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Growth, Development Spending, and Inequality in Indian States

Nishant Chadha ( is at the India Development Foundation, Gurugram. Bharti Nandwani ( is at the Indira Gandhi Institute of Development Research, Mumbai.

The relationship of economic growth from 1988 to 2012 in Indian states with poverty and inequality is analysed. The results suggest that in faster-growing states, poverty levels have declined, but poverty intensity has not changed and the highest increase is in inequality. An examination of the performance of development spending (which should mostly benefit the poor) incurred by the states indicates that though faster-growing states showed high spending on the development sector, development spending benefited the rich more effectively than the poor—contrary to the intent behind it—thereby raising inequality in the state.

The authors would like to thank the anonymous referee for valuable comments. They also thank Ashwini Deshpande and Shubhashis Gangopadhyay, and the participants for useful discussions at two workshops—the United Nations University World Institute for Development Economics Research workshop on between-group inequalities held in June 2016 at Helsinki, and the Duke–Shiv Nadar University conference held in April 2016 at Delhi. A part of this research work was done at Shiv Nadar University where Bharti Nandwani was a doctoral candidate.

The past quarter of a century has seen the Indian economy grow at impressive rates, especially since the economic liberalisation reforms of 1991. However, critics of the Indian growth story argue that this growth has been skewed across sectors. The agricultural sector, which continues to employ a large number of people, grew at a very sluggish pace. The most spectacular growth has been in the high-skilled services sector, whereas the manufacturing sector, typically the one that generates more employment, has not seen similarly impressive growth rates. One of the outcomes of this unequal growth across sectors has been that while the income of a few has grown, the majority have not benefited much, raising income inequality (Banerjee and Piketty 2005). Additionally, it is argued that the pattern of growth deepened caste-based inequality in India, as people belonging to high castes reaped the maximum benefits of the new opportunities (Shah et al 2017). It is important to better understand the source of this rise in inequality at a disaggregate level.

Since many growth-enhancing and redistributive policies are taken up at the state level, this paper does so by looking at the growth and inequality experience of Indian states from 1988 to 2012. In particular, this paper looks at the impact of economic growth on poverty and inequality1 using the fixed effects estimation technique. It uses consumption expenditure data from various rounds of the National Sample Survey (NSS) to construct inequality measures for Indian states from 1988 to 2012. Summary statistics indicate that most of the states have become unequal over time, consistent with the national trend. In addition, all states except one (Tamil Nadu) have experienced an increase in between-group caste inequality.

The empirical analysis begins with investigating the impact of growth on poverty levels. The headcount ratio (HCR) and poverty gap index (PGI) are used as measures of poverty. It is found that even though poverty has reduced in faster-growing states, the intensity of poverty as indicated by the PGI has not changed, suggesting that the reduction in poverty in the periods of growth is brought about by improvement in outcomes for households very close to the poverty line.

Additionally, this paper evaluates the impact of growth on inequality and finds that inequality has increased the most in faster-growing states, implying worsening of relative economic outcomes with growth.2 This finding, which lends empirical support to the arguments of critics of the Indian growth story, is somewhat contrary to the conventional wisdom, which suggests that sustained growth rates enable governments to redistribute income in an efficient manner, thus reducing the perverse impacts of growth.

To explain this observation of the not-so-equitable Indian growth story, this paper evaluates social sector spending, one of the important redistributive instruments of the state. The objective is to understand the extent to which faster-growing states make use of social sector spending and analyse its role in mitigating inequality. Public expenditure on social services is expected to aid in redistribution of income because these services provide “virtual income” to the poor by freeing up resources spent on privately provided services (Verbist et al 2012; Lustig 2012; Seery 2014). Among social services, the trend of per capita real expenditure on education is considered, as it provides the poor an opportunity to invest in human capital and improve their future income potential, an additional channel by which public expenditure on education is expected to narrow income disparities. But this expenditure has widened the inequality gap even though faster-growing states incur high expenditure on education—the results show—quite contrary to the intent. The puzzling result suggests that in Indian states neither growth nor social sector spending has benefited the poor as effectively as it has benefited the relatively well off. However, this finding is in line with the literature, which has shown that public developmental expenditure, especially in developing countries, does not fare very well in redistributing income. A number of explanations are offered for the positive impact of development spending on inequality.

Literature Review

The liberalisation reforms of 1991, which provided a much-needed boost to the Indian economy, generated huge interest in assessing the performance of Indian states in reducing poverty and inequality. Deaton and Drèze (2002) studied the evolution of poverty and inequality in Indian states from 1987 to 2000. Motiram and Vakulabharanam (2012) extended the analysis up to 2010 and presented a picture of between-caste inequalities at the national level. Subramanian and Jayaraj (2015) employed an alternate measure of inequality, proposed by Krtscha (1994), to study the trend in overall and between-caste inequalities. Topalova (2007) looked at the impact of globalisation reforms of 1991 on poverty and inequality in Indian districts. The broad findings of these studies are that even though India has achieved moderate success in reducing poverty level, inequality has widely increased over the past few decades.

This paper provides various measures of overall and between-caste inequalities for Indian states for a much larger sample period (1988–2012) and systematically documents that faster-growing Indian states are becoming unequal over time, even after reduced poverty levels. This is a contribution to the literature which assesses the impact of growth on poverty and inequality (Ravallion and Datt 1996; Datt and Ravallion 2002; Deaton and Drèze 2002; Besley et al 2007; Topalova 2007).

The literature, however, does not consider the impact on disparities along caste lines. This paper studies the impact of growth on between-caste inequality and addresses the gap. Additionally, this paper studies the trend in development spending in states to explain rising income disparities in faster-growing states. Results indicate that even though faster-growing states have higher social sector spending, the impact of this development spending on inequality is positive, that is, it increases inequality. This result is in line with existing studies, which find that development spending benefits the not-so-poor more effectively than the poor and, consequently, fails in redistributing income.

Rudra (2004) studies the impact of development spending on income distribution for a panel of developing countries and finds that public expenditure on education and other social services increases inequality in developing countries. More generally, there exists a literature that considers the impact of welfare spending on developmental outcomes, with cross-country evidence establishing that public expenditure does not have any positive impact on development outcomes (however, the evidence at the local level is somewhat mixed [Hanushek 1995]). The reasons put forward to explain the failure of public expenditure are concerned with the limited institutional capacity of the state, corruption, and the inefficiency of government bureaucracy. Rajkumar et al (2008) show that the observed limited success of public developmental expenditure reverses as governance becomes efficient. However, much of the existing literature concerns itself with cross-country evidence; there is not much analysis available at the subnational level. This paper’s attempt to fully understand the role played by growth and social sector spending in the evolution of inequality is the first attempt to the best of the authors’ knowledge to study the impact of development spending on inequality in Indian states.3

Data and Methodology

To construct inequality measures from 1988 to 2012, rounds of the NSS are used. The NSS is a household survey conducted in India, typically with a “thick”4 round every five years. It is representative at the state and district levels. Data are used from five thick rounds of the NSS—43rd (conducted in 1987–88), 50th (1993–94), 61st (2004–05), 66th (2009–10), and 68th (2011–12)—and from two “thin” NSS rounds. Thin rounds are conducted in the years between two successive thick rounds. Their sample size is smaller, and their data are representative only at the state and national levels.

The 51st thin round (conducted in 1994–95) and the 64th round (2007–08) are used because their sample sizes are larger than those of a thick round. These rounds are employed to collect information on household consumption expenditure to construct the measures of inequality. The analysis does not consider income inequality because reliable data on household income is not available for the period under study. Conceptually, consumption might capture a better measure of welfare than income because, in a developing country like India, people are paid in kind for a lot of the services they provide. A large part of the population receives transfers from friends and governments and therefore, income is hard to measure for a large part of the population.

The expenditure data reported at the current prices in the NSS rounds is adjusted for the differential price changes in rural and urban areas using the consumer price index (CPI) data from the Reserve Bank of India (RBI) database. This separates the impact of differential price change in rural and urban areas from the actual change in consumption. The consumption data for household size is adjusted and two widely used measures of inequality—the Gini coefficient and the Theil index—are constructed. Being scale-independent, these measures belong to the class of relative measures of inequality (Kolm 1976a, 1976b).

Other classes of inequality measures appropriately reflect absolute differences in income. It is sometimes argued that the inequality trends that relative measures suggest might not always be consistent with the trends produced by other absolute and “centrist” measures of inequality, which makes using an appropriate measure of inequality critical (Kolm 1976a, 1976b). To address this concern, a general notion of disparity, in addition to the Gini coefficient and Theil index, is considered. To measure inequality trends at the national level, two ratios of consumption are computed—P(), which is the ratio of consumption at the 90th and the 10th percentiles, and P(), which is the ratio of consumption at the 75th and the 25th percentiles. The inequality trends reported using these measures are in line with the trends produced by the centrist measures as documented by Subramanian and Jayaraj (2015).

Apart from the above measures that capture inequality at the household level, inequality is computed between broad caste groups to study the evolution of between-caste inequalities. In
India, caste has always been an important part of the social structure. It greatly influences social and economic status and confines people from disadvantaged castes mostly to menial jobs. States repeatedly target policies and programmes towards improving the welfare of disadvantaged groups, but discrimination along caste lines in various spheres has often been documented (Banerjee and Knight 1985; Borooah 2005). The pattern of growth that India experienced in the past few decades has not done much to correct these historical inequities; in fact, as some argue, it has further deepened these disparities by denying people from low castes access to better-paying (and protected) formal sector. This motivates an analysis of the experience of states in terms of caste-based inequalities and whether economic growth has had any role in reducing/widening these disparities. This analysis is performed by making use of information on broad caste groups of households, Scheduled Castes (SCs), Scheduled Tribes (STs), and others, from the NSS and by constructing three measures of between-caste inequalities: group-weighted coefficient of variation (GCOV), group-weighted Gini coefficient (GGini), and group-weighted Theil index (GTheil).

The GCOV, a common measure of regional disparities, is weighted by the population size of each group, so that changes in the position of small groups get less weight than those of larger groups (Mancini 2008). It is calculated by the formula 1/2 where µ is the overall mean, yr is group r’s mean value, R is the number of groups, and pr is group r’s population share.

The GGini compares every group with every other group as opposed to calculating the difference from the mean, using the formula , where r and s denote different groups.

The GTheil compares each group with the mean. It is especially sensitive to the lower end of the distribution. It can be used to divide the vertical inequality into within-group and between-group components. GTheil is calculated by .

Since these three measures of between-group inequality are highly correlated, the main empirical results are reported using GGini and GTheil.

Poverty is measured using PGI and HCR. The PGI is computed as , whereGi = (z – yi). I (yi < z). The HCR is computed as , where yi is the income of individual i, z is the poverty line, and I(.) is an indicator function taking a value of 1 if yi < z and 0 otherwise. The poverty line suggested by the 1993 Lakdawala expert group5 for the 43rd round (the starting round of the sample) is used in this paper. For the subsequent rounds, the current price information for rural and urban areas in the CPI data is used to re-evaluate the minimum expenditure required to qualify as non-poor for a given state.

The methodology used by the Lakdawala expert group to calculate the poverty line has been heavily criticised for arbitrarily choosing 1973–74 as the reference year and for not updating it to reflect the current cost of obtaining the nutritional norm (Patnaik 2007; Subramanian and Jayaraj 2015). Because of this, there is some disagreement in the literature on the extent of fall in poverty, even though there is still consensus—barring a very few papers, such as Patnaik (2007)—on the trend that poverty has followed in the past two-and-a-half decades, irrespective of the methodology. Therefore, while changes in the construction of poverty line could change the magnitude of the coefficients, it should not completely reverse the signs.

To look at the impact of economic growth on poverty and inequality, data on per capita net state domestic product (NSDP) from the RBI are used. Lagged data (by five years) on development expenditure incurred by states are obtained from the database of the Economic & Political Weekly to study the relationship between growth, welfare spending, and inequality. In the database, development expenditure is categorised into expenditure on social services, economic services, and general economic services. Social services include expenditure on education, health, family welfare, sanitation, welfare of SCs and Sts, urban development, and the like. This paper restricts its attention to expenditure on education because it is meant to benefit particularly the poor—by freeing up resources they would otherwise have spent on private education services, providing them the opportunity to invest in human capital, and increasing the likelihood of higher future income/consumption. On the other hand, the rich, who depend primarily on the private education system, should not be affected much by public expenditure on education.6 Therefore, state expenditure on education is expected to impact inequality. A five-year lag is used for development spending because redistribution policies such as expenditure on education take time to have any real impact on the income and consumption of individuals.7

Inequality in India and Indian States

National estimates of overall inequality in consumption expenditure are based on Chadha and Nandwani (2018) (Table 1, p 48). The Gini coefficient and Theil index (columns 1 and 2) indicate that compared to the beginning period in the sample, inequality fell in the country for a brief period, until 1994, before rising steadily until 2010, and falling slightly thereafter. Overall, the trend indicates that inequality has been on an upward trajectory since the beginning of the 1990s. This finding is also supported by the other two measures of inequality—the ratio of consumption between the 90th and 10th percentile, reported in column 3, and the ratio of consumption between the 75th and 25th percentile, reported in column 4.

Compared to all other years, inequality is particularly high in 2010, probably because the survey in 2010 was conducted after two successive years of drought. The high inequality could indicate that the drought affected the poor more than the rich. This is plausible, given that the poor in developing nations rarely have access to consumption-smoothening mechanisms, and adverse shocks impact consumption much more for the poor than for the rich. The inequality trend presented here is in line with that presented by Motiram and Vakulabharanam (2012), which shows that inequality has been rising steadily since 1988. Between-caste inequality followed the same trend, though its magnitude is much smaller, according to national estimates (Table 2). The rise observed after 1994 is not as sharp as the overall inequality. The overall trend indicates a marginal rise in disparities across caste groups. However, since the groups considered in creating these measures are broad, and consist of internally disparate groups that have many layers of hierarchy, considering only such broad groups is likely to underestimate the true disparity across these groups. But since the NSS collects information only on broad caste group affiliation (SCs, Sts, Other Backward Classes, and others) and not on what is referred to by many scholars as the operative unit of caste on ground, the jati (sub-caste), between-group inequality can be constructed only at this level.

Next, inequality estimates for Indian states are presented. The Gini coefficient indicates a wide variation in the trend in inequality across states (Table 3). While some states like Andhra Pradesh have become equal over time, others like Karnataka and Haryana have seen an increase in inequality. To better understand the evolution of inequality in states over the sample period, the Gini coefficient of consumption expenditure for different states is plotted in 1988 against that of 2012. Most states have remained unequal over this 25-year period (Figure 1). The group-weighted Gini coefficient for all the states is reported to analyse the trend of between-caste inequalities, which has also increased in almost all the states (Table 4, p 49). The group-weighted Gini is plotted in 1988 against that in 2012; between-caste inequality increased in almost all states except Tamil Nadu (Figure 2).

Growth, Education Spending, and Inequality

There has been an increase in inequality in Indian states over 1988–2012 and income has not increased at the same pace for the poor as for the rich. Given that this period also coincides with the country growing at impressive rates, this section analyses whether economic growth has been accompanied by any change in the income of the poor, both in absolute and in relative terms, using a panel data set of states. In other words, the relationship between economic growth and poverty/inequality is evaluated by empirically estimating the following regression equation:


where s indexes state and t indexes the survey round. Ys,t is poverty/inequality in state s at time t, and ln(nsdp)s,tis the logarithm of NSDP at time t.

Results are reported in Table 5. The dependent variable in columns 1 and 2 is HCR, which measures the proportion of households living below the poverty line (BPL) in a given state. The coefficient of growth is negative in the first column, suggesting that the growth has had a positive impact in reducing poverty levels. In column 2, the independent variable, economic growth, is lagged by one year. This allows for current spending to have delayed impact on poverty. Results are similar to those with economic growth at time t. Thus, the results show that faster-growing states saw—along with increasing average incomes—a fall in the incidence of poverty. This indicates an increase in welfare.

The impact on PGI (columns 3 and 4) indicates the intensity of poverty, as it is the mean distance BPL as a proportion of the poverty line (counting the non-poor as having a zero gap). The insignificant coefficient in the two columns, however, suggests that even though the number of poor people decreases as the state progresses, there is no change in the intensity of poverty. This can be possible only when households very close to the poverty line have managed to gain income sufficient to cross the poverty line but the conditions of the poorest of the poor have not changed. Overall, the result of this table suggests that the fall in poverty in fast-growing states has been brought about by households close to the poverty line.

The relationship between economic growth and inequality is estimated in columns 5 and 6. Results suggest that economic growth has a positive and significant impact on inequality,
implying that the poor in faster-growing states have not benefited from growth as much as the rich, and that economic growth has widened the rich–poor divide. Rising inequality—a discouraging development for the country—has many independent, adverse consequences, and it has received much attention in academic and policy circles. An additional deduction to be had from this finding is that growth process in India is still stuck in the initial phases of the Kuznets curve where even though everyone has benefited (increasing average growth rate and falling poverty), the rich have benefited more than the poor. Similar results are found for the Theil index (results have not been reported for brevity).

All the specifications in the table include state- and time-fixed effects. State-fixed effects allow us to distinguish the impact of levels of growth from changes in growth, whereas time-fixed effects control for any events or policies that impact all states.

One of the defining features of poverty in India is that people from disadvantaged castes are over-represented in this category even today. The evolution of between-group inequality is analysed in studying whether the impact of growth has been different for different caste groups. This group-based socio-economic backwardness has been met with various democratic and non-democratic protests posing an important question: Can sustained economic growth achieve what government action in the form of affirmative action and targeted policies has failed to do by providing economic opportunities to the marginalised castes and narrowing group-based disparities? Or will economic growth not prove beneficial for lower castes?

The impact of economic growth on group-based inequality is analysed (Table 6, p 50). All the columns show that unlike overall inequality, growth did not have the perverse impact of increasing between-group inequality. However, it is also true that economic growth has not been successful in narrowing it. This suggests that growth has had a limited relationship with caste-based inequalities. This result also suggests that faster economic growth is increasing inequality in India by increasing inequality within groups and not between them. This is an interesting result given that the focus of caste dynamics in India has been on rising disparities between groups rather than on within-group inequality.


Education spending and inequality: Following the seminal work of Kuznets (1955), there has been much interest in understanding the relationship between economic growth and inequality. Kuznets showed that at lower stages of development, growth might lead to an increase in inequality; but, as development progresses, growth may spill over to the whole economy and lower inequality, as the government may have more ability to redistribute and improve welfare spending. This suggests that there is some relationship between growth, public spending on social sectors, and inequality. It is this relationship which we appeal to in order to explain the above-documented result. In particular, in explaining the evolution of inequality in faster-growing states, the role of social sector spending, in the form of public expenditure on education, is studied. This is because it is meant to benefit the poor more than the rich, as it might be costly to obtain these services on the private market, and can potentially equalise the distribution of income/consumption. The following regression equation estimates this relationship:


where “education spendings,t–5” is the per capita real expenditure incurred by state s at time t–5 on public education. The five-year lagged education spending is used because expenditure on public education is expected to take time to have any real impact on education infrastructure—building schools, maintaining infrastructure, hiring teachers, and the like take time. An additional advantage of using lagged education spending is that it addresses endogeneity concerns, as it is unlikely that the current period’s inequality would have any impact on determining the social spending by states five years in the past. Equation 1 tests the impact of economic growth on inequality, whereas equation 2 tests whether the impact of economic growth changes once education expenditure by the state is controlled for.

The results (Table 7, columns 1 and 2) indicate that the correlation between growth and state spending on education is positive—as the growth rate of states increases, their per capita expenditure on education also goes up. This seems expected, given that higher economic growth enables states to devote a higher share to social services. However, what is disconcerting is that this public spending seems to benefit the rich more than the poor, as indicated in columns 3 and 4, where inequality is regressed on per capita real expenditure on education incurred by the state five years ago (along with the economic growth variable). Contrary to expectation, the coefficient of education expenditure is positive and significant, which suggests that the impoverished have not even been able to take advantage of welfare spending by the state. The coefficient of growth in the presence of education expenditure has fallen considerably and become insignificant, possibly suggesting that public education expenditure is driving the observed positive impact of growth on inequality.

These findings point to the grim reality of the inefficiency of state development spending in narrowing disparities. But these are consistent with the existing literature, which suggests that public expenditure on social services, particularly in developing countries, does not favourably impact developmental outcomes. In turn, that suggests the failure of the government’s redistribution programmes. But what is puzzling is that this observed positive impact on inequality of public spending on education is in contrast to the findings of another strand of the literature, which suggests that better provision of education and health facilities by the state helps in equalising income (Chadha and Nandwani 2018; Lustig 2012; Seery 2014; Verbist et al 2012). The main reason for these contrasting results is that this paper considers expenditure on public services whereas others consider actual provision of public goods; the huge gap between what is expended and what is delivered by states is now widely accepted.

To make sure that these results are robust to the way the education expenditure variable—five-year lagged expenditure on education—is constructed, the lagged education expenditure is constructed by averaging the four- and five-year lagged education expenditure. This ensures that the positive coefficient of education expenditure is not being driven by anything specific to the years used to construct our variable of interest. The result (columns 5 and 6) is consistent with the result in columns 3 and 4. The coefficient of education expenditure is positive and significant, and the coefficient of the growth term falls, suggesting that public education expenditure is driving the increase in inequality.

The impact of education expenditure on between-caste inequality is considered (Table 8, p 51). Results when reported using GGini (columns 1 and 2) suggest that there is no relationship between group-based disparities and development spending. However, when GTheil is used as the measure of inequality (columns 3 and 4), it is found that like overall inequality, expenditure on education increases between-caste inequality. Additionally, it is found that after controlling for education spending, the coefficient of growth becomes negative, suggesting that growth seems to narrow between-caste inequality. That growth becomes significant in predicting group-based disparities after education expenditure is added, suggests that development spending is an important omitted variable to consider while looking at the impact of growth on inequality, as failure to control for it positively biases the coefficient of growth, which can potentially lead to incorrect inferences.

Potential explanations for the positive impact of education spending: State expenditure on education should free up the resources that the poor spend on private education, provide them current “virtual income,” and let them make investments in human capital to improve their future consumption possibilities. But the results show that education expenditure increases inequality. The possible reasons for this puzzling result are explored.

Relationship between Public and Private Expenditure: There could be a positive or negative correlation between education expenditure incurred by the state and private education expenditure, which could affect inequality. One possibility is that state investment in education reduces education expenditure by the poor, which would result in an increase in measured consumption inequality. Since public expenditure on education is likely to take the form of investment in schools/colleges and education-related schemes, which provide the poor monetary incentives to attend school/college, this can allow the poor to reduce their personal expenditure on education thereby freeing up resources which are spent on private education.8 If these freed-up resources are not completely spent on other consumer items, and a part is saved, it will automatically reduce the consumption expenditure of the poor, resulting in consumption inequality. However, this explanation seems unlikely, given that public provision of schools and health centres reduces consumption expenditure inequality, and that it is unlikely that the personal expenditure of the poor falls with better public provision (Chadha and Nandwani 2018).

The other possibility is that state expenditure on education and the corresponding private expenditure are complementary—to be able to take advantage of public expenditure on education, households also need to incur a certain amount of private expenditure needed to obtain education, for example, expenditure to pay school fees, however minimal, and to buy books and uniforms, amongst others. Households that can afford to incur this private expenditure on education would then be in a position to take advantage of the public expenditure. It is quite possible that state expenditure on education has benefited the not-so-poor households—which can afford private expenditure on education—more than poor households.

To empirically test whether there is any relationship between private and public expenditure on education, this paper analyses the NSS data on private expenditure on education made by households. Private education expenditure data in NSS provide a break-up of the expenditure incurred on various items, including expenditure on books, library fee, tuition fee, and other such expenses. There is no evidence of correlation between private and public expenditure on education, which rules out any relationship between the two for the positive impact of education expenditure on inequality (Table 9).

Corruption: There is a near-consensus now that the implementation capability of Indian states is abysmal. So, another possible explanation is a combination of weak state capacity, inefficient bureaucracy, and corruption. This could result in education expenditure, meant to benefit the poor, getting confiscated by the not-so-impoverished, thereby increasing inequality. The reason why this explanation seems relevant to consider is that a part of expenditure on education is incurred in the form of education schemes that provide monetary benefits to the poor for making investments in education. When state capacity is weak, it is easier for the rich to take advantage of their political/economic influence to confiscate the benefits of such education schemes.

This explanation has received some previous scholarly attention. Kohli (2012) provides an account of the redistributive capacity of Indian states and argues that there is a huge gap between state’s ambitions and capacity when it comes to redistribution. More generally, Devarajan and Reinikka (2004) argue that the main reason why social sector spending has not been effective in poor countries is that resources are not utilised effectively, due primarily to institutional constraints, which results in non-beneficiaries taking advantage of state spending. Using cross-country evidence, Rajkumar et al (2008) show how lower corruption and better institutions reduce inefficiency in social sector spending. All this suggests that weak state capacity and inefficient utilisation of state funds could be possible reasons that education expenditure is raising disparities.9 However, in the absence of robust empirical evidence, this question is left to future research.


The paper provides estimates of overall and between-caste inequalities in consumption expenditure for India and its states between 1988 and 2012. The trends reported present a picture of rising disparities in the country, starting from the early 1990s. Additionally, the paper studies the impact of economic growth on poverty and inequality. The regression results show that poverty levels have fallen in faster-growing states because the income of households close to the poverty line has improved. This result points towards a moderate success, at best, of economic growth in improving the conditions of the poorest of the poor. Inequality has increased with rising growth, suggesting that even though everyone has benefited from the Indian growth process, the rich have benefited more than the poor.

To understand why this is happening, this paper analyses an important redistributive element of the state government, spending on social sectors, particularly education spending, and its impact on inequality. Even though faster-growing states have incurred high expenditure on education, this expenditure has widened the inequality gap, quite contrary to the intent. This is discouraging, given the increasing importance placed on the disbursement of development funds to improve developmental outcomes of the poor. The finding that development spending is driving a perverse outcome is puzzling, and it may be explained by weak state capacity and poor implementation capability. In contrast, no/weak relationship is found between growth and between-caste group inequality; however, this relationship changes once we add development spending, suggesting social sector spending to be an important aspect of the relationship between growth and inequality.


1 This exercise would also be useful in assessing the effectiveness of the much celebrated trickle-down process.

2 However, growth does not seem to impact caste-based inequalities.

3 Kohli (2012) provides a general account of redistributive spending in India and explains how that has been influenced by the political environment in the country. More generally, the paper argues that redistributive capacity of Indian states is abysmally low.

4 They are called thick because of their large sample size which makes them representative at the district level.

5 Many problems have been pointed out in the computation of the Tendulkar expert group’s poverty line. It is highly controversial. This paper does not use this recommendation.

6 Chadha and Nandwani (2017), based on 2011–12 NSS data show that 68% of the children belonging to rich households attend private schools.

7 Note that the above-made argument for education expenditure having an impact on inequality can also be said to hold for health expenditure and likewise, we find that the results for health expenditure are very similar to education (results not presented for brevity). However, in a poor country like India, people make expenditure on health mainly during a negative health shock, unlike education expenditure, which is relatively smoother. We, therefore, expect to capture the impact of public education expenditure on inequality better.

8 Whereas, this should not bring any change in the expenditure of the rich if they do not rely on public education system or are not entitled to benefit from the education schemes. Such an arrangement would automatically increase inequality in consumption expenditure.

9 This, however, does not imply that there cannot be other possible explanations.


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Updated On : 20th Mar, 2019


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