THE EFFECTS OF REMITTANCES ON POVERTY AND HUMAN

CAPITAL FORMATION IN NIGERIA

BY

ONAH GLORIA IFEYINWA PG/M.Sc./07/42935

A RESEARCH PROJECT PRESENTED TO THE DEPARTMENT OF

ECONOMICS, UNIVERSITY OF NIGERIA (UNN)

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE

AWARD OF MASTER OF SCIENCE (M.Sc) DEGREE IN

ECONOMICS

SEPTEMBER, 2010

TITLE PAGE

THE EFFECTS OF REMITTANCES ON POVERTY AND HUMAN

CAPITAL FORMATION IN NIGERIA

ii

CERTIFICATION

This is to certify that Miss Onah Gloria Ifeyinwa PG/M.Sc/07/42935 being the

researcher of this work has satisfactorily completed the requirement for the project

research for the partial fulfillment of the requirements for the award of Master of

Science (M.Sc) Degree in Economic in the Faculty of Social Sciences, University

of Nigeria, Nsukka.

The work covered in this project is original and has not been submitted in

part or full for any other degree of this University or any other University.

………………………….. Professor F.E Onah Date …………… Supervisor

………………………….. Prof. C.C. Agu Date …………… Head of Department

………………………….. Prof. E.O Ezeani Date …………… Dean Faculty of Social Science

…………………………………………. EXTERNAL EXAMINER

iii

DEDICATION

To the almighty God who in His infinite mercy and guidance, the potentiality of

this work, besides other academic successes are ensured.

And

To my parents Mr. and Mrs C.I. Onah , and Mr. L.E. Attah , who by their support

made this work possible.

iv

ACKNOWLEDGEMENT

It is with deep sense of gratitude that I acknowledge the efforts of these persons

who despite all odds stood by me throughout this programme. They include my parent

Mr and Mrs. C Onah, my brother Ifeanyi and other siblings in the family and Mr. L.E.

Attah who relentlessly inspired me in many dimensions to get my dream (academic

excellence) actualized

My unquantified appreciation also goes to my able and competent supervisor,

Professor. F.E Onah, for his painstaking supervision and excellent exposition to

economic research. Besides him are Mr. Nwosu Emma , Mr .R. Ezebuilo, Dr W. Fonta,

and Fr H.E. Ichoku who with their wealth of experience in research also painstakingly

read through the various write ups.

I am also grateful to Prof. Agu C.C, the Head of Department of Economics,

University of Nigeria, Nsukka., for his efforts, besides his subordinates in the drive for

greater standard in the department, and for imparting in me the world of Economics, not

only in theory but also pragmatic approach to economic problems.

Important to note here are friends, Odom Chika, Ndidi Ivoke whom the same

dream brought us to University of Nigeria, Nsukka , and their perception about this life is

a source of encouragement to me.

v

TABLE OF CONTENTS

Title Page - - - - - - - - i

Certification - - - - - - - - ii

Dedication - - - - - - - - - iii

Acknowledgement - - - - - - - - iv

Table of Contents - - - - - - - - v

List of Tables - - - - - - - - - vii

Abstract - - - - - - - - - viii

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study - - - - - - 1

1.2 Statement of the Problem - - - - - - 4

1.3 Objectives of Study. - - - - - - 5

1.4 Research Hypothesis - - - - - - 6

1.5 Significance/Justification of the Study - - - - 6

1.6 Scope of the Study - - - - - - - 7

CHAPTER TWO: LITERATURE REVIEW

2.1 Theoretical Literature - - - - - - 9

2.2 Empirical Literature - - - - - - - 13

2.3 Limitation of Previous Studies - - - - - 18

CHAPTER THREE: METHODOLOGY

3.1 Model Specification - - - - - - 20

3.2 Justification of the Model - - - - - - 27

3.3 Source of the Data - - - - - - - 27

3.4 Software Package - - - - - - - - 28

CHAPTER FOUR: DATA ANALYSIS

4.1 Data Interpretation - - - - - - 29

vi

4.1.1 The Difference on Poverty Level between receiving

and non-receiving remittances households - - - 29

4.1.2 Effects of remittances on different zone, sex, sector, and

quintiles - - - - - - - - 32

4.1.3 Effect of remittances on Education - - - - 34

4.1.4 Effect of remittances on Health Status –using Infant Mortality 35

4.2 Evaluation of Hypotheses - - - - - - - 35

4.2.1 Test of Hypothesis One - - - - - - 36

4.2.2 Test of Hypotheses Two - - - - - - 36

4.2.3 Test of Hypothesis Three - - - - - - 36

4.2.4 Test of Hypothesis four - - - - - - 37

CHAPTER FIVE: SUMMARY, POLICY RECOMMENDATION AND CONCLUSION

5.1 Summary - - - - - - - - 38

5.2 Policy Recommendation - - - - - - 39

5.3 Conclusion - - - - - - - - 40

REFERENCES - - - - - - - - 41

APPENDIX - - - - - - - - - 48

vii

LIST OF TABLES

Table 1: Counterfactual Situation of Households without Remittances. - 29

Table 2: Probit Model on the Effect of Remittance on Poverty between

Receiving and Non-Receiving Household. - - - - 30

Table 3: Heckman Selection Model-two step estimates. - - - 31

Table 4: The Effects of Remittances on Different Zone, Quintiles,

Sex and Sector - - - - - - 32

Table 5: the effect of Remittance of Education - - - - 34

Table 6: The Effect of Remittance on Infant Mortality. - - - 35

viii

ABSTRACT

This study examines the effects of remittances on poverty and human capital

formation in Nigeria, since remittances can alleviate poverty at household level by

helping to fund schooling, reducing child labour, increasing family health and expanding

durable ownership. The aim of this work is to fill the gap that has been left by

researchers on poverty and to know how it effects education and health status-using

infant mortality in Nigeria. It combines both cash and non-cash values received by

individuals in measuring the amount for remittances. This study employs the linear

regression model for estimating counterfactual per capita household expenditure without

remittance and educational attainment, probit regression model on the effect of

remittances on poverty to know the difference between receiving and non-receiving

households, Heckman selection model – twostep estimates to correct selection bias that

leads to inconsistent estimate, since migrants are not randomly selected from the pool of

households and linear probability model for infant mortality, which includes “one” if

household has any infant dead, and “zero” otherwise. Four null hypotheses were

formulated and tested. Some were rejected while some were accepted based on the

significant level of the variables co-efficient, using both t-statistics and z-statistics at 5%

level of significance. The result shows that remittances have positive effects on poverty

and human capital formation in Nigeria using estimated co-efficients of the variables.

Our policy recommendation is that receiving households should increase the benefit from

remittance by investing on business activities instead of over depending on the

remittances by working less which may negatively affect their labour supply.

1

CHAPTER ONE

INTRODUCTION 1.3 Background of the Study

Migration is re-location of residence from the country of origin to another

country. It has become one of the means of acquiring skills and improving

standard of living of both skilled and unskilled labour force (Quartey, 2006). In

Nigeria, many people, especially the young ones consider migration as a panacea

to economic problems because of the macroeconomic instability, corruption and

poor management of resources. That is why thousands of professionals especially

scientists, academics and even those in the medical fields migrate mainly to

Western Europe, the United States and the Persian Gulf states. At the same time,

unskilled Nigerians with little education migrate abroad to work as cleaners,

security guards, e.t.c.(Chukwuone, 2007).

These migrants remit a portion of their increased income to their families

back home. The money which the families receive from their migrant members

abroad is known as remittance. Remittances are referred to as unrequited transfer

sent by migrant workers back to relatives in their countries of origin (Juthathip,

2007). Remittances are person-to person flows, well targeted to the needs of the

recipients, who are often poor, and do not typically suffer from the government

problems that are associated with official aid flows. Fundamentally, remittances

are personal flows from migrants to their families and friends (Dilip and Sanket,

2007). Remittances can be inform of money, assets or informal or non-monetary

2

forms. Non- monetary forms include clothing, medicine, gifts, tools, and

equipment.

According to the World Bank (2003), remittances have become a rising

source of external funding for developing countries, reaching 80 billion dollars in

2002. In addition, remittances are the second largest source of foreign capital in

developing countries next to Foreign Direct Investment (FDI). Remittances can

form a “family welfare system” that can help to smooth consumption, alleviate

liquidity constraints and provide a form of mutual assistance (Manuel, Lindsay,

and Schnieder, 2006). There is evidence that it alleviates poverty at household

level in some countries by helping to fund schooling, reducing child labour,

increasing family health and expanding durable ownership (World Bank, 2006).

Remittances can improve a country’s creditworthiness and thereby enhance its

access to international capital markets for financing infrastructure and other

development projects (Ratha, 2007).

It is estimated that migrant remittance flows to developing countries now

surpass official development aids receipts in many developing countries (Rath,

2005). Global flows of migrant workers’ remittances were estimated at US $182

billion in 2004, 5.7 percent above their level in 2003, and 34.5 percent compared

to 2001 (World Bank, 2004). Although remittances to Sub-Saharan Africa are low,

5 percent of global estimate in 2003, Nigeria remains the single largest recipient in

sub-Saharan Africa (Maimbo and Rath, 2005).

3

International remittances enter Nigeria through formal and informal

sources. The Western Union Money Transfer Mechanism is one of the major

ways through which remittances enter Nigeria. Informal sources include relatives

and town unions and individuals entering Nigeria form their domicile foreign

countries (Chukwuone, 2007). In Africa, remittances are part of a private welfare

system that transfers purchasing power from relatively richer to relatively poorer

members of a family. For most part, remittances seem to be used to finance

consumption or investment in human capital, such as education, health and better

nutrition (Lopez – Cordova, 2004).

Remittances also play a crucial role in developing local capital market and

productive infrastructure as well as increasing effective demand for local goods

and services (Ratha, 2003). Remittances are also associated with increased

household investment in education and health which are human capital investment

and entrepreneurship, all of which have a high social return in most circumstances

(Juthathip,2007).

According to Chimhowu, Piesse, and Pinda, (2003) remittances have made

powerful contribution to the poor or vulnerable in majority of households and

communities. Remittances can also indirectly promote community development

through spillover mechanisms. First, increased consumption of migrant

households can generate multiplier effects. If the recipient families increase their

household consumption on local goods and services, this will benefit other

members of the community through the increase in demand, which stimulates

4

local production, thereby promoting job creation and local development. Second,

remittances are also found to prop up the formation of small-scale enterprises,

thereby, promoting community development. International remittances ease credit

constraints by providing working capital for recipients to engage in entrepreneurial

activities (Woodruff and Zeneto, 2001).

1.4 Statement of the Problem

Despite the increasing size of remittances both internal and external, it

seems that little or no effort has been made to investigate its impact on economic

development, especially on poverty and human capital formation in Nigeria.

United Nation (2003) report shows that poverty is deep with over 70% of people

earning less than US$1 a day. Adam (2005) observes that little attention has been

paid to examining the economic impact of these transfers on households in

developing countries despite the ever-increasing size of official international

remittances.

Juthathip (2007) observes that in developing countries, remittances help

households to set up their entrepreneurial activity and finance education and

health. Chukwuone (2007) observes that remittances has been implicated as a vital

source of income with crucial income smoothening effect and improving the

standard of living, but its contribution in Nigeria is not known.

According to Yang (2005), remittances have contributed to the stability of

recipient economies by compensating for foreign exchange losses due to

macroeconomics shocks. In many conflict countries, it provides a lifeline to the

5

poor. Muhammed and Naveed (2009) observe that remittances ease the household

expenditure burden of poor families and smooth their consumption patterns.

Income helps families to engage in several investment opportunities like human

capital, microenterprises and property. Nigeria does not put remittances of migrant

workers to their best use. Thus, the questions are:

1. What is the difference in poverty level between remittance-receiving and non-

receiving households?

2. What are the effects of remittance on different zones, quintiles, sex, and

sectors?

3. What are the effects of remittances on human capital formation (education and

health status)?

1.3 Objectives of Study

The main objective of this study is to analyze the effect of remittances on

poverty and human capital formation in Nigeria. Specifically, this study seeks to:

1. Examine the difference in poverty level between remittance-receiving and

non-receiving households.

2. Examine the effects of remittances on different zones, quintiles, sex, and

sector.

3. Ascertain the effect of remittances on education

4. Ascertain the effect of remittances on health status using infant mortality as

a proxy.

6

1.4 Research Hypothesis

1. There is no significant difference in the level of poverty between

remittance-receiving and non-receiving households.

2. Remittances have no effects on different zones, sex, sector, and quintiles

3. Remittances have no significant effect on education

3. Remittances have no significant effect on health status using infant

mortality as a proxy.

1.7 Significance/Justification of the Study

This study provides information, which will benefit Nigerians and help

them to make use of migrant workers’ remittances. It will also help in some

developmental goals such as spending remittances on education, health services,

shelter, community projects and proper developments.

To policy makers in the National Planning Commission, it will help in anti-

poverty policies, since remittances can help to reduce poverty. It will also help in

the initiation of policies to encourage the transfer of remitter’s funds through the

new micro-credit banks, thus facilitating the access of the poor to finance. Due to

this study, policies may come up to enhance remittances, which will help to

facilitate access to long term finance made available by remitters especially

through their investment in the capital market. Also, to researchers, it will have a

meaningful contribution to the existing literature.

This study is justified by the level of poverty and human capital formation

in Nigeria. Although, some recent studies have been carried out on the effects of

7

remittance on poverty and human capital formation, for example, those of

Juthathip (2007), Adam (2004), Bodin and Sinaia (2009), Yang and Martinez

(2005) and Pablo et.al. (2007), most of these studies focused on Central America,

Caribbean countries and Latin American countries. To the best of our knowledge,

a study of this nature has not been carried out in Nigeria. Even though few people

have studied the impact of remittance on poverty and inequality in Nigeria, for

example, Chukwuone (2007) and Anyanwu and Erhjakpor (2006) on how

remittance affects poverty in Nigeria, they have not studied its effect on human

capital development.

Furthermore, this study is justified by the fact that it will account for

comparison or difference in poverty level between remittance receiving and non-

receiving households and its effect on poverty and human capital formation.

1.8 Scope of the Study

The discussion of this study is on the effects of remittances on poverty and

human capital formation (education and health status) in Nigeria. The scope

covers only Nigerian households for 2004. The study would have covered, the

year 2009, but for the fact that the available household survey for Nigerian

households on remittances, poverty, education and health status is Nigeria

National Living Standard Survey (NNLSS) 2003/2004. According to National

Bureau of Statistics (NBS, 2009) the 2009 NNLSS is not yet out; it is still in

process.

8

We used NNLSS 2004 for health status because it gives us vital

information that we needed for the measurement of health status (infant mortality).

Also, instead of using the current data on poverty, which is Nigeria Core welfare

indicator, 2006, we used NNLSS 2004, because the latter gives us more

information on the variables that we needed in our work. In case of remittances,

we used remittance variables under transfer payment indicator in NNLSS 2004,

rather than those in the current transfers of Nigerian Balance of Payment Statistics,

because our study is based on the microeconomics aspect (households); so, we

used household’s data.

Besides the above problem, finance has been a serious constraint to the

study.

9

CHAPTER TWO

LITERATURE REVIEW

2.1 Theoretical Literature

Remittances are financial resource flows arising from the cross boarder

movement of nationals of a country (Kapur, 2004). Some scholars have identified

many theories to explain migrants’ remittances. According to Massey, Arango,

Hugo, Kouaouci, Pellegrino, and Taylor (1993), the review of economic motives

for migration included the neoclassical economic theory that identifies the cause

of migration as wage differentials, so that the net flow of migrants should be from

low wage to high wage areas. The new economics of migration has extended the

theory to the household level, in which migration represents a way to reduce risk

by diversifying income sources, and provide insurance against local shocks with

market failure, otherwise prevent the availability of such insurance. With decision

made at the household level, remittances could play important roles.

Another theory, the dual labour market theory, identifies the cause of

migration as the continuous needs of receiving countries foreign workers.

According to Bavallan et. al., (2003), the theory about remittances and migration

explains different mechanisms whereby remittances may either increase or

decrease inequality. Cattaneo (2005) views international remittances in two

contrasting views on the economy of labour sending country: In the first one, he

views remittances as a mechanism for economic development. In the second one,

he views remittances as an “illness” that weakens an economy. Remittances could

10

also indirectly affect labor supply by encouraging some remittances recipient

households to work less. This could reduce labour supply and reduce economic

growth (Juthatip, 2007). Chami, Fullenkamp, and Jahjah (2003), argued that

remittance transfers take place under conditions of asymmetric information in

which the remitter and recipient of transfer are separated by long distances. This

could lead to significant moral hazard problems where the latter is likely to be

reluctant in participating in labour market, limiting job search and reducing labour

effort.

Motivation to remit as reflected by some schools of thought includes risk

sharing and altruism which is the act of increasing the income, consumption or

standard of living of someone else, even to the detriment of one’s own standard of

living (Vanwey, 2004). The risk-sharing school maintains that remittances are

installments of individual risk management (Stark, 1991). The altruism or

livelihood school considers remittance to be an obligation to the household and

remittances are sent out of affection and responsibility towards the family

members at home (Rapport and Docquier, 2005). The second school of thought

sees both altruism and self interest as playing a role in motivation to migrate and

remit (Ballard, 2001; Clarke and Drinkingwater, 2001).

On the impact of remittances, two dominant perspectives are emerging in

the literature. The neo-liberal functionalist persuasion suggests that remittances are

beneficial at all levels particularly to individual, household, community and

national level (Orozco, 2002; Skeldon, 2002; and Ratha, 2003). Remittances are

11

seen to play a crucial role in developing local level capital markets and productive

infrastructure as well as increasing the effective demand for local goods and

services. On the other hand, those looking at remittances form historical-structural

perspective consider remittances to be responsible for creating dependent relations

between the sending and receiving countries (Portes and Borocz, 1989).

Lucas and Stark (1985) view remittances as the result of inter generational

contract between migrants and their parents in the home country. Ilahi and Jafarey

(1999) view remittances as repayments to the family who finances migration in the

first place. Gubert (2002) notes that remittances act like an insurance against

income shocks to the recipients in the country. Cox and Uretha (1998) view

remittances as exchange frameworks, where they represent a payment by the

migrants for the services by the family members, such as taking care of his or her

relatives or property.

Remittances are seen to cause inequality in household and macro-economic

distortion especially in countries with low GDP. Generally, it remains

controversial whether remittances have an overall positive or negative impact on

the receiving country’s economy and its migrant-producing communities (Page

and Plaza, 2005).

Remittances, Poverty and Human Capital Formation.

Remittance is a transfer of money by a foreign worker to his or her home

country (Juthatip, 2007). Poverty can be defined as the inability to attain a

minimal standard of living (World Bank, 1990). Human capital formation is health

12

and education. Health is the general condition of a person in all aspects or a

healthy state of well-being free from diseases (Davis, 2000). Education is the

knowledge acquired by learning and instruction. It can also be defined as an act or

experience that has a formative effect on the mind, character or physical ability of

an individual (Brown, 2000).

Migrant remittances can generate substantial welfare gain for migrants as

well as poverty (Dilip and Sanket, 2007). Remittances directly augment the

income of the recipient households. In addition to providing financial resources for

poor households, they affect poverty and welfare through indirect multiplier and

macroeconomic effects (Dilip and Sanket, 2007). Remittances help those poor

unskilled people to lower their poverty level and get better access to various social

services (Muhammad and Naveed, 2009).

Remittances are also associated with increased household investments in

health, education and entrepreneurship-all of which have a high social return in

most circumstances (Woodruff and Zenteno, 2001). Migrant remittance can help

to promote human capital investments especially by poor households. On the other

hand, the migration of household members that precedes the receipt of remittances

can have disruptive effects on family life, with potential negative consequences on

the educational attainment of children. Moreover, to the extent that in destination

countries most migrants tend to work in occupation requiring limited schooling.

Returns from investment in education may be lower for those that are envisaging

13

international migration, which also could tend to reduce the schooling of children

in migrants' households (Pablo, Pablo, and Humberto-Lopez,2007).

Gilbrill, (2006) argued that those in developing countries who do not have

family and friends in the Diaspora to remit to them do not enjoy the direct benefits

of remittances and amongst the poor, access to remittances may mean escape from

the grips of poverty, lack of access of remittances starkly highlight the on going

poverty. It is even proposed that amongst the unemployed and low-income people

of developing countries, lack of access to remittances has become a new economic

indicator of poverty. Also, over dependence on remittances puts pressure on

recipients to the extent that it may even negatively affect the development of their

own financial and economic resources. Dependent on regular remittances may

reduce the motivation of the recipients to be more industrious, venturesome,

and enterprising.

Bridi (2005) argues that international remittances do promote idleness on

the part of the recipients. Chami et. al. (2003) argue that migration and associated

remittances may create moral hazard problems, including disincentives to work

among migrant household members. 2.3 EMPIRICAL LITRATURE

Stark (1991) and Adams (1991) introduced the effort to assemble

household data that could shed light on the effect of remittances on welfare.

However, their findings are limited by small sample size. Some recent studies

have been carried out to estimate the impact of remittances on welfare. Adams

14

(2004) finds that remittances reduce the severity of poverty in Guatemala. He also

finds out that Guatemala families who report remittances tend to spend a lower

share of total income on food and non-durable goods, and more on durable goods,

housing, education and health.

Paulson and Miter (2000) find that households who are more insured by

remittances shift their portiofolios towards riskier investments. Adam and Page

(2003) in a study of poverty, migration and remittances for 74 low and middle-

income developing countries find that both international remittances (the share of

remittances in country GDP) have a strong statistical impact on reducing poverty

in developing world. Jongwanich(2007) finds out that remittances do have a

significant impact on poverty reduction and have only a marginal effect on growth

operation. Adams and Page (2004) used the result of household survey in 71

developing countries to analyze the impact of international migration and

remittances on poverty. They find that a 10 percent increase in per capital official

international remittances in a developing country will lead to a 3.5 percent decline

in share of people living on less than $1/person/day in that country.

World Bank (2006) concludes that remittances do reduce poverty but have

a mixed effect on inequality. For instance, Stark and Taylor (1989) find that

relatively poor households in rural Mexico are more likely to have members

migrate abroad and are better-off households. Yang and Choi (2007) find that

remittances help to compensate for nearly 65 percent of the loss in income due to

rainfall shock in Philippines. Wu (2006) finds that the remittance receiving

15

households in Aceh region of Indonesia were found to have recovered faster from

2004 Tsunami because of remittance provided by migrant members.

IMF (2005) finds positive and significant impacts of remittances on

poverty reduction. Sorensen (2004) finds that remittances reduced the numbers of

Moroccans living in poverty by 1.2 million. Perina (2006) finds out that

remittances contribute to poverty alleviation in Philippines. Dorantes and Pozo

(2006) find that remittances might defray migration related expenses and at the

same time alter household labour supply.

Esquivel and Huerta-Pineda (2006) investigate the effect of remittances on

poverty condition among Mexican households and find out that receiving

remittances reduces the household's probability of being in food-based and in

capabilities-based poverty by 7.7 and 6.6 percentage points respectively. The

authors concluded that these effects represent a reduction of around 36 and 23

percent in the corresponding poverty rates for a typical remittance-receiving

household vis-a-vis a comparable non-remittance receiving household.

Taylor, Mora, and Adam (2005), in as study in rural Mexico, find that

international remittances account for a sizeable proportion of total per capita

household income in rural Mexico and that international remittances reduce both

the level and depth of poverty. Yang and Martinez (2005) in a study in Philippines

find that remittances lead to reduction in poverty migrants' origin household.

Fajnzylber and Humberto Lopez (2007) in their study on impact of remittance in

Latin America find that nine out of eleven countries in Latin America and

16

Caribbean exhibit higher Gini co-efficient for non-remittance income, suggesting

that if remittances were exogenously eliminated inequality would increase.

However, there are few studies which present contrasting view. These

studies show that remittances do not benefit the plight of the poor people. In

particular, Stahl (1982) argues that since international migration can be an

expensive venture, it is only the better-off households that will be more capable of

producing migration and sending remittance.

Cattaneo (2005) finds that remittances do not have any impact on poverty

in 149 sending countries. Viet (2008) in a recent study on Vietnam, finds that

receiving foreign remittances had increased household income and consumption

remarkably, but decreased poverty only slightly for the remittance recipients. De

la Fuentes (2008) finds a negative and statistically significant relationship

between the foreign remittances and the threat to future poverty that rural

households could experience.

On education, Hanson and Woodruff (2003) find that remittances are

associated with higher education attainment in rural Mexico, in particular among

10-15 year old girls whose mother has low educational levels. For the case of

Elsalvador, Cox-Edwards and Uretha (2003) show that children from remittance-

recipient households are less likely to drop out from school, which they attribute

to the relaxation of budget constraints affecting poor recipient households. Acosta

(2006) shows that this result is stronger for girls and younger boys in Mexico.

Tabuga (2007) finds out that with remittances, households allocated more

to consumer goods and leisure. He finds that remittance induces household to

17

spend more on education, housing and durable goods. He also finds that it does

not induce household to spend more on goods like tobacco and alcohol and on

food regularly eaten outside.

Yang (2005) finds out that there is a positive impact on potentially

investment related to disbursement, particularly, education and on ownership of

durable goods. He also finds positive impact of remittances on education

investment, that remittances increase the likelihood of being a student in

Philippine household.

Lopez-cordove (2005) shows that higher remittances flows are associated

with lower illiteracy rate in Mexico municipalities but the evidence on the impact

on school attendance is mixed. Mckezie and Rappoport (2006), again for Mexico,

show that children aged 16 to 18 from household with migrants exhibit lower -

educational attainment levels, and that this negative effect is larger for those

whose mothers have higher level of schooling

On the health outcomes, Hildebrandt and Mckezie (2006) results for

Mexican, case show that migrant households have lower rates of infant mortality

and higher birth rates and weights. Moreover, they find evidence that migration

also raises maternal health knowledge and the likelihood that the child was

delivered by a doctor. On the other hand, preventive health care (breast feeding,

doctor visits and vaccinations) seems to be less likely for children from migrants'

households at the community level and are associated with lower infant mortality

rates.

18

Hilderbrandt and Mckenzie (2005) find that children in migrant households

have higher birth weight and are more likely to survive their first year of life in

Mexico communities. Pablo et al (2007) find that children in remittances-

receiving households have more weight and height than non-receiving households

in Latin America.

Kanaivpumi and Donate (1999) find that infant mortality increases during

early stages of the migration process, but gradually decreases as the volume of

remittances grows and migrations becomes "institutionalized" in Mexico. De Jong

and Stokes (2002) find that rural to urban migration significantly increases the

child survival chances in Senegal and Uganda. Woodruff and Zenteno (2001) find

that in Sri Lanka, the children in remittance-receiving households have higher

birth weight, reflecting that remittances enable households to afford better health

care.

2.3 Limitation of Previous Studies

There are volumes of studies that have attempted to investigate the impact

of remittances on poverty and human capital formation in different countries. The

findings of these studies remain inconclusive. Most of the studies reviewed, for

instance, Acosta (2006), Acosta et al (2007), Hanson and Woodruff (2003) among

others, did not check the effects of remittances on different zones, sector, sex, and

quintiles, as we have done.

19

In addition, studies of this nature reviewed especially in Nigeria focused

on the impact of remittances on poverty and inequality, but in this study we extend

it to human capital formation.

20

CHAPTER THREE

METHODOLOGY

This research will adopt ordinary least squares technique using

linear regression model and also make use of binary regression model.

Remittance generally, will be measured as amount remitted by a migrant member

of the household.

3.1 Model Specification

In order to capture the objectives of this study, we use the specified models

below:

To capture the difference in poverty between receiving and non-receiving

households, we have to take into consideration the counterfactual per capita

expenditure/income that the household would have had if the migrant had stayed

at home, otherwise we would be overstating or overestimating the true impact or

effect of remittance on poverty reduction, since the non-remittance income

reported by households with migrant cannot be a good representation of the

situation of the family prior to migration (Pablo et. al., 2007). It is also argued that

remittances are not to be an exogenous transfer but rather a substitute for home

earnings that migrants would have had if they had not decided to leave their

countries to work abroad.

One possible way to address this issue is in Acosta et al (2007) who impute

per capita household income for migrant household in the counterfactual scenario

of no migration no remittances. Clearly, this requires information about the

income of the household before the migrant left, and this information is in general

21

not available directly from household survey. As an alternative, Acosta et al

(2007) infer the counterfactual per capita income level for those households with

remittances on the basis of a reduced-form specification for the determinant of

income among households without remittances. More formally, this approach

involves estimating a model like:

log )1...(................................................................................iiii HXY

where:

iY = per capita non-remittance household expenditure

iX = vector of household characteristics (including household number, household

size, sector, zone, and quintiles)

iH = a set of characteristics of household head (including sex, age,

educational level attended ,occupation, and marital status)

i = error term

and and, are co-efficients or the parameters.

But in this study we are going to use per capital household expenditure

rather than income data or per capita household income because of the following

facts: Firstly, poverty economists prefer to use expenditure rather than income

data to identify poverty, since expenditure provides a more accurate measure of an

individual's welfare overtime; secondly, income data is prone to measurement

error especially, underreporting of income, which is prevalent in Nigeria

(Chukwuone, 2007).

22

Equation (1) above can be estimated using sub- sample of households that

do not receive remittances and also there is absence of information on migrant

characteristics, so it is necessary to make some basic assumptions about

information of migrant characteristics.

One additional aspect that needs attention is that OLS estimation of

equation (1) will be inconsistent if u is not independently and identically

distributed (iid). In other words, if migrants are not randomly selected from the

pool of households, estimates of equation (1) based on the sample of households

without migrant or remittances could suffer selection bias. To control this

possibility, we use the Heckman correction model, a two-step statistical approach

proposed by Heckman (1979). In the first step, we formulate a model, based on

economic theory, for the household propensity not to migrate or not to receive

remittances. The general standard specification for this relationship is a probit

regression model.

Probit model can be derived from an underlying latent variable model that

satisfies the classical linear model assumptions. Let y* be an unobserved, or latent

variable determined by:

)2.....(..................................................)0*(1,11111111* yyRZHXY

where

R1 = Remittances (total amount of cash and non-cash items received by

individual from their migrant members of their families or friends)

23

We introduce the notation 1 (.) to define the binary outcome. The function

1 (.) is called the indicator function, which takes on the value one (1) if the event

in the bracket is observed, and zero (0) otherwise. Therefore,

)0*1( observedyify

gmisorunobservedyify sin0*0(

We assume that 1 is independent of X1 and H1 and has the standard normal

distribution. In this case, 1 is symmetrically distributed above zero, which means

that :

)3.........(......................................................................)()( GzGz 1

where ' G = vector for explanatory variables

z vector for unknown parameters

the cumulative distribution function of the standard normal distribution.

From equation (2), we derive the response probability for :y

)4........(........................................)(1

,,(Pr,,0*Pr,,1Pr

1111111111111

11111111111111

RZHXRZHXRHXRZHXRHXyRHXy

In equation (3), we assumed that G is a vector for explanatory variables and z is a

vector for unknown parameters. So, in equation (5] we replace explanatory

variables with G and parameters with z. This implies that:

)5.....(..........................................................................................1Pr GzGy

24

where :

y = 1 (for no migration or no remittance received)

y = 0 (for migration or remittance received).

In the second step, we add to equation (1) a variable called inverse mill

ratio that is derived from the probit model:

2 2 2 1 1log ...............................................................(6)i i i i iY X H z R

where:

i = inverse mill ratio, which is the ratio of the probability density function over the cumulative distribution function of a distribution, defined as:

)7......(................................................................................

11111

111111

1 RZHXRZHX

i i

i

with

1111 / RZHXE iiii

where i is the error component in the expenditure equations and is the density

function for a normal standard variable.

Controlling for i allows the remaining unexplained component i , to have the

usual iid properties.

The Effect of Remittances on different Zones, Sex, Sector, and Quintiles

To capture the effect of remittances on different zones, quintiles, sex, sectors,

we adopt a dummy variables regression model by Gujarati (2004).

25

)8...(......................................................................43423121 iDDDDY

Y Per capita household expenditure

Y Remittances

1D Sector = 1 for urban, 0 = rural

2D = sex = 1 for male, 0 = male

3D = quintiles, 1 for quintiles 2, 3, 4, 5 and 0 = quintile 1

4D = zone = for South south and south east, 0 = otherwise

Assuming that the error term satisfies the usual OLS assumption, on taking

expectation of equation (9) on both sides, we obtain

E ( 1 2, 3 4 2/ 1, )i iY D D D D , for sector

E (12 , 3 4 3/ 1, , 0)i iY D D D D , for sex

E ( 3 1 2 4 4/ 1, , , 0) ,i iY D D D D for quintiles

E ( 4 1, 2 3 5/ 1, 0) ,i iY D D D D for zones …………….. (9)

E ( ii DDDDY )00,0,0/ 4321

Education:

To capture the effect of remittances on education, we specified a model

below:

)10..(......................................................................321 XARY o

where

26

Y = Highest level of education attended by the child

R = Remittances

A = Household characteristics (age of the children based on different

age groups - 5years, l0years above, 15years and 15 years above

including sector,, zone).

X = Characteristics of child's mother (include mother's education, mother

living, mother's work)

i = error term

and 31, ando are coefficients of the parameters.

Health

To capture the effect of remittances on health status using infant mortality,

we use the model specified by Bodin and Sinaia, (2009):

)11.......(................................................................................iiiii XRIM

where:

IM = dummy of whether household i had an infant die that is (1 = if the

household had; 0 = otherwise).

iR = Remittances

X = Characteristics of child’s mother (include mother’s education, mother

work, and mother living)

= error term

Xand , are co-efficient parameters

.

27

Justification of the model

In econometrics modeling, there are many models available for the

econometricians or researchers. However, the choice of a particular model

depends on whether that model is adequate to capture the objectives of the

researcher or not, thus, this study would adopt the method used in Acosta et al

(2007) about the counterfactual situation of household without remittance and

two-step estimation proposed by Heckman (1979) to avoid selection bias, so that

OLS will have desirable properties.

In the second objective, dummy variable regression model will be used to

capture the effects of remittances in different zones sector, sex, and quintiles. In

third objective, we introduced a model that will be used to capture the effect of

remittances on education. In the fourth objective, Linear Probability Model (LPM)

used by Bodin and Sinaia, (2009) will be used to capture the effect of remittances

on health status, because co-efficients in LPM are much easier to interpret than the

logit model.

Source of the Data

The data are obtained from the Nigeria National Living Standard

Survey (NLSS) 2004.

28

Software Package

The study would make use of STATA econometric software in its analysis.

29

CHAPTER FOUR

DATA ANALYSIS

4.1 Data Interpretation

The estimation of results which is depicted below shows the effects of remittances

on poverty and human capital formation in Nigeria.

4.1.1 The Difference on Poverty level between receiving and non-receiving

Remittances Households.

Table 1: Counterfactual Situation of Households without Remittances.

Dependent Variable Log Per Capita Household Expenditure (LogPCEXP).

Variables Co-efficient t-statistics p-values

Constant

Household Characteristics

Sector

Household number

Household size

Zone

Quintiles

Characteristics of Household head

Sex

Age years

Marital status

Occupation

Highest level of Education attended

7077.41

-1931.644

8.393

-654.619

-2331.064

14150.64

-6760.265

56.0145

1798.434

759.178

605.441

2.84

3.04

0.99

-6.29

-14.08

68.37

-7.47

2.92

11.46

-5.55

3.73

0.004

0.002

0.323

0.000

0.000

0.000

0.000

0.003

0.000

0.000

0.000

30

Table 1 above shows that household number, quintiles, age years, marital status,

and highest level of education attended have positive effects on per capita household

expenditure and this increased per capita by 18%, 23%, 7% and 8%. Household size,

zone, sector, sex and occupation, have negative effect on per capita household

expenditure by decreasing per capital household expenditure by 65%, 23%, 19%, 68%,

and 78%, using estimated variables co-efficients.

Apart from household number, other variables such as sector, household size,

zone, quintiles, sex, age years, marital status, occupation and highest level of education

attended are statistically significant, at 5% level of significance.

Table 2: Probit Model on the Effect of Remittance on Poverty between Receiving

and Non-Receiving Household.

Dependent Variable Poverty (Pover) – Using Log Per Capita Household

Expenditure (LogPCEXP).

Variables Co-efficient z-statistics p-values

Constant 10.956 7.88 0.000

Household characteristics:

Sector

Household number

Household size

Zone

Quintiles

.72458

-.00748 -.000297 .48675 -3.5169

3.14

-1.69 -0.01 7.92 -11.09

0.002

0.090 0.994 0.000 0.000

Characteristics of household head:

Sex

Age years

Marital status

Occupation

.049552

.00545

-.00325

-.032457

0.21

0.88

-0.07

-0.85

0.833

0.377

0.941

0.398

31

Highest level of education attend Remittances Log likelihood ratio Pseudo R2

.020322

9.75 -166.53259 0.8352

.033

0.44

0.742

0.663

Table 2 provides information about the effect of remittances on poverty between

receiving and non-receiving households using probit model. Remittances have positive

effect on poverty reduction. This implies that an increase in remittance decreases the

likelihood of being poor in remittance-receiving households than in non-remittance

receiving household by 9.8%. Other variables, apart from quintiles that have 3.5%

negative effect on poverty, have shown no effect on poverty reduction.

In case of level of significance, only sector, zone, and quintiles are statistically

significant, other variables including remittances are not statistically significant at 5%

level of significance. The model fit is good with Pseudo R2 equal to 0.8352.

Table 3: Heckman Selection Model-two step estimates.

Dependent Variable Poverty (Pover) –Using Log Per-Capital. Household

Expenditure (LogPCEXP).

Variables Co-efficient t-statistics p-values

Characteristics of household:

Sector

Household number

Household size

Zone

Quintiles

0.18833

0.00047 0.00962 0.01988 0.01373

21.10

3.14 7.98 10.38 3.32

0.000

0.002 0.000 0.000 0.001

Characteristics of household head:

Sex

Age years

0.10217

0.00336

9.89

14.21

0.000

0.008

32

Marital status

Occupation

Highest level of education attended Remittances.

0.00305

0.01595

0.03405

4.02

1.52

9.43

16.62

0.36

0.120

0.000

0.000

0.722

Inverse mill ratio Lambda.

0.067612

7.42

0.000

Rho Sigma Lambda

0.74659 0.09056106 0.67612

From the table 3, remittances have greater positive effect on poverty reduction for

receiving households than for non-receiving households by 4%. This implies that

remittances have decreased the likelihood of being poor in remittance households more

than in non-remittance households by 4% using Heckman selection model.

To test for the level of significance, remittances are not statistically significant

and other variables in the model are statistically significant. The inverse mill ratio which

acted as its own instrument is statistically significant. In case of rho = lambda/sigma, the

co-efficient of lambda has a z-statistics, 7.42, and it is statistically significant.

4.1.2 Table 4: The Effects of Remittances on Different Zone, Quintiles, Sex and

Sector.

Dependent Variable is Remittance (Remit)

Variables Co-efficient t-statistics p-values

Constant

Sector

Sex

Quintiles:

10080.19

750.663

1464.691

3.72

0.38

0.8

0.000

0.706

0.416

33

Quintile 2

Quintile 3

Quintile 4

Quintile 5

Zone 1: South-south

Zone 2: south-east

4311.197

480.4828

3625.636

1122.646

3162.646

-1211.402

1.55

0.18

1.38

0.44

1.60

-0.62

1.121

0.857

0.167

0.659

0.111

0.535

Sector =1 for urban, O =rural

Sex =1 for male, O =female

Quintiles =1 for quintile 2,3,4,and 5, 0=for quintile 1

Zone = 1 for south south-south east, 0= for south-west, north-central, north-east and

north-west.

The benchmark categories are rural, female, quintile 1, south-west, north-central,

north-east and north-west. As this regression result shows, the effects of remittance on

rural, female, quintile1, South West, North Central, North East and North West is about

10080.19, that of urban is higher by 750.663, male is higher by 1464, quintile 2 is higher

by 4311.197, quintile 3 is higher by 480.4828, quintile 4 is higher by 3625.636, quintile 5

is higher by 1122.646, south-south is higher by 3162.646 and south-east is lower by

1211.402. The actual remittances for quintile 2, 3, 4 and 5, zone 1, and zone 2, male and

urban can be obtained by adding differential remittances value mentioned in chapter 3

(equation 9) which are equal to 10831,11545, 14391, 10561, 13706, 11203, 13243, and

8869.

To find the level of significance, the estimated co-efficient for urban, male,

quintile 2,3,4,5, south-south and south-east are not statistically significant as their p-

values are 71%, 42%, 12%, 86%, 17%, 66%, 11% and 54% respectively. These imply

34

that remittances have more effect on rural, female, quintile 1 and south west, north-

central, north-east and north-west.

4.1.3 Table 5: the effect of Remittance of Education

Dependent Variable Education (EDAGE)

Variables Co-efficient t-statistics p-values

Constant

Characteristics of household:

5 years age group

10 years age group

15 years age group

Age group above 15 years

Zone

Sector

2.96598

.0005.2

.000886 0.012681 -.00039 -0.00025 -0.00374

228.85

0.19 0.21 3.19 -1.05 -0.32 -1.15

0.000

0.849 0.835 0.001 0.292 0.752 0.250

Characteristics of child’s Mother:

Mother’s education

Mother’s work

Mother living

Remittances

.000275 -.00012 -0.0050122 3.31

0.82 -0.79 -1.09 0.77

0.413 0.429 0.277 0.443

Table 5 above shows that 1% increase in remittances leads to 3.3% increase in the

educational attainment. Other variables such as children within 15 years age group, 5

years age group, 10 years group, zone, sector, mother’s education, mother’s work, mother

living have no effects on education.

Remittances, children within 5 years age group, 10 years age group, zone, sector,

mother’s education, mother’s work, mother living are not statistically significant.

Children within 15 years age group is statistically significant at 5% level of significant.

35

4.1.4 Table 6: The Effect of Remittance on Infant Mortality Dependent variable.

Infant Mortality (infm)

Variables Co-efficient t-statistics p-values

Constant 0.0364 0.79 0.431

characteristics of Household:

Sector

Household size

Zone

-0.0214

0.0025 -0.0044

-2.03

-1.80 1.70

0.043

0.72 0.090

Characteristics of child’s mother:

Mother’s education

Mother’s work

Mother living

Remittances

0.0001

-0.0014

0.0059

-1.66

0.06

-0.28

0.39

-0.12

s0.952

0.778

0.698

0.906

Table 6 above shows that 1% increase in remittance reduces the probability of child

being dying in the family by 1.66%. Sector, household size, zone, mother’s education,

mother’s work, mother living have no effects on infant mortality and are not statistically

significant as their p-values are about 4%,7%,9%,75%,77%,70% respective. Remittances

also are not statistically significant as its p-value is about 91%.

4.2 Evaluation of Hypotheses

In this section, three hypotheses have been tested in accordance with the analysis

of the result.

36

4.2.1 Test Of Hypothesis One

The Effect of Remittances on Poverty between Receiving and Non-Receiving

Households

From the result of households without remittances, variables such as household

size, zone quintiles, sex, age years, marital status, and highest level of education attended

are statistically significant. In this case, are reject the null hypothesis and accept

alternative hypothesis. Other variables such as sector, household number and occupation

are not statistically significant; so we accept null hypothesis and reject the alternative

hypothesis.

From tables 2 and 3 results, since remittances are not statistically significant we

accept the null hypothesis and reject the alternative hypothesis. This implies that

remittances have no significant effect on poverty level between receiving and non-

receiving household.

4.2.2 Test of Hypothesis Two

From table 4 results, the urban, male, quintiles 2, 3, 4, and 5, south-south and south-east

are not statistically significant, we accept null hypothesis and reject alternative

hypothesis. This implies that remittances have more effects on rural, female, quintile 1,

south-west, north-central, north-east, and north-west.

4.2.3 Test of Hypothesis Three

The Effect of Remittances on Education

From the result, remittances and other explanatory variables apart from children

within 15 years age group do not exert statistically significant effect on education. So we

37

accept the null hypothesis and reject the alternative hypothesis. This implies that

remittances have no significant effect on education.

4.2.4 Test of Hypothesis four

The Effect of Remittances on Infant Mortality

The result shows clearly that remittances do not have statistically significant effect on

infant mortality; we accept the null hypothesis and reject the alternative hypothesis.

38

CHAPTER FIVE

SUMMARY, POLICY RECOMMENDATION AND CONCLUSION 5.1 Summary This work studies the effects of remittances on poverty and human capital

formation in Nigeria. It explains how remittances can have negative effect on households

by reducing the motivation of recipients to be more industrious, venturesome and

enterprising and positive effect by helping to smooth consumption and improving the

standard of living.

From the results of the estimation on the effect of remittances on poverty

reduction and education, remittances yield positive contributions on poverty reduction,

educational attainment and yield negative effect on health status-using infant mortality.

Other variables in the work such as variables for household characteristics, characteristics

of household heads, and characteristics of child’s mother have both positive effects for

some variables and negative effects for others on poverty reduction, education and health

status.

To get the following results we employed econometric models such as linear

regression model for counterfactual situation of household without remittances and

educational attainment probit regression model for the difference in poverty level

between receiving and non-receiving households, Heckman selection bias in the model,

and the linear probability model for estimation of infant mortality.

This work also shows the effects of remittances on different zones, quintiles, sex,

and sector. From the estimated results, the effects of remittances on rural, female, quintile

1, South west, North central, North east, and North west is about 10080.19, while urban,

39

quintile 2,3,4,5, South south and South east are higher 750.663, 1464,

4311.197,480.4828, 3625.636, 1122.646, 3162.646 and 1211.402 respectively. Although,

urban, male, quintile 2,3,4,5, South south and South east are higher with different values,

their estimated co-efficients are not statistically significant. This implies that remittances

have more effect on rural, female, quintile 1, South west, North east, and North west. It

employed dummy variable regression model for the differences.

5.2 Policy Recommendation

Remittances-receiving households should not over depend on remittances by

working less which may negatively affect the development of their own financial and

economic resources, even though, it has been shown that remittances have positive

effects on poverty reduction and human capital development in Nigeria. This over-

dependency can also reduce labour supply and economic growth.

Another recommendation is that receiving households should diversify their

investment options especially those in rural areas, by using the higher share of their

remittances on business activities other than farming. Again, unskilled people should use

their remittance to engage in any of the skilled labour of their choice that would increase

their finance.

Recipient households should maximize the benefit from remittances by saving

part of the remittances received, especially those remittances that are received through

informal sources in the bank for any investment such as education, health problems or

any emergency that may occur.

40

5.3 Conclusion

This work has explored the effects of remittances on poverty and human capital

formation Nigeria. Remittances appear to lower poverty level, increase educational

attainment, and lower Infant mortality. Equations were estimated using data from Nigeria

living standard survey 2003/2004. We used linear regression model for estimating

counterfactual situation of households without remittances, probit regression model for

estimating difference in poverty level between remittances-receiving and non-receiving

households, linear probability model for infant mortality, and dummy variables

regression model for estimating the effects of remittances on different zones, quintiles,

sex, and sector. We also employed Heckman selection model-two step estimates to

control selection bias in the model.

There are two key findings from this work. Firstly, remittances seem to have a

positive effect on poverty reduction and human capital development. Secondly,

remittances have increased the volume of household expenditure through increase in

income.

41

REFERENCES

Acost, P. (2006) “Labour Supply, School Attendance and Remittances from

International Migration. The Case Elsalodor”. World Bank Policy Research Working paper 3903.

Acosta, C. Fajnzylber, P. and Lopez H. (2007) “What is the Impact of International Remittances on Poverty and Inequality in Latin American?”, World Development forthcoming.

Adam, R. and Page J. (2004) Do International Migration and Remittances Reduce Poverty in developing countries? Washington DC: World Bank.

Adam, R.H.(2004), “Remittance and Poverty in Guatemala”, World Bank policy Research Working paper 3418: Washington DC: World Bank.

Adam, R. (1991) “The Effects of International Remittances on Poverty, Inequality and Development in Rural Egypt”, International Food Policy Research institute Research Report 86, Washington: IFRI.

Adam, R. and Page J. (2003). International Migration, Remittance Reduce Poverty, in Development Countries, World Bank Policy Research Working Paper 3179, December.

Anyanwu J, C. Erhijakpor, A.E. (2006) Do International Remittances Affect Poverty in Africa? Paper Presented at the Africa Economic Research Consortium: Kenya , Nairobi

Ballard R (2001) The Impact of Kinship on the Economic Dynamic of Transnational Networks: Reflections on some South Asia Developments Center for Applied South Asian studies, University of Manchester, United Kingdom.

Bodin, C. and Sinaia, U., F. (2009) The Impact of Remittances on Health in Mexico. A research design. Published by Hewlet Foundation and by Gustava Rains, Yale University’s Citizenship Workshop.

Bridi, H. (2005) Consequences of Labour Migration for the Developing Country Management of Remittances. World Bank Brussels Office.

42

Brown,V.(2000). Children’s Services and Skills. www. Wordnetweb. Princeton.edu/perl/webwn.

Cattaneo, C. (2005) International Migration and Poverty: Cross Country Analysis. OECD Destination Countries, University of Sussex, World Bank Development

Chami, R. Fullenkamp, C. and Jahjah, S. (2003) “Are Immigrants Remittances Flows a Source of Capital for Development?” IMF Working Paper 01/189, International Monetary Fund, Washington Dc.

Chimhowu, A., Piesse, J. and Pinder, C. (2003); Assessing the Impact of Migrant Worker’s Remittances on Poverty. Presented at the EDIAS Conference on New Directions in Impact Assessment for Development: Methods and Practice, 24-25, November.

Chukwuone, N. (2007) “Analysis of impact of Remittances on poverty and inequality in Nigeria”, PEP Research Network General Meeting, www.pep-org.

Clarke K. and Drinkingwater S. (2001) “An Investigation of Household Remittance Behavior, Manchester School of Economics Studies, Discussion Paper, Manchester, England.

Cox, D, Eser, Z. and Jimenez E. (1998), Motives for Private Transfers Over the Life Cycle: Analytical framework and Evidence for Peru, Journal of development Economics, 55:57-80.

Cox, E and Uretha, A. (2003) International Migration for Mexican Transnational Communities. Economic Geography 74(1). 26-44.

Davis, C.B. (2000). Public Health and Health Insurance. www. Wordnetweb. Princeton.edu/perl/webwn.

De Haan, (2000) “Migrants Livelihood and Rights: the Relevance of Migration in Development Policies”. Social Development Working Paper 4, International Development, London.

De Jong, D. and Stokes, B. (2002) The Effects of Female Migration and Child Survival in Uganda and Senegal

43

De La Fuente, A. (2008) Remittances and Vulnerability to Poverty in Rural Mexico. UNU_WIDER Research Paper No.2008/17, February.

Dilip, R,. and Sanket, M. (2007) “ Increasing the Macro-economic Impact of Remittances on Development”, Development Prospects Group World Bank, Washington DC, November 26, 2007.

Dorantes, A. C. and Pozo, S. (2006) Migration Remittances and Male and Female Employment Patterns. American Economic Review 96(2). 222-226.

Esquivel, G. and Huerta-Pinida, A (2006), Inter-American Development Bank, Remittances and Poverty in Mexico: A Propensity Score Matching Approach, (IADB), September.

Fajnzylber, P. and Humberts to, L.(2007). Close to Home: The Development Impact of Remittances in Latin America. World Bank Washington DC.

Gilbril Faal (2006) Mitigating the Structural Imperfections and Nigerian Impacts of Remittances.

Gubert, F. (2002), Do Migrants Insure those Who Stay Behind? Evidence from the kayes Area (Western Mali ), Oxford Development Studies, Volume 30,pp. 267-87.

Hanson, G. H. and Woodruff, C. (2003)Emigration and Educational Attainment in Mexico, Mimeo and University of Califonia.

Heckman, J.J. (1979): “Sample Selection Bias as a Specification Error” Econometrical, 47, 153-161.

Hiderbrandt, N. and Meknezie, H. 2005): “The effects of Migration on Child Health in Mexico” Economic Journal of the Latin American and Caribbean Economic Association 6, 257-289.

Ilahi, N. and Jafarey, S (1999), Guest workers Migration, Remittances, and the Extended Family: Evidence from Pakistan, Journal of Development Economics, Volume 58, 485-512.

IMF (2005) World Economic Outlook: International Monetary Fund, April 2005, Washington, DC.

44

Juthathip, (2007) “Workers’ Remittances, Economic growth and Poverty in Developing Asia and Pacific countries”, UNESCAP Working Paper, WP/07/01, January.

Kanaiavpuni, S. and Donato, K. (1999), “Migradollars and Mortality: the Effects of Migration on Infants Survival in Mexico” Demography 36; 339-353.

Kapur, D. (2004) “Remittances: the New Development Mantra?” G-24 Discussion Paper No.29, UN Conference on Trade and Development. Gevena, United Nations .

Lopez-cordova, E. (2004) “International Migration to the High Income Countries: Consequences for Economic Development in the Sending Countries” World Bank Development.

Lopez-Cordova, E. (2005): “Globalization, Migration and Development: the Role of Mexican Migrants Remittances”, Economic Journal of the Latin American and Caribean Economic Association 6, 217-256.

Lucas, R. E. B, and Stark, O. ( 1985), Motivations to Remit: Evidence from Botswana. Journal of Political Economy 93(5): 901-917.

Maimbo,s S.M and Rather, D. (2005). Remittances: Development impact and future prospects. Washington D, World Bank

Manuel, O., Lindsay, B., and Schnieder, J. (2006) “Gender Specific Determinants of Remittances: Differences in Structure and Motivation. World Bank Gender and Development Group, PREM

Massey, D.S., Arango, J, Hugo, G. Kouaouci, A. Pellegrino A. and Taylor, J.E. (1993), Theories of International Migration: Review and Appraisal.

Mckenzie, D. (2005) “Migration Networks Migration Incentives, and.Education. Inequality in Rural Mexico” Inter-American Development Bank, Washington.

Mckenzie, D. and Rapport, H. (2006) “Can Migration Reduce Educational Attainment? Evidence from Mexico”,World Bank Policy Research working paper 3952.

45

Muhammed, S.and Naveed A. (2009) Determinant of Workers’ Remittances: Implication for Poor People of Pakistan.

National Bureau of statistics (NBS) 2009

Nigerian National living Standard Survey (NNLSS) 2004

Orozco, M. (2002) “Worker Remittance: the Human Face of Globalization”., Inter-American Development Bank Working paper.

Pablo A, Pablo, F. and Humberto Lopez, H.(2007) “The Impact of Remittances on Poverty and Human Capital” Evidence from Latin American Household Surveys. World Bank Policy Research Working paper 4247 , June.

Page, J. and Plaza S. (2005) International Migration and Economic Development: A Review of Global Evidence. Paper Presented at the African Economic Research Consortium: Kenya ,Nairobi.

Paulson, A.C. and Miller, D.(2000) “Informal Insurance and Moral hazard: Gambling Remittances in Thailand”.

Pernia, E. (2006) “Diaspora, Remittances and Poverty in RP’s Region”, UPSE Discussion Paper.

Portes, A. and Borocz, J. (1989) “A Contemporary Immigration: Theoretical Perspectives on its Determinants and Modes of Incorporation”. International Migration Review 23(fall): 606-60.

Quartey, Peter. (2006). Migration and Development: Challenges and Opportunities for Sending Countries, Ghana Country Case Study. Institute of Statistical, Social and Economic Research, University of Ghana, July, 2006.

Rapport, H. and Docquier, F. (2005) The Economics of Migrants’Remittance, IZA Discussion Paper, 1531, March.

Ratha, D. (2003) “Workers Remittances: An Important and Stable Source of External Development Finance”. In Global Development Finance: Striving for Stability in Development Finance, 157-175. Washington DC: World Bank Development Finance.

46

Ratha, D. (2005 ) “Workers’ Remittances. An Important and Stable Source of External Development Finance”. World Bank Development Finance.

Ratha, D. (2007). Leveraging Remittances for Development, Policy Brief, Migration Policy Institute, Washington D.C. World Bank.

Ravallina, N. M. and Robleza, E.J. (2003) The Contribution of Remittances On Income Inequality: A Decomposition Analysis. Report School of Economics, University of Philippines.

Rodriguez, E. (2000) Does International Migration Benefit Non-Migrant Household? Evidence from the Philippines households”

Skeldon, R. (2002) “Migration and Poverty” Asia Pacific Population Journal, 17(4): 67-82.

Sorensen, N.N. (2004) “Migrant Remittances as a Development Tool: the Case of Morocco”, Migrant Policy Research Working Paper Series, No.2, Gevena. International Organization for Migration, June.

Stahl, C. (1982), “Labour Emigration and Economic Development”, International Migration Review, Vol 22 1-4.

Stark, O. and Taylor, J. E. (1989), Relative Depriviation and International Migration. Demography 26, 1-14.

Stark, O. (1991) The Migration of Labor Cambridge: Basil Black well..

Tabuga, A. (2007) “International Remittances and Household Expenditure”, PIDS Discussion Paper Series 2007-18.

Taylor J.E., Mora, J. and Adams R, (2005) Remittances Inequality and Poverty:” Evidence from Rural Mexico. Mimeo, University of California.

Taylor, J. E. Mora, J. Adam, R. and Lope Fieldman, a (2005), Remittances, Inequality and Poverty: Evidence from Rural Mexico, Mimeo, and University of Califonia.

United Nigerians (2003) “Nigerian Country Profile”, Nigerian Country Office on drugs and Crime.

47

Vanwey, L. (2004) “Altruistic and Contractual Remittances between Male and Female Migrants and Households in Rural Thailand”. Demography, 41(4): 739-756.

Viet, C.N. (2008), Do Foreign Remittances Matter to Poverty and Inequality? Evidence from Vietnam.

Woodruff, C. and Zeneto, B. (2001) Remittances and Micro Enterprises in Mexico,. UCSD Graduate School of International Relations and Pacific Studies Working Paper, University of California.

.World Bank (1990). Poverty in Latin American: The Impact of Depression. Washington Dc.

World Bank (2004) Global Development Finance: Striving for Stability in Development finance. Washington DC. World Bank Development Finance.

World Bank (2004). “Global Development Financial 2004 Harnessing Cyclical Gains for Development” Washington DC: World Bank Development finance.

World Bank (2006) Gender and Development Group

Wu, T. (2006) The Role of Remittances in Crisis. An Aceh Research Study” Overseas Development Institute London, UK.

Yang D, and Choi, H.(2007) “Are Remittances Insurance ? Evidence from Rainfall Shocks in the Philippines” World Bank Economic Review 4(2), 2, Working paper 19-248.

Yang, D. (2005), International Migration, Human Capital and Entrepreneurship: Evidence From Philippines Migrants’ Exchange Rate Shocks Mimeo: University of Michigan.

Yang, D. and C. Martinez (2005) “Remittances and Poverty in Migrants’ Home Area: Evidence from the Philippines”. World Bank Interntional Migration and Development Research Group..

Yang, D. and Martinez, C.A. (2005) Remittances and Poverty in Migrants’ Households in Philippines.

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APPENDIX

. reg logpcexp sector hhno hhsize zone quinttr sex ageyrs marstat occgrp edgrp Source | SS df MS Number of obs = 19158 -------------+------------------------------ F( 10, 19147) = 868.27 Model | 1.0959e+13 10 1.0959e+12 Prob > F = 0.0000 Residual | 2.4167e+13 19147 1.2622e+09 R-squared = 0.3120 -------------+------------------------------ Adj R-squared = 0.3116 Total | 3.5126e+13 19157 1.8336e+09 Root MSE = 35527 ------------------------------------------------------------------------------ logpcexp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- sector | -1931.644 638.5827 -3.02 0.002 -3183.322 -679.9656 hhno | 8.392941 8.489105 0.99 0.323 -8.246451 25.03233 hhsize | -654.6185 104.0218 -6.29 0.000 -858.5103 -450.7266 zone | -2331.064 165.5691 -14.08 0.000 -2655.594 -2006.534 quinttr | 14150.64 206.9859 68.37 0.000 13744.93 14556.35 sex | -6760.265 904.6675 -7.47 0.000 -8533.493 -4987.037 ageyrs | 56.01448 19.15521 2.92 0.003 18.46858 93.56038 marstat | 1798.434 156.9421 11.46 0.000 1490.814 2106.054 occgrp | -759.1778 136.8771 -5.55 0.000 -1027.469 -490.8866 edgrp | 605.4405 162.466 3.73 0.000 286.9928 923.8882 _cons | 7077.41 2489.442 2.84 0.004 2197.885 11956.93 ------------------------------------------------------------------------------ . sum pcexp Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- pcexp | 19158 33659.05 42820.19 792.4576 2286242 . gen pover = 0 . replace pover = 1 if pcexp <= 33659.05 (12786 real changes made) tab pover pover | Freq. Percent Cum. ------------+----------------------------------- 0 | 6,372 33.26 33.26 1 | 12,786 66.74 100.00 ------------+-----------------------------------

49

Total | 19,158 100.00 . probit pover sector hhno hhsize zone quinttr sex ageyrs marstat occgrp edgrp remit Iteration 0: log likelihood = -1010.7617 Iteration 1: log likelihood = -411.01693 Iteration 2: log likelihood = -268.47039 Iteration 3: log likelihood = -203.27606 Iteration 4: log likelihood = -176.5298 Iteration 5: log likelihood = -168.03744 Iteration 6: log likelihood = -166.62914 Iteration 7: log likelihood = -166.53347 Iteration 8: log likelihood = -166.53259 Iteration 9: log likelihood = -166.53259 Probit regression Number of obs = 1460 LR chi2(11) = 1688.46 Prob > chi2 = 0.0000 Log likelihood = -166.53259 Pseudo R2 = 0.8352 ------------------------------------------------------------------------------ pover | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sector | .7245756 .2304429 3.14 0.002 .2729158 1.176235 hhno | -.0074766 .0044123 -1.69 0.090 -.0161245 .0011713 hhsize | -.0002974 .0367131 -0.01 0.994 -.0722538 .071659 zone | .4867465 .0614224 7.92 0.000 .3663608 .6071322 quinttr | -3.516859 .3171966 -11.09 0.000 -4.138553 -2.895165 sex | .0495517 .2355333 0.21 0.833 -.4120851 .5111884 ageyrs | .0054456 .0061694 0.88 0.377 -.0066462 .0175374 marstat | -.0032446 .0435122 -0.07 0.941 -.0885269 .0820377 occgrp | -.0324569 .0383716 -0.85 0.398 -.1076638 .0427501 edgrp | .0203217 .061714 0.33 0.742 -.1006356 .1412789 remit | 9.75e-07 2.24e-06 0.44 0.663 -3.41e-06 5.36e-06 _cons | 10.95752 1.390515 7.88 0.000 8.232156 13.68287 ------------------------------------------------------------------------------ Note: 1 failure and 348 successes completely determined. . heckman pover sector hhno hhsize zone quinttr sex ageyrs marstat occgrp edgrp remit, noconstant twostep select(pove > r = sector hhno hhsize zone quinttr sex ageyrs marstat occgrp edgrp remit, noconstant) rhosigma Heckman selection model -- two-step estimates Number of obs = 1460

50

(regression model with sample selection) Censored obs = 700 Uncensored obs = 760 Wald chi2(11) = 78681.11 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- pover | sector | .1883254 .0089266 21.10 0.000 .1708295 .2058212 hhno | .0004689 .0001493 3.14 0.002 .0001764 .0007615 hhsize | .0096214 .001205 7.98 0.000 .0072596 .0119831 zone | .0198818 .001915 10.38 0.000 .0161285 .0236351 quinttr | .0137285 .0041382 3.32 0.001 .0056178 .0218392 sex | .1021711 .0103345 9.89 0.000 .0819159 .1224263 ageyrs | .0033549 .0002361 14.21 0.000 .002892 .0038177 marstat | .0030487 .0020036 1.52 0.128 -.0008782 .0069756 occgrp | .01595 .0016906 9.43 0.000 .0126366 .0192634 edgrp | .0340607 .0020489 16.62 0.000 .0300449 .0380765 remit | 4.02e-08 1.13e-07 0.36 0.722 -1.81e-07 2.62e-07 sector | 1.524614 .1840023 8.29 0.000 1.163976 1.885252 hhno | .0006951 .0033538 0.21 0.836 -.0058783 .0072684 hhsize | .1112068 .0302119 3.68 0.000 .0519925 .170421 zone | .6029394 .0552881 10.91 0.000 .4945767 .711302 quinttr | -2.316909 .1414994 -16.37 0.000 -2.594243 -2.039575 sex | .7860772 .1952155 4.03 0.000 .4034619 1.168693 ageyrs | .0280863 .0049581 5.66 0.000 .0183686 .0378041 marstat | .0446092 .0377773 1.18 0.238 -.029433 .1186514 occgrp | .1095953 .031066 3.53 0.000 .0487071 .1704836 edgrp | .3285206 .0479942 6.85 0.000 .2344537 .4225875 remit | 2.24e-06 2.25e-06 0.99 0.320 -2.17e-06 6.66e-06 -------------+---------------------------------------------------------------- mills | lambda | .067612 .009081 7.45 0.000 .0498135 .0854105 -------------+---------------------------------------------------------------- rho | 0.74659 sigma | .09056106 lambda | .067612 .009081 ------------------------------------------------------------------------------ . tab sex, gen( sex) sex | Freq. Percent Cum. ------------+----------------------------------- male | 16,370 85.45 85.45

51

female | 2,788 14.55 100.00 ------------+----------------------------------- Total | 19,158 100.00 . desc sex1 -sex2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- sex1 byte %8.0g sex==male sex2 byte %8.0g sex==female . tab sector, gen( sector) urban or | rural | sector | Freq. Percent Cum. ------------+----------------------------------- urban | 4,646 24.25 24.25 rural | 14,512 75.75 100.00 ------------+----------------------------------- Total | 19,158 100.00 . desc sector1 - sector2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- sector1 byte %8.0g sector==urban sector2 byte %8.0g sector==rural . tab zone, gen(zone) zone | Freq. Percent Cum. --------------+----------------------------------- south south | 2,888 15.07 15.07 south east | 2,697 14.08 29.15 south west | 3,055 15.95 45.10 north central | 3,477 18.15 63.25 north east | 3,214 16.78 80.02 north west | 3,827 19.98 100.00 --------------+----------------------------------- Total | 19,158 100.00 . desc zone1 -zone6

52

storage display value variable name type format label variable label ------------------------------------------------------------------------------- zone1 byte %8.0g zone==south south zone2 byte %8.0g zone==south east zone3 byte %8.0g zone==south west zone4 byte %8.0g zone==north central zone5 byte %8.0g zone==north east zone6 byte %8.0g zone==north west tab quinttr , gen( quinttr) quintiles | Freq. Percent Cum. ------------+----------------------------------- 1 | 3,267 17.05 17.05 2 | 3,630 18.95 36.00 3 | 3,753 19.59 55.59 4 | 3,832 20.00 75.59 5 | 4,676 24.41 100.00 ------------+----------------------------------- Total | 19,158 100.00 . desc quinttr1 - quinttr5 storage display value variable name type format label variable label ------------------------------------------------------------------------------- quinttr1 byte %8.0g quinttr== 1.0000 quinttr2 byte %8.0g quinttr== 2.0000 quinttr3 byte %8.0g quinttr== 3.0000 quinttr4 byte %8.0g quinttr== 4.0000 quinttr5 byte %8.0g quinttr== 5.0000 . reg remit sex1 sex2 sector1 sector2 zone1 zone2 zone3 zone4 zone5 zone6 quinttr1 quinttr2 quinttr3 quinttr4 quinttr > 5 Source | SS df MS Number of obs = 1460 -------------+------------------------------ F( 8, 1451) = 1.56 Model | 9.2656e+09 8 1.1582e+09 Prob > F = 0.1316 Residual | 1.0760e+12 1451 741582767 R-squared = 0.0085 -------------+------------------------------ Adj R-squared = 0.0031 Total | 1.0853e+12 1459 743867158 Root MSE = 27232 ------------------------------------------------------------------------------ remit | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+----------------------------------------------------------------

53

sex1 | 1464.691 1801.132 0.81 0.416 -2068.41 4997.792 sex2 | (dropped) sector1 | 750.663 1989.056 0.38 0.706 -3151.07 4652.396 sector2 | (dropped) zone1 | 3162.646 1981.39 1.60 0.111 -724.0485 7049.34 zone2 | -1211.402 1951.358 -0.62 0.535 -5039.187 2616.383 zone3 | (dropped) zone4 | (dropped) zone5 | (dropped) zone6 | (dropped) quinttr1 | (dropped) quinttr2 | 4311.197 2779.945 1.55 0.121 -1141.944 9764.338 quinttr3 | 480.4828 2664.269 0.18 0.857 -4745.749 5706.714 quinttr4 | 3625.636 2622.936 1.38 0.167 -1519.516 8770.788 quinttr5 | 1122.646 2545.168 0.44 0.659 -3869.956 6115.249 _cons | 10080.19 2707.375 3.72 0.000 4769.404 15390.98 tab mothliv mother | living? | Freq. Percent Cum. ------------+----------------------------------- yes | 1,208 6.31 6.31 no | 17,950 93.69 100.00 ------------+----------------------------------- Total | 19,158 100.00 . tab mothliv, gen(mothliv) mother | living? | Freq. Percent Cum. ------------+----------------------------------- yes | 1,208 6.31 6.31 no | 17,950 93.69 100.00 ------------+----------------------------------- Total | 19,158 100.00 . desc mothliv1 - mothliv2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- mothliv1 byte %8.0g mothliv==yes mothliv2 byte %8.0g mothliv==no

54

. reg edage agegrp10 agehlt age1560 agegrp5 motheduc mothwrk zone sector remit mothliv1 mothliv2 Source | SS df MS Number of obs = 1460 -------------+------------------------------ F( 10, 1449) = 3.95 Model | .079456172 10 .007945617 Prob > F = 0.0000 Residual | 2.91437944 1449 .002011304 R-squared = 0.0265 -------------+------------------------------ Adj R-squared = 0.0198 Total | 2.99383562 1459 .002051978 Root MSE = .04485 ------------------------------------------------------------------------------ edage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- agegrp10 | .0008855 .0042768 0.21 0.836 -.007504 .0092749 agehlt | .012681 .0039809 3.19 0.001 .0048719 .02049 age1560 | -.0038802 .0036812 -1.05 0.292 -.0111013 .0033409 agegrp5 | .0005017 .0026329 0.19 0.849 -.004663 .0056665 motheduc | .0002749 .0003356 0.82 0.413 -.0003834 .0009331 mothwrk | -.0001196 .0001511 -0.79 0.429 -.000416 .0001769 zone | -.0002463 .0007782 -0.32 0.752 -.0017728 .0012802 sector | -.0037369 .0032478 -1.15 0.250 -.0101078 .002634 remit | 3.31e-08 4.32e-08 0.77 0.443 -5.16e-08 1.18e-07 mothliv1 | -.0050122 .0046043 -1.09 0.277 -.0140441 .0040196 mothliv2 | (dropped) _cons | 2.965976 .0129605 228.85 0.000 2.940552 2.991399 ------------------------------------------------------------------------------ . sum infmor Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- infmor | 83 -2.228916 1.810035 -7 0 . gen infm =0 . replace infm =0 if infmor <= -2.22892 (0 real changes made) . replace infm =1 if infmor <= -2.22892 (34 real changes made) . tab infm

55

infm | Freq. Percent Cum. ------------+----------------------------------- 0 | 19,124 99.82 99.82 1 | 34 0.18 100.00 ------------+----------------------------------- Total | 19,158 100.00 . reg infm sector hhsize zone motheduc mothwrk mothliv remit ageyrs Source | SS df MS Number of obs = 1460 -------------+------------------------------ F( 8, 1451) = 1.36 Model | .233207834 8 .029150979 Prob > F = 0.2089 Residual | 31.0654223 1451 .021409664 R-squared = 0.0075 -------------+------------------------------ Adj R-squared = 0.0020 Total | 31.2986301 1459 .021452111 Root MSE = .14632 ------------------------------------------------------------------------------ infm | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- sector | -.0214035 .0105568 -2.03 0.043 -.0421118 -.0006951 hhsize | -.0024953 .0013884 -1.80 0.072 -.0052187 .0002281 zone | .0043607 .0025692 1.70 0.090 -.0006791 .0094006 motheduc | .0000658 .0010946 0.06 0.952 -.0020814 .002213 mothwrk | -.000139 .0004939 -0.28 0.778 -.0011079 .0008298 mothliv | .0058678 .0151144 0.39 0.698 -.0237806 .0355162 remit | -1.66e-08 1.41e-07 -0.12 0.906 -2.93e-07 2.60e-07 ageyrs | .0004548 .0002775 1.64 0.101 -.0000896 .0009993 _cons | .0363685 .0461795 0.79 0.431 -.0542172 .1269541 ------------------------------------------------------------------------------s . . sum Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- state | 19158 18.73771 10.52677 1 37 sector | 19158 1.75749 .4286121 1 2 ric | 19158 1234.07 356.865 101 1909 hhno | 19158 26.45203 30.65116 1 717 caseid | 19158 2.66e+08 2.37e+08 3479 1.07e+09 -------------+-------------------------------------------------------- pid | 0 sex | 19158 1.145527 .3526403 1 2 relation | 19158 1 0 1 1 certbirt | 19158 1.861729 .3451934 1 2

56

ageyrs | 19158 47.42442 14.59712 13 99 -------------+-------------------------------------------------------- agemths | 0 marstat | 19158 2.279883 2.081485 1 7 agefbirt | 17519 25.77053 6.081043 0 77 spouhh | 14870 1.066711 .2495301 1 2 resonnu | 992 2.703629 1.342794 1 4 -------------+-------------------------------------------------------- spouseid | 13842 1.952969 .3498824 1 13 relig | 19158 1.523906 .5715749 1 4 fathliv | 19158 1.976459 .1516183 1 2 fathid | 311 3.643087 4.654302 1 61 fatheduc | 19158 2.978912 3.770117 1 12 -------------+-------------------------------------------------------- fathwrk | 19158 58.62663 10.40221 1 99 mothliv | 19158 1.936945 .2430675 1 2 mothid | 119 7.512605 8.268652 1 64 motheduc | 19158 3.174496 4.133011 1 12 mothwrk | 19158 54.58941 8.618561 1 99 -------------+-------------------------------------------------------- hhafh | 19158 .2104604 1.269406 0 12 livothh | 303 1.534653 .4996228 1 2 hhmem | 19158 1 0 1 1 hhsize | 19158 4.829105 2.908539 1 26 fao_adq | 19158 3.794103 2.259903 .73 21.12 -------------+-------------------------------------------------------- ctry_adq | 19158 3.689387 2.202687 .66 20.73 zone | 19158 3.674027 1.714638 1 6 s0month | 19158 6.488934 3.49355 1 12 s0year | 19158 3.656645 .4748412 3 4 weightea | 19158 1388.308 935.5943 451.6014 13924.5 -------------+-------------------------------------------------------- fpindex | 19158 1.075734 .230995 .63 1.77 nfpindex | 19158 1.049269 .1800106 .62 1.99 fdtotby | 19158 47852.04 46595.46 0 928815.5 fdtotpr | 19158 22242.41 37574.83 0 869223.2 fdtot | 19158 70094.45 60168.71 0 979574.8 -------------+-------------------------------------------------------- fdtotdr | 19158 66860.91 58867.87 0 1173965 edtexp | 19158 6883.55 25185.39 0 814950 hltexp | 19158 17874.85 77934.05 0 3713060 renthh | 19158 10682.47 12912.38 720 786600 nfdfqtot | 19158 13618.69 29431.86 0 1721558 -------------+-------------------------------------------------------- nfdiqtot | 19158 10481.07 167484.6 0 2.30e+07 nfdtot | 19158 59540.62 198674.8 1000 2.38e+07

57

nfdtotdr | 19158 57242.38 195298.4 925.9259 2.36e+07 hhexp | 19158 129635.1 218079.4 1200 2.43e+07 hhexpdr | 19158 124103.3 213228.5 1348.315 2.40e+07 -------------+-------------------------------------------------------- pcexpdr | 19158 31764.05 40543.31 850.0095 2198309 pcexp | 19158 33659.05 42820.19 792.4576 2286242 sharefd | 19158 .5809628 .2187835 0 .9877768 aeexpdr | 19158 40502.74 51631.01 1111.124 2782670 hhsize_1 | 19158 52704 27148.06 12 92516 -------------+-------------------------------------------------------- quinttr | 19158 3.157636 1.422383 1 5 popwt | 19158 6592.816 6361.12 451.6014 98128.37 corep | 19158 1.669433 .4704296 1 2 modp | 19158 1.494415 .4999819 1 2 corepr | 19158 1.809166 .3929688 1 2 -------------+-------------------------------------------------------- modpr | 19158 1.498643 .5000112 1 2 dpdpr | 19158 1.530222 .4990988 1 2 agegrp5 | 19158 10.13561 2.743927 3 15 pov | 19158 2.307809 .771208 1 3 age1560 | 19158 4.87791 1.457752 1 7 -------------+-------------------------------------------------------- sizehh | 19158 2.44039 .7817483 1 5 amtpd | 1011 3926.201 13825.08 100 400000 agehlt | 19158 4.232331 .7569653 2 5 agegrp10 | 19158 5.252062 1.283278 2 7 occgrp | 19158 5.129502 2.012693 0 9 -------------+-------------------------------------------------------- edgrp | 19158 2.773358 1.78268 1 6 englit | 19158 1.606848 .4884628 1 2 niglit | 19158 1.494676 .4999847 1 2 lit | 19158 1.146936 .3540514 1 2 edage | 19158 2.998799 .0346289 2 3 -------------+-------------------------------------------------------- enroll1 | 441 2.723356 .5920869 0 3 hholdex | 19158 401376.2 495322.5 3748.315 4.94e+07 infmor | 83 -2.228916 1.810035 -7 0 remit | 1460 13683.48 27273.93 0 350000 pce | 19158 .6673974 .4711578 0 1 -------------+-------------------------------------------------------- marstat1 | 19158 .6245955 .4842398 0 1 marstat2 | 19158 .1494937 .3565837 0 1 marstat3 | 19158 .0020879 .045647 0 1 marstat4 | 19158 .0125796 .111454 0 1 marstat5 | 19158 .0317361 .1753012 0 1 -------------+--------------------------------------------------------

58

marstat6 | 19158 .1155131 .3196485 0 1 marstat7 | 19158 .0639942 .2447489 0 1 sex1 | 19158 .8544733 .3526403 0 1 sex2 | 19158 .1455267 .3526403 0 1 sector1 | 19158 .2425097 .4286121 0 1 -------------+-------------------------------------------------------- sector2 | 19158 .7574903 .4286121 0 1 zone1 | 19158 .1507464 .3578109 0 1 zone2 | 19158 .1407767 .3478001 0 1 zone3 | 19158 .1594634 .3661172 0 1 zone4 | 19158 .1814908 .3854343 0 1 -------------+-------------------------------------------------------- zone5 | 19158 .1677628 .3736653 0 1 zone6 | 19158 .1997599 .3998302 0 1 mothliv1 | 19158 .0630546 .2430675 0 1 mothliv2 | 19158 .9369454 .2430675 0 1 infm | 19158 .0017747 .0420911 0 1 -------------+-------------------------------------------------------- quinttr1 | 19158 .1705293 .3761069 0 1 quinttr2 | 19158 .189477 .3918973 0 1 quinttr3 | 19158 .1958973 .3969002 0 1 quinttr4 | 19158 .2000209 .4000261 0 1 quinttr5 | 19158 .2440756 .429549 0 1 -------------+-------------------------------------------------------- pover | 19158 .6673974 .4711578 0 1