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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS

COPY RIGHT © 2013 Institute of Interdisciplinary Business Research

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ANALYSIS OF THE DETERMINANTS OF INCOME AND INCOME GAP BETWEEN

URBAN AND RURAL PAKISTAN

Liaqat Ali

PhD Scholar, Al-Khair University, Pakistan

Dr.Muhammad IsmaeelRamay

Head Graduate School of Business,

Al-Khair University, Pakistan

Dr. ZekeriyaNas

Yüzüncüyıl University, Van/Turkey

ABSTRACT

This paper examines the determinants of income and income gap in urban and rural areas of Pakistan by

using province, literacy, education, occupation, age, gender and marital status as predictors at individual level

for the Household Integrated Economic Survey (HIES) 2010-11 dataset. Traditional Mincerian model has

been estimated by applying the Ordinary Least Squares (OLS) method. Blinder-Oaxaca decomposition

method has also been used to analyze the income gap between urban and rural Pakistan. Results exhibit

literacy, education and occupation as the major determinants of income in Pakistan.Reading and writing skill

of individuals has been emerged as more important as compared to the numeracy skill. Lower levels of

education yields high returns in rural areas whereas higher levels of education give more return in urban

areas. Agriculture and fishery workers have emerged as the least earners followed by those engaged in low

paid elementary occupations. Individual characteristics like literacy, education, occupation and marital status

have been found as the major determinants of income gap.

Key Word: Income, income gap, urban and rural areas, Mincerian Model, Oaxaca-Blinder decomposition,

HIES,Pakistan

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INTRODUCTION

Pakistan is a developing country situated in South Asia, having total population of 180.7 million in 2012. The

total civilian labor force was59.3 million out of which 55.8 million was employed (Economic Survey, 2011-

12). At the time of independence from British Rule in 1947, Pakistan was an agrarian economy where the

contribution of agriculture was 53.2 percent in GDP during the fiscal year 1950 (Handbook of Statistics,

2010). However, major structural changes have occurred since then as the shares of agriculture, industry and

services sectors in GDP during financial year 2011-12was 21.1 percent, 25.4 percent and 53.5 percent

respectively (Economic Survey, 2011-12). Since its independence, Pakistan has faced varying economic

growth. Generally, during the civilian rules, growth has been slow whereas remarkable economic recovery

has been witnessed during three long periods of military rule. Despite being a poor country, lacking in

infrastructure,during the early periods of its history, economic growth rate of Pakistan was better as compared

to global average between 1950 and 1990. However, economy of Pakistanregistered a remarkable recovery

and average growth rate of GDP was recorded as 7 percent between 2003 and 2007. The result was increased

development spending by the government which in turn caused poverty head count decreased to 22.3 percent

in 2006 from 34.5 percent in 2001registering a decline of more than 10 percent (Economic Survey, 2007-08).

However, Pakistan's economic outlook has become stagnant since the beginning of 2008.Security concerns

arising from Pakistan’s active role in the war against terrorism has created great instability in the country and

consequently, foreign direct private investment has decreased to US $2201.3 Million for the fiscal year 2009-

10 from US $5409.8 Million in 2007-08 showing a decrease of around 60 percent (Handbook of Statistics,

2010). A massive capital flight from Pakistan to other countries has been incurred due to this insurgency.

Pakistan's economy has witnesseda high rate of inflation and widening trade deficits due to insurgency and

global increase in the prices of commodities. The GDP growth rate was recorded at mere 3.7 percent during

the fiscal year 2011-12 (Economic Survey, 2010-11). In 2008, consumer price index (CPI) based inflation

ratewas recorded as 21 percent (Economic Survey, 2007-08).To avoid bankruptcy in 2008, Pakistan

followeda tight fiscal policy backed by the IMF. The CPI basedrate of inflation was recorded at 13.81 percent

and 10.84 percent for the fiscal years 2010-11 and 2011-12 respectively (Economic Survey, 2011-12). The

average rate ofoverall inflation was recorded as 12.76 percentbetween 2007-08 and 2011-12 as compared to

food inflationwhich was recorded at 16.73 percent in the same period (Economic Survey, 2011-12).

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The disparities between rural and urban areas are quite common around the world in general and in less

developed countries like Pakistan in particular. The differences between urban and rural areas exist in number

of socio-economic indicators like unemployment, literacy rate, average household size, monthly consumption

expenditure and most importantly monthly income of the households. For example, the overall

unemployment rate in urban areas of Pakistan has increased to 8.8 percent in 2010-11 from 7.1 percent in

2008-09. However, in rural areas unemployment rate has remained same at 4.7 percent between 2008-09 and

2010-11 (Labor Force Survey, 2010-11). Literacy rate was 74 percent and 49 percent in urban and rural areas

respectively during 2010-11 (PSLM, 2010-11). The average household size was recorded as 6.38 persons per

household in Pakistan as compared to 6.19 and 6.49 persons in urban and rural areas respectively (Household

Integrated Economic Survey-HIES, 2010-11). Average monthly consumption expenditure per householdwas

Rs.23959(US $23

280.2) as compared to Rs.16919 (US $ 197.9) in rural areas during 2010-11 (HIES, 2010-

11). Similarly, average monthly household income in urban area was Rs.27664 (US $ 323.6) whereas it was

only Rs.18713 (US $ 218.9) in rural areas during 2010-11 against the overall average of Rs.21785 (US $

254.8) in Pakistan (HIES, 2010-11). The rise in inflation rate, adverse law and order situation, worsening

energy crises coupled with decline in the economic growth rate may have an adverse effect on employment,

and income and its distribution in the rural and urban Pakistan. In this background it is imperative to have a

study on determinants of income and income gap in the urban and rural areas of Pakistan. This study focuses

on the analysis of determinants of income and income gap between urban and rural Pakistan by using a

nationally representative latest available data set known as HIES 2010-11. The determinants of income gap

have been analyzed by applying theBlinder-Oaxaca decomposition method.

The rest of the paper is organized as follow: A brief literature review is presented in section 2. Methodology,

sources of data and descriptive statistics has been discussed in section 3. Empirical results are presented in

section 4. Finally, section 5 concludes the paper.

LITERATURE REVIEW

According to Kuznets (1955) inequality generally increases duringinitial stages of economic development

and declinesas the process of development moves forward. The inverse relationship between inequality and

23

The average exchange rate of Pak Rupees 85.5= 1 US $ for the year 2010-11, the period during which HIES was

conducted, has been used. Source: Economic Survey of Pakistan, 2011-12, Table 8.10, p.81

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development iscalled Kuznets Curve in economic literature. Kuznets’s main focus was on how the income

distribution is affected by migration flows from rural to urban areas during the process of development.

Kuznets curve was extended into spatial context by Williamson (1965). While using data from 24 countries

he proposed thatalong with increase in per capita incomes disparities among regionsexpands at first stage

then become stagnant and decline subsequently. He also found that during early stages of economic

development regional inequality rises while a regional convergence is followed by a mature growth (Lu,

2002).

A more accurate proof of Kuznets' hypothesis was provided by Robinson (1976). His proof was based on

differences in mean income among various sectors of an economy and a higher mean income or inequalities

in the growing sector were not required.

The Center for Rural Pennsylvania (2007) found statistically significant differences between rural and urban

middle-income households demographically, economically, and educationally.

Gaoand Cao (2006) using Holt-Winter non-seasonal exponential smoothing model found that income gap

between urban and rural China was widening due to the slow growthin income of residents of rural areas.

Hammond and Thompson (2006) using from labor market regions in the U.S. found that difference in

determinants of growth in labor market areas between metropolitanand non-metropolitan were statistically

significant.The subject matterof their research was to highlight the importance of human capital for economic

developmentbetween metropolitan and non-metropolitan areas. They suggestedto encourage educate& retain

and attract better educated residents for economicdevelopmentin these areas. One of their findings was that

investment in human capital had a more strong impact onthe growth of income in metropolitan regionsas

compared to non-metropolitan areas. They also found that the existence ofphysical infrastructure facilities

like colleges, universities, household facilities, and lower rates of taxes encourage accumulation of

humancapital in labor markets.

Sicular, Yue, Gustafsson, and Li (2006) analyzed the size of income gap between urban-rural China, its share

to inequality and factors responsible for the gap by using data from household surveys for the years 1995 and

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2002. They investigated income inequality in rural and urban areasfor various groups of population by using

Oaxaca- Blinder method of decomposition and found education as the alonecharacteristic of

householdswhose contribution towards the income gap was significant between urban and rural areas.

Smith (2007), presented empirical results indicating factors influencing the distribution of income in Soviet

Union. He found human capital and demographic factors havingeffect on a household’s standing in the

regional/national income distribution. He concluded that a high income household was more likely to have a

middle-aged, married, well-educated male in good health as its primary earner. He found occupation as less

important factor for income distribution as compared to self- employment for Soviet sample. He also found

larger differences in income of household headed by married couples and those headed by single individuals

in the Soviet Union.

Afonso,Schuknecht, Ludgerandand Tanzi(2008) founds that distribution of income is significantly affected

byperformance of education and redistributive public spending. They also found that efficiency as well as

effectiveness of social spendingin public sector is more in countries having a strong performance in

education.

Li &Xu(2008) studied the trend of disparities among the provinces as well within the provinces of the

People’s Republic of China (PRC) from 1978 to 2005. They found that contribution of disparities between

urban and rural areaswas more than 70 percentin regional disparities since the mid-1980s. They noticed the

accelerating urbanization process in the PRC since 2000 and the large scale urban-rural labor migration but

concluded that disparities between urban and rural areas continue to expandprincipally due to the

increasinggap between economic growth rate in urban and rural areas.

Aikaeli(2010) estimated linear models by applying a generalized least squares technique and using data from

Rural Investment Climate Survey of Tanzania (2005) found that incomes of households in rural areas is

significantly and positively affected by improvements in variableslike household labor force size, household

head’s education level, non-farm ownership of rural enterprise and land use in acreage. He also found that

income in households havingmale as their head was significantly higher than in households where female was

the head. He also noticed a positive effect of greater use of telecommunications and improvements in road

infrastructure on rural incomes at the community level.

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Leyaroand Morrissey (2010)analyzed the association between household characteristics such as size and

location of household, age, sector of employment and education of head of household and household income

using data from the Household Budget Survey of Tanzania for the years 1991/92, 2000/01 and 2007. They

found a positive association between household’s head years of schoolingand income and estimatedpremium

of each additional year of education as about 4.5 percent. They also found that average incomes of

manufacturing households were higher than agriculture households. However, within each broad sector

incomes appear to be higher in sub-sectors with higher tariffs and household income have tendency to

increase across both tariffs and education.

OrewaandIyanbe (2010) identifiedvarious characteristics including age, level of education, size of household

and sex affecting intakes of food calorie in rural and low-income households in urban Nigeria. Using data

from a cross sectional survey,they carried out Ordinary Least Squares (OLS) multiple regression analysis to

ascertain the factor responsible for the determination of calorie intake of household members per capita per

day.They found relationship between various factors such asage, level of education, size of household, sex

and salary income earners and daily per capita calorie intake as significant and positive.

XueandGao(2012) conducted the analysis of survey data from Zhejiang and Shaanxi areas of PRC. They

found that the income of rural migrantsmoving with their family members to urban areas either was under

recorded or was not recorded at all due to sample selection problems which caused overestimation of gap in

income between rural and urban areas by 41.26 percent. They estimated their own gap in income

betweenrural and urban areas which stood at 2.29 times in 2010 contrary to the existing number of 3.33 times

in statistics and concluded that income gap between rural and urban areas was overestimated by 13.65 percent

because of missing records arising primarily due tonon-coverage of migrants in the existing urban residents

surveys.

There is a long history of poverty and urban-rural income gap or income inequality in Pakistan. Poverty and

income gap in Pakistan was inherited at the time of its independence from the British rulers in 1947due to

some political reasons.Despite being an inherent phenomenon, the research on poverty and inequality in

Pakistan was started in the1960s when the first round of HIESwas launched in 1963 (Awan, 2007).

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Ashraf and Ashraf (1993) presented male-female earnings differentials for industrial subgroups for the

years 1979 and 1985-86 using data from the HIES by applying the Oaxaca (1973) and Cotton (1988) and

Neumark (1988) models.

Awan (2007) has reported number of studies explaining urban rural inequalities in Pakistan based on Gini-

coefficient which includes Nasim (1973), Ayub (1977), Jeetun (1978), Kruijk and Leeuwn (1985) and Adams

& Jane (1995). According to Awan (2007),despite having a rapideconomic development of the Ayub Khan

Era, results of these studies showed thatincome inequality registered a decline during the 1960s but increased

in the 1970s. The resultsdepicted a positive relationship between improvements in income distribution

andgrowth of GDP (Awan, 2007).

Awan, (2007) concluded that gaps in income characterized by level of education were significant implying

that inequality in income in Pakistan was raised from education distribution patternas well as the labor market

compensation to education. He also noticed the widening of gaps in income between uneducated and

educated workers in first employment as experience increases. However, the rate differs from individual to

individual and also by levels of education.

Farooq (2010)analyzed the impact of education inequality by applying technique of Gini-Coefficient using

data from PSLMSurvey of 2004-05. He found gender inequality in income distribution and noted that

inequality among male workers was higher as compared to their female counterparts. He also noted greater

income inequality in urban areas as compared to rural areas. He also found favorable effect of education on

income distribution.

Asadand Ahmad (2011) studied the link between consumption inequality and growth using data from HIES.

They calculated various measures of inequality including Coefficient of Variation, Mean Log Deviation,

Deciles Dispersion Ratio, Quintiles Dispersion Ratio, Atkinsion Index, Theil Index and Gini-coefficientand

found instability in inequality in consumption. They also found a declining share in consumption for

thepoorest 20 percent as well as for the middle 60 percent of population against the richest 20 percent whose

share registered a significant increase in rural and urban areas along with overall Pakistan.

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In sum, most of the existing studies on the issue of urban-rural income inequality in Pakistanare based

onGini-coefficients method. Rather than examine income inequality using Gini-coefficient method, this

study look at income gap between rural and urban areas from the aspect of individual characteristics. In this

study an attempt has been made to find how age, literacy skills, education level, occupation, marital status

and gender of individuals affect their income levelin urban and rural areas of Pakistan. Further, contribution

of those factors towards the income gap between these areas has also been analyzed. Moreover, this study

uses the nationally representative latest available household survey data and therefore enabling us to provide

the latest information about the state of urban-rural income gap in Pakistan.

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METHODOLOGY, SOURCES OF DATA AND DESCRIPTIVE STATISTICS

Methodology

Theoretical and Econometric Model of the Study

The theoretical model presented in the figure above can also be expressed in the form of a mathematical and

econometric relationship. For this purpose, model used by Su and Heshmati (2013) has been estimated with

some modifications to estimates the earnings functions and urban-rural income gap in Pakistan. OLS

methodhas been used toestimate the effects of personal characteristics on annual income of individuals both

for urbanand rural residents of Pakistan living in Punjab, Sindh, KPK24

and Baluchistan provinces. For this

purpose, individual level datahas been used. Thestandard model, as has been used by Su &Heshmati (2013),

isbased on the human capital earnings function developed by Mincer(1994):

(1) lnINCi= Xiβ +εi

24

Khyber-Pakhtoonkhwa (KPK); formerly was known as North Western Frontier Province (NWFP).

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wherelnINCi, the dependent variable, is the natural logarithm of the annual income for observation i, and Xiis

a vector of individual characteristics (set of independent variables) including a measure of literacy, education,

occupation, age, gender, province of residence and marital status. β is the vector of unidentified parameters

which will be estimated using OLS method, and εi is a random error term. εi is assumed to satisfy the

common properties of zero mean and constant variance (Su &Heshmati, 2013).

This paper is also aimed at to analyze the composition of urban-rural income gap in Pakistan. Theprocedure

developed by Oaxaca & Blinder (1973) and used by Su &Heshmati (2013),divides the total income gap into

two parts. The first part of the income gap is due to observable differences in productive

characteristicswhereas the residual gap is attributable to differences in the returns to the characteristics which

have been examined for urban and rural areas separately (Su &Heshmati, 2013).

Specifically, according to Su &Heshmati (2013) the total gap in income between rural and urban areas is

equal to:

(2)

where, is the observedurban-rural income ratio.By taking the logarithm formof equation (2) and

combining it with the estimated result of OLS equation (1), the urban-ruralincome gap in the notations used

by Su &Heshmati (2013),can be expressed as:

(3)

where and are the mean values of the natural log of urban and ruralannual income

respectively. and are vectors of the mean values of productive characteristics of the urbanand rural

residents. and are vectors of regression coefficients which have been obtained from the estimation of

separate regressions for urban and rural areas.

Following Oaxaca (1973) and the notations used by Su &Heshmati (2013), the above equation can be further

transformed fordecomposition purpose as:

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where, I is an identity matrix and Ω is a diagonal matrix of weights. According to equation (4), themean

difference in log annual income can be decomposed into two parts. The firstexpression on the right is the

portion of the income gap which can be attributed todifferences in average observable productive

characteristics ofurban-rural residents. Thedifference in the average characteristics is multiplied by the

weighted estimatedcoefficient from both urban and rural regressions. Those coefficients are explained as the

structure of incomeat individual level. The second expression on the right hand side is that portion of

theincome gap which can be attributed to differences in the rural and urban regression coefficients. In other

words, this can be treated as the difference in the returns to urban and rural residents for same

productivecharacteristics. In this way, the second component on the right hand side is generallyconsidered as

combined effect of discrimination and theeffects of other omitted variables (Su &Heshmati, 2013).

For the purpose of simplicity and following Reimers’s (1983) method where Ω=0.5I and I is an identity

matrix and the notation of Su &Heshmati (2013), the income gap in equation (4) is reduced to:

Source of Data and Descriptive Statistics

The Pakistan Bureau of Statistics (FBS) is responsible for the collection of income and expenditure statistics

in Pakistan. In the initial years the data collection exercise was not much comprehensive, as well as there

were irregular breaks in the data till 1960s. The first round of HIES was carried out in 1963. The

questionnaire of HIES was revised in 1990 in order to cater to the needs of new national accounting system.

The same revised questionnaire was used for four subsequent surveys (Awan, 2007). HIES was merged with

Pakistan Integrated household Survey (PIHS) in 1998-99 to collect socio–economic data at household level.

Subsequently, the name of the survey was changed in 2004 to Pakistan Social and Living Standards

Measurement (PSLM), which helps government in formulating the development plans at district level and

also provide data for monitoring the progress of different indicators which are to be monitored under

Millennium Development Goals (MDG).PSLM provides data on social as well as economic indicators in the

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alternate years. Under PSLM Surveys, HIES was conducted in 2004-05, 2005-06 and 2007-08 to provide

information on number of characteristics including income, savings, liabilities, consumption expenditure and

its patternfor urban and rural households at national and provincial level (HIES, 2010-11).

This study is based on the data of latest round of HIES 2010-11. The total sample size of the HIES

2010-11 was 1180 Primary Sampling Units (PSUs) and 16341 Secondary Sampling Units (SSUs)

(Table 1). The total numbers of urban and rural PSUs covered were 564 and 616 whereas the numbers

of SSUs covered in these areas were 6589 and 9752 respectively (Table 10). Keeping in view the

diversified nature of households in socio-economic indicators and variability of the characteristics at

individual level, both PSUs and SSUs have been selected from four provinces of Pakistan with an

appropriate representation from urban and rural areas. The province-wisedetail of coverage of the

survey is given in Table 1.

The number of sample PSUs covered in Punjab, Sindh, KPK and Baluchistan provinces are 512, 296, 208

and 164 as compared to sample SSUs which are 6954, 4098, 2954 and 2335 respectively (table 1). Both

PSUs and SSUs in Punjab, Sindh, KPK and Baluchistan provinces stands at 43 percent, 25 percent, 18

percent and 14 percent of total25

sample size respectively.

Stratification Plan:

Urban Area:

In urban areas,big cities with population of 0.5 million and more have been considered as a separate stratum

which have been further sub-stratified into three income groups known as low, middle and high.An

independent stratum was formed by grouping together the rest of the cities and towns in each division of the

provinces(HIES, 2010-11).

25

The percentage representation of provinces in the selected sample roughly corresponds to their overall shares in the

total population. For example, the shares of Punjab, Sindh, KPK and Baluchistan provinces in total population were

54.52 percent, 23.82 percent, 13.42 percent, and 5.12 percent respectively. Source: Economic Survey, 2011-12.

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Rural Area:

The population of each district in rural areasof Punjab, Sindh and KPK Provinces was considered a stratum in

contrast to Balochistan province where each Division was considered as aseparate stratum(HIES, 2010-11).

Sample Design

A random sampling scheme knows as stratifiedcompleted in two stages, was followed for the HIES 2010-11.

In the first stage, using number of households as measure of size, PSU were selected from villages and

enumeration blocks in rural and urban areas respectively following method of probability proportion to size.

In the second stage households (SSUs) 12 in urban areas and 16 in rural areas, were selected from PSUs by

using systematic sampling technique with random start(HIES, 2010-11).

Descriptive Statistics

The description of variables used in the estimation of Mincerian earning functions is presented in the table 2.

In this paper analysis has been restricted to Punjab, Sindh, KPK and Baluchistan provinces of Pakistan and

FATA26

, AJK27

, GB28

have been excluded from the analysis due to data limitations.Sindh province has been

used as a reference group. The overall literacy29

rate in Pakistan is 58% and it stands at 60%, 59%, 50% and

41% for Punjab, Sindh, KPK and Baluchistan provinces respectively (PSLM, 2010-11). Keeping in view its

importance in socio-economic conditions in a developing country like Pakistan, literacy skillshave been

included as separateexplanatory variables in the model.

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 (HIES, 2010-11). The total number of individuals covered in

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

26

Federally Administered Tribal Areas (FATA) are a semi-autonomous tribal area situated in northwestern Pakistan

bordering with Afghanistan. FATA are comprised of seven tribal agencies and six frontier regions. Tribal Areas are

Bajaur, Mohmand, Khyber, Orakzai, Kurram, North Waziristan, South Waziristan. The frontier regions are Peshawar,

Kohat, Bannu, LakkiMarwat, Tank, Dera Ismail Khan.

Source:http://en.wikipedia.org/wiki/Federally_Administered_Tribal_Areas. 27

Azad Jammu & Kashmir (AJK) 28

GilgitBaltistan (GB) previously known as Northern Areas has now been given status of a Province. 29

Literacy is defined in HIES as an ability of a person to read and write in any language with understanding and to

solve simple arithmetic sums.

http://en.wikipedia.org/wiki/Pakistan

http://en.wikipedia.org/wiki/Afghanistan

http://en.wikipedia.org/wiki/Agencies_of_the_Federally_Administered_Tribal_Areas

http://en.wikipedia.org/wiki/Frontier_Regions

http://en.wikipedia.org/wiki/Bajaur

http://en.wikipedia.org/wiki/Mohmand

http://en.wikipedia.org/wiki/Khyber_Pakhtunkhwa

http://en.wikipedia.org/wiki/Orakzai

http://en.wikipedia.org/wiki/Kurram_Agency

http://en.wikipedia.org/wiki/North_Waziristan

http://en.wikipedia.org/wiki/South_Waziristan

http://en.wikipedia.org/wiki/Federally_Administered_Tribal_Areas

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

survey was 51% (55713) and 49% (53468) respectively (HIES, 2010-11). However, final analysis has been

carried for 22164 individuals of age 10 years and above. In HIES, all individuals both male and female of age

10 years and older, are asked about their employment and income from main occupation, second occupation,

other work, income in-kind and pensions. Due to this reason analysis has been carried out for individuals of

age 10 years and older (Table 2).

In the final sample 43%, 27%, 16% and 14% were belonged to Punjab, Sindh, KPK and Baluchistan

provinces respectively (Table 3). Those who could read and write with understanding in any language and

solve simple arithmetic sums were 63% and 88% respectively (Table 3). 74% and 53% of individuals in

urban and rural areas were found to be able to read and write in any language with understanding against 91%

and 84% who could solve simple arithmetic sums in these areas respectively (Table 3). 36%, 26% and 45%

individuals in the sample received no formal education in overall sample, urban and rural area respectively

(Table 3). Only 18%, 27% and 10% of the respondents in the selected sample received college education

(sum of edu4, edu5, edu6 and edu7) in overall Pakistan and its urban and rural areas respectively (Table 3).

Majority of the individuals i.e. 43%, 34% and 51% were found to be employed in low paid elementary

occupation in overall sample, urban and rural areas correspondingly (Table 3). Clerks, service workers, shop

and market sales workers, which have been used as reference group in this study, have been emerged as

second highest occupation groups with 21%, 26% and 16% contribution in overall sample, urban and rural

areas in that order (Table 3). In the final sample, 42.20%, 41.90%, 13.5% and 2.4% of individuals in overall

Pakistan were found in the age groups 10-30, 31-50, 51-65 and 66 & above respectively, whereas proportions

of the sample in same age groups in urban and rural areas were found to be 41%, 42%, 15% and 3% and

44%, 42%, 12% and 2% (Table 3).

The dominant majority of the respondents i.e. 87.55%, 88.04% and 87.11% in the sample were found to be

male in Pakistan and its urban and rural areas against 12.45%, 11.96% and 12.89% who were to be females

respectively (Table 3). Likewise, the proportion of married subjects was quite high as compared to those who

were unmarried and widow/divorced (Table 3).

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EMPIRICAL ANALYSIS OF THE EARNINGS FUNCTION

OLS Estimation

Results of the Ordinary Least Square model calculated from Equation (1), for overall sample and urban and

rural sub-samples for the year 2010-11 have been presented in the table 1. Adjusted R Square for overall

sample and urban and rural sub-samples stands at 0.546, 0.503 and 0.579 respectively. The R-square valuesin

this study are quite high as compared to other studies for example Awan (2007) and Sicular, et al,. (2006). Su

&Heshmati (2013) states that R-squared tends to be low in Mincerian model because (i) the individual

incomes has a large dispersion so that makes regressions difficult to capture the marginal effects of each

variable; (ii) there might be some unobserved effects that researcher fail to capture using the selected

variables such as ability. However, these regression modelsexplainhow the income of an individual is

determined by demographic characteristics over time (Su &Heshmati, 2013).

The estimated coefficientsfor Punjab province have turned out to be negative depicting it as lagging behind in

income from Sind province (reference group) in overall (-0.054), urban (-0.015) and rural (-0.076) areas.

However, the coefficient of urban Punjab has turned out to be insignificant whereas in overall sample and

rural areas it is highly significant.The overall coefficient of KPK (-0.056) is very close to that of the Punjab

indicating a very litter difference between the income levels of two provinces. However, in urban and rural

areas these differences are more significant (Table 4). Same is the case with the Baluchistan province, where

the coefficients in overall sample and in urban and rural areas significantly differ from those of other

provinces. This is due to the unique socio-economic conditions of the province.

To assess the contribution of human capital towards income distribution, two factors i.e. literacy and

education of the individuals has been used in the estimation of the earnings functions. The ability to read and

write in any language with understanding and ability to solve simple arithmetic questions, the measures of

literacy,both have been emerged as significant contributors towards income determination in overall sample,

urban and rural areas except for the Lit1 in rural areas where although it has an expected sign but is

insignificant.The most important variable in the empirical model used in this study is education. People who

have received no education have been used as the benchmark and seven other categories have also been

defined, keeping in view the education system in Pakistan. According to the results,all the levels of education

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have been emerged as highly significant in both urban and rural areas as well as in overall sample except for

the Edu1 in urban areas where it has positive sign but is insignificant. It is apparent that in urban areas

primary education i.e. up to 5 years of schooling cannot be expected to significantly alter the income level

because of more competition in the job market in these areas. However, in rural areas even a primary

education may significantly change the income of the individuals as has been emerged in this study where

individuals having primary education earn 11% more than those who received no education. Lower levels of

education i.e. up to higher secondary and bachelor’s level give more returns in rural areas whereas higher

levels of education i.e. masters and professional degrees result in significantly higher income levels in urban

areas. This is due to more specialized and competitive job markets in urban areas and can be explained that

economic development and capital accumulation have been taken place more intensively in the urban areas,

thus education in those areas are more valued (Su &Heshmati, 2013). The overall fit the models i.e. adjusted

R-square rises significantly with inclusion of education achievement as one of the predictors. Contrarily

spending on education has littleeffect on the distributionof income (Afonso, et al., 2008).

Occupational returns have been presented in (Table 4). Legislators, senior professionals, professionals,

managers (Occu1) and technicians and associate professionals (Occu2), earn significantly higher income as

compared to those with shop & market sales workers and clerks & service workers (Occu3; the reference

group) in overall sample as well as in urban and rural areas. Workers in agricultural & fishery (Occu4) and

those engaged in craft and related trades activities (Occu5) earn significantly less than worker in the reference

category in overall sample and in urban as well as in rural areas. The earnings of plant and machine operators

and assemblers (Occu6) that constitute about 8% of the sample in overall, urban and rural areas are very close

to the workers in the reference group. However, it has been emerged as the only insignificant occupation

group in the analysis. The earnings of the individuals engaged in elementary occupations (Occu7) are

considerably lower than the workers in the reference category by 33% in overall sample and rural areas and

28% in urban areas (Table 4). In fact this group has been emerged as the least income earner among all

occupational categories.

The earnings of male workers are considerably high as compared to those of the female workers. For example

in overall sample male workers earn 124% higher than their female counterparts whereas in urban and rural

areas this gap stands at 95% and 146% respectively. Likewise, age and age-squared variables are also

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significant at 1% level in Pakistan and its urban and rural areas, showing the non-linearity of the earning

functions.

Married workers earn 11% more than those who were unmarried in overall Pakistan and in rural areas

whereas this gap stands at 16% in urban areas. The income gap between widow/divorced workers and those

who never married also exist and stands at 10%, 7% and 12% in overall Pakistan, and in urban and rural areas

respectively. Both the variable relating to marital status of the individuals are significant at 1% level in all

three areas i.e. Pakistan, Urban and Rural except the widow/divorced in urban areas which is significant at

10% level.

Income Gap Decomposition

There exists a log income difference of 0.47 between urban and rural areas during the year 2010-11 in

Pakistan (Table 5). The log income difference between urban and rural areas has further been decomposed

into province, literacy, education, occupation and marital status. Among provinces of Pakistan, the Punjab

has been emerged as the sole significant contributor towards the urban-rural income gap. Literacy skills both

reading and writing and numeracy collectively explains about 10% of the income gap between urban and

rural areas in Pakistan whereas the major contributor is the reading and writing skill (Lit1). Education is the

second largest contributor of the gap in income between rural and urban areas of Pakistan and its contribution

stands at 22% during 2010-11. The results for primary and middle school are negative which suggest that

urban-rural difference in lower level of education actually play a positive role to narrow the income gap (Su

&Heshmati, 2013). However, higher levels of education have overtaken this affect. Bachelors, Master and

Professional degrees have been emerged as major contributor towards the income gap between the urban and

rural residents. According to the results higher level of education inequality between urban and rural areas in

Pakistan has been the main source contributing towards the income gap between rural and urban areas. There

is a need to address the issue of education inequality by promoting access to higher levels of education as

well as improving the quality of education being assessed by residents these areas (Su &Heshmati, 2013).

Occupation has been emerged as the dominant source of gap in incomes between residents of rural and urban

areas and its contribution stands at 34%.The four categories of occupations i.e. legislators, senior

professionals, professionals, managers, associate professionals, agricultural & fishery workers and workers

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engaged in elementary occupations have been emerged as the positive contributors towards the log income

gap between urban and rural Pakistan. In contrast craft & related trades workers and plant & machine

operators & assemblers have been emerged as the occupations which can play a positive role to narrow the

income gap.

Gender has negative effect in explaining the urban and rural income gap whereas age act as a positive factor

to increase the log income gap between rural and urban Pakistan(Table 5). The contribution of the marital

status towards income differences between urban and rural residents in Pakistan has been positive and stands

at 7%. However, married individuals contribute positively towards the income gap in contrast to the widows

or divorced whose contribution is negative (Table 5).

In sum, most of the income gap is explained by the characteristics of individuals between urban and rural

areas in Pakistan. The decomposition employed in this paper has been able to explain the 65% of the gap in

income between rural and urban areas and resultantly 35% of the income gap remained unexplained. Su

&Heshmati (2013) have stated from Macpherson & Hirsch (1995) that unexplained part in income

decomposition analysis, is generally recognized as the discrimination or due to the non-existence of detailed

controls for all possible relevant factors of job characteristics and person specific skills.

CONCLUSION AND RECOMMENDATIONS

The gap between urban and rural income has been a serious issues for all the countries of the world in general

and for developing countries like Pakistan in particular. In this study determinant of income and income gap

in urban and rural areas of Pakistan have been analyzed by using the latest available nationally representative

data set know as Household Integrated Economic Survey (HIES) 2010-11. In this paper not only

determinants of urban-rural income have been analyzed but decomposition analysis for the urban and rural

income gap has also been carried out. According to the results literacy, education and occupation have been

emerged as the major determinants of income and its gap between urban and rural Pakistan. Reading and

writing skill of individuals has been emerged as more important resulting in higher income by 9% and 3% in

urban and rural areas respectively as compared to the numeracy skill. The income of individuals

havingprimary, middle, secondary, higher secondary and bachelor’s degree are higher in rural areas as

compared to their urban counterparts having similar qualification by 6%, 7%, 3%, 3% and 4% respectively.

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However, master and professional degree holders in urban areas earn more than individuals having same

degrees in rural areas by 13% and 67% respectively.As a result lower levels of education yields high returns

in rural areas whereas higher levels of education, particularly master and professional degrees, give more

return in urban areas. In this way lower level of education e.g. primary and middle school, play a role of

reducing the income gap for rural areas. This is evident from the negative contributions of these education

levels in the decomposition of income gap. However, higher levels of education, secondary school and

more,contribute significantly towards increasing thegap in income between urban and rural Pakistan. This

advocate for the promotion of higher education in the rural areas to enables their inhabitants to compete with

better educated and high skilled urban workers in the competitive job markets.

Agriculture and fishery workers have been emerged as the least earners occupations followed by those

engaged in low paid elementary occupations as compared to legislators, senior professionals, professionals &

managers and technicians & associate professionals whose earnings have been emerged as significantly

higher than other occupations. The study has also found that the earnings of male workers were higher than

those of the female worker by 95% and 146% in urban and rural areas correspondingly. Another finding of

the study is existence of the difference in income between married and unmarried workers which stands at

11% in overall Pakistan and in rural areas as compared to the difference of 16% between these workers in

urban areas. The variables relating to marital status of the individuals have been found significant in this

study.

In sum, individual characteristics like literacy, education,occupation and marital status have been found as the

major determinants of income gap in urban and rural Pakistan. This paper recommends promotion of literacy

skills, higher education as the policy options to reduce the urban-rural income gap.

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Annexure

Table 1 Province-Wise Coverage of the HIES 2010-11

Province Sample PSUs Sample SSUs

Total Rural Urban Total Rural Urban

KPK 208 120 88 2954 1913 1041

Balochistan 164 96 68 2335 1524 811

Punjab 512 256 256 6954 4019 2935

Sindh 296 144 152 4098 2296 1802

TOTAL 1180 616 564 16341 9752 6589

Source: Copied from HIES, 2010-11

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Table 2: Definitions of variables

Name of

Variables

Description

Ln Income

Logarithm of the total income

Province: Punjab

Sindh Reference group

KPK

Baluchistan

Literacy:

Lit1 Can read and write in any language

with Understanding

Reference group

Cannot read and write in any

language with Understanding

Lit2 Can solve simple arithmetic

questions

Reference group

Cannot solve simple arithmetic

questions

Education:

Edu0 None Received no education;

Reference group Edu1 Primary Received 5 years of education

Edu2 Middle school Received 8 years of education

Edu3 Secondary school Received 10 years of

education Edu4 Higher Secondary School Received 12 years of

education Edu5 Bachelor’s degree Received 14 years of

education Edu6 MA, M.SC, MCS, M.Phil/PhD Received 16 or 16+ years of

education Edu7 Professional degree Received in agriculture, law,

engineering, etc Occupation:

Occu1 Legislators, senior professionals,

Professionals, Managers

Occu2 Technicians and Associate

Professionals

Occu3 Clerks and Service Workers and

Shop and Market Sales Workers

(Reference Group)

Occu4 Skilled Agricultural and Fishery

Workers

Occu5 Craft and Related Trades Workers

Occu6 Plant and Machine Operators and

Assemblers

Occu7 Elementary Occupations

Age Age in completed years

Age Squared: Age squared Age*age

Gender: Male Reference group

Female

Marital Status: Never Married/ Nikkah

30 Reference group

Currently Married

Widow / widower and Divorced

30

The couples who are formally married but have not started living together. There were 103 individuals under this

category during 2010-11 (HIES, 2010-11).

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Table 3: Percentage distribution of the variables used in the Empirical Model

2010-11 (All in %)

Characteristics Overall Urban Rural

Province

Punjab

43.12 45.05 41.42

Sindh

26.87 28.65 25.31

KPK

16.02 14.24 17.58

Baluchistan

13.99 12.05 15.68

Literacy:

Lit1 Yes 62.85 73.54 53.45

No 37.15 26.46 46.55

Lit2 Yes 87.53 91.41 84.11

No 12.47 8.59 15.89

Education

Edu0

36.15 25.81 45.24

Edu1

16.28 14.25 18.07

Edu2

12.03 12.46 11.66

Edu3

17.23 20.09 14.73

Edu4

7.01 9.86 4.51

Edu5

5.86 8.83 3.25

Edu6

3.60 5.73 1.73

Edu7

1.83 2.97 0.83

Occupation

Occu1

8.19 11.50 5.28

Occu2

5.35 6.68 4.17

Occu3

20.61 26.35 15.56

Occu4

4.79 1.02 8.10

Occu5

10.35 13.02 8.00

Occu6

7.95 7.83 8.05

Occu7

42.76 33.59 50.83

Age cohort

10-30

42.20 40.52 43.68

31-50

41.90 41.71 42.07

51-65

13.50 14.86 12.31

66 and above

2.39 2.91 1.93

Gender

Male

87.55 88.04 87.11

Female

12.45 11.96 12.89

Marital Status

Unmarried

28.16 29.36 27.09

Married

68.47 66.89 69.86

Widow Divorced 3.37 3.74 3.04

Source: Household Integrated Survey, 2010-11, Author's calculations

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Table 4: Results for OLS estimation for 2010-11

Overall Urban Rural

Variables Coefficients T Coefficients T Coefficients T

(Constant) 8.503* (208.174) 8.589* (140.735) 8.441* (158.910)

Pun -0.054* (-4.766) -0.015 (-0.953) -0.076* (-4.896)

KPK -0.056* (-3.844) -0.143* (-6.681) 0.039** (2.045)

Bal 0.192* (12.752) 0.084* (3.746) 0.289* (14.783)

Lit1 0.079** (2.158) 0.092*** (1.685) 0.033 (0.702)

Lit2 -0.105* (-6.532) -0.080* (-2.912) -0.081* (-4.228)

Edu1 0.091** (2.521) 0.051 (0.923) 0.112** (2.434)

Edu2 0.200* (5.116) 0.144** (2.476) 0.206* (4.075)

Edu3 0.313* (8.110) 0.268* (4.674) 0.302* (6.045)

Edu4 0.459* (11.199) 0.395* (6.657) 0.425* (7.639)

Edu5 0.682* (16.145) 0.604* (10.023) 0.648* (10.839)

Edu6 0.922* (20.316) 0.867* (13.723) 0.742* (10.852)

Edu7 0.845* (16.612) 0.923* (13.417) 0.247* (3.055)

Occu1 0.319* (15.246) 0.333* (12.760) 0.243* (7.208)

Occu2 0.182* (8.084) 0.161* (5.545) 0.231* (6.809)

Occu4 -1.035* (-44.164) -0.346* (-5.271) -1.002* (-37.855)

Occu5 -0.120* (-6.849) -0.168* (-7.422) -0.064** (-2.426)

Occu6 -0.030 (-1.589) -0.042 (-1.563) -0.017 (-0.636)

Occu7 -0.328* (-25.590) -0.279* (-15.561) -0.330* (-18.351)

Male 1.238* (84.146) 0.945* (43.922) 1.460* (75.433)

Age 0.073* (36.636) 0.083* (29.001) 0.064* (24.008)

Age_sq -0.001* (-32.163) -0.001* (-25.969) -0.001* (-21.389)

Married 0.113* (7.767) 0.165* (7.910) 0.112* (5.766)

Widow_Divorced 0.102* (3.358) 0.074*** (1.748) 0.121* (2.920)

Adjusted R Square 0.546 0.503 0.579

Sample size 22164 10369 11795

a. Dependent Variable: ln_y. t-statistics are in parenthesis. *, **, *** shows significance at 1%, 5% and

10% levels respectively.

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Table 5: Decomposition of urban-rural income gap

Attributable to differences in characteristics

Variables 2010-11

log income difference 0.4671

Province: -8.0119

Pun 0.0249

KPK -0.0272

Bal -0.0351

Literacy: 9.7347

Lit1 0.0506

Lit2 -0.0051

Education: 22.4085

Edu1 -0.0129

Edu2 -0.0060

Edu3 0.0093

Edu4 0.0198

Edu5 0.0323

Edu6 0.0368

Edu7 0.0254

Occupation: 34.2061

Occu1 0.0254

Occu2 0.0011

Occu4 0.0776

Occu5 -0.0167

Occu6 -0.0019

Occu7 0.0743

Gender:

Male -0.4404

Age:

Age 0.8172

Age_sq -0.3620

Marital Status: 6.6948

Married 0.0322

Widow_Divorced -0.0009

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Total Explained 0.3038

Total Explained (%) 65.0322

Source: Authors Calculations

analysis of the determinants of income …journal-archieves32.webs.com/858-885.pdfexpenditure and...

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