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Abstract:
The current study investigates income inequalities among earners engaged in different occupations and professions in Pakistan using HIES data for 2010-11 and 2015-16, focusing on their yearly income. Income equation and differences of income between subgroups of the population are estimated by using the OLS method. The generalized Entropy (GE) Class method is employed to evaluate the contribution of different subgroups of household characteristics and different income sources in overall inequality. The regression-based decomposition method is used to assess decompose changes in income inequality by various socio-economic factors. OLS estimates conclude that all variables play a significant role in explaining the differences in income. All indices of GE method indicate that inequality within the group is a greater problem than inequality experiences between groups. The decomposition method shows a positive sign of inequality decomposition for most household characteristics and income sources which depicts that these determinants have greatly contributed to overall income inequality.
Key Words:
Income Inequality; Occupations and Professions, HIES, OLS, GE, Decomposition Analysis
Introduction
The economic performance of a country and the living standards of people are very much dependent upon the channel through which income is distributed in the economy. This distribution process has divided the economies into different subgroups according to income generation levels. Some segments of the population benefited more than the other segments, as Organization for Economic Co-operation and Development (OECD) explains that income inequality has always existed. Still, the growing concern is due to the gap between the rich and the poor of the world growing even larger, which is one of the main reasons for the poor economic growth of the countries (OECD, 2011). The share of national income going to the richest one percent has increased rapidly in most parts of the world since 1980. It is worth wise to note that the one percent captured double income growth as much as the bottom half of the global population from 1980 to 2016 (IPS, 2019).
The economic performance of Pakistan has been badly affected by the disparity in households' income distribution since the 1990s. Households belonging to the highest income level in the country earn three times more average income as compared to the lowest income earner. This situation is further aggravated by an uneven and wider distribution of income in urban areas (PBS, 2016). Pakistan being a developing country, has to undergo different stages of economic growth. According to the World Bank (2017) average annual growth rate of Pakistan from 1961 to 2016 was 5.17 percent. However, it remained at 3.88 percent between 2010 to 2016 due to different national and international crises, especially unfavorable balance of payment pressure. As a result of this slow growth process, many socio-economic problems like slow economic performance, widespread poverty, hunger, ill-health, social and political instability have emerged. This difference in the distribution of income has affected the living pattern, daily social interaction, mental and physical capabilities of people.
Moreover, differences of regions, nature of job status, and choice of occupation, experience, and education have structurally divided the population into different segments. Overall income inequality also appears due to variations in mean wages between occupations, as incomes rise for some occupations and fall for some occupations. Education level, return to education, and gender is some factors that affect occupation inequality. Differences in income and wages are also observed between individuals of the same occupations and professions, as some professions such as management and sales are heterogeneous while some occupations such as medicine, law, teaching, engineering, and nursing are homogenous, so inequality grows within occupations.
Financial institutes also showed their concern that increasing the trend of inequalities at a global level would result in a high risk of economic & political crises and underutilization of human capital leading to hampering economic growth and stability (SDN, 2015). The importance of root cause of these problems has been recognized at United Nations, where goal 10 of Sustainable Development Goals (SDGs) is exclusively dedicated objective along with other related goals such as the first five goals and goal 8 (decent work and economic growth) and goal 9 (Industry, Innovation, and Infrastructure) for the nations to reduce income inequality by 2030 under Sustainable Development Goals program.
Different scholars have analyzed income inequality among occupations in Pakistan, compared disparities between them, and presented strategies to reduce income inequalities using HIES and PSLM data. These researchers used the Gini Coefficient Index, Theil-T index, and regression-based decomposition measure of inequality in various components. The present study focuses on the yearly income of the earning members of a household, concerns on the contribution of each source, and differences in the synthesis of household income which is missing in the literature. This current research has covered this gap.
Literature Review
To explain income distribution, disparities of the income distribution and analyze the effects of macroeconomic variables, studies used regression analysis on time series data mostly with the help of the Gini coefficient. While household survey data was employed to determine inequality at the micro-level using decomposition techniques which include decomposition by population group and observed how various factors affected overall inequality within sub-group and between sub-groups. Decomposition by factor components was employed to analyze how various income sources affect total income inequality.
Scholars around the world have revealed a mixed trend of between-occupations and within-occupation income inequalities among earners engaged in different occupations. Between occupations, income inequalities were found by scholars like Goos & Manning (2007) in Great Britain and Mouw & Kalleberg, (2010) in the USA. Within-occupational income disparities in the USA were assessed by Sørensen (2000) and Kim & Sakamoto (2008), while Helpman et al. (2017) also found within occupation income inequality in Brazil. Some studies found a mixed trend of within and between occupational income inequalities like Williams (2012) in the United Kingdom, Xie, Killewald & Near (2016) in the USA, and Helland et al. (2017) in Norway. Bayar (2016) found regional income inequality according to household characteristics in Turkey.
For Pakistan, an analysis of disparities of income distribution has been conducted by various scholars. Kruijk (1987) revealed within occupational group wage dispersion by the income of labor and income from other sources (remittances). A higher level of inequality among skilled workers and a lower level of inequality among professionals than overall inequality in Pakistan was found by Ahmad (2002). Kemal (2003) observed the highest level of Gini coefficient amongst the skilled workers, legislators, senior officials, managers, and unskilled workers while the lowest Gini coefficient amongst professional groups. Akhtar and Sadiq (2008) found short-term and long-term trends rising earnings disparities within each occupational category. Naseer and Athar (2016) examined those factors that determine the level of income inequality in Pakistan and concluded that share of age in income inequality was height among all occupations followed by a share of gender, education, and professional categories of occupation was found in inequality (Gini index).
Theoretical Framework
Analysis of this study is based on the theoretical framework of Becker (1994) in a human capital model, which states that investment in the human capital increases the productivity of an individual through education and skills. So, individuals invest in human capital to enhance productivity and hence increase their income and wages. The following equation presents the human capital model: -?lnY?_i=?+X_j ?_j+ ?_i
Where lnY is a log of a yearly income of household members i, Xj represents row matrix of characteristics that determine and affect the income of individuals such as region / province, gender, age, education level, job status, and occupation, and ?i is the error term.
Several techniques are used to measure disparities of income among the population of a society. Most widely adopted techniques include Gini coefficient and Lorenz Curve, Gini coefficient and Atkinson index, coefficient variation of earnings, Generalized Entropy Class of inequality including Theil Indices, and decomposition measures of inequality. To compare disparities in income across regions, Shorrocks (1982) decomposed total inequality into sub-components and determined the contribution of each of subcomponents with the help of the following equation: -Y_i=?_j^n?Y_i^k (1)
Where Yk is the sum of component incomes obtained from K sources, n denotes the total number of income recipients. This equation also gives an analysis of total income inequality, which is estimated by inequality measure (labour income, capital income, or transfer income). Later on, Fields (2003) extended Shorrocks (1982) model on income-generating function as:- ?lnY?_i=?'Z_i (2)
lnY is a log of the gross income of householdi, ?' represents regression coefficients, and Zi is the matrix of independent variables which represents household characteristics.
Following Shorrocks (1980) Generalized Entropy class of indicators [GE(?)] consisting of Theil indices including two entropy formulae was presented by Theil (1967) to decompose and observe which of the different household characteristics or income sources is responsible for the overall level of inequality and uses Gini coefficient to examine the effect of these factors on change in inequality. The general formula for generalized entropy class of inequality differences is: -GE(?)= 1/(?(?-1)) ?1/N ?_(i=1)^N?(y/y ? )^? -1 ? (3)
Where ? is the mean income. The value of GE varies between 0 and ?, in which 0 shows equal distribution whereas high values give higher inequality levels.
This study uses the OLS method to estimate the income equation, adopts regression-based decomposition proposed by Fields (2003) to investigate income differences between population subgroups. To assess the contribution of different subgroups of household characteristics and different sources of income in overall inequality, the study decomposes household characteristics and sources of income into “between-group” and “within-group” inequality. This study also employs GE(?) consisting of Theil indices including two entropy formulae presented by Theil (1967) to decompose and observe which of the different household characteristics or income sources is responsible for the overall level of inequality and uses Gini coefficient to examine the effect of these factors on the change in inequality.
Data, Methodology And Model Specifications
This study is based on disaggregate micro-level data and accounts for inequality at the household member level and not on the entire household to determine factors that are responsible for bringing income inequality and make changes in inequality. The study not only emphasizes average yearly income but also concerns the contribution of each source and differences in the synthesis of household income. The study presents results at the aggregate and sub-level of household characteristics and income sources to estimate the contribution of specific characteristics/sources to the increase in inequality.
Data and Sampling
The present study employs micro data sets of Household Integrated Economic Survey (HIES) separately for the years 2010-11 and 2015-16 conducted by the Pakistan Bureau of Statistics (PBS), Government of Pakistan. The study uses data set of 23,662 household members from HIES (2011) and 36,407 household members from HIES (2016) to estimate income inequality among earners actively engaged in different occupations and professions in Pakistan, focusing on their yearly income. The study includes only that income that has been earned by individuals engaged in different occupations (primary occupation only) and excludes other kinds/sources of income like earning from secondary occupations, remittance, pensions, financial assistance, and scholarships. Moreover, the role of different levels of education, experience, nature of the job, gender, and provinces differences has also been studied at the subgroup population level with the help of decomposition analysis.
Model Specification
The following regression equation is being used for the main analysis:
lnY_i ?=??_0+ ?_1 gend_i+?_(2 ) age_i+?_3 age_i^2+?_4 priedu_i+?_(5 ) secedu_i+?_6 interedu_i+?_7 profedu_i+?_8 occu_i1+?_9 occu_i2+ ?_10 occu_i4+?_11 occu_i5+?_12 occu_i6+?_13 occu_i7+?_(14 ) punj_i+?_15 kp_i+?_16 balochist_i+?_17 paidemp_(i )+ ?_18 culti_i+ ?_i (1)
lnYi represents the yearly earnings of an individual "i”. The explanatory variables represent individual characteristics, ?0 represents intercept term, ?2 & ?3, are coefficients of the continuous variables, and ?1, ?4… ?18 are the coefficients of the dummy variables.
Variables Description
This study takes the yearly income of the earners as the dependent variable, which consists of wages and salaries from employment and earnings from self-employment/being employed or engaged in any sort of economic activity. The study takes the log of yearly incomes to determine the effect of relative changes in explanatory variables. Table-4.3 below shows a brief description of dependent and explanatory variables.
Table 1. Variables Description
| Variable Code width="203">Variable Description width="219">Definition | > lnY width="203">Yearly Income width="219">Logarithm of a yearly income of an individual | > gendi width="203">Gender of respondent i width="219">Female is the base category align="left">gendi = 1 if respondent i is male, 0 otherwise. | > align="left">agei align="left">agei2 width="203">Proxy for the experience. align="left">Age of the individual i align="left">Square of the age width="219">Interprets effects of two age coefficients together. align="left">The continuous variable takes proxy for experience align="left">Continuous variable | > align="left"> align="left">priedui align="left"> align="left">secedui align="left"> align="left">interedui align="left"> align="left">profedui width="203">Education of individual i (in the year of schooling). align="left">Primary Education align="left"> align="left">Secondary Education align="left"> align="left">Intermediate Education align="left"> align="left">Professional Education width="219">No education as base category. align="left">priedui = 1 if education level>0 and education level<=5, 0 otherwise. align="left">secedui = 1 if education level> 5 and education level<=10, 0 otherwise. align="left">interedui = 1 if education level>10 & education level<=12, 0 otherwise. align="left">profedui=1 if education level>12, 0 otherwise. | > align="left"> align="left"> align="left">occui1 align="left"> align="left">occui2 align="left"> align="left"> align="left">occui4 align="left"> align="left">occui5 align="left"> align="left">occui6 align="left"> align="left">occui7 width="203">Occupation of individual i align="left">Occupation 3 (base) align="left"> align="left">Occupation 1 align="left"> align="left">Occupation 2 align="left"> align="left"> align="left">Occupation 4 align="left"> align="left">Occupation 5 align="left"> align="left">Occupation 6 align="left"> align="left">Occupation 7 width="219">Clerk / Service Workers / Shop and Market Sales Workers as base category. align="left">occui1=1 if occupation of individual is Legislators/ Senior Professionals, 0 otherwise. align="left">occui2=1 if the occupation of individual is Professionals, Managers / Technicians / Associate Professionals, 0 otherwise. align="left">occui4=1 if the occupation of the individual is Skilled Agricultural &Fishery Workers, 0 otherwise. align="left">occui5=1 if the occupation of individual is Craft and Related Trades Workers, 0 otherwise. align="left">occui6=1 if the occupation of individual is Plant/ Machine Operator &Assembler, 0 otherwise. align="left">occui7=1 if the individual is associated in Elementary Occupations, 0 otherwise. | > align="left">punji align="left">KPI align="left">Balochistan width="203">Province of individual i align="left">Punjab align="left">Khyber Pakhtunkhwa align="left">Baluchistan width="219">Sindh as base category. align="left">punji=1 if lives in Punjab, 0 otherwise. align="left">kpi=1 if individual lives in KP, 0 otherwise. align="left">balochisti=1 if lives in Baluchistan, 0 otherwise. | > align="left">paidempi align="left">cultii width="203">Job Status of individual I align="left">Paid Employees align="left">Cultivators, Share Croppers, and Livestock width="219">Employers, Self Employed as base category. align="left">paidempi=1 if paid employee, 0 otherwise. align="left">cultii=1 if individual is Cultivators / Share Croppers / Livestock, 0 otherwise. |
Results And Discussion
The study analyzes the overall level of income inequality in Pakistan, determinants of income among household members, and their contribution towards income inequality.
Estimation of Determinants of Income among Earners
Results of the estimated coefficients along with t values in parenthesis are given in Table-5.1 for both study periods.
Table 2. Earnings Equation Results, 2010 and 2015
| Variables width="138" colspan="2">2015 width="153" colspan="2">2010 | > Coef. width="60">p>t width="75">Coef. width="78">p>t | > Gender (female as a base) Male width="78">1.321*** align="center">(118.69) width="60">0.000 width="75">1.122*** align="center">(73.48) width="78">0.000 | > Age width="78">0.085*** align="center">(27.02) width="60">0.000 width="75">0.082*** align="center">(21.07) width="78">0.000 | > Age Square width="78">-0.001*** align="center">(-24.42) width="60">0.000 width="75">-0.0008*** align="center">(-19.30) width="78">0.000 | > Education (No education as a base)>Primary width="78">0.238*** align="center">(24.72) width="60">0.000 width="75">0.154*** align="center">(13.60) width="78">0.000 | > Secondary width="78">0.400*** align="center">(35.11) width="60">0.000 width="75">0.373*** align="center">(25.60) width="78">0.000 | > Intermediate width="78">0.546*** align="center">(35.78) width="60">0.000 width="75">0.519*** align="center">(25.74) width="78">0.000 | > Professional width="78">0.925*** align="center">(64.05) width="60">0.000 width="75">0.837*** align="center">(42.72) width="78">0.000 | > Occupations (Clerks, Service base)>Legislators, Senior Professionals width="78">0.747*** align="center">(28.15) width="60">0.000 width="75">0.666*** align="center">(18.21) width="78">0.000 | > Professionals, Managers width="78">0.269*** align="center">(21.14) width="60">0.000 width="75">0.301*** align="center">(15.97) width="78">0.000 | > Skilled Agricultural& Fishery width="78">-0.168*** align="center">(-4.35) width="60">0.000 width="75">-0.263*** align="center">(-6.59) width="78">0.000 | > Craft and Related Trades width="78">-0.153*** align="center">(-13.08) width="60">0.000 width="75">-0.042** align="center">(-2.34) width="78">0.000 | > Plant Operators & Assemblers width="78">0.081*** align="center">(3.11) width="60">0.001 width="75">0.060*** align="center">(3.09) width="78">0.001 | > Elementary Occupations width="78">-0.148*** align="center">(-13.90) width="60">0.000 width="75">-0.164*** align="center">(-11.6) width="78">0.000 | > Job Status (Employers, Self Emp as base) Paid Employees width="78">-0.328*** align="center">(-32.39) width="60">0.000 width="75">-0.379*** align="center">(-27.34) width="78">0.000 | > Cultivators, Share Croppers, Livestock width="78">-0.245*** align="center">(-6.08) width="60">0.000 width="75">-0.015*** align="center">(-0.37) width="78">0.000 | > Province (Sindh as base) Punjab width="78">0.079*** align="center">(7.37) width="60">0.000 width="75">0.047*** align="center">(3.63) width="78">0.000 | > KP width="78">0.016* align="center">(1.81) width="60">0.070 width="75">0.116*** align="center">(8.26) width="78">0.000 | > Baluchistan width="78">0.157*** align="center">(12.11) width="60">0.000 width="75">0.341*** align="center">(21.11) width="78">0.000 | > Constant width="78">8.827*** align="center">(177.58) width="60">0.000 width="75">8.568*** align="center">(136.90) width="78">0.000 | > Mean Dependent Variable width="138" colspan="2">11.822 width="153" colspan="2">11.292 | > Number of Observations width="138" colspan="2">36407 width="153" colspan="2">23662 | > R-Squared width="138" colspan="2">0.551 width="153" colspan="2">0.464 | > F-test width="138" colspan="2">1942.487 width="153" colspan="2">889.122 | > *** p<0.01, ** p<0.05, * p<0.1 width="138" colspan="2">Prob> F width="153" colspan="2">0.000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variable width="84">Population Share width="62">Mean Income width="60">Income Share width="46">GE (0) width="46">GE (1) width="46">GE (2) width="41">Gini | > By Gender | > Male width="84" valign="top">0.90 width="62" valign="top">125646 width="60" valign="top">0.95 width="46" valign="top">0.33 width="46" valign="top">0.37 width="46" valign="top">0.72 width="41" valign="top">0.43 | > Female width="84" valign="top">0.10 width="62" valign="top">58852 width="60" valign="top">0.05 width="46" valign="top">0.73 width="46" valign="top">0.69 width="46" valign="top">1.18 width="41" valign="top">0.61 | > Within groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.37 width="46" valign="top">0.38 width="46" valign="top">0.7 width="41" valign="top">
| > Between groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.02 width="46" valign="top">0.018 width="46" valign="top">0.015 width="41" valign="top">
| > By Age Group | > 1 - 20 years width="84" valign="top">0.13 width="62" valign="top">51637 width="60" valign="top">0.05 width="46" valign="top">0.28 width="46" valign="top">0.23 width="46" valign="top">0.29 width="41" valign="top">0.36 | > 21 - 30 years width="84" valign="top">0.28 width="62" valign="top">91902 width="60" valign="top">0.21 width="46" valign="top">0.27 width="46" valign="top">0.26 width="46" valign="top">0.38 width="41" valign="top">0.37 | > 31 - 40 years width="84" valign="top">0.24 width="62" valign="top">131091 width="60" valign="top">0.26 width="46" valign="top">0.32 width="46" valign="top">0.31 width="46" valign="top">0.49 width="41" valign="top">0.40 | > 40 - 50 years width="84" valign="top">0.19 width="62" valign="top">153860 width="60" valign="top">0.25 width="46" valign="top">0.35 width="46" valign="top">0.34 width="46" valign="top">0.57 width="41" valign="top">0.43 | > 51 - 55 years width="84" valign="top">0.07 width="62" valign="top">173462 width="60" valign="top">0.09 width="46" valign="top">0.46 width="46" valign="top">0.47 width="46" valign="top">0.94 width="41" valign="top">0.49 | > > 55 years width="84" valign="top">0.09 width="62" valign="top">143239 width="60" valign="top">0.11 width="46" valign="top">0.51 width="46" valign="top">0.54 width="46" valign="top">1.32 width="41" valign="top">0.52 | > Within groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.34 width="46" valign="top">0.35 width="46" valign="top">0.72 width="41" valign="top">
| > Between groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.06 width="46" valign="top">0.05 width="46" valign="top">0.05 width="41" valign="top">
| > By Highest Level of Education | > No Education width="84" valign="top">0.38 width="62" valign="top">80543 width="60" valign="top">0.26 width="46" valign="top">0.31 width="46" valign="top">0.27 width="46" valign="top">0.34 width="41" valign="top">0.39 | > Primary width="84" valign="top">0.30 width="62" valign="top">98277 width="60" valign="top">0.25 width="46" valign="top">0.30 width="46" valign="top">0.31 width="46" valign="top">0.70 width="41" valign="top">0.39 | > Secondary width="84" valign="top">0.15 width="62" valign="top">139645 width="60" valign="top">0.17 width="46" valign="top">0.32 width="46" valign="top">0.34 width="46" valign="top">0.68 width="41" valign="top">0.41 | > Intermediate width="84" valign="top">0.07 width="62" valign="top">174919 width="60" valign="top">0.09 width="46" valign="top">0.35 width="46" valign="top">0.39 width="46" valign="top">0.84 width="41" valign="top">0.43 | > Professional width="84" valign="top">0.10 width="62" valign="top">253608 width="60" valign="top">0.22 width="46" valign="top">0.34 width="46" valign="top">0.31 width="46" valign="top">0.43 width="41" valign="top">0.42 | > Within groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.32 width="46" valign="top">0.31 width="46" valign="top">0.66 width="41" valign="top">
| > Between groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.08 width="46" valign="top">0.09 width="46" valign="top">0.10 width="41" valign="top">
| > By Occupation | > Legislators, Seniors, etc. width="84" valign="top">0.02 width="62" valign="top">401467 width="60" valign="top">0.06 width="46" valign="top">0.28 width="46" valign="top">0.25 width="46" valign="top">0.29 width="41" valign="top">0.39 | > Professionals, Managers etc. width="84" valign="top">0.11 width="62" valign="top">200372 width="60" valign="top">0.19 width="46" valign="top">0.36 width="46" valign="top">0.31 width="46" valign="top">0.44 width="41" valign="top">0.41 | > Clerks, Service workers etc. width="84" valign="top">0.20 width="62" valign="top">142251 width="60" valign="top">0.24 width="46" valign="top">0.34 width="46" valign="top">0.34 width="46" valign="top">0.60 width="41" valign="top">0.42 | > Skilled Agricultural etc. width="84" valign="top">0.15 width="62" valign="top">130030 width="60" valign="top">0.16 width="46" valign="top">0.48 width="46" valign="top">0.49 width="46" valign="top">1.25 width="41" valign="top">0.49 | > Craft and Related… etc. width="84" valign="top">0.09 width="62" valign="top">93685 width="60" valign="top">0.08 width="46" valign="top">0.39 width="46" valign="top">0.33 width="46" valign="top">0.47 width="41" valign="top">0.42 | > Plant Operators etc. width="84" valign="top">0.07 width="62" valign="top">108278 width="60" valign="top">0.07 width="46" valign="top">0.14 width="46" valign="top">0.14 width="46" valign="top">0.18 width="41" valign="top">0.28 | > Elementary Occupations width="84" valign="top">0.35 width="62" valign="top">71385 width="60" valign="top">0.21 width="46" valign="top">0.22 width="46" valign="top">0.19 width="46" valign="top">0.23 width="41" valign="top">0.32 | > Within groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.31 width="46" valign="top">0.31 width="46" valign="top">0.66 width="41" valign="top">
| > Between groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.08 width="46" valign="top">0.09 width="46" valign="top">0.11 width="41" valign="top">
| > By Province | > Punjab width="84" valign="top">0.42 width="62" valign="top">124716 width="60" valign="top">0.44 width="46" valign="top">0.51 width="46" valign="top">0.50 width="46" valign="top">1.04 width="41" valign="top">0.50 | > Khyber Pakhtunkhwa width="84" valign="top">0.16 width="62" valign="top">112257 width="60" valign="top">0.15 width="46" valign="top">0.37 width="46" valign="top">0.35 width="46" valign="top">0.57 width="41" valign="top">0.44 | > Sind width="84" valign="top">0.27 width="62" valign="top">108983 width="60" valign="top">0.25 width="46" valign="top">0.34 width="46" valign="top">0.38 width="46" valign="top">0.72 width="41" valign="top">0.44 | > Baluchistan width="84" valign="top">0.15 width="62" valign="top">127001 width="60" valign="top">0.16 width="46" valign="top">0.17 width="46" valign="top">0.17 width="46" valign="top">0.21 width="41" valign="top">0.32 | > Within groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.39 width="46" valign="top">0.39 width="46" valign="top">0.76 width="41" valign="top">
| > Between groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.002 width="46" valign="top">0.002 width="46" valign="top">0.002 width="41" valign="top">
| > By Job Status | > Employer, Self Employed width="84" valign="top">0.15 width="62" valign="top">186032 width="60" valign="top">0.24 width="46" valign="top">0.37 width="46" valign="top">0.39 width="46" valign="top">0.72 width="41" valign="top">0.45 | > Paid Employee width="84" valign="top">0.71 width="62" valign="top">101364 width="60" valign="top">0.61 width="46" valign="top">0.34 width="46" valign="top">0.32 width="46" valign="top">0.48 width="41" valign="top">0.42 | > Cultivators, livestock width="84" valign="top">0.14 width="62" valign="top">133619 width="60" valign="top">0.16 width="46" valign="top">0.47 width="46" valign="top">0.49 width="46" valign="top">1.25 width="41" valign="top">0.49 | > Within groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.36 width="46" valign="top">0.37 width="46" valign="top">0.73 width="41" valign="top">
| > Between groups width="84" valign="top">width="62" valign="top"> width="60" valign="top"> width="46" valign="top"> 0.03 width="46" valign="top">0.03 width="46" valign="top">0.03 width="41" valign="top">
| ||||||||||||||||||||||||||||||||||||||||||
Source: Author’s own calculation from HIES (2011).
Table 4. Inequality Decomposition by Sub Groups (for k = 1,2,3…K) – 2015-16
| Variable width="82">Population Share width="67">Mean Income width="59">Income Share width="48">GE (0) width="50">GE (1) width="50">GE (2) width="45">Gini | > By Gender | > Male width="82">0.87 width="67">227876 width="59">0.93 width="48">0.33 width="50">0.36 width="50">0.76 width="45">0.43 | > Female width="82">0.13 width="67">105430 width="59">0.07 width="48">0.95 width="50">0.93 width="50">3.23 width="45">0.67 | > Within groups width="82">width="67"> width="59"> width="48"> 0.41 width="50">0.40 width="50">0.87 width="45">
| > Between groups width="82">width="67"> width="59"> width="48"> 0.03 width="50">0.02 width="50">0.02 width="45">
| > By Age Group | > 1 - 20 years width="82">0.13 width="67">85604 width="59">0.05 width="48">0.32 width="50">0.25 width="50">0.31 width="45">0.37 | > 21 - 30 years width="82">0.28 width="67">163011 width="59">0.21 width="48">0.32 width="50">0.31 width="50">0.64 width="45">0.39 | > 31 - 40 years width="82">0.24 width="67">229357 width="59">0.26 width="48">0.34 width="50">0.29 width="50">0.44 width="45">0.40 | > 40 - 50 years width="82">0.19 width="67">277377 width="59">0.26 width="48">0.39 width="50">0.37 width="50">0.79 width="45">0.44 | > 51 - 55 years width="82">0.07 width="67">318351 width="59">0.10 width="48">0.45 width="50">0.43 width="50">0.72 width="45">0.49 | > > 55 years width="82">0.09 width="67">267837 width="59">0.12 width="48">0.56 width="50">0.59 width="50">1.57 width="45">0.54 | > Within groups width="82">width="67"> width="59"> width="48"> 0.37 width="50">0.37 width="50">0.84 width="45">
| > Between groups width="82">width="67"> width="59"> width="48"> 0.07 width="50">0.06 width="50">0.05 width="45">
| > By Highest Level of Education | > No schooling width="82">0.32 width="67">124787 width="59">0.19 width="48">0.40 width="50">0.33 width="50">0.55 width="45">0.42 | > Primary width="82">0.29 width="67">169327 width="59">0.23 width="48">0.30 width="50">0.28 width="50">0.45 width="45">0.39 | > Secondary width="82">0.17 width="67">222819 width="59">0.18 width="48">0.28 width="50">0.28 width="50">0.52 width="45">0.38 | > Intermediate width="82">0.08 width="67">277210 width="59">0.10 width="48">0.36 width="50">0.40 width="50">1.27 width="45">0.43 | > Professional width="82">0.14 width="67">441020 width="59">0.30 width="48">0.34 width="50">0.33 width="50">0.57 width="45">0.42 | > Within groups width="82">width="67"> width="59"> width="48"> 0.34 width="50">0.32 width="50">0.77 width="45">
| > Between groups width="82">width="67"> width="59"> width="48"> 0.10 width="50">0.11 width="50">0.12 width="45">
| > By Occupation | > Legislators, Senior, etc. width="82">0.02 width="67">809252 width="59">0.08 width="48">0.33 width="50">0.41 width="50">0.92 width="45">0.43 | > Professionals, Managers etc. width="82">0.16 width="67">352989 width="59">0.27 width="48">0.36 width="50">0.32 width="50">0.46 width="45">0.42 | > Clerks, Service workers etc. width="82">0.24 width="67">221292 width="59">0.25 width="48">0.28 width="50">0.27 width="50">0.39 width="45">0.39 | > Skilled Agricultural etc. width="82">0.09 width="67">187164 width="59">0.08 width="48">0.43 width="50">0.46 width="50">1.03 width="45">0.47 | > Craft and Related… etc. width="82">0.17 width="67">149238 width="59">0.12 width="48">0.49 width="50">0.34 width="50">0.42 width="45">0.44 | > Plant Operators etc. width="82">0.02 width="67">181915 width="59">0.02 width="48">0.19 width="50">0.20 width="50">0.29 width="45">0.32 | > Elementary Occupations width="82">0.29 width="67">126086 width="59">0.17 width="48">0.26 width="50">0.21 width="50">0.25 width="45">0.34 | > Within groups width="82">width="67"> width="59"> width="48"> 0.34 width="50">0.31 width="50">0.74 width="45">
| > Between groups width="82">width="67"> width="59"> width="48"> 0.10 width="50">0.12 width="50">0.15 width="45">
| > By Province | > Punjab width="82">0.42 width="67">213226 width="59">0.42 width="48">0.48 width="50">0.45 width="50">0.85 width="45">0.48 | > Khyber Pakhtunkhwa width="82">0.19 width="67">238047 width="59">0.22 width="48">0.38 width="50">0.43 width="50">1.29 width="45">0.45 | > Sind width="82">0.28 width="67">185628 width="59">0.25 width="48">0.45 width="50">0.43 width="50">0.76 width="45">0.47 | > Baluchistan width="82">0.11 width="67">224773 width="59">0.11 width="48">0.30 width="50">0.28 width="50">0.40 width="45">0.40 | > Within groups width="82">width="67"> width="59"> width="48"> 0.44 width="50">0.42 width="50">0.89 width="45">
| > Between groups width="82">width="67"> width="59"> width="48"> 0.04 width="50">0.004 width="50">0.004 width="45">
| > By Job Status | > Employer, Self Employed width="82">0.18 width="67">336768 width="59">0.28 width="48">0.37 width="50">0.42 width="50">1.03 width="45">0.45 | > Paid Employee width="82">0.73 width="67">183215 width="59">0.64 width="48">0.41 width="50">0.37 width="50">0.57 width="45">0.45 | > Cultivators, livestock width="82">0.09 width="67">192152 width="59">0.08 width="48">0.44 width="50">0.47 width="50">1.04 width="45">0.48 | > Within groups width="82">width="67"> width="59"> width="48"> 0.41 width="50">0.39 width="50">0.86 width="45">
| > Between groups width="82">width="67"> width="59"> width="48"> 0.03 width="50">0.03 width="50">0.04 width="45">
| ||||||||||||||||||||||||||||||||||||||||||
Source: Author’s own calculation from HIES (2016).
Gender
In Pakistan, women normally work in professions like teaching, medical, finance & management, clerical support, cleaning & domestic, and so on. The population share of male-headed households is higher than females. However, it had fallen from 90 percent in 2010-11 to 87 percent in 2015-16. This implies that females have gained greater access to income-generating opportunities in recent years. The number of female earners increased; their income shares also increased. However, income inequality for females increases as the Gini coefficient enhances. Results of GE (?) indices also indicate greater inequality problem within-group than inequality experiences between the group (table-5.3.1 & 5.3.2).
Age
The population share of household members belonging to the age group of 21-30 years is more than age groups of 31-40 years and 41-50 years, but income share is less than these age groups because the income of persons increases with experience gain with age. There is wider income inequality in the age group of greater than 55 years because of multiple reasons, for example, job status, savings, wealth etc., and this income disparity becomes wider in 2015-16 (table-5.3.1 & 5.3.2). All indices experience more inequality within-group inequality than between-group inequality.
Education Level
Within the highest level of education group, population share and income share of household members having no education are higher as compared to other categories. The major change is observed in the decline of the income share of household members having no education in 2015-16, as the population share of individuals having no education decreases (table-5.3.1 & 5.3.2). Among the individuals with a certain level of education, the income share of professional degree holders increased in 2015-16 because trained persons
possessing higher certificates, diplomas, and degrees belong to this category. However, these training and degrees within this group create greater income disparities among members. Education level improved in 2015-16 as compared to 2010-11 as well as inequality is common in the groups having above secondary school education as evident from GE (?) values (table-5.3.2 & 5.3.2). So, within-group inequality exhibits an increased problem than between the groups.
Occupation / Profession
Income shares of household members engage in professionals, managers, and technicians' group is more than those individuals who are engaged in other categories in the year 2015-16, which also confirms the results of improvement in education level as well as population share of these individuals in 2015-16 (table-5.3.1 & 5.3.2). Moreover, the number of persons engaged in clerical and services occupations also increases, while there is a decline in people working in elementary occupations, as well as drift, is also observed in other occupations in the year 2015-16 as compared to 2010-11. However, the study experiences more inequality within-group than inequality between groups for all indices.
Region
No change in population share in the provinces Punjab and Sindh is observed; however, a small change is seen in the other two provinces but not significantly between the two survey periods. Results of population share also reflect a change of income share in the same manner. All inequality indices register an increase for all provinces between 2010-11 and 2015-16 (table-5.3.1 & 5.3.2). This could be a result of increased development in these regions and the decline in the dependence on the traditional sector. There is more inequality within a group than between-group inequality for provinces.
Job Status
Population and income share of household members engaged as cultivators and livestock occupations decreased in 2015-16, however, more income disparity is witnessed than other categories. Gini coefficient for employed / self-employed remains the same in both study periods, rises for paid employees indicating a rise in inequality and decreases for cultivators, livestock, etc., indicating a decrease in inequality (table-5.3.1 & 5.3.2). The results of GE (?) depict that inequality within the group is greater than inequality experiences between the groups.
Overall study finds that education level and occupation exhibit greater inequality for both within-group and between-group than other variables for both study periods; however, between-group inequality rises in 2015-16. Moreover, all indices show that within the group, disparities of income are a greater problem than inequality experiences between the groups.
Inequality Decomposition by Factor Components
The regression-based decomposition methodology proposed by Fields (2003) enables the current study to measure how much inequality in yearly income is explained by various human and non-human capital characteristics of each earner. Table 5.4.1 and Table 5.4.2 provide results of decomposition disparities by factor components (Gini decomposition by income source) in Pakistan.
Table 5. Inequality Decomposition by Factor Component – 2010-11
| Source width="110">SK (Inequality Contribution) width="71">GK (Source Gini) width="110">RK align="center">(Gini Correlation from Source) width="86">Share in total inequality width="68">Percent Change | > Gender width="110">0.08 width="71">0.10 width="110">0.58 width="86">0.11 width="68">0.03 | > Age width="110">3.23 width="71">0.21 width="110">0.29 width="86">4.57 width="68">1.34 | > Education width="110">0.11 width="71">0.57 width="110">0.44 width="86">0.60 width="68">0.50 | > Occupation width="110">0.43 width="71">0.22 width="110">-0.39 width="86">-0.84 width="68">-1.27 | > Job-status width="110">0.18 width="71">0.13 width="110">-0.17 width="86">-0.08 width="68">-0.26 | > Province width="110">0.18 width="71">0.29 width="110">0.12 width="86">0.14 width="68">-0.05 | > Total income width="110">width="71"> 0.04 width="110">width="86"> width="68">
|
Source: Author’s own calculation from HIES (2011).
Table 6. Inequality Decomposition by Factor Component – 2015-16
| Source width="108">SK align="center">(Inequality Contribution) width="68">GK align="center">(Source Gini) width="108">RK align="center">(Gini Correlation from source) width="88">Share in total Inequality width="68">Percent Change | > Gender width="108">0.07 width="68">0.13 width="108">0.61 width="88">0.13 width="68">0.06 | > Age width="108">3.06 width="68">0.21 width="108">0.31 width="88">4.33 width="68">1.27 | > Education width="108">0.12 width="68">0.52 width="108">0.49 width="88">0.68 width="68">0.56 | > Occupation width="108">0.38 width="68">0.24 width="108">-0.40 width="88">-0.80 width="68">-1.18 | > Job-status width="108">0.08 width="68">0.25 width="108">-0.29 width="88">-0.12 width="68">-0.20 | > Province width="108">0.19 width="68">0.21 width="108">-0.05 width="88">-0.04 width="68">-0.24 | > Total income width="108">width="68"> 0.05 width="108">width="88"> width="68">
|