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Social Indicators Research

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11-07-2017

Spatial Income Inequality in India, 1993–2011: A Decomposition Analysis

Published in: Social Indicators Research | Issue 2/2018

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Abstract

Using income from nationally representative household surveys and district as the lowest level of aggregation, we examine the role of spatial factors in determining income inequality in India. In both rural and urban India, we find that within-district income differences account for majority of the income inequality in 2011. Moreover, between-state income differences are more important in explaining between-district inequality in rural India. In contrast, in urban areas it is the within-state income differences that play a more important role in explaining the between-district inequality. We find significantly smaller level of inequality but similar trends using the consumption expenditure. Finally, using data for 1993 and 2011, we find that although majority of the income inequality in rural India is explained by within-district income difference in both years, over time the share of between-district differences has increased and they account for a third of the total increase in rural income inequality between 1993 and 2011.

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Source: http://www.oecd.org/social/income-distribution-database.htm.

For Mexico and China the number is for the year 2012. Source: http://data.worldbank.org/indicator/SI.POV.GINI and http://www.economist.com/news/china/21570749-gini-out-bottle.

Authors calculation using data from: http://povertydata.worldbank.org/poverty/country/IND.

Source: Surging tides of inequality, The Hindu, July 11, 2015.

Authors calculations from NSS 50th and NSS 68th round of consumer expenditure surveys.

In contrast, the Gini based on consumption data in IHDS surveys are only 0.384 and 0.395 for 2004–2005 and 2011–2012, respectively.

There also exist 7 Union Territories which are governed by representative appointed by President of India.

Because of non-availability of income data for 1993, we cannot examine change in urban income inequality.

As is commonplace in this literature, we find that in each time period the district-level decomposition of total income inequality in rural India yield a smaller between-component when compared to the within-component. As a result one may argue that targeting the within-component will bring about a larger reduction in total inequality. Kanbur (2006) underscores the problem associated with such an approach. For instance, it is possible that the between-group component, although smaller in magnitude, has a large role to play in the change in the total inequality over time. We find that one-third of the increase in income inequality in rural India between 1993 and 2011 is due to between-district component.

Between-district component can be further decomposed: \(I(\mu _{1}e_{1},\mu _{2}e_{2},\ldots ,\mu _{k-1}e_{k-1},\mu _{k}e_{k})=\sum _{s=1}^{S}\frac{n_{s}}{n}\left( \frac{\mu _{s}}{\mu }\right) I_{s}+I(\mu _{s1}l_{1},\mu _{s2}l_{2},\ldots ,\mu _{sS-1}l_{S-1},\mu _{sS}l_{S})\), where \(n_{s}\) is number of districts in state \(s, \mu _{s}\) is mean income of state \(s, I_{s}\) is within-state (district-level) inequality and \(l_{s}\) is \(n_{s}\) vector of ones. We use publicly available Stata program “ineqdeco” written by Stephen P. Jenkins for our decomposition (Jenkins 1999).

IHDS data is publicly available from Inter-university Consortium for Political and Social Research (ICPSR). HDPI data can be accessed from NCAER on request. See ihds.info, Shariff (1999), and Desai and Vanneman (2015) for details.

According to Census 2011, these major 16 states accounts for 97.5% of the total rural population. In 2001, the state of Jharkhand, Chattisgarh, and Uttarakhand was carved out from Bihar, Madhya Pradesh, and Uttar Pradesh, respectively. In 2011 data these split states are recoded as parental states.

Shorrocks and Wan (2005) suggest a non-decreasing relationship between the number of groups and the magnitude of the between-group inequality. They argue that an increase in the number of groups will increase the opportunities for differentiating between the group mean values used in the calculation of between-group, thereby causing the value of between-group to rise. Some districts were split in two or more districts between 1993 and 2011. In that case we recoded the 2011 split districts to parental districts as identified in the 1993 data. In the 1990s, there were 466 districts in India (according to Census 1991), which implies an average population of 2.6 million per district in 2011 (Census 2011).

We dropped the households that has negative or zero income in 2011. It should be noted that although the IHDS rural sample is representative at the district level, the urban sample is considered representative at the state level. See http://ihds.info/faq/it-possible-draw-inference-about-particular-state-using-ihds-data. However, our objective is not to provide district level inequality estimates. We are only interested in using the district as the lowest geographic unit for aggregation purposes.

The different sources of household income include: (a) Farm income: value of production for sale and own consumption, and income generated from allied agricultural activities like cattle tendering; (b) Salary Income: salaries from regular employment; (c) Agricultural and non-agricultural wages: wages from casual employment in agriculture and non-agriculture activities; (d) Income from self-employment activities; (e) Income from rent, pension, remittances etc.

Household weight is multiplied by household size to obtain distribution of persons.

Most of the existing literature on inequality in India uses NSS consumption expenditure data.

Consumption expenditure information was also collected in the 2011 IHDS survey. The Gini calculated using IHDS consumption expenditure data suggests a marginally higher consumption inequality in urban India, while the other two measures—Theil and MLD—suggest a marginally lower consumption inequality in urban India.

Note that we cannot compare the inequality in urban India in 2011 to 1993 as the 1993 HDPI survey was only administered in rural India. Although the earlier wave of IHDS collected in 2004–2005 also has an urban sample, we do not use that as the time span is too short to capture the role of spatial factors.

We also computed consumption inequality in rural India using the NSS consumption expenditure data for 1993–1994 and 2011–2012. The Gini coefficient in consumption expenditure for rural India increased from 0.286 in 1993 to 0.311 in 2011–2012: an increase of 9%. Hence, inequality increased in rural India between 1993 and 2011 based on both income and consumption measures.

We also computed the change in consumption inequality using the NSS consumption expenditure data for 1999–2000 and 2011–2012. As discussed in the data section, we are unable to decompose the inequality at district level using the 1993 NSS consumer expenditure data as the 1993 data does not identify districts. The closest NSS consumer expenditure data available is 1999. These results are reported in Table 7 of the “Appendix” for both rural and urban India. We find that both within and between component contributed to increase in consumption inequality between 1999 and 2011. Hence, in terms of inequality trend, both consumption and income data exhibit similar patterns.

Table 8 in the “Appendix” presents the sample size of our rural sample for different states. Column (1)/(5) of Table 8 are the total number of households in the state, while column (2)/(6) are total number of individuals in the state. Column (3)/(7) (Max N) and column (4)/(8) (Min N) are the maximum and minimum number of individuals across districts within the state.

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Title: Spatial Income Inequality in India, 1993–2011: A Decomposition Analysis
Publication date: 11-07-2017
Published in: Social Indicators Research / Issue 2/2018
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-017-1683-4

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