The diagnostics of the previous section suggest that the SDI framework does a reasonably good job explaining wage growth of the past decade across both demographic groups and states. But what are the key structural trends underlying these changes? Five candidates for consideration are as follows: (1) urbanization, (2) National Rural Employment Guarantee Act (NREGA), (3) the rural construction “boom,” (4) falling rural female labor force participation (LFP), and (5) rising agricultural prices.
We begin by predicting log-wage changes from 2004 to 2011 for each group × state observation using the results in Table 3, column 4, i.e.,
$$ \widehat{\Delta \log \left({W}_{ist}\right)}={\widehat{\beta}}_0+{\widehat{\beta}}_1\left(\Delta {D}_{ist}-\Delta {S}_{ist}\right)+{\widehat{\beta}}_2\Delta {I}_{ist}. $$
(7)
Next, we construct predicted difference-in-differences across groups i and j within a sector as follows
$$ \hat{\Delta_{ij}\Delta \mathrm{log}\left({W}_{st}\right)}=\hat{\Delta \mathrm{log}\left({W}_{ist}\right)}-\hat{\Delta \mathrm{log}\left({W}_{jst}\right)} $$
(8)
or across sectors within group i using
$$ \hat{\Delta_{ur}\Delta \mathrm{log}\left({W}_{it}\right)}=\hat{\Delta \mathrm{log}\left({W}_{iut}\right)}-\hat{\Delta \mathrm{log}\left({W}_{irt}\right)}, $$
(9)
where subscripts u and r denote, respectively, urban and rural. Finally, we examine the bivariate associations between the predicted diff-in-diffs and each of the five structural wage drivers mentioned above.
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.Footnote 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
$$ {\Delta }_{ur}\Delta {P}^A=\left({\beta}_u^A-{\beta}_r^A\right){\sum}_c{s}_c\Delta \log {p}_c, $$
(10)
where \( {\beta}_j^A \) is the initial (i.e., 2004–2005) share of labor in agriculture for a state in sector (j = u, r), s
c
is the share of crop c in the total value of state agricultural production in base year 2003–2004, and ∆ log p
c
is the change in log price of crop c between the 2004–2005 and 2011–2012 crop marketing years for the 18 top field crops of India.Footnote 15 Intuitively, the labor market response to changes in agricultural prices is modulated by the output share of agriculture in the overall economy of the sector; if production is Cobb-Douglas, this output share is equivalent to the labor share.
The relationship between differential urban-rural agricultural price changes, as reflected in ∆
ur
∆PA, 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
∆PA 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
∆PA 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.