Impact of Migration and Remittances on Labor Supply in Pakistan

M. Phil Dissertation

by

Kamran Khan

Supervised by

Dr. G. M Arif

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Dedication

This work is dedicated to my beloved and sweet parents and siblings for their unconditional

support during my studies.

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Acknowledgment

First and Foremost, I would like to thank Almighty Allah for being my strength and guide in

writing of this thesis. Without Him, I would not have had the wisdom or the physical ability

to do so.

I express my gratitude to my supervisor, Dr. G.M Arif for his support, valuable comments,

and unwavering guidance throughout the course of this work. His special interest and

knowledge enabled me the right guidance and provided me much needed motivation.

I am also very thankful for all my class fellows. When times were tough, they gave me the

confidence and strength to keep pressing on to achieve all my goals. God bless them all. I

appreciate the feedback offered by Mr. Abid Ali, Mr. Abdul Hanan, Mr. Adeel Khalid, Mr.

Ahsan Iqbal, Mr. Ghulam Mustafa, Mr. Sajid Rafiq, Mr. Salman Ahmed, Mr. Shahzad

Mehmood, Mr. Mohsin Kiyani and Mr. Muhammad Khalil and Mr. Yasir Khan.

A very special thanks to Dr. Anwar Hussain, Dr. Sajid Amin, Dr. Shujahat, Dr. Wasim

Shahid for their help and suggestions regarding Econometric model and methodology. A

special thanks to Mr. Ali Shan and Mr. Masood Afshaque for their help during data handling.

I would also like to thank Mr. Raja Fawad Ahmed during estimation analysis.

I am also very thankful to my friends Mr. Abdul Malik, Mr. Hassan Mehmood, Mr. Hassan

Shahzad, Mr. Mansoor Shahzad, Mr. Syed Mehtab Gardezi and Mr. Waqar Azeem for their

unconditional support and encouragement during my research.

Finally, I thank everyone in my family for always being supportive of my education,

especially my Father and Mother who have all contributed to and encouraged me during my

studies.

Kamran Khan

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Table of contents

Dedication ...................................................................................................................................................... i

Acknowledgement………………………………………………………………………………………………………………………….….ii

Abstract ........................................................................................................................................................ vi

Chapter 1 ....................................................................................................................................................... 1

Introduction ................................................................................................................................................... 1

1.1 Magnitude of Remittances.................................................................................................................. 4

1.2 Remittances and Development........................................................................................................... 6

1.3 Objectives of Study ............................................................................................................................. 9

1.4 Organization of the Study ................................................................................................................... 9

Chapter 2 ..................................................................................................................................................... 10

Literature Review ........................................................................................................................................ 10

2.1 Migration and Immigration ............................................................................................................... 10

2.2 Impact of Remittances (Migration) on Labor Supply or Participation .............................................. 13

2.3 Remittances and Linkages ................................................................................................................. 24

Chapter 3 ..................................................................................................................................................... 28

Data and Methodology ............................................................................................................................... 28

3.1 Theoretical Considerations ............................................................................................................... 28

3.2 Definitions of Variables ..................................................................................................................... 31

3.2.1 Labor Supply or Labor Supply .................................................................................................... 31

3.2.2 Employed ................................................................................................................................... 31

3.2.3 Unemployed ............................................................................................................................... 32

3.2.4 Migration .................................................................................................................................... 32

3.2.4 Remittances ............................................................................................................................... 33

3.3 Data Source ....................................................................................................................................... 33

3.3.1 Pakistan Panel Household Survey (PPHS) 2010 ......................................................................... 33

3.3.2 Labor Force Survey (LFS) 2010-11 .............................................................................................. 34

3.4 Model and its Specification ............................................................................................................... 35

3.4.1 Logit Model or Regression ......................................................................................................... 35

3.4.2 Model Specification ................................................................................................................... 36

3.4.3 Tobit Model or Censored Regression ......................................................................................... 37

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3.4.4 Ordinary-Least-Squares (OLS) .................................................................................................... 37

Chapter 4 ..................................................................................................................................................... 39

Descriptive Statistics ................................................................................................................................... 39

Chapter 5 ..................................................................................................................................................... 46

Impact of Remittances on Labor Supply ..................................................................................................... 46

5.1 Descriptive Statistics of Explanatory Variables ................................................................................. 46

5.2 Overall Labor Supply Models ............................................................................................................ 48

5.3 Labor Supply or Participation by Gender .......................................................................................... 55

5.4 Youth Labor Supply Model ................................................................................................................ 58

5.5 Labor Supply behavior of Remittances receiving Households .......................................................... 60

5.6 Model for Overall Labor Supply (Only Remittances Receiving Households) ................................. 60

5.7 Gender level Analysis ........................................................................................................................ 63

5.8 Regional level Analysis ...................................................................................................................... 66

Chapter 6 ..................................................................................................................................................... 69

Impact of Migration on Working Hours ...................................................................................................... 69

6.1 Working hours of Individuals using LFS (2010-2011) ........................................................................ 69

6.2 Descriptive Statistics of Explanatory Variables from LFS (2010-11) ................................................. 69

6.3 Model for Overall Weekly Working Hours ........................................................................................ 71

Chapter 7 ..................................................................................................................................................... 74

Conclusion and Policy Implication .............................................................................................................. 74

7.1 Summary and Conclusions ................................................................................................................ 74

7.1.1 Summary .................................................................................................................................... 74

7.1.2 Conclusion .................................................................................................................................. 75

7.2 Policy Implications ............................................................................................................................ 76

7.3 Future research ................................................................................................................................. 77

References .................................................................................................................................................. 78

Appendix ..................................................................................................................................................... 86

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List of Tables

Table 1: Workers‟ Remittances in US$ Million ........................................................................................... 6

Table 2: Crude and Refined Labor Force Participation Rates and Unemployment Rates by Gender and

Area ............................................................................................................................................................. 40

Table 3: Refined Labor Force Participation Rates of International, Internal Migrant and Non-Migrant

Household by Gender and Area, PPHS-2010 and LFS 2010-11 ................................................................ 41

Table 4: Refined Labor Force Participation Rates by International and Internal Remittances across

Gender, PPHS-2010 .................................................................................................................................... 42

Table 5: Refined Labor Force Participation Rates by Age and Gender ...................................................... 43

Table 6: Percentage Distribution of Employed Migrant and Non-migrant Household Workers by

Weekly Working Hours, LFS 2010-11 ....................................................................................................... 45

Table 7: Mean, Standard Deviation and Range of the explanatory variables ............................................. 47

Table 8: Results of Logistic Regression Model using remittances dummy ................................................ 51

Table 9: Results of Logistic regression model using yearly remittances amount ....................................... 52

Table 10: Results of Logistic regression model using remittances dummy ................................................ 56

Table 11: Results of Logistic regression model using remittances dummy ................................................ 57

Table 12: Results of Logistic regression model using remittances dummy ................................................ 59

Table 13: Results of Tobit censored regression model ............................................................................... 61

Table 14: Results of Tobit censored regression model ............................................................................... 63

Table 15: Results of Tobit censored regression model ............................................................................... 64

Table 16: Results of Tobit censored regression model ............................................................................... 67

Table 17: Results of Tobit censored regression model ............................................................................... 68

Table 18: Mean, Standard Deviation and Range of the explanatory variables ........................................... 70

Table 19: Results of OLS and Tobit censored regression model on Weekly Working Hours of Migrant

Household Members ................................................................................................................................... 72

Table 20: Mean, Standard Deviation and Range of the explanatory variables ........................................... 86

List of Figure

Figure 1: Age Specific Labor force Participation Rates ............................................................................. 43

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Abstract

The study has used the PPHS-2010 to investigate how remittance flows affect the labor

supply of left behind. Logit and Tobit models are used in the analysis. It is found that

remittances are significantly and inversely related to overall labor force participation. This

negative impact is more pronounced for the male labor than of female labor for force

participation. Similarly, the negative impact of remittances is stronger in the labor supply of

rural households than in urban areas. In the case of youth sample (15-29 years), remittances

are found to be negatively associated with the supply of labor and all these findings are

consistent with theory and literature. Education significantly enhances overall labor supply.

Female participation in the labor market increases significantly when they have 10 or more

years of schooling in their account. A model is also estimated for working hours of household

members using the data of LFS 2010-11. It is found, by using OLS and Tobit models, that

internal migration (in-migration) is positively related to working hours of household

members. There is a need to educate remittances receiving households to invest money on

business to engage their inactive adult members in economic activities.

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Chapter 1

Introduction

“When God made man, he seemed to think it best to make him in the east, and let him travel

west.”1

People migrate from one place or from one country to another for better opportunities and

better living status or they respond to incentives at the place of their destinations. Migrants

send remittances to their families left behind. International remittances can have various

effects on labor markets in developing countries. On the one hand, remittances may allow

recipients to overcome the type of liquidity constraints that prevent the creation of new

enterprises. On the other hand, remittances can reduce labor force participation by increasing

the level of minimum wages at which members of migrant households are willing to work

(the reservation wage).2 Few important questions arise in this regard. Why does people or

household migrate? What happens when they migrate? An important question of particular

interest arises is how the departure of a household member impacts the labor market behavior

of those who stay behind? The study of labor movements across labor markets, whether

inside or across countries is an essential component in any debate of labor market equilibrium

and these labor movements help markets to reach a more effective distribution of resources.

Pakistan has been experiencing outflows of its worker to other countries on a large scale for

the last four decades. Not only the annual outflow of workers increased over time, the stock

of Pakistani have reached to 7 million. Remittances sent home by these migrants have

increased to their families also increased many folds; they were US$ 2885 million in 1982-83

1 The Pioneer form www.Wikipedia.com.

2 Topic-16 Remittances and Labor Force Supply and Participation from Social Science Research Council (SSRC).

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and fell to US$ 914 million in 1999-2000 but gradually moved to US$ 13 billion in 2012-13,

and expecting US$ 15 billion for the upcoming year [PBS and SBP (2013)]. The workers‟

remittances surpass the Foreign Direct Investment (FDI) and are also most important source

of foreign exchange reserves in Pakistan. Workers‟ remittances positively contribute to

economic growth, and poverty reduction.

Many studies have been conducted to understand how remittances affect the household labor

supply, working hours and employment status of non-migrant members. The common finding

is that remittances decrease the labor supply or labor force participation of household

members left behind and the labor supply pattern varies across gender and area/region.

There is growing evidence that as a result of the flows of remittances individuals in migrant

households will participate less in the labor market. The economic logic of this effect can be

easily understood by neoclassical labor, leisure choice model as remittances is the form of

nonlabor income will lift the budget constraint and increase the reservation wages of

receiving individuals which may in turn increase the consumption of leisure by supplying less

work or hours in the labor market [Mark R. Killing-Worth, (1983); Azam and Gubert,

(2006)].

In the case of Pakistan, only two studies have so far have so far been conducted to see the

impact of remittances on labor supply. First, the study conducted by Kozel and Alderman

(1990) analyzed labor force participation in urban Pakistan and the data used to estimate the

model taken from the International Food Research Institute (IFRI) and Pakistan Institute of

Development Economics (PIDE) 1986 survey. They used the model of wage determination

developed by Becker (1964) and Mincer (1974) which assumes that wage difference is

mainly due to difference in human capital. They argued that extended family structure as well

as worker remittances allows the younger, educated male to extend their job search period.

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Using Tobit and probit techniques they found that the labor supply is primarily explained by

participation as opposed to working hours conditional upon participation. Women

employment rates are low and do not vary greatly by age. Labor force participation rises with

increases in expected earnings, which is due to differences in human capital while female

labor supply respond dramatically as education level increases. Kozel and Alderman

concluded that the decrease in labor migration to the Middle East and continuous drop in

remittances may have much stronger impact than anticipated; not only the return migrants

attempt to enter into domestic work force but previously inactive men may find it necessary

to enter into work force as well.

Second, Arif (2004) examined the overseas migration effect on labor market participation of

left behind family members by using the LFS (2001-02) and PSES (2000-01). According to

him, a large number of Pakistanis living overseas, send remittances back to their families or

household members as a result of this a strong income effect is expected, which will reduce

the labor supply or the participation of the non-migrant members especially in case of

females. Labor force participation is found lower for workers belonging to migrant

households than the non-migrant households and the difference was significant among

females. Education positively affects the labor participation and play important role in labor

market participation, as high level of education is associated with more labor supply or labor

participation. Older individuals and males were more likely to participate in the labor market,

while headship which is mostly males in traditional country like Pakistan also affects the

labor participation. Overseas migration was found to significantly decrease the labor

participation of non-migrant members.

During the last decade, particularly since 9/11 event, both the outflows of Pakistani workers

and inflows of remittances have been gradually increasing. The Pakistani Diaspora counts as

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more than 7 million, and in 2012-13, Pakistan has been received about US $13 million as

workers‟ remittances only from banking or formal channels. But the impacts of these

remittances on the labor supply have not been examined. This study aims to extend not only

the earlier work done by Kozel and Alderman (1990) and Arif (2004) but also covers the

dimensions which were ignored in these studies such as rural-urban differential in labor

supply of remittances receiving household and hours worked in the labor market. With this

focus, the present study contributes to the existing literature on remittances and labor supply.

1.1 Magnitude of Remittances

Foreign remittances are the biggest source of external funding in many less developed

countries (LDCs). The labor movement has become a key feature of globalization, and

worldwide, migrants‟ earnings were US$ 440 billion in 2011 and more than $350 billion of

that total were sent to developing countries in the form of remittances. At the completion of

the 20th Century nearly 140 million individuals or approximately 2 percent of the world‟s

population lives in a place where they did not belong to. In 2010 the estimated international

migrants were at 214 million [World Migration Report 2010]. If it carries on rising at the

similar speed as in last 20 years, it might touch 405 million in 2050.3

The top migrant destination country is the United States, followed by the Russian Federation,

Germany, Saudi Arabia, and Canada. In 2010, the top recipient countries of recorded

remittances were India, China, Mexico, Philippines and France. As a share of GDP, however,

smaller countries such as Tajikistan (35 percent), Tonga (28 percent), Lesotho (25 percent),

Moldova (31 percent) and Nepal (23 percent) were the largest recipients in 2009. High-

income countries are the main source of remittances and in this regard United States is by far

3 See Migration Report 2010 and this IOM estimate is based on UN DESA, 2009.

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the largest, with $48 billion in recorded outward flows in 2009. Saudi Arabia ranks as the

second largest, followed by Switzerland and Russia.

The stock of the overseas Pakistani is presently around 7 million. Pakistan has set a target of

US $15 billion per year, and it is confident that strong double-digit growth in remittances

from Gulf region will help achieve its overall target of US $15 billion. According to World

Bank data, Pakistan has become the fifth largest remittances recipient developing country in

2011 after India ($58 billion), China ($57 billion), Mexico ($24 billion), and the Philippines

($23 billion). The World Bank estimated that the remittance flows are expected to continue

growing with global remittances expected to exceed $593 billion by 2014, of which $441

billion will flow to developing countries.

The workers‟ remittances in Pakistan increase more than tenfold from US$ 1bilion in 2001 to

US$ 12 billion in 2011, due to the increase in the migrant abroad, more remittances from

formal channels and due to the change in the skill composition of these migrant workers

abroad [Amjad, Arif and Irfan, (2012)].4 These remittances positively contribute to economic

growth in Pakistan at a time when all the resources are drying up due to poor law and order

situation and energy crisis. Remittances share to GDP has increased from only 1.5% in 2001

to 6.1% in 2013. The major source countries for remittances flows to Pakistan are Saudi

Arabia, USA, UK, and UAE.

4 See Explaining the Ten-fold Increase in Remittances to Pakistan 2001-2012 by Amjad, Arif and Irfan.

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Table 1: Workers’ Remittances in US$ Million

Year (FY) Remittances % of GDP

2001 1086.57 1.5

2002 2389.05 3.3

2003 4236.85 5.1

2004 3871.58 3.9

2005 4168.79 3.7

2006 4600.12 3.4

2007 5493.65 3.6

2008 6451.24 3.8

2009 7811.43 4.6

2010 8905.95 5.0

2011 11200.97 5.02

2012 13186.62 5.9

2013 13921.66 6.1

Source: PBS and SBP, 2013

1.2 Remittances and Development

For more than half a century, there have been heated debates on the sources of economic

growth of developing economies.5 For many developing countries, remittances represent a

major part of international capital flows surpassing foreign direct investment (FDI), export

revenues and foreign aid [Giuliano and Ruiz-Arranz (2005)]. Given the large size of

aggregate remittance flows, they are expected to have significant effects on the respective

5 See Lewis, (1954); Solow, (1956); Chenery and Strout, (1966); Denison, (1967); Myrdal, (1968); Harris-Todaro,

(1970); Schultz, (1979); Fields, (1980); Romer, (1986); Lucas, (1988); Barro, (1991); and Easterly, (2001).

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economies. In addition, remittances have been identified as a source of funding for economic

development.

Remittances affect the overall economy by contributing in GDP growth; they also help

improve the living standards of households, increase the overall human capital of their

members and contribute to lowering the poverty levels. Remittances are largely personal

transactions from immigrants to their families and relatives; they tend to be well directed to

the requirements of their receivers and their ability to reduce poverty. Household survey data

show that remittances have reduced the poverty headcount ratio significantly in several LICs:

by 11 percent in Uganda, 6 percent in Bangladesh and 5 percent in Ghana and in Nepal

remittances may explain a quarter to a half of the 11 percent reduction in the poverty

headcount ratio [Ratha (2007)]6.

Ratha (2007) argues that: “Remittances directly enhance the income of recipient households.

In addition to providing the financial means for unfortunate households, they affect poverty

and prosperity through indirect multiplier effects and also macroeconomic effects”.

At present remittances flows are more than double the official aid received by developing

countries (LDCs). According to the World Bank and the IMF, if informal channels of

remittances are included, then overall remittances could be as much as 50 percent higher than

the authorized record [World Bank (2010); IMF (2009)]. In 2009 in some countries economic

remittances have “become as huge as foreign direct investment” and in a large group of

developing countries, remittances denote a resource inflow that frequently surpasses a variety

of other balance of payments flows [IMF (2009)].

6 See Leveraging Remittances for Development By Dilip Ratha 2007.

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The recent literature has also paid more attention towards the effects of migration and

remittances on the labor markets of source countries. There is growing evidence that

individuals in migrant households supply less labor, meaning that they work fewer hours and

are less likely to be active in the labor market due to remittances. Regular transfers from

abroad in the form of remittances raise recipient‟s reservation wages and may also lower their

work efforts thus increasing their consumption of leisure. The unearned income from

remittances makes remaining household members “lazy” [Azam and Gubert, (2006), p. 426]

so that they “simply stop working and wait from month to month for the overseas remittance”

[Kapur, (2005), p. 152]. Emigration and remittances could at worst lead to a culture of

dependency in labor sending communities along with a reduction of productive activities,

labor shortages and other adverse economic impacts.

The growth of international migration has been accelerating at a steady pace in recent years

and is a becoming an important and emphasized issue, especially for the developing countries

[Eversole (2008), p 94; World Bank (2008)]. Due to the differences in per capita income

among countries with a large immigration or emigration, the movement of populations raises

several questions about what effects migration has on countries‟ economies and their

development. Can migration lead to income convergence between source and host countries

and foster economic development in the source countries [Gubert (2007), p 94-95]?

According to Adams (2003, p 4-5) remittance is the most visible product of international

migration and one of the best measures of the aspects of migration. Policymakers and

economists emphasize the importance of remittances to development and therefore it is

critical to see if this optimism is warranted [Barajas (2009), p 4; IMF (2009), p 12]. One way

remittances could affect household decision-making is by impacting on recipient household‟s

decision on how much labor it should supply, depending on if the receiving households see it

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as more profitable to supply more leisure after the extra type of income or not [Jadotte

(2009), p 5].

1.3 Objectives of Study

The objectives of the study are:

i. To analyze the impact of remittances on the overall labor supply or labor force

participation of the adult population in Pakistan;

ii. To examine age, gender and regional differences in labor force participation;

iii. To analyze the impact of migration on working hours of the employed labor force;

and

iv. To give suitable policy recommendations on migration, remittances and labor supply.

1.4 Organization of the Study

After discussing the introduction in chapter 1, subsequent part of the study is comprised of

six chapters. Review of some important and relevant studies is weaved up in chapter 2.

Chapter 3 is furnished with the definitions of the variables, data source and methodology

which would be employed in this study and chapter 4 holds descriptive analyses. Empirical

findings and scholarly discussion on the obtained results which best with the impact of

remittances on labor supply are set down in chapter 5, whereas, chapter 6 consists of

discussion on the impact of migration (in migration) on working hours of the household

members. Finally, this study will be concluded and some recommendations will be

suggested on the basis of the obtained results in chapter 7.

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Chapter 2

Literature Review

Migration is a very old and historical phenomenon as it‟s vastly discussed in the

anthropology, sociology and in economic. It has been taking place since the man emerges on

this planet and he moves from one place to another to just for having a better life without

taking into account any geographical limit or boundary and since then it is happening all

over the globe. Its dynamics and pyramids changed over the passage of time and nowadays

individuals, mostly migrated for economic prospective in simple words just for better

incentives. Several pioneer studies are conducted in economics literature related to migration

and immigration. So, the study will highlight some of the these studies and divide our

literature into three broad categories; migration and immigration, its impact on the labor

supply or participation and its impact on macro and micro level indicators or variables.

2.1 Migration and Immigration

If shed lights on the history of migration in economic literature, came across the pioneer

study conducted by Michel P. Todaro (1969), which first highlighted and raises this

important and crucial issue internal migration (i-e: rural to urban migration) and from there it

started a huge debate on this issue. According to Michel P. Todaro people migrated to urban

areas from rural areas just because of having better incentives and expected high wage rate

as compared to rural areas or agriculture sector having more chances of being unemployed.

According to him migration decision is primarily based on the rational behavior of the

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individual or household i-e: cost and benefit analysis not in terms of financial terms as well as

in psychological terms.7

Later on Harris and Todaro (1970) extended this work further in their paper and further

investigated and explored this in two sector analysis (Rural and Urban sector). They assume

that the rural sector specialize in agriculture production and part of which traded to the urban

sector in return for manufactured goods in which that sector specialize and continuing focus

on its assumption that the potential rural migrants behave as maximizers of expected utility.8

Another study was conducted by Michel P. Todaro (1980) said that typically migrants are not

a random sample of the population, basically they are young, energetic, better educated, less

risk averse and more achievement oriented. He argued that in spite of having many

significant modifications of basic Todaro/Harris-Todaro model the fundamental idea remain

the same hat people migrate from rural to urban areas because of differences in expected

urban real incomes compared to rural real incomes and having the chance of unemployment

in urban areas accelerated the internal migration in LDC‟s is considered not only a plausible

phenomenon but also a rational behavior. He argued that this general acceptance at the

theoretical level is also reflected at empirical level that “expected income differential” is one

of the most important explanatory variables in migration decision. This internal migration can

be further extended to international migration as well because as people migrated from their

country to abroad just for having more incentives and more expected wage or income and

better opportunities for them and those who left behind. So it seems quite plausible that this

internal migration is easily generalized to international migration without any loss of

generality so we can investigate it over cross border migration because the motive and

essence remain the same as in case of internal migration. Nowadays, lots of people migrate

7 This paper was written by Michal P. Todaro as a partial fulfillment of his Phd degree in 1967 which was later

on published in 1969. 8 See Migration, Unemployment and Development: A Two Sector Analysis by John R. Harris and Michel P.

Todaro (1970).

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from LDC‟s to other countries are having better incentives and economic opportunities so it

provides us a foundation and acts as a basic pillar to look into this issue more deeply. As now

on this issue become a heated debate among policy makers for both hosts as well as on native

countries or economies because of its effects and consequences for both countries and

economies.

If go back to the history of immigration, a classical study undertook by Borjas (1994) raising

the issue “The Economics of Immigration” and its outcomes and consequences. So from there

it started a huge heated debate on the economic impact of immigration on growth of host

countries and its adverse effect of employment opportunities of natives and also in decline in

their wages which motivated these countries to look more deeply and seriously the

immigration policy. He said immigration impact can be harmful or beneficial and it will vary

by time and place. Borjas concluded that although in start immigrants have the economic

disadvantage, but their economic opportunities, improved rapidly over time and even catch

up with native workers and the decade after that overtake the earnings of natives. He

concluded the little evidence is found that immigrants had an adverse impact on native‟s

employment opportunities. He concluded that a very important lesson learnt and observed is

that this phenomenon has long lasting and far reaching impact as we perceive. He finally

concluded that in the context of economic, cultural and political significance of this issue

immigration policy is now a main or central ingredient of debate in social policy in many

countries. He suggested that as an immigration issue primarily focuses on economic issues

and use the evidence provided by economic research to frame and formulate the discussion

because its impact will be felt many decades to come and further exploration is needed on

this issue.9

9 From The Economics Immigration by Borjas (1994).

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In this regard Borjas (1999) conducted a study as “The Economic Analysis of Immigration”

and said that flow of labor is very crucial aspects of discussion in labor market equilibrium

across or within countries and it helps to reach efficient allocation of resources in the labor

market. Immigration not only affects the economic opportunities of the host country, but also

source country as well and mostly the natives benefited from this immigration as long as

there exist a differential in productivity endowment between natives and immigrants and the

larger these benefits the greater the endowment differences. According to the classical study

by Borjas he argued that there is no literature of immigration has exploited that different

immigration policies are pursued by different countries as there is no rule of thumb and by

examining these differences we can come to know or evaluate its impact on labor market

outcomes on the host country and more importantly an issue or topic which is not addressed

is the economic impact of immigration on source country. Immigration impact on household

or people left behind welfare and more importantly of the labor market participation decision

of those who left behind is ignored in immigration literature.

2.2 Impact of Remittances (Migration) on Labor Supply or Participation

Different studies are undertaken focusing on gender issue by using a panel data set or

household surveys of different countries of the world examine the impact of remittances on

the overall labor supply or working hours patterns of both male and female and found

different patterns and behavior among male and female who remit receiving household. A

very detailed and interesting study by ITZIGSOHN (1995) analyzed the remittances impact of

low income households in Caribbean Basin‟s four capital cities using a survey conducted in

1991. A large number of households in each city for subsistence depend on remittances. Only

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the Dominican Republic remittances equally reached to every socioeconomic section, but

while it is not the case in the other three countries the one having more human capital and/or

income having more access to remittances. There are differences in remittances impact on

labor supply like in the case of Kingston city, Santo Domingo and Port-au Prince it decreased

labor supply and people quit from labor market while in Guatemala and Santo Domingo city

it contributed in way by sending fewer people to labor market or labor force. He argued these

remittance inflows allow individuals or people in low paying or weakest labor market

positions to get rid of this and involve or indulge in more easy or flexible jobs or

employment. These patterns are not same in every city and claimed that these remittances are

the key tactic for those low income households to cope with poverty in the Caribbean Basin.

He suggested that these inflows of remittances are very logical approach opted by these

households to confront with very tough labor market condition.

Another study to further examine this issue was conducted by Rodriguez and Tiongson

(2001) which investigated the impact of international migration on the labor supply of non-

migrants using household survey data in the Philippines and used probit model. Migrants

contributed by reducing the labor participation of non-migrants having lower incomes or

earnings from local labor markets. They used this income which comes in the form of

remittances for more leisure which is a kind of a benefit from having one migrant outside.

This effect will vary across gender as in case of male it will reduce labor supply when a

migrant belongs to a nuclear family, in case of females it reduces their chances to participate

in the labor market but it raises their probability if the migrant is educated. They estimated

that male labor participation decreases by 18.5%, while female labor participation reduces by

5.7% if migrants have tertiary education. They found male participation decreases by 27.7%

and female participation decreases by 12.5%. They concluded that the labor participation of

non-migrants declines as a result of remittances although it is not significant and massive, but

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it‟s stronger for males than the females. Finally a per-capita thousand pesos increase in

remittances declines the chance of employment of men and women 0.3% and 0.2%

respectively.

Amuedo-Dorantes and Pozo (2006) used panel data for Mexico and examined the remittances

impacts the decision to participate in the labor market on both male and female. By using

Amemiya Generalized Least Squares (AGLS) and IV-Tobit model they estimated the hours

worked by both male and female recipients in the home country. Remittances do not affect

the overall labor force participation but it changes the employment type. An increase in

remittance amount will decrease the participation in the formal sector work and with an

increase in informal sector work. They also found that with the receipt of remittances

Mexican males seem to prefer the flexibility of informal jobs, but the overall female labor

supply tends to decline with the receipt of remittances, but only in rural areas. As remittances

may increase or decrease the working hours, but this pattern may vary across gender and

areas or region and important type of work he/she has done. Rural females in Mexico appear

to use remittances as a means of escaping from low-paying types of employment in the

informal sector. They finally concluded that remittances cause variation in male labor force

participation in various types of employment, but decline in the overall labor supply of

females.

On the same ground in exploring this issue a study by Acosta (2007) used panel data from El

Salvador and found that the effects of international remittances on labor force participation

differ by gender. Receipt of international remittances, labor force participation falls much

more for women than for men i-e: urban females in remittance-receiving households are 42.2

percent more likely to quit the labor market, while urban males in remittance-receiving

households are only 9 percent more likely to quit. He finally concluded that both males and

females reduce their total hours worked per week upon receiving remittances.

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To explore this issue in a different way or dimension Carletto and Mendola (2008)

conducted a study in Albania by using Living Standards Measurement Study (LSMS) survey

of 2005 by examining the role of male migration on the labor market outcome by gender of

remittances receipts household. Male and female labor supply respond differently in response

to migration and by using Instrumental Variable (IV) estimation strategy after controlling or

accounting the endogeneity of migration and income effect from remittances will increase the

female participation in unpaid work, significantly raises the their participation in self-

employment but contributed to the decline in the female paid workers. By taking into the

account the other factors or control variables (like age, education and child caring) that are

very important and played a crucial role in defining whether to participate or not in labor

market female having less education with male migrant are more likely to alter her

occupations. Female participation or employment is greatly affected by male migration and

most probably earning potentials of these females, which ultimately improves their role in the

society.

Dermendzhieva (2008) in Albania, using Living Standards Measurement Survey (Albania

2005 LSMS), estimated the probability of household member to participate in the labor

market for male and female Linear Probability Model (LPM) is used separately. The

predicted effects of remittances using the instrumental variable approach were found

significant for only one male age group i-e: 46-60 years, having a combined effect 20 to 50%

decline in the probability of working. Although the expected negative impact on labor supply

as a consequence of remittances, but it is not empirically observed in case of female in recent

research or data. Using the instruments coefficients of having a migrant is found to be large

and positive while the coefficients of receiving remittances are large but negative for old age

males and females. He said that OLS estimates for female subsamples found to be

insignificant and signs of the estimated coefficients propose that effect of migration is

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downward biased while the effect of remittances are upwards biased as compared to the true

effects of variables.

Edwards and Oreggia (2008) investigated the impact of remittances on households in Mexico

and found that no differences in labor participation between receiving and non-receiving

households. They concluded that for women in the urban areas remittances increase labor

participation, and the possible explanation is that remittance contributes to the establishment

of family owned enterprises which could improve the labor market opportunities for women.

This study opposed the earlier studies which suggest a decrease in labor supply or working

hours of female due to remittances. A very interesting study in this regard is conducted by

Lokshin and Glinskaya which examined the male migration‟s impact on female labor force

participation who left behind because usually male are the household head and their decisions

affect the household life patterns.

Lokshin and Glinskaya (2008) investigated this issue in Nepal by using Nepal Household

Survey (2004), applying the Instrumental Variable Full Information Maximum Likelihood

method which is used to account for unobserved factors. Female labor supply is curtailed by

male migration for work and found sign considerable heterogeneity as a result of male

migration. They argued it highlights the important fact gender dimensional influence on the

wellbeing of people left behind as a result of male migration. They concluded that the policy

makers should take into the account these gender related aspects of migration in making

policies regarding economic development in Nepal.

Another very interesting and more extensive study which more deeply and econometrically

examined this issue was by Jadotte (2009) undertook a study in Haiti using ECVH-2001

containing 7,186 households. By using different econometric methods to remit decision,

probability of migration and for labor participation, a count regression model is used, models

for migration probability while 2SLQ model is used to examine to remit decision and also for

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its impact on the labor market. He found the similar negative impact of remittances on labor

supply as vastly observed in the previous literature and said that this negative effect is

significant among women in developing countries, and female wages are more sensitive than

male as a result of remittances. Using the IV-Tobit for working hours and IV-probit for

participation in the labor market he found the negative impact of remittances. He concluded

that to see the best picture and the dynamics of this migration and remittances on the labor

market outcome further more research is needed in the Republic of Haiti. The impact of

remittances doesn‟t appear crucial in determining the female participation.

Justino and Shemyakina (2012) used the Tajik Living standards Survey (2000), and argued

that both male average male and female in remittances household participate less in the labor

market and provide less working hours than others. They found that remittances affect male

participation more than the female participation in the labor market, while in the earlier

researches or studies female participation is found more sensitive to remittances. Remittances

negatively affect the male participation in the conflicted areas, but no effect labor

participation of female in conflict areas. They concluded in the end that by just using

remittances, which affect the participation decision in conflicted areas didn‟t tell the whole

story and give an incomplete picture, so further research is needed in this regard.

There are some studies conducted to investigate the remittances effect on the development of

rural areas and occupational choice by singing cross sectional data in developing countries

because it is assumed and observed in past literature that remittances has more impact on

rural areas than urban areas. A study conducted by Gubert (2002) by using the data of eight

villages in Kayes areas, by taking the implicit assumption that remittances serve as an

insurance contract between the migrant the household members left behind. The study used

the Powell‟s CLAD and beside some parametric to examine it. By taking both (internal and

International) remittances found that remittances from France were used to overcome or

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neutralize the loss in mainly comes in crops production and due to illness or death of a

household or family member. He argued that Powell‟s estimator is more consistent than two-

step Heckman‟s or Tobit estimator in the presence of heteroscedasticity. He concluded that

by making policy related to migration the policy makers must have to consider the insurance

and the welfare effect of remittances which it gave to households. A very comprehensive

study by Lucas theoretically investigated the migration‟s impact on rural development and its

consequences.

To examine this on theoretical grounds and viewing it more rigorously Lucas (2007)

theoretically examined the impact of internal and international migration on rural

development and some of the evidence pertaining to these effects in low income countries.

He found that South-south international migration may well be more important to rural

development in lower income countries than is migration of low-skill workers in the high

income countries and south-south migration remains largely neglected. He argued that the

rural sectors are far from homogeneous, rural-rural migration is important in its own right and

far more common than rural-urban migration in the low income countries and yet very little is

known about the patterns, causes and consequences of these movements. He argued that

effects from both internal and international migration upon rural development are manifold

but it is important to recognize that both migrations out of the rural areas and improvements

for those left behind are part of rural development. He concluded that links through labor,

replacement, chain migration, investments financed by remittances, insurance provided to the

community and its resulting changes in technologies adopted, and the multiplier effects of

remittance spending all help to raise living standards even for those who do not migrate out.

He concluded that there is fairly uniform agreement that both internal and international

migrations contribute to the absolute poverty reduction and migration may also enhance inter-

generational socioeconomic mobility though this remains to be explored. He finally

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concluded that it is often communities that are isolated from the migration process, perhaps

as a result of geographic separation that remain amongst the poorest.

Demurger and Shi (2012) undertook a study in China and by using a probit model examined

migrant or remittances effects on household member occupational choices of work. After

that, they estimated the impact of migration duration and remittances on migrant sending

households. They found that the migration has two fold effect on the occupational choice of

individuals in rural China, firstly it will increase local off-farm work which also indicates the

fact, when the migrant came back his/her village migrant will more likely to work in off-farm

sector while secondly overall sending a migrant to a city on motivates the left behind to work

in farming than off-farm activities.

A number of studies are conducted using cross sectional, several years of panel and

household surveys to analyze the migrant‟s remittances impact on household behavior and

overall labor supply of different countries using different models and techniques. A simple

study by Kim (2007) conducted in Jamaica to understand the functioning of the labor market

and simple cross sectional study using LFS and SLC and found that remittances are

negatively affecting labor market participation at the individual level. He argued that the

competitiveness of Jamaican economy will be affected by the remittance flows, but it will not

affect the working hours of household/individual. He found that at cluster level panel data

shows a negative impact of remittances on the labor force, mainly because people receiving

remittances have higher reservation wages. He finally concluded that there is a dire need to

conduct further research on this issue.

Farre, Gonzalez and Ortega (2009) investigated the immigration effect on the skilled native

women, using LFS (199-2008), LPR (1999-2008) and Decennial Census of 1991. The recent

inflows of immigration substantially increase the labor supply of the skilled native females,

but it had no effect on labor supply of highly skilled native males. Weekly working hours of

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females in high earnings occupations increased to 2.1 hours and allowed a skilled, educated

female to return to their job after the childbirth as soon as possible. Immigration increases

the probability to indulge in a part time employment by 4% during this period. They found

skill women living with elderly male will increase its participation by 5.9% and also work in

the labor market after the retirement of their husbands. Immigration overall increase the

employment rate of these women by 4.6% and its effects in urban areas are huge. They found

that immigration will increase 3 hours per week women employed in the high occupation

jobs, 5.7%, in part time employment of skilled, educated women, 8.4% with in-house elderly

males and 20.7% after the retirement of their husbands in urban areas.

Torrado (2010) investigated the effects of Nicaraguan migrant‟s income from international

migration on household behavior using a household 9-year panel data set. To overcome the

endogeneity problem instrumental model with fixed effect was estimated and variation in the

wages using the information of occupation, gender and gender was also used. Migration

income increases the probability of head of the household and it is stronger for poor

households. It will increase the probability of business ownership when migrants are not

heads but heads are more likely to invest in housing than own business. She purposed three

main hypotheses: insecurity, enabling and the last one are migration chain and done many

tests and exercise to test and predict them, but due to the data limitations cannot distinguish

between these three hypotheses. She concluded that these findings provide policy makers a

sign or indication to develop and provide a physical and financial infrastructure to promote

successful entrepreneurial activities.

Very detailed and comprehensive study by Emilsson (2011) investigated how the received

remittances affecting the recipient household labor supply by applying the neoclassical model

of labor-leisure choice, and by analyzing data on household income and expenditure surveys

from the Department of Statistics in Jordan for 2002, 2006, 2008. She found the results from

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regressions that remittances will negatively affect the labor participation of men and women

for all of the years 2002, 2006 and 2008 and this is consistent with the traditional

assumptions of leisure as a normal good which strengthens the study‟s hypothesis. She said

that the positive effects of remittances may offset by lower labor force participation. She

found that dividing the heads of households into different age groups, one can see that

remittances affect labor supply positively for some of the age groups, but there does not seem

to be any consistent trend of supplying more labor in any age group. She concluded that a

possible solution to reduce this negative effect could be to adopt mechanisms that benefit the

entrepreneurial activities in which remittances could be invested. She finally concluded that

the results of this study could be affected by omitting variable bias and therefore extended

data is needed and research on how remittances affect the Jordanian labor supply could be

extended to include uncertainty of the future streams of remittances in the analysis, since this

will probably affect the labor supply decision of households as well. A very fruitful and

interesting study is conducted in Spain to highlight the effect of skill native labor force (i-e:

male and female) on the employment patterns of both male and female due the recent

Immigration wave.

The recent and latest study by Gobel (2012) examined the role of migrant remittances on

labor supply in Peru using Peruvian household survey (ENAHO) for the year 2002 and 2006.

He employed a gender specific labor choice model that outlines the possible impacts,

especially on the self-employment sector. He used fixed effects and v effects as well as

instrumental approach estimations methods. He found that overall the impact of remittances

on labor supply is inconclusive while the fixed effect model of the instrumental variable

approach suggested that labor supply responds negatively to remittances. He found strong

evidence that remittances increase at least female self-employment and females live-in a

remittances receipts, household more likely to engage in own enterprises. He concluded that

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these remittances serve as to overcome the credit constraints and it is observed that both

genders tend to invest a part of the remittance but only women do increase their labor supply

in self-employment which reflects how strong their work success is constrained by liquidity

shortage. He finally concluded that overall no robust effect of reduced labor supply in

response to remittances ware found.

Immigration and remittances also affect the occupational choice of return migrant and serve

as tool or apparatus to overcome credit constraint and also to get rid of poverty. The study of

Mesnard (2004) analyzed the impact of international migration and remittances on

occupational choice of return migrants by using the data of Tunisia. He found that for return

migrants the likelihood of self-employment increases significantly with the amount of

savings from abroad. He also found that for each additional 1000 Tunisian dinars in savings,

the likelihood of a return migrant being self-employed increases by 18 percent. He concluded

that education is also important: a migrant with no schooling will might be engaged in self-

employment after return. He finally concluded that savings allow the poor workers to

overcome credit constraints for investment in small projects or business when returned.

To shed light on this issue and interrelate this issue with the poverty of household

Funkhouser (2006) comprehend a study by using 1998 longitudinal data and LSMS 2001

household survey in Nicaragua. He found that international migration does indeed tend to

reduce labor force participation. He found that when compared to non-migrant households,

households with migrants have fewer working members and less labor income. He concluded

that households with migrants are less likely to be poor, because migrant households receive

more in remittance income than they do for work in the local markets.

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2.3 Remittances and Linkages

Several studies are undertaken to investigate the macro level impact of immigration and

remittances besides its micro level impact for different countries showing the importance and

significance of it. A very extensive study conducted by Quartey and Blankson (2004) which

took both the macro and micro level datasets in Ghana using the Ghana Living Standards

Survey (GLSS). They found that the low of remittances increases in economic shocks time

by using the random effect model and hence counter-cyclical. In the time of economic shocks

household welfare will decreases, but it will be minimized by migrant remittance receipts, for

the one who owns land and crop farmers, ultimately having more welfare than the others who

don‟t have any land. Education of head will enhance the welfare while household head‟s age

was negatively related to the welfare. The role of remittances in consumption smoothing is

found to significant in food crop farmers. They argued that in Ghana, remittances are very

important to improve household welfare and consumption smoothing.

To show its significance at macro level Kharmeh and Sondos (2010) conducted a study and

found the impact of Jordanian worker remittances of macroeconomic variables was

significant. They found that household‟s final consumption expenditure increased by 33.6

percent per year, the government final consumption increased by 35.1 percent per year, the

total exports decreased by 39.5 percent per year and the gross capital formation increased by

31.4 percent all due to remittances and overall remittances contributed to increase in GDP by

7.1 percent. They finally concluded that impact of high rates of Jordanian worker remittances

on the household‟s final consumption expenditure was due to a high ratio of marginal

propensity to consume (MPC) in Jordan, which is estimated to be more than 90 percent.

Quartey and Blankson undertook an interesting and crucial study focused on Consumption

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smoothing of the household during the period of shock having remittances serving as means

of escape from this shock showing its importance.

Lucas (1987) examined temporary labor migration from five countries to South Africa's

mines. He found that in the short run emigration to South African mines showed reduction in

the crop production in areas of Lesotho, Botswana and Malawi while in the long run income

or the earning of the migrant boosted the crop production and accumulation of cattle expect

Lesotho. He found due to migrants accumulated earnings livestock and crop production,

improved in several countries in Southern Africa though it is also the fact that these

improvements were offset by labor withdrawal to the mines. He examined the gap between

the wage available in the South African mines and the domestic wage weighted by

probability of employment, it positively affects the desire to be a miner, but in Botswana and

Lesotho this assumed not to have any feedback effect on domestic wage and employment

because such wage jobs exist are largely in the public sector. He also found in Malawi and

Mozambique emigration to South Africa's mines has significantly inflated labor costs to the

local estate and plantation operators.

The study of Hanson and Woodruff (2003) which is conducted to examine the impact of

immigration and remittances on the educational attainment of household is considered very

important, highlighting this crucial aspect of remittances on both male and female which has

long lasting effects on labor supply of immigrants as well as on remaining household (non-

immigrant). According to notion the theory the correlation among schooling/education and

emigration is not clear. On the one side migration generate flow of remittances to migrant

household, which raises the income level and also relax their constraint, allows them to send

their children to school for more education, but on the other side it may disturb the family

that it might involve more household work than schooling. Migration was taken as an

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endogenous so the instruments, interaction between household characteristics and previous

migration pattern were used. OLS results found that in migrant household the mother having

the less than the 3 years of education, children will have more years of schooling while in

case of 0-15 year old girls it will increase the schooling 0.23 years, while using Instrumental

Variable (IV) it was 0.73 to 0.89. In case of boys sample OLS gave smaller or minor results,

but Instrumental Variable (IV) gave inconclusive results. They concluded that emigration will

loosen the household budget or credit constraints which will be used to finance more

education or schooling as consistent with theory.

Another study, which focused on performance of migrant household in agriculture sector

compared to non-migrant another insight of remittances is conducted by Azam and Gubert

(2004) conducted in western Mali. Migration brought flows of remittances in the migrant

household, by which these households adopted new imported technology in agriculture, but

unfortunately still these households have not been shown better agricultural performance than

the non-migrant household. Migration is the implicit insurance contract between the migrant

and the household members left behind gave rise to devious behavior which lead towards

technical ineptitudes and found that using household-specific fixed effect for the estimation

of the production function enable to reject our null hypothesis. They found that the reduction

in labor effort by farming households in migrant household was balanced by improving

technology and investment from the remittance receipts. They concluded that policies should

be made to help and support the non-migrant household or families rather than focusing on

migrant household, so it will craft adequate imitation to encourage the migrant household or

families to be more proficient.

The recent study conducted by Mendola and Paoli looked it into another dimension by

relating it to child labor and its impact on child labor exploring new doors of research.

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Mendola and Paoli (2012) empirically investigated the impact of international migration on

child labor by using a cross country dataset covering the majority of the developing countries.

Many child labor equations were estimated and found that international out-migration may

significantly reduce or offset child labor linked to poor labor market environments. They

found that the cross country difference in the correlation among parental skill and child labor

is just because of composition of skill differences between immigrants relative to natives. For

heterogeneous sub-samples they found that the young kids, boys and children living in rural

areas are the beneficiaries of the outmigration. They found that female outmigration on

average have more impact on child labor and this effect is much that stronger in case of

female headed household. They finally concluded that by controlling remittances, individual

level characteristics and also country level characteristics and fixing them, out migration will

reduce the child labor in poor households by making necessary changes in the local labor

market.

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Chapter 3

Data and Methodology

3.1 Theoretical Considerations

According to the neoclassical migration theory the core motive for labor migration is wage

dissimilarity among two geographical localities, depending on the labor demand and supply

in these localities. The regions with a lack of labor, but a surplus of capital have a high

comparative wage while regions with a high labor supply and a shortage of capital have a low

comparative wage and labor inclines to move from low-wage regions with high-wage regions

[Oberg, (1997): 24].

According to dual labor market theory, immigration is mostly instigated by pull factors in

more developed countries. It assumes that the developed countries labor market consists of

two segments: primarily, which requires high-skilled labor, and secondary, which is very

labor-intensive but requires low-skilled workers. Migrants get employment generally in the

secondary market because the natives do not want to work in low-status and low- paid jobs.10

The theory of the new economics of labor migration (NELM) states that migration tides and

forms cannot be described exclusively at the individual level and their economic enticements,

but those broader social units must be considered as well. One such social unit is the

household, migration can be observed as an outcome of risk aversion on the part of a

household that has not enough income and in this situation the household is in need of

additional resources that can be attained through remittances sent back by family members

10

See Piore, (1979); Massey et al, (1993); Gieseck et al, (1995); Frey and Mammey, (1996); MaCurdy et al, (1998).

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who participate in migrant labor overseas to relax their constraint [Taylor, (1999); Massey et

al, (1993)].

The relative deprivation theory states that realization of the income variance among

neighbors or other households in the migrant-sending community is a key reason in labor

migration. There is higher encouragement to migrate in regions that have a high level of

economic inequality, while in short run remittances may increase inequality, but in the long

run they may in fact reduce inequality. There are two stages of migration for a worker: first,

they invest in themselves by accumulating human capital and on the basis of this try to gain

or reap its benefits. In this manner successful migrants can use their new attained capital to

give to their children better schooling and better homes and living standards for their

families which relaxed their constraint further [Stark et al, (1988); Docquier and Rapopport,

(2003)].

World systems theory stares migration from a comprehensive view and global perspective,

and describes that interaction among different civilizations can be an imperative element in

social change within societies. It can be said that the advanced countries import labor-

intensive goods, which bases a rise in employment of unskillful workers in the under

developed countries (UDC‟s), which reduces the outflow of the migrants. On the other hand

the export of capital-intensive goods from developing countries to less developed countries

also balances, income and employment situations, thus also slow down migration. In either

way, this idea can be used to enlighten migration among countries that are geographically far

apart [Wallerstein, (1983), p. 18; Gosh, (1992); Amankwaa, (1995); Mouhond (1997)].11

11

For detail of these theories see Causality Chains in the International Migration Systems Approach by Roel Jennissen August 2007.

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The theoretical framework of this study is based on two perspectives: Neoclassical and

NELM. It considers that migration produces remittances, which increase the reservation wage

of receiving individuals or households. Remittances, a form of non-labor income, relax

households‟ budget constraint and allow them to enjoy more leisure. If remittances increase

the reservation wage of individuals in remittance-receiving household its impact will be

negative on their labor supply [see Killingsworth, (1985)]. The study had followed the

neoclassical model of labor-leisure choice as typically used by economists in labor supply

behavior [Borjas, (2008), p. 27] where remittances are thought to be a non-labor income. The

neo-classical combines work and leisure to maximize utility. Leisure is considered as a

normal good in the model that if one has more income or wealth he/she will demand more

leisure. Remittances are non-labor income for left behind, who may demand more leisure

than supplying labor in the market. It is also called “dependence” effect of the international

migration; the intensity of this depends upon family ties and dynamics [Rodriguez and

Tiongson, (2001), p. 713]. The effect of remittances on the labor supply may also be positive

as the recipient household or individual used it in entrepreneur or commercial activities

[Jadotte, (2009), p. 5].

The NELM states that migrant and non-migrant members of a household jointly decide

migration; the costs and returns of this migration are shared according to the implicit

contractual arrangement between these two parties. Most aspects of human behavior,

including the migration involve feelings and independent will and if a person migrates from a

family or household from one location to another to change his family/household or relative

position in the same reference group or to change his reference group. According to NELM

framework migration decision is taken by the entire household or family to maximize their

joint utility level following the pioneer works of Stark and Levhari (1982), Stark and Bloom

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(1985) and Taylor (1999) who examined the economic implications of international

migration in developing countries.12

Migration flows and pattern cannot explain only taking

into account the individual level economic incentives, but also consider its impact on their

family or household utility/welfare. Migration is viewed as a risk taken by household or

individual that has insufficient income and need an extra amount of capital which can be

achieved through remittances to relax their constraint. Migration decisions are often jointly

made by migrants and non-migrants by sharing the cost and returns with an implicit

agreement between two parties.

By taking these theoretical considerations, this study takes the position that households in the

need of additional resources decide to send their members abroad for employment. These

workers in turn send remittances to their families or households who left behind. Remittances

relax their budget constraint and increase the reservation wage of non-migrant members, and

this rise in reservation wage reduces the labor force participation of these non-migrant

household members.

3.2 Definitions of Variables

3.2.1 Labor Supply or Labor Supply

Labor supply refers to the hours worked by an individual at given real wage rate in the labor

market. It can be defined at a given wage the number of people or individuals who are willing

and able to work in any occupation or industry. Labor force consists of employed plus

unemployed [PPHS (2010), and LFS, (2010-11)].

3.2.2 Employed

12

For NELM see Stark and Levhari (1982), Stark and Bloom (1985) and Taylor (1999).

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The ILO defines a person „employed‟ who has worked at-least one hour in the week before

the interview [ILO, (1993)]. In case of Pakistan all persons 10 years of age and above who

worked at least for one hour during the reference period (a week before the survey) either as a

paid employee, self-employed or unpaid family helper are defined as employed [LFS, (2010-

11), and PPHS, (2010)].

3.2.3 Unemployed

In case Of Pakistan all persons 10 years of age or above who during the reference period are

without work either in paid or self-employment but available and seeking for work, or not

available for work due to illness, or temporarily laid off, or will take a job within a month or

an apprentice and is not willing to work are defined as unemployed [LFS, (2010-11), and

PPHS, (2010)].

3.2.4 Migration

It is the population movement from one place/district to another place/district/country at any

time of their lives [PPHS, (2010), and LFS, (2010-11)].

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3.2.4 Remittances

Remittances are the amount of money transferred from a foreign or domestic worker to

his/her home. International Monetary Fund (IMF) defined foreign remittances as the

migrant‟s current private transfer of money from the host country where they are living or

working to their country of origin [LFS, PPHS, and IMF (2008)].

3.3 Data Source

This study has used two data sources. The first, is the Pakistan Panel Household Survey

(PPHS-2010), that has information on both the remittances (internal and external) received

and labor supply by the sampled households. PPHS-2010 does not have information on the

number of weekly working hours. To overcome this limitation, the study has used the Labor

Force Survey [LFS (2010-11)] which provides information about the working hours of the

individuals but it does not have data on remittances. Moreover, the LFS only incorporates

information on in-migration. Despite this limitation it enables to examine the effect of

migration on working hours.

3.3.1 Pakistan Panel Household Survey (PPHS) 2010

The PPHS is a longitudinal dataset, conducted by Pakistan Institute of Development

Economics (PIDE) in 2001, 2004 and 2010. This study has used the last round of 2010. The

PPHS expanded the International Food Policy Research Institute (IFPRI) panel household

survey conducted only in four districts (Dir, Attock, Faisalabad and Badin) in 1980s to 16

34 | P a g e

districts from four provinces. In 2001 and 2004, the PPHS was a rural panel survey; however,

the urban sample was included in the last round carried out in 2010 [Arif and Nayab

(2012)]13

. The urban sample was added in all 16 districts. A selected district was the stratum

for the urban sample. All the urban localities in each district were divided into enumeration

blocks, consisting of 200 to 250 households in each block. In total, 75 urban enumeration

blocks (PSUs) were selected randomly for the third round (PPHS-2010). The total sample

size of the PPHS-2010 is 4142 households: 2800 rural and 1342 urban households.14

3.3.2 Labor Force Survey (LFS) 2010-11

The LFS is a nationally representative survey carried out regularly by Pakistan Bureau of

Statistics (PBS) since 1968. It collects broad sets of information on various dimensions of the

civilian labor force and collects data on socio-demographic characteristics of the population.

It also provides information about hours worked in main /subsidiary occupation. The 2010-11

LFS sample covers rural and urban areas of four provinces of Pakistan, according to 1998

Population census excluding FATA and military restricted areas which constitute 2% of the

population. The PBS has designed its own sampling frame for urban areas in which each

city/town is divided into enumeration blocks. Enumeration blocks in urban domain and

mouzas/dehs/villages in rural areas are taken as a Primary Sampling Units (PSUs) and the

listed households of the sampled PSUs are taken as Secondary Sampling Units (SSUs). The

entire sample of households (SSUs) is drawn from 2580 Primary Sampling Units (PSUs)

13

See Pakistan Panel Household Survey Sample Size, Attrition and Socio-demographic Dynamics PSDPS: by Arif and Nayab. 14

See Pakistan Panel Household Survey PRHS-PPHS (2010).

35 | P a g e

having 1204 from urban and 1376 from rural areas. The LFS 2010-11 provides data on

36,420 households.15

3.4 Model and its Specification

Labor supply or labor force participation and weekly working hours are the dependent

variable in this study. Different types of models are used to examine and investigate the

impact of migration (immigration) and remittances on labor supply or number of hours

worked. In the case of the labor force participation model, the dependent variable is binary

and discrete in nature. In this case, Linear Probability model does not work properly because

our results do not abide by the limits described by the binary character of dependent variable,

i.e. though they fall between zero and one but they are also scattered around zero and one.

This type of endogenous variables is better explained with the help of LOGIT and TOBIT

regression models using maximum likelihood estimation procedure because this model

ensures that the predicted probabilities lie between 0 and 1.16

3.4.1 Logit Model or Regression

The logistic regression model is used in the study as the dependent variable is dichotomous or

binary, so it best describes and estimate the relationship between dependent and independent

variables. Two main reasons for applying logistic regression are; firstly, logistic regression

model is extremely flexible, and secondly its results‟ interpretation is straight forward

15

Labor Force Survey (LFS) 2010-2011 by Pakistan Bureau of Statistics, Govt of Pakistan. 16

See [Kozel and Alderman (1990); Hayine and Gorman (1999); Arif, Kiani and Sheikh (2002); Dubois, Jeandidier and Berger (2003); G.M Arif (2004); Amuedo and Pozo (2006); Jamal (2007); Jadotte (2009) and Demurger and Shi (2012)].

36 | P a g e

[Montshwe (2006)]. Along with the assumptions on the error term of the model, labor supply

or labor participation status is predicated on the basis of computed probabilities. The logistic

regression model is considered as a powerful technique because it analyzes all types of

independent variables (e.g. Discrete, Continuous or mix of both) [Anka (2006)]. The

independent variables were examined in order to check their significance for our model and

final model contain all the independent variables believed to affect the labor supply or labor

force participation.

3.4.2 Model Specification

Logistic model can be written as:

Prob (LF = 1) =

Where

LS= Labor Supply y = 1 if Participating in Labor market

y = 0 if not Participating in Labor market

e = base to natural logarithm

z= + + + + + + +

+ + +

37 | P a g e

3.4.3 Tobit Model or Censored Regression

The study has used simple Tobit model, as used by [Kozel and Alderman (1995); Amuedo,

and Pozo (2006); Amuedo, and Pozo (2007)] to estimate our impact of remittances on labor

supply and on working hours of household as it has the advantage to capture as our dependent

(latent) variable can take discrete as well as continuous values as well. It is used to describe

the relationship between a nonnegative dependent variable and an independent variable

(or vector) . It follows the Maximum Likelihood (ML) principle to estimate.17

The model

supposes that there is a latent i-e: unobservable variable . The explanatory variable ,

linearly depend via a parameter (vector) β which determines the relationship the independent

variable (or vector) , and the latent variable , just in case of linear model. The value of

observable variable , is equal to latent variable , whenever the latent variable is above

zero and equal to zero otherwise.

{

}

3.4.4 Ordinary-Least-Squares (OLS)

In the one model, weekly working hours are used as a dependent variable. It is estimated by

using OLS procedure, which shows the direction of relationships among variables.

Yt = α +βXt +µt

17

Tobit model is proposed by James Tobin (1958).

38 | P a g e

Is an equation showing population regression function that is not directly observable. With

the help of sample data we obtain estimates of α and β.

ititit uXY

A basic assumption in the case of OLS is that the zero correlation of error term µt and other

regressors of the model. This means that the independent variables are all pre-determined or

are determined outside of the system. The dependent variable is a continuous variable and it

assumes the normality assumption, so OLS will yield or provide significant, consistent results

as it uses the error minimization principle.

To examine the remittances impact on labor force participation and for weekly working hours

following benchmark model/ equation 1, will be estimated:

Where

And

For i =1, 2 ……, n individuals. Where , measures labor force participation and weekly

working hours while, , shows remittances (dummy), yearly remittances, per capita monthly

remittances, and , is a vector of exogenous explanatory (Independent) household and

individual level variables and , is a disturbance or error term. includes; gender, age, age-

squared, which represent experience, household size, relationship to household, marital status

if (male or female), education level (no education, below matriculation, matriculation, and

above matriculation), dependency ratio (low, medium, high), and region/area variable (rural

or urban).

39 | P a g e

Chapter 4

Descriptive Statistics

This chapter presents a descriptive analysis of major variables used in this study to better

understand the current labor market situation in Pakistan. It sets out data on both the crude

and refined labor force participation rates from PPHS-2010 and LFS 2010-11 by gender and

area. Age specific participation rates are also calculated from both surveys while hours

worked weekly by migrants and non-migrant household workers are calculated from LFS

(2010-11).

There are not much differences in crude activity rates between the PPHS and LFS, although

the overall and female participation rates are a little higher in the former. Both the samples,

PPHS and LFS have used the same definition (see table 2). Similarly, the differences in

Refined Labor Force Participation Rate (RLFPR) between the two surveys are not

substantial. The Crude Activity rate is around 35 percent, while the RLFPR is around 47

percent, the RLFPR is higher in rural areas than in urban areas in both PPHS and LFS. The

unemployment rate is calculated at around 5.8 percent with no real difference between PPHS

and LFS. This comparison also shows the validity and soundness of PPHS-2010, which gives

the almost same participation rates as in case of LFS (2010-11). It is evident from table 2 that

more male participate in the labor market than the female in both rural and urban areas.

40 | P a g e

Table 2: Crude and Refined Labor Force Participation Rates and Unemployment Rates

by Gender and Area

PPHS-2010 LFS (2010-11)

Crude LFPR

Overall 35.22 32.83

Male 50.18 49.26

Female 18.47 15.60

Refined LFPR

Overall 47.33 45.69

Male 67.88 68.70

Female 24.87 21.67

Urban Area 42.07 39.54

Male 64.77 66.33

Female 17.32 10.65

Rural Area 49.44 49.05

Male 69.12 69.97

Female 27.94 27.57

Unemployment Rate 5.78 5.95

Source: Author‟s calculation by using PPHS-2010 and LFS 2010-11

In PPHS-2010, 211 households have at least one member working abroad; the number of

migrants was 263. There are more internal migrants (455) than the international migrants. All

overseas migrants sent remittances to their household members or families, but in case of

internal migrants more than 85% of migrant household received remittances from their

household members.

41 | P a g e

In table 3 RLFPR are calculated for international and internal migrant as well as non-

migrants. It is evident from the table 3 that the internal migrants have a higher participation

rate (57.2%) than international migrant households (41%). This difference has been in other

parts of world because international migrant households have more nonlabor income as

compared to internal migrant or non-migrant households, so their reservation wage increases

more than the latter. They enjoy more leisure than counterpart (internal migrant or non-

migrant) because of foreign remittances. Female participation among international migrant

household is relatively low at 18 percent. Whereas no major difference is found in the overall

activity rate between internal and international migrant households, in rural areas the

participation of the former is substantially higher (58.6%) than the later (40%). There is no

significant difference between the migrant household participation rate calculated from LFS

(44.73%) than the international and non-migrant household, although it is quite lower than

the internal migrant household (57.16%). Rural participation (50.95%) in migrant household

from LFS is higher than the urban participation (41.12%), but there is no significant

difference in these participation rates calculated from PPHS, expect the rural participation in

the international migrant household (40.29%).

Table 3: Refined Labor Force Participation Rates of International, Internal Migrant

and Non-Migrant Household by Gender and Area, PPHS-2010 and LFS 2010-11

PPHS-2010 LFS 2010-11

International Migrants Internal Migrants Non-Migrants Migrants

Overall 41.05 57.16 44.78 44.73

Male 60.38 76.01 65.58 73.96

Female 18.03 35.51 23.93 20.92

Urban 43.97 48.33 38.93 41.12

Rural 40.29 58.61 48.74 50.95

42 | P a g e

Source: Author‟s calculation by using PPHS-2010 and LFS 2010-11

In table 4 presents the RLFPR by the amount of remittances received by households

(international and internal). Both the international as well as internal remittances are divided

into three categories. There is not a substantial difference in both international and internal

but the former has little lower participation (34.7%) than the later (58%). Male participation

rates are higher than female participation in both international and internal remittances

receiving households. There is no significant difference in male participation in both

international and internal remittances receiving households. Female participation in

international remittances receiving household is quite lower than the counterpart females in

the internal remittances household, the female participation of the former is (15.3%) than the

later (33.9%).

Table 4: Refined Labor Force Participation Rates by International and Internal

Remittances across Gender, PPHS-2010

International Remittances Household Internal Remittances Household

Yearly Remittances Total Male Female Yearly Remittances Total Male Female

40000 to 200000

47.92

71.43

20.69

0 to 50000

56.42

76.38

34.31

Above 200000 to

500000

36.08 54.55 15.33 Above 50000 to

100000

56.19 75.44 33.90

Above 500000 34.66 58.17 24.23 Above 100000 57.96 74.99 37.07

Source: Author‟s calculation by using PPHS-2010

The age specific participation rates are presented in table 5. There is no significant difference

between age specific participation rates in both PPHS and LFS in all age categories.

Participation rate increases as the age increases, but it starts declining after the age 40-49

43 | P a g e

years, in both the samples PPHS (64%) and LFS (64.5%). The overall age-specific

participation rates from both samples depicts an inverted U-shaped (Figure 1). Both male and

female participation rates are also maximum in the 40-49 years and then start declining

afterwards. Female participation rates are substantially lower than the male participation in

all age groups. The female participation rate in 40-49 years is 31.9% in PPHS while it is

29.3% in LFS.

Table 5: Refined Labor Force Participation Rates by Age and Gender

PPHS-2010 LFS (2010-11)

Age (Years) Total Male Female Total Male Female

10-19 22.94 29.07 16.19 24.06 32.93 14.21

20-29 55.37 80.68 26.20 56.33 90.53 24.62

30-39 60.22 92.70 27.45 61.04 98.04 27.46

40-49 64.00 93.48 31.94 64.50 98.29 29.29

50-59 59.52 89.00 30.24 62.49 94.40 27.18

60-64 48.99 71.97 22.77 52.35 77.97 20.99

65 And Above 33.93 47.59 18.04 28.29 41.64 10.65

Source: Author‟s calculation by using PPHS-2110 and LFS 2010-11

Figure 1: Age Specific Labor force Participation Rates

44 | P a g e

Source: PPHS 2010 and LFS 2010-11

The table 6 shows the percentage distribution of migrants and non-migrant by hours worked

during the week preceding the survey. The LFS 2010-11 is used for this purpose as PPHS-

2010 does not have data about weekly working hours of the employed sample. The workers

who worked less than 35 hours during the week preceding the LFS 2010-11 are shown as

underemployed in the table. More than one-third of the employed women, either migrants or

non-migrants, are underemployed while the corresponding figure for male is only 13 percent.

This is due to the fact women prefer to work less and spend more time in household work

than in the labor market.

0-19 20-29 30-39 40-49 50-59 60-64 65+

PPHS LFS

45 | P a g e

Table 6: Percentage Distribution of Employed Migrant and Non-migrant Household

Workers by Weekly Working Hours, LFS 2010-11

Migrants Non-Migrants

Working Hours Groups Total Male Female Total Male Female

<35 Hours (Underemployed) 13.25 7.19 34.15 13.28 7.32 38.84

35-39 Hours 11.87 6.76 29.49 14.85 10.81 32.21

40-41 Hours 6.87 7.21 5.73 6.17 6.39 5.22

42-48 Hours 25.36 27.46 18.15 26.72 29.63 14.26

49-55 Hours 9.90 11.47 4.48 12.01 13.81 4.28

56 Hours And Over 32.75 39.93 8 26.97 32.05 5.20

Source: Author‟s calculation by using LFS 2010-11

Male migrants work for longer hours in the labor market than their non-migrant counterparts.

Table 6 shows that compared to 45% of male employed non-migrants, 51% of male

employed migrants worked for 49 hours and more in the labor market during the week

preceding the LFS 2010-11. In fact, more than one-third of male migrants worked longer than

55 hours during the reference week. It indicates the hard work of migrant workers at places of

their destinations to earn livelihood.

46 | P a g e

Chapter 5

Impact of Remittances on Labor Supply

This chapter examines how remittances affect labor force participation decision. The PPHS-

2010 has information about migration, whether internal or external, and also about

remittances (external or Internal), so it best serves our purpose to fulfill the objectives of this

study. The empirical analysis is carried into two steps, firstly, all sampled households are

included in different models of labor supply, and secondly, the labor supply analysis is

carried out for those households which received remittances (both internal and external) in

the year preceding the survey.

5.1 Descriptive Statistics of Explanatory Variables

Table 7 presents descriptive statistics of all explanatory variables used in the multivariate

analysis. The mean age of the labor force included in the analysis is 24.56 years, while the

average household size is 7.33 with standard deviation 3.69. The mean of gender if the male

is 0.52 while for a female is 0.48 with standard deviation 0.50. The relationship to household

with three categories is used in the analysis to mean of head (0.13), spouse/mother (0.14) and

other (0.72). The marital status variable with mean for married if the male is 0.18 while for

the other category the mean is 0.18. Urban area has the mean 0.30 while the rural area mean

is 0.70 with standard deviation 0.46. The dependency ratio with categories low, median and

high18

has the mean 0.39, 0.31 and 0.30 respectively. The education variable is used as a

categorical variable in the analysis, namely no education, below matriculation, matriculation

and above matriculation with mean 0.77, 0.14, 0.05 and 0.04 respectively. The main and 18

Dependency ratio is calculated by dividing the inactive population (age 0 to 14 years and age 65 years and above) to active population (age 15 to 64 years). 0 to 0.5 is coded as low, 0.51 to 1 as medium, while above 1 is coded as high dependency ratio.

47 | P a g e

important variable used in the study is remittances (internal and external), the average yearly

remittances are Rs.25624.72, while if decomposed into monthly remittances than average

monthly remittances are Rs.2135.39 and when remittances used as a dummy variable its

mean is 0.13.

Table 7: Mean, Standard Deviation and Range of the explanatory variables

Explanatory Variables Mean S.D Min Max

Demography:

Age 24.56 18.92 0 105

Age2

961.29 1352.45 0 11025

Household Size 7.33 3.69 2 43

Gender (Male=1) 0.52 0.50 0 1

Gender (Female=1) 0.48 0.50 0 1

Relationship to Household

(Head=1)

Relationship to Household

(Spouse/Mother=1)

0.13

0.14

0.34

0.35

0

0

1

1

Relationship to Household

(Other=1)

0.72 0.45 0 1

Married (if Male=1) 0.18 0.39 0 1

Others 0.18 0.39 0 1

Area (if Urban=1) 0.30 0.46 0 1

Area (if Rural=1) 0.70 0.46 0 1

Dependency Ratio (if Low=1) 0.39 0.49 0 1

Dependency Ratio

(if Medium=1)

0.31 0.46 0 1

Dependency Ratio (if High=1) 0.30 0.46 0 1

Economic:

Remittances (if dummy)

0.13

0.34

0

1

Yearly Remittances

Monthly Remittances

Socio:

25624.72

2135.39

95961.88

7996.82

0

0

1440000

120000

No Education

Below Matriculation

Matriculation

0.77

0.14

0.05

0.42

0.34

0.22

0

0

0

1

1

1

Above Matriculation 0.04 0.20 0 1

Source: PPHS-2010 micro-data

Note: Age, Age2, Household size and Remittances are taken as continuous variables and dummy variables are

included for all other explanatory variable categories.

48 | P a g e

5.2 Overall Labor Supply Models

To examine the effect of remittances on the overall labor supply of the sampled households,

two models have been estimated. In model 1, which is presented in table 8, for remittances

receiving households a dummy variable is used. Remittance receiving households are coded 1

and otherwise 0. In the second model the amount of yearly remittances received by

individual/households is used. Logistic regression is used for both models and the PPHS-

2010 is the data source. The results of the model presented in the table are found to be

significant, consistent, plausible and according to existing literature. There is always a

question regarding how correctly the model is specified and how good fit it. There are

number of goodness of fit or post estimations test available used after the logistic regression.

Hosmer-Lemeshow test is used for goodness of fit, as it is widely used now a day. As the

logistic regression is based on likelihood procedure convergence is achieved at 5th

iteration

with the likelihood value of -12805.601 (details of results presented below in the table 8).

In the second model all the control or explanatory variables are the same as used in the first

model, but the only difference is that in this model the remittance dummy is replaced by

yearly remittances. Again the logistic regression is used in this model and to check the

model‟s prediction and goodness of fit Hosmer-Lemeshow test in used. By using its

maximum likelihood principle the model got convergence at 5th

iteration having the log

likelihood value -12789.251. In table 9 results of this model are presented.

There is not much difference in the results of both models so the results are discussed

together. As has been argued earlier, remittances are likely to reduce the overall labor force

participation because remittances (non-labor income), increase reservation wage, which

relaxes the budget constraint and allow household members to enjoy more leisure over work.

49 | P a g e

In both models, remittances (either in the form of a dummy or yearly amount of remittances),

show a significant and negative effect on the labor supply. The only differences is in the level

of significance, in the dummy variable model it is significant at 5% (table 8) while in

absolute case it is significant at 1%. These results validate and affirm the main hypothesis,

that remittances reduce labor supply of household members. The marginal effect coefficient

of remittances in first model is -0.023 while in the second model it is -2.22e-07 showing its

net effect on labor supply. This result is in line with the existing literature [Mark

Killingsworth (1983)19

; Kozel and Alderman (1990); Funkhouser (1992); Rodriguez and

Tiongson (2001); Frank (2001); Arif (2004); Kapur (2005); Azam and Gubert (2006);

Amuedo-Dorantes and Pozo (2006); Jadotte (2009); Emilsson (2011)].

Age and age square variables used in both models are found to be significant with the

expected signs observed in the literature. Age is a very important variable in the context of

labor force participation; it has a positive association with the participation of household

labor in the labor market. In the first model, the marginal effect coefficient of age is 0.033

while in the second model it is also 0.033. Age square variable is also used with age as it

incorporates and count as experience, beside it is used to generate quadratic curve in analysis,

as one at the start of his/her working life participate more and reach its maximum at an age

and then start diminishing or decreasing. Age square is found to be significant at 1% in both

models with a negative sign.

Another important variable use in our analysis is household size, which is continuous in

nature. It is found to be negatively related to labor supply and is significant at 1% in both

models. In the first model it has the marginal effect coefficient of -0.10 and in the second it

has the marginal effect coefficient as -0.009. The logic may be that as household size mostly

19

See Neo-classical labor-leisure choice model by Mark R. Kilingsworth “Labor Supply”, Cambridge University Press, 1983.

50 | P a g e

consists of inactive (i-e: children and old age) people, so they will not participate in the labor

market or it might be the reason that it has more female in the house as they will not

participate much in labor markets in case of Pakistan [Alderman and Kozel (1990)].

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Table 8: Results of Logistic Regression Model using remittances dummy

Logistic Regression Model using Remittances Dummy (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Odd Ratios Coefficients Marginal Effect Significance

Economic:

Remit (Dummy) 0.902 -0.103 -0.023 0.024

Demography:

Age 1.161 0.149 0.033 0.000

Household Size 0.955 -0.046 -0.010 0.000

Age2 0.998 -0.002 -0.0004 0.000

Education:

*No Education

Below Matriculation 3.284 1.189 0.265 0.000

Matriculation 3.095 1.130 0.251 0.000

Above Matriculation 4.560 1.517 0.338 0.000

Dependency Ratio:

*Low

Medium 0.874 -0.135 -0.030 0.000

High 0.705 -0.349 -0.078 0.000

Relation to HH:

*Head

Spouse/Mother 0.066 -2.724 -0.607 0.000

Other 0.059 -2.836 -0.633 0.000

Marital Status:

*Others

Married Male 2.393 0.872 0.195 0.000

Gender:

*Female

Male 2.465 0.902 0.201 0.000

Area:

*Rural

Urban 0.409 -0.893 -0.199 0.000

Summary of Statistics:

N Hosmer-Lemeshow: 0.000

(23758) Source: PPHS-2010

52 | P a g e

Table 9: Results of Logistic regression model using yearly remittance amount

Logistic Regression Model using Yearly Remittances (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Odd Ratios Coefficients Marginal Effect Significance

Economic:

Remittances 0.999 -9.96e-07 -2.22e-07 0.000

Demography:

Age 1.161 0.150 0.033 0.000

Household Size 0.958 -0.043 -0.0096 0.000

Age2 0.998 -0.002 -0.0005 0.000

Education:

*No Education

Below Matriculation 3.282 1.189 0.265 0.000

Matriculation 3.136 1.143 0.255 0.000

Above Matriculation 4.665 1.540 0.344 0.000

Dependency Ratio:

*Low

Medium 0.860 -0.151 -0.034 0.000

High 0.692 -0.369 -0.082 0.000

Relation to HH:

*Head

Spouse/Mother 0.065 -2.733 -0.610 0.000

Other 0.058 -2.841 -0.634 0.000

Marital Status:

*Others

Married Male 2.378 0.866 0.193 0.000

Gender:

*Female

Male 2.475 0.906 0.202 0.000

Area:

*Rural

Urban 0.407 -0.899 -0.200 0.000

Summary of Statistics:

N Hosmer-Lemeshow: 0.000

(23758) Source: PPHS-2010

Education plays a key role in the labor supply decision, the higher the level of education, the

higher the possibility for participation in the labor market. In both models no education is

used as base category. All categories of education (below matriculation, matriculation, above

53 | P a g e

matriculation) are positively related to labor supply, and are significant at 1%. In both

models, results show that as compared to no education all other levels of education induce the

labor supply of household member. As the education level of a household member increases,

he/she is more likely to participate in the labor market (table 8 and 9). Above the

matriculation level of education sharply increases the participation in the labor market; its

marginal effect coefficient is 0.34 in both model and it is the highest [Kozel and Alderman

(1990); Mountford (1997); Stark et al., Stark and Wang (1997); Arif (2004); Mansuri (2006);

Ahmed and Azam (2010); Emilsson (2011)].

The dependency ratio is the ratio of dependent population (i-e: 0 to 14 years and 65 years and

above) to working-age population (15 years to 64 years). It has three categories, 0 to 0.5 is

labeled as low, 0.51 to 1 is labeled as medium while above 1 is labeled as high dependency

ratio and low dependency ratio is used as base category. Both the medium and high

dependency ratios are found to be inversely related to the labor supply at the 1 % level. In

both models, medium dependency ratio has the marginal effect coefficient -0.03, while in

case of high dependency ratio it is -0.08.

The “relation to household” is entered into the models as a categorical variable which has

three categories; head, which is used as a base category, spouse, and other. Both categories

are found to be significant and adversely affect the labor supply. The spouse category has the

marginal effect coefficient of -0.61, while the „other‟ category has the marginal effect

coefficient -0.63, showing that household members other than the head are less likely to

economically active in the labor market.

Marital status variable is decomposed into married if male (equal to 1) and otherwise 0. As

observed in the literature the married male category is positively related to labor force

participation in both models. Married male has the responsibility to run his household. In

54 | P a g e

both models it has the marginal effect coefficient 0.19, meaning that the male married is

likely to participate 19% more than other household members.

The last two variables used in the study are related to gender and location or area of the

household member. Gender is a dummy variable, and the female category is used as a

reference. Male is positively related to labor force participation and significant at the 1 %

level in both models, its marginal effect coefficient is (0.20) in both models indicated that

males are likely to participate 20% more in the labor market than the female. In the region

dummy, rural area is used as the base category. Results show that the population in urban

area is less likely to participate in the labor market than rural populations in both models. Its

marginal effect coefficient in both models is -0.20, meaning that the urban household

member is 20% less likely to participate in the labor force than its rural counterparts.

55 | P a g e

5.3 Labor Supply or Participation by Gender

According to above discussed models, labor force participation significantly varies between

male and female. It is worth modeling separately for the male and female samples. Two

models, one for the male sub-sample and another for female sub-sample, are estimated and

logistic regression with Hosmer-Lemeshow is used in these models. In the male labor supply

model, the convergence is achieved at the 5th

iteration with the log likelihood value -5639.59.

In the female labor supply model, the convergence is achieved at the 4th

iteration with log

likelihood value -6366.24. The prime purpose of doing this separate estimation is to check

and examine the labor supply behavior of male and female in the context of inflows of

remittances.

Remittances are significantly and negatively related to labor force participation in both

models; the only difference is in the level of significance, for males it is significant at the 1 %

level while for female at 5%. Remittances have more negative effect on the labor force

participation of males than the females as its marginal effect coefficient is -0.0.06, while for

female its marginal effect coefficient is -0.0002 (see table 10 and 11), and this result is in line

of previous literature [Rodriguez and Tiongson (2001); Amuedo and Pozo (2006);

Dermendzhieva (2009); Jadotte (2009); Emilsson (2011)].

There is no difference in the labor supply behavior of male and female while controlling for

their age and household size. In the male sample all levels of education are found to be

positively related to labor force participation, while in case of female sample only the

education level of matriculation or above has a positive impact on the supply of labor (tables

10 and 11). As the education of female increases, they participate more in the labor market

due to awareness and also to support her family in a better way.

56 | P a g e

Table 10: Results of Logistic regression model using remittances dummy

Logistic Regression Model for Male Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Odd Ratios Coefficients Marginal Effect Significance

Economic:

Remit (Dummy) 0.789 -0.237 -0.057 0.001

Demography:

Age 1.235 0.211 0.051 0.000

Household Size 0.954 -0.047 -0.011 0.000

Age2 0.997 -0.0029 -0.0007 0.000

Education:

*No Education

Below Matriculation 8.289 2.115 0.511 0.000

Matriculation 10.245 2.327 0.562 0.000

Above Matriculation 8.767 2.170 0.524 0.000

Dependency Ratio:

*Low

Medium 0.941 -0.061 -0.015 0.283

High 0.775 -0.254 -0.061 0.000

Relation to HH:

*Head

Spouse/Mother 0.179 -1.720 -0.415 0.000

Others 0.063 -2.759 -0.666 0.000

Married 1.941 0.663 0.160 0.000

Area:

*Rural

Urban 0.435 -0.834 -0.201 0.000

Summary of Statistics:

N Hosmer-Lemeshow: 0.000

(12418) Source: PPHS-2010

57 | P a g e

Table 11: Results of Logistic regression model using remittances dummy

Logistic Regression Model for Female Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Odd Ratios Coefficients Marginal Effect Significance Economic:

Remit (Dummy) .981 -2.15e-04 -0.0002 0.032

Demography:

Age 1.136 0.128 0.0164 0.000

Household Size 0.959 -0.041 -0.005 0.000

Age2 0.998 -0.002 -0.0002 0.000

Education:

*Matriculation

No Education 0.509 -0.675 -0.086 0.000

Below Matriculation 0.757 -0.279 -0.036 0.014

Above Matriculation 1.428 0.356 0.046 0.015

Dependency Ratio:

*Medium

Low 0.808 -0.213 -0.027 0.000

High 0.848 -0.164 -0.021 0.006

Relation to HH:

*Head

Spouse/Mother 0.156 -1.855 -0.238 0.000

Others 0.136 -1.993 -0.256 0.000

Married 0.727 -0.319 -0.041 0.000

Area:

*Rural

Urban 0.3999 -0.917 -0.118 0.000

Summary of Statistics:

N Hosmer-Lemeshow: 0.000

(11340) Source: PPHS-2010

In case of male sample low dependency ratio is used as base category, medium dependency

ratio is found to be insignificant while high dependency is inversely related to labor

participation with coefficient marginal effect coefficient -0.061. In female sample low and

high dependency ratios are found to be significant and negatively impact the labor force

participation. The logic or intuition may be that these categories are consist of a more

dependent population (i-e: children and old age) which are staying at home, and females look

58 | P a g e

after them, so females are less likely to participate in the labor market and indulge in

household work.

The effect of other variables such as relation to head of household, marital status, and

regional dummy on the labor supply of male or female sample is not different in their effect

on overall labor supply models, as discussed earlier.

5.4 Youth Labor Supply Model

A separate model for youth is estimated. Basically, it is an age specific labor force

participation model which only includes youth, aged 15 to 29 years. Logistic regression with

Hosmer-Lemeshow goodness of fit test is used. Convergence is achieved at 5th

iteration with

the log likelihood value of -5094.95. The same control variables are used which are used in

previous analysis or models.

Remittances are found negatively associated with the youth labor force participation, with a

marginal effect coefficient -0.029. Remittances will dampen the labor force participation by

2.9%, although it is significant at the 10 % level. Age is positively related to youth labor

force participation and significant at the 1 % level, while age square is found to be

insignificant. Household size negatively associated with the youth participation in the labor

market at 1% level with marginal effect coefficient -0.010.

59 | P a g e

Table 12: Results of Logistic regression model using remittances dummy

Logistic Regression Model for Youth Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Odd Ratios Coefficients Marginal Effect Significance

Economic:

Remit (Dummy) 0.892 -0.115 -0.029 0.097

Demography:

Age 1.127 0.119 0.0298 0.000

Household Size 0.960 -0.041 -0.010 0.000

Age2 0.999 -0.0007 -0.0002 0.265

Education:

*No Education

Below Matriculation 3.490 1.25 0.312 0.000

Matriculation 2.814 1.035 0.258 0.000

Above Matriculation 3.445 1.237 0.309 0.000

Relation to HH:

*Head

Spouse/Mother 0.157 -1.851 -0.462 0.000

Other 0.216 -1.532 -0.382 0.000

Marital Status:

*Others

Married Male 3.318 1.199 0.299 0.000

Gender:

*Male

Female 0.192 -1.648 -0.411 0.000

Area:

*Rural

Urban 0.539 -0.619 -0.154 0.000

Summary of Statistics:

N Hosmer-Lemeshow: 0.000

(9914) Source: PPHS-2010

By using no education as the base category, the analysis found that all levels of education (i-

e: below matriculation, matriculation and above matriculation) enhance the youth

participation in the labor market at 1% level of significance. As the education level increase

household members are more likely to participate in the labor market, table 12 shows that

where all categories of education are positively related to labor force participation and this

result is similar to other studies‟ findings [Amuedo and Pozo (2006);Emilsson (2011)].

60 | P a g e

The effect of other socio-demographic variables, including headship of the household, marital

status, gender and region on the youth labor supply are similar to the overall labor supply

models.

5.5 Labor Supply behavior of Remittances receiving Households

In this section, labor supply of only remittances-receiving households has been analyzed, as

has been in several studies carries out in different parts of the world [Amuedo, and Pozo

(2006a, b); Jadotte (2009); Emilsson (2011);]. This analysis will enable us to better

understand and examine the impact of remittances on the labor supply. As discussed in the

section, out of the total sample of more than 4000 household members, only 14.5% have

received remittances during the year preceding the survey. The analysis carried out in this

section has included these household members. The descriptive statistics for all relevant

explanatory variables is given in appendix (table 20).

5.6 Model for Overall Labor Supply (Only Remittances Receiving Households)

As our dependent variable in binary in nature, so in this instance, as described by Wooldridge

it is a corner solution model where one just participate or simply avoid it, so the Tobit model

will be used in this analysis which is enormously used in the literature.20

The Tobit model

follows the normal distribution as used by probit model by giving us unbiased, consistent and

efficient results. The Tobit model uses the maximum likelihood (ML) method to estimate the

relationship between dependent and independent variables. As the mostly studies related to

labor supply or participation uses this model as it is developed for this purpose see

[Wooldridge, 2002, p 517-520]. As proposed by many this type of corner solution or latent

20

See Estimations of relationships for limited dependent variables by James Tobin, Econometrica, Vol.26, No.1, (1958, pp. 24-36).

61 | P a g e

variable model, especially in the context of the labor market bestly explained by Tobit

„censored regression‟ model [see Tobin (1958); McDonald and Moffit (1980)21

; Fraser and

Wind (1986)22

]. To examine the impact of remittances on the labor force participation using

the Tobit regression model as proposed by Wooldridge [2002, p. 520; 2009, Ch. 17.2] and

Greene (2003) as it is extensively used in recent and previous literature [as Kozel and

Alderman (1990); Amuedo, and Pozo (2006); Jadotte (2009); Emilsson (2011)] so it will be

followed in our study.

Results of Tobit regression are presented in table 13 which also present their probabilities and

marginal efficient as its coefficient is interpreted carefully. Censoring is done from left at less

or equal to zero. Monthly per capita remittances is used in our analysis and found to be

adversely affecting the labor force participation of household members, showing that monthly

per capita remittances will decrease the participation of household member by 1.8% and

found to be significant at a level of 1%.

Table 13: Results of Tobit censored regression model

Tobit Censored Regression Model for Overall Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Coefficients Marginal Effect Probability

P(y>0|x) Significance

Economic:

Monthly Per Capita Remit -0.0377187 -0.0177602 -0.0203336 0.000

Demography:

Age 0.0530095 0.02496 0.0285767 0.000

Household Size -0.0129818 -0.0061126 -0.0069983 0.000

Age2 -0.0006708 -0.0003158 -0.0003616 0.000

Education:

*No Education

Below Matriculation 0.2837276 0.1482379 0.1521823 0.000

Matriculation 0.3119781 0.1694247 0.1664101 0.000

Above Matriculation 0.4862267 0.2844011 0.2522759 0.000

Dependency Ratio:

21

See The uses of Tobit analysis by John F. McDonald and Robert A. Moffit, The Review of Economics and Statistics, Vol.62, No.2, (1980, pp. 318-321). 22

See Why and when to use Tobit Analysis by Cynthia Fraser and Yoram Wind, Working paper 86-2.

62 | P a g e

*Low

Medium -0.168924 -0.0769199 -0.0905383 0.000

High -0.2051658 -0.0899932 -0.1092319 0.000

Relation to HH:

*Head

Spouse/Mother -0.6572085 -0.2292545 -0.3188852 0.000

Other -0.6891058 -0.3867507 -0.3551481 0.000

Marital Status:

*Others

Married Male 0.1928474 0.0970775 0.1039402 0.000

Gender:

*Female

Male 0.2820083 0.1315122 0.1509721 0.000

Area:

*Rural

Urban -0.2465139 -0.1054512 -0.1304515 0.000

Summary of Statistics:

N F( 14, 4347) = 332.84

(4361) Prob > F = 0.0000 Source: PPHS-2010

Age is found to be positively related with the labor force participation and significant at 1%.

While age square is found to be significant and negatively related labor participation.

Household size found to be significant and negatively impact the participation of household

members in the labor market. The logic may be that as household size mostly consists of

inactive (i-e: children and old age) people, so they will not participate in the labor market or it

might be the reason that it has more female in the house as they will not participate much in

labor markets in case of Pakistan [Alderman and Kozel (1990)].

In the model, no education is taken as a reference point and found that all levels of education

(below matriculation, matriculation and above matriculation) positively affect the

participation of household members. So as the education level increases, it will increase the

labor force participation.

Both medium and high dependency ratio categories negatively affect the labor force

participation with marginal effect coefficient -0.08 and -0.09 respectively. As in both the

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categories there are less active population, mostly children and old age people who are not

likely to participate in the labor market. The head of households are more likely to participate

in the labor market than other household members. The impact of marital status and region

dummy are similar models. Married males are more likely to be active in the labor market.

Similarly, labor force participation is higher in rural areas than in urban areas.

5.7 Gender level Analysis

According to our estimated results labor force participation significantly vary across male and

female, male participate more in the labor market than female (see table 14). Tobit model

censored regression is used and censoring is done at left meaning censoring at corner

solution. In the both samples monthly per capita remittances are found to be negatively

related labor force participation of both (male and female) and found to be significant at the 1

% level. Male labor force participation has more negative affect of remittances than the

female, as the marginal effect coefficient for male is -0.012 while its coefficient for female is

-0.005, and in the line with existing literature [Kozel and Alderman (1990); Funkhouser

(1992); Rodriguez and Tiongson (2001); Acosta (2006); Kim (2007); Edwards and Oreggia

(2008); Lokshin and Glinskaya (2008); Dermendzhieva (2009); Jadotte (2009); Ahmed and

Azam (2010); Emilsson (2011)].

Table 14: Results of Tobit censored regression model

Tobit Censored Regression Model for Male Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Coefficients Marginal Effect Probability

P(y>0|x)

Significance

Economic:

Monthly Per Capita Remit -0.0158702 -0.0121522 -0.0098115 0.000

Demography:

Age 0.0571331 0.0437482 0.0353216 0.000

Household Size -0.0023933 -0.0018326 -0.0014796 0.367

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Age2 -0.0007283 -0.0005577 -0.0004503 0.000

Education:

*No Education

Below Matriculation 0.3290068 0.26817 0.1737696 0.000

Matriculation 0.3320586 0.277257 0.1631202 0.000

Above Matriculation 0.4189393 0.3577141 0.1879597 0.000

Dependency Ratio:

*Low

Medium -0.1230325 -0.0925625 -0.0781962 0.000

High -0.1952585 -0.1416423 -0.130362 0.000

Relation to HH:

*Head

Spouse/Mother -0.1573654 -0.1125064 -0.1072286 0.192

Other -0.4041988 -0.3321846 -0.2067195 0.000

Married 0.1195796 0.0927805 0.0719378 0.001

Area:

*Rural

Urban -0.1197185 -0.0886939 -0.0779359 0.000

Summary of Statistics:

N F( 13, 2303) = 612.94

(2316) Prob > F = 0.0000 Source: PPHS-2010

In the male sample all levels of education are positively related to labor force participation

and boost the labor participation of male significantly (see table 15). While in female sample,

only the education level of matriculation or above person has an impact on the supply of

labor. The effect of other socio-demographic variables are not different from their effect on

overall labor supply models, as discussed earlier.

Table 15: Results of Tobit censored regression model

Tobit Censored Regression Model for Female Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Coefficients Marginal Effect Probability

P(y>0|x)

Significance

Economic:

Monthly Per Capita Remit -0.0271896 -0.0051902 -0.0059378 0.000

Demography:

Age 0.1114283 0.0212702 0.0243344 0.000

Household Size -0.0342837 -0.0065443 -0.0074871 0.00

Age2 -0.001189 -0.000227 -0.0002597 0.000

Education:

*Above Matriculation

65 | P a g e

No Education -0.7248813 -0.1809825 -0.1821585 0.000

Below Matriculation -0.783679 -0.1051433 -0.1337804 0.000

Matriculation -1.078458 -0.1159775 -0.1553817 0.000

Dependency Ratio:

*High

Low -0.2460091 -0.0464533 -0.0532813 0.024

Medium -0.305776 -0.0549928 -0.0641913 0.006

Relation to HH:

*Head

Spouse/Mother -1.206642 -0.174446 -0.2121564 0.000

Other -1.05451 -0.2661203 -0.2624584 0.000

Married -0.1786636 -0.0334585 -0.0385277 0.094

Area:

*Rural

Urban -0.5332408 0.0831117 -0.1015673 0.000

Summary Statistics:

N F( 13, 2032) = 52.46

(2045) Prob > F = 0.0000 Source: PPHS-2010

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5.8 Regional level Analysis

According to our estimated results labor force participation significantly varies across rural

and urban areas. Tobit censored regression is used for both rural and urban samples to

investigate the regional differences to participate in the labor market, and censoring is done at

the minimum or from left, less or equal to zero. In the both samples monthly per capita

remittances are found to be inversely related to labor force participation decision.

Remittances have more effect on the participation of rural household members than the

urban, the marginal effect coefficient for rural sample -0.015 while for urban sample it is -

0.003 [Funkhouser (1992); Rodriguez and Tiongson (2001); Amuedo, and Pozo (2006);

Gorlich, Mahmud and Trebesch (2010); Emilsson (2011)].

Education is found to positively relate to the labor force participation in both samples. No

education is used as base category and found that all levels of education are positively

affecting the labor force participation in both rural as well as in the urban sample (table 16 &

17). The effect of other socio-demographic explanatory variables such as age, household size,

head of household, marital status, and gender dummy on the labor supply of rural or urban

sample is not different from the overall labor supply models, as discussed earlier.

67 | P a g e

Table 16: Results of Tobit censored regression model

Tobit Censored Regression Model for Rural Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Coefficients Marginal Effect Probability

P(y>0|x)

Significance

Economic:

Monthly Per Capita Remit -0.030367 -0.0146411 -0.016405 0.000

Demography:

Age 0.0512236 0.0246969 0.0276722 0.000

Household Size -0.0096518 -0.0046535 -0.0052141 0.004

Age2 -0.0006505 -0.0003137 -0.0003514 0.000

Education:

*No Education

Below Matriculation 0.3094752 0.1668272 0.1653373 0.000

Matriculation 0.3009485 0.1663124 0.1602128 0.000

Above Matriculation 0.4125716 0.2400512 0.2155812 0.000

Dependency Ratio:

*Low

Medium -0.1418642 -0.0665355 -0.076366 0.000

High -0.1870345 -0.0847287 -0.100171 0.000

Relation to HH:

*Head

Spouse/Mother -0.6138931 -0.224522 -0.304206 0.000

Other -0.7162712 -0.4137116 -0.3655867 0.000

Marital Status: *Others

Married Male

0.2300004 0.119727 0.12371 0.000

Gender:

*Female

Male 0.2392488 0.1145777 0.1286326 0.000

Summary of Statistics:

N F( 13, 3601) = 297.51

(3614) Prob > F = 0.0000 Source: PPHS-2010

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Table 17: Results of Tobit censored regression model

Tobit Censored Regression Model for Urban Sample (Dependent Variable – Labor Supply)

Labor Supply=1 Explanatory Variables Coefficients Marginal Effect Probability

P(y>0|x)

Significance

Economic:

Monthly Per Capita Remit -0.0075098 -0.0031305 -0.0042502 0.000

Demography:

Age 0.0653173 0.0272276 0.0369663 0.000

Household Size -0.0422326 -0.0176047 -0.0239015 0.000

Age2 -0.0008475 -0.0003533 -0.0004797 0.000

Education:

*No Education

Below Matriculation 0.2247488 0.1028232 0.1287226 0.010

Matriculation 0.3486599 0.1735219 0.1995895 0.001

Above Matriculation 0.6594421 0.3696322 0.3632445 0.000

Dependency Ratio:

*Low

Medium -0.2469173 -0.0972618 -0.1370425 0.001

High -0.281745 -0.103817 -0.1530018 0.002

Relation to HH:

*Head

Spouse/Mother -0.4360622 -0.1454554 -0.226233 0.008

Other -0.626109 -0.3103467 -0.3501536 0.000

Marital Status: *Married Male

Others -0.7506476 -0.2212474 -0.3578651 0.000

Gender:

*Female

Male 0.3126933 0.1274504 0.1745405 0.000

Summary of Statistics:

N F( 13, 634) = 81.92

(747) Prob > F = 0.0000 Source: PPHS-2010

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Chapter 6

Impact of Migration on Working Hours

6.1 Working hours of Individuals using LFS (2010-2011)

One of the prime objectives of the study is examining the effect of migration on the working

hours of the employed household members. It might be possible that these households do not

quit from the labor market, but alter their working hours due to migration of a member in

their household. Migration from one place to another is taking place mainly due to better

economic incentives. In this chapter, weekly working hours of the employed sampled are

taken as the dependent variable, to examine their relationship with migration and other

characteristics. In this regard the question addressed is whether migration affects the working

hours positively or negatively. The LFS (2010-11) survey was used as the PPHS-2010 survey

did not have information regarding the weekly working hours. But the LFS does not have

information either on remittances or on out-migration. It only has the information related to

in-migration, which has been used to examine the relationship between the working hours

and migration.

6.2 Descriptive Statistics of Explanatory Variables from LFS (2010-11)

Weekly hours worked by the employed sample are used as a dependent variable and all

relevant variables which may influence the working hours are incorporated in the analysis as

explanatory variables. Mean age of the individual is found to be 33.92 years, while the

average household size is 7.67 with standard deviation 3.29. The mean of gender if the male

is 0.81 while the mean gender if the female is 0.19. The marital status variable is used with

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two categories married if male having the mean 0.54 with standard deviation 0.50. The

regional dummy variable is used with the mean 0.35 for urban area while 0.65 for rural area

(table 18).

Table 18: Mean, Standard Deviation and Range of the explanatory variables

Explanatory Variables Mean S.D Min Max

Demography:

Age 33.92 13.91 0 99

Age2

1344.38 1059.61 0 9801

Household Size 7.67

3.29

1

43

Gender (Male=1) 0.81 0.39 0 1

Gender (Female=1) 0.19 0.39 0 1

Relationship to Household

(Head=1)

Relationship to Household

(Spouse/Mother=1)

0.43

0.01

0.50

0.12

0

0

1

1

Relationship to Household

(Other=1)

0.55 0.50 0 1

Married (if Male=1) 0.54 0.50 0 1

Others 0.13 0.33 0 1

Area (if Urban=1) 0.35 0.48 0 1

Area (if Rural=1) 0.65 0.48 0 1

Dependency Ratio (if Low=1) 0.41 0.49 0 1

Dependency Ratio

(if Medium=1)

0.29 0.45 0 1

Dependency Ratio (if High=1) 0.30 0.46 0 1

Socio:

No Education

Below Matriculation

Matriculation

0.43

0.31

0.13

0.50

0.46

0.33

0

0

0

1

1

1

Above Matriculation 0.13 0.33 0 1

Source: LFS (2010-2011) micro-data

Note: Age, Age2, Household size are taken as continuous variables and dummy variables are included for all

other explanatory variable categories.

71 | P a g e

6.3 Model for Overall Weekly Working Hours

As our dependent variable, the weekly working hours, is a continuous variable, estimation is

done by simple, ordinary least squares (OLS) and then by Tobit censored regression as OLS

may yield biased, inconsistent and spurious results due to unobserved heterogeneity and

omitted variable bias as it is based on the linearity assumption. In general OLS on the whole

sample or on just the uncensored sample will give inconsistent coefficients [Green, 2003].

These types of models are not censored models it is best to refer these as corner solution

models, it is quite notable that here the issue is not data observability: but primarily interested

in E (y) and P (y=0) meaning its distribution so it is problematic to use OLS in this setting

(Wooldridge). To understand it further see the examples provided related to working hours of

married female presented and explained by Jeffery Wooldridge [Ch. 16, (2002)23

; Ch. 17.2,

(2009)24

]. So, first OLS will be used and then by Tobit model to estimate and check this issue

or phenomenon.

In-migration is positively related to working hours of the employed sample, in case of OLS

its coefficient is 1.03, while in case of Tobit model it is 0.98 and found significant at the 1 %

level. The intuitive and logic of this result could be that migration takes place for better

opportunities so migrant prefer more work and also they have to compete with local people in

the labor market. They have to strive harder and work more extensively, by supplying more

working hours in the market for their survival. Second thing is in LFS most of the in-

migration is taking place for Noneconomical purpose, so it might be the reason of this result.

Age is positively related to working hours; in the early age workers work for longer hours in

the labor market while in a later or old age they will curtail their working hours. This

23

See Wooldridge (2002), Econometric Analysis of Cross Section and Panel Data, Chapter 16. 24

See Wooldridge (2009), Introductory Econometrics, 5th

Edition, Chapter 17.2.

72 | P a g e

negative or diminishing effect is captured by age square which is negatively related to

working hours (table 20). Age square variable has the expected sign. Another variable used in

the study household size, which is a continuous variable, it was found to be significantly and

inversely related to working hours. As a matter of the fact its coefficient in OLS is -0.142,

while its coefficient in Tobit model is -0.139.

Table 19: Results of OLS and Tobit censored regression model on Weekly Working

Hours of Migrant Household Members

Variables OLS Tobit E(y/x, y>0) Significance

Demography:

Migration Age Age2

Household Size Education:

*Above Matriculation

No Education

Below Matriculation Matriculation

Dependency: *Low

Medium High

Relation to HH:

*Head

Spouse/Mother Other

Marital Status: *Others

Married Male

Gender:

*Female Male

Area:

*Urban Rural

1.031

0.695

-0.009

-0.142

4.370

4.782

4.222

-0.427

-0.431

-2.441

0.131

1.488

13.840

3.403

0.996

0.695

-0.009

-0.141

4.430

4.830

4.249

-0.424

-0.428

-2.413

0.155

1.471

13.872

3.420

0.979

0.683

-0.009

-0.139

4.351

4.736

4.156

-0.417

-0.421

-2.379

0.153

1.447

13.654

3.358

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.001

0.001

0.000

0.372

0.000

0.000

0.000

σ 12.84 12.86 N=70730

Adj.R2/log likelihood 0.22 -279412.11

F(14,70715)=1411.87 LR chi2(14)=17353.65

Prob>F=0.000 Prob>chi2=0.000

73 | P a g e

Source: LFS 2010-11

Education is found to be positively related to working hours of employed sample. It is evident

from the table all levels of education, enhance or boost the working hours of household

members, and in both OLS and Tobit model, they have almost the same magnitudes.

Both the medium and high dependency ratios are found to have a negative impact on the

working hours. It is because these categories consist of children and old age (inactive people),

who are less likely to participate in the labor market. These results are according to the theory

and literature.

Marital status plays a very crucial role to participate in the labor market or whether, to

provide more working hours in the labor market. Married male is found to be positively

related to working hours employed sample and in OLS its marginal effect coefficient is 1.49,

while in Tobit model it is 1.45. Married male is more extensively participating and supply

more working hours in the market than their counterparts and other household members.

Males are likely to provide more working hours in the labor market, than the females. The

employed rural sample works for longer hours than their urban counterparts (table 19).

74 | P a g e

Chapter 7

Conclusion and Policy Implication

7.1 Summary and Conclusions

7.1.1 Summary

This study has examined the impact of remittances on the labor supply of household

members. It has used the PPHS-2010 micro-data which provide all information necessary to

analyze the relationship between remittances and labor force participation. The study has also

used the LFS 2010-11 to examine the relation between migration and working hours. The

dependent variable labor supply or labor force participation is binary in nature, so Logit and

Tobit models are used to examine the impact of remittances on the labor supply of household

members. Working hours of employed household member are used as a dependent variable to

examine the impact of migration (in-migration) on working hours by using OLS and Tobit

models.

The results show that remittances significantly reduce overall labor force participation.

Education is positively related to labor force participation of household members. Age also

turns out to be positively related to labor force participation while age square has shown a

negative association with labor supply. Household size has a significant and negative effect

on the participation of adult population. The dependency ratio is inversely related to labor

force participation. Married males are more likely than the married females as well as

unmarried persons to be active in the labor market. The rural adult population is more active

in the labor market than its urban counterparts.

A special model for youth (15-29 years) is also estimated; remittances have a negative impact

on the labor force participation of youth. Separate estimations are also conducted for male

75 | P a g e

and female samples and remittances turns out to be negatively related to labor force

participation. Separate estimations are conducted for rural and urban areas. Remittances turn

out to be negatively associated with labor force participation, however, its effect is stronger in

rural areas.

A model is estimated related to working hours, and by applying OLS and Tobit model;

migration has a positive effect on the working hours of the employed labor force. Age turns

out to be positively related to the working while the effect household size on the working

hours is negative. All levels of education turn out to be positively related to working hours

using above matriculation as a base category in both OLS and Tobit models. Dependency

ratio reduces the number of hours supplied in the labor market. Married males are likely to

work to work longer hours.

7.1.2 Conclusion

The study highlights some important factors relating to labor supply in Pakistan.

Remittances are negatively related to labor force participation as these remittance

inflows are kind of a non-labor income, which raises the reservation wages of these

remittances receiving households allows them enjoy more leisure and they participate

less in the labor market.

Education has a positive effect and enhance the labor supply of household members;

education is a proxy of human capital and as one has more human capital

accumulation he/she may participate more in the labor market than those who has no

education.

Migration (in-migration) is positively associated to the working hours, because

internal migration is primarily taking place for better economic opportunities and

76 | P a g e

incentives, but in LFS most of the internal migration is taking place for

Noneconomical purposes or reasons, so it might be the cause of this positive relation.

7.2 Policy Implications

Having concluded our main findings, now, on the wake of pragmatically obtained results, the

study recommends some suggestions.

Remittances reduce the labor force participation of left behind household members.

There is a need to promote more saving in remittances receiving households so that

the inactive population can be involved in a business.

Remittances are permanent inflows to households which may create disincentives for

the adult population in receiving households and household members wait month after

month and it creates moral hazard.

It seems like that all remittances are used in consumption and daily needs of the

households, so might cause inactivity in remittance receiving household and

ultimately, less remittances are available for investment. If the household members

also participate in the labor market than they have more income and can fulfill their

daily need for their earned labor income and these remittances are invested (in

business or education).

There is need of collective policy which includes awareness in inactive household

members to play their part and participate in the labor market; and rural financing

activities should be thrived which demonstrate this adverse effect of remittances on

labor supply.

In remittances receiving household the inactive household members due to

dependency on remittances may indulge in unethical activities like crimes or drug or

in rogue elements, as they are not productive members of society.

77 | P a g e

7.3 Future research

The study just examined the impact of remittances on labor supply, ignoring the economic

and social implication caused by this inactivity, which could be explored in the future for

both household and economy level.

The study does not have information regarding remittances and working hours of the

household members, so in future if a survey which provides information on these is available,

then one could analyze this issue more deeply.

The research could be extended to include uncertainty of the future inflows of remittances,

which may affect the labor supply of the household members.

78 | P a g e

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Appendix

Table 20: Mean, Standard Deviation and Range of the explanatory variables

Explanatory Variables Mean S.D Min Max

Demography:

Age 25.56 19.25 0 105

Age2

1044.84 1423.80 0 11025

Household Size 10.53 5.40 2 38

Gender (Male=1) 0.53 0.50 0 1

Gender (Female=1) 0.47 0.50 0 1

Relationship to Household (Head=1)

Relationship to Household (Spouse/Mother=1)

0.12

0.13

0.32

0.34

0

0

1

1

Relationship to Household (Other=1) 0.75 0.43 0 1

Married male 0.19 0.39 0 1

Others 0.19 0.39 0 1

Area (if Urban=1) 0.17 0.38 0 1

Area (if Rural=1) 0.83 0.38 0 1

Dependency Ratio (if Low=1) 0.47 0.50 0 1

Dependency Ratio

(if Medium=1)

0.33 0.47 0 1

Dependency Ratio (if High=1) 0.20 0.40 0 1

Economic:

Yearly Remittances

Monthly Remittances

Monthly Per capita Remit

Socio:

182800.8

15233.4

3.49

192380.8

16031.74

3.68

0

0

0

1440000

120000

27.52

No Education

Below Matriculation

Matriculation

0.71

0.16

0.07

0.45

0.37

0.25

0

0

0

1

1

1

Above Matriculation 0.06 0.23 0 1

Source: PPHS-2010 micro-data