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S3H Working Paper Series

Number 06: 2016

Determinants of Income Inequality

among the Earners in Pakistan

Saira Naseer

Ather Maqsood Ahmed

February 2016

School of Social Sciences and Humanities (S3H) National University of Sciences and Technology (NUST)

Sector H-12, Islamabad, Pakistan


Faculty Editorial Committee

Dr. Zafar Mahmood (Head)

Dr. Najma Sadiq

Dr. Sehar Un Nisa Hassan

Dr. Lubaba Sadaf

Dr. Samina Naveed

Ms. Nazia Malik


Number 06: 2016

Determinants of Income Inequality

among the Earners in Pakistan

Saira Naseer Graduate, School of Social Sciences and Humanities, NUST

[email protected]

Ather Maqsood Ahmed Professor, School of Social Sciences & Humanities, NUST

[email protected]

February 2016

School of Social Sciences and Humanities (S3H) National University of Sciences and Technology (NUST)

Sector H-12, Islamabad, Pakistan

mailto:[email protected]

iii

TABLE OF CONTENTS

Abstract…………………………………………………………………………………………...vii

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

2. Literature Review ........................................................................................................................................... 4

3. Data ................................................................................................................................................................. 7

4. Methodology .................................................................................................................................................. 7

4.1 Theoretical Model ................................................................................................................................... 7

4.2 Methodological Framework ................................................................................................................... 8

4.3 Regression Model .................................................................................................................................. 10

5. Results and Discussion ............................................................................................................................... 13

5.1 Descriptive Statistics ............................................................................................................................. 13

5.2 Estimation of the Gini Coefficient ..................................................................................................... 14

5.3 Estimation of the Coefficients ............................................................................................................ 15

5.4 Estimation of Factor Inequality Weight ............................................................................................ 19

5.5 Contribution to Change in Inequality during 2005-2010 ................................................................ 26

6. Conclusion and Policy Recommendations .............................................................................................. 29

APPENDIX ..................................................................................................................................................... 32

REFERENCES................................................................................................................................................ 36

iv

LIST OF FIGURES

Figure 1: Lorenz Curves for the Distribution of Earnings in Pakistan, 2005 and 2010 ........................ 15

Figure 2: Plot of Actual Income and Predicted Income against the Number of Earners 2005 ........... 19

Figure 3: Plot of Actual Income and Predicted Income against the number of Earners 2010 ............ 19

LIST OF TABLES

Table 1: Variables Description ....................................................................................................................... 12

Table 2: Descriptive Statistics ........................................................................................................................ 13

Table 3: Gini Coefficients for the year 2005-6 and 2010-11 ..................................................................... 14

Table 4: Earnings Equation Results, 2005-06 and 2010-11. ...................................................................... 17

Table 5: Average yearly Income by Age and Education Group (2005) ................................................... 18




Table 9: Factor Inequality Weight of the Variables in 2005 ...................................................................... 22

Table 10: Factor Inequality Weights of the Variables in 2010 .................................................................. 25

Table 11: Contributions to the Change in Gini Coefficient during 2005-10 .......................................... 28

Table 12: Consolidated Contributions to the Changes in Gini Coefficient during 2005-10 ................ 29

v

LIST OF ACRONYMS

PRSP I: Poverty Reduction Strategy Program I

PRSP II: Poverty Reduction Strategy Program II

HIES: Household Integrated Economic Survey

OLS: Ordinary Least Square

GDP: Gross Domestic Product

VAT: Value Added Tax

CV: Coefficient of Variation

ILO: International Labor Organization

ADB: Asian Development Bank

RCRE: Research Centre for Rural Economy

NSS: National Sample Survey

LFSS: Labor Force and Socio-Economic Survey

LIS: Luxembourg Income Study

ECLAC: Economic Commission for Latin America Caribbean

LFS: Labor Force Survey

SHIW: Survey of Household Income and Wealth

PSLM: Pakistan Social and Living Standard Measurement

RSES: Rawalpindi Socio-Economic Survey

PRHS: Pakistan Rural and Household Survey

PIHS: Pakistan Integrated Household Survey

KPK: Khyber Pakhtunkhwa

vii

ABSTRACT

Growth strategy adopted in Pakistan has failed to reduce poverty for two reasons. First, it has not

been pro-poor, and second, it exacerbated the income inequality situation in the country that has

further compounded the poverty situation. The historical data confirms that during high periods of

growth, the emergence of high levels of inequality not only decreased the growth momentum but

also reduced the poverty-decreasing effect of the growth. On the other hand, periods of low growth

were marked by undue increases in poverty due to inequality. Since, inequality in income is the main

hurdle to alleviate poverty effectively, understanding the structure of income inequality is important

to undertake poverty reduction program. The present study accomplishes this task by examining the

factors that determine the level of income inequality in Pakistan and knowing the drivers of the

changes in the income distribution by utilizing Household Integrated Economic Survey (HIES) for

the year 2005-06 and 2010-11.This was achieved by estimating the level of income inequality among

earners using the Gini index approach and re-estimating the determinants of earnings using the

standard augmented Mincerian model. Further, the determinants of inequality and its change were

estimated through the regression-based decomposition methodology proposed by Fields (2003). The

study has found a gradual decline in the Gini index from 0.52 in 2005 to 0.44 in 2010 due to the

policies adopted by the government under the Poverty Reduction Strategy Program (PRSP-II). The

results have further shown that after age, the share of gender in inequality (Gini index) was highest

for both the study periods, followed by education, and professional categories of occupation. The

study predicts that continuation of the well-directed policies would positively contribute to the

decline in inequalities further.

Keywords: Income Inequality, Regression based Decomposition, Gini Index.

1

1. Introduction

Poverty reduction policies after the Washington consensus have mainly focused on encouraging

economic growth. However, since late 1990s the relevance of income distribution among population

has been ‘brought back in from the cold’ [Atkinson (1997)]. It is making its way back onto the

development policy agenda in many developing countries [Kanbur and Lustig (1999)]. This outcome

may have been due to the disappointment with the development model which placed exclusive

emphasis on growth and its “trickle-down”1 effect that envisages sinking down of growth in average

incomes automatically to benefit the poor. However, the basic idea of the trickle-down effect that

the higher investment would decrease the poverty through employment and real wages was

ineffective2 as the growth has not been sustained for a reasonable period of time to have its effect.

In fact, in many situations the pro-growth development agenda has led to the decline in poverty at

the expense of worsening inequality.

During the decade of the 1990s, the abundance of high-quality data on income distribution from a

number of countries has allowed a thorough empirical testing of the standing debate on the relative

importance of redistribution and growth in reducing poverty. While the debate is still inconclusive,

the cross-country evidence has allowed many development economists to contend that an unequal

income distribution is a serious hurdle to effective poverty alleviation measures [Ravallion (1997)].

On the other hand, White and Anderson (2001) and Knowles (2001) have found that growth is the main

tool for reducing poverty. However, they along with Ravallion (2001) have stated that the imperative

of growth for combating poverty should not be misinterpreted to mean that “growth is all that

matters”. No doubt that growth is a necessary condition for poverty alleviation, but inequality also

matters and should not be deleted from development agenda.

Inequality matters for reducing poverty as it not only affects poverty directly but indirectly as well

through its link with the economic growth [Naschold (2002)]. In explaining the direct effect White

and Anderson (2001) found poverty to be very sensitive to changes in income distribution, i.e., small

1 Trickle-down was the dominant development thinking in the 1950s through 1980s. It implies a vertical flow from rich to the poor that happens on its own accord. The benefits of the economic growth go to the rich first, and then in the second round the poor begin to benefit when the rich start spending their gains. Thus, the poor benefit from economic growth only indirectly through a vertical flow from the rich. It implies that the proportional benefits of growth going to the poor will always be less.

2 Kanbur and Lustig, 1999

2

favorable changes in the income distribution of the population at the lower end barely increases the

Gini coefficient and decreases the poverty more than for the same change in growth. However,

Rodrick (1999), Birdall et al. (1995) and Bourguignon (1999) have found inequality in the physical

and human capital assets (land, education, gender, ethnicity, etc.) to reduce growth, thereby slowing

down the reduction in poverty indirectly.

The importance of studying income distribution in the context of Pakistan stems from the fact that

Pakistan is a low income country characterized by high levels of the poverty. Since, Pakistan has

never been able to maintain a sustained growth rate; poverty levels in Pakistan have fluctuated

considerably. Jamal (2009) while studying the growth periods of 1988-99 and 2000-05 in Pakistan

has found that growth is not distributionally neutral. It has not only increased inequality during the

high growth periods but also reduced the poverty-decreasing effect of growth. However the periods

of low growths are marked by undue increases in poverty due to the inequalities.

The growth performance of the last three decades confirms that growth is negatively related to the

poverty and positively to the inequality. Starting with the 1980’s Pakistan saw high economic growth,

accompanied by a decline in poverty and increase in inequality. However, the growth declined in the

1990s that resulted in the rise in poverty while inequality decreased. In the early half of the 2000,

Poverty Reduction Strategy Program (PRSP-I) was adopted with the aim of achieving pro-poor

growth by focusing on different aspects of poverty including high economic growth3. While the

policy led to a sharp decline in poverty, it was ineffective in reducing inequality, as it increased from

0.28 to 0.30 in 2005. The second phase of poverty reduction program continued from 2008 to 2011,

but due to macroeconomic instability many of its objectives could not be achieved as the growth

declined, thereby increasing poverty [Jamal (2009)].

Since, inequalities in the income are the main hurdle to the effective poverty alleviation.

Understanding the structure of income inequality is important to reduce poverty. This can be done

by examining the factors that determine the level of income inequality in Pakistan and knowing the

drivers of the changes in the income distribution. Such knowledge is highly relevant to the policy

3 The PRSP was launched in December 2003 for three years; four pillars were adopted under it to address different aspects of poverty: high economic growth; governance and consolidating devolution; investment in the human capital; bringing the poor, vulnerable and backward regions into mainstream of development, and to make marked progress in reducing existing inequalities.

3

makers as it enables them to decide whether and how to take corrective action4. Gaining better

insights into the determinants of inequality also helps the decision makers to assess the distributional

effects of existing and new poverty reduction and growth policies. This is relevant not only for

policies specifically aimed at making the distribution of income more equitable, but also for

assessing whether any proposed new policy is likely to affect the distribution of income.

Much of the previous work on inequality in Pakistan mainly comprises of one of the two traditional

decomposition approaches, i.e., decomposition by income sources (wage and self-employment

income) or population subgroups (male and female or primary school vs secondary or higher

education). The major drawback of these approaches is that they only tell us what income sources or

population subgroups explain inequality but fail to find the contribution of individual determinants

to it, since by design their analysis does not incorporate household endowments and characteristics,

their conclusions tend to remain vague as to whether and how to address income inequality. Thus,

the information provided by these two methods is of little use to the policy makers who seek to

address this issue.

The present study therefore tries to bridge the gap in the earlier studies by adopting a regression

based decomposition methodology proposed by Fields (2003) using the HIES data for the year 2005

and 2010. The method starts by first estimating the Mincer type income generating function, and

then it uses the estimated coefficients to find the relative factor inequality weight, i.e. the

contribution of the individual determinants to inequality. The main advantage of the method is that

the contribution remains the same no matter what inequality measure is used in the analysis. Unlike

the previous two approaches, this method has the ability of going beyond decomposing inequality

simply in terms of income components or discrete population categories and has the ability to

include any variable that may explain the inequality such as economic, demographic, social, and

policy variables both discrete and continuous. Moreover, the method also manages the problem of

endogeneity ignored by the earlier two methods.

The study attempts to achieve the following objectives;

4 For example knowing what factors account for inequality helps the policy makers to decide whether the existing inequalities are due to unchangeable characteristics i.e. province, region, gender etc whose distribution cannot be changed or due to the factors that can be changed through policy for example increasing access to the education.

4

To estimate the level of income inequality among the earners.

To examine the determinants of the income among the earners.

To estimate the contribution of the determinants to the inequality level.

To find the contribution of the determinants to the change in Gini index.

To draw policy implication from the study to help decrease inequality among the earners.

2. Literature Review

Since understanding the structure of inequality is important for the effective poverty alleviation. A

number of empirical studies conducted in the past try to explain income distribution using

household survey data. These studies normally employ decomposition methods. Depending upon

the question that is raised, different methods can be utilized to address income inequality. This

section sheds light on the existing literature in this area.

Kruijk (1985) examines the development of poverty and income inequality in Pakistan for the year

1969 and 1979 using Household Integrated Economic Survey (HIES). The paper uses various

poverty and inequality indicators to measure inequality at national, urban and rural level. Findings

suggest decline in poverty from 1969 to 1979 but increase in income inequality. The elements

contributing to the increase in inequality are as follows; increase in the earnings inequality of the

rural areas, increase in participation rates of urban and rural households, sectoral shift from urban to

rural areas and increase in inequality between urban and rural areas.

Kruijk (1987) evaluates the two main sources of income i.e. labor income and non-labor income by

further subdividing labor income into the occupational groups and non-labor into income from

property, housing and other sources. The data for the study is taken from HIES for the year 1969-70

and 1979-80. The study uses Household Equivalence Scale to measure inequality and adopts Theil-T

index as an inequality measure for the decomposition analysis. The results suggest that the bulk of

income inequality in Pakistan is generated by labor income inequalities within the occupational

groups and by inequalities of income from other sources, namely remittances.

Adams (1994) clarifies the impact of rural non-farm income on income distribution by making two

contributions; first he uses the decomposition techniques to pinpoint contribution of different

sources of rural income (including non-farm income) to total inequality, second he decomposes

5

nonfarm income inequality into the different sources. The study uses Gini coefficient and CV

approach to carry out the decomposition analysis for the year 1986-87, 1987-88 and 1988-89. The

results indicate that non-farm income is not only the inequality decreasing source of income but in

any year it accounts for a small proportion of the overall inequality. Further decomposition of non-

farm income suggests self-employment and unskilled labor income to be the major income sources

for the poor followed by the government employment.

Nasir and Mahmood (1998) analyze the distribution of individual earnings in the labor market, with

the aim of finding the causes of dispersion in the workers earnings. The data for the study is taken

from the Household Integrated Economic Survey (HIES) for the year 1993-94. The study first uses

the OLS method to establish the determinants of income among earners and then uses variance of

log earnings method to find the extent of inequality among the earners. Findings suggest age groups

belonging to the two extremes (youngest and oldest) have the highest contribution in explaining

inequality in the personal earning. Furthermore education not only increases the earnings but is the

highest contributor to inequality followed by sector of employment, region, gender, marriage, and

other characteristics.

Ahmad (2001) estimates income inequality among occupations in Pakistan for the year 1992-93

using Household Integrated Economic Survey (HIES). The study first calculates Gini coefficient

using household per capita income for different occupations and then compares inequalities among

them. Findings suggest inequality to be highest among the skilled workers and lowest among

professionals. Inequality among skilled workers is higher than the overall inequality in Pakistan and

inequality among professional to be lower than the national inequality.

Idrees (2001) analyzes inequality in the individual earning in Pakistan by using Household Integrated

Economic Survey (HIES) for the years 1992-93, 1996-97, 1998-99 and 2001-02. The study uses

alternative approaches, i.e., Gini coefficient, Theil’s measures and Atkinson’s indices to carry out the

analysis. Findings suggest inequality in the earning to be much higher than the inequality in the total

household income. Further decomposition of the inequality based on individual characteristics

suggest higher inequality among youngest and eldest groups of earners, female earners, and earners

with low level of education, earners working as employers and members of producer’s cooperatives,

and the earners employed in services industries other than social & personal services. On the other

6

hand, the degree of inequality is lower among the middle-age groups, the highly educated workers,

paid workers and the workers employed in the primary products industries namely agriculture,

forestry, hunting & fishing.

Naschold (2009) study the microeconomic determinants of income inequality in rural Pakistan for

the year 1986-87 and 1988-99 using Pakistan Rural Household Survey (PRHS). The paper

decomposes two measures of inequality i.e. Gini coefficient and Thiel index into the human capital

variables using Fields (2003) methodology. The inequality estimates are based on household income

per adult equivalent. Findings suggest Physical assets, particularly land, to be the most important

broader category for explaining income inequality, followed by location, demographic characteristics,

and education.

Ali and Akhter (2014) examine the determinants of income and income gap for male and female

workers in Pakistan for the year 2010-2011. The study uses education, industry province, literacy,

occupation, job status, age, marital status and region as explanatory variables to estimate earning

equations separately for males and females by applying the OLS. Blinder-Oaxaca decomposition

method is used to analyze earnings gap between males and females. Findings suggest that earnings

increases with the level of education for both the male and female workers, however earnings are

significantly higher for females as compared to the males. Professions such as manages,

professionals and technicians contribute highest to the earnings for both males and females as

compared to the other professions. Female paid employees earn more than their employers & self-

employed counter parts as compared to the males. Individual characteristics like education, job

status, marital status, and occupation and are the major determinants of income gap between male

and female workers in Pakistan.

The literature above reveals that most of the existing studies on inequality decomposition in Pakistan

either focus on sub group/source decompositions or rely on the simple regression based approaches

(i.e., Ordinary Least Square method and Blinder-Oaxaca methodology) to establish the determinant

of income and income gap. The present study combines the two concepts in the sense that, it first

establishes the determinants of income through OLS in Pakistan and then, uses the estimated

coefficients from the income regression to establish the determinants of inequality by exploiting the

resemblance to source decomposition analysis. The method is only used by one more study in

7

Pakistan to find the determinants of rural income inequality at household level but since there is not

a single research on the determinants of inequality among the earners on the national level. The

present study fills the research gap left out by the earlier studies.

3. Data

The study uses Household Integrated Economic Survey (HIES) data for the years 2005-06 and

2010-11 conducted by Pakistan Bureau of Statistics (PBS). The total number of households covered

in the survey is 15435 in 2005-6 and 16338 in 2010.

HIES (2005-06) provides information about 110816 individuals out of which 43501(39.2%) live in

urban areas and 67315(60.47%) live in rural areas. The total number of individuals covered in

Punjab, Sindh, KPK and Baluchistan provinces are 43255 (39.03%), 27468 (24.79%), 23848

(21.52%) and 16245 (14.66%) respectively. The proportion of male and female individuals covered

in the survey is 50.4% (55831) and 49.6% (54985), respectively.

HIES (2010-11) provides information about 109181 individuals out of which 43120 (39.5%) live in

urban areas and 66061 (60.5%) live in rural areas. The total number of individuals covered in

Punjab, Sindh, KPK and Baluchistan provinces are 43089 (39.5%), 27265 (25%), 21708 (19.9%) and

17119 (15.7%) respectively (HIES, 2010-11). The proportion of male and female individuals covered

in the survey is 51% (55713) and 49% (53468), respectively.

Since the main purpose of the analysis is to study the determinants of inequality among the earners.

The final analysis is carried out for economically active earners, i.e., 23784 individuals in 2005 and

23656 in 2010.

4. Methodology

4.1 Theoretical Model

The study uses the framework provided by the human capital model. The theory states that people

invest in the human capital in the hope of earning more in the future. Numerous variables are used

in the literature to estimate the determinants of income some of which are continuous while others

are dummy variables. The basic idea of the human capital model is that; investment in the human

capital increases the productivity of an individual i.e. through education. As labor wages are aligned

8

with the productivity of the labor, so as the productivity increases the wages also increase.

Individuals continue to invest in the human capital until marginal returns equal the marginal cost.

The standard human capital model is specified as:

… (1)

where, lnY, the dependent variable, is the log of yearly income of an individual i, is a row matrix

of individual characteristics including measures such as education, occupation, age, gender, province,

job status. represents the error term with the normal distribution i.e. the zero mean and constant

variance.

4.2 Methodological Framework

Four types of analyses are carried out to shed light on the inequality in income for the year 2005 and

2010. First, the study uses income data to construct a Gini coefficient using the Lorenz curve

method. Second, it uses OLS regression to examine the determinants of income among the

individuals. Third, it uses the coefficients estimated from the OLS regression to find the percentage

contributions of these determinants to the Gini coefficient by using Field’s methodology and lastly it

examines the extent to which these characteristics account for the change in Gini coefficient over

time.

Step 1: Estimating Gini Coefficient

In the first step the study estimates the inequality index, since the main purpose of the analysis is to

examine the determinants of inequality and its change (3rd and 4th step). For this purpose Gini

coefficient is computed by using the formula proposed by Lorenz (1905), i.e.,

Gini-coefficient = Area between Lorenz Curve and Diagonal / Total Area under Diagonal … (2)

The Gini-coefficient varies between the limits of 0 and 1.

Step 2: Determinants of Income

In the second step, the study estimates the determinants of income among earners by using the OLS

regression. The income generating function used for this purpose is similar to (1)

9

Step 3: Determinants of the Level of Inequality (Fields Method)

In the third step, the coefficients obtained from the OLS regression are used to find the percentage

contribution of the variables to the level of inequality (Gini coefficient), also known as the factor

inequality weights, sj:

⁄ = ( ) ⁄ 5 … (3)

where represents the estimated coefficient from the OLS regression of the characteristic of

an individual, and represents the value taken on by the characteristic. and σ are

the standard deviation of and of , respectively and is the correlation between

factor and . Therefore, indicates the share of characteristic in inequality (Gini

index), due to the fact that is unequally distributed among the earners. The positive implies

that is an inequality-increasing factor whereas the negative means that factor decreases the

inequality. The are summed to one, ∑ = 1, where is the inequality arising from the

omitted variables and ∑ = i.e. the explanatory power of the income regression determines the

proportion of inequality explained. Equation (3) clarifies that the factor inequality weights will be

large if (i) is large, i.e. characteristic has a large return; (ii) varies highly relative to yearly

income; or (iii) there is a high correlation between the characteristic and the yearly income.

Step 4: Determinants of Change in Inequality

In the fourth step, the factor inequality weights are further used to examine the extent to which

changes in Gini coefficient over time are explained by changes in the distribution of the

characteristics. For this at least two comparable household surveys are required that are done in the

different time periods so that the same income regression equation can be estimated for each period.

Let represent any inequality measure at time period , represent the relative factor inequality

weight of characteristic of an individual in the time period . Then, the difference in inequality

can be expressed in terms of each period’s factor inequality weight and inequality index.

5 The formula allows us to decompose any measure of inequality as long as the decomposition rules are followed (Shorrocks, 1982) and the model is log liner, as the percentage contribution of the variables to the inequality remains the same. The inequality indices satisfying these conditions are the Gini coefficient, the Atkinson index, coefficient of variation, generalized entropy family, and the various centile measures. The derivation of the formula proposed by Fields (2003) is given in the appendix.

10

∑ + ( ) … (4)

Therefore, the percentage contribution of the jth factor to the difference in inequality becomes

⁄ , where ∑ … (5)

Unlike the decomposition of the level of inequality explained in the previous section, the

decomposition of changes in inequality depends on the inequality index used in the analysis. Hence,

the factor inequality weights in the decomposition of changes in inequality, the , are no longer

independent of the choice of inequality measure used.

4.3 Regression Model

Following regression equation is used for the main analysis;

+ + +

represents yearly earnings of an individual “i”. The independent variables male, age,

education, occupation, province, and job status represent individual characteristics. represents the

intercept of the equation, are the coefficients of the continuous variables and the rest are the

coefficients of dummies.

Education plays an important role in human capital formation by raising the productivity of an

individual and hence the labor income. However the returns to education are not uniform, since

different school years impart different skills and therefore affect the earnings differently as pointed

in many earlier studies. Most of these studies use dummy variables to capture the effect of different

levels of education. In order to examine the effect of school years at different levels of education,

Van Der Gaag and Vijverberg (1989) divided the years of schooling according to the school systems

of Cote d’ Ivore. Similarly Khandker (1990) also used years of primary, secondary and post-

secondary schooling in wage function for Peru. Both studies found significant differences in returns

to education at different levels of education. Following the literature the present study also divides

schooling into categories to capture the differences in returns for different levels of education.

11

The existence of vast gender gap in the average earnings of the male and the female workers has

been reported by many earlier studies. Ali and Akhtar (2014) found earnings of the males to be five

times more than that of the females; their finding is consistent with the studies by Nasir (1998) and

Ali (2013). Therefore the present study accounts for the gender gap by introducing gender dummy

in the regression model.

Although HIES provides information on the highest level of education attained by an individual. It

lacks information regarding the age of school admission for the study period 2005-6 and 2010-11.

The admission age is particularly important to estimate the potential experience of an individual

earner. The experience indicator used by Mincer (1974) is an appropriate proxy especially for the

earners of the United States as they are admitted to the school at an exact age of 6. However, this

assumption is not true in case of Pakistan, since there is no uniform age to start school. The present

study therefore avoids the use of potential experience (AGEi- 6 - EDUCATIONi ) and uses age as

proxy to the variable experience.

The provinces of Pakistan exhibit different socio – economic characteristics and cultural values. Rate

of return to education is noted to be different for different provinces that highlight not only the

existence of differences in the market opportunities but also the uneven expansion of social services

across provinces [Irfan and Khan (1985); Khan and Shabbir (1991); Shabbir (1994); and Haq

(1997)]. These differences are accounted for, by the use of provincial dummies in the regression

model.

Employment status of earners is another factor that can be associated with the level of earnings and

earnings inequality. Ali and Akhtar (2014) while examining the determinants of income and income

gap found employers/self-employed to earn more than the other categories of earners. Following

literature the study divides job status into three categories. Along with the other factors literature

confirms occupation to be the major determinant of income as well [Ali (2013); Alejos (2003)].

Table 1 presented in the following provides a brief description of the variables used in the analysis.

12

Table 1: Variables Description

Variable Definition

LNY Logarithm of yearly income of an individual

Gender:

Male

Female

Dummy variable (1 if male, 0 otherwise)

Base category

Age:

Age square:

Continuous variable

Continuous variable

Education:

No Education

Primary Education

Secondary Education

Intermediate Education

Professional Education

Base category

Dummy variable (Primary = 1 if edu>0 & edu<= 5, 0 otherwise)

Dummy variable (secondary = 1 if edu >5 & edu<= 10 , 0

otherwise)

Dummy variable (intermediate=1 if edu >10 & edu<= 12, 0

otherwise)

Dummy variable (Professional =1 if edu> 12 , 0 otherwise)

Occupation:

Occupation 1

(Legislators, Senior Professionals)

Occupation 2

(Professionals, Managers Technicians and

Associate Professionals)

Occupation 3

(Clerks and Service Workers and Shop and

Market Sales Workers)

Occupation 4

(Skilled Agricultural and Fishery Workers)

Occupation 5

(Craft and Related Trades Workers)

Occupation 6

(Plant and Machine Operators and

Assemblers)

Occupation 7

(Elementary Occupations)

Dummy variable (1 if Occupation 1, 0 otherwise)


Base category

Dummy variable (1 if occupation 4, 0 otherwise)




Province:

Punjab

Sindh

KPK

Baluchistan

Dummy variable (1 if Punjab, 0 otherwise)

Base category

Dummy variable (1 if KPK, 0 otherwise)

Dummy variable (1 if Balochistan, 0 otherwise)

Job status

Employers, Self Employed

Paid Employees

Cultivators, Share Croppers and Livestock

Base category

Dummy variable (1 if paid employees, 0 otherwise)

Dummy variable (1 if cultivators/ share cropper/livestock, 0

otherwise)

13

5. Results and Discussion

5.1 Descriptive Statistics

Table 2 provides descriptive statistics of the variables by utilizing Household Integrated Economic

Survey data for the time periods 2005-06 and 2010-11. Sample for the year 2005 contains 23784

individuals with average yearly income of 64937.02 RS. Among them, 20,625 are males and 3109 are

females. According to the statistics, average age of the earner in the sample is 36 years with majority

belonging to Punjab (41percent) followed by Sindh (27 percent), KPK (17 percent), and Balochistan

(13 percent). More than half the individuals in the sample are paid employees. As majority of the

individuals have the qualification of secondary or below, elementary and clerical posts employees the

highest percentage of the workers.

Sample for the year 2010 contains 23656 individuals with average yearly income of 121671 RS.

Among them, 21,184 are males and 2,472 are females. Average age of the individuals is 36 years,

same as the sample of earners in 2005. The rest of the variables observe the same trend with the

little variation in the percentage contributions.

Table 2: Descriptive Statistics

Variable 2005

Mean

2010

Mean

Yearly Income 64937.02 121671.4

Gender

1. Male 2. Female

.87

.13

.90

.10

Age

Age square

36.03

1500.5

36.46

1515.40

Education

1. No Education 2. Primary 3. Secondary 4. Intermediate 5. Professional

.410

.168

.269

.059

.091

.385

.163

.285

.066

.098

Occupation

1. Legislators, Senior Professionals

2. Professionals, Managers Technicians and

Associate Professionals

3. Clerks and Service Workers and Shop

and Market Sales Workers (Reference

.063

.058

.211

.075

.049

.197

14

Group

4. Skilled Agricultural and Fishery Workers

5. Craft and Related Trades Workers

6. Plant and Machine Operators and

Assemblers

7. Elementary Occupation

.193

.108

.070

.292

.146

.099

.075

.355

Province

1. Punjab 2. Sindh 3. KPK 4. Balochistan

.419

.274

.175

.130

.422

.271

.162

.142

Job status

1. Employers, Self employed 2. Paid Employees 3. Cultivators, Share Croppers, Livestock

.189

.632

.177

.152

.709

.137

Source: Calculated from 2005-06 and 2010-11 Household Integrated Economic Survey.

5.2 Estimation of the Gini Coefficient

The estimates of the inequality index Gini computed by using the Lorenz curve method in the Table 3 given

below indicates decline in inequality among the earners from 0.52 in 2005 to 0.42 in 2010. The decrease in

Gini coefficient is also evident by the inward movement of the Lorenz curve towards the line of equality

since the green line in the Figure 1 gives the Lorenz curve for 2005 and the red line gives the Lorenz curve

for the year 2010.

Table 3: Gini Coefficients for the Years 2005-6 and 2010-11

Year Gini Coefficient

2005 0.5223

2010 0.4489


15

Figure 1: Lorenz Curves for the Distribution of Earnings in Pakistan, 2005 and 2010

5.3 Estimation of the Coefficients

The Ordinary Least Square (OLS) estimates of the equation 1 are provided in the Table 4 for both

the study periods. The values of R2 indicate that the independent variables can explain 49 to 48

percent of the variation in the dependent variable which is good enough for the cross sectional data

sets. Examining the variables individually, this study finds that most of the estimated coefficients are

significant at 1 percent level with a few significant at 5 percent.

The estimates show income of the male workers to be higher than that of the female workers by 142

percent in 2005 and 114 percent in 2010. Examining the income profiles of the male and female

workers as given in the Table 5 to 8, it is clear that the main reason for the differences in income is

discrimination in the labor market.

The variable age has a positive sign, while age square has a negative in both the study periods. Both

the variables are significant and correctly signed as postulated by the human capital theory, implying

that income increases with age but at a decreasing rate.

The study considers five educational categories i.e. no-education, primary, secondary, intermediate

and professional (Table 4). Results indicate that the estimates of the coefficients of education are not

only positive but are increasing with rise in education level, demonstrating a curved association

between income and education. The categories primary, secondary, intermediate and professional

educations earn more than the base category by 15, 31, 54, and 87 percent in 2005 (Table 4).

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

equality

lorenz 2010

lorenz 2005

16

However, in 2010 the income differential arising from different educational dummies has declined as

compared to the 2005. The result is consistent with Psacharopoulos (1994) who also found the

educational returns to decline over time.

Estimates of the coefficients for Punjab and KPK are negative for both the study periods; indicating

that these provinces lag behind in income as compared to the base category by -6 and -17.4 percent

in 2005 and -5 and -9 percent in 2010 (Table 4). However, the coefficient for the province of

Balochistan is positive for both the study periods as compared to the base category implying that

people in Balochistan earn more than the people of Sindh by about 3.8 percent in 2005 and 22.4

percent in 2010. The signs of the variables are consistent with the earlier studies (Nasir and Nazli,

1999) and (Ali and Akhtar (2014)).

To capture the income differential due to occupation seven categories are introduced. The income

of the workers engaged as legislators/senior professionals, professionals/managers technicians and

plant/machine operators are both positive and significant in both the study periods; implying that

the workers belonging to these occupations earn more than those belonging to the base category

(Table 4). However, the workers belonging to the agriculture/fishery, craft/trade workers, and

elementary occupations earn less than those in the base category since low income individuals are

employed in these occupations (PRSP-II). The findings are consistent with the earlier studies by (Ali

(2013)) and (Ali and Akhtar (2014)).

Job status is introduced as an independent variable in the model by introducing three dummy

variables namely employers/self-employed, paid employees and cultivators/share croppers/live

stock. The dummy employers/self-employed is used as the base category. According to results paid

employees and cultivators/share croppers/ live-stock lag behind in income as compared to the base

category by -36 and -27 percent in 2005 and -37 and -5 percent in 2010 (Table 4).

Since the value of R2 is compromised in log linear and linear log models (Stock and Watson, 2007).

The predicted and the actual log of income is plotted against the numbers of earners to explain how

well the model fits the data (Figures 3 and 4). The actual log of income is represented by the scatter

plot whereas the predicted log of income is represented by the red line. Examining the graphs of

both the years as presented in the Figures 3 and 4, it is clear that the fitted line passes right from the

17

center of the scatter plot for the actual log of income and has a slight curvature to it. However, the

fitted line in both the figures is unable to capture the deviation in the actual log of income. Since the

volume of the data is large and we want our model to remain parsimonious hence capturing

maximum curvature, keeping in view that unnecessary complications are avoided in the final

functional form of the model.

Table 4: Earnings Equation Results, 2005-06 and 2010-11

Dependent Variable: Logarithm of Annual Income (T Values Are Given In Parenthesis)

Variable 2005 2010

Gender Male 1.42 (89.27)* 1.14(78.23)*

Age Age square

.088(50.42)* -.0009(-43.01)*

.085(52.10)* -.00088(-43.81)*

Education 1. No Education (omitted) 2. Primary 3. Secondary 4. Intermediate 5. Professional

.155(10.41)* .317(23.63)* .548(23.26)* .87(38.50)*

.098(7.66)* .290(25.35)* .494(25.46)* .833(42.89)*

Occupation 1. Legislators, Senior Professionals 2. Professionals, Managers Technicians and Associate

Professionals 3. Clerks and Service Workers and Shop and Market

Sales Workers(omitted) 4. Skilled Agricultural and Fishery Workers 5. Craft and Related Trades Workers 6. Plant and Machine Operators and Assemblers 7. Elementary Occupations

.513(20.07)* .288(11.57)*

-.186(-5.94)* -.121(-6.28)* .097(4.42)*

-.176(-11.26)*

.420(20.20)* .297(13.23)*

-.200(-5.27)* -.057(-3.33)* .049(2.61)*

-.153(-11.31)*

Province 1. Punjab 2. Sindh (omitted) 3. KPK 4. Balochistan

-0.06(-4.82)*

-.174(-11.19)* .038(2.27)**

-.050(-4.64)*

-.09(-7.21)* .224(15.86)*

Job status 1. Employers, Self Employed (omitted) 2. Paid Employees 3. Cultivators, Share Croppers, Livestock

-.36(-25.62)* -.27(-8.43)*

-.378(-28.41)* -.053(-4.68)*

Constant 7.65(189.59) 8.64(227.01)

R2

F-statistic N

0.4935 1286.46 23784

0.4813 1218.41 23656


18

Table 5: Average yearly Income by Age and Education Group (2005) Male

Education 0-9 10-13 14 +years

Age 10-20 27701 29504 42647

21-25 38322 47982 119985

26-30 47469 63069 125109

31-35 57799 73333 128423

36-40 64898 86031 150782

41-45 69977 98355 222123

46-55 65633 97497 275821

56+ 5340 8685 1925

Source: HIES 2005-6.

Table 6: Average yearly Income by Age and Education Group (2005) Female Education 0-9 10-13 14+years

Age 10-20 12293 12589 18300

21-25 8573 17000 37478

26-30 9793 26667 82089

31-35 26314 55900 218310

36-40 13282 27940 120400

41-45 15736 35776 178202

46-55 14179 24966 170215

56+ 1072 1687 1687


Table 7: Average yearly Income by Age and Education Group (2010) Male

Education 0-9 10-13 14 +years

Age 10-20 52786 55303 55600

21-25 73328 84310 133353

26-30 88823 110707 197731

31-35 103732 134671 244715

36-40 114270 157012 309850

41-45 120394 161457 310583

46-55 126516 178501 399000

56+ 9287 15063 2444


Table 8: Average yearly Income by Age and Education Group (2010) Female Education 0-9 10-13 14+years

Age 10-20 25016 27867 41644

21-25 22173 46674 75804

26-30 28321 67464 140784

31-35 27024 62609 165545

36-40 29242 59728 266602

41-45 31627 62191 267072

46-55 35079 90741 376185

56+ 1424 3484 1698.5


19

Figure 2: Plot of Actual Income and Predicted Income against the Number of Earners 2005

Figure 3: Plot of Actual Income and Predicted Income against the number of Earners 2010

5.4 Estimation of Factor Inequality Weight

The regression-based decomposition methodology proposed by Fields (2003) enables the current

study to measure how much the inequality in yearly income is explained by the various human and

non-human capital characteristics of each earner. To answer this question, the study uses the

estimates the coefficients, as given in Section 5.3, to calculate the factor inequality weight, ,

attributed to each of the individual characteristics.

20

Tables 9 and 10 present how the factor inequality weight of each variable is computed. The positive

value of the means that the variable is inequality increasing whereas the negative value means that

the variable is inequality decreasing. While the coefficients estimated in the section 5.3 indicate the

significance of the variables in determining workers yearly income or earnings differential, factor

inequality weight in the current section reveal that only a few variables significantly determine the

inequality level.

Examination of Factor Weight Inequality in the year 2005

Table 9 presents the factor inequality weight of each variable in 2005. As a whole, the included

variables can explain 49.35 percent of the inequality in the yearly income, equivalent to the value of

coefficient of determination ( ). All the variables have a positive except the variables primary

education and age square. The positive means that the variables are inequality increasing whereas

the negative means that the variables are inequality decreasing. The study concludes gender, age

and education to be the major inequality increasing factors with their consolidated shares of 21.32

percent, 10.08 percent, and 9.37 percent, respectively (Table 12).

Gender

Male earn more than the females, as females are not treated equal to their male counterparts in the

labor market and are given lower compensation thus, widening the income gap between the two.

Due to this gender male provides highest contribution to the inequality with the contribution being

21.32 percent.

Education

Primary education decreases the inequality but its effect is very small as it contributes only about

-0.244 percent. The effect drives from the fact that the variable is distributed in the favor of low

income individuals and also from the fact that it enables them to engage in more income generating

opportunities compared to the non-educated individuals, as most employers prefer literate

employees. However, secondary, intermediate and professional educations are inequality increasing

with their contribution being 1.24 percent, 1.36 percent and 7.01 percent, respectively. The rise in

inequality shares of the education variables show that the returns to education are based on

investment the more the individuals invest in education the more they earn.

21

Age

Among all the inequality increasing factors the consolidated contribution of age is second highest at

almost 10.08 percent (Table 12). Age contributes positively to inequality whereas age square

contributes negatively to it (Table 9). The result can be explained by the fact that age is distributed in

favor of the individuals with high income, so with age, experience increases people get promoted

which improves their ability to generate income as compared to the low income individuals.

Occupation

Legislators/senior professionals provide the highest contribution to inequality (2.45 percent) since

the variable is distributed in the favor of the high income individuals. The coefficients obtained

through the OLS regression show that the individuals in the above occupation earn not only in

excess of the individuals in the base category but more than any individual in the other occupations,

thus widening the income gap. Moreover, the correlation between legislation and income is positive

and greater than any other occupational category.

Similarly the coefficients of the professionals/managers technicians and plant and machine

operators show the individuals in these categories earn less than the legislators/senior professionals

but more than the remaining occupations, thus leading to the positive contribution to inequality.

Workers employed in agricultural/fishery, craft/trade workers, elementary occupations earn less

income compared to the base category as more low income/poor individuals are engaged in these

occupations (Asian development bank and PRSP- II). These occupations contribute positively to the

inequality with their contribution being 0.33%, 0.52% and 1.419 % which can be attributed to the

low returns.

Province

The coefficients obtained through the OLS regression show that the individuals in Punjab and

KPK earn less than the base category Sindh. The contribution of these provinces to inequality is

positive which can be attributed to the low return. However the individuals in Balochistan not only

earn more than the base category but even more than the other provinces thus leading to a large

income gap between the population and thereby contributing positively to inequality.

22

Job status

Individuals working as the paid employees and cultivators/share croppers earn less income as

compared to the employees/self-employed (base category). However, these variables contribute

positively to inequality with the contributions being 2.28 percent and 0.25 percent, which can be

attributed to low returns.

Table 9: Factor Inequality Weight of the Variables in 2005

Variable Standard deviation of Xj

Correlation Of

LNY)

Factor inequality

weight

Gender Male 1.4273 .3370 0.4815 0.2132

Age Age square

.0880 -.0009

14.2130 1157.397

0.2372 0.1756

0.2731 -0.1723

Education 1. No Education (Omitted) 2. Primary 3. Secondary 4. Intermediate 5. Professional

.1552

.3175

.5487

.8797

.3743

.4437

.2367

.2881

-0.0456 0.0962 0.1140 0.3007

-0.0024 0.0124 0.0136 0.0701

Occupation 1. Legislators, Senior Professionals 2. Professionals, Managers

Technicians and Associate Professionals

3. Clerks and Service Workers and Shop and Market Sales Workers(omitted)


5. Craft and Related Trades Workers 6. Plant and Machine Operators and

Assemblers 7. Elementary Occupations

.5136

.2884

-.1862

-.1216 .0979

-.1760

.2445

.2346

.3947

.3114 .2557

.4547

0.2120

0.1324

-0.0498

-0.1495 0.0778

-0.1926

0.0245

0.0082

0.0033

0.0052 0.0017

0.0149

Province 1. Punjab 2. Sindh (omitted) 3. KPK 4. Baluchistan

-0.0607

-.1745 .0387

.4935

.3802 .3372

-0.0605

-0.0007 0.0816

0.0016

0.000043 0.00098

Job Status 1. Employers, Self Employed

(omitted) 2. Paid employees 3. Cultivators, Share Croppers,

Livestock

-.3657 -.2740

.4820

.3824

-0.1409 -0.0268

0.0228 0.0025

LNY 1.0863

Unexplained Variation ( 1 - ) 0.5064

Source: Calculated from 2005-06 Household Integrated Economic Survey.

23

Examination of the Factor Weight Inequality in the year 2010

When compared to 2005, a slight increase in the Unexplained Variation in 2010, indicate that the

variables included in the model do not explain the inequality in the yearly income as accurately as

before (Table 9 & 10). In 2010, the included variables explain about 48.13 percent of the inequality

with majority being inequality increasing like before as indicated by the positive sign of their factor

inequality weights. The study concludes gender, age and education to be the major contributor to

inequality with their consolidated shares of 15.1 percent, 11.64 percent, and 10.61 percent,

respectively (Table 12).

Gender

The gender dummy male contributes 15.10 percent to the inequality, in comparison to 21.32 percent

in 2005 thus showing a significant decline in inequality (Table 9 & 10). The decrease in inequality

arises from the fact the gender earnings gap has been reduced from 2005 to 2010. However, the

effect is still large enough as it is the single most important factor determining the earnings gap

between the two genders as evident by the coefficient.

Education

When compared to 2005, intermediate and professional education worsens the inequality in 2010

while primary and secondary education decreases the inequality (Table 9 & 10). The contribution of

primary education in decreasing inequality slightly increases to -0.29 percent in 2010. The effect exist

as the variable is distributed in favor of the individuals with less income and also from the fact that

the primary education enables the individuals to engage in more income generating opportunities as

compared to the non-educated individuals as employers prefer literate employees. Secondary

education is inequality increasing factor in both the years but its contribution to inequality declines

from 2005 to 2010, suggesting the role of secondary education in decreasing inequality. Now

considering the inequality increasing factors, the contribution of intermediate and professional

education to inequality increases from 2005 to 2010.

Age

Compared to 2005, the consolidated contribution of age increases from 10.08 percent to 11.64

percent in 2010; implying that the age contributes more to inequality in 2010 (Table 12). The result

can be explained by the fact that age is distributed in favor of the individuals with high income, so

24

with age, experience increases people get promoted. This improves their ability to generate income

as compared to the low income individuals.

Occupation

The inequality contribution of agriculture/fishery workers, craft/trade workers declines from 2005

to 2010 as their correlation coefficient improves (Table 9 & 10).

Legislators/senior professionals contribute maximum to the inequality in 2010 since the variable is

distributed in the favor of the high income individuals. The coefficients obtained by the OLS

regression show that the individuals in the above occupation earn not only in excess of the

individuals in the base category but more than any individual in the other occupations, thus

widening the income gap. Similarly the coefficients of the professionals/managers technicians and

plant and machine operators show the individuals in these categories earn less than the

legislators/senior professionals but more than the remaining occupations, thus leading to the

positive contribution to inequality.

Workers employed in craft/trade workers and elementary occupations earn less income as compared

to the base category. Although more low income individuals are engaged in these occupations.

These occupations contribute positively to the inequality with their contribution being 0.16 % and

2.15 % which can be attributed to the low returns. Those employed in agricultural/fishery also earn

less as compared to the base category (clerks/service workers). But it emerges as the inequality

decreasing factor.

Province

The share of the inequality declines from 2005 to 2010 for the province of Punjab and rises for

KPK due to increase in negative correlation with income (Table 9 & 10). The contribution of

Punjab to inequality is 0.166 percent in 2005 which declines to 0.1385 percent in 2010 whereas the

contribution of KPK increases from 0.0043 percent to 0.02 percent in 2010. The decline for Punjab

indicates the narrowing income gap between the province and the base category but as Punjab still

get low returns as compared to Sindh the variable contributes positively to inequality. However,

Baluchistan contributes more to the inequality in 2010 as compared to 2005. The rise in inequality is

attributed to the fact that Baluchistan earns more in 2010 as compared to 2005 as evident by the

coefficient, thus leading to the larger income gap and higher contribution to inequality.

25

Job status

The contribution of paid employees to inequality increases from 2.28 percent in 2005 to 3.63

percent in 2010 (Table 9 & 10). This increase can be explained by the increase in income gap as paid

employees are earning less in 2010 as compared to the base category (employees/self-employed) in

2005.While the earning of the cultivators/sharecroppers improves from 2005 to 2010 and its

correlation becomes positive. Therefore the variable becomes inequality decreasing.

Table 10: Factor Inequality Weights of the Variables in 2010

Variable Standard Deviation

of Xj

Correlation of

LNY)

Factor Inequality

Weight

Gender Male 1.1493 .3059 .3924 0.1510

Age Age square

.08503 -.00088

13.6376 1101.696

0.2726 0.2152

0.3461 -0.2297


.0989

.2902

.4940

.8332

.3700

.4517

.2498

.2982

-0.0742 0.0756 0.1231 0.3000

-0.0029 0.0108 0.0166 0.0816

Occupation 1. Legislators, Senior Professionals 2. Professionals, Managers Technicians

and Associate Professionals 3. Clerks and Service Workers and Shop

and Market Sales Workers(omitted) 4. Skilled Agricultural and Fishery

Workers 5. Craft and Related Trades Workers 6. Plant and Machine Operators and

Assemblers 7. Elementary Occupations

.4202

.2971

-.2006

-.0574 .0492

-.1537

.2637

.2174

.3540

.2986

.2640

.4787

0.2241

0.1396

0.0134

-0.0898 0.0465

-0.2679

0.0271

0.0098

-0.0010

0.0016 0.0006

0.0215


-.0502

-.0979 .2248

.4939

.3692 .3499

-0.0510

-0.0068 0.1187

0.0013

0.0002 0.0102

Job status 1. Employers, Self-employed (omitted) 2. Paid employees 3. Cultivators, Share Croppers, Livestock

-.3789 -.0532

.4540

.3447

-0.1931 0.0280

0.0363 -0.0005

LNY .9134

Unexplained Variation ( 1 - ) 0.5187

Source: Calculated from 2010-11 Household Integrated Economic Survey.

26

5.5 Contribution to Change in Inequality during 2005-2010

The regression based decomposition methodology enables the current study to clarify how changes

in the individual characteristics explain the change in the inequality over time. Using equation (x) and

the values of Gini coefficient, this study assesses the contribution of each characteristic to the

change in inequality.

⁄ …(x)

The sign of the factor weight determines the direction of the contribution to inequality. If the sign is

positive then the variable is inequality increasing whereas the negative sign depicts the variable to be

inequality decreasing. However, if the factor weight declines in the current year as compared to the

previous year, it indicates the ability of the variable in lessening the inequality in the yearly earnings.

Factor inequality weights are given in the first two columns of the Table 11 for the year 2005 and

2010. Comparison of the factor inequality weights show that the variables improving the inequality

are male, primary education, secondary education, agriculture/fishery workers, craft/trade workers ,

plant/machine operators , Punjab and cultivator/sharecroppers. On the contrary variables increasing

the inequality in 2010 are age, intermediate, professional, legislators/senior professionals,

professionals/managers technicians, elementary occupations, paid employees, KPK and Balochistan.

The third column gives the numerator of the equation (16), the negative values in the third column;

indicates the variables ability in lowering the Gini coefficient. If the value is positive it works in the

opposite direction. The variables improving the Gini coefficient are male, primary education,

secondary education, legislators/senior professionals, agricultural/fishery workers, craft/trade

workers, plant/machine operators, Punjab, and cultivator/sharecropper/livestock.

Last column describes how each variable explains the change in Gini coefficient. Since, the Gini

coefficient improves to 0.44 in 2010 as compared to 0.52 in 2005, the negative values in the last

column indicate the variables that account for increasing the Gini coefficient and vice versa.

Examining the effect of each factor on the change in inequality enables this study to verify the

effectiveness of government policies.

The improvement in the inequality (Gini coefficient) due to gender and primary education can be

attributed to the effectiveness of the government policies under Poverty Reduction Strategy

Program II (PRSP-II). The policies were aimed at the elimination of all kinds of discrimination

27

against women including wage discrimination under International Labor Organization (ILO)

assistance, and on increasing accessibility of primary education to the masses.

Similarly, agriculture was also given high priority under PRSP-II as majority of the poor

concentrated in the rural areas are employed in it. The policy aimed at Self-reliance in commodities,

food security through improved productivity of crops as well as development of livestock and dairy

through: (i) development of new technologies; (ii) more productive use of water through precision

land leveling and high efficiency irrigation systems; (iii) promoting production and export of high

value crops; (iv) accelerating the move towards high level activities, such as livestock rearing, dairy

production, fisheries, and horticulture; (v) creating necessary infrastructure; and (vi) ensuring

availability of agricultural credit.

Similarly, livestock contributes 11 percent to the GDP; almost about 30 to 35 million people in the

rural areas depend upon it. Since the sector is highly labor intensive it has a potential to generate

income for small farmers as well as for the landless and for rural women. Therefore micro credit was

made available under the PRSP-II covering provision of goats, sheep and dairy animals on personal

guarantee without any formal collateral. Livestock producers were to be provided with improved

animal feed and artificial insemination facilities. Since the low income individuals are employed in

the categories agriculture/fishery workers and cultivators/sharecroppers/livestock, policies aimed at

improving their incomes leads to the reduction in Gini coefficient. Other variables improving the

Gini coefficient are secondary education, craft/trade workers, plant/machine operators and Punjab.

Return to the secondary education decline in 2010 in reference to the base category thereby reducing

its inequality share in 2010 and contributing to the decline in Gini coefficient.

Since people with the low incomes are usually employed in craft/trade workers, therefore the

improvement in the return in 2010 as indicated by the coefficient leads to the decline in factor

inequality weight in 2010 and thereby improvement in the Gini coefficient. However, plant and

machine operators earn more in 2005 as compared to the 2010 in comparison to the base category

thereby reducing its inequality share in 2010 and contributing to the decline in Gini coefficient.

Return to the Punjab improve in 2010 as compared to 2005 in comparison to the base category

thereby reducing its inequality share in 2010 and contributing to the decline in Gini coefficient.

28

Table 11: Contributions to the Change in Gini Coefficient during 2005-10


The first two columns of the Table 12 give the consolidated factor inequality weight; obtained by

summing all the variables in the same category for the 2005 and 2010. Comparison of both the

columns indicates that the contribution of gender to inequality is highest in both the study periods

but its value drops from 21 percent in 2005 to 15 percent in 2010. Age which is used as a proxy to

Variable Factor Inequality

Weight 2005

Factor Inequality

Weight 2010

Sj weighted by Gini Coefficient (Sj2010Gini2010-sj2005Gini2005)

Contribution to Change in Gini

Coefficient

Gender Male 0.2132 0.1510 -0.0435 0.5930

Age Age square

0.2731 -0.1723

0.3461 -0.2297

0.0126 -0.01310

-0.1727 0.1782


-0.0024 0.0124 0.0136 0.0701

-0.0029 0.0108 0.0166 0.0816

-0.000048 -0.0016 0.00034 0.000010

0.000654 0.02217 -0.0047 -0.00014

Occupation 1. Legislators and Senior

Professionals 2. Professionals, Managers

Technicians and Associate Professionals

3. Clerks and Service Workers and Shop and Market Sales Workers(omitted)


5. Craft and Related Trades Workers

6. Plant and Machine Operators and Assemblers

7. Elementary Occupations

0.0245

0.0082

0.0033

0.0052

0.0017

0.0149

0.0271

0.0098

-0.0010

0.0016

0.0006

0.0215

-0.00063

0.00011

-0.00217

-0.00199

-0.000619

0.0018

0.0086

-0.00157

0.0295

0.0271

0.0084

-0.0254


0.0016

0.000043 0.00098

0.0013

0.0002 0.0102

-0.000252

0.000067 0.0040

0.0034

-0.00092 -0.0553

Job Status 1. Employers,Self employed

(omitted) 2. Paid employees 3. Cultivators, Share

Croppers, Livestock

0.0228 0.0025

0.0363 -0.0005

0.0043 -0.0015

-0.0596 0.0208

Residual 0.5064 0.5187 0.4312

29

the variable experience emerges as the second highest contributor to inequality in both the years.

However its contribution to inequality increases from 10 percent in 2005 to 11 percent in

2010.Education comes in at third with the contribution 9.37 percent to inequality in 2005 and 10.61

percent in 2010.Occupation contributed nearly the same in both the years with contribution of 5.78

percent 2005 and 5.96 in 2010.The role of province and job status in explaining inequality increased

from 0.26 and 2.53 percent in 2005 to 1.17 and 3.58 percent in 2010. The third column gives the

contribution of the factors to the change in the inequality.

In the third column, the negative value of the factor inequality weight weighted by the Gini

coefficient confirms the role of the variables in decreasing inequality. The fourth column reveals that

during 2005-2010, Gini coefficient is improved by gender, age, education, and occupation. The share

of gender in reducing Gini coefficient is highest at 59.30 percent as compared to the other factors.

Occupation, education, and age contribute 4.68, 1.79, and 0.5 percent to the decline in Gini

coefficient.

Table 12: Consolidated Contributions to the Changes in Gini Coefficient during 2005-10


6. Conclusion and Policy Recommendations

Growth policy adopted to decrease poverty in Pakistan has always been characterized by high levels

of inequality. The existence of high levels of inequality during the periods of high growth not only

decreased the growth but also reduced the poverty decreasing effect of the growth whereas the

periods of low growth were marked by undue increases in poverty due to inequality. Therefore,

there is need to reduce poverty without increasing inequalities. To achieve this objective the study

looks beyond the growth dimension and tries to gain better understanding of underlying structure of

Variable Factor Inequality Weight 2005

Factor Inequality

Weight 2010

Sj weighted by Gini Coefficient (Sj2010Gini2010-sj2005Gini2005)

Contribution to Change in Gini

Coefficient

Gender 0.2132 0.151 -0.0435 0.5930

Age 0.1008 0.1164 -0.0004 0.0055

Education 0.0937 0.1061 -0.00132 0.01796

Occupation 0.0578 0.0596 -0.00344 0.0468

Province 0.0026 0.0117 0.00388 -0.0528

Job Status 0.0253 0.0358 0.0028 -0.0388

Residual 0.5064 0.5187 0.4312

30

income inequality by examining the factors that determine the level of income inequality and its

change. Since income of the individuals depend upon various factors among which gender, age,

occupation, job status and province of residence are likely to be prominent. The study investigates

the role of these factors in creating inequalities among the individuals by using regression-based

decomposition proposed by Fields (2003) for the year 2005-06 and 2010-11.

The study fist estimates the income regression as per the requirement of the Fields(2003) method.

Results suggest all the estimated coefficients to be significant; implying that all the variables have a

significant role in explaining differences in income. Gender emerges as the most important

determinant of income among the earners in both the study periods followed education,

employment status and occupation. Gender dummy male contributes highest to the income in both

the years followed by intermediate and professional education. The contribution of the categories of

education to income slightly decreases from 2005 to 2010. Among the occupational categories,

professional categories of occupation earn more than the base category whereas the rest earn less in

both the years. The variable age, which is used as a proxy to the variable experience, confirms the

trend postulated by the human capital theory.

Next, the estimates of the coefficient from the income regression are used to find the inequality

share of each variable to the Gini coefficient. Variables with positive factor inequality weight are

inequality increasing whereas the variables with negative factor inequality weight are inequality

decreasing. Almost all the variables have the same sign for the factor inequality weight for both the

years except for agriculture/fishery and cultivators/share-croppers/livestock. Among the policy

relevant variables, gender provides the highest contribution to the inequality among the earners due

to the discriminatory behavior of the employers against the female workers. Similarly, higher levels

of education also increase the inequality among the earners as higher investment leads to higher

returns thus widening the income gap. Occupations such as legislators/senior professional,

professionals/managers technicians and plant/machine operators are mainly distributed in the favor

of the individuals with high income, thus leading to the positive contribution to inequality whereas

the workers with the low income are employed in the agricultural/fishery, craft/trade workers,

elementary occupations (Asian development bank and PRSP- II) which also contribute positively to

the inequality due to the low returns. Similarly, individuals working as the paid employees and

31

cultivators/share croppers also contribute positively to the inequality as they earn less than the

employees/self-employed (base category).

Further the decomposition approach allows the study to examine the effect of these factors on the

change in inequality, i.e., Gini coefficient. The Gini coefficient in 2005 fell from 0.55 to 0.44 in 2010.

The improvement in the inequality (Gini coefficient) due to gender and primary education can be

attributed to the effectiveness of the government policies under Poverty Reduction Strategy

Program (PRSP-II). The policies were aimed at the elimination of all kinds of discrimination against

women including wage discrimination under International Labor Organization (ILO) assistance, and

on increasing the accessibility of primary education to the masses. Similarly, agriculture and livestock

were also given high priority under PRSP-II as majority of the poor concentrated in the rural areas

are employed in it. Since the low income individuals are employed in the categories agriculture/

fishery workers and cultivators/sharecroppers/livestock, policies aimed at improving their incomes

lead to reduction in Gini coefficient.

Since inequality is the main hurdle to the effective poverty alleviation, therefore adoption of the

well-directed policies aimed at reducing inequality is imperative to decrease poverty in the long run.

The present study suggests the following policy options;

The government needs to continue the policies adopted under the Poverty Reduction

Strategy Program II (PRSP-II) concerning elimination of gender discrimination, increasing

accessibility of primary education to the masses and expansion of the agriculture and fishery

sector in Pakistan to reduce the inequality further.

Higher level of education should be made accessible to the poor masses.

Incentives should be given for the expansion of the occupations such as craft/trade

workers, and plant/machine operators to reduce inequality further.

Policies concerning paid employees should be adopted to improve their incomes.

32

APPENDIX

REGRESSION BASED DECOMPOSITION METHODOLOGY

Derivation of the Model

To explain the level of income inequality computed by an inequality index a decomposition method

was proposed by Field’s (2003). The method starts with the income generating function based on

the human capital model in which income is expressed as a function of variables such as education,

experience, gender etc. The decomposition is based on the estimates of the income generating

functions similar to the equation (1)

∑ (3)

The subscript “i” refers to each individual, t denotes years, and “j” denotes the number of

variables/constants

The income generating function given above can be rewritten in matrix form as

(4.a)

Where

[ ] (4.b)

[ ] (4.c)

The regression decomposition given focuses on decomposing the log variance of the income which

is an inequality measure.

Theorem

Let A1,…,Ap and B1,…,BQ be the sets of two random variables , and a1,…,ap and b1,….,bq be the two

sets of constants. Then according to the theorem proposed by mood and boas; covariance between

the two random variables can be written as.

[∑ ∑

] ∑ ∑

[ ] (5)

Now applying this theorem in the context of a single random variable in Y such that

∑

the covariance for the single variable Y is obtained as

[∑ ] ∑ [ ]

(6)

33

But because the left-hand side of equation (6) is the covariance between lnY and itself, it is simply

the variance of lnY. Thus, equation (6) can be rewritten as

∑ [ ] (7.a)

Dividing equation (7.a) by (lnY), equation (7.b) is obtained as

∑ [ ] ⁄ ∑

(7.b)

where each is called the relative factor inequality weight. The relative factor inequality

weight gives the proportion of the log variance of income (inequality) explained by the variable (age,

education etc) or in other words it tells how much inequality in the total income is explained by a

certain variable.

[ ] ⁄ (7.c)

if the last element of Z is excluded which is the factor inequality weight of the error term, then the

remaining relative factor inequality weights can be represented as,

∑ [ ] ⁄ ,

Which sum exactly equal to the goodness of fit of the regression model. i.e. R2 (ln Y)

Since the ordinary correlation coefficient is related to the covariance as in equation (8).

[ ] [ ] ( ) ⁄ (8)

Therefore, given the income-generating function (4.a-4.c), let sj (ln Y) denotes the share of the log-

variance of income that is explained by the j’th explanatory factor and R2(lnY) be the fraction of the

log-variance that is explained by all of the Z's( variables) taken together except the error term. Then

the log-variance of income can be decomposed as equation (9.a).

[ ] ⁄

( ) [ ] ⁄ (9.a)

where

∑ (9.b)

and

∑ (9.c)

34

Equation (9.c) is similar to equation (9.b), except that the later excludes the residual term. Therefore,

we can rewrite the fraction that is explained by the j’th explanatory factors, Pj (ln Y), as

⁄ (9.d)

Equations (9.a-9.d) provide a full and exact decomposition of the log variance of income.

Extension to the other measures of inequality

Fields extended the decomposition to the other inequality measures by borrowing from the literature

of additive factor components (Shorrocks,1982). The decomposition rule is applicable to all the

inequality indices written as the weighted sum of incomes.

∑ (10)

where is the income of individual i, N is the total number of the individuals, Y = (Y1, Y2,…..,YN)

and is a weighting factor. Now, If an individual gets income from j different sources than his

total income is represented as the sum of the component incomes

.(such as pension,

employment income and transfers)

∑

(11)

By substituting (11) into (10), equation (10) can be expressed as the sum of source specific

components. i.e. if there are only three sources of income(pension, employment income and

transfers) then the inequality in the total income comprises of the sum of the inequalities of these

sources given by ∑ .

∑ ∑

∑

(12)

Now the share of the income source j in the inequality in the total income is given by, , also

known as the relative factor inequality weight

∑

⁄ (13)

Since, this results in the different decomposition rules for different inequality indices as weights

vary for the indices which will generate a different way of assigning factor contributions for

each index. Shorrocks (1982) proposed the solution to this problem by presenting a unique

decomposition rule;

( ) ⁄ (14)

Such that ∑ for any inequality index which is continuous and symmetric and for which

35

Nearly all the inequality indices satisfy these conditions, including Gini coefficient, the Atkinson

index, coefficient of variation, generalized entropy family, and the various centile measures.

This result can be further extended to the income generating functions given by Fields to account

for income inequality. As the standard income generating function is expressed as

(4.a)

The function has the same additive form as the equation expressing total income as the sum of

income from each source

∑

(11)

When the inequality of the above is decomposed Shorrocks (1982) obtains

( ) ⁄ Such that ∑ ,

which has the same form as 7 with replacing and replacing . Now taking advantage of

this similarity and applying shorrocks theorem, we get the following result.

[ ] ⁄ [ ] ⁄ (9.a)

As long as the decomposition rules are followed and the model is log liner, any measure of

inequality can be decomposed, because the percentage contribution of the variable to the inequality

will remain the same. The inequality indices satisfying these conditions are the Gini coefficient, the

Atkinson index, coefficient of variation, generalized entropy family, and the various centile

measures.

36

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