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Beyond the Poverty Line: A Multidimensional Analysis of Poverty in Pakistan

Azam Chaudhry*, Theresa Chaudhry**, Muhammad Haseeb***, and Uzma Afzal****

1. Introduction

The issue of poverty is both simple and complex: at one level, the implicit or explicit objective of most economic policymakers in developing countries is to reduce the levels of poverty in a country; at another level, the tools that should be used are constantly open to debate. The debate in almost all developing countries concerns the correct policy mix between those that target economic growth, which have the potential to reduce poverty as overall income levels rise, and those policies that target poverty directly, such as social safety nets or income transfers.

There is little question that long-term growth reduces poverty, but in a country such as Pakistan, where growth is sporadic at best, the question that arises is what can be done to reduce poverty for those who will not benefit from growth for years or even decades. If we add to this the fact that development has been devolved to the provinces in Pakistan after the 18th Constitutional Amendment, the future of poverty alleviation initiatives is quite simple: Either we explicitly acknowledge that the state is only concerned with economic growth and wait for growth to reduce poverty—while pursuing intermittent and idiosyncratic poverty interventions that assist the poor but do little to move them out of poverty—or we clearly focus the limited resources of the state on explicitly targeted poverty interventions.

The purpose of this chapter is not to evaluate poverty reduction initiatives in Pakistan or to predict what will happen to poverty in the country over the next decade. Rather, we want to see what has been happening to poverty over the last decade by looking at poverty in unique ways: First, we look at what has been happening to poverty if we expand the definition of poverty to include not just income but also other basic necessities such as health and education. After this, we decompose poverty into two distinct categories in Pakistan: poverty as a result of the circumstances into which one is born (such as where one was born or the education of one’s parents) as opposed to poverty as a result of one’s own efforts (such as the level of education one obtains). The principle objective of our analysis is move the debate in Pakistan beyond the “poverty line” as defined in income terms and toward a more comprehensive discussion of what really makes people “poor” and the best ways of targeting poverty in Pakistan. We believe that, in an environment characterized by constrained resources and intermittent poverty reduction interventions, the only way one can make an active difference to the level of poverty is to clearly identify which areas to focus on.

The chapter is structured as follows: Section 2 discusses some of the literature on poverty in Pakistan as well as the more recent literature on poverty measurement. In Section 3, we look at a wider view of poverty, which includes health and education, and then at poverty trends from this wider perspective. In Section 4, we decompose poverty in that which can be explained by circumstances and that which can be explained by effort. We then see what happens to poverty if we were to equalize the circumstances in which people were born. Finally, we present some conclusions from our analysis.

2. Measuring Poverty and the Literature on Poverty in Pakistan

2.1. Trends in Poverty and Inequality in Pakistan

On average, poverty has decreased in Pakistan over the last decade or so, after having risen in the 1990s. The proportion of people living below the international poverty line, i.e., on less than USD 1.25 a day, went from 58.5 percent in 1990 to 36 percent in 2001/02, finally falling to 22.6 percent in 2004/05 (World Bank, 2008, 2009). According to the national poverty line, poverty rose steadily from 17.32 percent in 1987/88 to 25 percent in 1993/94, dipping to 21.6 percent in 1996/97 before rising sharply to 30.6 percent in 1998/99, and finally peaking at 34.5 percent in 2000/01. After that, significant declines occurred, culminating in a poverty headcount of 22.3 percent in 2005/06—almost identical to the poverty rate seen in 1990/91. Households are vulnerable to shocks, such as the food price crisis in the latter part of the last decade, economic slowdowns, political turmoil, and other disruptions that can reverse gains in poverty alleviation, moving vulnerable households back into poverty.

Khan (2011) and Anwar (2009) reach similar conclusions about the overall fall in poverty when looking at other dimensions of wellbeing besides income per capita. Khan (2011) finds that multidimensional poverty fell by five percentage points between 1998 and 2008 to 38 percent. However, it was not a steady decline as steep increases in poverty occurred both in 2001/02 and 2005/06. At the end of the period studied, poverty continued to be higher in rural than urban areas, but the gap had narrowed. Naveed and Islam (2011) also consider multidimensional poverty in Pakistan, noting that the most common deprivations faced by households (in order of importance) are due to child mortality, lack of land ownership, and children not enrolled in school.

Anwar (2009) uses household data over the 2000–05 period to look at trends in consumption and other measures of wellbeing across income deciles, finding that inequality falls in terms of “opportunities” as measured by literacy, school enrollment rates (at all levels), child immunization, pre- and postnatal care utilization, access to electricity, and access to modern water and sanitation. However, he notes that income inequality likely increased over the period since the richest two deciles increased their share of consumption.

Shocks such as those to food prices can lead to large increases in poverty, though these are usually transient. Ivanic, Martin, and Zaman (2012) find that the spike in food prices in late 2010 that caused wheat prices to double and led to 65–75 percent increases in sugar, maize, and soybean and palm oils, led to a net increase in poverty of 44 million people around the world. Further, they estimate that the poverty headcount in Pakistan increased by two full percentage points (the second largest increase in the sample) due primarily to the fact that the steep rise in global wheat prices was largely passed on in the form of higher local prices. 

The World Bank (2011) also studies the effect of increasing food prices (particularly wheat) between 2006 and 2010, finding that households would have needed 27 percent more income between 2006 and June 2008 to maintain household utility levels in the presence of the price increases (increasing further over the course of the year), and that caloric availability fell by around 8 percent at the height of the food price crisis in 2008. The study finds that households sold assets to absorb the shock of higher food prices, and that those that owned land were somewhat protected. 

Chaudhry and Chaudhry (2008) calculate the elasticity of the poverty gap (depth of poverty) with respect to food prices as 2.1 percent and 0.44 percent with respect to energy. Kurosaki (2006) also focuses on the effects of risk and shocks on poverty when decomposing poverty into chronic and transient components. He finds in Khyber Pakhtunkhwa (KP) that during 1996–99, more than half the population was always poor, 13 percent were usually poor, and 16 percent were occasionally poor. Earlier research by Alderman (1996) on the IFPRI panel from the late 1980s of rural households in Pakistan demonstrates that, even though households engage in precautionary saving, they experience a reduction in per capita consumption and sell assets when faced with multiple shocks. 

2.2. Measurement of Poverty

The simplest and historically most commonly used indices to measure poverty include the poverty headcount, the poverty gap, and the squared poverty gap. These indices are known as the Foster-Greer-Thorbecke (FGT) measures of poverty. However, in recent years, newer measures of poverty have been developed to capture dimensions of poverty that are not contained in the FGT indices, such as poverty across time or different dimensions of poverty. These include measures that capture the detrimental impact of income fluctuations (see Kurosaki, 2006) or measures that capture multiple dimensions of wellbeing (such as Alkire & Foster, 2011) in the spirit of Sen’s capabilities approach. Alkire and Foster (2011) first calculate deprivation on individual dimensions, such as consumption, health, education, or empowerment, after which the number of dimensions for which a person is deprived can be summed. 

As we have seen, there is increasing recognition among development economists that poverty is more than simply the shortfall of income or consumption below a certain threshold, but rather the deprivation of households along multiple dimensions. In fact, the relationship between consumption levels and deprivation can be weak, as noted by Naveed and Islam (2010) in their analysis of selected districts in Punjab and KP. According to their analysis, the official poverty line has missed a large share of those who would be considered among the multidimensional poor. Measuring poverty by the official poverty line, only around 18 percent would be considered poor in these two provinces, but the share of households deprived in either five or six dimensions is 36 or 25 percent, respectively. 

Similarly, McLeod (2006) finds for a larger group of countries including Pakistan that calculations of poverty through household expenditure surveys do not correlate highly with capabilities; rather, national accounts estimates of consumption growth are better predictors of improvements in wellbeing. 
Kurosaki (2006) notes that one of the unattractive features of the FGT squared poverty gap measure is that it does not capture that income fluctuations have more serious welfare implications the greater the depth of poverty. Further, he finds that decompositions of poverty into chronic and transient components using the FGT squared poverty gap is not robust to changes in the poverty line, while the Clark-Watts measure which incorporates constant relative risk aversion, performs significantly better.

3. Incorporating Education and Health into Poverty Measures

In this section, we analyze unidimensional and multidimensional measures of poverty using household-level data on income, health, and education in Pakistan. The reason for this is that poverty is a multidimensional concept in that certain segments of the population may be simultaneously deprived in numerous dimensions but their poverty can be underestimated if one looks only at their income. Similarly, certain segments of the population may be better off when assessed across various dimensions of deprivation as compared to a simple analysis of their income. 

For the purposes of this analysis, we focus on three dimensions of poverty: income, education, and health. As is the standard practice (see Labar & Bresson, 2011), when looking at educational deprivation, we have restricted our sample to individuals above the age of 20. In order to measure each type of deprivation, we use the following data for 2004, 2008, and 2010 from the Pakistan Social and Living Standards Measurement Survey: for income deprivation we use household per capita income, obtained by dividing the total household income by na, where n is the number of household members and a is an equivalence factor. For education, we use the number of years of education obtained by the household member. For health, we use a combination of indicators, including household sources of water and household sanitary conditions.

The income-based poverty line we use is USD 1.08 per day in 2004 and USD 1.25 per day in 2008 and 2010. For education-based poverty, we take a person to be below the poverty line if they have not completed primary education; for health-based poverty, we take a person to be below the poverty line if they do not have a clean source of drinking water. 

First, we look at unidimensional comparisons of poverty over time. Here, we look at the poverty trend using each of these indicators across gender, provinces, and rural/urban divides. We then apply unidimensional stochastic dominance tests to gauge what has been happening to poverty, using these indicators separately. 

Following this, we look at multidimensional poverty using all the indicators. Following Alkire and Foster, we use both the intersection and union approaches to poverty identification. The intersection approach implies that a person is considered poor if they are deprived in all dimensions of poverty, namely income, health, and education. In the union approach, a person is considered poor if they are deprived in at least one dimension of poverty. As for the unidimensional analysis, we initially look at the trend in multidimensional poverty across gender, provinces, and between rural and urban areas. 

3.1. Analysis of Unidimensional Poverty in Pakistan over Time

The unidimensional poverty analysis checks to see if poverty has fallen when one looks at the three indicators of deprivation, income, health, and education, separately over time. We start with a look at the statistics on poverty levels, using each of these three indicators over time, and then carry out a stochastic dominance analysis to see if poverty has fallen significantly over the last decade in Pakistan. 

Looking at poverty from the perspective of income, Table 17.1 shows the overall percentage of people below the poverty line for the years 2004, 2008, and 2010. What the numbers show is that that there is a significant decline in the number of people below the poverty line between 2004 and 2008 from approximately 20 to 16 percent. From 2008 to 2010, the percentage of people below the poverty line rises to approximately 16.5 percent but this number is not statistically different from the corresponding number from the 2008 data, which implies that the percentage is approximately the same between 2008 and 2010. 

Table 17.1: Percentage of population below the income-based poverty line for the entire population


Source: Authors’ calculations.

Table 17.2 shows what has happened in the rural and urban areas in terms of income-measured poverty: Between 2004 and 2008, there is a significant decrease in the percentage of the urban population living below the poverty line, from approximately 11.5 to 9.2 percent. After this fall, the level of urban poverty stays approximately the same from 2008 to 2010. A greater fall occurs in rural poverty from 2004 to 2008, with the percentage of the rural population living below the poverty line falling from approximately 25 to 19 percent. The level of rural poverty rises between 2008 to 2010 from approximately 19 to 20 percent, but this increase is again not statistically significant.

Table 17.2: Percentage of population below the income-based poverty line for the entire population: Rural vs. urban breakdown


Source: Authors’ calculations.

Finally Table 17.3 shows the breakdown of income-based poverty across the provinces. Again, there is a significant decrease in the percentage of people living below the poverty line in all four provinces between 2004 and 2008, with the greatest decrease occurring in KP where the percentage of people living below the poverty line falls from 34 to 22 percent. In Punjab, Sindh, and Balochistan, this trend of falling poverty continues between 2008 and 2010, but what is striking is that the percentage of people living below the poverty line rises significantly in KP between 2008 and 2010 from 22 to 26 percent. 

Table 17.3: Percentage of population below the income-based poverty line for the entire population: Provincial breakdown


Source: Authors’ calculations.

Moving on to the second indicator of poverty, education, we focus on the percentage of people without primary education. Because individuals below a certain age may either still be in the process of obtaining education or still have the opportunity to complete their primary education, we have concentrated on individuals above the age of 20. The overall breakdown of people above the age of 20 who have not completed their primary education is shown in Table 17.4. As the numbers illustrate, there was a significant decrease in the number of people above the age of 20 without primary education between 2004 and 2008, from approximately 59 to 55.5 percent, but there is no statistically significant decrease in this number between 2008 and 2010.

Table 17.4: Breakdown of population above the age of 20 who have not completed their primary education (%)


Source: Authors’ calculations.

Similarly, Table 17.5 shows that there is a significant fall in the number of men and women above the age of 20 without primary education from 2008 to 2010, with the percentage of men without primary education falling by almost four percentage points from 44.6 to 40.6 percent and the percentage of women without primary education falling from 73.9 to 70.5 percent. Again, there are no significant changes in these numbers between 2008 and 2010. 

Table 17.5: Breakdown by gender of population above the age of 20 who have not completed their primary education


Source: Authors’ calculations.

Tables 17.6 and 17.7 show the breakdown of people above the age of 20 without primary education in rural and urban areas and across provinces between the years 2004 and 2010. The three important points to note are: First, the significant decreases in the percentage of people without primary education between 2004 and 2008 (with a fall from 40.4 to 37.3 percent in the percentage of adults without primary education in urban areas and a corresponding fall from 70.4 to 66 percent in the percentage of adults without primary education in rural areas); second, the disparity in the percentage of adults without education when comparing rural and urban areas and the provinces of KP and Balochistan versus the provinces of Punjab and Sindh (with the former having a significantly higher percentage of adults without primary education than the latter); third, the lack of any significant change in all these percentages between 2008 and 2010.

Table 17.6: Rural/urban breakdown of population above the age of 20 who have not completed their primary education


Source: Authors’ calculations.

Table 17.7: Province-wise breakdown of population above the age of 20 who have not completed their primary education


Source: Authors’ calculations.

The final dimension of poverty that we focus on is health, as measured by the source of drinking water available to individuals. As discussed above, we have characterized the sources of drinking water into categories above and below the poverty line. The overall picture for the years 2004 to 2010 is given in Table 17.8. Here we see an interesting reversal in the trend observed for the other indicators of poverty: From 2004 to 2008, the percentage of people below the poverty line as measured by their source of drinking water increased from approximately 11 to 15 percent, and this higher level was also observed in 2010. 

Table 17.8: Breakdown of population by access to drinking water (%)


Source: Authors’ calculations.

Tables 17.9 and 17.10 show where this increase in poverty has occurred: Table 17.9 shows the breakdown between rural and urban poverty in terms of sources of drinking water and the numbers show a significant increase in the poverty rate (or, in other words, a significant deterioration in the sources of drinking water) in rural areas with an increase from 16 to 22 percent of the rural population falling below the poverty line in terms of sources of drinking water, while the percentage of people with poor sources of drinking water in the urban areas is significantly lower at around 1.5 percent. 

Table 17.9: Rural-urban breakdown of population by access to drinking water


Source: Authors’ calculations.

Table 17.10 shows the provincial breakdown of people below the poverty line in terms of sources of drinking water; the numbers are striking because of the significantly lower levels of poverty in Punjab and Sindh compared to KP and Balochistan. Table 17.10 also shows where the greatest increase in poverty, as measured by the sources of drinking water, has occurred in Balochistan, with the number increasing significantly from 31 to 49 percent of the population being classified as poor in terms of their sources of drinking water. Finally, Tables 17.9 and 17.10 show that, like income-based and health-based poverty, there has been no significant change in poverty as measured by sources of drinking water between the years 2008 and 2010.

Table 17.10: Province-wise breakdown of population by access to drinking water


Source: Authors’ calculations.

Thus, the numbers show that income-based poverty and education-based poverty declined between 2008 and 2010 while poverty measured by sources of drinking water increased between these years. Additionally, the numbers for all three poverty indicators show no significant changes between 2008 and 2010. 

The obvious question that arises is whether these changes over time are significant for poverty when measured in terms of income as well as when measured in terms of education and health. Table A17.1 gives the results of unidimensional stochastic dominance tests for each of the poverty measures. The results are interesting in that they show that income-based and education-based poverty fell significantly between 2004 and 2008 while health-based poverty did not change significantly (even though the results imply that it actually rose, these results were not significant). 

Moreover, even though the results seem to be implying an increase in all three poverty measures between 2008 and 2010, none of these are statistically significant, implying that there was no significant change in poverty between 2008 and 2010. A comparison of these different types of poverty measures between 2004 and 2010 yields the same results as those between 2004 and 2008: income- and education-based poverty decreased but the health-based poverty measure did not show a significant change over this period. 

The rural/urban breakdowns of these unidimensional poverty measures given in Tables A17.2 and A17.3 are informative: Between 2004 and 2008, all three measures of poverty indicate that urban poverty fell significantly, whereas only income- and education-based poverty fell during in rural areas. What is interesting is that there was no significant change in income-based poverty in either rural or urban areas between 2008 and 2010, but there was a significant decline in education- and health-based poverty in urban areas between 2008 and 2010, which was not matched by any significant changes in education- and health-based rural poverty. Over the longer period 2004–08, urban poverty declined using all three measures of poverty while only income- and education-based poverty declined in the rural areas during this period. 

The province-wise breakdowns of unidimensional poverty measures are given in Tables A17.4–A17.7. It is worth noting, first, that while income-based poverty fell in all the provinces between 2004 and 2008, education-based poverty fell in three of the four provinces (the outlier being Balochistan) and health-based poverty only fell in Punjab and KP. The second interesting observation is that, while there was no significant change in income-based poverty for all three provinces between 2008 and 2010, the only province that experienced a decline in education- and health-based poverty was Sindh. Finally, the statistics show that only KP did not experience a net decline in income-based poverty over the longer period 2004–10, while Balochistan was the only province that did not experience a net fall in education-based poverty during this period. Over the longer period, it seems that only Punjab experienced a significant decline in health-based poverty levels. 

3.2. Analysis of Multidimensional Poverty

This section looks at multidimensional poverty, using a combination of income, health, and education indicators, and then performing tridimensional dominance tests (using these indicators) to see if multidimensional poverty has fallen over time in Pakistan. 

Our approach uses the “intersection” of the various poverty measures to measure the poverty rate. By this we mean that we consider an individual to be above the poverty line if they fall below the poverty cutoff in at least one of the indicators while they are considered to be above the poverty line if all three of their indicators lie above the poverty cutoff. So an individual is considered to be above the poverty line if they are considered not poor in terms of income, health, and education; if any of these indicators lie below the poverty cutoff, they are considered poor. 

Though we acknowledge that this is a far more stringent criterion for being above the poverty line than most commonly used measures, our rationale is that it allows us to build a more comprehensive picture of the state of poverty in Pakistan. So if 70 percent of the population lies below the poverty line according to this multidimensional measure, it does not imply that the figures we obtained for poverty above are incorrect; rather, it implies that, from the point of view of development, 70 percent of the population is still deprived in at least one dimension of poverty. 

Table 17.11 shows the percentage of the overall population that is poor in terms of at least one criterion. Here, we see that, according to our multidimensional view of poverty, almost 73 percent of the overall population is considered to be deprived in at least one of our key criteria (income, health, and education). This number falls to approximately 70 percent in 2008 after which is remains constant in 2010. 

Table 17.11: Percentage breakdown of overall population below the multidimensional poverty line (income, education, and health)


Source: Authors’ calculations.

Table 17.12 shows the rural/urban breakdown of multidimensional poverty, indicating the stark difference between multidimensional poverty in the urban and rural areas: Urban multidimensional poverty is almost 25 percent lower than rural multidimensional poverty (56 percent as opposed to 82 percent), and though there is a slight fall in both poverty levels between 2004 and 2008 (after which it stays relatively constant), the wide gap between urban and rural multidimensional poverty persists. 
Table 17.12: Rural/urban breakdown of overall population below the multidimensional poverty line (income, education, and health)


Source: Authors’ calculations.

Table 17.13 shows the provincial breakdown of multidimensional poverty. What is striking is the large gap between the level of multidimensional poverty in Punjab and Sindh versus that in KP and Balochistan. So while multidimensional poverty falls in Punjab, Sindh, and KP between 2004 and 2010 (with the most significant falls taking place in Punjab and Sindh), there is no significant decrease in multidimensional poverty in Balochistan over this period.

Table 17.13: Province-wise breakdown of overall population below the multidimensional poverty line (income, education, and health)

Source: Authors’ calculations.

4. Separating Circumstances from Effort in Income Determination

A critical but often ignored question in the discussion on poverty is the impact of opportunities available to individuals on their poverty status. Obviously, there is a significant difference between the economic opportunities available at birth to individuals in Pakistan and the same amount of effort may be enough to take one individual out of poverty but insufficient in another’s case. Roemer (1998), who formalized this idea, suggested decomposing economic outcomes such as income into circumstances and effort. By circumstances, we mean those exogenous factors that a person has no control over, such as gender, family background, or place of birth; by effort, we mean those factors that are affected by the choices made by individuals (or families), such as education or occupation. 

In this way, economic outcomes such as income, and therefore poverty, are jointly determined by factors that are beyond the control of individuals and factors that a person can control through his/her choices. This is a powerful idea because not only can one say that a person’s income lies below the poverty line, but one can also associate this income with observed circumstances beyond the control of individuals. In other words, we divide the determinants of income in Pakistan into factors determined by the circumstances into which an individual was born, and factors that are determined in his/her own life. 

The small budgets and limited capacity that developing countries such as Pakistan have to work with limit their ability to equalize economic outcomes for individuals. For these reasons, in addition to notions of redistributive justice, a policy that aims to equalize opportunities is an attractive approach because it combines the idea of personal responsibility (that outcomes should vary with effort) with the recognition that many individuals are born into disadvantaged circumstances through no fault of their own. 

Drawing on the concept of effort versus circumstances delineated by Roemer (1998), Bourguignon, Ferreira, and Menendez (2007) use data from Brazil on men’s hourly earnings and determine that between 10 and 37 percent of the earning inequality for men aged 26 to 60 could be eliminated if they faced equal circumstances, which included race, place of birth, parents’ education, and father’s occupation. In their exercise, income is determined by three major factors: circumstances, effort, and unobserved individual characteristics (such as intrinsic ability and motivation). Circumstances are fully exogenous to income. However, they allow individual effort, including education, occupation, and migration, to be determined in part by circumstances. The motivation for this, in the case of education for instance, is that the value that parents place on education may depend in part on the family’s socioeconomic status, thus influencing the amount of education attained by their offspring, regardless of ability or intrinsic motivation. 

This analysis is intuitively appealing, but it requires very specific data on circumstances such as place of birth and parents’ education levels, as well as data on effort, such as education level and occupation. Though much of the latter information is available in household-level datasets in Pakistan, the former is not available in virtually any dataset. Thus, we take a unique approach to this problem: Using the PSLM data for 2010/11, we restrict our sample to working men aged 20 to 30 who are living with their fathers. This helps minimize the sample selection issue that would arise if men living with their fathers differed fundamentally from those who had left, in ways that were not exogenous to income. 

For example, sample selection bias could occur if men who are more intrinsically motivated are more likely to move away from their parents and earn higher incomes. Since the majority of men in the 20–30-years-old age bracket will not yet have moved from their natal household, we feel that taking this subsample will minimize the sample selection bias. Given the different circumstance and effort variables that we consider, we lose additional observations due to missing data. We use the characteristics of the fathers in the households and separate the impact on income of the circumstances into which these men were born from that of their efforts. 

4.1. Persistence of Income and Educational Attainment over Generations

However, before studying the joint effects of circumstance and effort variables, it is useful to take a step back and look at the persistence of key economic characteristics across generations. These are referred to as Galtonian regressions and take the form
                                                                                                          (1)

where refers to the attainment of the current generation, in this case income or education, and refers to the attainment of the previous generation. The coefficients in each case indicate the persistence across generations and therefore allows us to measure intergenerational mobility. The greater the elasticity , the greater is the persistence of inequality across generations; an elasticity of 1 would indicate that incomes are perfectly correlated between generations of the same family.

Table 17.14 reports the results of these Galtonian regressions of intergenerational persistence of income and educational attainment. For each of the regressions, we run the Galtonian regression on two samples; in the first, we use the largest sample available for men aged 20 to 30, and in the second we use a smaller sample that is comparable with that used in the upcoming regressions to compute the effects of unequal opportunity on income. 

Table 17.14: Galtonian regressions of intergenerational persistence: Income and education


Source: Authors’ calculations.

The results of Table 17.14 tell us that the income of men aged 20 to 30 has an elasticity of approximately 0.4 with respect to their fathers’ income. That is, on average, a young man’s income tends to be 4 percent higher if his father’s income is 10 percent higher. When it comes to education, the attainment of men aged 20 to 30 is much more highly correlated with that of their mothers (elasticity 0.3–0.5) compared to their fathers (elasticity of 0.1). A young man’s education tends to be more significantly affected by the level of education of his mother compared to that of his father.
On the whole, the results imply that parents’ income and education are significant in determining if young men lie above or below the poverty line. 

4.2. The Effect of Effort and Circumstances on Income

In this analysis, following Bourguignon et al. (2007), we analyze separately the factors that affect a person’s income level into those factors that a person faces at birth and has no control over, or “circumstances”, and those that are determined during the life of a person, or “effort”. We then analyze how the equalization of circumstances can reduce the inequality of outcomes. In determining these, two critical points emerge: First, we can assess which factors should be targeted in the effort to reduce poverty in Pakistan. Second, we can see that, in an environment of limited resources, what would be the impact on inequality of trying to simply reduce the unequal circumstances into which people are born instead of generally targeting the poor equally throughout Pakistan. 

In our estimations, the circumstances that we consider are the average educational attainment of the man’s parents (AEP), the father’s total annual income from all sources (FY), occupation dummies for the father (FOD), the household’s wealth index (W), and regional dummies for place of residence (RD). The regression equation for the total effect of circumstances on income is:

                         (2)

where w is the yearly total income from all sources for men aged 20 to 30. 

These results show that average parental educational attainment and household wealth has no real impact on earnings. However, the father’s income and occupational status have impacts that are both large and statistically significant. More specifically, the children of sharecroppers have the lowest incomes while the children of farmers who own their land have slightly higher incomes. Interestingly, people with parents who are wage-paid employees tend to have higher incomes than the children of farmers while the highest incomes are those of children with parents who are self-employed or are employers. So, if one wanted to target poverty reduction funds, one should aim to focus on areas with significant populations of landless agricultural workers. 

The results also show that the region of residence plays an important role. Men living in the urban areas of northern Punjab have the highest incomes, followed by those in Karachi. With the exception of KP, men in urban areas generally have higher incomes than their rural counterparts. Thus, in terms of targeting, central Punjab, southern Punjab, and northern Punjab are areas in which income levels tend to be the lowest after controlling for all other factors. 

If, instead, we want to compute the direct effect of circumstances, in other words controlling for individual efforts, we start with the following expression:

                                    (3)

The circumstance variables, C, are as above. The effort variables, E, chosen by the individual male aged 20 to 30 are education (ED) and the decision to work in the agricultural sector (AG). The regression specification we use to estimate the direct effect of circumstances C, controlling for efforts is:



    (4)

The estimation results for equation 4 can be found in Table A17.9. The results show that the individual’s own education has the expected positive and nonlinear relationship with earnings; an additional year of education increases annual total income by about 3.9 percent, emphasizing the important role of education in reducing poverty. 

The father’s income and occupational status have important effects on earnings. The elasticity of own income with respect to the father’s income is 31 percent. Compared to men whose fathers are sharecroppers, those with fathers with higher occupational status also earn more, with the increase in wage rising with the occupational status. Men with fathers who are owner-cultivators, wage earners, or have their own business earn 20, 38, and 46 percent more than those whose fathers are sharecroppers. The region of residence is also important, with the highest incomes earned by those in northern urban Punjab and Karachi. Thus, the circumstances into which people in Pakistan are born have a significant impact on their future income levels. 

Following Bourguignon et al (2007), we compare the actual earnings distribution with the theoretical distribution generated by equalizing circumstances and controlling for individual efforts using an inequality measure, I. In other words, we measure income inequality by equalizing the circumstances into which every person in the sample was born. Indices of inequality for the distribution of actual earnings, as well as the hypothetical earnings distributions computed with equalized circumstances and the residual inequality are summarized in Table 17.15. 

In our results, equalizing all circumstances reduces the Theil index from 0.5 to 0.46—a fall of about 8.2 percent—when using the regressed sample of 1,493 observations. If we expand the sample to include observations for which effort variables are missing, the reduction in inequality is 7 percent as measured by the Theil index. Thus, differing circumstances at birth lead to significant differences in income, and equating circumstances across Pakistan could have a significant impact on poverty reduction. 

Table 17.15: Decomposition of inequality measures due to unequal opportunities

Source: Authors’ calculations.

5. Conclusions

This chapter’s analysis is significantly different from typical analyses of poverty. We have looked at poverty as a multidimensional phenomenon and found that there are more significant differences between the rural and urban areas and between provinces if one expands the definition of poverty to include income levels, health indicators, and educational attainment. 

Urban multidimensional poverty is almost 25 percent lower than rural multidimensional poverty and though there is a slight fall in both poverty levels between 2004 and 2008, the gap between urban and rural multidimensional poverty persists. Additionally, the provincial breakdown of multidimensional poverty shows a large gap between the level of multidimensional poverty in Punjab and Sindh versus that in KP and Balochistan. While multidimensional poverty falls in Punjab, Sindh, and KP between 2004 and 2010, there is no significant decrease in multidimensional poverty in Balochistan over this period. 

We also adopt a new approach to looking at the factors that affect income levels in Pakistan. By dividing the factors that affect income into the circumstances into which people are born (and which they have no control over) and those that people can influence through their own efforts, we are able to gauge if targeting poverty reduction initiatives can have a significant impact on income levels. 

We find that factors such as education have a significant impact on determining a person’s income. Thus, an obvious way to reduce poverty would be to promote education in the least developed areas. A more interesting result is that the circumstances into which people are born have a significant impact on whether they will have low incomes or not; put another way, controlling for all the usual factors such as education, just being the child of a less educated father or sharecropper significantly reduces the levels of income a person will have and increases the chances that he/she will fall below the poverty line. We also find a significant decrease in income inequality if we equalize the circumstances under which people are born.

This final result is critical because normal policy reduction initiatives have a household-level focus: they are less concerned about targeting regions or communities and more concerned about targeting the lowest-income households. Our results imply that, in a situation where resources are constrained, there can be significant reductions in income inequality if one focuses on equalizing the circumstances under which people are born. This may mean that one should not have to target households with the lowest incomes, but rather target areas that have the most significant persistence of low incomes.


* The author is an associate professor and dean of economics at the Lahore School of Economics.

** The author is an associate professor of economics at the Lahore School of Economics.

*** The author is a research associate at the Centre for Economics and Business, Lahore School of Economics.

**** The author is an assistant professor of economics at the Lahore School of Economics.

The dimensions of deprivation (and cutoff) considered were education (less than primary completed, child not enrolled), health and nutrition (an underweight woman in household, one death under age 5), housing (mud house), electrification, safe drinking water, (no covered sources), sanitation (lack of proper toilet), assets (none), livelihood (household head unemployed or in elementary occupation), child status (not enrolled in school), cooking fuel (dirty fuel used: wood, dung, or coal), land ownership (< 2 acres agricultural or some nonagricultural land), and consumption (less than the official poverty line).

We could also use the “union” approach in which an individual is considered poor if they lie below the poverty cutoff in all three dimensions of poverty. However, there are two problems with this approach: First, it might underestimate poverty; and second, the number of observations in our sample that meet this criterion is severely limited.

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posted by S A J Shirazi @ 12/25/2013 05:52:00 PM,

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