metadata: authors: 0: author: Jacoby,Hanan G.
1: author: Dasgupta,Basab
count: India
docna: 0: docna: Changing wage structure in India in the
            post-reform era : 1993-2011
docty: Policy Research Working Paper
subtopic: Construction Industry,Mining & Extractive Industry (Non-Energy),Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Food Security,Business Cycles and Stabilization Policies,Rural Labor Markets
teratopic: Social Protections and Labor,Poverty Reduction,Macroeconomics and Economic Growth
abstracts: cdata!: This paper documents the changing
            structure of wages in India over the post-reform era, the
            roughly two-decade period since 1993. To investigate the
            factors underlying these changes, a supply-demand framework
            is applied at the level of the Indian state. While real
            wages have risen across India over the past two decades, the
            increase has been greater in rural areas and, especially,
            for unskilled workers. The analysis finds that, in rural
            areas, the changing wage structure has been driven largely
            by relative supply factors, such as increased overall
            education levels and falling female labor force
            participation. Relative wage changes between rural and urban
            areas have been driven largely by shifts in employment,
            notably into unskilled-intensive sectors like construction.
geo_reg_key: 517198,517212,517191
repnme: repnme: Changing wage structure in India in the
            post-reform era : 1993-2011
display_title: Changing wage structure in India in
            the post-reform era : 1993-2011
listing_relative_url: /research/2015/09/25094714/changing-wage-structure-india-post-reform-era-1993-2011
topicv3: Agriculture and Food Security m1326205 435,Mining Services m1326410 1701,Labor Markets m1326488 855,Manufacturing Sectors m1326521 1281,Mining Sector m1327113 554
publishtoextweb_dt: 2015-09-28T00:00:00Z
historic_topic: Social Protections and Labor,Poverty Reduction,Macroeconomics and Economic Growth
no_of_pages: 30
author_keys: 0: author_key: 84800
1: author_key: 817685
model_extractions: WPS7426 Policy Research Working Paper 7426 Changing Wage Structure in India in the Post-Reform Era

# 1993–2011

_Hanan G. Jacoby Basab Dasgupta_ Development Research Group Poverty and Inequality Team September 2015
Policy Research Working Paper 7426

# **Abstract**

This paper documents the changing structure of wages in India over the post-reform era, the roughly two-decade period since 1993. To investigate the factors underlying these changes, a supply-demand framework is applied at the level of the Indian state. While real wages have risen across India over the past two decades, the increase has been greater in rural areas and, especially, for unskilled workers. The analysis finds that, in rural areas, the changing wage structure has been driven largely by relative supply factors, such as increased overall education levels and falling female labor force participation. Relative wage changes between rural and urban areas have been driven largely by shifts in employment, notably into unskilled-intensive sectors like construction.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hjacoby@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Changing Wage Structure in India in the Post‐ Reform Era:  1993‐2011**

By Hanan G. Jacoby<sup></sup> and Basab Dasgupta<sup>†</sup> JEL codes: J21, J23, J24, J31 Keywords: Labor Supply, Labor Demand, Rural Wages in India

>  DECRG, World Bank, 1818 H St. NW, Washington DC 20433 (hjacoby@worldbank.org).  We thank Rinku Murgai and Ambar Narayan for helpful suggestions.

> † GWADR, World Bank (bdasgupta@worldbank.org)
# **I. Introduction**

We investigate changes in the structure of wages in India over the post‐reform era, the roughly two‐decade period since 1993, paying particular attention to recent trends in the wages of rural workers, especially the unskilled. Poverty reduction, much of it concentrated in rural areas, has accelerated over the last few years, largely due to increased earnings from non‐agricultural wage employment (Balcazar et al. 2015). An exploration of the fine‐grained details of India’s labor market transformation will thus help us to better understand this poverty decline.

Our approach hews closely to the Supply‐Demand‐Institutions (SDI) framework pioneered by Katz and Murphy (1992) and Bound and Johnson (1992). The idea is to divide the workforce into imperfectly substitutable demographic groups; e.g., by gender, education, and age. The twist, in our case, is to also cut the data by rural/urban, recognizing that, to a large extent, rural and urban India constitute distinct labor markets, or at least are far from being perfectly integrated. Thus, our apparatus allows us to investigate, for example, changes in wages of the rural unskilled _relative_ to their urban counterparts.

A second point of departure from conventional SDI analysis is its application at the state level, treating each Indian state (or group of states) as having separate urban and rural labor markets. A state‐level approach provides the requisite degrees of freedom for econometric analysis (see Juhn and Kim, 1999, for a related study of US states). In particular, SDI decomposes wage changes for a group into supply shifts (changing group employment shares), demand shifts (changing industrial composition biased for or against a group), and wage‐premia shifts (essentially, movements into or out of structurally low‐paying jobs). We then take the analysis a step further by investigating the key state‐level drivers of recent relative wage trends; i.e., we ask what types of supply or demand shifts were particularly influential in explaining the changing wage structure in India over the last decade.

There is a modest literature exploring India’s wage structure using data from NSS’s Employment‐Unemployment surveys. Hnatkovska and Lahiri (2013) consider rural‐urban wage convergence in India from 1983‐2009 using a model of long‐run structural transformation, but they do not decompose supply and demand factors behind the more recent wage trends. While Chamarbagwala (2006), like us, uses a supply‐demand decomposition, it is focused on the impact of trade liberalization over the earlier 1983‐99 period (see also Azam, 2010).

The organization of the paper is as follows. We begin in Section II by defining our groups and industries and then documenting how real and relative wages in India have changed over the past two decades. In Section III, we review the SDI framework and apply it to the national level. Next, we turn to the state‐level SDI analysis in Sections IV and V, followed by conclusions in Section VI.

2
Patterns of industrial employment have changed rather dramatically in rural India over the past two decades. As seen in Figure 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 over the last two decades. By contrast, the urban picture is one of relative stasis, with more modest expansions of services and construction over the same period.

<!-- Start of picture text --> Rural Urban<br>1993‐94 1993‐94<br>1.00 1.00<br>0.75<br>0.75<br>0.50<br>0.50<br>0.25<br>0.25<br>0.00<br>0.00<br>2011‐12 2011‐12<br>1.00 1.00<br>0.75 0.75<br>0.50 0.50<br>0.25 0.25<br>0.00 0.00<br>Uneducated young male Educated young male Uneducated young female Educated young female<br>Uneducated old male Educated old male Uneducated old female Educated old female<br>Services<br>Services<br>Construction Professional<br>Construction Professional<br>and Utilities<br>Utilities<br>Agri.+ Forestry+Fishing Mining, Manufacturing<br>Agri.+ Forestry+Fishing Mining, Manufacturing and<br>Construction Professional Services Construction Professional Services<br>and Utilities Utilities<br>Agri.+ Forestry+Fishing Mining, Manufacturing Agri.+ Forestry+Fishing Mining, Manufacturing and<br><!-- End of picture text -->

**Figure 3:  Share of demographic group in each industry**

5
Representation of the 8 demographic groups in each industry is shown for both rural and urban areas in Figure 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. Both rural and urban areas have seen a gradual up‐skilling of the workforce over the past two decades, across all industrial sectors.

# **B. 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 1986‐87 and CPI‐IW with base‐year 1982. We converted these indices to have a uniform base‐year 2004‐05. 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 Appendix Table B.2 for details on the CPI calculation for the smaller states).

<!-- Start of picture text --> 0.120<br>0.080<br>0.040<br>0.000<br>Rural Urban<br>1993‐04 2004‐12<br>Uneducated young male Educated young male Uneducated young female Educated young female Uneducated old male Educated old male Uneducated old female Educated old female Rurall average Uneducated young male Educated young male Uneducated young female Educated young female Uneducated old male Educated old male Uneducated old female Educated old female Urban average<br><!-- End of picture text -->

**<u>Figure 4: Annual average real wage changes by demographic group</u>**

6
Mean _annualized_ changes in log real wages by group are shown in Figure 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 (Figure 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.

<!-- Start of picture text --> 0.06<br>0.04 0.06<br>0.07<br>0.07<br>0.06 0.09<br>0.07<br>0.04 0.04 0.05 0.06<br>0.04 0.06<br>0.06 0.06 0.06 0.05<br>0.02 0.03 0.02 0.02 0.02 0.03 0.04 0.02 0.04 0.02<br>0.00 0.01<br><!-- End of picture text -->

**Figure** **<u>5: Annual average real wage changes for unskilled labor by state</u>**

# **C. Changes in relative wages within and across sectors**

For ease of presentation under the first major column heading of Table 1, we aggregate mean relative wage changes across pairs of demographic groups using the respective (base‐year) shares of wage‐earners as weights. Thus, for example, the change in the wage for rural educated males relative to rural uneducated males is computed as a weighted average of the corresponding mean wage changes for old and young rural males in each of these educational categories.

7
**Table 1:  SDI decomposition of relative wage changes** **_within_ rural and urban India**

|||R|elative wage|||Supply|||Demand|||Institution||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||1993-94 to<br>2004-05<br> <br>|2004-05 to<br>2011-12|1993-94 to<br>2011-12|<br>1993-94 to<br>2004-05<br> <br>|2004-05 to<br>2011-12|1993-94 to<br>2011-12|1993-94 to<br>2004-05|2004-05 to<br>2011-12|1993-94 to<br>2011-12|1993-94 to<br>2004-05|2004-05 to<br>2011-12|1993-94 to<br>2011-12|
|Educated/uneducated|Male|-0.09|-0.24|-0.33|0.50|0.41|0.91|-0.29|-0.37|-0.64|-0.08|0.03|-0.01|
||Female|-0.10|-0.31|-0.41|0.82|0.70|1.51|-0.15|-0.03|-0.27|-0.07|-0.08|-0.09|
|Old/Young|Male|0.10|-0.06|0.04|0.11|0.30|0.41|0.25|0.11|0.38|0.00|-0.07|-0.07|
||Female|0.05|-0.03|0.02|0.37|0.38|0.75|0.07|0.00|0.09|-0.06|0.04|-0.02|
|Male/Female||0.13|-0.15|-0.01|-0.06|0.34|0.28|0.28|0.31|0.47|0.41|-0.13|0.11|
|Educated/uneducated|Male|0.11|-0.13|-0.03|0.07|0.32|0.38|-0.06|-0.11|-0.17|-0.22|0.01|-0.15|
||Female|0.04|-0.17|-0.13|-0.03|0.43|0.40|-0.01|-0.02|-0.04|-0.23|-0.05|-0.19|
|Old/Young|Male|0.10|-0.16|-0.06|0.07|0.24|0.32|0.00|-0.05|-0.05|-0.14|-0.08|-0.19|
||Female|0.01|-0.16|-0.15|0.13|0.12|0.25|0.00|-0.01|0.00|-0.13|-0.02|-0.12|
|Male/Female||0.03|-0.14|-0.11|-0.17|0.15|-0.02|0.02|0.02|0.02|0.25|-0.07|0.05|
|Urban/Rural||-0.27|-0.09|-0.36|0.28|0.20|0.48|-0.35|-0.24|-0.60|-0.08|-0.12|-0.17|

The first two rows of Table 1 (denominated in log changes) 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. Much of these relative gains occurred in the most recent decade (2004‐11). 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.

**Table 2:  SDI decomposition of relative wage changes** **_between_ rural and urban India**

|||1993-94 to<br>2004-05<br> <br><br>R|2004-05 to<br>2011-12<br>elative wage|1993-94 to<br>2011-12<br>|1993-94 to<br>2004-05<br> <br>|2004-05 to<br>2011-12<br>Supply|1993-94 to<br>2011-12|1993-94 to<br>2004-05|2004-05 to<br>2011-12<br>Demand|1993-94 to<br>2011-12|1993-94 to<br>2004-05|2004-05 to<br>2011-12<br>Institution|1993-94 to<br>2011-12|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Urban / RuraL Educated|Male|-0.17|0.01|-0.16|-0.38|-0.20|-0.57|‐0.22|‐0.11|‐0.30|-0.21|-0.12|-0.26|
||Female|-0.07|-0.02|-0.09|-0.73|-0.25|-0.99|‐0.03|‐0.02|‐0.05|-0.08|-0.12|-0.18|
|Urban/ Rural uneducated|Male|-0.37|-0.10|-0.47|0.05|-0.10|-0.05|‐0.45|‐0.37|‐0.77|-0.07|-0.10|-0.13|
||Female|-0.21|-0.16|-0.37|0.12|0.01|0.13|‐0.17|‐0.04|‐0.28|0.08|-0.15|-0.08|
|Urban /Rural old|Male|-0.29|-0.09|-0.38|0.06|0.01|0.07|‐0.52|‐0.37|‐0.89|-0.19|-0.10|-0.22|
||Female|-0.16|-0.12|-0.29|0.05|0.14|0.19|‐0.19|‐0.04|‐0.32|0.00|-0.17|-0.16|
|Urban / Rural Young|Male|-0.28|0.00|-0.28|0.10|0.07|0.16|‐0.27|‐0.22|‐0.46|-0.06|-0.09|-0.11|
||Female|-0.12|0.00|-0.12|0.29|0.40|0.69|‐0.13|‐0.03|‐0.23|0.06|-0.12|-0.06|
|Urban/Rural|Male|-0.35|-0.08|-0.44|0.09|0.00|0.09|‐0.43|‐0.32|‐0.73|-0.15|-0.10|-0.19|
||Female|-0.25|-0.09|-0.34|0.20|0.19|0.39|‐0.17|‐0.04|‐0.28|0.01|-0.16|-0.13|
|Urban/Rural||-0.27|-0.09|-0.36|0.28|0.20|0.48|‐0.35|‐0.24|-0.60|-0.08|-0.12|-0.17|

Looking across the urban‐rural divide in Table 2, the striking pattern is wage convergence, albeit skewed toward the unskilled. Overall, wages for uneducated males rose by around 47% relative to their urban counterparts; the corresponding figure for uneducated females is 37%. However, much of these gains occurred in the earlier decade of the post‐reform era, especially for males. Similar, but substantially smaller, relative gains were experienced by educated rural workers. Aggregating across groups, rural wages rose a modest 9% relative to urban wages over the last decade, following a 27% increase in the first decade.

8
where Wit and S;, t-l

> log ~~(M2)Wat)~~ _1ir= ~~py~~ <u>= [De</u> _— Si, , Dt Alog (“**)Wat) _= ~~1<~~ [AD, _— AS;].

. The parameter o t — Xt —

> Sise = log@#*) ~~Nis~~

where N;,; is the group’s employment in the sector and N,; sector. Shifts in supply, AS;s¢
AD ~~Nikst Nkst~~ istist = YkXie ~~——~~ <u>Nest</u> Alogog( ~~(——~~ <u>Nee)</u> where k indexes industry. So, the first term in the sum is the share of demographic group i in industry k’s employment and the second term is the growth rate in the share of industry k employment in overall sectoral employment. Intuitively, ADjs; demographic groupi (initially) predominates in relatively fast-growing industries. As with supply shifts, ADj<¢ log(Wise) = log(Wise ) + Li Piste Piske where W,¢, is the competitive market wage given groupi skills, p;,,¢ is the industry k wage premium for group i at time t, and @j5x4¢ = Nisxz Nis; is the proportion of group i workers in industry k. Based on p;,;,¢ estimated from wage regressions, the institutions index (A/) for group iis the list = LelAPiskePiske-1 + Piske-vAPiske] that a group with a higher AI;,,
Comparison of group‐shares across NSS rounds, as shown in Figure 6, indicates that uneducated rural males (young and old) are shifting out of agriculture most rapidly.

<!-- Start of picture text --> 1993‐94 2011‐12<br>100% 100%<br>75% 75%<br>50% 50%<br>25%<br>25%<br>0%<br>0%<br>Rural Urban Rural Urban<br>Agri.+ Forestry+Fishing Construction Mining, Manufacturing and Utilities Professional Services<br>Figure 6: Industry share of employment by demographic group<br>Educated Old Male Educated Old Male<br>Educated Old Male Educated Old Male<br>Educated Young Male Uneducated Old Male Educated Old Female Educated Young Male Uneducated Old Male Educated Old Female Educated Young Male Uneducated Old Male Educated Old Female Educated Young Male Uneducated Old Male Educated Old Female<br>Uneducated Young Male Educated Young Female Uneducated Old Female Uneducated Young Male Educated Young Female Uneducated Old Female Uneducated Young Male Educated Young Female Uneducated Old Female Uneducated Young Male Educated Young Female Uneducated Old Female<br>Uneducated Young Female Uneducated Young Female Uneducated Young Female Uneducated Young Female<br><!-- End of picture text -->

# **B. 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, especially for females, spread rather evenly across the two sub‐periods. Meanwhile, relative demand for educated workers fell, especially for males. 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, we see that shifts in relative supply were not a decisive factor behind the wage gains of uneducated workers vis‐a‐vis the educated. There were, however, big drops in relative demand for 11
unskilled male labor in urban areas, with smaller declines in the case of females. The institutions index also moved 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).

# **IV. State‐level SDI Analysis**

We now compute changes in mean log wages, ∆����, ∆����, and ∆���� separately for each major state or group of adjacent states.<sup>5</sup> Our “data set”, therefore, consists of 448 = 2 x 2 x 8 x 14 observations for 2 decadal intervals, 2 sectors (rural/urban), 8 demographic groups, and 14 states. Note that in treating a state as, for all intents and purposes, a distinct labor market, we are assuming that changes in, say, labor supply within a given state are not driven by inter‐state migration. This assumption seems reasonable as a first approximation given India’s historically low mobility <u>(see Hnatkovska and Lahiri, 2013).</u>

**Figure 7:  Bivariate relationship between state/group wage changes and supply shifts**

> 5 State groups consist of Chhattisgarh with Madhya Pradesh (called Madhya Pradesh); Uttaranchal with UP (called Uttar Pradesh); Jharkhand with Bihar (called Bihar); Seven Sisters in the Northeast with Sikkim (called seven sisters); Goa, D & N Havelli, D& Diu with Maharashtra; A&N Island with West Bengal (called West Bengal); Lakshadweep with Kerala (called Kerala) and Pondicherry with Tamil Nadu (called Tamil Nadu).  Finally, Haryana, Punjab, Himachal Pradesh, Delhi, and Chandigarh and combined into Northern states.

12
**Figure** **<u>8:  Bivariate relationship between state/group wage changes and demand shifts.</u>**

Bivariate scatterplots (fig. 7) reveal that increases in supply are strongly associated with wage declines in each period. Increases in demand, by contrast, are associated with wage increases (fig. 8). This is all as it should be, but to properly assess the SDI framework we need to control for both supply and demand shifts simultaneously.

To do so, we run a series of regressions of state mean log‐wage changes on the SDI shift variables. The first such regression, shown in Table 3, uses the full dataset, thus including log‐wage changes between 1993‐2004 and 2004‐2011. Among the independent variables is a dummy for the second decadal change. Results in the first column of Table 3 show that increases in supply lead to lower wages, conditional on the demand shift. Likewise, increases in demand increase wages, conditional on the supply shift. Moreover, we cannot reject the null hypothesis that the coefficient on supply is equal to minus the coefficient on demand; i.e., that only net demand shifts matter for wages (cf., equation (1)).

13
and/or supply shifts on the other. Do AS;,;;, AD;;;, and, for that matter, AJ;;; predetermined. However, to test this proposition, we instrument AS;;, ADj;¢ and Alist their lagged values AS;.,_;, ADis¢_; and Alj<¢_, demand shifts AD,,, — AS;,;. are now included along with AI;,, specifications, the coefficient on the institutions index Al;,;
Alog(Wis) = Bo + BAD ist — ASist) + BoAlise yAlog(Wst) = Alog(W,s¢) _ Alog(W,st) urAlog(W,z) = Alog(Wyyt) _ Alog(W,,+) eduneaAlog(W,-+) eduneaAlog(W,-)
Consider the change in the employment share of construction in rural areas of each of the 14 state‐groups. The top left panel of Figure 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.

<!-- Start of picture text --> Kerala Kerala<br>R-sq=.66<br>Gujarat Gujarat<br>Northern States Northern States<br>Maharashtra Maharashtra<br>RajasthanWest Bengal West Bengal Rajasthan<br>Tamil Nadu OrissaUttar Pradesh Uttar Pradesh Orissa Tamil Nadu<br>Andhra Pradesh Andhra Pradesh<br>Madhya Pradesh Bihar Bihar Madhya Pradesh<br>KarnatakaSeven Sisters Karnataka Seven Sisters R-sq=.34<br>.4 .6 .8 1 .2 .3 .4 .5 .6<br>diff. log rural construction share 68-61 diff. log rural services share 68-61<br>Kerala Kerala<br>Gujarat Gujarat<br>Northern States Northern States<br>Maharashtra Maharashtra<br>RajasthanTamil NaduWest Bengal Orissa Uttar Pradesh R-sq=.02 Tamil NaduRajasthanWest Bengal OrissaUttar Pradesh R-sq=.04<br>Andhra Pradesh Andhra Pradesh<br>Madhya Pradesh Bihar Madhya PradeshBihar<br>Seven Sisters Karnataka Seven Sisters Karnataka<br>-.3 -.2 -.1 0 -.3 -.2 -.1 0<br>diff. share NREG job-card ed/uned 68 diff. share NREG worker ed/uned 68<br>diff. log rel. wage ed/uned 68-61 Fitted values<br>Figure 9: Drivers of changes in educated vs. uneducated wages within rural India<br>-.15 -.15<br>-.2 -.2<br>-.25 -.25<br>-.3 -.3<br>-.35 -.35<br>-.15 -.15<br>-.2 -.2<br>-.25 -.25<br>-.3 -.3<br>-.35 -.35<br><!-- End of picture text --> It is interesting to contrast the labor market impacts of the above compositional shifts to those of NREG (National Rural Employment Guarantee). Phase‐in of NREG began at around the mid‐point of our 2004‐2011 window. Analyses of NSS data preceding the 68<sup>th</sup> (2011‐12) round provide mixed evidence as to the rural wage impacts of NREG expansion (see Azam, 2012; Zimmerman, 2013; 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 16
# ~~<u><mark>ine</mark></u>~~
mfAlog(W,) <!-- Start of picture text -->
| | | | |<br><!-- End of picture text -->
# **B. Urban vs. rural India**

In the remainder of our analysis, we contrast urban and rural wage changes for _unskilled_ labor. In particular, we use equation (9) to compute ∆��∆������ ��� separately for uneducated males (figure 15) and for uneducated females (figure 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 B.1 in the appendix).

<!-- Start of picture text --> Tamil Nadu Tamil Nadu<br>Andhra Pradesh Andhra Pradesh<br>Kerala Gujarat Gujarat Kerala<br>Rajasthan Rajasthan<br>Maharashtra Maharashtra<br>Orissa Northern States Orissa Northern States<br>R-sq=.03<br>Bihar Bihar<br>Seven Sisters Seven Sisters<br>R-sq=.34<br>West Bengal West Bengal<br>Karnataka Uttar Pradesh Madhya Pradesh Madhya Pradesh Uttar Pradesh Karnataka<br>-1 -.8 -.6 -.4 -.2 -.4 -.3 -.2 -.1<br>diff. log rel. urb/rur construction share 68-61 diff. log rel. urb/rur services share 68-61<br>Tamil Nadu Tamil Nadu<br>Andhra Pradesh Andhra Pradesh<br>Kerala Gujarat Gujarat Kerala<br>Rajasthan Rajasthan<br>Northern StatesMaharashtraOrissa MaharashtraOrissaNorthern States R-sq=.13<br>Bihar Bihar<br>Seven Sisters Seven Sisters<br>R-sq=.04<br>West Bengal West Bengal<br>Madhya Pradesh<br>Karnataka Uttar PradeshMadhya Pradesh Utt a r Pradesh Karnataka<br>0 .1 .2 .3 .4 0 .2 .4 .6<br>diff. log rel. urb/rur female LFP 68-61 Inter-censal urban pop. share growth<br>DD log-wage uneducated male urb/rural-68/61 Fitted values<br>-.04 -.04<br>-.06 -.06<br>-.08 -.08<br>-.1 -.1<br>-.12 -.12<br>-.04 -.04<br>-.06 -.06<br>-.08 -.08<br>-.1 -.1<br>-.12 -.12<br><!-- End of picture text -->

**Figure 15:  Drivers** **<u>of changes in urban vs. rural wages for males.</u>**

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<!-- Start of picture text --> Kerala Kerala<br>Karnataka Karnataka<br>Tamil Nadu Tamil Nadu<br>Rajasthan Northern StatesAndhra Pradesh Andhra Pradesh Rajasthan Northern States<br>R-sq=.05 West Bengal R-sq=.04 West Bengal<br>Orissa Orissa<br>Uttar Pradesh Maharashtra Uttar Pradesh Maharashtra<br>Madhya Pradesh Gujarat Gujarat<br>Madhya Pradesh<br>Seven Sisters Seven Sisters<br>Bihar Bihar<br>-1 -.8 -.6 -.4 -.2 -.4 -.3 -.2 -.1<br>diff. log rel. urb/rur construction share 68-61 diff. log rel. urb/rur services share 68-61<br>Kerala Kerala<br>Karnataka Karnataka<br>Andhra Pradesh Tamil Nadu Tamil Nadu<br>Rajasthan Northern States RajasthanNorthern StatesAndhra Pradesh R-sq=.41<br>West Bengal West Bengal<br>Orissa Orissa<br>Maharashtra Uttar Pradesh Maharashtra Uttar Pradesh<br>Madhya Pradesh Gujarat Madhya Pradesh Gujarat<br>Seven Sisters R-sq=.31 Seven Sisters<br>Bihar Bihar<br>0 .1 .2 .3 .4 0 .2 .4 .6<br>diff. log rel. urb/rur female LFP 68-61 Inter-censal urban pop. share growth<br>DD log-wage uneducated fem. urb/rural-68/61 Fitted values<br>Figure 16: Drivers of changes in urban vs. rural wages for females.<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br><!-- End of picture text --> For males, the construction sector stands out as the key _relative_ wage driver, with higher construction growth strongly associated with higher wage growth (R<sup>2</sup> =0.34), whereas for females the corresponding association is actually negative, albeit weak (R<sup>2</sup> =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 (R<sup>2</sup> =0.31), a pattern essentially absent with respect to male wages (R<sup>2</sup> =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 figures 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.

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urAP4 = (Bi ~ Be) dic ScAlog Dwhere pA is the initial (i.e., 2004-05) share of labor in agriculture for a state in sector (j = U,T), Sc 2003-04, and Alog p, that the agricultural labor share differential 64 — BA Referring to equations (4) and (7), one can see that Bf — BA urAlog(W,t) mechanical correlation with B/ — BA urAlog(W,,) urAP4 in regressions on 64 — BA
been attenuated and, as a result, the demand for unskilled labor has not fallen as much due to structural transformation.

<!-- Start of picture text --> Andhra Pradesh<br>Tamil Nadu<br>Gujarat Rajasthan partial R-sq=.62<br>Maharashtra<br>Kerala<br>Karnataka<br>Orissa<br>Northern States<br>Seven Sisters<br>Madhya Pradesh<br>West Bengal<br>Bihar<br>Uttar Pradesh<br>-.05 0 .05<br>diff. log price index change urb/rural<br>diff. log rel. wage urb/rural uneducated 68-61 Fitted values<br>.05<br>0<br>-.05<br><!-- End of picture text -->

**<u>Figure 17: Urban vs. rural wage changes and agricultural prices</u>**

# **VI. Conclusions**

Real wages have risen across India in the past two decades, but the increase has been greater in rural areas and, especially, for unskilled workers. Broadly speaking, the changing wage structure _within_ rural areas has been driven largely by relative supply factors, such as increased overall education levels and falling female LFP, whereas the changing wage structure _between_ rural and urban areas has been driven largely by shifts in employment, notably into unskilled‐intensive sectors like construction. Notwithstanding the rural construction boom, the recent expansion of the national public‐works program (NREG) throughout rural India does not appear to be associated with shifts in the structure of wages (i.e., to the advantage of the unskilled) over the last decade. Finally, while structural transformation—the gradual movement of labor out of agriculture—has been the dominant trend of the last two decades in rural India, our evidence suggests that the recent upturn in agriculture’s terms of trade may have muted the commensurate decline in demand for unskilled rural labor, contributing to growth in wages for the rural unskilled relative to their urban counterparts.

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