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Accounting for income inequality: empirical evidence from India

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Abstract

This paper decomposes income inequality using the regression-based decomposition technique. The paper analyses the role of education, experience, employment status, industry and their interactions in accounting for differences in income and its inequality in India over the past three decades. The results clearly show that education is the most dominant factor contributing to inequality in both rural and urban areas. Separate inequality decomposition analyses for each employment status group show that although education is the most important factor contributing to inequality for the salaried, and to an extent for the self-employed workers, it is much less important for the casual workers. Other factors such as household size, size of land holdings, and regional variations contribute to increasing inequality among casual and self-employed worker households.

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Notes

  1. This arises out of a comparison between non-comparable surveys since they give different inequality coefficients. In the 55th round the question on consumption of clothing, footwear, education and institutional health were asked with a reference period of 365 days and that on food consumption for 30 and 7 days, separately. In all the earlier rounds and the 61st round the reference period was uniform where the respondents were asked about their consumption in all categories for the past 30 days. The non-availability of uniform reference period in 1999–2000 makes it non-comparable with other surveys.

  2. We provide a more detailed discussion on this issue and in particular its relevance for this paper, in our earlier work Bigotta et al.(2015).

  3. Deaton (1997) recommends a correction of the critical value for large samples based on Schwarz (1978), Leamer (1978) and Chow (1983). For our case with only one restriction, Leamer’s critical value is given by (T − K)(T 1/T − 1) (cf. Leamer 1978, p. 114) which goes to ln(T) as T goes to \(\infty\).

  4. As we mentioned above, the estimated shares will be the same for all the inequality measures satisfying the six assumptions made by Shorrocks (e.g. Gini, Theil’s inequality index, coefficient of variation).

  5. For example, the total contribution of industry groups is made up of five terms (dummy variables): mining and manufacturing; electricity, gas and water; construction; low-skilled service sector; and high-skilled service sector. This aggregation is also done for social groups, employment status, education levels, industry groups and regions.

  6. Note that if a regression coefficient is not significant then its contribution to income inequality should not be considered. So we aggregate the shares resulting from coefficients that are not significant with the share of residuals and set to zero the shares for the insignificant parameters (in the table these are marked with ‘–').

  7. This could unfortunately be due to the nature of available data. In the analysis we compare Scheduled Castes (SCs) and Scheduled Tribes (STs) with "Others", the latter being an all-encompassing category that includes everyone else. This is a large heterogeneous category that includes castes that are very low in the hierarchy, not necessarily very different from the SCs and STs in terms of status and economic conditions. It is possible that this classification actually underestimates the relative disadvantage of SCs and STs with respect to the ‘higher’ castes.

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Correspondence to Uma Rani.

Appendices

Appendix 1: data sources, variable names and definitions

The data are based on multiple rounds of the employment–unemployment survey along with the consumption expenditure survey undertaken by the National Sample Survey Organisation (NSSO) every 5 years, covering all the major Indian states. We use four rounds corresponding to the years 1983 (38th round), 1993–1994 (50th round), 2004–2005 (61st round) and 2011–2012 (66th round). The detailed characteristics of all household members including sex, age, caste/religion, marital status, relation to the household head, education level, employment status, occupation, industry and the region are provided in the survey. The monthly per capita consumption expenditure that is used as a proxy variable for income is obtained for the same set of households from the Consumer Expenditure Survey. The sample is restricted to the age group 15–75 years and the variables are defined below:

Age: age of the individual.

Household size: number of persons in the household.

Gender: dummy variable, indicating female = 1, 0 otherwise.

Land size: per capita land possession obtained as land owned by the household divided by the number of persons in the household.

Social group: social group consists of Scheduled tribes, Scheduled castes and others (other backward caste and forward caste). Two dummy variables for scheduled tribes and scheduled castes are constructed with “others” as the reference category.

Religion: religion comprises Hindus, Muslims, Christians and others. We have constructed three dummy variables for Hindus, Muslims and Christians separately, with “others” as the reference category.

Education: we classify education into five categories: Illiterate, primary, middle, secondary and above secondary. We generate four dummy variables for illiterate, primary, middle and secondary and the reference category is “above secondary”.

Employment status: the employment status categories that we consider are self-employment, casual worker, salaried and unemployed. Self-employment comprises own account workers, employers and unpaid family workers; salaried workers comprise regular salaried and wage employee and casual workers comprise casual labour in public works or other types of works. We create three dummy variables with “unemployed” as the reference category.

Industry: we aggregate the industries classified under NIC (National Industrial Classification) to six industry groups with similar qualitative characteristics: agriculture (comprises agriculture, forestry and fishing); manufacturing (comprises mining and manufacturing); electricity, gas and water; construction; low-skilled services sector (comprises trade, hotels and restaurant, transport and personal services) and high-skilled services sector (comprises banking and insurance, communication, real estate, business services and public administration). The categorisation of the service sector into two groups is justified on the basis of skill and capital requirements. “Agriculture” is used as reference category and we constructed five dummy variables for each of the other industry groups.

State dummies: We have generated state dummies for 15 major states in India and the remaining states are used as the reference category. The 15 major states for which we have generated dummies are Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal.

The dummy variables for education, employment status and industry are constructed for both the head of the household and other members of the household who are in the labour force.

We have included three work-related characteristics (education, employment status and industry group) separately for the workers other than the household head in the regression. For this, we have computed the proportion of workers in the household that are in a specific category education (5), employment status (4) and industry group (6), so that in each category the proportion adds to one for each household. These proportions are included in the regression.

Appendix 2

See Tables 5, 6 and 7.

Table 5 Gini coefficients for 15 major states by employment status of the head of household
Table 6 Results of regression for urban and rural India. Dependent variable: log (income)
Table 7 Share of inequality and change in inequality using Gini indices, rural and urban India

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Rani, U., Krishnakumar, J. & Bigotta, M. Accounting for income inequality: empirical evidence from India. Ind. Econ. Rev. 52, 193–229 (2017). https://doi.org/10.1007/s41775-017-0012-9

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