Changing wage structure in India in the post-reform era: 1993–2011

2.1 Definitions: groups and industries

We analyze three rounds of the NSS, the 50th (1993–1994), 61st (2004–2005), and 68th (2011–2012), thus covering an 18-year span. Workers between 12 and 65 years of age are divided into eight demographic groups, consisting of the 2 × 2 × 2 interaction of male/female, educated (completed secondary level or above)/uneducated (less than completed secondary), and young (12–29)/old (30–65). In addition, we construct aggregates for these eight demographic groups by sector (urban/rural), yielding 16 groups in total.⁴

Wage earners are defined as those engaged in “gainful activities,” as recorded in their “usual principal status” in the NSS, but not self-employed. The usual principal status also serves as our basis for categorizing individuals into industry groups below. We focus on principal status because this accounts for the preponderance of the reference period of 365 days preceding the date of survey.⁵

Figure 1 shows the representation of each demographic group among sector-specific wage earners. The proportion of uneducated young males and females fell across the board from 1993 to 2012, but the decline was more pronounced in rural than in urban areas (5 versus 3 percentage points for males and 6 versus 2 percentage points for females). Among the older cohorts, the proportion of uneducated also declined but more gradually. Of course, the flip side has been the increasing share of educated among wage earners, especially in rural areas. Despite these trends, uneducated males still make up a little more than half of the workforce in rural wage labor markets.

We next define five broad “industry” or occupational categories: (1) agriculture (inclusive of forestry and fishery); (2) construction; (3) manufacturing (inclusive of mining and utilities); (4) professional (including, inter alia, financial, public administration, education, and health); (5) services (inclusive of wholesale/retail trade and domestic service).⁶ SDI analyses using developed country data, and even Chamarbagwala’s (2006) study of urban Indian wages, typically use a much more fine-grained industrial classification. However, sample size considerations constrain us to only five. Rural India is predominately agricultural; manufacturing has, until very recently, accounted for much less than 10% of rural employment. Given the typical NSS sample, there would simply not be enough wage earners in each category to support a very detailed classification. This concern is only reinforced in our state-level analysis, where state-wise wage-earner samples are much smaller.

Patterns of industrial employment have changed rather dramatically in rural India over the past two decades. As seen in Fig. 2, from around three quarters in the early 1990s, the share of rural labor employed in agriculture had, by 2011, declined to around one half. The two main rural growth industries are services and construction, with the latter’s employment share more than quadrupling (from 3 to 13%) over the last two decades, a finding consistent with anecdotal reports of India’s rural construction “boom.” By contrast, the urban picture is one of relative stasis, with more modest expansions of services and construction over the same period.⁷

Representation of the eight demographic groups in each industry is shown for both rural and urban areas in Fig. 3. Educated workers, obviously, predominate in the professions, whereas wage jobs in rural construction are largely held by unskilled males, even more so than in agriculture and quite substantially more so than in services. Looking at trends by industry over the past two decades, Fig. 3 shows (with 2004–2005 omitted for brevity) a gradual up-skilling of the workforce across the board. Between 1993–1994 and 2011–2012, the share of rural uneducated young males and females in agriculture fell by half, from 23 to 12% for males and from 11 to 6% for females. In rural construction, however, while the share of uneducated young females declined by 10 percentage points, that of young uneducated males remained virtually unchanged. Meanwhile, the share of educated young males nearly doubled in both rural manufacturing and in services over this period but stayed around 11% in professional jobs. Even more dramatic increases in industry employment shares between 1993–1994 and 2011–2012 occurred for older educated males, both in rural and urban areas. For educated women, by contrast, the employment share has only increased significantly in professional jobs.

2.2 Changes in real wages

Information on weekly wage earnings and days worked per week is available for regular and casual workers. In the case of those who perform multiple jobs in the week, we calculate average daily wages by dividing weekly wage income from all sources by total number of days worked. To compute real wages, we use the state-level Consumer Price Index for Agriculture (CPI-AL) and Industrial Workers (CPI-IW). Originally, the CPI-AL was available with base year 1986–1987 and CPI-IW with base year 1982. We converted these indices to have a uniform base year 2004–2005. CPI-AL is used to deflate wages in rural areas and CPI-IW in urban India. Because these deflators are not available for some of the small states, we used available information for larger states either adjacent to them or from which they had been split (see Table 5 in Appendix for details on the CPI calculation for the smaller states).

Mean annualized changes in log real wages by group are shown in Fig. 4 across each of the two sub-periods. Evidently, wages have been rising in real terms over the past two decades for all groups and especially in rural areas. There has also been a marked acceleration in wage growth in recent years, most pronounced in urban India as well as among the unskilled (those with less than secondary education). Looking across states (Fig. 5), we see big real-wage gains for unskilled workers in the south and east of the country, with W. Bengal being a notable exception. The remainder of our analysis will largely ignore this overall rising tide to focus on why some “boats” have risen faster than others.

2.3 Changes in relative wages within and across sectors

The first three columns of Table 1 report relative wage trends denominated in log changes. Rows 1 and 2 indicate that the wages of uneducated rural workers rose relative to those of educated rural workers (hence the negative sign) for both males and females. Specifically, unskilled men (women) saw their wages rise by 33 (41) % relative to skilled men (women) since the early 1990s. Much of these relative gains occurred in the most recent decade (2004–2011). Note that the urban unskilled also experienced relative wage gains in the second period, but not quite as much as their rural counterparts. Overall, rural females (especially the unskilled) gained ground on rural males in the last decade.

3.1 Conceptual framework

Suppose we have a CES production function for aggregate output that depends on just two types of labor (ignore capital), types a and b. Katz and Autor (1999), e.g., show that

where w _it are wages for type i in time t, D _t is an index of relative demand shifts favoring group a, and S _t is an index of relative supply shifts favoring group a. The parameter σ represents the aggregate elasticity of substitution in production between labor of type a and b.⁸ A key implication of the model is that only net demand shifts (i.e., net of supply shifts) matter for relative wages. Differencing Eq. (1) over time, using the notation ∆x _t  = x _t  − x_t − l, delivers

Thus, on the left-hand side of Eq. (2), we have a difference-in-differences in mean log wage for two groups over time.⁹ These diff-in-diffs are precisely what is reported in Tables 1 and 2 for, respectively, within and between sector contrasts.

where N _ist is the group’s employment in the sector and N _st is total employment in the sector. So, for example, the relative supply of uneducated young males in rural areas is the log ratio of the number of uneducated young males to total number of workers employed in rural areas. Note that employment includes self-employment in agriculture or in a household enterprise and hence the employed are a much larger set than wage earners, especially in the rural sector. Shifts in supply, ∆S _ist , are assumed predetermined; that is, not caused by changes in relative wages. In the state-level analysis, we will have the opportunity to test this assumption.

where k indexes industry. So, the first term in the sum is the share of demographic group i in industry k’s employment (e.g., share of young unskilled men in agriculture) and the second term is the growth rate in the share of industry k employment in overall sectoral employment (e.g., growth in agriculture as a share of rural employment). Intuitively, ∆D _ist is larger when demographic group i (initially) predominates in relatively fast-growing industries. As with supply shifts, ∆D _ist is taken as exogenous with respect to changes in relative wage structure; once again, this is testable.

The “institutions” component of SDI boils down to allowing for industry wage premia. A wage premium measures the extent to which a given type of worker (demographic group) is paid more (or less) when working in a particular industry. Labor market institutions matter insofar as wages are not determined solely by the interaction of skill endowments and skill prices—i.e., by the competitive market for skills. A salient example in the case of India is agricultural labor. On average, jobs in agriculture pay around a third less than those outside of agriculture, holding location and type of worker constant (see Table 6 in Appendix for estimates of industry dummy variable coefficients from standard log-wage regressions).¹¹

where \( {W}_{ist}^c \) is the competitive market wage given group i skills, ρ _iskt is the industry k wage premium for group i at time t, and φ _iskt  = N _iskt /N _ist is the proportion of group i workers in industry k. Based on ρ _iskt estimated from wage regressions, the institutions index (∆I) for group i is the change in the entire wage-premium term or

Returning to the case of India’s agricultural sector, we can see that groups with a high value of ∆I _ist have to be moving out of agriculture relatively quickly. This is because ρ_{iskt − l} is large and negative (e.g., − 0.22 in 1993–1994), whereas ∆ρ _iskt is rather small (− 0.05 = − 0.27 + 0.22 from 1993–1994 to 2011–2012; see Table 6 in Appendix). Comparison of group shares across NSS rounds, as shown in Fig. 6, indicates that uneducated rural males (young and old) are shifting out of agriculture most rapidly.

3.2 All-India decomposition

SDI metrics at the national level are reported under, respectively, the second, third, and fourth major column headings of Table 1. So, why did the wages of educated workers decline relative to the wages of uneducated workers in rural India? First off, there was a substantial increase in relative supply of educated workers spread rather evenly across the two sub-periods. Thus, we see that the relative supply of educated men rose by 0.50 log points (or 50%) from 1993–1994 to 2004–2005 and again by 0.41 log points from 2004–2005 to 2011–2012; the corresponding figures for educated women are 0.82 and 0.70, respectively. Meanwhile, relative demand for educated workers fell, especially for males (by 0.29 log points in the earlier period and 0.37 log points in the later period). And, finally, there were modest declines in the institutions index for educated relative to uneducated workers. In other words, uneducated workers moved out of (low-paid) agricultural labor faster than educated workers. Similar, but less pronounced, patterns are seen for educated vs. uneducated workers in urban India (rows 6 and 7).

In Table 2, we compute urban vs. rural SDI changes. Focusing on unskilled labor (rows 2 and 3), we see that shifts in relative supply were not a decisive factor behind the wage gains of uneducated workers vis-a-vis the educated. For example, the supply of urban educated males fell by only 0.05 log points over the last two decades relative to that of rural educated males; the corresponding figure for females indicates a rise in relative supply of 0.13 log points. There were, however, big drops in relative demand for unskilled male labor in urban areas (0.77 log points over the 1993–1994 to 2011–2012 period), with smaller declines in the case of females (0.28 log points). The institutions index also moved slightly against the urban unskilled. The story of wage gains by the rural unskilled relative to their urban counterparts is, therefore, one of changing patterns of industrial employment rather than one of changing relative supplies (as was the case within the rural sector).

5.1 Within rural India

We look first at rural areas and, in particular, at wages of educated rural workers (old/young and male/female taken together) relative to uneducated. Each panel of Fig. 9 shows a scatterplot of \( \hat{\Delta_{\mathrm{ed},\mathrm{uned}}\Delta \mathrm{log}\left({W}_{rt}\right)} \) against a relevant driver. Having now aggregated wage changes across all eight demographic groups, we end up with 14 data points, which is to say one \( \hat{\Delta_{\mathrm{ed},\mathrm{uned}}\Delta \mathrm{log}\left({W}_{rt}\right)} \) for each state group.

Consider the change in the employment share of construction in rural areas of each of the 14 state groups. The top left panel of Fig. 9 shows that higher construction shares are strongly positively associated with the predicted growth in wages for the uneducated relative to educated. Indeed, differences in construction industry growth explain about two thirds of the variation in the relative wage growth predicted by the SDI framework. The same exercise using the rural services share, an industry which also employs significant numbers of unskilled workers and which also expanded in relative terms over the last decade, shows a similar pattern but a weaker association with wages. In sum, the rural construction boom appears to have been an important, if not the main, driver of unskilled relative wage growth within rural India.

It is interesting to contrast the labor market impacts of the above compositional shifts to those of National Rural Employment Guarantee (NREG). Phase-in of NREG began at around the mid-point of our 2004–2011 window. Analyses of NSS data preceding the 68th (2011–2012) round provide mixed evidence as to the rural wage impacts of NREG expansion (see Azam 2012; Zimmermann, L: Why guarantee employment? Evidence from a large Indian public-works program, unpublished; Imbert and Papp 2015). However, NSS68, for the first time, provides individual level data on NREG registration (job-card holding) and take-up (i.e., NREG employment in the last 12 months). This allows us to construct, for each state, the proportion of each demographic group that are job-card holders or who have worked in NREG.

Looking across state groups in Fig. 10, there are huge differences in NREG registration rates, with Rajasthan and MP topping the list, although rates of participation in this massive public works program are actually highest in the far east of India (“Seven Sister” states). Also relevant for our analysis is the large registration and participation gap between the educated and uneducated, with much higher NREG involvement among the latter (Fig. 11). Thus, we have in the two bottom panels of Fig. 9 plots of the predicted log-wage diff-in-diffs against the state-wise differences in NREG participation shares (job card on the left; worker on the right) between educated and uneducated groups. Given Fig. 11, all of the NREG share differences are negative (educated have lower registration and take-up than uneducated). What we do not see is much of a relationship between NREG participation and wage growth (the slopes are positive, but the R²’s are essentially zero). Put differently, states in which NREG has (presumably) expanded relative employment opportunities for unskilled labor more do not appear to have experienced differential growth in net demand for unskilled labor. This is, of course, not to say that NREG has been ineffectual as a safety net for the poor, only that it is evidently too small of a labor market intervention to have detectable general equilibrium effects.¹⁴

Next, using the same approach, we consider what has been driving changes in relative wages of men vs. women in rural India over the last decade. In this case, we compute \( \hat{\Delta_{m,f}\Delta \mathrm{log}\left({W}_{rt}\right)} \) by aggregating wage changes for all male (m) and female (f) demographic groups within the rural sector of each state. Here, we introduce another potentially relevant factor, the change in female LFP rate, which counts as labor force participants the self-employed, regular, and casual wage earners, as well as the unemployed seeking jobs. Figure 12 shows massive declines in female LFP in rural areas of most states, whereas Fig. 13 shows much more muted ones in the corresponding urban areas.

The top left panel of Fig. 14 provides striking confirmation that this recent movement of women out of the rural labor force explains much of the predicted increase in their wages relative to those of men; the R² of the associated bivariate regression is 0.84. By contrast, changes in the rural construction share (top right panel) or in women’s participation in NREG relative to men’s (bottom panels) explain next to nothing.

5.2 Urban vs. rural India

In the remainder of our analysis, we contrast urban and rural wage changes for unskilled labor. In particular, we use Eq. (9) to compute \( \hat{\Delta_{ur}\Delta \mathrm{log}\left({W}_{it}\right)} \) separately for uneducated males (Fig. 15) and for uneducated females (Fig. 16). On the x-axis in each panel in the next two figures is the urban-rural difference in log shares of construction employment (top left), services employment (top right), and female LFP (as a share of all females of working age). The bottom right panel of each of the figures considers the change in the urban (state) population share between the 2001 and 2011 population censuses (see Figure 19 in Appendix).

For males, the construction sector stands out as the key relative wage driver. A fall in the urban-rural construction industry share differential over time is associated with a decline in the urban-rural wage differential (R² = 0.34), whereas for females, the corresponding association is negative, albeit weak (R² = 0.05). Relative growth in the service sector, by contrast, bears little relationship to relative wage changes for either males or females. As for female LFP, we again see a strong correlation with wage growth. In states where women have withdrawn from the labor force faster in the countryside than in cities, rural wages of females have risen faster than urban wages (R² = 0.31), a pattern essentially absent with respect to male wages (R² = 0.04).

Next, we ask whether the growth of cities has in and of itself led to changes in SDI at the state level. By far, the fastest urbanization over the last decade occurred in Kerala, which is clearly an outlier in the bottom right panels of Figs. 15 and 16. Nevertheless, even with Kerala excluded, the story is clear. Faster urbanization is associated with greater urban wage growth relative to rural areas for both genders, but especially for females. Moreover, this latter effect is not driven merely by correlation between falling female LFP and urbanization; it survives virtually intact after controlling for the relative change in female LFP. Thus, it appears that in rapidly urbanizing states, the demand for female labor, as reflected in their wages, has been growing faster in cities than in the countryside.

As a final exercise, we turn to the agricultural commodity price boom of recent years as an explanation for the relative rise in rural wages. Jacoby (2016) uses variation across Indian districts in the shares of different crops in production to show that districts experiencing relatively higher agricultural prices over the 2004–2009 period also saw higher wages for unskilled labor. Adapting this approach to the state-level analysis of this section and extending the price data to 2011–2012, we construct the following measure of differential agricultural price change

The relationship between differential urban-rural agricultural price changes, as reflected in ∆ _ur ∆P ^A , and relative wage changes, as reflected by \( \hat{\Delta_{ur}\Delta \mathrm{log}\left({W}_{it}\right)}, \) is complicated by the fact that the agricultural labor share differential \( {\beta}_u^A-{\beta}_r^A \) affects both quantities independently. Referring to Eqs. (4) and (7), one can see that \( {\beta}_u^A-{\beta}_r^A \) and \( \hat{\Delta_{ur}\Delta \mathrm{log}\left({W}_{it}\right)} \) are mechanically related. In particular, since unskilled workers shifted out of agriculture into construction and other services over the last decade, the demand index for unskilled workers is dominated by a weighted average of the proportion of each of these industry’s shares of unskilled labor, where the weights are, essentially, the growth rates of employment in the respective industries. In a state where agriculture had a larger initial employment share, the growth rate of agriculture employment tends to be smaller and, hence, there appears to be a greater increase in demand for unskilled labor. The upshot is that, in considering the bivariate relationship between ∆ _ur ∆P ^A and \( \hat{\Delta_{ur}\Delta \mathrm{log}\left({W}_{it}\right)} \), we must partial out this mechanical correlation with \( {\beta}_u^A-{\beta}_r^A. \) Figure 17 thus plots the residuals of \( \hat{\Delta_{ur}\Delta \mathrm{log}\left({W}_{it}\right)} \) against those of ∆ _ur ∆P ^A in regressions on \( {\beta}_u^A-{\beta}_r^A \) across the 14 state groups. Consistent with Jacoby (2016), the figure shows that rural wages of the unskilled (males and females combined) have risen faster relative to urban wages in states where the terms of trade for agriculture have improved by more. Evidently, in states benefitting differentially from the agricultural commodity boom, the secular decline in agriculture (and the associated decline in demand for unskilled labor) has been attenuated.