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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS
COPY RIGHT © 2013 Institute of Interdisciplinary Business Research
858
MAY 2013
VOL 5, NO 1
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.
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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