Decomposition analysis of earnings inequality in rural India: 2004–2012
© The Author(s). 2016
Received: 2 June 2016
Accepted: 4 October 2016
Published: 8 December 2016
We analyze the changes in earnings of paid workers (wage earners) in rural India from 2004/05 to 2011/12. Real earnings increased at all percentiles, and the percentage increase was larger at the lower end. Consequently, earnings inequality declined. Recentered influence function decompositions show that throughout the earnings distribution, except at the very top, both changes in “worker characteristics” and in “returns to these characteristics” increased earnings, with the latter having played a bigger role. Decompositions of inequality measures reveal that although the change in characteristics had an inequality-increasing effect, chiefly attributable to increased education levels, inequality declined because workers at lower quantiles experienced greater improvements in returns to their characteristics than those at the top.
JEL:JEL Classification: J30, J31, O53
KeywordsEarnings Inequality Earning distribution Rural India
In their discussion of India’s economic growth, Kotwal et al. (2011) point to the existence of two Indias: “One of educated managers and engineers who have been able to take advantage of the opportunities made available through globalization and the other—a huge mass of undereducated people who are making a living in low productivity jobs in the informal sector—the largest of which is still agriculture.” This paper is about the second India that mainly resides in its rural parts. Agriculture, the mainstay of the rural economy, continues to employ the largest share of the Indian workforce, but its contribution to gross value added (GVA) is much smaller. In 2011, the employment shares of agriculture, industry, and services were 49, 24 and 27 %, respectively, whereas their shares in GVA were 19, 33, and 48 %, respectively (GOI 2015). In addition, between 2004/05 and 2011/12, real gross domestic product (GDP) in these sectors grew at 4.2, 8.5 and 9.6 % per annum, respectively, making agriculture the slowest growing sector of the economy (authors’ calculations based on RBI 2015). Given these figures, the concern about whether high overall GDP growth has benefitted those at the bottom, and to what extent they have benefitted compared to those at the top, is even more pertinent for rural India. We therefore focus on rural India and examine how real earnings of paid workers (wage earners) evolved over the 7-year period between 2004/05 and 2011/12.
Several studies have documented that along with the high growth rates of GDP that have characterized the Indian economy since the 1980s, there has been an increase in inequality.1 However, most of these studies have either focused on consumption expenditure (Sen and Himanshu 2004; Cain et al. 2010; Motiram and Vakulabharanam 2012; Jayaraj and Subramanian 2015; Datt et al. 2016)2 or on earnings of paid workers in urban India (Kijima 2006; Azam 2012a). Two notable exceptions are Hnatkovska and Lahiri (2013) and Jacoby and Dasgupta (2015). Hnatkovska and Lahiri (2013) focus on wage comparisons between rural and urban areas between 1983 and 2010. They find that urban agglomeration led to a massive increase in urban labor supply that in turn reduced the rural-urban wage gap. Unlike Hnatkovska and Lahiri (2013), we focus exclusively on rural India to provide a more detailed picture of the changes within this sector. Jacoby and Dasgupta (2015) adopt the supply-demand-institutions (SDI) framework pioneered by Katz and Murphy (1992) and Bound and Johnson (1992), to decompose wage changes between 1993 and 2011 in both rural and urban India. We use a very different approach, namely, the recentered influence function (RIF) decomposition developed by Firpo, Fortin, and Lemieux (2009) to study earnings evolution in rural India.3 Jacoby and Dasgupta (2015) decompose the change in an indirect measure of wage inequality, namely, the relative wages of educated and uneducated workers, into changes in employment shares of different demographic groups and changes in the industrial composition. In this paper, we focus on direct measures of inequality such as the Gini and the 90/10 percentile ratio, and decompose changes in these measures into changes in worker characteristics and changes in returns to these characteristics. Our finding that the change in returns to characteristics is driving the decline in earnings inequality in rural India is a novel one. Moreover, we document changes not just at the mean but also at various quantiles. It is important to do so because several studies have found that earnings inequality is mainly concentrated at the upper end. For India, Azam (2012a) and Kijima (2006) find this for urban wage earners and Banerjee and Piketty (2005) find it for income tax payers. We use unconditional quantile regressions to account for the effects of workers’ characteristics at different quantiles and thereby make inferences about their effects on earnings inequality. Finally, we use the RIF decompositions to divide the overall change in earnings inequality into a composition effect (the component due to changes in the distribution of worker characteristics) and a structure effect (the component due to changes in returns to these characteristics).
We find that during the period from 2004 to 2012, real earnings among paid workers increased at all percentiles and the percentage increase was greater at lower percentiles. Consequently, earnings inequality declined in rural India. The RIF decompositions reveal that throughout the earnings distribution, except at the very top, both the composition effect and the structure effect increased earnings, with changes in the latter having played a bigger role. Decompositions of inequality measures reveal that in spite of the composition effect having had an inequality-increasing role, inequality fell because workers at lower quantiles experienced greater improvements in returns to their characteristics than those at the top. Earnings inequality increased as workers acquired higher levels of education. At the same time, lower returns to higher education reduced inequality.
The rest of the paper is organized as follows. Section 2 discusses the methodology used to analyze the change in earnings. Section 3 describes the data and the analysis sample. Section 4 presents the results, and Section 5 concludes.
We briefly explain the RIF regression for unconditional quantiles, followed by the RIF decomposition technique. For a detailed exposition of this and other decomposition techniques, see Fortin et al. 2011.
2.1 Unconditional quantile regressions
Unconditional quantile regressions (UQR) introduced by Firpo et al. (2009) help us examine the marginal effects of covariates on the unconditional quantiles of an outcome variable. UQR differ from the traditional quantile regressions (Koenker and Bassett 1978) in that the latter examine the marginal effects on the conditional quantiles. For instance, if we observe that the conditional quantile regression coefficients for college education increase as we move from the first to the ninth decile, we can say that having more people with a college education would increase earnings dispersion within a group of individuals having the same vector of covariate values. However, in order to claim that college education increases overall earnings dispersion (among all individuals irrespective of their covariates), we need to rely on unconditional quantile regressions. To understand UQRs, we begin with the concept of an influence function (IF).
2.2 RIF decomposition
The RIF decomposition divides the overall change in any distributional statistic into a structure effect (due to the changes in returns to characteristics/covariates) and a composition effect (due to the changes in the distribution of covariates). Compared to other decomposition methods such as the Machado-Mata (Machado and Mata 2005), the RIF decomposition has the added advantage of further dividing the structure and composition effects into the contribution of each covariate. In this way, it is closest in spirit to the decomposition method proposed by Blinder (1973) and Oaxaca (1973).
The detailed decomposition of the structure effect has a limitation when categorical variables are included as covariates. The choice of the omitted or reference group (for caste, education, industry, occupation, or state of residence in our analysis) can influence the contribution of each covariate to the structure effect. Since the choice of the reference categories is arbitrary, results of the detailed decomposition can vary. Existing solutions to the omitted category problem come at the cost of interpretability (see Fortin et al. 2011). To ensure the robustness of our results regarding the contribution of factor-specific structure effects, we use several specifications, each of which uses a different set of omitted categories for the categorical variables.
Though the above discussion on RIF decomposition focused on quantiles, it is also applicable to any other distributional statistic. We present the RIF decomposition for quantiles as well as selected inequality measures including the Gini.
We use two rounds of the nationally representative Employment Unemployment Survey (EUS) conducted by the National Sample Survey Organization (NSSO) for the years 2004/05 and 2011/12. Our target population is wage earners between the ages of 15 and 64 (working age), living in rural areas4 of 23 major states of India.5
In both years, wage earners constituted around 25 % of the rural working age population.6 Nominal earnings are converted into real terms (2004/05 prices) using consumer price indices provided by the Labour Bureau, Government of India.7 We also trim the real earnings distribution of each year by dropping 0.1 % of observations from the top and the bottom.8 Ultimately, our analysis sample consists of, 44,634 workers in 2004/05 and 36,050 in 2011/12. This corresponds to about 104 million paid workers in 2004/05 and about 118 million in 2011/12.
In this section we present our findings related to the evolution of the earnings distribution in rural India between 2004/05 and 2011/12.
4.1 Changes in the distribution of earnings from paid work
4.1.1 Changes in earnings inequality
Inequality measures for real weekly earnings from paid work
Variance of log earnings
The decrease in inequality is also reflected in the variance of log earnings and in the Gini coefficients. The Gini of real weekly earnings fell from 0.462 to 0.396.11 This is in sharp contrast to the picture in urban India where earnings inequality remained virtually unchanged over the period: The Gini of real weekly earnings in urban India was 0.506 in 2004/5 and 0.499 in 2011/12. Jayaraj and Subramanian (2015) use consumption expenditure data (also from the NSSO) and find that between 2004/05 and 2009/10, the Gini declined from 0.305 to 0.299 in rural India. For urban India, it increased from 0.376 to 0.393. It is noteworthy that while the direction of change in rural inequality that they find using consumption expenditure is the same as what we find using earnings, this is not the case for urban inequality. This makes a strong case for studying both consumption and earnings inequality.
4.1.2 Wage rates or days worked: decomposition of the variance in log earnings
Decomposition of earnings inequality
2 ∗ Cov[ln(W), ln(D)]
Change over time
In both years, the covariance between wage rates and days worked was positive implying that highly paid workers worked more number of days. Also, earnings inequality was largely on account of inequality of wages rates rather than inequality of days worked or because highly paid workers also worked for a longer time: Over 70 % of the earnings inequality was due to inequality of wage rates.13
The last row of Table 2 presents the decomposition of decline in earnings inequality as seen in the decrease in the variance of log earnings. About 50 % of this decline was due to a decline in inequality of wage rates. The rest was due to a decrease in inequality of days worked (about 30 %) and a weaker relationship between highly paid workers working more number of days (about 20 %).
4.2 Unconditional quantile regression results
Descriptive statistics, wage earners in rural India
Number of observations
Mean log real weekly earnings (Std. dev.)
Mean age (Std. dev.)
Caste categories (%)
Education categories (%)
Primary and middle
College and beyond
Wholesale and retail trade
Administrators and managers
Sales and service workers
Craftsmen and machine operators
Laborers and unskilled workers
We classify industries into seven categories: agriculture, manufacturing (including mining), construction, utilities, wholesale and retail trade, public administration (including defense), and other services (including education, health, real estate, and finance). Over the period, the major change in the industrial distribution came primarily from agriculture, which saw a 12 percentage point decrease, and construction, which saw a roughly equivalent increase.15
The first row of plots in Fig. 5 shows that the coefficients for being male were positive and significant, implying the presence of a gender earnings gap. The UQR male coefficients were decreasing across deciles: In 2011/12, the male coefficient value was 0.69 at the first decile, 0.44 at the median, and 0.40 at the ninth decile. This is termed as the “sticky floor” effect and shows that while men earned more than women throughout the distribution, the penalty for being female was more pronounced at the bottom of the distribution.17 The decreasing UQR coefficients also mean that having a greater proportion of men would reduce earnings inequality among wage earners. This was unambiguously true for 2004/05 as the coefficients decline monotonically across deciles, and it was true for the lower part of the 2011/12 distribution.
The second through fourth rows of plots in Fig. 5 show the presence of caste earnings gaps, though we do not see such gaps in all parts of the distribution. In 2004/05, the UQR coefficients for ST, SC, and Other Backward Classes (OBC) vis-à-vis “Others” show that there was an earnings penalty for all three groups at the upper deciles but not at the lower ones.18 In 2011/12, the caste penalty for ST persisted, although, unlike 2004/05, it was experienced at the lower deciles. Surprisingly, the caste penalty for SC and OBC disappeared in 2011/12. Interestingly, in the regressions without industry and occupation controls, the caste earnings gap for SC and OBC persisted even for 2011/12. This suggests that in 2011/12, the caste earnings gaps were overwhelmingly because of occupation and industrial segregation by caste.
The fifth row of Fig. 5 indicates that returns to being married moved from being insignificant at lower deciles to being positive at upper ones. Thus, if the proportion of married individuals were to increase, earnings inequality among wage earners would increase. Except at the ninth decile in 2004/05, there was no penalty for being Muslim in both years.
Figure 6 examines coefficients for various education categories vis-à-vis the illiterates. First, there is clear evidence of positive returns to education. Additionally, in 2004/05, for each education category, there was a monotonic increase in returns as we moved up the earnings distribution, with an especially sharp increase at the ninth decile. This pattern persisted in 2011/12 for all categories except primary and middle: For instance, the coefficient of “college and beyond” was 0.22 at the first decile, 0.28 at the median, and 1.7 at the ninth decile. Thus, educating the illiterate population would increase earnings dispersion.19 Figure 6 also reveals how the impact of education on earnings dispersion changed over time. The profile of UQR coefficients across deciles was flatter in 2011/12 than what it was in 2004/05 revealing that the inequality enhancing effect of education weakened over the period. The detailed decomposition of the structure effect in Section 4.3.3 shows this more formally.
4.3 RIF decomposition results
Next we turn to RIF decompositions to understand the factors behind the changes in the real earnings distribution. We first present the aggregate decomposition followed by the detailed decompositions of the composition and structure effects.
4.3.1 Aggregate decomposition of change in earnings
The total difference is decomposed into the structure (dashed) and the composition effects (dotted). Both components made significant contributions to the overall increase in earnings over the 7-year period. The only exception to this is at the 19th vigintile (95th percentile), where the structure effect is not significant. Thus, the contribution of the structure effect to the overall increase in earnings was positive and much larger than the composition effect at all but the top vigintile.21
An important conclusion from the decomposition is that most of the decline in inequality occurred because the returns to characteristics improved a lot more at lower percentiles. In fact, it is clear that while changing characteristics did lead to an improvement in real earnings throughout the distribution, it had an inequality-increasing effect: The composition effect increased sharply after the eighth decile, implying that had “returns to characteristics” been held constant over the period, earnings inequality would have risen.
Decomposition of changes in inequality measures from 2004/05 to 2011/12
Value in 2004/05
Value in 2011/12
Aggregate decomposition of total change
Detailed decomposition of the composition effect
Detailed decomposition of the structure effect
In summary, the aggregate decomposition of all inequality measures reveals that the decline in inequality came exclusively from the structure effect, but the detailed decomposition that follows presents a more nuanced picture.
4.3.2 Detailed decomposition of the composition effect
Before we move to the detailed decomposition of the structure effect, we would like to remark on the inclusion of industry and occupation as separate factors in the decomposition. Changes in the composition of and returns to industry and occupation may be partly driven by changes in education. To that extent, we should not be including them as controls if we are interested in studying the overall contribution of education. Following the decomposition literature, we also estimate Table 4 without industry and occupation controls. The results are in Appendix 1.23 Comparing with Table 4, one major difference with regard to the composition effect is that without industry and occupation controls, the change in distribution of education plays a significant role even in the bottom of the distribution (as seen by the 50-10 measure). Otherwise, the conclusions are qualitatively the same.
4.3.3 Detailed decomposition of the structure effect
The bottom panel of Table 4 presents the decomposition of the structure effect. Both the 90-10 and the Gini decompositions reveal that education, occupation, and being married were largely responsible for the negative structure effect. Further, comparing the 50-10 and 90-50 measures shows that for all three characteristics, it was changes in returns at the top end of the distribution that mainly contributed to the overall negative structure effect.
This was also noted in Fig. 6 where the returns to education (with illiterates as the base category) actually declined at the higher end of the wage distribution, whereas returns did not change significantly in the middle. The same is true for the return to higher occupations (with laborers and unskilled workers as the base category). Comparing with Appendix 1 (without industry and occupation controls), the conclusions broadly remain the same.
The contribution of returns to industry in Table 4 is interesting: it changed in such a manner that it had an inequality decreasing effect at the bottom and an inequality-increasing effect at the top as seen by the negative and positive effects for the 50-10 and 90-50 measures, respectively. It is therefore not surprising that it has an insignificant contribution toward the 90-10 measure.
In Table 4, the contribution of the “constant” term to the overall structure effect is large and statistically significant. It is hard to give a meaningful interpretation to it as it depends on the choice of omitted categories for categorical variables. As described in Section 2.2, the choice of omitted category affects the decomposition of the structure effect. We test for the sensitivity of our results vis-à-vis choice of omitted categories by re-estimating Table 4 using two additional specifications presented in Appendix 2. Given that returns to education were largely driving the structure effect, in the first specification we change the omitted category for education from illiterates to the highest educational category, namely, “college and beyond”. As seen in Appendix 2, the returns to education are now positive (vis-à-vis college and beyond) and the constant term is now negative. The broad conclusions are therefore the same. In the second specification, we convert all categorical variables into dummy variables by defining the variable to be “0” for the omitted category and defining it to be “1” for the remaining categories.24 Education continues to explain a large part of the composition and structure effects.
4.4 Robustness check using state poverty lines
Recall that we used the Consumer Price Index – Rural Labourers (CPI-RL) to deflate nominal earnings to 2004/05 prices. These price indices do not account for spatial price adjustment across states. As a robustness check, we use state-level poverty lines computed using the Tendulkar methodology (Planning Commission 2014) which account for spatial variation across states. We replicate Tables 1 and 4 using state-level poverty lines and present them in Appendix 3. Our results are robust to the choice of deflators.
Using nationally representative data from the Employment Unemployment Survey, we examine the changes in real weekly earnings from paid work for rural India from 2004/05 to 2011/12.
For wage earners who constituted about a quarter of the rural working age population, we find that their real earnings increased at all percentiles. Using consumption expenditure data that span the entire population, other studies25 have also documented an improvement in all parts of the distribution. Taken together, there is clear evidence that economic growth in the post-reform period (after the early 1990s) has been accompanied by a reduction in poverty.26 At the same time, according to official estimates, in 2011/12, 25.7 % of the rural population was below the poverty line. This figure represents about 216.7 million poor persons, a large number of people living below a minimum acceptable standard.27
Our analysis also reveals that earnings inequality in rural India decreased over the 7-year period, and about half of the decline can be accounted for by the decline in daily wage inequality. However, while the rural Gini fell over this period, it remained virtually unchanged in urban India. This suggests that the dynamics of earnings is different for the two sectors. This could be because the underlying structural characteristics are different across the two sectors. For example, while agriculture is the largest employer in rural India, for urban India it is services. It could also be the result of different redistributive policies followed in the two sectors. These aspects need to be recognized when designing future policies to tackle inequality in the two regions.
Aggregate decompositions of the change in inequality measures reveal that the change in returns to worker characteristics was mainly responsible for the decrease in earnings inequality. Further detailed decompositions reveal that higher levels of education in the population contributed to an increase in earnings inequality, while lower returns to higher education contributed to a decrease. Rural India experienced a construction boom during this period that also contributed to the decrease in earnings inequality.
Some studies (Datt et al. 2016; Thomas 2015) have attributed the tightening of the rural casual labor market between 2000 and 2012 to the expansion of schooling and to the construction boom. Others (Azam 2012b; Berg et al. 2015; Imbert and Papp 2015) have found that the MGNREGS (Mahatma Gandhi National Rural Employment Guarantee Scheme), a large-scale employment guarantee scheme initiated in rural India in 2005, led to an increase in casual wages.
One cannot be certain that this trend of rising casual wages and declining earnings inequality will continue into the future. Regardless of the underlying causes of the recent decline in earnings inequality in rural India, volatility in global crop prices and the drought conditions currently experienced by large parts of the country because of two consecutive weak monsoons are important reminders that policies designed to foster employment opportunities and wage growth of unskilled workers outside of agriculture are crucial for improving the economic wellbeing of the second part of India.
Finally, we end with the caveat that although India has the lowest Gini value among the BRICS countries,28 and we find that earnings inequality declined in rural India between 2004/05 and 2011/12, these facts mask extreme deprivations and inequities in access to health care, education, and physical infrastructure such as safe water and sanitation (Drèze and Sen 2013). One needs to be cognizant that extreme inequalities prevail in many other dimensions beyond earnings and consumption expenditure.
A notable exception is Dutta (2005). For the period, 1983–1999, at the all-India level, she finds an increase in wage rate inequality among regular salaried workers, but a decrease among casual labor.
There are some advantages in looking at consumption expenditure instead of earnings (Goldberg and Pavcnik 2007). The former are a better measure of lifetime wellbeing and suffer from fewer reporting errors. In spite of this, we feel that it is important to juxtapose the two to get a complete picture. This is especially important as the two measures may exhibit different trends. Krueger and Perri (2006) document this for the USA and then develop a model to show how income inequality can affect consumption inequality.
It is hard to establish the superiority of one approach over the other. In the SDI framework, changes in supply (changes in employment shares of demographics groups) and demand (changes in industrial composition) are assumed exogenous and therefore unaffected by changes in the relative wage structure. In the RIF decomposition, the feedback between changing characteristics and changing returns is ignored. Both these assumptions ignore general equilibrium effects.
In 2004/05, 75.3 % of India’s working age population lived in rural areas, while in 2011/12 this figure was 71.1 %.
In 2004/05 India had 28 states and 7 union territories. We excluded the states and union territories for which there were no price deflators. The 23 included states are Andhra Pradesh, Assam, Bihar, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Manipur, Meghalaya, Orissa, Punjab, Rajasthan, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, and West Bengal. In both years, they constituted 99.3 % of India’s rural working age population.
In 2011/12, of the remaining rural working age population, 30 % were self-employed, 2 % were unemployed, and 43 % were not in the labor force. The main reason for restricting our analysis to wage earners is that the EUS does not collect earnings data for self-employed individuals. Kijima (2006) imputes the earnings of the self-employed using Mincerian equations estimated on the sample of regular wage/salaried workers. We refrain from this imputation as it imposes identical returns to covariates for both sets of workers, an assumption that may not be true.
We use the Consumer Price Index – Rural Labourers (CPI-RL), the relevant price index for rural areas.
While we are aware that this may underestimate our inequality measures, we do this in order to remove potential data entry errors.
The poverty line is based on the methodology proposed by the Tendulkar Committee in 2009. The committee was appointed by the Planning Commission, Government of India.
Using consumption expenditure data (also collected by the NSSO), for the period between 2004/05 and 2009/10, Jayaraj and Subramanian (2015) find a similar pattern of an increase in real consumption expenditures at all deciles for rural India, with the highest growth occurring at the third and fourth deciles.
If we consider daily wage rates instead of real weekly earnings, the Gini fell from 0.398 to 0.358. This indicates that it is wage rates, and not so much the time spent working, that is driving the decrease in earnings inequality. We study this in detail in the next sub-section where we show the same result by decomposing the variance in log earnings.
Although the variance of log weekly earnings allows us to quantify a “wage rate effect”, a “workday effect”, and a “covariance effect”, it does not necessarily fall when one rupee is transferred from a rich worker to a poor one. However, this limitation is inconsequential since we have shown (using the Lorenz curves) that inequality has unambiguously fallen over time.
Admittedly, as there are bounds to the number of days worked, ranging from half a day to 7 days, this may have partly contributed to the lower inequality of days worked.
Scheduled Castes and Tribes (SC and ST, respectively) are administrative categories and represent groups of castes and tribes that are entitled to benefits from affirmative action policies such as reservations in educational institutions and government jobs to overcome historical social and economic discrimination against them. OBC stands for Other Backward Classes and is a collective term used by the Government of India to classify other castes that are socially and educationally backward (for details on the caste system, see Deshpande 2011).
Following the literature on earnings regressions, we also estimated the regressions and decompositions without the industry and occupation controls. The results are qualitatively the same and are available from the authors on request.
Deshpande et al. (2015) also find a sticky floor for 1999/2000 and 2009/10 among regular salaried workers in India.
The “Others” group includes, but is not confined to, the Hindu upper castes as the EUS data do not allow us to isolate the Hindu upper castes. Consequently, this four-way division understates the gaps between the Hindu upper castes and the most marginalized ST and SC groups (Deshpande 2011).
This finding for rural India is similar to the evidence presented in Azam 2012a for regular salaried workers in urban India. Using conditional quantile regressions on EUS data for 1983, 1993/94, and 2004/05, he finds that returns to secondary and tertiary education have increased over time and are larger at higher quantiles.
The results based on the other counterfactual that relies on the characteristics of 2011/12 and returns of 2004/05 are very similar and are available on request.
Standard errors for Table 4 (and for all its variants in various appendices) were calculated using 1000 replications of the bootstrap procedure followed by Fortin et al (2011). The basic codes for this are available from Fortin’s website http://faculty.arts.ubc.ca/nfortin/datahead.html and were suitably modified for this paper.
We decided to present the decomposition with industry and occupation controls in the main text because as noted earlier there was a massive shift from agriculture to industry which we believe was largely exogenous to education. Because this change has been widely discussed in related literature on the Indian economy, we feel that readers may be more interested in the specification that includes industry and occupation controls, despite the endogeneity issue that it suffers from.
We had to exclude controls for state of residence, as there is no natural criteria of classifying the states as high or low.
Using NSS data on consumption expenditure from 1957 to 2012, Datt et al. (2016) provide direct evidence that growth in India has been accompanied with a decline in poverty, especially after economic reforms were initiated in the early 1990s.
The corresponding figures for below poverty line population in urban India are 13.7 % (53.1 million).
According to estimates from the World Bank, the Gini values for BRICS countries are as follows: Brazil-0.539 (2009); Russia-0.397 (2009); India-0.339 (2009); China-0.421 (2010), and South Africa-0.630 (2008). These are available at Gini Index (World Bank Estimate) http://data.worldbank.org/indicator/SI.POV.GINI. Accessed on June 1, 2016.
Brazil, Russia, India, China, South Africa
Consumer Price Index – Rural Labourers
Employment Unemployment Survey
Gross domestic product
Gross value added
Mahatma Gandhi National Rural Employment Guarantee Scheme
National Sample Survey Organization
Other Backward Classes
Ordinary least squares
Recentered influence functions
Supply, demand, and institutions
Unconditional quantile regressions
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 290752. The views expressed in this paper are those of the authors and do not reflect the views of Statistics Canada or of other institutions that the authors are affiliated to. We are grateful to participants at the Nopoor India Policy Conference in Delhi, and to an anonymous referee and the editor for many insightful comments, which greatly improved the paper.
Responsible editor: David Lam
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 290752. The funding agency had no role in collecting data, interpreting the results, or writing the manuscript.
Availability of data and materials
This paper uses the Employment Unemployment Survey data collected by the National Sample Survey Organization (NSSO), Government of India. This data is available for purchase from the NSSO. http://mail.mospi.gov.in/index.php/home
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