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Income Mobility among Social Groups

Thiagu Ranganathan (thiagu.r@iegindia.org), Amarnath Tripathi (amarnath@iegindia.org) and Ghanshyam Pandey (ghanshyampndy@yahoo.com) are at the Institute of Economic Growth, New Delhi.

Looking at income mobility across different social groups in India using the India Human Development Survey data from 2004–05 and 2011–12, different notions of mobility are calculated. Average mobility across quintiles is seen to be higher among backward castes. Higher inter-temporal mobility among households belonging to Scheduled Castes and Other Backward Classes is noticed, while positional movement is similar across different social groups. Per capita absolute income changes are seen to be the highest for forward castes, while per capita directional income changes were highestfor sc households.

India is a country with high income inequality with gini coefficient of income at 0.532 in 2004–05 (Himanshu 2015). The gini coefficient of income of rural households during the same period was 0.513 which increased to 0.529 in 2011–12.1 The Indian economy registered a high economic growth between 2004–05 and 2011–12. There are various views on how this economic growthand increasing inequality will reconcile in future. While some believe that inequality will decline if high growth continues over a sustained period of time (Bhagwati and Panagariya 2014), others hold that such high and increasing inequality is an area of concern that needs to be addressed immediately (Weisskopf 2011). What actually happens is partly an empirical issue, and it is only with time that we will have an idea of what would actually happen. A related concept that could provide us some leading answers is that of income mobility.

Mobility is a multifaceted concept where specific context needs to be defined properly. It could be measured in the intergenerational or intra-generational context. Also, mobility could be measured using various indices, including occupation, education, income, consumption, earnings, and so on. There are many studies in the Indian context that look at intergenerational mobility of occupation, education, and income (Sharma 1970; Ramachandran 1990; Kumar et al 2002; Majumder 2010; Motiram and Singh 2012; Hnatkovska et al 2013; Nandi 2013; Reddy and Swaminathan 2014; Ahsan and Chatterjee 2015; Azam and Bhatt 2015; Reddy 2015). But, there have been few studies that have looked at intra-generational income mobility in India (Gaiha 1988; Dreze et al 1992; Pradhan and Mukherjee 2015).

The dearth in intra-generational income mobility could be due to at least two reasons. Unlike inequality and inter-generational mobility, intra-generational income mobility does not have clear normative interpretations when looked at in isolation. While high intra-generational income mobility could be seen as a sign of dynamism, it could also be a reflection of uncertainty associated with a constantly fluctuating income stream (Jantti and Jenkins 2013). Also, analysis of intra-generational income mobility requires longitudinal data collected over short time periods for analysis. Such data is rarely available in the Indian context.

To address the first concern, we calculate different indices that look at different notions of mobility. By looking at the changes in these different indices, we try to interpret the changes in different notions of mobility. In the Indian context, any question of mobility has to be addressed in the background of rigidities imposed along different social groups. So, we also compare mobility across different social groups.

Data and Methodology

In our analysis, we use the data collected from two rounds of IHDS conducted in 2004–05 and 2011–12. The survey was large-scale, nationally representative, and conducted under the supervision of the National Council of Applied Economic Research (NCAER) in collaboration with the University of Maryland. The survey covers almost all the states and union territories of India except Andaman and Nicobar Islands and Lakshadweep. It used two-stage stratification and was conducted over a sample of 27,010 rural households (from 1,503 villages) and 13,126 urban households (over 971 urban blocks) in 2004–05. In 2011–12, the survey team reinterviewed around 83% of the households as well as split households (if located within the same village or town). It also selected an additional replacement sample of 2,134 households in this round. In total, the 2011–12 survey was conducted among 42,152 households.

For the purpose of our analysis, we use only those rural households that were surveyed in both 2004–05 and 2011–12. There were households that were split after 2004–05 and the split households in the same villages were resurveyed in 2011–12. Since the survey did not mention whether there were households that were split and not in the same village, we did not include any of the split households in our analysis. In all, there were 19,831 households that fit the above criteria and have been included for the purpose of our analysis. The survey also collects information related to the income of the household from different sources of income (agriculture, labour, remittances, business, and other income sources) as well as total income. We use the total household income for our analysis.

There are various indices in literature that have been used to measure mobility, but not all the indices measure the same concept of mobility. Fields (2006) identifies six notions of mobility and associates different mobility indices to these notions. The six notions identified are time-independence, positional movement, share movement, income flux, directional movement of incomes, and mobility as an equaliser of long-term income.

Given this background, we proceed with the analysis as follows. First, we create a transition matrix for the whole population and for different social groups by classifying the households into income quintiles. We measure some summary statistics from these matrices. Importantly, we focus on the probability of households staying in the first and fifth quintile in both the years. We also measure the probability of upward mobility and downward mobility across all groups. We also measure a mobility statistic M defined by Prais (1955) and Shorrock (1978). This is the average probability across all quintiles that the householdwill leave its quintile in 2004–05 to a different quintile in 2011–12. The statistic is defined as follows:

where, pii is the probability of the household staying in the same income quintile in 2011–12 as in 2004–05.

Then, we calculate the followingindices which indicate the six notions of mobility.

For time-independence, we calculate the following mobility index:

I time–independence = 1– rINCOME2004–05, INCOME2011–12 ...(2)

where rINCOME2004–05, INCOME2011–12 is the Pearson’s correlation between household’s income in 2004–05 and 2011–12. This index increases as correlation decreases, indicating lesser time-dependence between incomes in two periods. The changes in incomes may not get reflected in changes in positions/ranks.

For positional movement, the following index is calculated:

I positional_movement = 1–ρ INCOME2004–05, INCOME2011–12 ...(3)

where ρINCOME2004–05, INCOME2011–12 is the Spearman’s rank correlation between household’s income in 2004–05 and 2011–12. Higher index would imply higher mobility in positions among households. Both the indices mentioned could theoretically vary between 0 and 2 though we would not expect these indices to go above 1. The three next indices do not have any bounds and measure the per capita changes in shares and “incomes as follows:

Another index that visualises mobility as an equaliser of long-term incomes is the following:

where Incomeaverage is the average income of the household in 2004–05 and 2011–12 and G(.) is the Gini coefficient function.

Findings

We categorise rural households into the following social groups: forward castes, Other Backward Classes (OBCs), Scheduled Castes (SCs), and Scheduled Tribes (STs) and use real per capita household incomes of 2004–05 and 2011–22 as the indicator of well-being.2 Before we look at mobility of the different social groups, we first look at the situation in 2004–05. Figure 1 indicates the distribution across quintiles for different social groups.

We see that there are variations across quintile distributions in each social group. Among forward caste households, 37% were in quintile 5 (Q5), and only 14% were in Q1. For OBC households, 22% households were in Q1, and 18% in Q5. The rest of the OBC households were evenly distributed among the three other quintiles. Among SC and ST households, there were only 13% and 16% households, respectively in Q5. Taking this as the base, we plot the transition matrices.

The transition matrix plots the probability of a household staying in a particular quintile in 2004–05 and moving to a particular quintile in 2011–12. Figures 2(a)–(e) indicate the transition probabilities at the country level for all households and for different social groups.

The figures indicate in general a high persistence at extreme quintiles and a higher mobility at intermediate quintiles. The information in the transition matrices is summarised in Table 1. We find that the average mobility statistic, M, is lowest for households belonging to forward castes. This statistic is higher for households belonging to lower caste groups. If we look at the probability of a household staying in Q1 in 2011–12, given a household was in Q1 in 2004–05, it is the lowest for forward caste households and OBCs with the probability value at 29%. For SC households, this is 33%, and for STs, it is 40%.

The persistence is stronger at Q5, particularly for forward caste households. The probability of staying at Q5 in 2011–12, given it was in Q5 in 2004–05, is 53% for forward caste households; this was 44%, 40%, and 41% for households belonging to OBC, SC, and ST respectively. The probability for upward mobility is higher than probability of downward mobility for SCs and forward castes and the reverse is true for STs and OBCs. In particular, the probability of downward mobility was 11% higher than that of upward mobility for ST households.

In summary, we find that there is persistence at lowest and highest quintiles, with higher castes having a considerably higher persistence at Q5 and lower castes having the same at Q1. But average mobility is high among backward castes. We also observe that probability of downward mobility and upward mobility are quite similar for SCs while probability of downward mobility is much higher than upward mobility for STs. To further explore the mobility across different social groups, we calculate the different indices of mobility. Table 2 presents the indices for all households and different social groups.

From Table 2, we observe that there are different trends across different indices. Time-independence is lower for households belonging to forward castes and STs as compared to those in OBCs and SCs. The positional mobility across different social groups is almost the same. The per capita change in absolute shares was highest for households belonging to forward castes followed by OBCs, SCs and STs. The per capita income changes were highest for households belonging to forward castes, OBCs, and lower for SC and ST households. The per capita directional change was highest for households belonging to forward castes and SCs while it is lower for OBC, forward caste, and ST households. Mobility had the least equalising impact among SC households. The equalising impact was highest for forward castes and lowest for ST households.

On further looking into the details of real per capita incomes of households for 2004–05 and 2011–12, we find that the mean incomes of households belonging to SCs increased by 65% as compared to 54%, 55%, and 40% for those belonging to forward castes, OBCs, and STs, while the standard deviation increased by 155% compared to 65%, 41% and 21% for forward caste, OBC, and ST households. Gini coefficient of real per capita income of households belonging to SCs increased by 7.8% while it increased by 2%, 3% and 3.5% among households belonging to forward castes, OBCs, and STs. The increasing inequality among SC households in the recent past has been documented recently by others as well (Singh et al 2015). Such mobility could be in line with the structural changes occurring in the economy.

The period 2004–05 to 2011–12 saw an increase in real wages in agriculture after a long time. Since most of the agricultural labourers belong to SC households, it might be that they benefited the most from it. Also, this was a period where a large population also shifted their dependence from agricultural labour to non-farm labour. Here again, the benefits could have been higher for those who moved to casual labour and this might not have been even for all SC households as non-farm employment opportunities may not be accessible to all. Some households belonging to SC could also have benefited from affirmative action, but the effect of this is not likely to be significantly high in the short seven-year period. These conjectures are also confirmed if we look at the ratio of nominal household incomes in 2011–12 to those in 2004–05. This ratio is highest for SC households for farm income, agricultural labour income, casual labour income, salaries, and total income. However, we also find that the average ranks of SCs declined in 2011–12 compared to 2004–05 indicating a positional decline.

In summary, the article finds high persistence at the top and lower persistence at the bottom of income distribution. In this context, welfare programmes such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) that are self-targeting might be more efficient. Also, mobility in the lower income distribution could be due to shocks caused by weather and other factors. So, mobility might not always be seen in a positive sense (Krebs et al 2013). The article also finds lesser mobility among forward castes and STs, but higher downward mobility among STs compared to forward castes and vice versa. There seems to be higher mobility among OBCs across measures of various indices. High mobility is observed in SCs, though it seemed to have caused higher inequality among SC households, although without much positional movement.

These findings have to be interpreted with caution as they are based on two years. As more rounds of such surveys are conducted, we might have better picture of trends in mobility.

Notes

1 Among a smaller set of rural households that were common to 2004–05 and 2011–12 (and were not split), Ranganathan et al (2016) find the gini of rural household incomes to have increased from 0.536 to 0.557.

2 We use average consumer price index (for agricultural labourers) (CPI–AL) to deflate the incomes of both the years to 1986–87 prices and divide it by number of members in the household to get the per capita figures.

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Updated On : 13th Oct, 2017

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