0.1

Middle School 999 99.9 1 0.1

High School 988 98.8 12 1.2

Boy's Degree College 1000 100

Girl's Degree College 1000 100

Technical College 1000 100

Madrasah (Boys) 1000 100

Madrasah (Girls) 1000 100

Satisfaction with Education Facility 447 44.7 553 55.3

Source: Calculated from Primary Data Collected in 2016-17

Generally, education is considered an important instrument to reduce poverty. Fabre & Augersaud-Veron (2004) estimated the effect of poverty and educational policies on child labor, growth and school attendance. Education has economic objectives along with many other objectives. As, Babatunde & Adefabi (2005) argued that education is triggering economic growth through many factors like enhancing the employment opportunities, improving health facilities, reducing fertility and poverty level, improving technological development and source of political

116 | P a g e

stability. Education is a critical dimension of socio-economic development across the globe. It plays a significant role in the employment generation as well as in the creation of high income. However, the literacy rate in Pakistan particular in the rural areas is very low. Table 14 presents the state of education of the household children and household’s literacy in the Jhang district. The estimates indicate that 39.4 and 5.4 percent of housing units have no male and female children in school, respectively. While the remaining have 1, 2, 3 and more than male and female children in the school, respectively. Moreover, the results indicate that the overall literacy rate of the household’s members is not good. Table 14 shows that 23.0 and 15.8 percent of families have not illiterate male or female members of their households, respectively. While the remaining all families have at least one illiterate male and female members in their household. Apart from this, the results also explicate that more than half i.e. 53.8 and 58.0 percent housing units have only 1 or 2 literate male or female members of their households, respectively. While the remaining housing units have more than 2 literate male and female members in their households. This implies that overall literacy rate, as well as male and female literacy rate in the Jhnag district, are relatively low compared to other areas of Pakistan. Table 4.14 Children in School and Household’s Members Literacy Rate

Variables Male Female

Frequency Percent Frequency Percent

Children in School None 394 39.4 54 5.4 1 293 29.3 396 39.6 2 313 31.3 442 44.2 3 61 6.1 More than 3 47 4.7

Household’s Literacy

Illiterate Members in Households

None 230 23.0 158 15.8 1-2 645 64.5 592 59.2

117 | P a g e

3-4 116 11.6 236 23.6 More than 4 9 0.9 14 1.4

Literate Members in Households

None 22 2.2 9 0.9 1-2 538 53.8 580 58 3-4 386 38.6 356 35.6 More than 4 54 5.4 55 5.5

Source: Calculated from Primary Data Collected in 2016-17

4.4 Socio-economic Characteristic of Households

4.4.1: Ownership of Assets

4.4.1.1 Household Landholdings In the rural areas of Pakistan, farming is the main source of income. Hence, the ownership of the land is generally considered the main source of the food and income in rural communities. However in Pakistan particularly in the rural areas, the ownership of land is highly unequal. The unequal distribution of land results in a tenancy arrangement such as sharecropping which eventually leads to the high prevalence of absolute poverty particularly in Pakistan especially in the rural areas. Second, the lack of land ownership compels the people especially the poor households to seek other sources of income such as agricultural labor and daily wage labor. Since the agricultural and daily wage labors are considered the primary factors of rural poverty across the globe particularly in Pakistan. Table 15 reports the estimates of landholding in the Survey Conducted in 2016-17. The results indicate that 36.3 percent of households had no cultivated land while 63.7 percent of households had a land holding of various scales in the Jhang district. For example, 2.6 percent of households had a land holding of up to 5 acres, 42.4 percent housing units had 6 to 10 acres and 12.4 percent households had 10 to 20 acres cultivated the land. The remaining 6.2 percent of households own greater than 20 acres of cultivated land suggesting a highly skewed land ownership pattern in Jhang district. These estimates imply that a small segment of households holds a large size of land and there is a highly unequal distribution of cultivated land in the Jhang district.

118 | P a g e

Table 4.15 Cultivated Land per Housing Unit

Land in Acres Frequency Percent

No Cultivate Land 364 36.4

Up to 5 acres 26 2.6

6-10 acres 424 42.4

10-20 acres 124 12.4

21-30 acres 31 3.1

31-40 acres 1 0.1

40 acre or more 30 3.0

Source: Calculated from Primary Data Collected in 2016-17

4.4.1.2 Livestock Population After the farming, the livestock is the second major source of income generating and food in the rural areas of Pakistan. Livestock production is closely associated with rural poverty since livestock is considered a principal asset in a rural economy like Pakistan. It plays a major role in the socio-economic development of rural communities of Pakistan. Table 16 present the livestock population (cows, buffalos, Goats, sheep etc.) in the Jhang district. The estimates explicate that 96.4 percent has own livestock while only 3.6 percent of households do not have livestock in the Jhang district. Moreover, results show that out of who owned livestock, 28.6 percent households had up to 3 livestock population, 31.6 percent housing units had 4-6 livestock population, 11.5 percent households had 7-10, and 8.1 percent households had 11-15 livestock population while the remaining 16.5 percent households had more than 15 livestock population.

Table 4.16 Livestock Population per Housing Unit

Number of Livestock Frequency Percent

None 36 3.6

119 | P a g e

Up to 3 286 28.6

4 - 6 316 31.6

7 - 10 115 11.5

11 - 15 81 8.1

16 - 20 145 14.5

More than 20 21 2.1

Source: Calculated from Primary Data Collected in 2016-17

4.4.1.3 Total Assets of the Household It is conventional wisdom among the economic experts and policymakers that assets of the households perform a principal role in the socio-economic conditions of a community. Households who had a lot of physical assets like land, livestock, business etc., are economically resourceful that can be read from the opposite end as well. However, there is a highly unequal distribution of assets especially land and access to water supply in the rural areas of Pakistan which causes an incidence of poverty among the rural communities. In the current study, the physical assets occur in the form of land, livestock, house, equipment such as tractors, thresher etc., household appliances such as electronic goods and personal cars of the households. Table 17 reports the value of total assets of the households. The finding indicates that assets are highly unequally distributed in the Jhang district. For example, only 14. 7 percent of households have assets worth more than Rs. 5000000 while the remaining, 1.5 percent households possess assets worth 1 to 500000 rupee, 8.9 percent households have assets worth 500001 to 1000000 rupee, 18.2 percent families have assets worth 1000001 to 2000000 rupee and 56.7 percent households have assets worth 2000001 to 5000001 rupee in the Jhang district.

Table 4.17 Total Assets of the Household

Value of assets in Rupee Frequency Percent

1-500000 15 1.5

120 | P a g e

500001-1000000 89 8.9

1000001-2000000 182 18.2

2000001-5000000 567 56.7

More than 5000000 147 14.7

Source: Calculated from Primary Data Collected in 2016-17

4.4.5 Employment Categories and Earning Members per Housing Unit

The people in the Jhang district were involved in various income-generating activities. However, the agricultural especially farming is a preferred source of income in the Jhang district. The estimates indicate that around 48 percent of households have the main source of income is farming and 13.2 percent of households are involved in the livestock to generate the income. On the other hand, a small segment of population i.e. 17.1 and 13.9 percent of households have the main source of the income service sector and small or medium business, respectively. 5.6 percent of households are associated with daily wage labor and rest of the 2.0 percent are involved in other income generating activities like transport, overseas employee etc. To sum up, the livelihood of the people in the Jhang district highly depend on farming and livestock.

Table 4.18 Sources of Income

Frequency Percent

Farming 482 48.2

Livestock 132 13.2

Services (Government/Private) 171 17.1

Business 139 13.9

121 | P a g e

Daily wage labor 56 5.6

Other 20 2.0

Source: Calculated from Primary Data Collected in 2016-17

Furthermore, the economic dependency ratio (household member not working or earning/working members of the household) is the prime factor of the poverty in Pakistan, particularly in rural areas. Most of the household’s members in Pakistan especially in the rural areas depend on other members of the household. In the rural areas of Pakistan, mostly females are not allowed to participate in the economic generating activities due to social norms etc. Secondly, most of the male members of the households also unemployed in the rural areas of Pakistan. The prime reasons for the unemployment in the sampled areas are a lack of industries or other employment creating business activities. Table 19 shows the number of earning members per households in the Jhang district. The results indicate that the working member’s ratio in the Jhang district are very low and on average 2.03 members (including both male and female) of the household are working or earning. However, the average household size in the Jhang district is 7.98 and average economic dependency ratio is around 3 in the Jhang district. Moreover, the estimates in Table 19 demonstrate that 44.7 percent families have only 1 male earning or working member in their households while 26.1 and 29.2 percent housing units have 2 and 3 male earning members in their households, respectively. On the other side, only 18.8 percent of household’s females are participating in the economic generating activities. However, only 1 female member is earning in these households. This implies that the majority of the females in the Jhang district do not participate in the economic generating activities.

Table 4.19 Earning Members per Household Male Female

Number of Earners

Per Households Frequency Percent Frequency Percent

122 | P a g e

None 816 81.6

1 447 44.7 184 18.4

2 261 26.1

3 292 29.2

Source: Calculated from Primary Data Collected in 2016-17

4.4.6 Income Dynamics in Jhang District

4.4.6.1 Income from Agriculture The agricultural sector is the main source of income in the Jhang district. The majority of the people in the sampled areas are involved in agricultural activities as a principal source of income. The results indicate that around 70% and 60% have income from farming and livestock, respectively. However, there is a huge variation in term of income from farming and livestock among the households. For example, 38.7% and 52.6% of households have income worth 1 to 5000 rupees per month from farming and livestock, respectively. In the income group of 5001 to 10000 rupee per month from farming and livestock, there are 18.5% and 5.1% households, respectively. The remaining 5.7% and 5.3% of households have monthly income from farming are worth 10001 to 15000 rupees and more than 15000 rupees, respectively. This signifies that there is the highly unequal distribution of agricultural income in the Jhang district. Only a small segment of population i.e. 10.8 percent of households have more than 15000 rupees monthly income from farming in the sampled area.

Table 4.20 Income from Farming and Livestock

123 | P a g e

Farming Livestock

Income per Month Frequency Percent Frequency Percent

None 317 31.7 423 42.3

1 - 5000 387 38.7 526 52.6

5001 - 10000 186 18.6 51 5.1

10001 - 15000 57 5.7

More than 15000 53 5.3

Source: Calculated from Primary Data Collected in 2016-17

4.4.6.2 Income from Business In the Jhang district, a small portion of the population is involved in the business-related activities to generate the income. The results in Table 21 explicate that only 21.3 percent of households have income from the business. The income from business has been divided into four income categories with monthly income; 1 to 10000 rupees (5.4 percent), 10001 to 20000 rupees (8.7 percent), 20001 to 30001 (4.3 percent) and the remaining 2.9 percent households monthly income from the business is more than 30000 rupees.

Table 4.21 Income from Business

Income per Month Frequency Percent

None 787 78.7

1 - 10000 54 5.4

10001 - 20000 87 8.7

20001 - 30000 43 4.3

More than 30000 29 2.9

Source: Calculated from Primary Data Collected in 2016-17

124 | P a g e

4.4.6.3 Income from Services In the contemporary world, the service sector is playing a crucial role in the rural economy. However, in the Jhang district, a very small number of people work in the government offices and private sectors employees. Table 22 presents the household’s monthly income from services including government and private in the Jhang district. The findings explicate that around 62% of households have income from the services. The monthly income from the services sector has been divided into four income groups. However, in the income group of 1-10000 rupee per month, there are 9.0 percent households; in the income group of 10001 to 20000 and 20001 to 30000 rupee per month, there are 41.6 and 6.4 percent households, respectively. The remaining 5.8 percent households have monthly income from the services is more than 30000 rupees. ; in the income group of Rs. 20001-30000, there are 5.7% people; while in the income group of more than Rs.30000 there are 2.8% Households.

Table 4.22 Income from Services

Income per Month Frequency Percent

None 372 37.2

1 - 10000 90 9.0

10001 - 20000 416 41.6

20001 - 30000 64 6.4

More than 30000 58 5.8

Source: Calculated from Primary Data Collected in 2016-17

4.4.6.4 Per Month Income of the Households from all Sources Table 23 presents the families monthly income from all sources in the Jhang district. The households have placed into five different income groups with monthly family income; 1 to 10000 rupees (12.6 percent), 10001 to 2000 rupees (37.4 percent), 20001 to 30000 rupees (32.4 percent), 30001 to 50000 rupees (13.7 percent), and the remaining 3.9 percent household’s monthly family income more than 50000 rupees in the Jhang district. These estimates signify that

125 | P a g e

there is high-income inequality in the Jhang district. Only 3.9 percent of households have monthly income more than 50000 rupees.

Table 4.23 Total Monthly Income of the Household

Income per Month Frequency Percent

1-10000 126 12.6

10001-20000 374 37.4

20001-30000 324 32.4

30001-50000 137 13.7

More than 50000 39 3.9

Source: Calculated from Primary Data Collected in 2016-17

4.4.7 Household’s Expenditure

4.4.7.1 Total per Month Expenditure of the Households Household’s expenditure is the amount of the total final consumption made by resident households to meet their daily needs like food, clothing, housing, transport, energy etc., and durable goods. The results show that majority of the households i.e. 75.1 percent have very low level monthly total expenditure (up to 20000 rupees per month) in the Jhang district. Moreover, the findings indicate that a minor portion of the households i.e. 1.9 percent, have a large amount total monthly expenditure (more than 30000 rupees per month) while the remaining 23.0 percent households have total expenditure 20001 to 30000 rupee per month.

Table 4.24 Total Expenditure of the Household

Expenditure per Month Frequency Percent

1-10000 43 4.3

10001-20000 708 70.8

126 | P a g e

20001-30000 230 23.0

30001-50000 18 1.8

More than 50000 1 0.1

Source: Calculated from Primary Data Collected in 2016-17

4.4.7.2 Food Expenditure Generally, the households in the rural areas of Pakistan are spending the large share of their income on food. Table 25 reports the monthly food expenditure of the households in the Jhang district. The findings indicate that 54.5 percent of households have a very low level of food expenditure that is 1 to 10000 rupees per month. 42.4 percent households have food expenditure worth 10001 to 20000 per month and the remaining 3.1 percent of households have a high level of food expenditure that is more than 15000 rupees per month in the Jhang district.

Table 4.25 Food Expenditure of the Household

Expenditure per Month Frequency Percent

1 - 5000 109 10.9

5001 - 10000 436 43.6

10001 - 15000 424 42.4

15001 - 20000 23 2.3

More than 20000 8 0.8

Source: Calculated from Primary Data Collected in 2016-17

4.4.8 Income and Expenditure Table 26 presents the difference between the income and expenditure of the households in the Jhang district. The results of the field survey August 2016 explicate that 77.6 households have income is very less than their expenditure while only 0.1 percent of households have income is

127 | P a g e

more than their expenditure. The remaining 22.3 percent households have income equal to their expenditure in the Jhang district.

Table 4.26 Balance of Income and Expenditure

Balance of Income Frequency Percent

Your income is very less than expenditure 776 77.6

Your income is more than your expenditure 1 .1

Equal 223 22.3

Source: Calculated from Primary Data Collected in 2016-17

4.5 Poverty Level in Jhang District

The prime objective of the present research is to assess the poverty situation in Jhang district including the all four sub-districts for Jhang District keeping in view the detailed socioeconomic profile of the poor residing in these areas. An attempt has been made to find the incidence, depth, and severity of poverty. Through this study we can find the hierarchy of the poor, characteristics correlate to the in and out of poverty and what should be the effective measures for the alleviation of poverty. As the alleviation of poverty is the individual household phenomenon, the income distribution pattern and individual household poverty gap would lead towards the actual increase in income needed for the household to be out of the poverty trap. A lower value indicates that most of the poor are bunched around the poverty line. A higher value of poverty gap indicates the bad condition of the poor. The severity of the poverty is shown by the squared of the poverty gap. So more the poverty gap, the more would be the severity of the poverty. Table 4.27 Categorization of Poverty with respect to Income Level in Pakistan

Poverty Category Income Level

128 | P a g e

Extremely Poor 50 % of the poverty line or less 1515

Ultra Poor 50%-75% 2273

Poor 75%-100% 3030

Vulnerable 100%- 125% 3788

Quasi Non-poor 125%-200% 4545

Non-poor 200% of the poverty line and above 6060

Source: National Poverty Report 2015-16 (Ministry of Planning and Reform)

4.5.1 The Head-count Index The most commonly used measure of poverty, the head-count index, and share of poor households based on this measure. The head-count index Po can be defined as follows:

H Po = 100 N

Po measures the percentage of households that fall below the specified poverty line. H is the total number of households below the poverty line. N is the total number of households.

Headcount measures depict the proportions of people below the poverty line / causing poverty incidence and its value ranges from 0-1. HeadCount Measures does or does not locate the exact position of the poor with reference to the poverty line and this denies the distributional consideration within the poor population.

4.5.2 Poverty Gap This indicates the aggregate poverty depth of the poor relative to the poverty line. This is a good indication of the depth of poverty in that it depends on the distance of the poor below poverty line i.e., the average consumption gap between the actual expenditure of the poor and the poverty line. Poverty gap also represents the total amount of income necessary to raise everyone, who is below the poverty line up to that line. Estimating Procedure for this indicator as follow:

129 | P a g e

n 1 Z−Yi P = ∑ ( ) n i=1 Z

Where

P = Poverty Gap (Distance of the poor below the poverty line).

Z = Poverty line determining expenditure.

Yi = Consumption Expenditure of the ith poor household.

A poverty gap measure is the one, which is more commonly used for measuring of poverty. It is also called a poverty income deficit. It gives the distance between the income of the poor and the poverty line. Its value ranges over the interval. Poverty gap measures are insensitive to the numbers of poor people under the poverty line (Sen, 1976). Squared Poverty Gap measures the difference between the poor i.e. inequality among the poor relatively gives more weight to the very poor as compared to the less poor.

4.5.3 Decomposition of Poverty in Jhang District According to the field survey 2016-17, poverty levels vary in Jhang District as shown in Table 28

Table 4.28 Poverty Estimates of Households of Jhang District at Glance

Poverty Measures Poverty Estimates

Poverty Incidence 54.3%

Poverty Depth 0.36

Severity of Poverty 0.13

Source: Calculate from the Survey results

To measure the extent of poverty i.e. poverty ratio or headcount ratio, three different poverty lines are used. According to the results of the poverty measures, 54.3% of households are poor that shows that more the half of the population is below the poverty line which is very critical situation for this district and also for the authorities of this Province and Pakistan. The poverty depth is about 0.36 which means that 36% of the poverty line is required to escape poverty in the

130 | P a g e

Jhang District. The severity of poverty is estimated at 0.13%, implying that there is 13% inequality among the poor. Put differently, a higher weight is placed on those households who are further away from the poverty line. These studies are FBS (2002), Malik (1996) and Chaudhry (2003). Social Policy and development center (SPDC) also reported the poverty situation for the country annually and in the and also in the same period that as per the latest conducted HIES (2016) that in Pakistan 37.9% people are living under the Poverty line. They also bifurcate this and estimated that 31.0% of people are poorer are in urban areas and 41.2% poor people are in rural areas of Pakistan. They also reported that Severity is 8.2 and Depth is 2.5 in all over Pakistan as per their estimates however in rural areas it is 9.0 and 2.8 respectively and in urban areas these are 6.7 and 2.1 respectively. (SPDC, 2017)

Table 4.29 Magnitude of Poverty in Jhang District with respect to the Poverty Bands (Income Level) Income Poverty Frequency % Income Poverty Frequency % Bands Bands Level Level

Less than Poor 543 54.3% 1515 Extremely 163 16% 3030 Poor 2273 Ultra poor 196 20%

3030 Poor 184 18%

More than Non 457 45.7% 3788 Vulnerable 189 19% 3030 Poor 4545 Quasi Non- 71 7% Poor 6060 Non- poor 197 20%

Source: Calculate from the Survey results

Further Classification of all Poverty band with respect to the Income level is also highlighted in the above-mentioned table 4.29. Which show that out of total almost 54.3% poor people, 16% are categorized as extremely poor households, whose income are less than Rs. 1515 (per capita) and 20% people are ultra-Poor in the Jhang district whose income is less than 2273 and more than Rs.

131 | P a g e

1515, and remaining 18% people are categorized as poor and their income level is from Rs. 2273 to Rs. 3030. So overall all 54.3% of people in the Jhang district as per the primary survey conducted in 2016-17 are under the poverty line and the remaining 45.7% are not categorized as Non-Poor in Jhang District. In Non-Poor there are also three categories which are categorize as Vulnerable, Quasi Non-Poor and Non –Poor. There are 19% people who are Vulnerable (whose income is less than Rs. 4545 and more than Rs. 3030). These are the people whore considered as Non Poor but there is also a chance (to some extent) that they can be poor in near future if Govt. or they themselves not take interest for increasing their income and 7% people who are Quasi Non poor (Income level from Rs. 4545 to Rs. 6060) and only 20% who are considered as Non- Poor category and their Income are more than 6060.

4.5.4 Decomposition of Poverty in all four Sub Districts of Jhang

There are four Sub Districts in the Jhang District which are Jhang Sub District, Shorkot Sub District, AhmedPur Sial Sub District and Athara Hazari Sub District. Poverty estimate and categorization of Poor and Nonpoor category for each sub district (Tehsil) are as follow.

4.5.4.1 Poverty Estimates in Jhang Sub District Table 4.30 Poverty Estimates of Households of in Jhang Sub District

Poverty Measures Poverty Estimates

Poverty Incidence 51.3%

Poverty Depth 0.36

Severity of Poverty 0.13

Source: Calculate from the Survey results

The headcount poverty index is given by the percentage of the population that lives in the household with consumption per capita less than the poverty line. The survey data results of the poverty measures indicate that 51.3% of households are poor(below the poverty line). The poverty depth is about 0.36 which means that 36% of the poverty line is required to escape

132 | P a g e

poverty in the Jhang Sub District. The severity of poverty is estimated at 0.13, implying that there is 13% inequality among the poor. Put differently, a higher weight is placed on those households who are further away from the poverty line. 51% people are poor in this sub district which is less than the total poor people are as reported in the Jhang District which is 54.3%.

Table 4.31 Magnitude of Poverty in Jhang Sub District (Tehsil) with respect to the Poverty Bands (Income Level) Income Poverty Frequency % Income Poverty Frequency % Bands Bands Level Level

Less than Poor 197 51.3% 1515 Extremely 59 15% 3030 Poor

2273 Ultra poor 75 20%

3030 Poor 63 16%

More than Non 187 48.7% 3788 Vulnerable 73 19% 3030 Poor Quasi Non- 21 5% 4545 Poor

6060 Non- poor 93 24%

Source: Calculate from the Survey results

Poverty band is also very much showing the same picture as of the Jhang District. In Jhang Sub district ,51.3% people are Poor and 48.7% are non-Poor. 15% are extremely poor, 20% are ultra- poor and 16% are in the poor category. And 19%, 05%, and 24% are Vulnerable, Quasi Non- poor and Non-Poor respectively. 15% people are extremely poor and very alarming sign (income level is less than Rs. 1515)

4.5.4.2 Poverty estimates in ShorKot Sub District Table 4.32 Poverty Estimates of Households of in ShorKot Sub District

Poverty Measures Poverty Estimates

133 | P a g e

Poverty Incidence 57.6%

Poverty Depth 0.36

Severity of Poverty 0.13

Source: Calculate from the Survey results

According to the results of the poverty measures, 57.6% of households are poor. In this sub district almost 2/3 of the population is under the poverty line and special attention is required for reducing the poverty specifically in the sub district. The poverty depth is about 0.36 which means that 36.9% of the poverty line is required to escape poverty in the ShorKot Sub District. Shorkot sub-district is having a worse number in poverty as compared with the overall figures of the Jhang District. The severity of poverty is estimated at 0.13, implying that there is 13% inequality among the poor.

Table 4.33 Magnitude of Poverty in Shor Kot Sub District

Income Poverty Frequency % Income Poverty Frequency % Bands Bands Level Level

Less than Poor 186 58% 1515 Extremely 60 19% 3030 Poor

2273 Ultra poor 68 21%

3030 Poor 58 18%

More than Non 137 42% 3788 Vulnerable 57 18% 3030 Poor Quasi Non- 22 7% 4545 Poor

6060 Non- poor 58 18%

Source: Calculate from the Survey results

In Shorkot sub-district 19% people are living under the extreme poverty line which means that 19% of the total households are having the income less than Rs. 1515. Overall 58% people are

134 | P a g e

Poor in the Sub District and 42% are non-Poor. And from those 58% poor households in the Shorkot Sub-District, 19 %, 21% and 18% are Extremely poor, Ultra poor and Poor respectively. And from Non-Poor category 18%, 07%, and 198% are Vulnerable, Quasi Non-poor and Non- Poor respectively.

4.5.4.3 Poverty Estimates in Athara Hazari Sub District Table 4.34 Poverty Estimates of Households in Athara Hazari Sub District

Poverty Measures Poverty Estimates

Poverty Incidence 56.0%

Poverty Depth 0.38

Severity of Poverty 0.14

Source: Calculate from the Survey results

Poverty Depth and Severity of Poverty is higher as compared with in all four sub-districts of Jhang district. According to the results of the poverty measures, 56.0% of households are poor. The poverty depth is about 0.38 which means that 38% of the poverty line is required to escape poverty in the Atthara Hazari Sub District. The severity of poverty is estimated at 0.14, implying that there is 14% inequality among the poor.

Table 4.35 Magnitude of Poverty in Atthara Hazari Sub District

Income Poverty Frequency % Income Poverty Frequency % Bands Bands Level Level

Less than Poor 75 56% 1515 Extremely 30 22% 3030 Poor

2273 Ultra poor 21 16%

3030 Poor 24 18%

More than Non 59 44% 3788 Vulnerable 30 22%

135 | P a g e

3030 Poor Quasi Non- 13 10% 4545 Poor

6060 Non- poor 16 12%

Source: Calculate from the Survey results

In Attahara Hazari sub-district 14% household is living under the extreme poverty line. Poverty bands for Poor categories are also very much vary in the Atthara Hazari sub District. 56% people are Poor in the Sub District and 44% are non-Poor. And from the 56% poor in the Atthara Hazari Sub District, 22 %, 16%, and 18% are Extremely poor, Ultra poor and Poor respectively. And from Non-Poor category 30%, 13%, and 16% are Vulnerable, Quasi Non-poor and Non- Poor respectively.

4.5.4.4 Poverty Estimates in Ahmed Pur Sub District Table 4.36 Poverty Estimates of Households of in Ahmed Pur Sub District

Poverty Measures Poverty Estimates

Poverty Incidence 53.5%

Poverty Depth 0.28

Severity of Poverty 0.08

Source: Calculate from the Survey results

According to the results of the poverty measures in Ahmed Pur Sub District, 53.5% of households are poor. The poverty depth is about 0.28 which means that 28% of the poverty line is required to escape poverty in the ShorKot Sub District. The severity of poverty is estimated at 0.08, implying that there is 8%% inequality among the poor.

Table 4.37 Magnitude of Poverty in Ahmed Pur Sub District

Income Poverty Frequency % Income Poverty Frequency % Bands Bands Level Level

136 | P a g e

Less than Poor 85 54.3% 1515 Extremely 14 9% 3030 Poor

2273 Ultra poor 32 20%

3030 Poor 39 25%

More than Non 74 45.7% 3788 Vulnerable 29 18% 3030 Poor Quasi Non- 15 9% 4545 Poor

6060 Non- poor 30 19%

Source: Calculate from the Survey results

Poverty band is also very much vary in the Ahmed Pur Sub District. 54.3% people are Poor in the Sub District and 45.7% are non-Poor. And from the 54.3% poor in the Ahmed Pur Sub District, 14 %, 32% and 39% are Extremely poor, Ultra poor and Poor respectively. And from Non-Poor category 29%, 15%, and 30% are Vulnerable, Quasi Non-Poor and Non-Poor respectively.

4.5.5 Comparison of Poverty numbers in all four Sub-districts in Jhang District According to the survey Conducted in 2016-17, Shorkot sub-district is the poorest sub-district in all of four sub-districts of Jhang district, where Poverty incidence is is reported as 57.6% and after that Athara Hazari is second poorest sub-district where 56% poverty rate is reported.

Poverty Depth is highest in Athara Hazari sub-district which is 0.38 that means 38% of the poverty line is required to escape poverty in that Sub District. The severity of Poverty is 15.4% in Athara Hazari sub-district and it is the highest in all four sub-districts. That means, in Athara Hazari Subdistrict, 15% of a gap is among the poor and such volume of resources is needed to bring these households closer to the poverty line or above it. Most extremely poor are living in the Atthara Hazari Sub District where is reported as 22% of the total household and whose income level is less than Rs. 1515.

137 | P a g e

4.6 Correlates of Poverty in Jhang District Following are the complete descriptions of all of the identified correlates of poverty in Jhang district. All variables are already discussed in detail in the previous chapter and we are finding their relationship with poverty. All variables are micro variables and can be managed by the households easily at the household level.

4.7 Decomposition of Poverty in Jhang District by Household Characteristics A poverty profile describes the pattern of poverty, but is not principally concerned with explaining its causes. Yet a satisfactory explanation of why some people are poor is essential if we are to be able to tackle the roots of poverty. Among the key causes, or at least correlates, of poverty are: Household and individual characteristics: Among the most important are: Demographic: household size, age structure, dependency ratio, gender of head of the Household etc. Economic: Profession, employment status, hours worked, property owned etc. Social: Health and nutritional status, education, shelter etc.

This section discusses the major correlates of poverty for poor households based on primary data analysis. The literature has identified several factors associated with the dynamics of poverty. The changing socio-demographic and economic characteristics of the household have been considered as the key drivers of poverty. Regarding the demographic characteristics, larger household size and/or dependency ratio are associated with chronic poverty as it put an extra burden on a household’s assets and resource base (Jayaraman and Findeis, 2005; Ssewanyana, 2009). Changes in household size and age structures (young, adult and elderly) are also linked with the movements into and out of poverty because of their distinct economic consequences (Bloom et al, 2002). Additional children not only raise the likelihood of a household to fall into poverty but it also leads to intergenerational transmission of poverty due to a reduction in school attendance of children with a regressive impact on poorer households (Orbeta, 2005). Households headed by the female are more likely to be chronically poor (John and Andrew, 2003);

138 | P a g e

4.7.1 Decomposition of Poverty by Educational Attainment Various characteristics of the household have direct or indirect bearings on the income- generating activities or consumption pattern of the households. These economic aspects of the individual household determine the living standard of the household by which the poverty status has been measured. The first demographic characteristic is the educational attainment of the head of the household. According to a most recent economic survey of Pakistan, the literacy rate is 56 percent (males are almost 69 percent and females are only 44 percent) which is extremely low also compared with the south Asian countries. A person is considered literate if she can read and write her name. Therefore these rates are quite alarming.

Table 4.38 Decomposition of Poverty by Educational Attainment

As %age of Poor Household Headcount Poverty Severity Education level Households (%) index Depth of Poverty

Post Graduate 0% 1% 0% - -

Graduate/Equivalent 13% 21% 7% 0.33 0.11

Intermediate/College 9% 7% 5% 0.32 0.10

Matric (SSC) 36% 41% 19% 0.25 0.06

Illiterate 48% 31% 26% 0.45 0.20

Source: Primary data Analysis

A number of studies have shown that the increase in human capital reduces the likelihood of being chronic poor or transient poor. Such evidence from literature has been seen in the milieu of the education of the head of the household (Wlodzimierz, 1999; Arif et al., 2011) as well as the education of the children to overcome the persistent poverty (Davis, 2011). Education is human capital and one of the 2015 human development goals. Around one billion people were unable to read and sign their names at the start of the 21st century (IFAD, 2007). How developed countries become prosper. There are many reasons and one is more important is education. Education of

139 | P a g e

the head of households has a significant and negative relation with chronic poverty or being poor the heads of the families are mostly illiterate (31%) and having only matric degrees (41%). The small number is headed by people who are educated persons; having a Bachelor or Intermediate Only 1% of people who are Household Head are having Post Graduate/Master Degrees. The table shows that all three measure headcount index, depth, and severity are worse among the households that are headed by illiterate persons. The results also show that as a level of education of households increase, the incidence, depth, and severity of poverty decrease. It can indicate that the level of education of household head has a negative relationship with the level of poverty in the target area. Rodriguez and Smith (1994) used a Income regression model to estimate the effect of different economic and demographic variables on the probability of a household being in poverty in Costa Rica. The source of the data was from National Household- Income (1986). Their results showed that poverty was higher for the household whose heads had a lower level of education. Examining separately in our subject district in Pakistan, it has been observed that the education of the head of the household is negatively and the dependency ratio is positively related to the poverty status of the household. Better education can be an effective tool for reducing poverty and enhancing economic growth in Pakistan specially Jhang District.

Table 4.39 Decomposition of Poverty by Educational Attainment in all four Subdistricts

Post Graduate Graduate/Equiva Intermediate/C Matric (SSC) Illiterate lent ollege Poverty Freq. % Freq. % Freq. % Freq. % Freq. % Bands Jhang Sub District Poor 0 0% 60 68% 9 31% 72 46% 88 85%

Non 8 100% 28 32% 20 69% 83 54% 16 15% Poor

Total 8 88 29 155 104

ShorKot Sub District Poor 0 0% 16 27% 9 36% 60 48% 101 89%

140 | P a g e

Non 0 100% 43 73% 16 64% 65 52% 13 11% Poor

Total 0 59 25 125 114

Atthara Hazari Sub District Poor 0 0% 3 23% 3 21% 32 60% 38 68%

Non 8 100% 10 77% 11 79% 21 40% 18 32% Poor

Total 8 13 14 53 56

AhmedPur Sial Sub District Poor 0 0% 24 48% 14 21% 30 40% 31 91%

Non 0 0% 26 52% 53 79% 45 60% 3 9% Poor

Total 0 50 67 75 34

Source: Primary data Analysis

Education has economic objectives along with many other objectives. Any nation cannot be developed without having the education which actually plays a very crucial role in the building of human economic growth. Raja (2005) argued that education is the first step in the path of the development process along with the reduction in poverty in any nation. It is a two-way process, on one side, it increases the economic growth and on the other side, it reduces poverty and increases productivity. It plays a very crucial role in the building human capabilities and enhances economic growth through skills and knowledge. Investors are more interested in that country, where there is ample stock of human capital. We have drawn a strong and significant relationship between educational attainment and rural poverty. Survey results conclude that in Jhang District the households with no educational attainment have the highest incidence, depth, and severity of poverty. There is evidence that with a rising level of educational attainment, all three measures of poverty fall.

141 | P a g e

4.7.2 Decomposition of Poverty by Job Structure In Pakistan, the agriculture sector employed more than 45% of the population. Out of which, 85% are the small farmers. Sabir, Hussain & Saboor (2006) have used the sample of 300 small farmers of central Punjab to investigate the status of poverty among them. They found that education is the factor that could reduce poverty. However, old age of the head of household, the large size of household, small output and low price, insufficiency of infrastructure and dependency ratio are the few determinants of high poverty in central Punjab, Pakistan.

Table 4.40 Decomposition of Poverty by Job Structure

As %age of Poor Household Headcount Severity of Job Structure Poverty Depth Households (%) index Poverty

Private Job 38% 42% 21% 0.29 0.08

Government 0.14 0.02 9% 15% 5% employee

Farmer 51% 35% 28% 0.46 0.21

Own Business 1% 8% 1% 0.08 0.01

Source: Primary data Analysis

In order to have an idea about the poverty decomposition/ status of persons engaged in different occupations, the incidence of poverty has been calculated for major occupation groups. Results show that incidence of poverty is highest among the daily wage worker and lowest among the government employees According to field survey Conducted in 2016-17, the poverty incidence, depth, and severity are high in the households that are headed by farmers. The results also show that more than 51 % poor households are headed by farmers. The all three measure headcount ratio, depth, and severity are low in the household that is headed by the government and private servants. More than 35% poor people are living in households that are mange by farmers. People who are having own businesses are not poor. This indicates that secure job and proper flow of income has direct implication for poverty status. People are more secure in the government sector, so they are less poor, while people working in

142 | P a g e

agriculture sector/ farming are not secure with their earnings. People with secure jobs like Govt. employees and having own business have more capacity to absorb economic shocks.

. Table 4.41 Decomposition of Poverty by Job Structure in all sub-districts Private Job Government employee Farmer Own Business Poverty Frequency % Frequency % Frequency % Frequency % Bands Jhang Sub District Poor 76 47% 21 35% 99 82% 1 3%

Non Poor 87 53% 39 65% 22 18% 39 98%

Total 163 60 121 40

ShorKot Sub District Poor 73 53% 16 34% 96 83% 1 5%

Non Poor 66 47% 31 66% 20 17% 21 95%

Total 139 47 116 22

Atthara Hazari Sub District Poor 30 56% 7 39% 39 71% 0 0%

Non Poor 24 44% 11 61% 16 29% 8 100%

Total 54 18 55 8

AhmedPur Sial Sub District Poor 30 45% 6 22% 46 82% 3 27%

Non Poor 36 55% 21 78% 10 18% 8 73%

Total 66 27 56 11

Source: Primary data Analysis

143 | P a g e

4.7.3 Decomposition of Poverty by Family Type

Table 4.42 Decomposition of Poverty by Family Type

As %age of Household Headcount Poverty Severity of Family Type Poor (%) index Depth Poverty Households

Nuclear family 15% 19% 8% 0.33 0.11

Joint family 85% 81% 46% 0.37 0.13

Source: Primary data Analysis

The joint family system is the dominant mode of living in the Jhang District. Due to it has increased the dependency ratio among the families in the area. The evidence shows that 81% of poor people/households are living in joint families. The results also show that the incidence, depth, and severity of poverty are worse among the joint families. 85% of the total Poor households are Poor in the Jhang District.

Table 4.43 Decomposition of Poverty by Family Type in all four sub-districts

Nuclear family Joint family Poverty Bands Frequency % Frequency % Jhang Sub District Poor 51 53% 152 53%

Non Poor 45 47% 136 47%

Total 96 288

Shorkot Sub District Poor 17 31% 169 63%

144 | P a g e

Non Poor 38 69% 99 37%

Total 55 268

Atthara Hazari Sub District Poor 10 50% 65 58%

Non Poor 10 50% 47 42%

Total 20 112

AhmedPur Sial Sub District Poor 10 50% 75 54%

Non Poor 10 50% 64 46%

Total 20 139

Source: Primary data Analysis

In all over Pakistan joint family system is the dominant mode of living in Pakistan and the same is the case of Punjab and also in Jhang District. All four sub-districts of Jhang have reported that dominant mode of living is Joint family system and due to that the dependency ratio is also on higher side due to a low number of earners in all four sub-districts in Jhang District and because of that poverty rate is on the higher side in the joint family system.

4.7.4 Decomposition of Poverty by Age of the Head of House Hold Age of the household head is playing a very important rule in the reduction of poverty. Changes in household size and age structures (young, adult and elderly) are also linked with the movements into and out of poverty because of their distinct economic consequences (Bloom et al, 2002). Additional children not only raise the likelihood of a household to fall into poverty but it also leads to intergenerational transmission of poverty due to a reduction in school attendance of children with a regressive impact on poorer households (Orbeta, 2005). According to the table, the all three measure, headcount index, depth and severity of poverty are worse among the households that are headed by aged people (60 years and above). The results also show that as an age of household head increase, the all three measures of poverty increase. The tendency shows

145 | P a g e

that the age of the household head has a positive relationship with the incidence of poverty in Jhang District. Two very common observations regarding the gender of the head of households and poverty from earlier studies are as follows: first, only a small proportion of households around 7 percent are headed by females. Second, in terms of poverty these female-headed households are not different from male-headed households.

Table 4.44 Decomposition of Poverty by Age of the Head of House Hold

Age of As %age of Poor Household (%) Headcount Poverty Depth Severity of Household Households index Poverty Head

Up to 25 3% 2% 2% 0.15 0.02

25-40 22% 24% 12% 0.24 0.06

40-60 57% 58% 31% 0.39 0.15

60+ 18% 17% 10% 0.43 0.19

Source: Primary data Analysis

Age wise distribution in all four sub-districts of Jhang district are shown in the table in 4.45

Table 4.45 Decomposition of Poverty by the age of the Household in all four sub-districts

Up to 25 25-40 40-60 60+ Poverty Freque % Frequen % Frequency % Frequency % Bands ncy cy Jhang Sub District Poor 7 70% 49 51% 113 51% 26 48%

Non Poor 3 30% 48 49% 110 49% 28 52%

Total 10 97 223 54

146 | P a g e

ShorKot Sub District Poor 5 71% 36 49% 114 58% 31 67%

Non Poor 2 29% 38 51% 82 42% 15 33%

Total 7 74 196 46

Atthara Hazari Sub District Poor 3 100% 18 55% 37 51% 17 68%

Non Poor 0 0% 15 45% 36 49% 8 32%

Total 3 33 73 25

AhmedPur Sial Sub District Poor 3 100% 16 48% 43 52% 23 58%

Non Poor 0 0% 17 52% 40 48% 17 43%

Total 3 33 83 40

Source: Primary data Analysis

As far as the age of the household head is concerned, we have concluded empirically that the older the household head, the lower the incidence, depth and severity of poverty given his or her work experience. This situation might be because of the high dependency ratio and low participation rates among the households of sub-districts of Jhang District.

4.7.5 Decomposition of Poverty by House Structure Table 4.46 Decomposition of Poverty by House Structure

House As %age of Poor Household (%) Headcount Poverty Severity of Structure Households index Depth Poverty

Pucca 55% 67% 30% 0.31 0.10

147 | P a g e

(cemented)

Kaccha 42% 25% 23% 0.43 0.19 (mud)

Mix(Pucca 3% 8% 2% 0.27 0.07 (cemented) & Kaccha (mud))

Source: Primary data Analysis

Table 4.46 shows that all the three measures, incidence, depth and severity of poverty are worse among the families who are living in Kaccha (mud) houses. About 42% of the poor are living in the Kaccha (mud) houses. The results also that the all three measures, headcount ratio, depth and severity of poverty are very low among the households that are living in Pucca (cemented) houses.

Table 4.47 Decomposition of Poverty by House Structure in all four sub-districts

Pucca (cemented) Kaccha (mud) Mix(Pucca (cemented) & Kaccha (mud)) Poverty Frequency % Frequency % Frequency % Bands Jhang Sub District Poor 114 42% 82 94% 1 4%

Non Poor 157 58% 5 6% 25 96%

Total 271 87 26 7%

ShorKot Sub District Poor 96 47% 89 94% 1 5%

Non Poor 110 53% 6 6% 21 95%

148 | P a g e

Total 206 95 22 7%

Atthara Hazari Sub District Poor 60 56% 13 76% 2 22%

Non Poor 48 44% 4 24% 7 78%

Total 108 17 9

AhmedPur Sial Sub District Poor 28 33% 46 96% 11 44%

Non Poor 58 67% 2 4% 14 56%

Total 86 48 25

Source: Primary data Analysis

The results show that household/residence in a Kaccha (mud) house was positively and significantly Correlated with the probability of being poor. In all four sub-districts of Jhang district, it is observed that household living in the Kaccha (mud) houses and Mix( Kaccha (mud) and Pacca) are having the more probability of being Poor.

4.7.6 Decomposition of Poverty by House Roof Type Table 4.48 Decomposition of Poverty by House Roof Type

As %age of Poor Household (%) Headcount Poverty Severity of Roof Type Households index Depth Poverty

Concrete 19% 12% 10% 0.43 0.18

Mud/Kachta 45% 31% 24% 0.48 0.24

Wood/Bamboo 17% 35% 10% 0.47 0.22

Iron Sheet 19% 23% 10% 0.37 0.13

Source: Primary data Analysis

149 | P a g e

The table shows that all the three measures, incidence, depth and severity of poverty are worse among the families who are living in that house where Roof type is of Mud.

Table 4.49 Decomposition of Poverty by House Roof Type in all four sub-districts

Concrete Mud/Kaccha Wood/Bamboo Iron Sheet (mud)

Poverty Bands Frequenc % Frequency % Frequency % Frequen % y cy Jhang Sub District Poor 42 86% 84 73% 30 24% 42 43 %

Non Poor 7 14% 31 27% 94 76% 55 57 %

Total 49 115 124 97

ShorKot Sub District Poor 33 87% 94 88% 28 24% 63 67 %

Non Poor 5 13% 13 12% 87 76% 31 33 %

Total 38 107 115 94

Atthara Hazari Sub District Poor 4 80% 32 80% 23 42% 16 47 %

Non Poor 1 20% 8 20% 32 58% 18 53

150 | P a g e

%

Total 5 40 55 34

AhmedPur Sial Sub District Poor 23 92% 32 73% 14 25% 8 30 %

Non Poor 2 8% 12 27% 42 75% 19 70 %

Total 25 44 56 27

Source: Primary data Analysis

4.7.7 Decomposition of Poverty by No. of Room per House

Table 4.50 Decomposition of Poverty by No. of Room per House

As %age of Poor Household (%) Headcount Poverty Severity of Room Households index Depth Poverty

1 Room 13% 7% 7% 0.48 0.23

2 Rooms 39% 29% 21% 0.42 0.18

3 Rooms 20% 47% 11% 0.27 0.08

4 & than 8% 16% 4% 0.15 0.02 4 Rooms

Source: Primary data Analysis

According to the field survey conducted in 2016-17, the families living in houses having 01 Rooms and 02 Rooms have a direct positive relationship with poverty in Jhang District. Also, Poverty Depth and Severity are also very high in those families. Which was also reported by the Buhman et al, (1988) who have concluded in that research that in Nigeria. According to NPC

151 | P a g e

(2004), 50% of the populations live below the poverty line and from those poor households about 55% of the urban populations live in single rooms, and 62% of the population has no access to primary healthcare facilities. A number of people per room are the main determinant of poverty in the Jhang District.

Table 4.51 Decomposition of Poverty by No. of Room per House in all fours sub-districts.

1 Room 2 Rooms 3 Rooms 4 & more than Rooms Poverty Frequency % Frequency % Frequency % Frequency % Bands Jhang Sub District Poor 19 95% 107 89% 59 31% 12 24 %

Non Poor 1 5% 13 11% 134 69% 39 76 %

Total 20 120 193 51

ShorKot Sub District Poor 28 93% 87 90% 54 38% 17 32 %

Non Poor 2 7% 10 10% 89 62% 36 68 %

Total 30 97 143 53

Atthara Hazari Sub District Poor 6 100 38 90% 23 39% 8 30 % %

Non Poor 0 0% 4 10% 36 61% 19 70

152 | P a g e

%

Total 6 42 59 27

AhmedPur Sial Sub District Poor 17 100 28 85% 34 45% 6 18 % %

Non Poor 0 0% 5 15% 42 55% 27 82 %

Total 17 33 76 33

Source: Primary data Analysis

4.7.8 Decomposition of Poverty by Bathroom Situation in the House

Table 4.52 Decomposition of Poverty by Bathroom Situation in the House

As %age of Poor Household Headcount Poverty Severity of Bathroom Households (%) index Depth Poverty

Outside 40% 47% 22% 0.29 0.08

Inside 60% 53% 33% 0.41 0.17

Source: Primary data Analysis

According to the field survey Conducted in 2016-17, the families who are Bathrooms are inside the House is poorer than the families whose Bathrooms are outside the Houses. Also, Poverty Depth and Severity are also very high in those families.

Table 4.53 Decomposition of Poverty by Bathroom Situation in the House in all four sub- districts.

153 | P a g e

Outside Inside Poverty Bands Frequency % Frequency % Jhang Sub District Poor 54 45% 143 54%

Non Poor 67 55% 120 46%

Total 121 263

ShorKot Sub District Poor 87 51% 99 64%

Non Poor 82 49% 55 36%

Total 169 154

Atthara Hazari Sub District Poor 35 42% 40 78%

Non Poor 48 58% 11 22%

Total 83 51

AhmedPur Sial Sub District Poor 39 40% 46 74%

Non Poor 58 60% 16 26%

Total 97 62

Source: Primary data Analysis

4.7.9 Decomposition of Poverty by Land Holding Table 4.54 Decomposition of Poverty by Land Holding

154 | P a g e

As %age of Household Headcount Poverty Severity of Land Poor (%) index Depth Poverty Holding Households

No Land 42% 36% 23% 0.29 0.09

Up to 05 0% 3% 0% 0.23 0.05 Acres

05 Acres - 58% 58% 31% 0.41 0.17 40 Acres

40 + acres 0% 3% 0% 0.56 0.31

Source: Primary data Analysis

Being from an agricultural country, most of the people of Pakistan have farming as their primary source of living. This source is shrinking with the division of lands amongst the family members and depriving honorable way of living to the families- once well of. Only 37% of rural households own land and around 35 million people in rural areas are poor- representing about 80% of Pakistan’s poor. Hence, rural poverty is found to be strongly correlated with lack of asset in rural areas. Poverty estimates using the official poverty line suggest the high prevalence of rural poverty in all provinces in Pakistan. The incidence of landlessness is common in rural areas. The land holding is a key factor with regard to the incidence, depth and severity of poverty the tendency of landholding depicted negative relation with the three poverty measures i.e. incidence, depth, and severity of poverty. More agricultural land holding less is the incidence of poverty and less land means the poverty in its acute form. Table 4.54 shows that as a household’s landholding is having mix type of relationship with Poverty. The result also shows that poverty incidence, depth, and severity very high among landless household and the households that have less than 40 acres size of land. This tendency shows the negative relationship between landholdings and the incidence of poverty. The results also show that poverty incidence, depth, and severity are worse among landless households.

155 | P a g e

Table 4.55 Decomposition of Poverty by Land Holding in all four sub-districts.

No Land Up to 05 Acres 05 Acres -40 Acres 40 + acres Poverty Frequen % Frequency % Frequency % Frequency % Bands cy Jhang Sub District

Poor 78 55% 2 18% 116 54% 1 6%

Non 65 45% 9 82% 98 46% 15 94% Poor Total 143 11 214 16

ShorKot Sub District Poor 85 71% 0 0% 101 55% 0 0%

Non 35 29% 10 100% 84 45% 8 100% Poor Total 120 10 185 8

Atthara Hazari Sub District Poor 27 53% 0 0% 47 63% 1 25%

Non 24 47% 4 100% 28 37% 3 75% Poor Total 51 4 75 4

AhmedPur Sial Sub District Poor 36 72% 0 0% 49 46% 0 0%

Non Poor 14 28% 1 100% 57 54% 2 100%

Total 50 1 106 2

Source: Primary data Analysis

156 | P a g e

Khan (2008) reported that poverty status is clearly related to land holdings. But this does not match with our analysis as we have found the mix correlation of poverty with the landholding. As people who have no land and also from 5 acres to 40 acres have a positive relationship with poverty.

4.7.10 Decomposition of Poverty by Household Size Table 4.56 Decomposition of Poverty by Household Size

As %age of Household Headcount Poverty Severity of HH Size Poor (%) index Depth Poverty Households

1-4 4% 6% 2% 0.28 0.08

5-6 7% 19% 4% 0.30 0.09

7-10 65% 57% 36% 0.38 0.14

10+ 24% 19% 13% 0.34 0.12

Source: Primary data Analysis

Changes in household size and age structures (young, adult and elderly) are also linked with the movements into and out of poverty because of their distinct economic consequences (Bloom et al, 2002). Additional children not only raise the likelihood of a household to fall into poverty but it also leads to intergenerational transmission of poverty due to a reduction in school attendance of children with a regressive impact on poorer households (Orbeta, 2005). Larger family size and high dependency ratios are associated positively with chronic poverty and negatively with the desired state of “never poor‟. Movement into and out of poverty is also more common among large households with high dependency ratio than among small households. As far as the household size is concerned, we have concluded that as household size increases the all three measures, headcount index, depth and severity of poverty is also rising. It can indicate the positive relationship between poverty and household size. The results also show, on average 8 and above members in a household imply the highest incidence, gap, and severity of poverty. 89% of poor households have 7 and more than 7 members in a household. We have also

157 | P a g e

concluded that optimal household size is 1 to 4 members, as it experiences a lower headcount index, depth, and severity of poverty. This gave the direct implication of family size and incidence of poverty so family size is positively related with the existence of poverty. Family size is important because as we increase the family size the burden upon the pool of resources of any family will increase and practically we have lesser and lesser resources for the welfare of individuals. Large families are more prone to poverty. Household size is positively related to the incidence of poverty. The large household was more likely to be poor than small household because larger households probably had more young children, that encounter financial burden due to the high cost of living, education, health and other social as well as social activities and vice versa. As expected, family size and dependency ratio are important predictors. Both determinants are highly correlated with household expenditure in urban as well as in rural areas. Similarly, as the expected number of earners also turned out significant positive determinants of household expenditure. In contrast, unemployment of head of household is negatively associated with expenditure. In the rural context, amount of agriculture land, ownership of livestock and non- residential property are all correlated positively with household expenditure. Further, non-farm Households and wage employment play a dominant role in determining the level of consumption. These two variables have negative and significant coefficients. The coefficient associated with households with large farm size is also statistically significant, and as expected, with a positive sign. The quality of housing structure in terms of material used and housing services/utilities are important indicators of standard of living. The estimated functions indicate that telephone connection (landline) in the rural area and RCC roofing in both urban and rural areas are significant and positive determinants of household consumption expenditure. Moreover, low housing congestion, represented by rooms per person, appears as a positive and significant correlate.

Table 4.57 Decomposition of Poverty by Household Size in all fours sub-districts.

1-4 Members 5-6 Members 7-10 Members 10+ Members Poverty Frequency % Frequency % Frequency % Frequency %

158 | P a g e

Bands Jhang Sub District Poor 11 42% 13 18% 90 43% 53 72%

Non-Poor 15 58% 61 82% 120 57% 21 28%

Total 26 74 210 74

ShorKot Sub District Poor 4 21% 10 17% 125 67% 47 78%

Non Poor 15 79% 48 83% 61 33% 13 22%

Total 19 58 186 60

Atthara Hazari Sub District Poor 3 33% 7 28% 44 61% 12 46%

Non Poor 6 67% 18 72% 28 39% 14 54%

Total 9 25 72 26

AhmedPur Sial Sub District Poor 3 33% 7 28% 44 61% 21 75%

Non Poor 6 67% 18 72% 28 39% 7 25%

Total 9 25 72 28

Source: Primary data Analysis

The statistical results indicate that size of household is the prime determinant of the per capita income of the household as a 1 % positive increase in the size of the household would decrease more than 1% per capita income of the household. It implies that the size of the household can add well to the level of poverty incidence in the Jhang district, Punjab. This result is in the agreement with the previous literature; for instance, Pakistan Integrated Household Survey (1998-99) reported that the poorest segment of the country is mostly lived in larger households with an average family size of 8.4 persons in the poorest quintile compared with 6.2 in the non-

159 | P a g e

poor quintile. Malik (1992) for most developing and low-income countries and Chaudhry et al (2009) for the village of Betti Nala in Tehsil Jatoi, district Muzaffargarh in southern Punjab, Pakistan also documented the similar results in their studies.

4.7.11 Decomposition of Poverty by Dependency Ratio Dependency ratio, dependents (children and elder) as the proportion (%) of working age (15-64) population, shows the demographic pressure on society or concerned households. In high fertility regime, children outnumber the working age population. This pattern shifts to elder persons in case of low fertility regime. Although fertility in Pakistan has declined from more than 6 children per women in 1980s to around 4 children per women at present, it is still high in the region (NIPS, 2013). The modest decline in fertility during the last three decades, however, has brought a gradual change in age structure, with a declining share of young children. This dependency ratio allows us to calculate the burden weighing on members of the labor force within the household. How many people are working and how many household members are not working within the household. The dependency ratio is the ratio of the number of family members not in the labor force (whether young or old) to those in the labor force in the household. One might expect that a high dependency ratio will be associated with greater poverty. In Central Punjab, Sabir, Hussain & Saboor (2006) conducted the research and have used the sample of 300 farmers(Small) and investigated the status of poverty among them. They concluded that along with the education dependency ratio is the major determinant of high poverty in central Punjab, Pakistan Table 4.58 Decomposition of Poverty by Dependency Ratio

Dependency As %age of Poor Household Headcount Poverty Severity of Ratio Households (%) index Depth Poverty

0.1 - 2 6% 16% 3% 0.26 0.07

2.1 - 4 48% 43% 26% 0.26 0.07

4.1 - 6 12% 12% 7% 0.41 0.17

160 | P a g e

Above 6 34% 29% 18% 0.50 0.25

Source: Primary data Analysis

Two demographic factors, household size, and dependency ratio, have a significant and positive relationship with poverty, suggesting that high fertility which contributes to a rise in child dependency and family size, is likely to lower the standard of living. Poverty in Pakistan especially in the rural area is due to the high dependency ratio. The table shows that the dependency ratio is positively correlated with incidence, depth, and severity of poverty in Jhang District. An increase in the dependency ratio leads to raising the headcount index, depth and severity of poverty. A decline in the dependency ratio is likely to help improve the economic status of a household as several studies have empirically shown a linear relationship between poverty and dependency ratio (Arif and Farooq, 2012). The results also show that the dependency ratio 2 and above 2 persons imply the highest incidence, depth, and severity of poverty. The all three measures, headcount index, depth and severity of poverty are lower among the households where the dependency ratio is 2 or less than 2 persons.

Table 4.59 Decomposition of Poverty by Dependency Ratio in all four sub-districts.

0.1 - 2 2.1 - 4 4.1 - 6 Above 6 Poverty Frequency % Frequency % Frequency % Frequency % Bands Jhang Sub District Poor 33 21% 261 61 65 54 184 63% % %

Non 123 79% 170 39 55 46 109 37% Poor % %

Total 156 431 120 293

ShorKot Sub District Poor 17 29% 85 54% 28 55 67 56%

161 | P a g e

%

Non 41 71% 71 46% 23 45 52 44% Poor %

Total 58 156 51 119

Atthara Hazari Sub District Poor 6 12% 81 63% 25 64 74 71% %

Non 46 88% 47 37% 14 36 30 29% Poor %

Total 52 128 39 104

AhmedPur Sial Sub District Poor 4 15 53 63% 7 47% 21 64% %

Non 23 85 31 37% 8 53% 12 36% Poor %

Total 27 84 15 33

Source: Primary data Analysis

The incidence, depth, and severity of poverty have positive relationships with the dependency ratio. The high dependency ratio results in an increase in the incidence, depth, and severity of poverty. In Shorkot sub-district it is observed that the dependency ratio is more than 6.0 and due to that percentage of poor households are also on the higher side. However in the same scenario results are totally different in Atthara Hazari District where non-Poor People those where the dependency ratio is 6.0 or more than that. Overall in all over the district majority of the household have dependency ration is more than 6.0 and also poverty level is on the higher side in that household. It is also concluded through our survey results that in the one household, earning hand is only one and average people of a household are 7 to 8. On the earning of one man

162 | P a g e

another member of the family depending on this one. This is a high dependency ratio which intensifying poverty. The joint family system is causing more expenses. Household size is positively related to the incidence of poverty. The large household is more likely to be poor than small household because larger households probably had more young children, that encounter financial burden due to the high cost of living, education, health and other social as well as social activities and vice versa.

4.7.12 Decomposition of poverty by Male-Female Ratio

Table 4.60 Decomposition of poverty by Male-Female Ratio

As %age of Household (%) Headcount Poverty Severity of Male -Female Poor index Depth Poverty Ratio Households

0.00 - 0.5 21% 21% 11% 0.40 0.16

0.51 - 0.75 21% 27% 12% 0.08 0.01

0.75 - 1 15% 16% 8% 0.36 0.13

Above 1 36% 36% 19% 0.24 0.06

Source: Primary data Analysis

The evidence shows that the Male-Female ratio (members) has no significant effect on the poverty in the target area. Because the all three measures, incidence, depth and severity of poverty are almost same in witnessed all type of female-male ratio (members) of the household. Majority of the household in Jhang districts are having Male – Female ratio.

Table 4.61 Decomposition of poverty by Male-Female Ratio in all four sub-districts.

0.00 - 0.5 0.51 - 0.75 0.75 - 1.0 Above 1

163 | P a g e

Poverty Frequency % Frequency % Frequency % Frequency % Bands Jhang Sub District

Poor 114 54% 115 43% 82 52% 193 53%

Non 96 46% 154 57% 77 48% 169 47% Poor Total 210 269 159 362

ShorKot Sub District Poor 46 52% 52 50% 31 52% 68 52 %

Non 43 48% 53 50% 29 48% 62 48 Poor %

Total 89 105 60 130

Atthara Hazari Sub District Poor 40 56% 43 57% 32 58% 71 59%

Non 31 44% 33 43% 23 42% 50 41% Poor Total 71 76 55 121

AhmedPur Sial Sub District Poor 40 56% 43 57% 32 58% 71 59%

Non 31 44% 33 43% 23 42% 50 41% Poor Total 71 76 55 121

Source: Primary data Analysis

164 | P a g e

4.7.13 Decomposition of poverty by Female-Male Ratio Female-male ratio of members is considered a sex ratio. Mostly female members in a household in rural areas evade working because of their customs and religious norms. Therefore their attitude towards participation is rather discouraging. It is also stated that the higher female-male ratio of workers has an inverse relationship with the headcount index, depth, and severity of poverty. The tendency shows that in Jhang District there is a negative relationship between poverty and the female-male ratio of workers. The results also show that the households with above 1.0 female-male ratio of workers imply the highest headcount index, depth, and severity of poverty.

Table 4.62 Decomposition of poverty by Female-Male Ratio

As %age of Household (%) Headcount Poverty Severity of Female -Male Poor index Depth Poverty Ratio Households

0.00 - 0.5 15% 16% 8% 0.41 0.17

0.51 - 0.75 18% 16% 10% 0.36 0.13

0.75 - 1 16% 19% 9% 0.46 0.21

Above 1 52% 49% 28% 0.32 0.10

Source: Primary data Analysis

Chaudhry & Rehman (2009) have estimated that, in Pakistan, the size of household and female to male ratio has the significant and positive impact on poverty. While female to male enrollment ratio, literacy ratio of female to male, education of head of household and ratio of earners of female to male have been significantly and negatively affected the rural poverty. The same of the case is in our study where it is observed the majority of the household have a higher female-male ratio and it is reported as above 1.0 and also at there, the poverty rate is also on higher side.

Table 4.63 Decomposition of poverty by Female-Male Ratio in all four sub-districts.

0.00 – 0.5 0.51 - 0.75 0.75 - 1.0 Above 1

165 | P a g e

Poverty Frequency % Frequency % Frequency % Frequency % Bands Jhang Sub District Poor 27 47% 38 63% 28 42% 104 52 %

Non Poor 30 53% 22 37% 39 58% 96 48 %

Total 57 60 67 200

ShorKot Sub District Poor 29 55% 35 66% 33 52% 89 58%

Non Poor 24 45% 18 34% 30 48% 65 42%

Total 53 53 63 154

Atthara Hazari Sub District Poor 12 57% 7 35% 15 56% 41 62%

Non Poor 9 43% 13 65% 12 44% 25 38%

Total 21 20 27 66

AhmedPur Sial Sub District Poor 11 38% 17 61% 10 36 47 64% %

Non Poor 18 62% 11 39% 18 64 27 36% %

Total 29 28 28 74

Source: Primary data Analysis

166 | P a g e

It is observed that in all four sub-districts female to male ratio is on the higher side and those household is also living below the poverty line. In Jhang sub-district 70% of the total household who are living under the poverty line where female-Male ratio is above 1.0

4.7.14 Decomposition of Poverty by No. of Adults in Household.

Table 4.64 Decomposition of Poverty by No. of Adults in Household.

No. of Adults As %age of Poor Household (%) Headcount Poverty Severity of Households index Depth Poverty 2 27% 38% 15% 0.37 0.14

3 28% 26% 15% 0.45 0.20

4+ 46% 36% 25% 0.30 0.09

Source: Primary data Analysis

Our survey results confirmed that the number of adults leads to poverty in the household in Jhang District. The No. of adults where it is reported as more than 2 is supposed to be poorer than where it is 2 or less than 2. Poverty depth and severity of poverty are also on the higher side where no. of adults is more than 02.

Table 4.65 Decomposition of Poverty by No. of Adults in Household in all four sub-districts of Jhang Districts

Poverty Bands 2 Percentage 3 Person Percentage 4+ Person Percentage Person Jhang Sub District Poor 55 38% 50 51% 92 66%

Non Poor 91 62% 49 49% 47 34%

167 | P a g e

Total 146 99 139

ShorKot Sub District Poor 42 35% 54 63% 90 76%

Non Poor 77 65% 32 37% 28 24%

Total 119 86 118

Atthara Hazari Sub District Poor 22 44% 22 65% 31 62%

Non Poor 28 56% 12 35% 19 38%

Total 50 34 50

AhmedPur Sial Sub District Poor 26 41% 24 53% 35 69%

Non Poor 37 100% 21 47% 16 31%

Total 63 45 51

Source: Primary data Analysis

In all four sub-districts of Jhang district, it is confirmed that majority of the household of 2 (in nos.) adults and from these households majority of the household are not under the line of poverty. It is also confirmed that in all four sub-districts where adults are more than two there are more chances of a household falling under the poverty line.

4.7.15 Decomposition of Poverty by total Amount of Assets in the Household Land, especially arable land, is an asset that the poor covet the most. Land ownership is directly proportional to the well-being of a household. The more land owned, the greater the well-being experienced by a household. The poor own very little or no land, and very poor are landless. The lack of land ownership compels the poor to seek other sources of income such as agricultural labor or off-farm daily wage labor. This increases the poor’s dependence on others for food and 168 | P a g e

income. Ownership of a home is also an important factor distinguishing the well-off from the poor. The condition of homes and ownership of simple material possessions such as clothes and household items are also considered when differentiating well-being and ill-being. The rich own jewelry and fine clothes. The poor have tattered clothes and are at times barefoot.

Table 4.66 Decomposition of Poverty by total Amount of Assets in the Household

Total Assets As %age of Poor Household (%) Headcount Poverty Severity of Households index Depth Poverty Less than 1 Mln 29% 16% 16% 0.34 0.11

1 Mln - 5 Mln 68% 76% 37% 0.24 0.06

5 Mln to 10 Mln 2% 6% 1% 0.17 0.03

10 Mln and 1% 3% 0% 0.03 0.00 above. Source: Primary data Analysis

The results show that the Amount of total assets is having a negative relationship with the poverty line. Where the total assets are high poverty level is on the lower side and where the amount of total assets is lower side, the poverty level is on the higher side. 92% people are having less than 5 Mln as assets (as reported, estimated figures) and from this population , 97% people are reported as poor (under the poverty line). Poverty Depth and Severity of Poverty ratio are also on the higher side in both factors.

Table 4.67 Decomposition of Poverty by total Amount of Assets in the Household of all four sub-districts

Poverty Less % 1 Mln - % 5 Mln to % 10 Mln % Bands than 1 5 Mln 10 Mln and Mln above. Jhang Sub District Poor 62 94% 128 47% 4 15% 3 19%

169 | P a g e

Non 4 6% 147 53% 23 85% 13 81% Poor Total 66 275 27 16

ShorKot Sub District Poor 54 96% 129 52% 2 17% 1 17%

Non 2 4% 120 48% 10 83% 5 83% Poor Total 56 249 12 6

Atthara Hazari Sub District Poor 14 74% 55 51% 1 20% 0 0%

Non 5 26% 52 49% 4 80% 3 100% Poor Total 19 107 5 3

AhmedPur Sial Sub District Poor 23 100% 57 46% 5 45% 0 0%

Non 0 0% 68 54% 6 55% 0 0% Poor Total 23 125 11 0

Source: Primary data Analysis

It is important to build assets for sustainable livelihood, natural capital, social capital, physical capital, human capital and financial capital through social mobilization to harness peoples potential to help themselves. The people and communities at the grassroots level are more effective in reducing poverty and achieving wellbeing by mobilizing the underutilized creativity of the poor, local resources and local knowledge. The experience across countries including Pakistan indicates that in the absence of a participatory process, most interventions designed for helping the poor have not always been successful in reaching the poor and the conventional

170 | P a g e

macro development interventions are inadequate for poverty reduction and human development (Mahbubul Haq, 2005). The total assets of the households are also an important factor to determine the level of poverty incidence. The total assets of the households include the tangible goods (land, livestock, house, Television, car, agricultural equipment and machinery, home accessories etc.) and its financial assets (cash and saving etc.). In All four sub-districts of Jhang district, it is reported that people/households are having more assets they do not fall under the poverty line and where the total assets are less than 05 million the household is having more chances to be under the poverty line. It is also concluded that only 135 households are categorized as poor where total assets of the households are having assets more than 10 Million and these are only in Jhang districts. However, in Ahmed pur sial, no household is reported as having assets of above 10 Million.

4.7.16 Decomposition of Poverty by No. of Children in the Household Table 4.68 Decomposition of Poverty by No. of Children in the Household

No. of As %age of Poor Household Headcount Poverty Depth Severity of Children Households (%) index Poverty

0-1 11% 13% 6% 0.17 0.03

2 11% 15% 6% 0.46 0.21

3-4 31% 32% 17% 0.37 0.13

5-6 36% 34% 20% 0.37 0.14

6+ 10% 7% 6% 0.42 0.17

Source: Primary data Analysis

The data confirmed that where the number of children is more than 2, the households are having more chances of falling in the poverty line and the same has also reported in (Orbeta, 2005) who have reported that in household more or additional children not only raise the likelihood of a household to fall into poverty but it also lead to intergenerational transmission of poverty due to reduction in school attendance of children with a regressive impact on poorer households. In 171 | P a g e

Jhang District the majority of the household have from 3 to 6 children and that household where a number of children are from 5 to 6 have more poverty ratio compared with another household.

Table 4.69 Decomposition of Poverty by No. of Children in the Household in all four sub- districts of Jhang District.

Poverty 0-1 % 2 % 3-4 % 5-6 % 6+ % Bands Chil Childr Childre Childr Childr dren en n en en Jhang Sub District Poor 22 40% 23 39% 56 49% 76 58% 20 50%

Non 33 60% 36 61% 58 51% 55 42% 5 50% Poor Total 55 59 114 131 40

ShorKot Sub District Poor 16 42% 23 48% 57 55% 72 65% 18 82%

Non 22 58% 25 52% 47 45% 39 35% 4 18% Poor Total 38 48 104 111 22

Atthara Hazari Sub District Poor 4 21% 8 38% 17 40% 15 38% 12 92%

Non 15 79% 13 62% 25 60% 24 62% 1 8% Poor Total 19 21 42 39 13

AhmedPur Sial Sub District Poor 12 75% 6 27% 33 57% 28 51% 6 86%

172 | P a g e

Non 4 25% 16 73% 25 43% 27 49% 1 14% Poor Total 16 22 58 55 7

The data shows that in all four sub-districts of Jhang District, the majority of the household have from 3 to 6 children and that household where a number of children are from 5 to 6 have more poverty ratio compared with another household.

4.7.17 Decomposition of Poverty by Household’s Livestock Population. Ownership of livestock is also an important indicator of well-being or ill-being. A well-off household typically owns between 100-150 sheep, whilst a better off household owns about 50 sheep. The poor sometimes own one or two sheep and the very poor do not own any livestock. Livestock numbers have, however, been adversely affected by the persistent drought in the area. Falling livestock numbers also affect the poor, especially women, as caring for livestock owned by others is an important income source for them. Typically, livestock populations consist of cows, sheep, and goats. Backyard poultry is also a source of income and nutrition for the poor. There are very few buffaloes. Falling livestock numbers have had a devastating impact on the livelihoods of local people, particularly the poor and women.

Table 4.70 Decomposition of Poverty by Household’s Livestock Population.

Live Stock As %age of Poor Household Headcount Poverty Severity of Population Households (%) index Depth Poverty

No Livestock 19% 12% 10% 0.43 0.18

1 - 2 45% 31% 24% 0.48 0.24

3 - 6 17% 35% 10% 0.47 0.22

173 | P a g e

Above 6 19% 23% 10% 0.37 0.13

Source: Primary data Analysis

In the rural areas, livestock forms a key saving and income-generating source in the economy. The table shows that the livestock population has no significant effect on poverty in Jhang District. Local people keep cows, sheep, goats and poultry the results of the primary data analysis show that the headcount index, depth, and severity are high when live stocks population is 1-2 in any number of livestock of the households. We also concluded that the depth and severity of poverty are lower among the households that have no livestock or less than 2 live stocks.

Table 4.71 Decomposition of Poverty by Household’s Livestock Population in all four sub- districts.

No Livestock 1-2 3-6 Above 6 Poverty Frequency % Frequency % Frequency % Frequency % Bands Jhang Sub District Poor 42 86% 84 73% 30 24 42 43% %

Non 7 14% 31 27% 94 76 55 57% Poor %

Total 49 115 124 97

ShorKot Sub District Poor 33 87% 94 88% 28 24% 63 67%

Non 5 13% 13 12% 87 76% 31 33% Poor Total 38 107 115 94

Atthara Hazari Sub District

174 | P a g e

Poor 4 80% 32 80% 23 42% 16 47%

Non 1 20% 8 20% 32 58% 18 53% Poor Total 5 40 55 34

AhmedPur Sial Sub District Poor 23 92% 32 73% 14 25% 8 30%

Non 2 8% 12 27% 42 75% 19 70% Poor Total 25 44 56 27

Source: Primary data Analysis

4.8 Regression analysis of Level of Poverty and its determinants in Jhang District

To achieve the objective of determining the relationship between poverty status and socioeconomic characteristics, as discussed earlier, a measure of poverty was needed. In most definitions of poverty, income tends to play an important role in determining whether a household is poor. Studies on poverty usually begin with the adoption of a specific poverty line described in terms of income. A study by You et al. (2014) in rural China found that the incidence of multidimensional poverty increased as income poverty declined. The income measure of poverty is, however, one-dimensional and may not include other forms of poverty that could simultaneously afflict households. Identification of factors that are strongly linked to poverty is an important aspect in developing successful strategies intended for poverty reduction. We have used the Income regression model to investigate the correlates of poverty in the Jhang district. For the income regression model, we have estimated the four different equations (1, 2, 3, and 4). Most of the variables are calculated or derived from each other, therefore; there is high chance of the multicollinearity in the estimated equation. To control the multicollinearity in the model, we have estimated the four different equations (1, 2, 3 and 4) by dropping some variables in each equation. Secondly, a log-linear model is preferred due to ease of interpretation and its superior fit. Therefore, we are taken the log of all the variables in the equation (1, 2, 3 and 4).

175 | P a g e

Almost all the equations produced the similar results in term of size and sign. The results of the income regression model are summarized in Table 4.72.

Table 4.72 Regression Analysis (04 Models)

Dependent Variable: LOG(PCI) Sample (adjusted): 2 999 Included observations: 720 after adjustments Model 1 Model 2 Model 3 Model 4

Variable Coeff t-Stat Coeff t-Stat Coeff t-Stat Coeff. t-Stat C 9.57 9.75 11.43 12.37 7.92 8.57 8.17 9.29 LOG(HS) -1.00 -4.76 -0.98 -5.06 -1.04 -2.31 - - LOG(HHAGE) - - -0.02 -1.27 -0.04 -1.55 -0.304 -4.78 LOG(EDR) 0.06 1.20 0.09 1.37 -1.14 -3.40 -0.95 -2.87 LOG(EANPH) 0.86 5.01 0.96 6.05 -0.90 -1.52 0.156 1.82 LOG(TDR) -2.51 -3.35 -2.81 -4.05 -4.06 -5.97 -4.14 -6.07 LOG(CHILR) 1.37 2.86 1.52 3.41 2.42 5.52 2.46 5.62 LOG(AGEDR) 1.37 4.92 1.38 5.32 1.79 7.07 - - LOG(FMR) -0.18 -4.64 -0.05 -1.36 0.07 1.72 -0.02 -0.78 LOG(TAST) 0.04 1.75 - - 0.15 3.20 0.13 2.77 LOG(LIVST) 0.14 1.86 0.13 1.69 - - - - LOG(LANDH) 0.48 1.05 1.18 2.80 0.55 1.21 1.26 2.79 LOG(TASSETS) 0.04 0.75 -0.01 -0.29 0.15 3.20 0.13 2.77 D1 - - - - 0.58 8.04 0.53 7.35 D2 - - -0.52 -10.94 -0.34 -7.00 -0.32 -6.66 D3 - - - - -0.21 -3.87 -0.19 -3.56 Model Diagnostics R-squared 0.29 0.39 0.457 0.445 Adjusted R-squared 0.28 0.38 0.447 0.435 S.E. of regression 0.54 0.50 0.470 0.471 Akaike info criterion 1.61 1.46 1.350 1.351 F-statistic 26.21 38.03 42.465 45.418 Prob (F-statistic) 0.00 0.00 0.000 0.000 Durbin-Watson stat 1.62 1.86 1.78 1.92

176 | P a g e

The estimated results show that the coefficient value of the household size is negative and statistically significant at the reasonable level of significance in all three models. The statistical results indicate that size of household is the prime determinant of the per capita income of the household as a 1 % positive increase in the size of the household would decrease more than 1% per capita income of the household. It implies that the size of the household can add well to the level of poverty incidence in the Jhang district, Punjab. This result is in the agreement with the previous literature; for instance, Pakistan Integrated Household Survey (different year reports) reported that the poorest segment of the country is mostly lived in larger households with an average family size of 8.4 persons in the poorest quintile compared with 6.2 in the non-poor quintile. Malik (1992) for most developing and low-income countries and Chaudhry et al (2009) for the village of Betti Nala in Tehsil Jatoi, district Muzaffar Garh in southern Punjab, Pakistan also documented the similar result.

Moreover, the results of the income-regression models indicate that the age of the household head is negative in our equations but statistically significant in model 4 only. The results show that a 1% increase in the age of the head of the household head will result in a 0.30% decrease in the per capita income of the household. This signifies that age of the head of household has significantly contributed to the incidence of poverty in the Jhang district. These results are generally consistent with the findings of previous empirical studies; for example, Alam and Hussain (2013) for Khyber agency, FATA, Pakistan documented that the age of the head of the household has a significant effect on the level of incidence of poverty. However, these results in the contradiction with Sekhampu (2013) for South African Township, they concluded that the age of head of household is negatively related to the probability of being poor.

Furthermore, the statistical estimates show that the level of education is also significantly related to the per capita income of the household. D2 is the dummy variable in income-regression model which shows the impact of the level of education on per capita income of the household. The values of the dummy variable D2; 1 in such case when the head of the household has Matric and less than Matric education, 0 otherwise. However, the coefficient value of D2 is negative in all three models and highly significant. The estimated results show that the education level of the head of household has a significant effect on the incidence of poverty in the Jhang district.

177 | P a g e

Although, the findings show that less than matric education of the head of the household is significantly reduced the per capita income of the household and increase the level of incidence of poverty in the Jhang district. This implies that the household with the highly educated head of the household has a greater potential to exploit resources and technology and avoid poverty in the Jhang district of Punjab, Pakistan.

The number of workers/earners per household is the main component of the household income generation. The coefficient value of the earners per household has a positive sign and statistically significant at a reasonable level of significance in all models except model 3. The estimated results explicate that a 1% increase in the workers per household will result in an around 0.90% rise in the per capita income of the household. It means that the number of potential income earners per household has substantially enhanced the income of the household and reduced the level of poverty incidence in the Jhang district. In addition, the economic dependency ratio (not working members of the household/ household working members) has a negative sign and highly significant in model 3, 4. However, it has a positive sign in model 1 and 2 but statistically insignificant in both models. The results of the model 3 and 4 show that a 1% positive change in the economic dependency ratio (EDR) causing an around 1% reduction in per capita income of the household. These results imply that economic dependency ratio significantly reduces the per capita income of the household and increases the incidence of poverty in the Jhang district. Likewise, the coefficient values of the total dependency ratio (not working age population/working age population) are negative in all four models and significant at the 1 % level of significance. The estimated coefficient of the total dependency ratio is greater than unity in all four models, implying that the household income is highly responsive to changes in the total dependency ratio. The results clarify that a 1% positive change in the total dependency ratio causing more than a 2% reduction in the household per capita income. It signifies that an increase in the total dependency ratio has considerably reduced the per capita income of the household and increased the level of poverty incidence in the Jhang district. Moreover, the results of the income regression model suggest that Female-Male ratio (number of females in the household/ number of males in the household) has a negative influence on the per capita income of the household in the Jhang district. The statistical results indicate that coefficient values of the Female-Male ratio are correctly signed in three models, but statistically significant in model 1

178 | P a g e

only. Overall, the results suggest that Female-Male ratio is inversely associated with per capita income of the household in the Jhang district. These results generally consistent with the common view that the level of poverty is higher in the households in the rural areas that have a high Female-male ratio. This is mainly because female members of the households in rural areas do not participate in income generation activities. Furthermore, the results show that child dependency ratio [number of children (0-14 years in Household/ Adults (15-64)] and aged dependency ratio [number of age above 64 years/ Adults (15-64)] have positive signs and statistically significant in all four models. The results imply that a 1% positive change in the child dependency ratio and the aged dependency ratio will result in more than 1% rise in the per capita income of the household. These results suggest that child dependency ratio and aged dependency ratio have a significant impact on the per capita income of the households as well as on the reduction of the level of poverty incidence in the Jhang district. The positive sign of the child dependency ratio is not surprising because the child in the rural areas of Pakistan has frequently contributed to income generation activities, therefore, the positive relationship between household income and child dependency ratio is possible.

Among the independent variables in the income regression models, livestock of the household is positively associated with the per capita income of household and livestock has a negative impact on rural poverty. The statistical findings indicate that coefficient values of livestock variable are correctly signed in all equations except model 3, but statistically significant in model 1 and 2 only. Moreover, the degree of coefficients of livestock is very low. A 1 unit increase in the livestock population causing only a 0.13 % rise in per capita income of the households in the Jhang district. In conclusion, the estimated results suggest that livestock has a significant influence on the household income as well as on the reduction of poverty incidence in the Jhang district, Punjab. Additionally, the dummy variable D3 has also captured the effect of livestock on the household income in income-regression model. The values of the dummy variable D3; 1 in such case when households have no livestock, 0 otherwise. The estimated results reveal that coefficient values of D3 are negative and statistically significant in both model 3 and model 4. It means that the per capita income is lower in those households which have no livestock. These results imply that the contribution of the livestock sector toward the household income and reduction of rural poverty is quite significant in Pakistan in general and Jhang district, Punjab in particular.

179 | P a g e

In the rural areas, the ownership of the agricultural land is considered the prime factor that can play a critical role in the reduction of poverty. The statistical evidence shows that coefficient values of the landholding in model 2 and model 4 are positive and highly significant. However, the degree of the coefficients of the landholding in both models is greater than unity, implying that household income is highly responsive to changes in the landholding per household. A 1 unit increase in the landholding (area in acres) will result in an approximately 1.20% rise in the per capita income of the household. Additionally, the dummy variable D1 also captured the influence of the ownership of the agricultural land on the household income and poverty in the income- regression model. The values of the dummy variable D1; 1 if the households have a farming land, 0 if landless. The statistical estimates indicate that the coefficient values of D1 are positive and highly significant in both model 3 and 4. It signifies that the per capita income is higher in those households which have a farming land. Overall, the results suggest that the ownership of the agricultural land is significantly contributed to the income of the rural households in Pakistan.

The total assets of the households are also an important factor to determine the level of poverty incidence. The total assets of the households include its tangible goods (land, livestock, house, Television, car, agricultural equipment and machinery etc.) and its financial assets (cash and saving etc.). The statistical results of the income regression models show that coefficient values of the total assets are positive and statistically significant. The results indicate that a 1% increase in the total assets will result in a 0.13% rise in the per capita income of the household in the Jhang district. These results imply that total assets have a significant effect on the household income as well as on the reduction of poverty incidence in the Jhang district, Punjab.

4.9 Logistic regression Results and Discussion Logistic regression analysis is commonly undertaken to explore the influence of various household-level characteristics on the probability of being poor. In logistic regression model, the dependent variable is a binary or dichotomous taking two values 0 and 1 showing the probability of occurrence of an event. Logistic regression determines the impact of multiple independent variables presented simultaneously to predict membership of one or other of the two dependent

180 | P a g e

variable categories. (Field, 2009). We have used the logisitc regression to examine the poverty determinants in terms of various qualitative and quantitative variables in the Jhang district. In the logisitc regression only multicollinearity assumption is created a serious problem in the model and other assumptions are not required in the Income regression . For this purpose, we have estimated the four different equations by dropping some variables in each equation to avoid the multicollinearity problem. The results of all four logistic regression models are summarized in Table 4.73.

Table 4.73: Results of the logistic regression Dependent Variable: Pov: 1 if the household is Poor; 0 otherwise Model 1 Model 2 Model 3 Model 4 Variable Coeff z-Stat Coeff z-Stat Coeff z-Stat Coeff. z-Stat C 8.289 3.695 8.379 3.730 9.289 4.251 11.105 5.673 HS 0.327 5.905 0.309 6.341 0.296 5.556 0.276 6.077 HHAGE -0.008 -1.656 -0.008 -1.734 -0.007 -1.600 -0.007 -1.570 EDR -0.039 -0.695 - - -0.037 -0.671 - - EANERPH -0.270 -1.808 -0.176 -2.838 -0.248 -1.687 -0.172 -2.951 TDEPRATIO -0.012 -3.355 -0.012 -3.299 - - - AGEDDEPRATIO - - - - -0.009 -2.919 -0.008 -2.563 CHILDDEPRATIO 0.012 3.171 0.011 3.112 FMRATIO 0.134 2.141 0.135 2.167 LTASSETS -0.724 -4.780 -0.742 -4.964 -0.762 -5.184 -0.885 -6.648 LOG(LIVESTOCK) -0.160 -2.112 -0.149 -2.011 -0.191 -2.614 0.068 5.963 LANDHOLDING 0.080 6.405 0.078 6.369 0.075 6.272 -0.169 -2.366 D1 0.116 0.595 0.095 0.493 D2 1.206 7.644 1.202 7.636 1.297 10.056 1.286 10.060 D3 0.725 2.633 0.717 2.607 0.748 2.733 0.750 2.764 D4 0.411 2.295 0.400 2.241 0.322 1.887 D5 -0.379 -2.029 -0.388 -2.092 -0.342 -1.853 -0.390 -2.185 D6 -0.536 -3.443 -0.531 -3.414 -0.589 -3.974 -0.614 -4.197 D7 0.556 4.017 0.557 4.017 0.593 4.862 0.518 4.514 D8 0.191 0.871 0.150 0.713 0.206 0.947 0.225 1.076 D9 0.793 3.400 0.810 3.474 0.811 3.528 0.806 3.497 Total observations 964 964 964 964 Log Likelihood -445.34 -445.59 -448.07 -449.98 LR statistic 441.436 440.95 435.99 432.168 Prob(LR statistic) 0.000 0.000 0.000 0.000 McFadden R-squared 0.331 0.331 0.327 0.324

181 | P a g e

The results (Table 4.73) of logistic regression based on the survey data conducted in 2016-17 indicate that the coefficients of household size are positive and significantly different from zero at the reasonable level of significance in all four equations. It implies that, with an increase in the household size, the likelihood of being poor will rise in the Jhang district, Punjab. Furthermore, the coefficient values of a dummy variable (D8) are positive in all equations but statistically significant in Model 4. This indicates that households having more than 8 family members are more likely to be poor. Additionally, the dummy variable D6 (1 if the nuclear family system, 0 otherwise) captured the effect of the nuclear family system on the probability of being poor. Negative and significant coefficients of the D6 in all four equations show that the nuclear family system is negatively correlated with the probability of being poor. Overall, these results suggest that an increase in household size and larger family size have significantly risen the probability of being poor in the Jhang district of Punjab.

The other demographic factor that increases the likelihood of being poor is the dependency ratio. The evidence in Table 4.73 shows that the coefficients of the economic dependency ratio (EDR) and the total dependency ratio (TDR) are positive and highly significant in all cases. This implies that a higher dependency ratio will result in an increase in the probability that a household is poor. Furthermore, the statistical results in Table 2 explicate that the coefficients of the Female- Male ratio (FMR) are positive and statistically significant in all four equations except model 4. It indicates that the Female-Male ratio has a positive and significant effect on the probability of being poor in Jhang district. More interestingly, the coefficients of the child dependency ratio (CHILDDR) are negative in both estimated equations and highly significant. Likewise, the coefficient of the aged dependency ratio (AGEDDR) is negative and statistically significant in both models. These results signify that an increase in the child dependency ratio and aged dependency ratio will decrease the probability that a household is poor in the Jhang district. Overall, the results based on the Income regression show that dependency ratio has a significant influence on the probability of being poor in the Jhang district.

Furthermore, the results of the logistic regression reported in Table 2 show that the coefficients of the earner per household (EANERPH) are negative in all three models as expected but statistically significant in model 2 and 3. These results indicate that an increase in the earners per household significantly reduce the probability of being poor in the Jhang district. Additionally,

182 | P a g e

D2 and D3 have captured the effect of the occupation of household heads. The dummy variable D2 takes the value of 1 if the head of the household is farmer and 0 otherwise. Likewise, the dummy variable D3 takes the value of 1 if the head of the household head is daily wager and o otherwise. The statistical evidence in Table 2 shows that both D2 and D3 have positive coefficients and highly significant in all cases. It implies that the households headed by the farmer and daily wager are more likely to be poor.

Another important factor is the education level of the head of the household. Education plays an important role in the reduction of poverty and improving the socio-economic status of households. The statistical results in Table 2 indicate that the coefficient value of the D1 (1 if the head of household is illiterate, 0 otherwise) is positive and statistically significant in both models. This implies that the probability of being poor is higher for those households that are headed by illiterate. Moreover, the coefficients of the dummy variable D7 (1 if the head of household has a matric or less than matric education, 0 otherwise) are positive in all four equations but statistically significant in models 3 and 4. It means that the households headed by the member who has a matric or less than matric education are more likely to be poor in the Jhang district. The results signify that educational attainment is significantly related to the likelihood of being poor in the Jhang district, Punjab.

The ownership of the agriculture land and livestock is considered good for the reduction of the rural poverty in Pakistan. The estimated results of the logistic regression in Table 2 reveal that the coefficients of the landholding and livestock are negative and statistically significant in all four models. This indicates that an increase in the landholding and livestock of the households will reduce the probability of being poor in the Jhang district. Moreover, the evidence shows that the dummy variable D4 (1 if the household has a farming land; 0 otherwise) has negative signs in all three equations but statistically significant in model 3 only. On the other hand, the dummy variable D9 (1 if the household has no livestock; 0 otherwise) has positive signs and statistically significant in all four equations. This implies that ownership of farming land and livestock have a significant impact on reducing the probability that a household is poor. Similarly, the total asset of the households has also a negative and significant effect on the probability of being poor as the estimated coefficients of the total assets in all three models are negative and highly significant. Overall, the results of the logistic regression suggest that an increase in the landholding, livestock

183 | P a g e

and total assets of the household have considerably decreased the probability of being poor in the Jhang district.

184 | P a g e

Chapter 05- Conclusion and Policy Recommendation

In the light of the analysis of poverty, I argued that poverty is a complex phenomenon and that the causes of poverty are multidimensional. It further means that there is no single or just a few solutions for the problem. Secondly, poverty is an individual problem within a societal context which should rather be understood and interpreted within a economic-political and socio-cultural framework. The “blame” for poverty can rarely be placed on individuals. Poverty is also no respecter of persons. Thirdly, the impact of poverty on people and the environment is enormous. It does not only affect the quality of life of billions of people, but in many cases their dignity and humanness. For this reason poverty and its implications have a strong moral claim on society. The world is faced with the greatest issue of poverty and the same is with the Pakistan which has been facing this problem since its foundation (International Monetary Fund, 2010). Poverty is an integral part of the human condition, though its existential aspects differ from culture to culture and from region to region. As such, experts on the subject cannot reduce it to a single factor but focus on the forces which combine to cause the vicious cycle of poverty with the result that different tools are applied to analyze it for findings which may be used to alleviate it. The research study on “An assessment of magnitude and correlates of Poverty in Jhang District” was carried out with the objective to document the status and trend analysis of poverty situation in all four sub-districts of Jhang District Punjab. Keeping in view the above picture, this study was conducted to quantitatively determine poverty incidence in Jhang District with a sample of 1000 households in its. Jhang District is from one of the oldest subcontinent’s district. The Jhang district is in a well-known province of Pakistan, Punjab. The Jhang is one of the districts of Pakistan which are stricken by the poverty and most of them are the rural residents of the district (Bhutto et al, 2007). The analysis of the study carried out on the basis of primary data and all data have been collected through a specifically designed questionnaire in the selected villages of all four sub-districts (Tehsil) of Jhang District, Punjab. The information including in the questionnaire regarding household size, the income level of the household and expenditures of the household (expenditure of Food, rent, health, education, clothing, social and transport) and all other social and demographic characteristics of the household. The sample size was selected in all four sub-districts (Tehsil) of Jhang District on the basis of population/ number of households in each Tehsil. After that in each Tehsil different Union Councils was selected and from each

185 | P a g e

Union Councils, different villages have been selected through probability sampling technique. In the second phase, we employed simple random sampling trough out the all four sub-districts for Jhang District and selected different households from each village based on convenience and willingness of the respondents to answer our question. We have used two distinct approaches: (і) poverty profile, and (іі) an econometric approach in our empirical analysis. This study has used bivariate and Multivariate analysis (income regression model and logistic model) to determine how various indicators of the poverty such as socioeconomic and demographic and social characteristics of households affect the poverty incidence in Jhang District. In short, the above sample was used to make the required analyses based on consumption poverty approach and income dynamics. After analyzing the data some seminal results are: • According to the survey conducted in 2016-17, Average household size is 8 persons and average earner per household is 2.0 persons in Jhang District. Average Household Size in non-poor households is 7.0 and in the poor household are 9.0. Poverty does depend upon respondent social characteristics like Household size. That concludes that in Jhang District A typical poor household is large and includes many children (dependency ratios in poor households are high); • Average Room per House in Jhang District is 2.7 and Average person per Room are 2.9 • Average earner per family in the Non-poor household is 2.19 people per household and in the poor household, it is 1.78 people per household. Which conclude that dependency ratio is one of the major factors in determining the poverty and where it is high; Poverty is also high in Jhang District which confirmed that Poverty does depend upon household characteristics such as dependency ratio. • According to the survey conducted in 2016-17, 54% are below the Poverty Line in Jhang District. Depth and severity of poverty are 36% and 13% respectively. It is also noted that 16% of people are extremely poor whose income is Rs. 1515. The people of the area are found poorer compared to that of 33% in Punjab Province and 36% in Pakistan and 31% in the world. • In all sub-districts poverty level is also on the higher side which is 51.3 %, 57.6 %, 56.0% and 53.5% in Jhang Sub District, Shorkot Sub District, Attahara Hazari Sub District and Ahmed Pur Sial District respectively. In Shorkot Sub District Poverty level is highest as compared with other sub-districts.

186 | P a g e

• Majority of the extremely poor people are living in Attahara Hazari Sub District where its magnitude is 22% and in another sub-district extremely poor people are 15 %, 19% and 9% in Jhang Sub District, Shorekot sub-district and Ahmed pur sial sub-district respectively. In Attahara Hazari Sub District majority of the household are having the occupation of farming. • Poverty depth and poverty severity are also on the higher side in Attahara Hazari sub- district which 38% and 14% respectively. • In other three sub-districts which are Jhang sub-district, Shorkot sub-district and Ahmed Pur Sial poverty depths are 36 %, 36%, and 28% respectively and poverty severity is 13% and 13% and 8% respectively. • Majority of families were headed by the farmer, illiterate and aged persons in Jhang Sub District. Education is the most significant factor that distinguishes the poor from the non- poor • 31% people are illiterate in Jhang District, 41% people are a farmer by profession an average age for the Head of the household is 48 years in Jhang district. • The all three measures, headcount index, depth and severity of poverty were worse among the households that are headed by illiterate, farmer, and age persons. • 80% people in Jhang district are living in the Joint family system and in joint family system poverty is also on higher side. • Average Male per household is 3.7 and average female per household is 4.1. • 66% household in Jhang District is living in Pacca constructed home. And Poverty is on the higher side in that household who are living in Kaccha (mud) houses that confirmed that Poverty does depend upon household characteristics such as household quality. In all four sub-districts, Jhang sub-district have the majority of household (66%) who are living in Pacca constructed houses and in Ahmed Pur Sial majority of people are living in Kaccha (mud) houses (30%). • Agriculture inputs are not cheaper, irrigation facilities are not enough, need for tube wells but electricity problems, things are not marketing in the proper way, landlords take a big portion of agricultural profit while farmer remains poor. • The population is increasing without planning. That is why dependency ratio is increasing,

187 | P a g e

• The majority if the household has not availed any banking facility from the Bank. • Livestock is also the source of income for a rural poor. There is no proper treatment available for sick animals. • Inflation is also causing poverty. Food items are expensive. Basic items such as sugar, flour, and oil are going out of the hands of the poor. • In the income regression analysis that size of household is the prime determinant of the per capita income of the household as a 1 % positive increase in the size of the household would decrease more than 1% per capita income of the household. It implies that the size of the household can add well to the level of poverty incidence in the Jhang district, Punjab. The results of the logistic model shows that the age of the households head, household size, household head is illiterate, household head is farmer, household head is daily wager or labor, residence in Kaccha (mud) house was positively and significantly correlated with the probability being poor while households satisfaction with education facilities and household have members in abroad for income purpose are negatively and significantly correlated with the probability of being poor. • Illness and disability are also factors that increase human insecurity of the poor. Malaria, typhoid, jaundice, and cholera are prevalent in Jhang District. • Almost half of the population consists of female but due to social and cultural constraints, they are unable to participate actively to improve their living condition to achieve sustainable development. So more emphasis should be given to female community organization and gender development. • Microfinance can be an effective tool to reduce poverty and empower women, but the interest rate is high. So the interest rate should be low to meet the productive needs of the rural poor. • The results of the logistic regression suggest that an increase in the landholding, livestock and total assets of the household have considerably decreased the probability of being poor in the Jhang district. Moreover, the results show that an increase in the earners per household significantly reduce the probability of being poor in the Jhang district. Education plays an important role in the reduction of poverty and improving the socio- economic status of households. The results signify that educational attainment is significantly related to the likelihood of being poor in the Jhang district, Punjab. The

188 | P a g e

ownership of the agriculture land and livestock is considered good for the reduction of the rural poverty in Pakistan. The estimated results of the logistic regression revealed that the coefficients of the landholding and livestock are negative and statistically significant in all four models. This indicates that an increase in the landholding and livestock of the households will reduce the probability of being poor in the Jhang district. The ownership of farming land and livestock has a significant impact on reducing the probability that a household is poor. Similarly, the total assets of the households have also a negative and significant effect on the probability of being poor as the estimated coefficients of the total assets in all three models are negative and highly significant.

The core objectives of the study are to find the causes/factors of the poverty level in the target area, which are found to be socio-economic, demographic and social factors. In Social factors person per room, house structure, education, and Health were the main correlates and , In Socioeconomics factors total assets, Landholding, Livestock and in demographic factors age of household head, person per room, family size, family type, education of head of household and occupation of the head of the household are the main correlates of poverty in the Jhang Districts. population, earners per household are the main in origin, i.e., joint family system headed by an elderly person sticking to the old patterns of generating income, illiteracy, especially that of women, dependency ratio, lack of education.

The need of the hour is that the people of the area may be introduced to new patterns of thinking to change their lives for the better. Awareness programmes may be launched to prepare the new generation for the changes and challenges ahead. In this regard, the people women in the area may be educated and empowered to enable them to actively participate in income-generating activities on sustainable grounds.

The data read with facts and figures and analyzed under Poverty Consumption Approach will provide policy makers and planners, whether relating to Government or NGOs, with enough tangible material and strong socioeconomic background for initiating developmental schemes, projects, and programmes. The study may also be used as an active source of motivation to stimulate other researchers in the field.

189 | P a g e

Growth and education that generates income and employment for the poor of the country can be critical for poverty reduction. Poverty can also be reduced by introducing social safety programs to the lower socio-economic segment of Pakistan’s society. Better education can be an effective tool for reducing poverty and enhancing economic growth in Pakistan

5.1 Conclusions

Jhang District is characterized as having common social, economic and demographic features. In which the most general sort is landlessness and limited access to land. Large families distinguished poor households in many ways. As a result, the dependency ratio increase, lower education attainment occurs, and health service access is lacking. Lack of education also limits them to follow family planning programmes which cause higher population. The rural poor also have limited access to basic amenities such as sanitation, clean drinking water, and electricity. Findings of the study revealed that public policy on poverty reduction lacks a coherent long-term strategy. Similarly, various poverty alleviation programmes have failed to address the problems of rural poverty and inequality in all four sub-districts of Jhang District. It is important to examine these issues and address them on a long-term sustainable basis for all households at the macro and micro level. Private individuals, industrialists, philanthropists, landlords, businessmen, and farmers all stakeholders collectively have to play a positive role in this regard. The primary and secondary data used in this study would help policymakers to design result- oriented programmes that would address poverty (Its magnitude and Correlates) in the study area and also in the whole province.

5.2 Recommendations

On the basis of conclusions drawn from primary data analysis the following policy recommendations are developed and presented as follows:

190 | P a g e

Provincial and Federal Government, Private and Public sector corporations, local entrepreneurs and businessmen should launch massive efforts for job creation and employment generation in all four sub-districts of Jhang District especially in Atthara Hazari sub-district where the magnitude of poverty is on the higher side. There are no established economic zones or industry sites in the whole district. So local administration of Jhang should try to establish industrial sites in Jhang, by those business activities in Jhang should be picked up and find some good investing opportunities. Due to cheap land, cheap skilled labor opportunities are wide open for private and public sector investors.

Government agencies along with the Private NGOs should start grooming and teaching the head of the household and the members of the families regarding micro correlates of the poverty and convincing them to fight by themselves and not only waiting for Government support. Poor communities may be encouraged to participate in planning and development dialogues. There is a need to start different public welfare schemes at grass root level for improving the livelihood of the poor. Local Bodies and provincial level bodies should address the real problems of the poor in their respective communities. Delivery of essential services and basic necessities of life would reduce the burden of Poverty. There is also a need to encourage the active participation of rural women in income generating jobs through a very strong social mobilization. Rural leadership and community organization developed programs may be launched by major NGOs and public sector organization.

Microcredit facilities should be provided to poor households by the Govt. institutions and other financial institution in Jhang District. Apart from this, Credit agencies should provide free interests loans to the poor so that they can be self-reliant. There is a need to create conditions in which the poor are either given or enabled to acquire their assets and a peaceful environment to benefit from those assets.

Public spending on basic social, economic and socio-economic services should be increased. It is recommended that the government should introduce new housing schemes through public-private partnership so as to support those who are extremely and ultra-poor category.

191 | P a g e

Infrastructural development is needed in all four sub-districts of Jhang District to reduce the level of poverty among the poor. There is a need for capacity building this will help the VDOs to bring people together to solving their own problems. Ensure sustainability of income generating activities in their respective areas and strong and regular feedback regarding solutions of problems related to water supply, road construction, the building of a primary school, bridge or rural health center.

Rural poor in Jhang District should involve themselves in other socio-economic activities such as monitoring input market and agro-processing in order to diversify their means of livelihood so as to generate more income. The diversification of income source of the farming household heads for both poor and non-poor households can help to reduce the risk associated with income from a single source especially a very risky enterprise such as agriculture/farming. The productivity of the land is equally very important in obtaining higher yields and, subsequently, higher incomes and lower incidence of poverty. Greater investment in human capital and public amenities as a strategy for poverty alleviation is required Growth in Pakistan must be translated into education enhancement and poverty reduction activities. Growth and education that generates income and employment for the poor of the country can be critical for poverty reduction. Poverty can also be reduced by introducing social safety programs to the lower socio-economic segment of Pakistan’s society. The government should also focus on the quantity and quality of education that, in turn, leads to more researches in the country. It is also recommended that the linkages among education, poverty and economic growth may further be explored and generalized by including other macroeconomic variables other than physical capital. Poverty reduction and education enhancing strategies must be adopted to accelerate the economic growth of the country.

5.3 Follow up Studies This research work should be repeated after some time again to identify the magnitude, number, intensity dimension, nature and correlates of poverty problems which may arise with the passage of time in all four sub-districts of Jhang District, In this regards different waves or set of data

192 | P a g e

may be gathered and compared with gap of some years like after change of every government in Pakistan at Provincial level and federal level. In particular, this type of studies may be conducted in Southern Punjab Districts like Dera Ghazi Khan, Rahim Yar Khan or in the selected districts of Sindh Province like Tharparkar or Sakrand district where the incidence of poverty is reported to be high. The same study can be conducted in KPK and Baluchistan Provinces as well. • There is a need for research that will create a better understanding of the linkage between growth and poverty and help to formulate policies that seek to maximize the effect of growth on poverty.

193 | P a g e

References

Achia, T. N., Wangombe, A., & Khadioli, N. (2010). A logistic regression model to identify key determinants of poverty using demographic and health survey data. European Journal of Social Sciences, 13(1), 38-45. Addison, T., Harper, C., Prowse, M., Shepherd, A., Barrientos, A., Braunholtz-Speight, T., ... & Moore, K. (2008). The Chronic Poverty Report 2008-09: Escaping Poverty Traps. Manchester: Chronic Poverty Research Centre, Brooks World Poverty Institute.

Afzal, M., Malik, M. E., Begum, I., Sarwar, K., & Fatima, H. (2012). Relationship among education, poverty and economic growth in Pakistan: an econometric analysis. Journal of Elementary Education, 22(1), 23-45.

Ahmed, M. U., & Uddin, M. (2004). Socio-demographic correlates of rural poverty in Bangladesh: a case study of Gaibandha Sadar and Tanore Upazilas. Bangladesh e-journal of sociology, 1(2), 50-66.

Akanbi, O. A. (2015). Structural and institutional determinants of poverty in sub-Saharan African countries. Journal of Human Development and Capabilities, 16(1), 122-141.

Akhtar, S., Ahmad, S. M., & Cheema, I. N. A, Kkan, MN Sarwar, L., S. Bashir and M. Sadiq (2007). Ranking of districts by quality of housing indicators: A comparison from census 1998 and Core Welfare Indicator Questionnaire (CWIQ) 2004-05 data. Center for Research on Poverty Reduction and Income Distribution (CRPRID), Islamabad.

Alam, M. M., & Hussain, S. I. (2013). Estimating the magnitude and correlates of poverty using consumption approach in Khyber Agency (FATA). Developing Country Studies, 3(12), 42-53.

Ali, M., & Nishat, M. (2009). Do foreign inflows benefit Pakistani poor? The Pakistan Development Review, 48(4-II), 715-738.

Alkire, S., & Foster, J. (2011). Understandings and misunderstandings of multidimensional poverty measurement. The Journal of Economic Inequality, 9(2), 289-314.

194 | P a g e

Alkire, S., & Santos, M. E. (2010). Acute multidimensional poverty: A new index for developing countries. United Nations development programme human development report office background paper, (2010/11).

Alkire, S., Roche, J. M., Ballon, P., Foster, J., Santos, M. E., & Seth, S. (2015). Multidimensional poverty measurement and analysis. Oxford University Press, USA.

Alkire, S., & Santos, M. E. (2014). Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index. World Development, 59, 251-274.

Amjad, R., & Kemal, A. R. (1997). Macroeconomic policies and their impact on poverty alleviation in Pakistan. The Pakistan development review, 39-68.

Anka, L. M. (2009). Empirical analysis of the determinants of rural poverty in Sindh province of Pakistan (Doctoral dissertation, University of Sindh Jamshoro Sindh Pakistan).

Anwar, T. (2003). Trends in Inequality in Pakistan between 1998-99 and 2001-02. The Pakistan Development Review, 42(4), pp-809.

Anwar, T., & Qureshi, S. K. (2002). Trends in absolute poverty in Pakistan: 1990-91 and 2001. The Pakistan Development Review, 859-878.

Anwar, T., Qureshi, S. K., Ali, H., & Ahmad, M. (2004). Landlessness and rural poverty in Pakistan. The Pakistan Development Review, 855-874.

Ariana, P. and Naveed, A. (2009). 'Health'. In Deneulin, S. and Shahani, L. (eds.), 'An Introduction to Human Development and Capability Approach: Freedom and Agency'. London: Earthscan.

Arif, G. M. (2006). Targeting efficiency of poverty reduction programs in Pakistan. Asian Development Bank, Pakistan Resident Mission.

Arif, G. M., Iqbal, N., & Farooq, S. (2011). The persistence and transition of rural poverty in Pakistan: 1998-2004. Working Papers & Research Reports, 2011.

195 | P a g e

Arif, G. M., & Farooq, S. (2014). Rural poverty dynamics in Pakistan: Evidence from three waves of the panel survey. The Pakistan Development Review, 53(2), 71-98.

Arif, G. M., Nazli, H., Haq, R., & Qureshi, S. K. (2000). Rural Non-agriculture employment and poverty in Pakistan [with Comments]. The Pakistan Development Review, 1089-1110.

Aslam, Q. (2004). The problem of poverty in Pakistan-theory and reality. Economic Journal, 37, 13.

Akhtar, S., Saboor, A., Mohsan, A. Q., Hassan, F. U., Hussain, A., Khurshid, N., ... & Hassan, I. (2015). Poverty dynamics of rural Punjab and over time changes. J. Anim. Plant Sci, 25(5).

Atkinson, A. B. (2003). Multidimensional deprivation: contrasting social welfare and counting approaches. The Journal of Economic Inequality, 1(1), 51-65.

Atkinson, A. B. (2017). Monitoring global poverty: Report of the Commission on Global Poverty. World Bank, Washington.

Awan, M. S., Waqas, M., & Aslam, M. A. (2011). Multidimensional poverty in Pakistan: Case of Punjab province.

Awan, M., Waqas, M., & Amir, A. (2012). Multidimensional Measurement of Poverty in Pakistan. University Library of Munich, Germany.

Awan, M. S., Waqas, M., & Aslam, M. A. (2015). Multidimensional measurement of poverty in Pakistan: provincial analysis. Nóesis: Revista de Ciencias Sociales y Humanidades, 24(48), 55- 72.

Babatunde, M. A., & Adefabi, R. A. (2005, November). Long run relationship between education and economic growth in Nigeria: Evidence from the Johansen’s cointegration approach. In Regional Conference on Education in West Africa.

Bank, W. (1995). Pakistan Poverty Assessment, Report No. 14397-PAK”, p.1&17

Bank, W. (2002). Pakistan Poverty Assessment. Poverty in Pakistan, Vulnerabilities, Social Gaps, and Rural Dynamics. South Asia Region.

196 | P a g e

Bank, W. (2002). Pakistan Poverty Assessment: Poverty in Pakistan Vulnerabilities, Social Gaps, and Rural Dynamics”, Washington, D.C. (Report No. 24296-PAK).

Bank, W. (2016). Monitoring Global Poverty: Report on the Global Commission on Poverty.

The World Bank. Washington, DC

Bank, W. (2017). Monitoring Global Poverty: A Cover Note to the Report of the Commission on Global Poverty, chaired by Prof. Sir Anthony B. Atkinson. The World Bank. Washington, DC.

Barrientos, A., Hulme, D., & Shepherd, A. (2005). Can social protection tackle chronic poverty?. The European Journal of Development Research, 17(1), 8-23.

Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39(227), 357-365.

Bhide, S. (2004). Correlates of incidence and exit from chronic poverty in rural India: Evidence from panel data.

Bloom, D., Canning, D., & Sevilla, J. (2003). The demographic dividend: A new perspective on the economic consequences of population change. Rand Corporation.

Bourguignon, F., & Chakravarty, S. R. (2003). The measurement of multidimensional poverty. The Journal of Economic Inequality, 1(1), 25-49.

Burki, A. A., & Khan, M. A. (2008).Impact of Higher Wheat Prices on Poverty in Pakistan: Futuristic of Food Security.

Browne, S. (2012). United Nations Development Programme and System (UNDP). Routledge.

Cavendish, W. (2000). Empirical regularities in the poverty-environment relationship of rural households: Evidence from Zimbabwe. World development, 28(11), 1979-2003.

Chaudhry, I. S., & Rahman, S. (2007). The impact of gender inequality in education on rural poverty in Pakistan: an empirical analysis. European Journal of Economics, Finance and Administrative Sciences, 15(1), 174-188.

197 | P a g e

Chaudhry, I. S., & Malik, S. (2009). The Impact of Socioeconomic and Demographic Variables on Poverty: A Village Study. Lahore Journal of Economics, 14(1).

Chaudhry, I. S., Malik, S., & Ashraf, M. (2006). Rural Poverty in Pakistan. Pakistan Economic and Social Review, 44(2), 259-276.

Cheema, I. A. (2005). A Profile of Poverty in Pakistan: Centre of Research on Poverty Reduction and Income Distribution Planning Commission Islamabad.

Cheng, E. (2007). The demand for microcredit as a determinant for microfinance outreach- evidence from China. Savings and Development, 307-334.

Chronic Poverty Research Centre (CPRC), 2004, “The Chronic Poverty Report” website; www.chronicpoverty.org

Chronic Poverty Research Centre (CPRC), 2008–09, The Chronic Poverty Report, website: www.chronicpoverty.org

Cochran, W. G. (1977). Sampling Techniques. John Wiley & Sons. New York. Cooper, E. (2010). Inheritance and the intergenerational transmission of poverty in Sub-Saharan Africa: policy considerations. Chronic Poverty Research Centre Working Paper, (159).

Civil Secretariat (FATA), Government of Pakistan 2006, FATA Sustainable Development Plan (2006-2015)”, http://www.fata.gov.pk/subpages/sdp.php

Drèze, J., & Khera, R. (2010). The BPL census and a possible alternative. Economic and Political Weekly, Davis, P. (2011). The trappings of poverty: the role of assets and liabilities in socio-economic mobility in rural Bangladesh. Chronic Poverty Research Centre Working Paper, (195).

Fabre, A., & Augeraud-Véron, E. (2004, August). Education, poverty and child labour. In Econometric Society 2004 Far Eastern Meetings (No. 738). Econometric Society.

Fan, S., Hazell, P., & Thorat, S. (2000). Government spending, growth and poverty in rural India. American journal of agricultural economics, 82(4), 1038-1051.

198 | P a g e

FAO. (2007). Global plan of action for animaundpl genetic resources and the Interlaken declaration. In International Technical Conference on Animal Genetic Resources for Food and Agriculture.

Ferreira, F. H., Chen, S., Dabalen, A., Dikhanov, Y., Hamadeh, N., Jolliffe, D., ... & Serajuddin, U. (2015). A global count of the extreme poor in 2012: data issues, methodology and initial results. The World Bank.

Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage

Finance Division, Government of Pakistan, 2007/08, ‘Human Development for the 21st Century’ “Poverty Reduction Strategy Paper-II”, Chapter

Gazdar, H. (2011). Social protection in Pakistan: in the midst of a paradigm shift?. Economic and Political Weekly, 59-66.

Geda, A., De Jong, N., Mwabu, G., & Kimenyi, M. (2001). Determinants of poverty in Kenya: A household level analysis. ISS Working Paper Series/General Series, 347, 1-20. Government of Pakistan. (2014). Economic Survey 2013- 14. Finance Division. Economic Adviser’s Wing, Islamabad

Government of Pakistan. (2014). Economic Survey 2013-14. Finance Division. Economic Adviser’s Wing, Islamabad. IFAD, (2011). Rural poverty report. Available at www.ifad.org/rural/rpr/2011/background/9.pdf

Government of Pakistan (2016), “Pakistan Economic Survey (2015-16)”, Ministry of Finance, Government of Pakistan

Government of Pakistan (2016),. Household Integrated Economic Survey, Federal Bureau of Statistics, Statistics Division, Government of Pakistan, Islamabad.

Government of Punjab (2008), Planning and Development Department, Bureau of Statistics. Multiple Indicator Cluster Survey Punjab 2007-08, District Jhang

Government of Punjab, 2009, Punjab Cities Improvement Investment Program project report , Jhang City Profile , 2009

199 | P a g e

Haq, R., & Bhatti, M. A. (2001). Estimating poverty in Pakistan: The non-food consumption share approach (Vol. 183). Pakistan Institute of Development Economics.

Haq, R., & Zia, U. (2009). Does governance contribute to pro-poor growth? Evidence from Pakistan (No. 2009: 52). Pakistan Institute of Development Economics. Haq, R., & Zia, U. (2013). Multidimensional wellbeing: An index of quality of life in a developing economy. Social Indicators Research, 114(3), 997-1012.

IFAD. 2007. Results Measurement Framework for reporting on progress achieved against the IFAD Strategic Framework 2007-2010. EB 2007/91/R.2.

Ivanic, M., & Martin, W. (2008). Implications of higher global food prices for poverty in low- income countries. The World Bank.

Jackman, R. W. (1973). On the relation of economic development to democratic performance. American Journal of Political Science, 17(3), 611-621.

Jafri, S. Y., & Khattak, A. (1995). Income inequality and poverty in Pakistan. Pakistan Economic and Social Review, 37-58.

Jefferis, K. R., & Kelly, T. F. (1999). Botswana: Poverty amid plenty. Oxford Development Studies, 27(2), 211-231.

Jamal, H. (2007). Income poverty at district level: An application of small area estimation technique.

Jamal, H. (2014). Growth and income inequality effects on poverty: The Case of Pakistan (1988- 2011).

Jamshed, A., Rana, I. A., Birkmann, J., & Nadeem, O. (2017). Changes in vulnerability and response capacities of rural communities after extreme events: Case of major floods of 2010 and 2014 in Pakistan. Journal of Extreme Events, 4(03), 1750013.

Jolliffe, D. (2014). A measured approach to ending poverty and boosting shared prosperity: concepts, data, and the twin goals. World Bank Publications.

200 | P a g e

Jolliffe, D., & Prydz, E. B. (2015). Global poverty goals and prices: how purchasing power parity matters. The World Bank.

Jolliffe, D., & Prydz, E. B. (2016). Estimating international poverty lines from comparable national thresholds. The World Bank.

Kaplinsky, R. (2013). Globalization, poverty and inequality: Between a rock and a hard place. John Wiley & Sons.

Kaplinsky, R., & Morris, M. (2014). Developing industrial clusters and supply chains to support diversification and sustainable development of exports in Africa. Report for the African Export- Import Bank. Cairo: African Export-Import Bank.

Kaplinsky, R., & Morris, M. (2016). Thinning and thickening: productive sector policies in the era of global value chains. The European Journal of Development Research, 28(4), 625-645.

Keister, L. A. (2008). Conservative Protestants and wealth: How religion perpetuates asset poverty. American journal of Sociology, 113(5), 1237-1271.

Khan, A. U., Saboor, A., Hussain, A., Sadiq, S., & Mohsin, A. Q. (2014). Investigating multidimensional poverty across the regions in the Sindh province of Pakistan. Social indicators research, 119(2), 515-532.

Khan, A. U., Saboor, A., Hussain, A., Sadiq, S., & Mohsin, A. Q. (2014). Investigating multidimensional poverty across the regions in the Sindh province of Pakistan. Social indicators research, 119(2), 515-532.

Khan, A. U., Saboor, A., Hussain, A., Sadiq, S., & Mohsin, A. Q. (2014). Poverty assessment as a multidimensional socio-economic concept: the case of the Rawalpindi region in Pakistan. Asia Pacific Journal of Social Work and Development, 24(4), 238-250.

Khan, M. H. (2013). Participatory Rural Development in Pakistan: Experience of Rural Support Programmes.

Lerman, R. I. (2002). Impacts of Marital Status and Parental Presence on the Material Hardship of Families with Children.

201 | P a g e

Lipton, M., & Ravallion, M. (1995). Poverty and policy. Handbook of development economics, 3, 2551-2657

Lohana, H. R. (2009). Poverty dynamics in rural Sindh, Pakistan. Chronic Poverty Research Centre Working Paper, (157)

Loughhead, S., & Mittal, O. (2000). Urban poverty and vulnerability in India: A social policy perspective. Social change, 30(1-2), 33-54. Magongo, J., & da Corta, L. (2011). Evolution of gender and poverty dynamics in Tanzania. Available at SSRN 1896143

Malik, S. (1996). Determinants of rural poverty in Pakistan: A micro study. The Pakistan development review, 171-187.

Malik, K. (2014). Human development report 2014: Sustaining human progress: Reducing vulnerabilities and building resilience. United Nations Development Programme, New York.

Mehta, A. K., & Shah, A. (2003). Chronic poverty in India: Incidence, causes and policies. World Development, 31(3), 491-511.

Mehta, A. K., & Shah, A. (2001). Chronic Poverty in India: Overview Study, Chronic Poverty Research Centre. University of Manchester, 55-143.

Minasyan, G., & Mkrtchyan, A. (2005). Factors Behind Persistent Rural Poverty in Armenia. Universitäts-und Landesbibliothek Sachsen-Anhalt.

Moore, K. (2005). Thinking about youth poverty through the lenses of chronic poverty, life- course poverty and intergenerational poverty. Chronic Poverty Research Centre Working Paper, (57).

Naveed, A., & Ali, N. (2012). Clustered deprivation: District profile of poverty in Pakistan. Sustainable Development Policy Institute.

Naveed, A., & Islam, T. U. (2012). A new methodological framework for measuring poverty in Pakistan. Sustainable Development Policy Institute.

202 | P a g e

Nelson, G. C., Rosegrant, M. W., Koo, J., Robertson, R., Sulser, T., Zhu, T., ... & Magalhaes, M. (2009). Climate change: Impact on agriculture and costs of adaptation (Vol. 21). Intl Food Policy Res Inst.

Okrasa, W. (1999). Who Avoids and Who Escapes from Poverty during the Transition? Evidence from Polish Panel Data, 1993 96. The World Bank.

Orbeta Jr, A. C. (2005). Poverty, vulnerability and family size: evidence from the Philippines. Poverty Strategies in Asia, 171.

Pakistan Economic Survey, FY 2007/08, Government of Pakistan. www.finance.gov.pk/survey

Pakistan Economic Survey, FY 2013/014, Government of Pakistan. www.finance.gov.pk/survey

Please Vijaya, R. M., Lahoti, R., & Swaminathan, H. (2014). Moving from the household to the individual: Multidimensional poverty analysis. World Development, 59, 70-81.

Pogge, T. (2007). Freedom from poverty as a human right: who owes what to the very poor?. UNESCO.

Pogge, T. (2007). Severe poverty as a human rights violation. Freedom from poverty as a human right: who owes what to the very poor, 11-53.

Pogge, T. (2004). The first United Nations millennium development goal: A cause for celebration?. Journal of Human Development, 5(3), 377-397.

Pogge, T. (2005). Severe poverty as a violation of negative duties. Ethics & International Affairs, 19(1), 55-83.

Qureshi, S. K., & Arif, G. M. (1999). Profile of poverty in Pakistan, 1998-99. Pakistan institute of development economics.

Qureshi, S. K., & Arif, G. M. (2001). Profile of Poverty in Pakistan, 1998-99. Pakistan Institute of Development Economics, Islamabad. MIMAP Technical Paper Series, 5.

Raja, N. (2005). Humanization of education in Pakistan through Freire’s concept of literacy. Asia Pacific Education Review, 6(1), 1-6.

203 | P a g e

Ravallion, M. (1992). Poverty comparisons: a guide to concepts and methods. The World Bank.

Ravallion, M. (1994). Measuring social welfare with and without poverty lines. The American economic review.

Ravallion, M. (1999). Issues in measuring and modeling poverty. The World Bank.

Ravallion, M. (2009). The developing world's bulging (but vulnerable)" middle class". The World Bank.

Ravallion, M. (2010). Poverty lines across the world. The World Bank.

Ravallion, M., Chen, S., & Sangraula, P. (2008). Dollar a day revisited. The World Bank.

Ravallion, M. (1990). On the coverage of public employment schemes for poverty alleviation. Journal of Development Economics, 34(1-2), 57-79.

Ribas, R. P., & Machado, A. F. (2007). Distinguishing chronic poverty from transient poverty in Brazil: developing a model for pseudo-panel data. Brasília: International Poverty Centre, 219- 239.

Rodriguez, A. G., & Smith, S. M. (1994). A comparison of determinants of urban, rural and farm poverty in Costa Rica. World Development, 22(3), 381-397.

Roul, A. (2005). Lashkar-e-Jhangvi: Sectarian Violence in Pakistan and Ties to International Terrorism. Terrorism Monitor, 3(11), 177-178.

Sabir, H. M., Hussain, Z. A. K. I. R., & Saboor, A. B. D. U. L. (2006). Determinants of small farmers poverty in the Central Punjab (Pakistan). Journal of agriculture and social sciences, 2(1), 10-12.

Sadeghi, J. M., Toodehroosta, M., & Amini, A. (2001, July). Determinants of poverty in rural areas: Case of Savejbolagh farmers in iran. Economic Research Forum for the Arab Countries, Iran and Turkey.

204 | P a g e

Salahuddin, T., & Zaman, A. (2012). Multidimensional poverty measurement in Pakistan: time series trends and breakdown. The Pakistan Development Review, 493-504.

Santos, M. E. (2011). Human capital and the quality of education in a poverty trap model. Oxford Development Studies, 39(01), 25-47.

Sekhampu, T. J. (2013). Determinants of poverty in a South African township. Journal of Social Sciences, 34(2), 145-153

Sen, A. (1976). POVERTY: AN ORDINAL APPROACH TO MEASUREMENT. Econometrica (Pre-1986), 44 (2), 219.

Sen, A. (1982). Poverty and famines: an essay on entitlement and deprivation. Oxford university press.

Sikander, M. U., & Ahmed, M. (2008). Household determinants of poverty in punjab: a logistic regression analysis of MICS (2003-04) data set. In 8th Global Conference on Business & Economics.

Susheela, H., Surendra, H. S., Padmaja, N., & Anuradha, J. (2000). Prevelence of poverty in rural households of Dharwad district. Karnataka Journal of Agricultural Sciences, 13(1), 228-229.

Sugema, I., Irawan, T., Adipurwanto, D., Holis, A., & Bakhtiar, T. (2010). The impact of inflation on rural poverty in Indonesia: An econometrics approach. International Research Journal of Finance and Economics, 58, 51-57.

Thompson, A., Traub, B. J., & White, R. P. (1983). Socio-Demographic Predictors of Rural Poverty: A Regional Analysis. Department of Agricultural Economics & Rural Sociology, North Carolina A & T State University.

Thompson, A., Traub, B. J., & White, R. P. (1983). Socio-Demographic Predictors of Rural Poverty: A Regional Analysis. Department of Agricultural Economics & Rural Sociology, North Carolina A & T State University.

UNDP, 2000. Annual Report of United Nations Development Programme UNDP Secretariat New York USA

205 | P a g e

UNDP, 2006. Annual Report of United Nations Development Programme UNDP Secretariat New York USA.

UNDP, 2006. National Human Development Report Poverty, Growth and Governance,

UNDP, 2012. Pakistan National Report , Social audit of Local governance and delivery of Public services 2012, Pakistan

You, J., Wang, S., & Roope, L. (2014). Multi-dimensional intertemporal poverty in rural China. University of Oxford Centre for the Study of African Economies Working Paper Series No. WPS/2014–3.

206 | P a g e

QUESTIONNAIRE

General Information

From Number Village Union Council Town

Interviewer Name Time of Interview Date of Interview

Q A1 Resulted of Interview

1 Interview conducted 2 Interview declined

3 Partially Completed Other………………………………………………

207 | P a g e

SECTION I: Personal Information of the Head of Household & Basic Household Statistics

Q1 Head of the Household ------Q2 Gender of Respondent (Don’t ask, just record)

1. Male 2. Female

Q3 What is your marital status?

1 Married 2 Never married / engaged 3 Divorced/separated 4 Widowed

Q4 Age (in completed years) ------Q5 Which is the highest class/degree that your head of the household have passed/obtained

0 Illiterate 1 Below Primary

2 Primary pass/ Middle fail 3 Middle pass/ Matric fail

4 Matric (SSC) 5 Intermediate/College (No degree)

6 Graduate/ equivalent 7 Post Graduate

8 Professional degree 9 Madrassah (Seminary Degree)

Q6 Occupation of the household head

1- Government employee 2- private employee 3- own business

4- Farmer 5- Employed in Maddrassah 666- Other------

Q7 What is current employment status of the head of household?

1- Permanent employee/business 2- contract employee/temporary business

3- Daily wager 4- Retired 5- Student 666 Other------Q8 Which Type of family you have?

1. Nuclear family 2. Joint family Q9 What is the total number of family members living in the household? Male Female

208 | P a g e

a. (060 ------e. Total ------Q10 Do you have CNIC card? 1 Yes 2 No Q11 How many members of your household have CNIC card? ……………………………………………………. Q12 How many children of your household have Form B? …………………………………………………………………. Q13 Are you a permanent resident of this village/Area?

1. Yes 2. No

Q14 Duration of your stay in this village

------

Q15 From where did your forefather come

1. Within Pakistan 2. India 3. Any other

Q16 From which tribe do you belong?

1. ------2. ------3------

Q17 What is the ownership status of your house?

1. Own house 2. Rented 3. Tenants

Q18 What is the structure of your current house?

1 Pucca 2 Katcha 3 Pucca & Katcha 666 Other ------Q19 Which materials use in the wall of your house.

1 Baked Bricks/Blocks/Stones 2 Un-Baked Bricks/ Earth bounded 3 Wood/Bamboo

666 Others------

209 | P a g e

Q20 Which materials do you use in the roof of your house?

1. Iron Sheet 2. Concrete 3. Wood/Bamboo 4. Mud/Kachta

666 Others------

Q21 What is the most important source of drinking water at your house?

1 Tap inside the house 2 Tap outside the house 3 Tube well 4 Hand pump

5 Open well 6 Natural source (Pond/Lake/Stream/River/Spring) 7 Canal

666 Others------

Q22 How many rooms are there in your house?

------

Q23 What is the latrine situation in your house?

1 Inside 2 Outside 3 No Latrine

Q24 If the above question inside or outside then what type latrine your household have?

1 Pucca 2 Katcha 3 Katcha and Pucca

Q25 What is the Bathroom situation in your house?

1 Inside 2 Outside 3 No Bathroom

Q26 If the above question inside or outside then what type Bathroom your household have?

1 Pucca 2 Katcha 3 Katcha and Pucca

Q27 What is the Kitchen situation in your house?

1 covered area 2 Open area 3 Other------

Q28 Most important source of lighting at your house

1 Electricity 2 Kerosene oil 3 No

666 Others------

Q29 Most important source of fuel used for cooking

1 LPG/Gas 2 Electricity 3 Kerosene oil 4 wood 5 Coal/lignite/charcoal

210 | P a g e

6 Other - purchased (firewood/crop residue/cow dung cake)

7 Other - not purchased (firewood/crop residue/cow dung cake

666 Other ------

Q30 Which of the following communication facilities/sources use in your house.

A Land line phones 1 Yes 2 No B Cellular/ Mobile networks 1 Yes 2 No C Cable/TV 1 Yes 2 No D Radio 1 Yes 2 No E Internet 1 Yes 2 No G Newspapers 1 Yes 2 No

Q31 The road linking your house to local market is ------

1. Blacktop 2 Jeepable (Made of Bricks) 3 Katcha track

666 Other------

Q32 How many health facilities available In your area and your family have access to it.

Number of Facilities Sector Hospital Clinic Dispensary BHU

A Govt./Semi-govt. facilities

B Private Sector Facilities

Welfare/Non-Profit C Facilities

Alternative Medicine D (Hakeem/Homeopathic)

Total

Q33 Major diseases in the last three months

1 ------2------3------

Q34 Is the children immunization facility in your area accessible to your family?

211 | P a g e

1 Yes 2 NO

Q35 Do you immunize your children against basic diseases regularly?

1 Yes 2 NO

Q36 Do you vaccinate pregnant female in your household.

1 Yes 2 NO

Q37 Do you satisfy with health facility in your area.

1 Yes 2 No

Q38 Does your household own any agricultural land?

1 Yes 2 No

Q39 How much land does your family own?

1 Up to 5 acre 2 6-10 acre 3 10-20 acre 4 21-30 acre 5 31-40 acre 6 40 acre or more

Q40 What are the main sources of irrigation water?

(1)-Barrani water (2)-Tube well (3)-Canals (4)-River (5)-Streams

666-Others------

Q41 Number of the Assets of the household and its value.

Numbers Value (Rs) Numbers Value (Rs)

1-land ------9-House ------

2-Livestock ------10-personal car------

3-Tractor ------11-Motor cycle------

4-Thresher ------12- Computer------

5-Tubewell/pump------13-Tv/radio ------

6-shop/business ------14 saving (cash etc) ------

212 | P a g e

7-Loan given ------15 sewing match ------

16 Jewlary ------666-Other Assets ------

Q42 Are banks present in this area/accessible to you? 1. Yes 2. No

Q43 What are the sources of credit in this area? 1. Banks 2. Business partners 3. Relatives 4. Friends 5. Private moneylenders 6. Manufacturer 666 Other------Q44 Do you have taken any loans in the last six months?

1. Yes 2. No

Q45 Loans (Rs)

From whom Amount taken utilization

1. Friends/relative ------

2. Banks ------

3. Shopkeepers ------

4. Government ------

5. Private lender ------

6. Others ------

Q46 How many children are going to school

Boys ------Girls------Total------

Q47 Level of Education of the household members

Male (number) Female (number)

Not literate ------

literate ------

Q2 Q48 How many of the following education facilities are available in your area/village for which your family have easy access.

Number of Schools

Public Private Welfare Total

213 | P a g e

A Girls’ primary schools

B Boys’ primary school

Girls’ middle & High C school

Boys’ middle & High D school

E Girls’ degree college

F Boys’ degree college

G Technical college

H Madrassah

Q49 What is the attitude of the head of household about education?

1 Positive 2 Negative

Q50 Do you satisfied with education facility in your area?

1 yes 2 No

Q51 Are you the main earner in your family?

1. Yes 2. No

Q52 What is your relation to the main earner of your family? 1 Father 2 Mother

3 Brother 4 Sister

5 Spouse 6 Other………………………………………………

Q53 What is the occupation of the main earner of the family? ______

Q54 How many of your family members are earning

Male ------Female ------

Q55 Has any member of your family migrated to another area, inside or outside the country to earn income?

214 | P a g e

1 Yes 2 No

Q56 If yes how many ------

Q57 If Yes then where

1 ------2 ------3------4------

Q58 Have they migrated permanently or temporarily?

1. Permanently 2. Temporarily

Q59 How often do they visit the family?

1- Once a year 2 - Once every two years 3 – Once More than two years

Q60 Do they send back money regularly?

Yes 2. No

Q61 Income detail per month/day/season (Rs) of the household(Clean later to per month)

1. Agricultural (crop, rice etc)/season------2. Livestock ------

3. Business ------4. Service------

5. Labor------6. Pension------

7. Rent ------8. Remittances------

9. Daily wagers------9. Other------

10.Total/month------

Q62 How does your household spend their income? What is the most significant item that your household spends its earnings to procure each month?

1 Food items 2 Health

3 Non-Food items 4 Education

666 Other------

215 | P a g e

Q63 Expenditure detail per month (Rs) of the household

1 Food------2 Clothing ------

3 Housing------4. Medical expenses------

5 Educations------6. Transport------

7 social functions------8. Other------

9 Total/month------

Q64 Which the following statement better describe your income and expenditure

1. Your income very less than your expenditure

2. Your income is less than your expenditure.

3. Your income is much more than your expenditure

4. Your income is more than your expenditure

5. Equal

Q65 When your expenses exceed of your income, how do you meet your expenses?

1. Savings 2. Borrow from your friend and relative

3. Borrow from bank 4 Private lender

666. Others------

Q66 What are the main sources of your earnings of your household?

(1)-Agriculture (2) - Govt Service

(3)- Private Service (4) - Transport

(5)- Small business (6) - Medium business

(7)- Large business (8)-Rental Income

666 Other------

Q67 What are the hurdles in your main sources of earnings?

1. Irrigation 2. input 3. Lack of money 4. lack of skill/Education 5. Govt legislation 6. Others------

216 | P a g e

Q68 What are the four main reasons of poverty in your area

1 ------2 ------

3 ------4 ------

217 | P a g e