THE POLITICAL ECONOMY AND REGINAL DISPARITIES IN PAKISTAN

A disparity between the standards of living applying within a nation. Then it is difficult to

quantify the prosperity or poverty of a region.

Regional disparity in Pakistan:

In Pakistan, historically, regional economic disparity has been an important political

issue. During the 1960's the economic disparity between East and West Pakistan fuelled

the movement for provincial autonomy in East Pakistan and subsequently the

movement for national independence in what became Bangladesh in 1971.During the late

1970's and 1980's the issue of regional disparity between the provinces of

what remains of Pakistan has acquired an explosive potential. However, this is an issue

that has been charged by emotion, and it may be time now to begin a serious analysis

to enable effective policy formulation for overcoming the problem.

It is important to note that not only does the overall growth rate of provincial income

vary between provinces but recent research sug- gests that there is also considerable

inter-provincial variation in the level of poverty and changes over time. What is

interesting is that the pattern of variation in the inter-provincial economic growth rates

may not be congruent with the pattern of variation in the inter-provincial poverty levels.

Therefore, the emotional charge of regional identities mobilized on the basis of differing

regional economic growth rates could be mitigated by the fact that a province like the

Punjab for example, with a relatively high provincial growth rate also has

a relatively high level of poverty measured in terms of the percentage of population

below specified calorific norms.

TH MECHANISM

The early studies on regional disparities focused on economic inequality between East-

West Pakistan. The first major study on regional disparity within (West) Pakistan was

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conducted by Hamid and Hussain in which they estimated district-level value added in

large scale manufacturing and agriculture, and also district level economic and social

infrastructure. The study showed that not only inter-provincial inequality increased over

time, but also the degree of inequality within provinces accentuated. What was interesting

was that the regional disparity was positively correlated with the level of growth, i.e., the

rank ordering of intra-provincial inequality was congruent with the rank ordering of

provincial growth rates. The study indicated that when growth occurs within the

framework of the market mechanism there is a cumulative tendency for the relatively

developed regions to grow faster than the relatively less developed regions.

The developed regions enjoy internal and external economies, and lower costs of

production relative to other regions which make the initiating region cumulatively more

advantageous for further investment.

The specific factors underlying cumulative divergence in the

attractiveness of regions for further investment and hence increased disparity

in regional growth rates are: Concentration of communications, banking

facilities, public utilities, technical know- how, trained manpower, and

maintenance facilities. Conversely, as growth is concentrated in the developed

region, it pulls capital and skilled labour from the backward region, thereby

adversely affecting the age composition, skill and capital endowment of the

backward areas.

CHANGE IN SPATIAL CONCENTRATION OF INDUSTRY

The evidence shows that in 1959-60, as much as 39 percent of the value added in industry

is accounted for by Karachi. This is followed by Lahore and Faisalabad. These three

districts together accounted for 60 percent of the value added industry. The rest of the

industry was fairly evenly distributed across the local core and the inner periphery. Over

time the local cores, inner periphery and outer periphery all gained at the expense of the

national core, although at the end of the period, Karachi still accounted for 35 percent of

value added in industry, and the Central Punjab districts constituted 19 percent. In

Central Punjab the most rapidly industrializing district is Sheikhupura, in

northern Punjab it is Jhelum, and in Sind the most dynamic district in terms of industrial

growth is Dadu.

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INCIDENCE AND INTENSITY OF POVERTY: THE REGIONAL DIMENSION

In 1991 a recent paper, Aly Ercelawn has estimated both the incidence and the

intensity of poverty in each of the provinces of Pakistan for rural and urban households

respectively. This has been done by first specifying the minimum expenditure required

for a daily intake of 2550 calories per adult equivalent, using existing dietary patterns.

The calorie-expenditure function on the basis of which the expenditure norm was derived

allowed for both provincial and locational differences.

The incidence poverty indicated the percentage of households below the poverty line.

Poverty line is defined as the expenditure below that required for a calorific intake of

2550 calories daily per adult equivalent.

The intensity of poverty estimates were based on the widely recognized

proposition that an intake of between 70 to 80 percent of the calorific

norm over a sustained period constitutes very high risk of starvation and

undernourishment. The results of Ercelawn's study suggest that in Pakistan, the incidence

of poverty highest in the Punjab and lowest in the NWFP. The percentage of households

below the poverty line in rural areas are approximately 31 percent in

Punjab, 27 percent in Baluchistan, 18 percent in Sind and 15 percent in NWFP.

In urban areas while Punjab has the highest incidence of poverty, Sind has the lowest.4

Thus the percentage of urban households below the poverty line are approximately 25

percent in Punjab, 23 percent in Baluchistan, 14 percent in NWFP and 0 percent in Sind.

If we define the intensity of poverty as the percentage of households

unable to acquire more than 75 percent of the calorific norm, then Ercelawn's estimates

show that for the rural areas the intensity of poverty is highest in Baluchistan and lowest

in Sind. The percentage of households unable to reach 75 percent of the calorific norm in

rural Pakistan is 19 percent in Baluchistan, 10 percent in Punjab, 12 percent in NWFP

and 6 in Sindh.

Disparity in Health Sector:

The factors determining the health behaviors may be seen in various contexts: physical,

socio-economic, cultural and political. Therefore, the utilization of a health care system,

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public or private, formal or non-formal, may depend on socio-demography factors, social

structures, level of education, cultural beliefs and practices, gender discrimination, status

of women, economic and political systems environmental conditions, and the disease

pattern and health care system itself.

A main driver for the health seeking behavior is the organization of the health care

system. In many health care systems there is tension between the public and the private

health sector. The private health sector tends to serve the affluent; thus the public sector

resources should be freed for the poor. A dynamic cooperation, either formal or informal,

between the two sectors is a must but the private sector is rarely taken into account in

health planning scenarios.

The public and private sector may complement or substitute for each other. There are

very often resource mixes with doctors working in the public sector also establishing

their own private practice. Features of the service outlet and confidence in the service

provider also play a major role in decision making about the choice of health facility.

Regional Disparity in Educational Sector in Pakistan:

Pakistan emerged as an independent nation in 1947 breaking apart from India on the basis of a two-nation theory. The Muslim majority province of East Bengal (subsequently East Pakistan) joined Punjab, North-West Frontier Province, Sind and Baluchistan to form the undivided Pakistan. However, after a quarter-century of union with West Pakistan, the Eastern part of Pakistan broke away in 1971As economic activity becomes increasingly knowledge based, disparities in

educational opportunity play a more important role in determining the

distribution of income and poverty. A greater equity in the distribution of educational

opportunities enables the poor to capture a larger share of the benefits of

economic growth, and in turn contributes to higher growth rates.

In contrast, large-scale exclusion from educational opportunities results in lower

economic growth and persistent income inequality. This research appraises

education inequalities in Pakistan at the district level. To summarize district performance

in terms of education, a District Education Index (DEI) is prepared. Further, it

explores the socioeconomic inequalities in education by linking DEI with the

level of district economic development. Education helps to improve living standards and

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enhance the quality of life, and can, thereby, provide essential opportunities for all. Many

of the world’s states, through international conventions and commitments, have

recognized education as a human right. In a rapidly changing world,

education has become more important than ever. Faced with increasing

globalization, the rapid spread of democracy, technological innovation, the emergence

of new market economies, and changing public/private role, countries need

more highly educated and skilled populations, and individuals need added

skills and information to compete and thrive.

Typically, poverty and inequality are perceived of in terms of income. Differences in

income and wealth matter because they define opportunities for reducing poverty. But

differences in income reflect deeper inequalities in life-chances, including inequalities

in education. Wide income disparities tend to coexist with under-investment in human

capital that translates into lower long-run economic growth. The empirical evidence

suggests that there is a high correlation between income and education levels,

as well as between income and educational inequalities.

The relationship between education and income equality is linked to the returns

associated with education. Consider the present situation where the nature of

technological change and the globalization trend are manifested by a rapidly

increasing relative demand for technologically skilled workers.

If the demand for unskilled labor is contracting or growing at a slower rate than the

demand for skilled labor, then wage inequalities will increase. The gap between rich

and poor will then start to widen and income inequality will continue to

grow. Moreover, if there is a large disparity in the educational opportunities

between the rich and poor, mainly educated workers capture the benefits of economic

growth. This, in turn, exacerbates income inequality.

Beyond the discussion on the importance of education for economic growth, the value of

education to the individual is so high that access to education is recognized as a

human right in international law. Countries ratifying the United Nations (UN)

convention on the Rights of the Child recognize the right to education “on

the basis of equal opportunity”.

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Therefore to promote this right to education, access should be provided without

any discrimination.

The characteristics to which discrimination in education provision could be

linked are wide-ranging. Gender, ethnicity and disability are obvious examples and

are explicitly mentioned by the UN Convention. But the list should also include

spatial discrimination in terms of low or no provision of education facilities.

Literacy rate

It needs to be highlighted that from census to census the definition of literacy has been

undergoing a change; resultantly the literacy figure has vascillated irregularly during the

last 5 census. An update of the five censuses is as under:

Year ofcensus

Male Female Total Urban RuralDefinition ofbeing "literate"

Agegroup

1951 19.2% 12.2% 16.4% -- --One who can read a clearprint in any language

All Ages

1961 26.9% 8.2% 16.3% 34.8% 10.6%One who is able to read withunderstanding a simple letter in any language

Age 5 and above

1972 30.2% 11.6% 21.7% 41.5% 14.3%One who is able to read andwrite in some language with understanding

Age 10 and Above

1981 35.1% 16.0% 26.2% 47.1% 17.3%One who can read newspaperand write a simple letter

Age 10 and Above

1998 54.8% 32.0% 43.9% 63.08% 33.64% One who can read a newspaper Age 10 and

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and write a simple letter, in any language Above

2004 66.25% 41.75% 54% 71% 44%

2009 69% 45% 57% 74% 48%

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Table below shows the literacy rate of Pakistan by province.

Province Literacy Rated 1981 1998 2009

Punjab 20.7% 27.4% 46.6% 59%

Sindh 30.2% 31.5% 45.3% 59%

Khyber-

Pakhtunkhwa15.5% 16.7% 35.4% 50%

Balochistan 10.1% 10.3% 26.6% 45%

Table below shows the literacy rate of Federally Administered Areas.

Region Literacy Rate

1981 1998 2007

Islamabad 47.8% 72.88% 87%

Azad Kashmir 25.7% 55% 62%(2004)

Gilgit-Baltistan 3% (female) 37.85% 53%(2006)

Tribal Areas 6.38% 17.42% 22%

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School attendance

Population aged 10 & over that has ever attended school, highest and lowest figures by region.

Islamabad has the highest rate in the country at 85%, whilst Jhal Magsi has the lowest rate at

20%.

Province Highest Lowest

Punjab Rawalpindi (77%)Muzaffargarh and Rajanpur

 (40%)

Sindh Karachi (78%) Jacobabad (34%)

Khyber-Pakhtunkhwa Abbottabad (67%) Upper Dir (34%)

Balochistan Quetta (64%) Jhal Magsi (20%)

Gender disparity

Among other criticisms the Pakistani education system faces is the gender disparity in

enrollment levels. However, in recent years some progress has been made in trying to fix this

problem. In 1990-91, the female to male ratio (F/M ratio) of enrollment was 0.47 for primary

level of education. It reached to 0.74 in 1999-2000, showing the F/M ratio has improved by

57.44% within the decade. For the middle level of education it was 0.42 in the start of decade

and increased to 0.68 by the end of decade, so it has improved almost 62%. In both cases the

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gender disparity is decreased but relatively more rapidly at middle level.

The gender disparity in enrollment at secondary level of education was 0.4 in 1990-91 and 0.67

in 1999-2000, showing that the disparity decreased by 67.5% in the decade. At the college level

it was 0.50 in 1990-91 and reached 0.81 in 1999-2000, showing that the

disparity decreased by 64%. The gender disparity has decreased comparatively rapidly at

secondary school.

However, the gender disparity is affected by the Taliban enforcement of a complete ban on

female education in the Swat district, as reported in a January 21, 2009 issue of the Pakistan

daily newspaper The News

Disparity in Agriculture sector

The stagnant agriculture sector of the Fifties was transformed into a dynamic one in

Pakistan by the technological breakthroughs made in the early Sixties. The installation of private

tubewells, introduction of high-yielding varieties (HYVs) for various crops, the rising use of

chemical fertilizers and insecticides and the mechanization of tillage operations have ensured

growth rates of agricultural output unknown in the Indo-Pakistan subcontinent.

Although the desirability of these technological changes in terms of growth cannot be

doubted, it was argued in many studies that the technology would likely lead to increasing rural

income disparities in Pakistan thus thwarting the desired impact of growth on economic

development [Alavi (1976); Falcon (1970); Gotsch (1976); Gotsch (1976a); Griffin (1974); and

Hamid (1974)].

The conclusion that rural income distribution worsens is attributed in part to interclass disparities

but to a large extent to growing income differences among the various regions [Alavi (1976);

Falcon 0970); Gotsch (1973); and Griffin (1974)]. In making the case for growing regional

income disparities, these studies held the view that the benefits of the technology would remain

restricted to irrigated areas alone.

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Barani areas, with their dependence on natural precipitation alone, would be severely

constrained in the adoption of high-yielding varieties (HYVs) and the application of chemical

fertilizers (Falcon 1970). These apprehensions led to the conclusion of rising interprovincial

disparities; Punjab with an elaborate system of canals and private tubewells is likely to make

tremendous gains in agricultural production relative to other provinces especially Balochistan

and NWFP where agricultural production has shown no visible signs of improvement [Alavi

(1976); Falcon (1970); and Griffin (1974)].

MEASURING INEQUALITY IN AGRICULTURE SECTOT IN PAKISTAN

An empirical investigation of the trend of regional income distribution in agriculture

requires consistent time-series data on value added by agriculture in various regions. As there is

a general lack of consistent time-series data on national accounts disaggregated by regions, one

must look for the alternative sources of data of regional incomes.

Among the most viable of these sources is the valuation of crop-production data by

districts. This adds tremendously to data requirements and calculation work. Not only does it

requires time-series data on production estimates of each and every agricultural commodity by

districts but also relevant price statistics to arrive at gross value of agricultural output at the

district level.

Although highly laborious, we had no option but to undertake the task of preparing

estimates of gross value of agricultural output for each district since 1960-61 to 1982-83. As a

first measure of regional disparity, we aggregate the district data into shares of provinces in gross

value of agricultural output.

In order to proceed further, the gross value of output of each district was divided by its respective

population in agriculture to arrive at per capita incomes. Although agricultural population

estimates for districts are not available other than population census years, they were interpolated

on the basis of inter-censal growth rates of agricultural population in various districts.

The calculation of per capita incomes was essential to arrange districts from lowest to

highest per capita income and to calculate their income shares. The Gini coefficients reported in

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this paper have been based on cumulative shares of population and of incomes of the various

districts ordered by the lowest and highest per capita income.

(iv) How To. Measures of Regional disparity:

Interest of income inequality has led to development of several ways of measuring it .two

types of measuring are of interest static and dynamics. Static measures provide a snapshot of

these inequalities at a point of time whereas the dynamic measures the capture historical trends.

These are described in below turn:

(a) Static measures of regional disparity:

The measurement of regional disparity is an arduous task and no single statistical is able to

capture its myriad dimensions. The selected measures are briefly described in the following

paragraphs.

(i) Maximum to minimum ratio (MMR)

A comparison of the per capita GRDP (gross regional domestic product) of the region

with the highest income to the region with the lowest income (minimum per capita GRDP)

provides a measure of the range of these disparities.

If this measure is small (close to 1), then it would mean that the different regions have

relatively equal incomes. If this measure is large, then the interpretation is more problematic, as

it does not tell us if the high ratio is due to substantial variation in the distribution of per capita

GDRPs or the presence of outliers. Nevertheless, maximum to minimum ratio (MMR) provides a

quick, easy to comprehend, and politically powerful measure of regional income inequality.

(ii) Coefficient of variation

The coefficient of variation (CV) is one of the most widely used measures of regional in-equality

in the literature. The CV is a measure of dispersion around the mean. This dispersion can be

calculated in a few different ways.

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Several authors have used the standard deviation of the logarithm of real per capita

GRDP. In this study, however, theCV as defined below attempts to capture the dispersion of per

capita GRDP. This measure is standardized and can be used to make comparisons between

countries and over time (especially if GRDP data are only available in current prices). In the

following analysis, the CV is calculated in two ways.

The first, the ‘‘simple coefficient of variation’’ is an unweighted measure as given below:

where yi is the GRDP per capita of state i, N is the number of regions and is the mean per capita

GRDP. is computed as the mean of the per capita GRDPs without weighting them by population

as follows:

This is slightly different from Williamson’s (1965, p. 11) formula for the unweighted

CV,where the mean GRDP per capita, , is taken asthe national mean per capita GRDP. The

Williamson measure is not appropriate in this application as it uses a weighted measure for the

denominator and an unweighted one for the numerator. CVu varies from 0 for perfect inequality

(equal percapita GRDP for the different regions) to for perfect inequality (only one

region has all the GDP). Although this measure can be used for comparisons of regional

disparities in countries across time, it is problematic for comparisons between countries because

the inequality value is sensitive to the number of regions.

This problem is somewhat overcome by the ‘‘weighted coefficient of variation,’’ where

each regional deviation is weighted by its share in the national population. This measure is

calculated as given below:

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Yi is the GRDP per capita of region i, and is the national mean per capita GDP. P is the

national population and pi is the population of state i.CVw varies from 0 for perfect equality to

for perfect inequality where region i has all the GDP. This is better than CVu for

cross country comparison as the measure of inequality depends not on the number of regions but

on the population proportion of the regions

.

(i) Relative mean deviation (Rw )

Following Kakwani (1980, 1990) and Williamson (1965, p. 16) we also compute the relative

mean deviation of per capita GRDP as follows:

yi is the GRDP per capita of region i, and is the national mean GRDP per capita. P is the

national population and pi is the population of state i. Rw is a measure weighted by population

proportions of the regions.

As CV is computed by squaring differences, it could be unnecessarily sensitive to

outliers. Rw, which avoids this problem can thus be used to check the CV results. Rw varies

from 0 for perfect equality to 2 for perfect inequality. Kakwani (1980) divides Rw by 2 to get his

measure of relative mean deviation, as it gives the desirable property of the measure becoming

equal to 1 for perfect inequality. But, as we use Rw only to check our CV results for outlier

effects, we do not follow Kakwani (1980) in this regard.

(iv) Gini index (G)

The Gini index like the CV is widely used in

the inequality literature. Following Kakwani(1980), we compute the unweighted Gini indexas

follows:

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Yi and Yj are the GRDPs per capita of region and j respectively. n is the number of regions, and

is the unweighted mean of the per capita GRDPs. Gu varies from 0 for perfect equality to 1

for perfect inequality. The Gini index thus measured is the arithmetic average of n(n-1)

differences of per capita GRDPs, taken as absolute values divided by the maximum possible

value of this average, .

The weighted Gini index, which weights each difference of per capita GRDPs by respective

population proportions is calculated as shown below:

is the national mean per capita GDP. pi and pj are the populations of regions i and j

respectively. P is the national population, and n the number of regions. Gw varies from 0 for

perfect equality to 1-(pi/P) for perfect inequality. If pi is small compared to P, i.e., if the region

with a small proportion of the population produced all the GDP then the value for perfect

inequality would approach 1.

(v) Theil index (T )

A final measure of inequality used in this paper is an information or entropy measure of

inequality. Following Theil (1967), it is computed as follows:

xi is the GDP share of region i of region i and qi is the population share of region i. For equal per

capita GRDPs, i.e., with GRDPs proportional to regional populations, T takes a value of 0. For a

case where region i produces the entire GDP, T becomes, where P is total population of

the country, and pi is the population of state i. Note here that as the population share of region i

goes down, T rises if region i produces the entire GDP.

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(b) Dynamic concepts of regional inequality:

While a snapshot view of regional income disparities is illuminating, a longer-term

perspective is more helpful in ascertaining the impact of public policies. This requires

developing time profile of static measures and discerning whether these inequalities appear to

diminish he so-called convergence hypothesis) or accentuate (divergence hypothesis) over time.

A strong convergence hypothesis suggests that quality in factor productivity and income

levels will be achieved regardless of initial conditions provided diffusion and adoption of

technological change is unrestrained.

A weak convergence hypothesis, on the other hand, requires competitive market

structures to send the right signals for allocation of productive factors (see Boldrin, 2001). Under

the weak convergence hypothesis, differences in technology alone do not explain the differences

in factor productivity.

Lack of competitive price signals such as those observed with regional incentives and

subsidies, infant industry protection, barriers to trade etc. may perpetuate regional differences in

factor productivity and income.

At the conceptual level, regional convergence is assured under perfect competition,

constant returns to scale with no external effects and free and cost-less mobility of factors across

relatively homogeneous (with respect to resource endowment, topography, composition of

population, human capital, political and legal environment, informal culture, etc.) regions within

the nation state.

This requires that political units are commensurate with reasonably large geographic

areas with reasonably diverse endowments so that regional income differentials are attributable

to policy and institutional considerations rather than simply to irreversible acts of nature.

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For example, one should not expect to have convergence among three completely

heterogeneous regions comprising solely desert, mountainous and arable lands. Regional

convergence becomes more difficult to achieve under increasing returns to scale and with

externalities of investment and growth. Strong nonconvergence (divergence) hypothesis places a

greater emphasis on path dependency (initial conditions matter), increasing returns to scale, and

externalities of investment as sources of differences in factor productivity and growth (see

Krugman, 1991; Romer, 1990).

Realization of increasing returns to scale and/or agglomeration economies under perfect

mobility in one of the regions and not others would accentuate regional divergence. Divergence

would also happen if factors are either unable (due to impediments) or unwilling (say age and

ethnicity consideration) to move.

Under strong divergence hypothesis, inequality in levels of income and resource

endowments will prevent convergence in regional growth rates. Under a weak divergence

hypothesis, attainment of a minimum threshold of physical and knowledge capital in the leading

regions is necessary for persistence in the divergence of growth paths.Thus some regions that

attain the minimum thresholds in these factors may form ‘‘clubs’’ or ‘‘growth poles’’ and may

grow faster than others and achieve ‘‘club convergence.’’

Publicpolicies to break this regional concentration of powers may have tradeoffs between

national growth and overcoming regional inequalities (for a discussion of this issue in the

European Union context, please see Boldrin, 2001). In federal countries regional inequalities are

likely to be given significant importance in evaluating any tradeoffs that may be observed.

various countries.

Two statistical concepts are helpful in look-ing at the dynamics of regional inequalities.

First, a reduction in the dispersion of regional income over time is termed as convergence.

Second, any catching up in incomes by relatively poorer regions through faster growth is called b

convergence (see Barro & Sala-i-Martin, 1995, p.383).

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we have estimated a growth equation based on a log-linear approximation around the

steady state of the Solow growth model. This is similar to the one suggested in Barro and Sala-i-

Martin (1992, 1995). The model is as follows:

where yi;t is the per capita GRDP of the ith region at time t, yi;tT is the per capita GRDP in the

ith region at the beginning of the time interval under study and ui is an independently distributed

error term. For our purposes, we rearranged Eqn. (8) and estimated the following convergence

equation:

Convergence and convergence analyses may not always give similar results. For instance,

there could be a significant decrease in convergence (divergence), with a change in the ranks

of the regions.

A poorer region could grow so fast that it could overshoot on the richer side, leading to greater

overall dispersion . Convergence analysis could capture this divergence as convergence, or it

may show an absence of either. Therefore, it is important to do both types of analysis to

understand what is really happening.

Conclusion:

The measures of regional disparities show that a sizable disparity in per capita incomes

continuously persists among the four regions of Pakistan. The extent of regional disparities in

labor productivity and employment ratio are also high and have increasing trend since 1982. In

the case of two regions, the disparity in per capita incomes. The estimation results of regional

population functions indicate that relatively higher job opportunities give incentive to people to

move from one to another region.

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