Intragenerational labor mobility in the Indian labor market

The study uses panel data from the India Human Development Survey (IHDS) conducted for the years 2004–2005 (IHDS-I) and 2011–2012 (IHDS-II) (Desai and Reeve, 2015a, 2015b). Both IHDS-I and IHDS-II are representative at the national level. IHDS-I has a sample size of 41554 households (215754 individuals), out of which 83% original and split households were resampled in IHDS-II. While the rural sample for IHDS is based on stratified random sampling, the urban sample was drawn using a stratified sample of towns and cities within states selected by probability proportional to population sampling.

The focus of the study is on labor mobility characterized by the movement of workers across labor market statuses. We divide the labor force into eight labor market status groups: farmworker,

The farmworker segment includes workers involved in the cultivation of crops and those involved in animal care.

A person from outside the workforce who is currently enrolled in an educational institution is considered a student. Persons from outside the workforce who are not students comprise the “Unemployed and Not in the Labor Force” (UNLF) category. In the case of self-employed persons, there may be multiple household members engaged in a nonfarm business. In such a scenario, we follow Deshpande and Sharma (2016) and classify the person who has worked the maximum number of hours within the household as the primary decision-maker of the business

There are a small number of cases in which the total number of hours worked is similar for two or more workers. In such cases, we use information on age of the worker and identify the more aged worker as the primary decision-maker.

Existing attempts in the literature to investigate labor market mobility have mainly relied on the presence of wage gaps, controlling for all available characteristics of workers. However, this approach has been criticized for its inability to control for all the productive characteristics of workers (Rosenzweig, 1988). Further, a significant monetary wage gap across sectors may simply indicate compensatory wage differentials for differences in nonpecuniary rewards to jobs as workers seek to equalize utility across sectors (Maloney, 1999).

Given the criticisms of the wage analysis approach, other studies have relied on evaluation of the transition of workers across sectors over time as a method to evaluate mobility in the labor market. Some studies have applied both mobility analysis and wage regressions to get a more robust and comprehensive picture (Duryea et al., 2006; Pagés and Stampini, 2009).

Following the empirical literature, the present study relies on three tools to understand the patterns of mobility across sectors and how such mobility is associated with individual characteristics and earnings. First, the study examines the transition probabilities of workers moving across sectors using transition matrices. The study then looks at the characteristics of the movers with reference to stayers using multinomial logistic regression. Finally, the study looks at the consequences of mobility on earnings with reference to the stayers using fixed-effects regression analysis.

We discuss below the characteristics of the workers engaged in the different employment sectors.

Table 1 shows that farmworkers as a group have the largest share in the workforce, closely followed by casual workers. Employment sectors such as employers and regular workers are concentrated in urban areas relative to other groups. The age composition of the workforce shows that unpaid workers, students, and the UNLF categories are significantly younger than the other groups. OAW, employer, and regular worker have the most aged workforce in the sample (Table 1). The gender composition of the employment groups shows the OAW, employer, regular worker, and casual worker groups being considerably male dominated, with the females mostly concentrated in the UNLF and farmworker groups.

The education profiles of the workers over both the periods show regular workers and employers doing significantly better, followed by the OAWs and unpaid family workers, with the remaining groups having the most inferior educational outcomes (Table 1). Our results dispel the notion of self-employed having a significantly lower education status than their wage-employed peers (Robinson and Sexton, 1994). The caste composition shows that the General and Other backward castes (OBC) category workers have a higher representation in better employment outcomes such as employers and regular workers, whereas the Scheduled castes/Scheduled tribes (SC/ST) groups are involved mostly in casual work. Similarly, we find considerable variation in the distribution of workers by religion and marital status.

Finally, judging in terms of incomes and assets, we find employers and regular workers doing significantly better, followed by OAWs and unpaid family workers, with the casual workers doing the worst in this regard (Table 1). In line with the literature, we also find variability in earnings to be much higher among the self-employed group relative to the wage-employed peers (Åstebro and Chen, 2014). Consistent with the international literature, we also find the number of work hours to be quite high among the self-employed (Blanchflower, 2004) and regular workers, whereas they are found to be lower among the casual workers.

An interesting feature of the Indian labor market is the large share of workers who are involved in >1 job. Table A1 in Appendix shows that >10% of the workers in the workforce are involved in >1 job, and that this number has increased over the investigated period. Further, we find that a majority of such multiple jobholders are involved in farm and wage work. The phenomenon of multiple jobholdings is seen to be more common among casual workers and OAW, being quite rare for farm and regular workers (Table A1 in Appendix). This issue is discussed further in the next few sections.

We now look at the transition matrices of the workers across the various employee groups.

A first look at the P-matrix shows that all the transition probabilities are significant at the 1% significance level. The corresponding 95% multinomial confidence intervals are reported in Table A8 in Appendix. The diagonal elements of the P-matrix show labor turnover to be very high for the self-employed groups, whereas it is very low for the wage-employed group and farmworkers (Table 2). We also find high two-way mobility between farmwork and casual work, which may signify the seasonal nature of agricultural work as workers move intermittently between agriculture and casual work to eke out a living. We look at this issue further in the next section.

Considerable two-way mobility is also noticeable among the wage-employment groups (between casual and regular workers) and the self-employment groups (among OAWs, employers, and unpaid family workers) (Table 2). Finally, the significant movement of people from outside the workforce (composed of students and UNLF) into the farmworker and casual worker groups signifies the role that these groups serve as points of entry for new entrants into the workforce (Table 2). Such a phenomenon may imply that workers move into such sectors to gain work experience (either voluntarily or due to the limited supply of formal [or regular] job opportunities) before moving into better-paying activities.

Looking at the T-matrices, we observe that most of the t indices have near-unity values, signifying random worker transition (Table 4). The indices from the V-matrix also indicate, in most cases, a high level of symmetry among the various sectoral flows (Table 3). However, in some instances, we also notice a high tendency of workers to move between the self-employed categories of employer, OAW, and unpaid family worker (Table 4). The v indices in such cases are not very symmetric, especially with reference to movements associated with unpaid family workers (Table 3). A high tendency of mobility for workers is also noticeable among the casual and regular worker categories, with the v indices being asymmetric (Tables 3 and 4).

Although the analysis of mobility until this point has provided us with a good idea of the extent of movement of workers across different employment groups, it does not tell us anything about the characteristics of workers making a move to other job categories. Hence, we use the multinomial logistic regression results to get a better idea of the different attributes of workers associated with mobility. We complement the discussion of the results from the multinomial logistic regression with the logistic regression results illustrating the characteristics of the stayers.

Given the large movements of workers from farmwork to casual work, we first look at the characteristics of such movers (Table 2). We find that with reference to staying in farmwork, movements into casual work is mainly undertaken by the young, the less educated, males, the poor, and the backward castes (Table 5). Given the relatively high poverty rates for such movers before and after moving (as seen from Table A4 in Appendix) and the attributes of the workers moving from the former to the latter, movements out of agriculture into casual work is likely to be driven by distress, undertaken mainly by workers to supplement their meager family incomes.

Looking at the issue further, we find that most of the movers from farmwork to casual work hold multiple jobs, simultaneously working in the farm and in casual work in both the periods. At the same time, the number of such multiple jobholders almost tripled over the period (Table A7 in Appendix). Further, most of such movement from farmwork to casual work is not into work provided under the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), but private casual work, indicating that such work is not due to the lure of relatively better MGNREGA work.

We do not present the results in regard to this contention. Results are available upon request.

Interestingly, around 17% of the workers undertake the opposite movement, from casual work to farmwork. Such movers are generally better off in terms of education, caste affiliation, and assets (Table 6). The incidence of multiple jobholdings also falls dramatically among such movers (Table A7 in Appendix). Such differences in the characteristics of the movers from farmwork to casual work, and vice versa, offer some argument against the possibility that movements between farmwork and casual work are random and that the movement from the former to the latter represents genuine symptoms of distress among the farmers.

The findings tally with those in the literature, which finds evidence of declining profitability as well as rising risks and indebtedness in Indian agriculture. This is despite the silver lining of rising agricultural productivity in recent years (Binswanger-Mkhize, 2013; Deokar and Shetty, 2014; Mishra and Reddy, 2010). Our findings are also in line with the global trend of rising diversification of farm-based households into nonfarm activities (Davis et al., 2010; Deere, 2005; J. Lanjouw and Lanjouw, 2001). Our results are also similar to those of Lanjouw and Shariff (2004), who find that much of the diversification into nonfarmwork by the poor is into casual work.

Given the high t indices between OAW and employers, we take a look at the attributes of movers into employer jobs. We see that relative to staying in OAW, moving into employer category is mainly undertaken by the young, those with better educational background, and the wealthy (Table 5). This may be an indication of the presence of liquidity constraints in the economy, as small well-to-do businesses with enough financial capital undertake investments to enlarge their business (Table 5). The very low initial poverty rates among the OAWs undertaking the move into employer category further corroborates this contention (Table A4 in Appendix). Such presence of liquidity constraints can also be seen as an evidence of the limited role of microfinance programs in promoting entrepreneurial growth. This is corroborated by our finding of the limited impact of social capital – proxied by memberships in self-help groups (SHGs) and savings groups – on labor mobility, as discussed below.

We also notice high mobility of workers from casual work into regular work. We see that movers from casual work to regular work have better endowments in terms of higher education, fluency in the English language, as well as being more likely to be rich, from urban areas, and belonging to privileged castes and religions (Table 6). The scenario of low turnover for regular workers, the high tendency for casual workers to move into regular work, and the superior characteristics of such movers points to a situation where casual workers queue for scarce well-paying regular jobs, and it is mainly the better endowed of the casual workers who get to move into regular work.

Consistent with this view, we find that the reverse movements of workers from regular work into casual work are mainly associated with the less educated and the poor (Table 6). Looking at the persistence in casual or regular work, we find that, consistent with our earlier conjecture, education, fluency in English, and household wealth are positively associated with survival in regular work but negatively related with survival in casual work (Table A5 in Appendix).

We conclude the section with a few general remarks:

First, our results, in general, show a consistent pattern wherein the occupational status of the father/husband’s father of the household head has a significant influence on the mobility patterns of the workers (Tables 5 and 6;Tables A2, A3, and A5 in Appendix). In general, workers within a family tend to move into or persist in the employment status related to the occupation of the father/husband’s father of the household head. Hence, workers whose father/husband’s father of the household head were in sales-related occupations are more likely to move into (or persist in) self-employment work (Tables 5 and 6; Tables A3 and A5 in Appendix). Similar results are evident for the farmwork and wage work groups. The results corroborate the literature on the impact of parental occupations on self-employment entry and survival (Parker, 2009; Simoes et al., 2016). The results also support the literature on limited intergenerational occupational mobility in India (Reddy, 2015).

Second, the mobility patterns display a definite gender pattern, with males more likely to move into (or persist in) paid work categories such as OAW, employer, casual worker, and regular worker, whereas females are more likely to move into (or persist in) unpaid family work or outside the workforce (Tables 5 and 6; Tables A2 and A3 in Appendix). This is consistent with the literature, which finds high mobility within the workforce for men and high mobility into joblessness for women (Royalty, 1998; Theodossiou and Zangelidis, 2009).

Third, we fail to find any general impact of social capital, proxied by membership in socio-political institutions such as SHGs, savings groups, and so on, on labor market mobility (Tables 5 and 6; Tables A2 and A3 in Appendix). This is also borne out by the existing literature, which finds a limited impact of microfinance programs on income growth and employment prospects (Gopalaswamy et al., 2016; Mohapatra and Sahoo, 2016).

Fourth, we also find that in line with the findings in the literature, SCs/STs are more likely to be exiting self-employment and less likely to be entering and surviving in self-employment (Table 6; Table A5 in Appendix) (Ahn, 2011; Fairlie, 1999). The results show that despite the rhetoric on Dalit capitalism and some stray success stories, the scenario on SC/ST-owned businesses looks bleak (Iyer et al., 2013; Kapur et al., 2014). Our results tally with the existing literature, which shows that relative to the rest of the economy, SC/ST-owned businesses continue to fare poorly in their entrepreneurial pursuits (Deshpande and Sharma, 2016; Iyer et al., 2013). Lastly, education is found to positively affect mobility and survival in the more-favorable-outcome groups, such as employer and regular worker (Tables 5 and 6; Tables A2, A3, and A5 in Appendix).

We finally take a look at the impact of labor mobility on individual earnings. Taking a look at Tables A4 and A6 in Appendix, we find that movements into statuses, such as OAW, employer, and regular worker, from other sectors (such as farmwork and casual work) are associated with positive changes in household consumption levels and wage levels. The aggregate statistics on consumption and wage changes associated with worker mobility, however, do not control for individual-level attributes. To look at the impact of worker mobility on earnings, controlling for observable and time-invariant unobservable worker attributes, we look at the results of the fixed-effects regression analysis.

As can be seen from Table 7, earnings are significantly higher in the employer group as compared to all other sectors. The finding that employers earn significantly more than OAWs, along with our earlier observation about the attributes of the movers from the OAW group into the employer category, offer further support to our earlier inference on employer category being the more-desired employment status. Our findings are also similar to those found in Vietnam and Madagascar (Nguyen et al., 2013; Nordman et al., 2016). Similarly, casual workers fare the worst in terms of wages in comparison to all other segments. OAWs and regular workers are positioned in between the extreme cases of casual workers and employers (Table 7).

We see that regular workers earn significantly better than their casual worker counterparts (Table 7: Column 4). This result, in combination with our earlier evidence on a lower turnover in regular work, as well as the superior attributes of the workers moving from casual to regular work, provides ample evidence in favor of the conjecture that regular work is a more-favorable outcome vis-à-vis casual work. Our findings are synonymous with the literature on Latin America, Vietnam, Madagascar, and South Africa, which finds overwhelming evidence of a formal wage premium over the informal-sector wage workers (Duryea et al., 2006; Nguyen et al., 2013; Nordman et al., 2016; Pagés and Stampini, 2009).

Lastly, we find significant heterogeneity in the earnings penalty/premium from sectoral change by education groups. Specifically, the earnings penalty/premium from sectoral change is found to be more prominent for the highly educated (Table A10 in Appendix). On the other hand, we find the earnings penalty/premium from moving across the self-employment groups to be muted for the SC/ST castes (Table A12 in Appendix). However, we fail to find much heterogeneity in the earnings changes of workers from sectoral change by gender and age groups (Tables A9 and A11 in Appendix). The results emphasize the moderating role of education in influencing the earnings gains/losses from sectoral mobility.