DO PAKISTANI FEMALE HOME-BASED WORKERS EARN LOWER …

DO PAKISTANI FEMALE HOME-BASED WORKERS EARN LOWER WAGES THAN WOMEN WORKING OUTSIDE THE HOME?

A Thesis submitted to the Faculty of the

Graduate School of Arts and Sciences of Georgetown University

in partial fulfillment of the requirements for the degree of

Master of Public Policy in Development Management and Policy

By

Asma Saeed, M.B.A.

Washington, DC April 15, 2014

ii

Copyright 2014 by Asma Saeed All Rights Reserved

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DO PAKISTANI FEMALE HOME-BASED WORKERS EARN LOWER WAGES THAN WOMEN WORKING OUTSIDE THE HOME?

Asma Saeed, MBA

Thesis Advisor: Robert W. Bednarzik, Ph. D.

ABSTRACT

This paper analyses whether the wages of female home-based workers (HBW) in Pakistan are

on average less than those female workers who work outside the home. The federal monthly

minimum wage in Pakistan for unskilled workers, as of 2013, is Rs. 10,000 (roughly $100) per

person per month. While according to HomeNet Pakistan estimates, many of these women barely

make a quarter of that in a month, little empirical analysis is available to support these claims.

This paper carries out a regression analysis comparing wages for both groups of women. The

main dependent variable of interest is monthly wage/income for women and the main

independent variable is a dummy variable for the place of work; i.e. inside the home or not.

Certain age brackets have been chosen as per the availability of data for more detailed study.

Data for the annual labor force survey have been obtained from the Pakistan Bureau of Statistics,

and the International Labor Organization (ILO) definition for home-based work has been used

which does not include agricultural workers. The regressions results show that women home-

based workers earn substantially less than those women who work outside the home. Thus, this

study provides a statistical basis to advocate for a national home based workers policy in

Pakistan in order to instill minimum standards of wages for this part of the labor force.

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The research and writing of this thesis is dedicated to everyone who helped me all through this process. I would like to thank my family for their support. I would especially like to thank my father, M. Saeed, for helping me obtain my

data from the Pakistan Bureau of Statistics, without which this thesis would not have been possible. I would like to thank Mike Barker at MSPP for guiding me through the intricate initial stages of the thesis writing process. And finally, I would also like to thank my thesis advisor,

Professor Bednarzik for his support and guidance. All the mistakes made are my own

Many thanks, ASMA SAEED

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TABLE OF CONTENTS

INTRODUCTION 1

BACKGROUND: HOME BASED WORKERS POLICY IN PAKISTAN 4

LITERATURE REVIEW 5

Working from Home 5

Informal Sector Matters 7

Informal Sector Penalty 8

Wage Comparisons in Developing Countries 9

CONCEPTUAL FRAMEWORK AND HYPOTHESIS 13

Hypothesis 13

DATA AND METHODS 15

Data Source 15

Analysis Plan 17

RESULTS 19

Descriptive Results 19

Income and Home-based Work 19

Regression Results 20

Ordinary Least Squares 20

Shortcomings of the Model 26

Propensity Score Matching 28

POLICY IMPLICATIONS 34

APPENDIX 37

REFERENCES 46

1

INTRODUCTION

Gender inequality is a common problem in most developing countries. This issue

manifests through the existence of unequal standards of work, pay and rights in the eyes of the

law. Beyond these formal levels of inequalities, however, another set of problems exist in the

informal work force, which includes but is not limited to even lower pay for women and very

low and unhealthy workplace conditions, with women often working from small rooms within

their own homes. In countries like Pakistan, are many international and national Non-

Governmental Organizations (NOGs)work for the rights of women in general and informal

workers, or home-based workers (HBWs) as they are sometimes called, in particular in order to

elevate their status equal to that of formal workers with all the rights they deserve under the

International Labor Organization (ILO) conventions. According to HomeNet Pakistan estimates,

there are approximately 19.7 million women in Pakistan who work from their homes to support

their families as of 2001. They make up around 77 to 83 percent of the entire female work force

in the country(Homenet Pakistan, 2001). According to HomeNet South Asia, some ILO figures

show that around 65 percent of the non-agricultural work force of women is comprised of home-

based workers (HomeNet South Asia, 2011). These are staggering figures, which makes it even

more problematic that a majority of these women are not included in most official surveys or

counted as part of the formal the work force. Even the survey used for this study, the Labor

Force Survey of Pakistan, does not have an explicit section dealing with home-based workers.

The federal monthly minimum wage in Pakistan for unskilled workers, as of 2013, is Rs.10,000

(roughly $100) per person per month (WageIndicator.Org, 2013). Many of these women barely

make a quarter of that in a month (UN Women, 2012). Their wages are not based on some set

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standards, rather on the pieces they produce. Many of them produce intricate handicrafts,

garments and even a variety of sports equipment. By many standards, these are skillful workers

whoshould be paid according to their expertise in their fields and the talents they utilize in the

production of these items. Even compared to the average of the region,these figures are

problematic - in neighboring India, 51 percent of the non-agricultural informal work force is

women compared to 65 percent (HomeNet South Asia, 2011).

In addition to low wages, women home-based workers have no decision making power and

no ‘employment’ benefits, including paid time off or health benefits. Since most of the

workforce is informal, there are very little data available about the working conditions of these

women. Many of these women are forced by circumstance to depend entirely on the informal

network of ‘middlemen’ they work for the sale of their goods, and many work in small rooms for

up to 18 hours a day with minimal compensation(HomeNet Pakistan, 2005). ILO’s work

convention on Home Work (No.177) adopted in Geneva in 1996, states that home-based workers

are eligible to receive decent pay as defined by ILO, protection against discrimination and a

certain level of working conditions including safety and health. However, this convention has not

been ratified by the Government of Pakistan.

Pakistan’s GDP, as of 2012, was $231 billion, as reported by the World Bank.

Unemployment rate was 7.7 percent in 2012, which is very high as compared to neighboring

India which has 3.85 percent unemployment rate with a population of over a billion people

(Trading Economics, 2012). The informal workers are usually not reported as part of the

workforce and hence have an unknown impact on this figure (Homenet Pakistan, 2001).

However, if the home-based workers were to be included as part of the employed labor force, it

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is likely to have positive impact on the employment rate of the country.

The aim of this study is to quantify the wage differential between the women home-based

workers in Pakistan as compared to those women who don’t work from home. The aim of the

results of this study is to advocate for a formal, home-based workers policy in Pakistan to be

implemented at the national level. A statistical analysis examining the relationship between

wages for working at home as opposed to working outside the home was thus undertaken. The

study shows that having lower wages is correlated with working at home for women in Pakistan.

Women who work from home, on average, earn approximately PKR 975 – 1440 less than those

women who don’t work from home. This result shows that there is a need for a formal home-

based workers policy in Pakistan that effectively makes women home-based workers part of the

work force in Pakistan. This will not only have an impact on monthly wages of women who

work from their homes but will also change the overall labor force figures and possibly increase

the percentage of women’s participation in the work force. It should be noted that throughout

this study, income and wages will be used interchangeably since for many of the women in the

sample set, there is no steady source of income and wages are earned either on an hourly basis or

on the basis of each piece of product produced by them.

4

BACKGROUND: HOME BASED WORKERS POLICY IN PAKISTAN

Although Pakistan has a very large population of home based workers, the road to

formalizing a home-based workers policy has been a tumultuous one. The problem has not

become any simpler after the passing of the 18th amendment which gives the provincial

governments in Pakistan more autonomy to create and implement their own policies (News

Desk, 2014). The provinces currently are in different stages of development of a home-based

workers policy, with Sindh and Punjab taking the lead. However, little change can actually

happen with regards to implementation unless budget provisions are made catering to women

who work from their homes. Meanwhile, these women are not considered ‘workers’ under the

law and hence have no protection in terms of either wages or working conditions. Most

provinces only have rough estimates of the number of female home based workers and even the

labor force survey only has a few questions pertaining to this very important sector of the labor

force. Smaller scale surveys for home-based workers have been carried out, but nothing at the

same scale as the labor force survey. Apart from the problems that exist due to the deficiencies at

the legislative level, these women often also fall prey to the ‘middlemen’ that buy their products

in bulk and sell it to different markets in the country. These middle men often take the majority

of the profits through the sale of this merchandise and pay a very minimal rate to the women who

actually manufacture these goods.

Limited data are only part of the problem. As long as the provinces do not make a real

commitment to formalizing home-based work through the ratification and implementation of a

home-based workers policy, no real change can happen, and this can only happen once the

provincial governments realize the advantages of doing so for the economy as a whole.

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LITERATURE REVIEW

Working from Home

Millions of women in South Asia suffer from domination from their male counterparts who

often exploit the skills of women and use them for profit. Due to cultural and religious

restrictions, women are often unable to leave the house, and this coupled with extreme cases of

poverty often force women to use their homes as their place of business. The disadvantage of

their inability to move without restrictions means those who can move freely can take advantage

of their skills and sell their products at a much higher margin, pocketing the difference.

Many studies have examined the different aspects of home-based workers; however there is a

gap in our knowledge since very few look at home-based workers particularly in a detailed,

empirical manner. Most studies assume that home-based workers are being exploited and earn

much less than they deserve, and theoretically, this argument has merit (Akhtar, 2011). However,

searching did not reveal any empirical studies on this issue in Pakistan; specifically, studies

analyzing the difference in wages between informal and formal women workers were not found.

Differences in women’s wages can be due to factors like exploitation and not due to a

difference in their skill set (Akhtar, 2011). To further explore this issue, this study utilizes labor

force data from around 36,000 households from all provinces of Pakistan collected by the

Pakistan Bureau of Statistics in 2010-11. Data collection was carried out by regional teams

employed in both urban and rural areas through a stratified random sampling method

(Government of Pakistan, 2013).

This study has two basic theoretical foundations; the Human Capital Theory, which in its

simplest form, asserts that a person’s wages or earnings are directly related to their level of skills,

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including but not limited to, education, experience and inherent ability (Becker, 1965). The

Human Capital theory actually builds on the Mincer Equation, which states that income depends

on education and experience, which is the second basis for this study (Mincer, 1958). In addition,

using existing body of knowledge, it appears that women home-based workers in Pakistan are

being exploited. For example, the ‘middle men’ actually take most of the profit from the sale of

the products produced by the women because of their inability to leave their homes to work

(Roots for Equity, 2011).

A number of studies on home-based workers in Pakistan have been done through

international organizations like the ILO and United Nations Entity for Gender Equality and the

Empowerment of Women (UN Women) and local national and international NGOs like

HomeNet Pakistan and Sungi. A study carried out by the ILO in Pakistan goes in some detail and

compares wages earned by women home-based workers to men working from home. It found

that in many cases, women earned less than 60 percent of what men earned in comparable jobs.

This figure includes women who are self-employed, unpaid family workers and those generally

engaged in low skilled, low wage economic activities (Akhtar, 2011). Lack of awareness and a

lack of monitoring are two of the major reasons a more bottom-up approach has not been

successful in bringing about a change in working conditions and wages of home-based workers.

Women are generally less aware of labor rights and hence do no voice their concerns. Moreover,

it is very challenging to monitor at a precise level the exact number of home-based workers

present in Pakistan.

Although numerous studies have been carried out, the availability of data is always an issue,

especially with regards to the more rural areas of the country and even more so in areas that are

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at high risk in terms of security. Also, the demographic figures for population also tend to change

frequently due to migration in cases of natural disasters and civil unrest. In addition, when

changes in working hours occur, they are especially difficult to monitor since most informal

workers do not follow any formal work schedule or have any system of tracking in place. The

ILO report used Labor Force Survey Data from 1999-2009 to track the trends of these home-

based workers. The numbers of home-based workers have increased from roughly 1.22 million at

the beginning of the century to 1.62 million in 2009; however they reached a peak of 2.01

million in 2006.

Although educational attainment amongst women in Pakistan is increasing, problems still

persist (Akhtar, 2011). Most home-based workers have less than secondary school education,

which is equivalent to grade 5. However, the share of HBWs receiving any type of Technical

Educational Vocational Training (TEVT) increased by 30.5 percentage points during the recent

decade. For females, this share ‘increased even faster from 8.1 in 1999-2000 to 45.5 percent in

2008-09’. The report also noted a ‘many-fold discrete jump in HBWs acquiring TEVT in the

latest 2 years, whether on-job or off-job training’(Akhtar, 2011). Although education and

training among women is growing, wages remain low. For example the ILO report noted that per

piece earnings of the home-based workers is much less than the government minimum wages per

month (Akhtar, 2011).

Informal Sector Matters

In most developing nations the informal sector not only exists but is growing. Even as

countries move towards economic and social development, the informal sector persists. Instead

of expecting development to automatically formalize the labor market, it is imperative that new

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institutions and policies are made in order to manage the informal sector which has its own needs

separate from that of the formal sector (Bangasser, 2000). Some insight into the coming years

can be inferred from two ILO documents - new style Program and Budget 2000-01 and the

Report of the Director General “Decent Work" These talk about how the 'urban informal sector is

both not at the “centre of the stage” but still never far from the institution’s concerns' (Bangasser,

2000). The informal economy makes up a significant portion of the total economic structure

since they offer a much more flexible production model. These kinds of models were not

expected to persist beyond the early stage of development by the classical economists (M. A.

Chen, Jhabvala, & Lund, 2001).

Informal work does not always end up in the official statistics of a country. A study by Chen

asserts that, according to those who have had opportunity to work closely with women in the

informal sector, the actual size of this sect of the labor force is much larger than usually reported.

If labor force figures were to include unpaid housework and paid informal work, the figures

would be much higher (M. Chen, 1999).

Informal Sector Penalty

The gender gap in wages has long been talked about in the world of social and public policy.

One of the reasons can be that women are concentrated in small, non-competing firms. However,

men on average earn more than women in comparable jobs in both developed (Macpherson &

Hirsch, 1995) and developing countries (Weichselbaumer, 2005). The gender wage gap has

usually been shown using statistical models that control for all other characteristics that influence

wages. However in the case of countries like Pakistan, the social structure is such that it assigns

different roles to the sexes, and women are responsible for running the household. This coupled

9

with high poverty levels often force women to work from the home as well. However, their

gender seems to be a limiting factor for their income.

A study carried out in Argentina by Pratap in 2006 asserts that for the same kind of job,

workers employed in the informal sector earn less than those employed in the formal sector

irrespective of their gender. These results could be due to the size effect, i.e. bigger organizations

pay more regardless of the sector, and also level of competition determines wage level, and those

industries with less competition offer lower wages. Besides discrimination against women, the

authors stipulate that unobservable characteristics are present, including but not limited to non-

wage benefits offered in the formal sector that are absent in the informal sector (Pratap, 2006).

Wage Comparisons in Developing Countries

The basic distinction between the formal and the informal sector is that employment in the

formal sector is protected by laws and rules in terms of wages and working conditions. Basic

wage models suggest that wages should be based on the skill set of the worker at an individual

level, and interaction of demand and supply of jobs at an aggregate level. However, this model

fails when it comes to the informal economy (Mazumdar, 1974). For example, in the case of

Pakistan, there are many other factors involved that may cause the market to deviate from the

efficient level of wages. The existence of middle men, the restricted movement of employees in

terms of where they can work, cultural, social and religious traditions, lack of awareness of labor

rights and absence of formal home-based workers policy protections are some the main reasons

for this.

Several other studies like Chen, 2001 and Khotkina, 2007 have shown that wages in informal

sector are lower than in the formal sector. Some argue that an individual’s ‘worth’ in terms of

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what wage they deserve is dependent on the bundle of their skills as well as their demographic

profile (J. Heckman, 1987). According to this argument, the wages of employees working in

formal sector should differ from those working in the informal sector since the informal

employees have characteristics that may make them ‘less desirable’. Foremost among them is

their inability to work outside the home and that they live in isolated areas from which it is

difficult to travel to work.

Magnac, 1991 showed that a “more important a feature of labor markets than segmentation is

the presence of comparative advantages for individuals between the various economic sectors”.

The paper uses short term data from Columbia in 1980 and divided women into formal and

informal sector. Using a probit model, the author rejected the hypothesis that the wage level was

the same across sectors (Magnac, 1991).

Theorists like Magnac, 1991 stipulated that the formal and informal sectors are in essence

competitors, and those that cannot find employment in the formal sector do so in the informal

sector. It is, however, difficult to test this assumption since the criteria of formal/informal sector

categorization is fluid and differs from country to country. In Pakistan, an organization is said to

be working in the formal sectors if it keeps written records. Home-based work is categorized

simply the place the work is carried out, so potentially, formal work could be carried out from

one’s own home. Gong, 2004 and associates examined the formal and informal movement across

these sectors, and found that there are restrictions to entry in the formal sector and movement

across the sectors is not free. This is in line with the assumption here that women home-based

workers cannot easily find comparative work in the formal sector (Gong, 2004).

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In Bolivia, it was found that, using household survey 1989 data, wages were higher in the

formal sector (Pradhan, 1995). Moreover, Pradhan (1995) showed that on average, higher

educated females fared better in the formal sector, but low skilled workers fared better in the

informal sector. The difference was thought to be a function of the different demands of the

sectors. The formal sector has a higher demand for highly educated workers while the informal

sector has a higher demand for a lower level of skill set concentrated in a specific area. Also,

social and demographic characteristics are important wage determinants as shown by a study of

males in Panama (J. J. Heckman & Hotz, 1986).A study carried out in El Salvador, Peru and

Mexico by Marcouiller in 1997 showed that there is a premium attached to working in the formal

sector (Peru and El Salvador); however sometimes, unexpectedly, there is a premium to working

in the informal sector (Mexico). The results were found to be the same for men as well as

women. The assumption is that working in the informal sector is a last resort for those who

cannot work in the formal sector for whatever reasons. The unexpected results in Mexico can be

attributed to actually a risk premium or even to the existence of social security, which however

does not explain why a similar premium is not seen in the other two countries (Marcouiller,

1997).

There is a gap in the literature with reference to Pakistan. Although several qualitative

studies on this subject exist, most of them focus on the wage differential between the sexes and

does not delve into why women workers at home may be earning differently. In addition, the

empirical studies do not go into detail to analyze the relationship between wages and home-based

work. This study aims to quantify this relationship. Moreover, other important factors may

naturally cause bias in a ‘free economy’. For example, the penalty associated with restriction in

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freedom of movement can lead to the difference in wages. The aim of this study is to examine

the nature and magnitude of this ‘penalty’.

13

CONCEPTUAL FRAMEWORK AND HYPOTHESIS

Hypothesis

This study will test the hypothesis that, for women, there is no relationship between wages

and being a home-based worker in Pakistan, keeping all other variables constant.

H0: For women, there is no relationship between wages and being a home-based worker in

Pakistan

H1: For women, there isa relationship between wages and being a home-based worker in

Pakistan.

Exhibit 1: Variables in the Model and Justification

Symbol

Variable

Name Definition

Predicted

Relationship

Rationale/previous

studies

Y income Income earned in PKR per month N/A N/A

β1 home_work Dummy equal to 1 if the respondent works from home Negative Akhtar, 2011

β2 age Continuous variable from 15-70 years Positive Becker, 1965; Data

Heckman and Hotz 1986

Β3 agesq Squared of the age variable Positive Becker, 1965


Β4 educ Ten education dummies from pre-school to PhD (baseline

= no education) Positive Becker, 1965; Data

β5

Married

Dummy equal to 1 for married females

Positive

Mazumdar 1981


Pradhan and van Soest

1992

Β6 HHsize Continuous variable for number of household members Positive Becker, 1965

Β7 hrs_worked Continuous variable hours worked in a month Positive Becker, 1965

Β8 size Four dummy variables for size of organization (ref = size

more than 20 people) Positive Pratap, 2006

Β9 rural Dummy equal to 1 if in rural area Positive (J. Heckman, 1987)

Β10 formal Dummy equal to 1 if working in a formal enterprise Positive Pratap, 2006

Β11

voc Dummy equal to 1 for vocational training

Positive

Pradhan, 1995

Mazumdar 1981

Becker, 1965

β12 Fem_head Dummy equal to 1 if female headed household Negative Doane, 2007

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The variables included in the model are those for which data were available and which are

directly related to both wages/income and being a home-based worker. Exhibit 1 shows the

expected direction of the relationship between all the independent variables as per our

observation of the data and per literature. Home-based work is expected to pay less than work

done outside the home; hence the relationship between the home-work variable and

income/wages is expected to be negative. In the same way, the other variables may have a

positive or negative relationship with the main dependent variable. Figure 1 in the appendix, for

example, shows a somewhat likely positive relationship between income and age within the

sample. As can be seen, most women of working age earn less than approximately PKR 50,000

per month, and the highest concentration is around less than PKR 20,000 per month, between the

ages of 20 and 40. Figure 2 shows the same relationship between income and age-squared,

providing us with justification for adding the quadratic term into the model. Figure 3 shows how

income differs between the different levels of education and is somewhat higher as education

advances.

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DATA AND METHODS

Data Source

The data were obtained from the Pakistan Bureau of Statistics and were gathered in 2010 for

the annual Labor Force Survey 2010-11. This is a household level survey carried out by trained

personal hired by the Pakistan Bureau of Statistics. Field offices are located in all the provinces

of the country and the surveyors go door to door to collect this information from the head of the

household. Although these data are collected at the household level, detailed information for all

the household members that are ten years of age or more is collected. The sample size for the

survey is 36,000 households which results in over 260,000 observations in the data set - a

national representative sample of the population of Pakistan1.

There are several limitations with the data with regards to our main dependent variable of

interest. Since income is a culturally sensitive issue, most respondents are hesitant to report it.

For this reason, there are many missing values with regards to this variable. However, since this

is a cultural issue and not associated with any specific level of income or type of work

environment, there should be no bias if the only the observations for which income was reported

are considered2. All missing and 0 values of income have thus been dropped from the sample. An

informal test was carried out to see if there was some correlation between the missing income

1Labor Force Survey Methodology: This annual survey is carried out using a stratified sampling method in which samples are taken from various enumeration blocks that are considered Primary Sampling Units (PSUs). Rural and Urban PSUs are of different areas to account for the difference in population density. The methodology used was developed using the 1998 Population Census, updated in 2003 and is considered by the Government to result in an accurately representative sample http://www.pbs.gov.pk/sites/default/files/Labourpercent20Force/publications/lfs2010_11/methodology.pdf 2 Phone conversations with representatives from the Pakistan Bureau of Statistics carried out on December 2nd, 2013 confirmed this. Representatives included the Director for the Labor Force Survey unit, Mr. Rai Shad, the Chief Mr. AmjadJaved and Mr. Noor Ahmed Shahid. The phone call lasted over 20 minutes and a follow-up call was made on December 5th, 2013.

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variable and our main independent variable of interest, home-work. Since the percent of missing

and income is same for both, it seems that the missing income is random and not correlated with

one specific group. As table 1 below shows, just an informal look at the numbers shows that

there is no specific group of the population for which income is missing more than

proportionally. Both those who work from home and those who do not have around 85-88

percent observations that have the income variable missing, hence this suggests that the fact that

people refused to report their incomes is not a trait that is predominant in a specific sect of the

society rather is a cultural phenomenon that effects the entire population and should not be

biasing our results.

Table 1: Impact of Missing values for Income Variable – Pakistan 2011

Income home_work

not working from

home Total

Total 2657 362 3019

Percent 88.01 % 11.99% 100.00%

Missing 59,160 10,164 69,324

Percent 85.34% 14.66% 100.00%

Total 61,817 10,526 72,343

Source: Pakistan Labor Force Survey, 2011

There are also many similar variables which may be combined to make one relevant variable.

In addition, there are detailed questions about previous jobs but not about the income from

previous jobs. Hence, only the income from primary jobs will be considered.

It should be noted that literacy and experience were two variables that were considered in

earlier versions of the model but subsequently dropped because they were highly correlated to

other variables in the model and were also not explaining a statistically significant portion of the

variation in the income variable. Instead, the quadratic term for age was added since literature

17

shows that the relationship between age and income is rarely linear. In addition, a dummy

variable for female headed households was also added to the model in order to account for the

fact that many female heads of households often have to take lower paying jobs in order to

support their families.

Analysis Plan

According to an ILO report of home-based workers carried out in 2011, women home-based

workers are usually categorized as women over the age of 15 working at home in some sort of

activity for which they are compensated in cash or kind. Usually, agricultural activities or any

activities that contribute towards agriculture are not included. Although formal retirement age of

workers in Pakistan is 62 years for both men and women3, there are many instances where older

women are working. Since the sample shows that there are very few women over the age of 70

that are actually working (see figure 1 below), 70 years will be the cutoff point for this study.

The population studies will be women, from 15 to 70 years of age and will be termed the

working-age population.

There are about 127,000 females in the sample - after restricting the sample by age, the

sample size was reduced to about 74,000, of which, approximately 22 percent considered

themselves to be part of the labor force. The analysis will thus include around 3000women

between the age of 15 and 70 years who consider themselves to be part of the labor force and/or

doing some sort of work in return for monetary or non-monetary compensation. It should be

noted here that the number of observations in each regression can differ depending on data

availability for all variables being used in the model; hence, the sample size varies between

3http://oly.com.pk/civil-servants-retirement-age-increased-to-62/

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2,881 and 3,019.

This study will use a basic OLS regression to estimate the relationship for working age

women of monthly wages and being a home-based worker in Pakistan. A matching model will

also be used to compare to the OLS results4.

Income = β0 + β1Home_Work + β2Age+ β3aAgesq + β4Educ + β5Married + β6HHsize +

β7hours_worked + β8Size + β9Rural + β10Formal + β11Voc + β12Fem_head + e

This model is based on the Mincer (1958) equation which says that income depends upon

education and experience. Becker (1965) further built upon this model with the human capital

theory, saying there are other factors also associated with income, education and experience

especially around the allocation of time between wage earning activities and all other kinds of

activities, and the trade-off between these two. Exhibit 1 provides a list of all the variables and

their justification for being in the model and have been discussed in detail in the literature review

(Becker, 1965).

4 Matching uses a predictive dependent variable, which helps correct for the strong OLS assumptions of linearity and other functional form issues. Most matching models use the two – step model to calculate the predicted probability of working from home. Matching allows the comparison of observations characteristics and hence minimizes all observed heterogeneity. However, matching, similar to OLS, does NOT control for unobserved correlations.

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RESULTS

Descriptive Results

Income and Home-based Work

Table 2 below shows that there is an observed difference between the average wages/income

of home-based workers and those who do not work at home.

Table 2: Income and Home-Work– Pakistan 2011

Income

Not

Working

from

Home

Percent Working

from Home Percent Total

0-1000 125.00 5% 36.00 10% 161.00

1000-3000 756.00 28% 219.00 60% 975.00

3000-10,000 1022.00 38% 95.00 26% 1117.00

10,000-25,000 573.00 22% 11.00 3% 584.00

25,000-45,000 153.00 6% 1.00 0% 154.00

45,000-90,000 28.00 1% 0.00 0% 28.00

Total 2657.00 100% 362.00 100% 3019.00


The data show that there are a disproportionately large number of home-based workers,

around 96 percent who earn PKR 10,000 or less, which is the minimum wage in many parts of

Pakistan. As we would expect, the higher income earners are those that do not work from home.

However, a large majority (71 percent) of women working outside the home still only earn

minimum wage or less. A better understanding of the relationship between earning less and

working from home will be garnered through a regression analysis.

Education may help increase income, and those that are higher educated do earn more, with

specialized education related to earning the most - see figure 3 in the appendix.

20

Regression Results

Ordinary Least Squares

Table 3 shows a series of Ordinary Least Squares (OLS)regressions using a different number

of control variables in each regression. Robust standard errors were used because of

heteroskedasticity5. By itself, working from home is associated with a reduction in income by

over PKR. 6000 for women working from home as compared to women who do not work from

home. However, since we have not included any control variables in the model, effects of other

related factors may have been mistakenly attributed to the home_work variable6.

5 See section figure 4 in appendix 6 The model has certain specification issues that have been discussed on page 26 – see appendix for more details.

21

Table 3: Multiple Regression Models

VARIABLES

Income and

home_work

Income

and age

variables

Adding

education

variables

Adding

dummy

for

‘married’

Adding

variable for

household

size

Adding

hrs

worked

/month

Adding

variable for

ent. size

Adding

‘formal’

dummy

Adding

dummy

for

training

Adding

dummy

fem_head

Variable

Means

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 8363.274

home_work -6,049*** -5,465*** -1,456*** -1,442*** -1,437*** -558.0** -907.7*** -875.6*** -981.7*** -975.2*** 0.15

(-23.19) (-20.25) (-5.559) (-5.422) (-5.396) (-2.028) (-3.154) (-3.034) (-3.026) (-2.999)

age 744.2*** 319.4*** 122.8 103.7 77.70 41.82 34.96 33.21 33.39 32.95

(9.008) (4.647) (1.392) (1.181) (0.899) (0.489) (0.409) (0.389) (0.391)

agesq -8.406*** -1.714* 0.613 0.839 1.196 1.494 1.556 1.582 1.581 1280.13

(-6.780) (-1.677) (0.494) (0.678) (0.974) (1.224) (1.274) (1.298) (1.297)

kg_ed 2,912*** 3,064*** 3,037*** 3,134*** 2,994*** 2,874*** 2,864*** 2,880*** 0.02

(2.835) (2.983) (2.956) (3.090) (2.948) (2.826) (2.813) (2.823)

prim_ed 3,061*** 3,300*** 3,329*** 3,240*** 3,127*** 3,075*** 3,047*** 3,055*** 0.10

(5.114) (5.391) (5.441) (5.402) (5.216) (5.108) (4.989) (5.009)

mid_ed 2,319*** 2,592*** 2,647*** 2,647*** 2,782*** 2,812*** 2,785*** 2,787*** 0.09

(6.521) (6.966) (7.050) (7.380) (7.289) (7.237) (7.082) (7.095)

matric_ed 5,038*** 5,203*** 5,266*** 5,581*** 6,146*** 6,263*** 6,247*** 6,241*** 0.10

(13.53) (13.85) (14.01) (14.57) (15.86) (16.19) (16.12) (16.12)

inter_ed 5,822*** 6,146*** 6,219*** 6,751*** 7,686*** 7,724*** 7,716*** 7,704*** 0.05

(14.13) (14.76) (14.85) (16.01) (18.24) (18.37) (18.30) (18.26)

undergrad_sc 24,450*** 24,740*** 24,786*** 24,858*** 25,165*** 25,151*** 25,150*** 25,150*** 0.00

(15.02) (15.19) (15.24) (15.15) (15.11) (15.14) (15.14) (15.15)

undergrad 9,544*** 9,870*** 9,971*** 10,510*** 11,495*** 11,547*** 11,540*** 11,536*** 0.03

(19.56) (20.04) (20.12) (20.26) (22.16) (22.40) (22.31) (22.27)

grad 14,845*** 15,289*** 15,384*** 16,049*** 16,650*** 16,575*** 16,567*** 16,562*** 0.01

(20.11) (20.46) (20.43) (20.70) (21.85) (21.77) (21.72) (21.74)

doct_ed 33,834*** 34,472*** 34,438*** 34,891*** 35,283*** 35,232*** 35,223*** 35,190*** 0.00

(5.705) (5.744) (5.814) (5.872) (5.714) (5.687) (5.689) (5.679)

21

22

VARIABLES

Income and

home_work

Income

and age

variables

Adding

education

variables

Adding

dummy

for

‘married’

Adding

variable for

household

size

Adding

hrs

worked

/month

Adding

variable for

ent. size

Adding

‘formal’

dummy

Adding

dummy

for

training

Adding

dummy

fem_head

Variable

Means

married 1,741*** 1,552*** 1,532*** 1,083*** 1,024** 1,025** 1,031*** 0.67

(4.513) (3.808) (3.769) (2.707) (2.563) (2.566) (2.586)

hhsize -238.8** -243.6** -225.9** -216.6** -217.1** -210.1** 2.52

(-2.202) (-2.265) (-2.169) (-2.068) (-2.069) (-1.986)

hrs_mth 22.22*** 20.30*** 20.55*** 20.53*** 20.49*** 138.84

(8.235) (7.517) (7.606) (7.605) (7.579)

size_med -5,859*** -4,521*** -4,516*** -4,503*** 0.00

(-11.09) (-7.815) (-7.809) (-7.782)

size_large -5,780*** -4,004*** -4,003*** -4,008*** 0.00

(-9.425) (-5.749) (-5.748) (-5.748)

size_xlarge -1,245 651.9 608.6 612.1 0.00

(-1.088) (0.540) (0.502) (0.505)

formal -2,092*** -2,091*** -2,080*** 0.01

(-4.945) (-4.946) (-4.923)

voc 207.7 213.4 0.07

(0.612) (0.629)

fem_head 160.4 0.51

(0.616)

Constant 9,088*** -5,243*** -4,918*** -2,463* -1,437 -4,886*** -3,217** -2,989** -2,977** -3,071**

(45.74) (-4.218) (-4.579) (-1.916) (-1.096) (-3.720) (-2.490) (-2.311) (-2.302) (-2.382)

-

Observations 2,980 2,980 2,980 2,980 2,980 2,881 2,881 2,881 2,881 2,881

R-squared 0.040 0.082 0.426 0.431 0.432 0.458 0.488 0.490 0.490 0.491

Prob> F <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Robust t- statistic in parenthesis

** Results significant at the 0.05 level *** Results significant at the 0.01 level

22

23

For example, according to the literature, there are many other factors that may also have a

direct impact on the level of income, like age, education, etc. The human capital theory

specifically talks about the importance of opportunity cost in terms of time. For example, if an

employee spent time going to school or vocational training, then that time has cost in terms of

lost potential income. It is imperative to account for these activities if we are to gain a more

complete understanding of all the factors that affect income (Becker, 1965). We test this theory

by adding a few control variables at a time and observing the changes to the home_work

coefficient. It should be noted that in all our results, the home_work coefficient remains

statistically significant at the 0.01 level, which means we can be reasonably confident that there

is a negative relationship between working from home and monthly income in the population

working people from which this sample was drawn.

The results in column 10 show the final OLS model used with all the control variables. All

coefficients except for those on the age and age-squared variables are statistically significant. All

have an impact on income that seems robust when compared to the constant figure, which is

PKR. 3,071. The overall model seems strong with an R2 of 0.491 and an F-value of less than

0.001.

Education. All the education dummies are statistically significant, and the coefficients show

what we would expect – that higher education is correlated with earning more income as

compared to earnings of the population that has less than Kindergarten level of education.

Something we may be surprised to see is the fact that those with graduate degrees actually earn

less than those that hold undergraduate degrees in science, on average. This might be due to a

few reasons – either this population is unusual in this specific sample and have less income on

24

average, or they hold graduate degrees in subjects that earn less. For example, a Masters of Arts

typically earns less than a graduate who is an engineer. More study would need to be done in

order to determine the source of this anomaly.

Marital Status.The dummy variable for marriage is also highly statistically significant. The

coefficient on the married dummy indicates a negative relationship between income and marital

status, implying that those who are married earn less than those who are not. This is also

consistent with most of the literature - a study by (Gong, 2004) showed that married women with

children have a larger tendency to work at home since the flexible working hours allows them to

also care for their families. The study also talks about how for women, their role in the

“household is significant when explaining labor market behavior, with important differences

between married women and single women”. For example, married women in Pakistan have less

of a pressure to care for their families and hence may settle for lower paying jobs.

Household Size and Hours of Work. The variable on household size is added because those

with bigger families may be more likely to work. However, larger households had a negative

relationship with income for women, perhaps illustrating women are forced to stay home to take

care of large families. As table 5 shows, a significant portion of women who are working from

home workpart-time. This further illustrates that those working from home may have greater

home responsibilities; hence, they are more likely to work part time.

25

Table 4: Home-Based Work and Hours Worked- Pakistan 2011

Part

Time % Full Time % Total

Not Home-Work

653 1%

61,164 99% 61,817

Home-Work

4,322 41%

6,204 59% 10,526

Total

4,975 7%

67,368 93% 72,343


Since our coefficient increased (became less negative) when this variable was added into the

model, it can be assumed the omission of the variables for hours worked in a month was biasing

the coefficient on home_work downward.

Enterprise Size. Unexpectedly, the dummy variables on enterprise size are negative though

all are statistically significant. The literature(Pratap, 2006) tells us that those who work in larger

enterprises tend to earn more, however here; the regression indicates a negative relationship

between the size of the enterprise and income as compared to the base population which is 5 or

less per enterprise. One possible reason for this may again be the fact that informal organizations

may have fairly large number of workers but they are working in isolation from one another and

hence are in reality, more like an organization of one where one woman works per household.

For this reason, even a larger enterprise may not be able to reap the benefits of economies of

scale due to fragmentation. In addition, even if a cottage industry, for example, has more than 10

employees, and they each work in their own homes, they can be exploited and paid less than the

industry average because they have limited information as to what competitive wage they should

be getting.

26

Formal Work. The variable on formal, as expected, was positively related with the income

variable. As the literature shows, formal sector employees on average tend to earn more than

informal sector workers (Mazumdar, 1974). On the other hand, research has also shown us that

the formal and informal sector are closely linked, especially in the developing countries where

the only thing separating the two are the economic regulations which govern the former but not

the latter (M. A. Chen et al., 2001).

Training and female-headed households. The addition for the dummy variable for vocational

training does not give us a statistically significant coefficient, which implies that we cannot make

a confident determination about the relationship between having vocational training and monthly

income for this particular population. Literature shows that not only do incomes vary of a person

is the head of the household (Gong, 2004) but that women who are the primary bread winners in

a family often consider themselves the head of the household (Doane, 2007), hence it has been

included in this model. Adding the variable for female headed households does not give a

statistically significant result, but we keep it within the model anyways because it does cause a

change in the coefficient on home_work which is now PKR. -975.2This means that the average

monthly income of women who work at home is PKR 975.2 less than those who do not work

from home in our population, keeping other variables in our model constant.

Shortcomings of the Model

As Figure 5 in the appendix shows the residuals are not randomly distributed. This shows

that there is likely some model specification error. This is confirmed by the Linktest and Ramsey

tests – see figures 6 and 7 in the appendix.

27

One of the main reasons for model misspecification can be self-selection bias – this means

that the decision of women to work from home may not be random. There are many reasons that

effect a woman’s decision to work from home or not, and although this model aims to account

for many of them through the inclusion of control variables, it is often impossible to account for

all of them in real world conditions due to limitation of data in some cases, or because some

factors simply cannot be measured. For example, the availability of jobs in terms of quantity as

well as quality and cultural and societal factors that limit the freedom of women are often the

biggest factors that affect their decision on working from home, and it is almost impossible to

account for such factors in this model unless a good instrument can be found. Unfortunately, the

limitations of the data do not allow us to find a valid instrument in this specific case, though this

is an area which warrants further study.

There is also likely to be some heteroskedasticity in the sample. As can be seen from figure 4

in the appendix, the residuals when plotted on a scatter plot are clearly not random, hence there

is a heteroskedasticity problem. The same is confirmed by a White’s test – the Chi-sq was 186

with a P-value = 1.5e-50 – hence we reject the null that there is homoscedasticity – see Figure 4a

and 4b in the appendix. This issue was dealt with by using robust standard errors in all the

regressions.

As can be seen from table A in the appendix, the variables in the model have fairly low level

of correlation with each other, which means that multicollinearity is likely not a problem. Age

and age-squared are almost perfectly collinear but that is not unusual within quadratic terms.

Another potential problem is posed by the missing values for the income variable – these

missing values may be correlated with either working from home, income or any of our control

28

variables. If, for example, higher earning people are less likely to report their income (as is often

the case) and also more likely to not work from home, it means our coefficient on home_work is

too small (in real terms) and inclusion of these variables would actually result in a higher

difference between average wage of those who work from home as opposed to those who do not.

Figure 8 in the appendix illustrates that most respondents reporting zero income worked from

home, It is highly possible that many areas where most women work from home, which tend to

be rural areas or urban slums, were under-represented in the sample. Then the actual coefficient

might be more or less extreme, depending upon whether the excluded women earn more or less

than the average of the sample. In any case, these questions are the kind that can only be

answered through further research.

Propensity Score Matching

In matching models7 like propensity score matching, the main idea is to come up with a

probability of working from home as opposed to not working from home taking into

consideration all the observables that define a person’s abilities. Since in essence we are

comparing two groups that are similar in all their observables except for the treatment (which in

this case is working from home) hence we can assume that the only difference in their income is

caused by working or nor working from home. Lastly, matching has common support by design,

which is described by methodologists as the condition that all the regions spanned by the

covariates contain members of both the treatment (home_work) and control (not home_work)

group (Murnane & Willett, 2011). In OLS, this is not the case since we compare a range of

7Care should be taken to not assume that matching takes care of any omitted variable bias problem – unless we can successfully argue that there are no variables that are omitted from the model, correlated with monthly and also correlated with working from home, this coefficient only gives us our best estimation of the relationship between monthly income and home_work and does not represent a causal relationship

29

observations that may or may not exist in the actual sample, hence are likely to get an incorrect

coefficient. Such designs have been used in the past to assess the policy impact of a certain

‘treatment’ on a sample group, however the same logic applies to non-experimental design like

this as long as the main independent variable is binary. As Graham says in his Handbook of

Social Economics:

“Associations or reference groups, such as families, co-workers, neighbors and classmates, define (partially) isolated environments in which social interaction takes places. These interactions may, in turn, affect the acquisition of human capital, the availability of employment opportunities, or even influence one’s aspirations and values.” (Graham, 2011)

Also later:

“The goal is to recover the match production function from these data and evaluate the effects of alternative assignments or ‘matching’ on the distribution of outcomes” (Graham, 2011).

How can these influence and interactions be accounted for? Agodini and Dynarski talk

about how “the propensity score method estimates impacts by comparing outcomes of a

treatment group with outcomes of a select group of individuals who, on average, are similar to

the treatment group along a wide array of observed characteristics” (Agodini & Dynarski, 2004).

Rosenbaum and Rubin (1985) say that:

“a non-experimental method not often used by evaluators—propensity score matching—yields impact estimates that are close to those produced by an experimental design. Propensity score methods estimate impacts by comparing outcomes of program participants with outcomes of a select group of individuals who, on average, are similar to participants along all the characteristics that are related to the outcomes of interest” (Rosenbaum & Rubin, 1985).

The matching model involves a first stage repression which is done through a probit model

where our main independent variable of interest (home_work) is taken as the main dependent

30

variable of interest and all the control variables are used as the main independent variables of

interest. Once each value in the sample has a probability of being either a 0 or a 1 in the

home_work dummy, then they are compared to their nearest partner in the other group. This

way, we are potentially comparing two virtual people with exact same characteristics, where one

works at home and the other one does not. In this case, we can safely assume that in the absence

of other unobserved covariates, the difference in monthly income represents a more confident

estimation of the income ‘gap’ between these two populations in the real world. Matching

coefficients are usually more accurate than simple OLS coefficients for the reasons discussed

above.

For this regression, the first stage regression resulted in propensity scores that ranged from

0.0084 to 0.8 with the final number of blocks being 7. Within these blocks, the balancing

property is satisfied. It should be noted that the common support option was selected, hence there

are observations under each block, though naturally there are fewer under the home_work group

as compared to the no home-work group (see figures 9 and 10 in the apendix).

The second stage regression was carried out using four methods – nearest neighbor

propensity score matching, radius matching, weighting and stratification. The results for these

are shown in table 5 along with the OLS result for comparison.

31

Table 5: Matching model coefficients compared to OLS

Nearest

Neighbor

Matching

Radius

Matching (0.1) Weighting Stratification OLS

VARIABLES . . . .

attnd -1,442**

(-2.476)

attr -1,873***

(-7.524)

attk -1,464***

(-2.843)

atts -1,408***

(-4.438)

home_work -975.2***

(-2.999)

Observations 2,980 2,980 2,980 2,980 2,881

** Results significant at the 0.05 level *** Results significant at the 0.01 level

The propensity score estimation, also known as the ATT8 estimation with Nearest Neighbor

Matching method is -1441.547 and is statistically significant at the 0.05 level. Standard error was

bootstrapped. This means that, keeping other variables in the model constant, and assuming there

is no omitted variable bias, the home based working women earn around PKR 1,442 less per

month than those who do not work at home. ATT estimation with the Radius Matching9 method

using a radius of 0.1 is -1872.929 and is statistically significant at the 0.01 level, meaning that

the home based working women earn around PKR 1,873 less per month than those who do not

8Average Treatment on the Treated – looks at the observations that actually received the treatment as opposed to those that were actually selected for treatment. 9 This method identifies observations that occur within a certain ‘radius’ as specified within the model. These observations are close to each other within a certain parameter and differ only in one way – the application of the ‘treatment’ which in this case is the binary independent variable of interest: home-work

32

work at home. ATT estimation with the Kernel Matching method (weighting)10 is -1464.286

and is statistically significant at the 0.01 level, meaning that the home based working women

earn around PKR 1,464 less per month than those who do not work at home. ATT estimation

with the Stratification method11 is -1407.978 and is statistically significant at the 0.01 level.

Standard error was bootstrapped. This means that, keeping other variables in the model constant,

and assuming there is no omitted variable bias, the home based working women earn around

PKR 1,408 less per month than those who do not work at home.

Standard error is lowest for radius matching though this increases slightly if a narrower

margin for the radius is chosen. Even so, the four results are comparable in terms of the

coefficients except for the radius coefficient which is even higher (in real terms) than the others,

leading us to believe that the true relationship coefficient is possibly somewhere between these

two extremes. In any case, matching gives us a stronger coefficient than OLS, which means that

there was likely some self-selection bias which was biasing the coefficient upwards – or making

it less negative than it actually is. To put the figure of PKR 1400 in perspective, let’s look at the

mean income for both the groups as shown in Figure 11 in the appendix. The mean income for

the women who work from home is a little less than PKR 3000, hence a potential increase in

income of PKR 1400 means an increase in income of around 47 percent. This figure is also

around 0.15 standard deviations away from the mean of the overall income variable, which is

also pretty significant

10 This method gives different weights to the propensity scores of each observations where those that are unusual are given more weight than those that are expected. For example, if an educated person earns more, that is as expected and hence is given less weight. In this way, if the sample has an unusually high number of a certain demographic, it can be controlled for. 11 The propensity scores for each observation are divided into similar blocks and only the observations within a block are compared with each other.

33

Hence in absence of more data, we can conclude that there is likely a strong relationship

between earning less and working at home as compared to not working from home in the

population from which this sample was drawn, keeping other variables constant.

34

POLICY IMPLICATIONS

ILO’s work convention on Home Work (No.177) adopted in Geneva in 1996, states that

home-based workers are eligible to receive, among other things, decent pay, protection against

discrimination and safe working conditions. This convention has not been ratified by the

Government of Pakistan. This study has helped determine an empirical basis for claiming that

female home-based workers are paid lower. This study empirically demonstrates that the women

who work at home earn lower wages on average as compared to those who work outside the

home, and should thus be used as a basis for arguments regarding formulation of a home-based

workers policy in Pakistan.

Reduction in Poverty is one of the main goals under the UN Millennium Development Goals

2015 (United Nations, 2000). The prevalence of the informal work industry, of which home-

based work is a large part, has been linked in earlier studies with development and deemed a

natural part of the growth process, even necessary for development.

However, coupled as it is with gender inequality and low-paid work for some of these

women home-based workers, it can actually lead to an increase in the poverty level. In South

Asia, particularly, a study carried out by HomeNet South Asia has shown persistent poverty and

inter-generational poverty related to the existence of home-based workers, particularly in cases

where the home-based workers are not part of a home-based workers organizations. In absence

of formal policies governing pay and working conditions of informal workers, home-based

workers organizations are the only way to provide them (Doane, 2007). Other studies in various

parts of the world have also linked informal industries with growth in poverty (Khotkina &

Khotkina, 2007)

35

Although there is a whole spectrum of home-based workers’ earnings, depending on the kind

of product they produce, generally speaking, poverty levels are higher due to low wages, poor

working conditions and the absence of even basic benefits, as many case studies have shown. It

is therefore important for studies like these to show empirically how the wages of women home-

based workers are lower than they should be in order to advocate for a more formal system of

decent employment, as per the ILO definition (International Labor Organization, 1999).

To date, few policies have specifically addressed the rights and concerns of female home-

based workers (Carr, 2000).Although interchangeably referred to as the cottage industry or the

informal sector, the truth is that most of these women work in isolation from each other and very

few sectors actually employ women home-based workers in groups - the Pakistani Sports

industry being an example of one that does (Siegmann, 2008). Hence, it is of special importance

that some initiative be taken to not only formulate policies specifically for home-based workers

but also implement and monitor them.” A matrix of rights consisting of the right to work,

broadly defined, safe work, minimum income and social security are identified as core issues for

informal workers” (Unni, 2004).

This study has shown that holding all other variables in the model constant, there is a

difference of approximately PKR. 1,400 between women home based workers and the women

who work outside of the home. Although there is need for further study with a more complete

data set that can instrument for cultural and societal factors that effect a woman’s decision to

work from home, it is likely that controlling for such factors will increase this differential,

especially since this data set is skewed towards women who work outside the home. In addition,

there is also need to carry out a separate study to assess the working conditions of these women –

36

monetizing other perks of formal work as opposed to home-based work is also likely to increase

this differential further.

Of the control variables in this model, the variables related to the enterprise seem to have the

most impact on income/wages, like weather the enterprise they are working is big in size and

whether it keeps formal records. This seems to imply that even in the absence of formalizing

home-based work, some form of a home—based workers union which allows them to argue for

their rights as a bigger enterprise could have a highly positive impact on income.

The regression results also show us that, as expected, education has a big impact on

income/wages, but for home-based workers, vocational training has a very slight impact. Hence,

the skill level of these women is not in question, rather there are other factors that are causing

them to earn less. In this case, focusing on a vocational training program will not be very helpful.

Literature and observation tells us that one of the biggest reason for the wage differential can be

exploitation by the middle men of these home based workers – there needs to be further study in

this area to show this through empirical evidence. If this turns out to be the case, a formal

mechanism needs to be put in place monitoring the activities of these middle men, and in an ideal

case, informal networks for home based workers can be set up that eliminate the need for middle

men altogether as these women are allowed to sell their own product. This can be supported by

local small business owner programs and funding mechanisms can be introduced to support these

women becoming business owners.

Poverty and family size, as in most developing countries, also play a factor here, and

government programs catered towards these issues can also decrease the wage differential in the

long term.

37

APPENDIX

Figure 1: Income and Age

02

00

00

400

00

600

00

800

00

100

00

0

20 40 60 80 100age

income Fitted values

38

Figure 2: Income and Age-Squared

Figure 3: Income and Education

02

00

00

400

00

600

00

800

00

100

00

0

0 1000 2000 3000 4000 5000agesq

income Fitted values

02

00

00

400

00

600

00

800

00

100

00

0

No Nur KG Prim Mid Matr Inter Eng Med Comp Agr Oth Grad DocEducation level 1-14

income income_mean2

Figure 4a: Heteroskedasticity

39

40

Figure 4b: White’s test for Heteroskedasticity

White's general test statistic : 635.9322 Chi-sq(186) P-value = 1.5e-50

(59153 missing values generated)






















. whitetst

Figure 5: Model Specification

Figure 6: Link Test

_cons 1545.813 258.926

_hatsq .0000195 2.05e-0

_hat .5452011 .051669

income Coef. Std. Er

Total 2.7083e+11 2909 9

Residual 1.3773e+11 2907 4

Model 1.3310e+11 2 6

Source SS df

. linktest

41

65 5.97 0.000 1038.115 2053.511

06 9.48 0.000 .0000154 .0000235

97 10.55 0.000 .4438883 .646514

rr. t P>|t| [95% Conf. Interval]

93099610.1 Root MSE = 6883.2

Adj R-squared = 0.4911

47377997.7 R-squared = 0.4915

6.6549e+10 Prob > F = 0.0000

F( 2, 2907) = 1404.65

MS Number of obs = 2910

42

Figure 7: Ramsey Test

Figure 8: Distribution of Home_work variable

Figure 9: Propensity Score Matching – Stage 1

Prob > F = 0.0000

F(3, 2885) = 130.97

Ho: model has no omitted variables

Ramsey RESET test using powers of the fitted values of income

. ovtest

Total 2,980 100.00

1 357 11.98 100.00

0 2,623 88.02 88.02

home_work Freq. Percent Cum.

. tab home_work if income!=0

99% .8782516 .9291667 Kurtosis 4.397966

95% .7287617 .9276452 Skewness 1.591493

90% .5963804 .9227979 Variance .0515677

75% .2251138 .9221565

Largest Std. Dev. .2270853

50% .0918134 Mean .1910509

25% .0400239 .0084806 Sum of Wgt. 1826

10% .0178585 .0083803 Obs 1826

5% .0129598 .0083617

1% .0094063 .0083593

Percentiles Smallest

Estimated propensity score

in region of common support

Description of the estimated propensity score

43

Figure 10: Propensity Score Matching – Stage 1b

Figure 11: Mean Income for both groups – home_work and not home_work

Over Mean Std. Err.

[95%

Conf. Interval]

Income Home_Work 8971.664 197.1052 8585.189 9358.138

NotHome_Work 2996.812 167.9827 2667.44 3326.184

Note: the common support option has been selected

Total 1,470 356 1,826

.8 10 38 48

.6 39 90 129

.4 54 78 132

.2 129 57 186

.1 319 52 371

.05 388 31 419

.0083593 531 10 541

of pscore 0 1 Total

of block home_work

Inferior

and the number of controls for each block

This table shows the inferior bound, the number of treated

44

Table A: Correlation Matrix

home_

w~k age agesq kg_ed

prim

_ed

mid

_ed

mat

ric~

d

inter

_ed

und

erg~

c

und

erg~

d grad

doct

_ed

mar

ried

hhsi

ze

hrs_

mth

size_

med

size_

l~e

size_

x~e

form

al voc

fem_

head

home_

work 1

age -0.06 1.00

agesq -0.05 0.98 1.00

kg_ed 0.09 -0.04 -0.03 1.00

prim_e

d 0.21 -0.10 -0.09 -0.03 1.00

mid_ed 0.10 -0.07 -0.06 -0.02 -0.05 1.00

matric_

ed -0.02 -0.06 -0.05 -0.04 -0.08 -

0.06 1.00

inter_e

d -0.10 -0.09 -0.09 -0.04 -0.08 -

0.06 -

0.11 1.00

underg

rad_sc -0.05 0.00 -0.01 -0.02 -0.04 -

0.03 -

0.05 -

0.05 1.00

underg

rad -0.14 -0.04 -0.06 -0.05 -0.10 -

0.08 -

0.13 -

0.13 -

0.06 1.00

grad -0.14 -0.03 -0.06 -0.04 -0.09 -

0.07 -

0.12 -

0.12 -

0.06 -

0.15 1.00

doct_ed -0.02 0.05 0.05 -0.01 -0.01 -

0.01 -

0.02 -

0.02 -

0.01 -

0.02 -

0.02 1.00

marrie

d -0.02 0.42 0.35 -0.02 -0.09 -

0.07 -

0.03 -

0.08 0.00 -

0.04 -

0.07 -

0.01 1.00

hhsize -0.01 -0.24 -0.21 -0.01 0.04 0.04 0.04 0.06 0.00 0.07 0.07 -

0.01 -

0.39 1.00

hrs_mt

h -0.21 0.01 0.00 -0.01 0.02 0.03 -

0.02 -

0.05 0.04 -

0.06 -

0.06 -

0.01 -

0.01 0.03 1.00

size_me

d -0.06 -0.14 -0.13 -0.03 -0.04 0.01 0.09 0.10 0.00 0.09 -

0.01 -

0.01 -

0.14 0.09 -

0.04 1.00

45

home_

w~k age agesq kg_ed

prim

_ed

mid

_ed

mat

ric~

d

inter

_ed

und

erg~

c

und

erg~

d grad

doct

_ed

mar

ried

hhsi

ze

hrs_

mth

size_

med

size_

l~e

size_

x~e

form

al voc

fem_

head

size_lar

ge -0.08 -0.10 -0.10 -0.02 -0.03 -

0.03 0.00 0.09 -

0.01 0.11 0.10 0.01 -

0.14 0.08 -

0.04 -

0.06 1.00

size_xla

rge -0.01 -0.01 0.00 0.01 -0.03 0.03 -

0.03 -

0.01 0.02 0.03 0.05 -

0.01 -

0.02 0.02 0.01 -

0.03 -

0.03 1.00

formal -0.06 -0.18 -0.16 -0.03 -0.05 0.01 0.07 0.10 0.00 0.12 0.03 -

0.01 -

0.20 0.13 -

0.02 0.37 0.51 0.30 1.00

voc 0.45 -0.08 -0.08 0.04 0.15 0.09 0.02 -

0.03 -

0.03 -

0.06 -

0.04 -

0.01 -

0.03 0.02 -

0.09 -

0.04 -

0.03 0.07 0.00 1.00

fem_he

ad -0.05 0.01 0.01 -0.02 -0.03 -

0.02 0.00 0.03 0.00 0.00 0.00 0.02 0.01 -

0.10 0.02 -

0.05 -

0.01 -

0.02 -0.05 -

0.05 1.00

46

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