Technical Change and Gender Wage Inequality:
Long Run Effects of India’s Green Revolution∗
Preliminary - Not for Citation or Circulation
September 2016
Anthony Louis D’Agostino†
This paper estimates the effect of India’s Green Revolution on the gender wage gap
in agricultural labor markets. Using newly-digitized data on groundwater availability,
I exploit the importance of reliable irrigation in adopting high-yielding variety (HYV)
seeds, to generate plausibly exogenous variation in long-run productivity growth. With
a 30-year district panel, I first demonstrate that groundwater-abundant locations had
higher HYV adoption rates, crop revenues, and produced more crop cycles per year.
Second, I estimate the impact on casual laborers’ wages and find that treated district
male laborers, but not female, received a significant wage increase, widening the wage
gap. Third, using both census and household-level data, I find that women exited agricultural wage labor in favor of own-farm work. This result is consistent with both an
income effect from higher earnings for both cultivators and male laborers, and the expansion of farm mechanization which may have generated gender-biased labor effects.
Whereas earlier work has framed technical change as shrinking the gender wage gap,
this paper offers a counterexample of exacerbating it.
JEL Codes: J16, J43, O12, Q16, Q25
Keywords: Gender gap, technical change, agricultural labor, Green Revolution
∗
I thank Supreet Kaur, Wolfram Schlenker, and Kiki Pop-Eleches for their advice and encouragement. I
thank Emily Breza, Ram Fishman, Eyal Frank, Suresh Naidu, and seminar participants at Columbia University and the Interdisciplinary PhD Workshop in Sustainable Development for helpful comments. Shruthi
Jayaram provided outstanding research assistance. I thank Andrew Foster, Yoshifumi Usami, and ICRISAT
for data assistance. This research was funded in part by grants from The Endeavor Foundation and the
Columbia University Center for Development Economics and Policy. All errors are mine.
†
School of International and Public Affairs, Columbia University. 420 West 118th Street, New York, NY
10027. [email protected]. http://www.columbia.edu/∼ald2187
1
1
Introduction
The gender wage gap is a feature of nearly every labor market in the world and the subject
of numerous decomposition analyses to identify its underlying causes.1 Significant legal
and institutional resources have been directed to narrowing this gap, though economic
development itself may be the most effective catalyst for pay equality; in the cross-section,
the wage gap is inversely related to per capita income, partially reflecting greater human
capital opportunities available to women in developed economies.2 Consider technologylimited agrarian societies in which a worker’s physical characteristics are a determinant
of her/his marginal productivity, advantaging male workers.3 As economies diversify and
labor-saving technologies displace manual work, this relationship weakens.4 That technical
change and service sector growth narrows the wage gap has been empirically demonstrated,
but has largely drawn on instances in developed country labor markets.5 Mobility frictions,
constraints in human capital investment, the influence of social norms, and weaker state
capacity in pay equality regulation and enforcement may generate dissimilar results in a
developing country context, especially one in which ‘brawn’ remains a determinant of labor
productivity.
In this paper I estimate the effect of technical change on the gender wage gap in India
using a combination of 30 years of district-level data, decadal population censuses, a nationally representative household panel, and repeated cross-sections of the National Sample
Survey’s Employment Unemployment survey. I use the introduction of high-yielding variety
seeds (HYVs) in 1966 as an exogenous technology shock to agricultural productivity which
1
See Goldin (1990); Blau and Kahn (1992, 2000); Goldin (2014) for overviews of trends and developments in gender wage gaps, particularly in the United States. Blinder (1973); Oaxaca (1973) pioneered the
decomposition approach, while O’Neill and Polachek (1993); Oaxaca and Ransom (1994); Appleton et al.
(1999); Brown and Corcoran (1997) offer more recent empirical applications.
2
See Duflo (2012) for a summary of evidence on the ‘empowerment-development’ view.
3
Pitt et al. (2012)
4
Goldin (1990) offers this as an explanation for the improvement in women’s relative earnings over the
19th and 20th century in the United States, while Galor and Weil (1996) develop a growth model featuring
positive complementarities between female labor and capital.
5
Black and Spitz-Oener (2010); Tick and Oaxaca (2010)
2
in turn generated an immediate, positive labor demand shock through two key channels.6
First, higher yields created more work to be done, especially in the post-harvest season.7
Second, the faster-maturing new seed varieties enabled farmers to harvest multiple crop
cycles per year, whereas traditional varieties often limited them to one. By focusing on the
wages of only agricultural laborers, empirical attention is restricted to tasks performed by
both sexes and limits the impact of occupational composition on observed effects.
In conjunction with more intensive usage of fertilizer, pesticide, and irrigation, HYVs
were the central component of the Green Revolution ‘technology package.’ HYV adoption
in itself could not guarantee higher yields, as their water-sensitivity meant that excessive or
inadequate irrigation would generally depress yields (Foster and Rosenzweig, 1995, 2010).
Consequently, farms located above groundwater aquifers who could privately supply irrigation without reliance on surface water supplies or rainfall therefore possessed a relative
advantage in utilizing Green Revolution practices. With this relationship in mind, I exploit
the quasi-random assignment of groundwater resources to generate plausibly exogenous
cross-sectional variation using newly-digitized data from the country’s first hydrological resource assessment. I validate the constructed groundwater measure’s suitability as a Green
Revolution proxy and find in a difference-in-differences framework that from 1966-1987
groundwater-rich districts persistently held a 21 percentage point lead in HYV adoption,
≥20% higher growth in unit area revenues, increasingly specialized in HYV-eligible crops,
and significantly increased their usage of both irrigation and phosphorous-based fertilizers. Results are robust to the inclusion of state×year fixed effects, and dropping the most
successful Green Revolution states of Punjab, Haryana, and Uttar Pradesh. These results
validate the groundwater proxy variable and indicate the measure’s potential value as a
source of exogenous variation in future analyses of the Green Revolution.
6
While subsequent variety breeding efforts were carried out domestically, the initial HYV seeds were
developed in Mexico and the Philippines, making the HYVs a suitable target for studying exogenous technical
change (Foster and Rosenzweig, 1996).
7
(Foster and Rosenzweig, 2010)
3
I next estimate the effect of agricultural productivity gains on agricultural laborers’
daily wages with newly-digitized historical data compiled in the Agricultural Wages of India.
Using a triple difference strategy, I estimate that men in groundwater-rich districts received
an average 9-15% larger wage premium than women for 1966 through 1987. Subsetting
to observations for which task-specific wages are available for both men and women for
a given district-month confirms this point estimate and rules out differential sorting into
higher- and lower-productivity tasks, or employment in better-paid and worse-paid months,
as driving the wage gap increase. Next, I draw on time-use records from employment
surveys conducted in 1983 and 2004 to test whether sex-biased mechanization of agricultural
processes may explain wage dynamics, and find that whichever sex provided the dominant
share of labor in the first survey extended their lead in the second.
To test mechanisms derived from a basic conceptual model of a village economy
production function, I rely on multiple, individual-level datasets with details on household
structure, labor supply, and occupational choice. I find that men and women in Green
Revolution areas residing in households with at least one male household member engaged
in respond asymetrically to the productivity shock; between 1970 and 1982, males are more
likely to become agricultural laborers, and women less likely. This corroborates districtlevel evidence from the decadal population census showing a relatively stronger exit from
paid agricultural work for women in groundwater-rich districts, and an increased likelihood
of identifying both as a cultivator and a part-time worker.8 Lastly, I provide suggestive evidence of an income effect driving down female labor force participation (FLFP), in higher
expenditure households. Though this may be indicative of privately-motivated leisure-labor
tradeoffs, it is also consistent with a ‘female seclusion’ norm documented in ethnographic
research (Dyson and Moore, 1983; Boserup, 1989), which is especially dominant in northern states. Under this norm, female engagement with non-relative males is costly to the
8
The Census classifies workers engaged in work for fewer than 183 days as a ‘marginal worker’, which
for interpretive simplicity I refer to as a ‘part-time’ worker.
4
household (and potentially to employers), which restricts female LFPR when household
subsistence can be maintained without her wages. Their withdrawal from wage labor is also
consistent with the downward-sloping portion of the U-shaped FLFP curve documented
in Goldin (1990) and Mammen and Paxson (2000).9 I find supporting evidence in survey
responses that women who primarily engaged in domestic activities alone claimed their lack
of working was not due to inability to find work, but from social and/or religious norms
that discouraged it.
These channels alone cannot explain increased wage inequality in Green Revolution
locations. Holding the male-female elasticity of substitution fixed over time, in the absence
of a negative female labor demand shock, as women leave the laborer workforce then female
wages should rise. I instead find the opposite. However, my conceptual model predicts that
technology-enabled labor productivity increases for male workers will widen the wage ratio. Accordingly, I offer suggestive evidence that groundwater-rich locations increased their
investment in tractors and tractor-powered farm equipment. As per various ethnographic
studies on gender and agriculture in India, social customs govern that men are more likely to
operate such equipment. Since usage of such equipment would plausibly require less ‘brawn’
than usage of hand-operated tools, women could have tests for composition effects of the
labor pool show limited support for differences in observables that results which though
it remains unclear why such a technology could not also be used by women, especially if
women already command lower wages. That I find heightened wage inequality holds even
within tasks performed by both men and women, rules out a gendered division of labor
which prohibits women from performing tasks involving highly productive equipment (e.g.,
tractors) as a standalone explanation. I therefore posit that even if the Green Revolution
amplified labor-capital complementarities, the persistence of male-biased wage gains further
requires either some unobservable productivity differences between the sexes, which is not
9
While these authors explicitly refer to structural transformation and male-biased industrial expansion,
a similar logic applies here with the rationalization of efficient agricultural practices.
5
evident in the respondent characteristics data available to the econometrician, and/or a
social norm reinforcing gender specialization in the use of productive capital.
This paper offers one of the few empirical treatments of a long-run productivity
shock on a developing country’s gender wage structure. Whereas a substantial body of
work has leveraged temporary labor demand shocks in such contexts, evaluating more persistent shocks in the vein of developed country analyses like Black and Spitz-Oener (2010)
have likely been hampered due to data scarcity issues and the limited instances of nonendogenous technology adoption which can generate sufficient variation to be empirically
useful
10
This is fundamental given that technical change in an advanced economy often
severs the relationship linking worker characteristics to productivity, and therefore settings
for which this relationship still holds may offer contrary guidance on the inequality effects
of technology. This paper also contributes to work exploiting India’s Green Revolution to
study the diffusion of new technologies (Munshi, 2004; Foster and Rosenzweig, 2010), human capital investment (Rosenzweig, 1990; Foster and Rosenzweig, 1996), and effects on
wages and labor supply (Bardhan, 1970; Rosenzweig, 1978).
2
Overview of Indian Agriculture and the Green Revolution
The empirical setting for this paper is rural India from the 1950s through the 1980s. In
1961, the rural population share exceeded 80% and a similar fraction of the rural workforce
engaged in some combination of cultivation and agricultural labor. The primary sector
contributed 42% of GDP (1960/61) and suffered from low productivity and nominal productivity growth (Parikh and Binswanger-Mkhize, 2012).11 Rice and wheat are the two
most dominant crops, the former grown across the country throughout the year, while
10
See especially Rose (2001); Jayachandran (2006); Kaur (2014) as examples addressing Indian labor
markets in timeframes that partially overlap this paper’s.
11
Gollin et al. (2002) hypothesize farm productivity and agricultural labor force share are inversely
related, which applies appropriately to the Indian context vis-a-vis other countries at a comparable level of
development.
6
wheat planting occurs between October and December and is harvested April through May
(Krishna Kumar et al., 2004).12 These crops alone account for 61% of the 122 million
hectares under foodgrain production in 2014/15.
Local, rural labor markets across the country match employers, typically landowners
with substantial farm holdings, with laborers hired-in to perform a range of agricultural
and non-agricultural tasks that vary by location and season.13 Laborers represent a large
share of the country’s workforce: the 1961 Census counts 31 million agricultural laborers
among a total rural population of 211 million. The agricultural labor pool largely consists
of smallholders and landless laborers, the former potentially complementing own-account
labor with hired-in workers. Key agricultural tasks include sowing, transplanting, weeding,
reaping, harvesting, and threshing. For each task, men and women often receive different
wages, each which fluctuate throughout the year. Crop choice is a key driver of task
composition, with wetland agriculture, for example, imposing greater labor demands on
weeding and irrigation than dryland.
Unlike in many other countries, Indian women actively participate in these agricultural wage markets. There is substantial geographic variation in participation rates, but
throughout is higher from lower-caste/scheduled tribe, small-holder, and landless households, as well as from those with fewer males (Boserup, 1970; Bardhan, 1979) either because
of male out-migration or the death of the male earner. Women working in agricultural wage
markets represented 25% of employed rural women in 1961, compared to 16% for rural men
who were more likely to be engaged in non-agricultural work than women (Bardhan, 1977).
For the country, 18% of adult rural women were counted as main or marginal agricultural
laborers in 1961, rising to 24% by 1981. The female share of main and marginal agricultural
laborers was constant at 45% for 1961 and 1981, and 38% if using the more conservative
12
Also see the Crop Calendar of Major Crops published by the Indian Council of Agricultural Research.
Bardhan (1983) examines labor arrangements in West Bengal whereby laborers are tied to landlords
under long-term contracts. Using National Sample Survey data from 1983, I find ‘attached’ workers comprise
fewer than 0.2% of all surveyed workers.
13
7
main worker definition. Indian agriculture is marked by a strong sexual division of labor.
Women are primarily responsible for the ‘control-intensive’ tasks of transplanting, weeding, and threshing (Paris, 1998). Ploughing is performed almost exclusively by men, while
harvesting is shared and exhibits strong regional variation in wage ratios.
2.1
Technical Change in Indian Agriculture: The Green Revolution
The 1966 introduction of high-yielding variety crops (HYVs), also known as modern varieties, marked the Green Revolution’s commercial arrival to India.14 Initially available for
rice, wheat, maize, and sorghum, the HYVs were the product of international research efforts
to increase crop productivity and strengthen food security in developing countries. Since
research conducted for the dissemination of the 1966 varieties was undertaken by overseas
agricultural research centers, particularly in Mexico and the Philippines, the HYVs are a
near-ideal episode of exogenous technical change (Foster and Rosenzweig, 1996). After their
release, domestic research programs continued improvements to the stock of new varieties
by further selecting for traits enhancing the crops’ agroecological and dietary suitability.
The availability of HYVs impacted food production in three key ways. First, the
newly-available dwarf varieties selected for shorter crop stalks that could support more
grain without breaking or falling over (lodging) (Evenson and Gollin, 2003), a major limitation facing traditional varieties. Second, the new varieties were faster maturing, often
cutting more than 40 days from the period of planting to harvest. Farmers became able
to grow two and potentially three crop cycles a year instead of the historic single cycle
(Pingali, 2012). This contributed to crop intensification practices that supplanted traditional crop diversification techniques.15 Third, the new varieties were responsive to higher
fertilizer concentrations. Through extension efforts, farmers were advised on appropriate
14
While adoption of limited quantities of HYVs began in the 1960s, the first bulk federal seed purchase
was in 1966 (Chakravarti, 1973).
15
Pinstrup-Andersen and Hazell (1985) notes how this narrowed the genetic diversity of seed varieties.
8
fertilizer application levels to maximize crop productivity, which were significantly higher
than levels used with traditional varieties. These effects collectively increased farm output
and labor productivity, spurring laborer wage gains and even creating labor shortages in
some locations.(Cleaver, 1972)
HYV adoption was rapid, particularly for wheat, as seen in Figure 1. In the first year
of their availability, 90% of the districts in this paper’s sample were planting some amount
of HYVs, rising to 98% by 1970. Within a decade, HYV wheat comprised more than
70% of all planted wheat area. Rice HYV adoption was substantially slower, requiring two
decades before achieving 50% coverage. This disparity was largely due to the immediate
yield gains wheat HYV adopters experienced, in part from potential similarities in the
agroecological systems of wheat-growing locations in India with Sonora, Mexico where the
wheat HYVs were bred. As well, the first generation of rice HYVs suffered a host of
setbacks. They were particularly vulnerable to disease and pests, and several varieties
possessed undesirable cooking properties a ‘chalky’ taste (Swaminathan, 1969; Chakravarti,
1973). In the 1980s, the decade of highest overall growth in output to agricultural worker
(Parikh and Binswanger-Mkhize, 2012), new rice varieties superior to those available at the
Green Revolution’s start were released, marking a second phase that triggered yield growth
rates exceeding those in the 1960s and 1970s.
Irrigation and fertilizer access constraints were often formidable impediments to successful adoption. Farmers who did not adhere to HYV cultivation guidelines often met
with yields below those achieved with traditional varieties, all the while incurring higher
input costs. This was especially the case with fertilizer, as farmers would apply levels below the recommended guidelines to little effect, because of access constraints or imperfect
knowledge (Byerlee, 1992). Access to controlled irrigation was essential for varieties with
higher fertilizer requirements and to take advantage of faster maturation periods (Sengupta
and Ghosh, 1968). Consequently, the Green Revolution prompted a wave of tubewell installations and pumpset purchases, as seen in Figure A1. The possession of groundwater
9
resources allowed farmers to utilize irrigation at their own discretion, sidestepping problems
of unreliable supply or timing plaguing users of tank and canal irrigation, especially those
at the latter end of the distribution chain. Outside the main kharif season, farmers could
not depend on the availability of canal irrigation (Sengupta and Ghosh, 1968).
3
Theoretical Model
In this section I develop a basic framework in the spirit of Krusell et al. (2000) and Acemoglu
and Autor (2011) to model the effect of a technology shock on the gender wage ratio. First
consider a closed, perfectly competitive, village economy consisting of I labor-supplying
households and J labor-demanding farms. Each farm’s output can be modeled as a CobbDouglas production function using Kj capital, Lj aggregate labor, Zj land, and a firmspecific total factor productivity term Aj to produce total output Qj . Capital and labor
are adjustable in the short-run, while land adjusts only in the long-run. Lj is a constant
elasticity of substitution aggregator of male and female inputs with some specified elasticity
of substitution, σ =
1
1−ρ ,
captures the percent change in demand for male (female) labor
from a 1% increase in female (male) wages. Sex-specific and time-varying productivity
parameters φm and φf sum to unity and capture both underlying gendered productivity
differences (e.g., brawn versus brain) and input-complementarities. All factors receive their
marginal products.
First assume that production of Qj involves a single task and that male and female
labor are separately homogeneous. Firms solve the following problem, dropping j subscripts
for notational ease,
γ
max π = AK α Z β (φf Lρf + φm Lρm ) ρ − wf Lf − wm Lm − rK
K,Lm ,Lf
10
(1)
Equilibrium wages are
∗
wm
=
γφf Lρ−1
γφm Lρ−1
m Q
f Q
, wf∗ =
L
L
(2)
and taking the log of their ratio produces,
ln
∗
Lf
φm
wm
= ln
+ (1 − ρ)ln
∗
wf
1 − φm
Lm
(3)
Holding constant the elasticity of substitution between male and female labor, changes in the
gender wage ratio are due to relative changes in the sex-specific technology φi parameters,
or the ratio of total labor. Adding female worker-hours applies upward pressure to the wage
ratio given output as concave in all factors. Since ρ < 1, the second term is positive only
when Lf > Lm . Wage equality obtains when φi = 0.5 and Lf = Lm , or when female labor
supply is scarce enough to offset male-biased productivity growth.
Now consider I price-taking households with labor supply decisions responsive to
prevailing male and female wages and no access to banking or borrowing facilities. Each
household consists of one m male and one f female, each possessing 1 unit of time divided
into labor, Lm , Lf , and leisure, lm , lf . Utility of the consumption good c is quasi-concave
and continuously differentiable. The econometrician does not observe the distribution of c
between household members, and marginal utility of consumption is assumed equal between
them. Assume unitary household decision-making over joint consumption and leisure, but
for which disutility of labor, D(L), may differ by sex. Households solve the following problem
11
with c the subsistence consumption minimum, omitting i subscripts for notational ease,
max u(c, lm , lf ) = ln(c − c) − Dm (Lm ) − Df (Lf )
c,lm ,lf
c ≤ wm Lm + wf Lf + r
s.t.
(4)
0 ≤ Lm ≤ 1
0 ≤ Lf ≤ 1
Equilibrium obtains when all markets clear, such that
J
X
j
LD
jm =
I
X
LSim ,
i
J
X
J
X
LD
jf =
I
X
j
I
X
(5)
i
j
Qj =
LSif
ci
i
Household i’s optimal labor supply decision satisfies
0 (L∗ )
Dm
m
0
Df (L∗f )
=
∗
wm
wf∗ ,
equalizing the
ratio of marginal labor disutilities to the equilibrium wage ratio. Assume that Df0 =
0 , µ 1, either because labor is physically more burdensome for females, or beµDm
cause households incur social costs from female labor, as per purdah or seclusion norms.
Consequently, even under wage parity, Lf < Lm .
Proposition 3.1. Only technical change which is factor-biased will impact the wage ratio.
That growth in factor-neutral A raises wage levels, but not the wage ratio, is evident
in Equation 3. Wage growth in the presence of factor-biased technology growth favors the
biased factor. This may arise from underlying ability differences in working with technology.
If men are better able to work with a newly available technology, then this increases the
male wage premium.
Proposition 3.2. Additions to the capital stock affect the wage ratio only through differential effects on the gender-specific technology parameters.
12
Denote Qm as the partial of Q with respect to Lm and Qmk the mixed partial with
respect to Lm and K. Since φi ∝ Qi for i ∈ {m, f }, sex-biased capital complementarity
implies that Qik > Q−ik leading to an increase in the first right-hand side term if malebiased, holding constant the labor ratio. These changes can be caused by ergonomic factors,
such as manual tasks for which worker productivity is differentiated by physical attributes,
or by institutional factors which lead to sex-biased task sorting. For example, if productivity
gains result from a subset of capital more likely to be used by male workers, under sufficiently
inelastic male labor supply the wage gap will widen.
Proposition 3.3. A technology shock impacts relative wages through changes in worker
quality.
While the model assumes labor homogeneity for each sex at a given moment, changes
in ability over time resulting from labor force composition changes will also impact the
wage ratio. Since φi measures relative differences in marginal productivity, a function of
underlying ability and the technology pre-multiplier, a widening in ability differences can
induce an increase in wage inequality.
Proposition 3.4. Male-biased technical change can amplify female withdrawal from labor
participation.
This follows from the rise in male wages and the relative convexity of the female
labor disutility function.
4
Data
This section provides an overview of all datasets used in the analysis. Table 1 reports
summary statistics for key variables. Details on data construction are provided in the
online data appendix.
13
4.1
4.1.1
District-Level Data
Agricultural Laborer Wages
Laborer wages are taken from the Agricultural Wages of India (AWI), an annual volume
compiled by the Ministry of Agriculture’s Directorate of Economics and Statistics. Monthwise wages by gender are reported by agricultural operation, though discretion over data
collection procedures given to states resulted in differences in labor classification.16 Reported wages are the sum of cash and cash-converted in-kind wages.
At the time of writing, no single data source satisfies the data demands for estimating
the effect of the Green Revolution on the gender wage gap, namely operation-specific male
and female wages across the country for years before and after 1966. Existing wage datasets
suffer either from a problem of aggregation without adequate description, limited geographic
coverage, or collection starting after 1966. For example, the first round of the National
Sample Survey Employment Unemployment module which is available to researchers is
from 1983. While the World Bank India Agriculture and Climate dataset offers wage data
starting in 1956, only male wages are recorded and no information is available about which
reporting center or operation a value is drawn from. Usami (2014) constructs a dataset
with monthly reporting from 1973 onwards, selecting one reporting center per district,
which I complement by digitizing records for July and January from the 1958-1971 AWI
volumes.17 I supplement this with additional months when necessary to ensure at least
one operation×month record includes concurrent male and female wages for any districtyear, where possible. This exercise is essential for states like Punjab, Uttar Pradesh, and
16
States were largely consistent in their chosen classification over time, though states like Bihar did
change. In general, Andhra Pradesh, Assam, Gujarat, Himachal Pradesh, Karnataka, Kerala, Maharashtra,
Orissa, and West Bengal reported wages by ‘field labor’ and ‘other labor’ categories. Bihar, Madhya Pradesh,
Punjab, Tamil Nadu, and Uttar Pradesh reported wages by ‘ploughing’, ‘sowing’, ‘weeding’, ‘reaping and
harvesting’, and ‘other labor’. As per the Ministry of Agriculture’s standard pro-forma for data collection,
‘other labor’ includes wages for “coolies employed for watering the fields, load-carriers, coolies, well-diggers,
laborers cleaning silt from water-ways, embankments, etc.”
17
Districts may have selected multiple reporting centers, especially if intra-district wage variation was
perceived as high. In the 1980s, AWI collection procedures converged to a single reporting center per district.
14
Bihar which are less likely to report female wages, consistent with their low female labor
participation rates. AWI data is de facto unbalanced as the composition of reporting centers
changes over time and wage values for a given month of year may be available in year t but
not in t + 1. I run robustness tests to ensure my findings are not due to data collection
artefacts.18 Real wages, where used, are constructed using state-level Consumer Price
Index for Agricultural Laborers (CPIAL) data (Besley and Burgess, 2000). To ensure
consistency with the agricultural data, I apportion data to 1961 boundaries using Kumar
and Somanathan (2015) as a guideline for all post-1961 data. Complete details are available
in the data appendix.
4.1.2
Agricultural, Demographic, and Employment Data
Data on agricultural inputs and outputs come from the World Bank India Agriculture and
Climate dataset balanced panel of 271 districts in 13 states for 1956-1987. To ensure temporal consistency, the data providers apportioned statistics to 1961 district boundaries.
Crop-specific statistics at district×year frequency are available for planted area by traditional and high-yielding varieties, irrigated area, quantity produced, area, and intensity of
inputs including fertilizer, tractors, and bullocks. Area under HYV cultivation is available
for wheat, rice, maize, sorghum, and pearl millet, referred to as ‘HYV-eligible crops’, and
all total HYV measures are a composite of these five crops. World Bank data is merged
with district-level population census data, also fixed at 1961 boundaries, which includes
demographic data and employment totals by broad occupation categories (e.g., cultivation,
agricultural labor, manufacturing, and household industry) (Vanneman and Barnes, 2000).
This dataset covers the decadal censuses for 1961-1991, with a subset of variables only
available through 1981.
18
See Kurosaki and Usami (2016) for a detailed analysis of complications associated with usage of raw
AWI data.
15
4.2
Household/Village-Level Data
To test mechanisms, I incorporate two sources that record earnings and employment specifics
by family member. The first is the National Council of Applied Economic Research’s Rural Economics & Demographic surveys (NCAER ARIS-REDS), a nationally-representative
household panel collected from more than 240 villages across all major states. I use the
1970/71 and 1982/83 rounds.19 In 1970, 52% of working males and 33% of working females
identified agricultural labor as their primary activity, with the respective values being 19%
and 31% for self-employed farming. Non-agriculture wage labor is relatively rare, claimed
by less than 1% of the sample.
I also use the periodic National Sample Survey Organisation’s Employment & Unemployment surveys (Round 38 (1983) and Round 60 (2004)) which capture family memberlevel details on total time worked, wages earned, and agricultural activities performed for
the seven days preceding the survey interview, as well as comprehensive socio-economic
characeristics of both each family member and the household (Minnesota Population Center, 2016).20 Respondents report their labor supply as a fraction of the day for the seven
days preceding the interview, along with the total earnings over that week. The NSS surveys
are repeated cross-sections identifiable down only to the level of district, or region in the
case of the 1983 round. Since the NSS Employment surveys are not merged with any other
district product, I use 1981 district boundaries when constructing the relevant groundwater
measure, and taking the weighted spatial average for constructing region-level values.
4.3
Groundwater Coverage
I digitized the 1969 Geohydrological Map of India, produced by the Geological Survey of
India, to provide data on groundwater resources available before the Green Revolution in19
The subsequent round was conducted in 1999, more than 30 years after the Green Revolutio’s start.
Data from surveys in 1968 and 1969 lack individual-level details.
20
Activity-level data does not become available in REDS until 1999.
16
tensified the adoption of tubewells with long-run impacts on groundwater availability.21 For
district-level analyses, the preferred groundwater variable is the district area share covering
any unconsolidated aquifer, illustrated in Figure 2.22 I use the GPS coordinates of REDS
villages to identify whether they are atop an unconsolidated aquifer, coding groundwater
as a binary variable when used for village analyses.
4.4
Weather Controls
Temperature and rainfall impact agricultural productivity and are therefore controlled in
all of the district panel models. I use India Meteorological Department’s daily temperature
product at 1◦ × 1◦ resolution, which is available for 1951-2013 (Srivastava et al., 2009). I
compute growing degree days using single sine-wave interpolation (Schlenker and Roberts,
2009) and aggregate daily values to season totals. This approach captures nonlinearities in
crop response to extreme temperatures and in various settings has outperformed average
temperature exposure over a time period. I use monthly precipitation from the Climatic
Research Unit (University of East Anglia) TS 3.23 at 0.5◦ × 0.5◦ resolution, available for
1901-present, and sum by season. The respective ranges for kharif and rabi seasons of June
1-September 30 and October 1-February 28 are fixed, constant across space and years, and
consistent with earlier work (Krishna Kumar et al., 2004; Guiteras, 2009; Auffhammer et
al., 2012). District values are computed based on the pixel containing a district’s centroid
using 1961 administrative boundaries. The set of variables included in specifications which
include weather controls are contemporanoeus and one year lagged instances of kharif
rainfall and rainfall2 , rabi rainfall and rainfall2 , kharif growing degree day sums for [10◦ C,
24◦ C) and [32◦ C, +∞), and rabi growing degree day sums for (−∞, 10◦ C), [25◦ C, 31◦ C),
21
Rud (2012) uses similar data from the 1982 National Atlas of India to estimate the effect of electrification
on industrial growth.
22
The geohydrological map includes further classification into thicknesses of ≥ 150 meters, 150 ≥ 100
m., ≤ 100 m., and piedmont zones with wide seasonal fluctuations in water storage. My approach therefore
estimates the average marginal effect across these categories.
17
and [32◦ C, +∞).
5
Empirical Strategy
To estimate the Green Revolution’s effect on the gender wage gap, I exploit the quasirandom placement of groundwater resources, which while of some benefit prior to 1966,
experienced a significant increase in usefulness with the introduction of HYV crops. Here I
describe the difference-in-differences estimation procedure I use on district-level data that
spans before and after 1966, and a similar procedure using household-level data which
leverages data that has only been collected after the Green Revolution’s start.
5.1
District-Level Estimation
The basic district-level specification uses a difference-in-differences (DID) estimator of the
form,
ydt =
X
0
βτ (Groundwaterd % × φt ) + Xdt ξ + γd + φt + dt
(6)
τ ∈[1956,1987]
τ 6=1965
for district d in year t, to estimate effects on labor and agricultural outcomes. The omitted
year is 1965. Models using agricultural wage data further add month and labor operation
fixed effects. The generalized version of this DID model substitutes the year dummy ×
groundwater interactions with an indicator function assuming unity starting in year 1966,
interacted with groundwater coverage. Time-varying controls include the set of weather
variables described in Section 4.4. Since the groundwater measure is static and fixed at
1969 values based on the geohydrological map, Green Revolution effects are estimated off
of cross-sectional variation whose marginal effect on an outcome of interest changes after
1966. Furthermore, adoption of Green Revolution practices encompassed infrastructure in-
18
vestments (e.g., drilling of tubewells and purchase of pumpsets) and more intensive application of other inputs like fertilizers and pesticides. Since these are endogenously determined,
they are bad controls and not included in the model. I instead employ the groundwater
measure as a proxy for Green Revolution intensity, showing in Section 6.1 positive covariance between groundwater access and input intensities of quantities relevant to the Green
Revolution. Therefore while immediate interpretation of β is as the marginal effect of
groundwater access, the more appropriate interpretation is the marginal effect of Green
Revolution intensity.
For efficiency gains, select specifications replace annual time dummies with pooled
intervals and are appropriately described. In district-level specifications, the groundwater
treatment variable is continuous and therefore estimates relative differences across locations
having different endowment levels, while the coefficient interpretation compares districts
with full coverage against those with none. The key identifying assumption underlying
this model are that the parallel trends assumption holds (i.e., the evolution of treated
district outcomes would have been similar to those for untreated districts in the absence of
treatment) such that Cov(, Groundwater × φt ) = 0. I present evidence in Section 6 that
this assumption is satisfied.
An amplified specification adds time-invariant characteristics, Sd , fully interacted
with a vector of year dummies. This allows non-groundwater variables like soil and topographic conditions to have flexible, time-varying effects on outcome variables. This specification is,
ydt =
X
0
0
βτ (Groundwaterd % × Postt ) + Γτ (Sd × φt ) + Xdt ξ
τ ∈[1956,1987]
τ 6=1965
+ γd + φt + f (ρd , φt ) + dt (7)
and allows for flexibly specified (e.g., state-level, region-level) time trends. Time-invariant
19
local characteristics are absorbed by district fixed effects, but trends or more idiosyncratic
movements in wage determinants may drive changes to the outcome variable and so I
perform robustness checks that include district trends, state trends, or state by year fixed
effects to isolate district-varying Green Revolution suitability.
In all district-level models I cluster standard errors by ‘NSS region’, an administrative
unit used in the 1983 NSS survey round that clubs together districts possessing comparable
topographical characteristics within a state. This allows for arbitrary correlation in errors
across all district-year pairs for districts within a region. Clustering on region results in
K = 53 clusters. Districts vary immensely in size, and so I weight by a district’s 1961 gross
cropped area for models with agricultural outcomes as a dependent variable. Estimates
are then interpreted as the marginal effect for a hectare of cropped land. For models
with wage outcomes, I weight by a district’s 1961 agricultural laborer population divided
by the number of district-year wage report observations. This corrects for ‘oversampling’
induced by differences across states in wage collection procedures. West Bengal, for example,
collects wage data from a single reporting site for every year, whereas Gujarat reports
from an average of 6.3 sites. Not correcting for this arbitrary imbalance would result in
heavy-reported states having more influence over parameter estimates for reasons entirely
independent of the data generating process.
5.2
Estimation Procedure for Household/Individual-Level Data
I analyze the REDS and NSS data using the following basic specification,
0
R
yihvt = β R (1(Groundwaterd ) × φt ) + Xihvt ξ R + γvR + φR
t + ihvt
(8)
for individual i from household h in village v for year t, using R subscripts differentiate
parameter estimates from district-level specifications. Models using household-level data
simply drop all i subscripts. Sampling probability weights calculated by NCAER [NSS] are
20
used in all models and standard errors are clustered by village [district/NSS region].23
Cross-sectional models drop the village fixed effects. Pre-Green Revolution householdlevel baseline data is not available since REDS began data collection in 1968. As a result,
the groundwater variable in panel models is interacted with a year dummy instead of a
post dummy. As before, since groundwater is time-invariant, it drops out in panel estimation when not interacted with time. Person- and household-specific controls include age,
religion, caste, education, and household size. Models using NSS data adopt a similar specification, but with standard errors clustered by NSS region (1983) or district (non-1983
rounds). When run as a panel at the village-level, village fixed effects absorb time-invariant
characteristics that affect wage and labor outcomes. Of especial interest is the role that crop
choice plays in determining both total labor intensity, as well as gender-specific allocations,
which is captured in the village fixed effects assuming that crop composition is static.
Some models are restricted to cross-sectional methods since the variables of interest
were not collected in all rounds. Second, the lengthy time gaps between rounds allow for
substantial change in household composition and characteristics, diminishing the likelihood
that a household fixed effect is truly capturing time-invariant household features. Additionally, the sample composition changes from round to round due to attrition, in/out-migration,
and new respondents to replace attrited households. Furthermore, a household-level panel
analysis restricts the sample to households appearing in all included rounds, and presents
potential sample selection bias.
For the remainder of this paper I refer to ‘treated’ locations as those with relatively
more abundant groundwater and is applicable to districts, villages, and households based
on their geographic location.
23
NCAER weights correct for a survey design which oversampled villages participating in Intensive Agriculture Development Program and Intensive Agriculture Area Program schemes, and high-income households.
21
6
6.1
Results
Verifying Suitability of Groundwater as a Green Revolution Proxy
This section features results from tests of the groundwater measure’s suitability as a proxy
for Green Revolution intensity. A reliable proxy must hold predictive power over HYV
adoption, which in turn should provide explanatory power over the yield gains and changes
in cropping decisions associated with the Green Revolution.
Figure 3 plots the β’s from equation (6) in which the HYV share of total cropped
Areacdt | c ∈ {HYV eligible crops}, is regressed on
Total Areacdt
groundwater coverage. Districts with relatively more groundwater had higher HYV adop-
area of HYV-eligible crops,
PC
HYV
PC
tion rates over the whole period, gradually increasing gains from 1966 introduction and
stabilizing in the early 1980s at a relative difference of ≈3.5 percentage points for each 10
percentage point increase in groundwater coverage. These results include state×year fixed
effects to control for state-level policies potentially affecting HYV adoption decisions, such
as minimum support prices and the public distribution system. Results from a model without these fixed effects produce a similar pattern, but with the relative gain stabilizing in
the early 1970s at ≈2.3 percentage points.
Beyond introducing the option to plant HYVs in lieu of traditional varieties, the
Green Revolution had wide-reaching effects on input practices and crop productivity. Groundwaterrich districts benefited relatively more from these changes, given their better access to the
irrigation resources needed for proper HYV utilization. First, the irrigated share of cropped
area increased by 13 percentage points for districts entirely covering groundwater aquifers as
seen in Table 2’s Panel A. Columns 2-4 show crop substitution away from staple crops like
groundnut, barley, and pulses, for which no HYVs were available, towards more intensive
cultivation of rice and wheat, the dominant HYV-eligible crops, which comprised a rising
share of total cropped area. While these results indicate compositional effects, there is little
22
evidence that the Green Revolution provoked expansion of land under cultivation, as seen in
column 5 which measures the maximum area cropped in a given season. This suggests the
Green Revolution did not have an effect on labor demand through the channels of manual
labor associated with land preparation and irrigation construction for new land brought
into production. On the contrary, the point estimate suggests relatively slower agricultural
expansion. Instead, farmers were able to capitalize on the faster-maturing characteristic of
HYVs to achieve multiple harvests in a year, shown in column 6 as a 6.7 percentage point
increase. Lastly, phosphorous fertilizer application registered a significant jump, at 59% for
full groundwater districts, in line with HYVs’ fertilizer-responsiveness.
Treated areas experienced sizable gains in unit area revenue, seen in Panel B.24
Wheat and rice yields were respectively 20% and 16% higher for treated districts after
1966, contributing the majority of the observed 21% unit area revenue increase, computed
using crop-weighted, nominal average prices for 1960-1965 and gross cropped area. This
is noteworthy since production increases in treated districts drove down local farm harvest prices, which would penalize revenue effects. More traditional staple crops like jowar
(sorghum) and bajra (pearl millet) enjoyed higher yield growth in Green Revolution areas,
but are imprecisely estimated. Maize yields experienced a sizable relative effect of -30%,
exacerbated by a reduction in maize planting share in treated districts where pre-Green
Revolution productivity was higher.
Table A1 presents robustness results for wheat yields, rice yields, and unit area
revenues, the primary agricultural outcomes by which HYV introduction would trigger
differential labor market impacts. Column 2 restricts the sample to districts reporting
agricultural wages, which increases the effect size for each outcome, but on a lower average
for wheat yields and unit area revenue. The results are largely robust to adding state×year
fixed effects and dropping Punjab, Haryana, and Uttar Pradesh, the states most commonly
associated as Green Revolution success stories.
24
Reliable cost of cultivation data needed to construct profit estimates are unavailable over this period.
23
6.2
Agricultural Laborer Wage Response to Green Revolution
The preceding section demonstrated that groundwater-rich districts experienced significantly larger gains in crop output and unit area revenues as a result of the newly available
HYVs and associated increases in production inputs. In this section I detail the wage
responses for male and female agricultural laborers and then evaluate evidence for the
mechanisms identified in Section 3.25
Figure 4 plots the time-varying coefficients of groundwater coverage on log nominal wages, estimated separately by sex with 1965 the omitted year. In these models, all
agricultural operations which both men and women perform are included, which leaves out
By the mid-1970s and early-1980s, male wages in treated districts rose 15-25% more above
pre-1966 levels than in untreated districts. The wage premium decays over the 1980s during which a second generation of HYVs, especially for rice, improve productivity in areas
that did not fully benefit from the first generation of varieties. This finding corroborates
case study evidence of male wage increases prompted by both higher labor demands (larger
yields to be collected, harvested, and threshed) and in the improved bargaining position
laborers found themselves vis-a-vis landowners facing labor supply deficits. Female wages
in treated districts stagnated throughout the period, registering no significant post-Green
Revolution increase.26 Results when weighting with 1961 rural population are comparable
to those when weighting by 1961 agricultural laborer population.
25
While annual wage data is available only from Agricultural Wages in India, these results can be compared against the less frequent REDS and NSS products by estimating as single year cross-sections, seen in
Table A3. The strong correspondence across the results, especially between AWI and NSS rules out that
effects from the AWI analysis are an artefact of data aggregation or employer reporting bias. The REDS data
suggests that women in groundwater-rich areas have a lower wage gap than elsewhere, since the groundwater
term is positive but female × groundwater is not significantly different from zero. Whereas the NSS wages
are always reported by the respondent, the REDS 1970 collects wages from the household while for 1982 are
collected at the village-level by local officials. The correspondence in these results provides reassurance that
analysis using the district-level data is not generating spurious results driven by reporting compliance with
minimum wage laws, or other institutional features that could influencing reporting differently for officials
than wage-earners themselves.
26
Women in groundwater-rich districts have overall higher wages than women elsewhere over the 19581987 period, with each 10 percentage point increase in groundwater coverage translating to a 3.5% (p < 0.01)
increase in daily wages. The analagous value for male workers is 1.8% (p = 0.11).
24
I then estimate the vertical gap in Figure 4 by pooling male and female wages using
a triple difference estimator of the following specification,
log wagesiodst =
X
0
βτ (Groundwaterd × 1(Male) × φt ) + βτ1 (1(Male) × φt ) (9)
τ ∈[1956,1987]
τ 6=1965
0
+ βτ2 (Groundwaterd × φt ) + β (Groundwaterd × 1(Male)) + Xiodst ξ
+ρ1(Male) + γd + δo + φt + iodst
which adds worker gender i performing agricultural operation o to equation (6). I use an
indicator for male workers to simplify interpretation, such that an increase (decrease) in
β 0 , the coefficients of interest, exacerbates (narrows) the wage gap. Figure 5 presents the
results, confirming male-biased wage growth in Green Revolution districts, peaking in the
late 1970s and converging to zero by the mid-1980s before registering a rebound. What
appears as a positive pre-trend stems from the larger relative wage loss experienced by
women workers from the start of the sample to 1962, as seen in Figure 4. Taking an estimate
of the average post-period treatment effect leads to a male wage premium semi-elasticity of
0.14 (p < 0.01).
6.2.1
Testing for Sorting as Possible Explanation of Increased Wage Gap
I now consider whether asymmetric sorting, into peak and off-peak periods, and across
agricultural operations, might explain the wage response. Under the previous specification,
a growing wage gap could be partly explained by male workers in Green Revolution areas
shifting into higher productivity operations, leaving women to perform less productive tasks,
synonymous with a rise in
φm
φf
from Equation 3. While this specification includes fixed
effects for each agricultural operation, it is not flexible enough to control for operationspecific productivity which may be correlated with Green Revolution technologies. As well,
since wages are not provided for every month, a combination of men working during peak
25
periods and women working during off-peak could drive the observed male wage growth.27
To address both task and time sorting, I use the operation-specific (o) wage ratio,
wodtm
wodtf ,
as the dependent variable and subsample to district×month×operation records for which
both male and female wages are available.28 This ensures that male and female wages are
compared only for the tasks for which they both participate. This de facto drops all wage
data for ploughing, which men near-exclusively perform, as well as female sower wages after
1973.29 For efficiency gains I further pool estimates into multi-year intervals, shown in
Figure 6, which includes results from interval widths of 3, 4, and 5 years to demonstrate
robustness to interval choice. For maximal comparability, 1966 is an interval’s left-most edge
in each model. The results approximate those from the earlier triple difference estimate,
with a rising male wage premium of 9-15% persisting through at least the early 1980s, and
then decaying to pre-1966 levels. This limits, but does not rule out a sorting explanation
for the rising wage gap; men and women may be simultaneously engaged in harvesting, but
differentially into higher- and lower-value crops. Alternatively tasks may be divisible, with
men and women sorting by sub-task.
6.3
Impact of Green Revolution on Non-Wage Labor Outcomes
I have shown that groundwater-rich locations experienced better agricultural outcomes and
that male laborers received nearly all the wage gains associated with the Green Revolution’s
productivity improvements. In this section I investigate whether compositional changes in
the laborer workforce can explain the rising wage gap, and then examine women’s time-use
27
This presumes that wage availability in a given month is indicative of that task being performed in
the stated time, which Kurosaki and Usami (2016) call into question given the occasional reporting of task
wages outside the timeframes customary for a given location.
28
Operations for which male and female wages may be simultaneously reported vary by time-frame.
Before 1972, these operations are field labor, reaping & harvesting, sowing, weeding, and other labor. From
1973, only field labor and harvesting are available for both sexes.
29
Sower wages frequently equal field labor and harvesting wages. Since ploughers’ wages often substantially exceeds those of other agricultural tasks, dropping this operation from analysis mechanically dampens
the wage ratio level, but not necessarily the magnitude of its change in time.
26
data to identify if workday length varies in correspondence with groundwater.
6.3.1
Effect on Occupational Choice
I first use census data to estimate the effects of groundwater access on occupational choice,
focusing on the rural population share working as cultivators or agricultural laborers as
a main (primary) or marginal (secondary) worker, or not working.30,31 Table 3 presents
estimates relative to the omitted 1961 × groundwater variable. Despite substantial wage increases in treated districts, the extensive-margin male agricultural labor response is inelastic.
The share of rural women working as laborers was more responsive, with groundwater-rich
districts experiencing a relative 10 pp decline between 1961 and 1981, leading to an 8 pp
drop in the female share of agricultural laborers. Column 5 suggests that women exiting
agricultural labor substituted into own-farm cultivation, and also cut back on their total
labor supply as seen in the last column. These women did not leave productive work altogether. Columns 8 and 10 show they were only more likely to not be working a main job
lasting more than half the year. Instead, they were more likely to engage in cultivation for
a part of the year, and as I show later, become more heavily involved in domestic activities.
Census evidence can only offer a broad overview of labor outcomes, so I now turn
to microdata for a more granular view. Table 4 presents extensive (Panel A) and intensive
(Panel B) margin responses using the 1970 and 1982 REDS rounds. The top panel displays
results for occupation outcomes based on a single, primary occupation affiliation and all
results are from linear probability models.32 Women in groundwater villages were less likely
30
Cultivators are workers engaged in any aspect of own-farm production which also includes labor supervision. Census respondents can report participation in multiple occupations, hence the sum of dependent
variable means exceeding unity for men. While the determination if specific to the census year, respondents
are categorized as main workers if they work more than 183 days in the previous year in that sector.
31
Occupation data is not disaggregated by age group, but was collected across all ages. Consequently the
very young comprise a non-negligible share of the ‘non-working’ population. Since children under 10 years
old work in agriculture, all children cannot be correctly removed from the non-worker population. Models
using occupation shares constructed using the ≥ 15 population produce similar results to those in Table 3.
32
The 1970 round collected individual ‘secondary occupation’ data whereas the 1982 round did not. I
therefore focus only on primary occupation status.
27
in 1982 (than in 1970) to report being an agricultural laborer, consistent with the finding
above. The point estimate for men is similar, but noisier.
Narrowing to the subsample whose household head or at least one male in the house
identifies as an agricultural laborer, clearer divergence between the sexes arises. This distinction is important because women working without their husbands or other male relatives
face stigma and restrictions, social or physical, on their outside working options. In the 1970
REDS round, 87% of women who identified as an agricultural laborer lived in households
where at least one male was also an agricultural laborer (76%) or resided in households with
no males older than 15 (11%). Consequently, a substantial share of the women exiting (or
not joining) agricultural labor are drawn from this group, with a large -0.26 estimate on
women in groundwater villages living with at least one agricultural laborer male.
Intensive margin results from Panel B, for both unconditional and conditional on
positive employment, show noisy estimates that cannot reject the null of no change in the
number of days laborers worked between 1970 and 1982. In agriculture (non-agriculture),
employed women report working an average of 158 (143) days which are both below the
183 day cutoff employed by the census to identify as a ‘main worker’. Consequently, some
of the difference in occupational choice model results between census and REDS data may
arise from differences in how occupation is coded.
6.3.2
Tests of Composition Effects in Laborer Markets
Having demonstrated the Green Revolution’s effect on the gender composition of farm workers, I now investigate other compositional effects that might explain the increase in laborer
wage inequality. In the absence of worker productivity data, I examine worker observables
that might plausibly be related to productivity, but find little support that compositional
differences can explain increased inequality. The sample here is the subset of workers identifying as agricultural laborers or reporting a positive number of days performing agricultural
28
laborer work.33 In Table 5 I show results for a range of outcomes, using linear probability
models for binary outcomes, and showing side-by-side results for male and female laborers
against the rest of the population (’Others’).
Column 1 corroborates the finding above of lower female participation. Restricting
attention to the population below the age of 40 both sharpens this estimate and strengthens
the magnitude. Columns 3-6 show weakly significant results for a downward shift in male
laborer age. Plotting the age distribution of all laborers by REDS round reveals that in
1970, men between the age of 35 and 55 were better represented than young males. By
1982, this pattern reversed with much of the mass contributed in the 18-30 age range and
then monotonically declining above 30. This pattern is further accentuated for men in
groundwater villages. Laborers’ literacy rates in Green Revolution villages improved for
both men and women, as did the same for the general population. There is suggestive
evidence that the average per capita expenditure of workers was relatively lower for Green
Revolution districts in 1982. Importantly, estimates for men and women are comparable
which indicates that a nutritional efficiency explanation is unlikely. I find no evidence
that the rates of landlessness changed across rounds in either laborer or non-laborer groups.
While caste is another important aspect of laborer characteristics and a margin along which
the worker pool might shift, caste details were not collected in the 1970 round.
6.3.3
Gender Composition of Agricultural Operations
One byproduct of the Green Revolution was increased capital investment in agricultural
equipment. By more intensively employing farm machinery, landowners both shortened
completion time against manual performance, and provided a buffer against rising wagebills. Since agricultural operations had already exhibited some degree of sex selection, and
that not all tasks were equally exposed to mechanization, movement in the wage ratio might
33
About 7.6% of the combined male and female sample not reporting agricultural labor as their primary
activity stated a positive number of days worked as a laborer.
29
be explained by a shift in the gender division of major agricultural operations.
I use the National Sample Survey Employment Unemployment rounds for 1983 and
2004 to determine whether gendered sorting into agricultural tasks can provide an alternate
explanation for the wage ratio effects. While the test in Section 6.2.1 ruled out differences in
wages across operations as an explanation, this test examines whether capital substitution
or changes in crop composition induced sex-biased productivity growth, causing one sex
to increase its share of work performed for specific operations. Survey respondents specify
how much time over the seven days preceding the interview were spent engaged in various
agricultural tasks. For each round, I sum the number of days spent by sex for each state
and for the country in each operation.34 Figure 7 indicates that for the whole country, there
is not compelling evidence that the gender composition of the main activities of ploughing,
sowing, transplanting, weeding, and harvesting have demonstrably shifted towards one sex
or the other. However, this masks substantial heterogeneity across individual states. In
states where men overall supply the majority of agricultural labor, the male share of labor
performed increased further, exemplified by Haryana in Figure 7b. In contrast, states like
Himachal Pradesh ( 7c) and Karnataka ( 7d) where women play a substantial if not primary
role in agriculture shifted towards further female representation. The consequence of these
opposing trends is a rising inter-state disparity of female labor share over time, which
balances out in aggregate.35
6.3.4
Women’s Time Use Patterns
An alternative explanation for the wage ratio response is women simply working fewer hours
per day, since wages are measured on a daily and not hourly basis. In Table 8 I present
cross-sectional results using the REDS 1982 Demographic Questionnaire which recorded
34
REDS does not begin recording individual labor supply by operation until 1999.
In most states women’s share of sowing increased, and those which did not were primarily northern
states.
35
30
women’s time-use for a representative day in each of three agricultural seasons.36 The
sample for each model is conditioned on respondents reporting a positive number of hours
worked. In Season 1, taken as the harvest time (November) for rice growing areas and
the sowing period (October) for wheat areas, women in groundwater-rich areas performed
fewer hours of paid labor (combined salaried, wage, and other labor), but no less time
when restricting attention to wage labor alone. While the wage labor category encompasses
both agricultural and non-agricultural work, 79% of women reporting positive time in this
category lived in households with heads with a primary occupation in farming, fishing, and
hunting. While the effect size of 0.06 hours on women from groundwater villages has a
relatively wide confidence interval, it is positively signed and small in magnitude compared
to the mean wage-day length of 7.8 hours. Were the 1982 profile representative of time-use
in 1970, changes in women’s workday length may not provide an explanation for Green
Revolution relative wage movements.
Notably, women in treated villages, despite spending less time in total work and
in agriculture, also report 33 fewer minutes of leisure time during the main season. The
preceding columns indicate that time not spent in wage-earning activities is shifted into
unpaid domestic tasks, including a range of food preparation and cleaning activities, such as
the time-consuming grinding and pounding of grain (Panel B, column 4), and the collection
of fuel and water for the household.
6.4
Mechanisms Driving Female Labor Supply and Wage Ratio Response
The results thus far indicate the Green Revolution was a positive agricultural labor demand
shock impacting labor markets with inelastic local supply. Men responded by diversifying
their labor supply between own-account and on-farm holdings and began to considered
36
Comparable data for 1970 is not available for estimating changes in time-use patterns. Fewer than 200
women out of the 15,419 included in the sample report time-use in salaried activities for any season and
fewer than 101 women reported positive time spent in wage labor outside of the main season.
31
themselves as self-employed farmers instead of laborers. Women did not, both less frequently
identifying as a self-employed farmer and in groundwater-rich areas also less likely to be
primarily an agricultural laborer. What explains the asymmetric response between the
sexes?
I consider the role of household income on female labor supply decisions. While female earnings mechanically raise total income, because of prevailing female seclusion norms,
labor participation may also incur disutility as discussed earlier. Such a relationship will
visibly approximate an income effect. I test this using binned scatterplots in Figure 8 with
log total household expenditure as the running variable in row 1, which uses REDS 1970 and
1982 data, and log monthly per capita expenditure in row 2 which uses NSS Employment
Survey 1983 data. Figure 8a shows a negative correlation between household expenditure
and an agricultural laborer primary status, for both men and women. This is intuitively
reasonable since wages for agricultural laborers are on the lower end of the distribution and
therefore richer households are less likely to have laborer earners. For men, one way to
secure higher earnings is to be a self-employed farmer, as seen in 8b. However, household
expenditure has no predictive power over the likelihood of women also being self-employed
farmers, indicating gender-specific supply responses.
While the REDS data did not use a consistent approach for obtaining information
on domestic work, I use the NSS Employment Survey data for 1983 to get cross-sectional
variation in monthly per capita expenditure and test this on whether female respondents
are primarily engaged in domestic tasks only. I find that in households coded as agricultural
laborers, the likelihood of performing only domestic tasks is rising in expenditure, from a
mean value of 0.4 to 0.5 in the top expenditure bin. This relationship is stronger for selfemployed farming households who have higher expenditure patterns to begin with. The
probability that women engage exclusively in domestic work is also increasing, with a lower
bin level exceeding 54%. While these tests cannot make causal claims - richer men marrying
women who prefer domestic work could also explain this pattern - this provides suggestive
32
evidence for further inquiry into the determinants of female labor force participation and
the potential role social norms may play in governing the relationship between household
income and female labor supply.
Furthermore, income gains from the Green Revolution helped finance the purchase of
mechanized farm equipment. Figure 9 presents results using the World Bank district panel
of a relative increase the in unit area availability of tractors, and a gradual tapering off in the
density of bullocks. The increasing availability of tractors allowed farmers to utilize tractordrawn equipment, which presumably was more productive than bullock-drawn counterparts.
The first available census of farm equipment was conducted in 1981/82 at the state-level, and
Figure 10 shows results from that data using a simple cross-sectional OLS model regressing
z-scores of all equipment types listed in the census, on state-level groundwater coverage
with all observations weighted by their gross cropped area in 1961. In general, treated
states were likely to have more units of tractor-drawn and powered equipment per unit
of land. Many of the coefficients on items listed under bullock-powered or hand-operated
suggest no difference between treated and untreated states. If rules similar to those applied
to ploughmen are also applied to farm equipment operated by a tractor, or electricitypowered, then differences in capital stock would explain the male wage premium in Green
Revolution locations as a product of a gender productivity gap.
7
Conclusion
In this paper I have estimated the effect of the Green Revolution on gender wage inequality
in Indian agricultural laborer markets. Using plausibly exogenous groundwater resources
as a proxy for Green Revolution intensity, I find groundwater-rich areas were better suited
to exploiting the newly available high-yielding varieties and experienced significant agricultural growth. Using annual, district-level wage data I find this positive labor demand shock
was male-biased, and increased pre-existing wage inequality levels in Green Revolution dis-
33
tricts. Using multiple, individual-level datasets on labor allocation, time-use, and earnings,
I examine occupational choice, selection, and sorting as potential explanations for changes
in the wage ratio.
This paper contributes to an important literature on the effects of technology on
gender wage inequality, and provides a counterexample to the conventional wisdom by
showing that men appear to be the sole beneficiaries of this episode of productivity growth.
With significant resources now being targeted at sub-Saharan Africa and the promotion
of Green Revolution 2.0 policies, this example from India provides strong evidence that
technological gains may be unequally distributed with a potential corrective role for policy.
34
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Figures
Figure 1: Crop-Wise HYV Share of Gross Cropped Area
Notes: The vertical axis represents the fraction of cropped area by each crop grown with
high-yielding varieties. Values are based on total area under cropping in India. The thick
black line denotes the area-weighted share of all five HYV-eligible crops.
Data: Ministry of Agriculture, Government of India
40
Figure 2: District Area Share Covering Unconsolidated Aquifers
Notes: District groundwater coverage is computed as the share of district area covering
groundwater aquifers classified as unconsolidated formations under unconfined conditions,
using 1961 Census of India district boundaries. REDS villages are marked with red stars
with groundwater coverage a binary outcome of geographic location atop an unconsolidated
format.
Data: Geohydrological Map of India 1969
41
Figure 3: Time-Varying Effect of Groundwater Coverage on HYV Coverage
Notes: HYV coverage is defined as the share of rice, wheat, maize, sorghum, and pearl millet
area planted with high-yielding varieties (HYVs). Plotted coefficients are year-varying effects
of groundwater coverage, measured in percent [0,1], with the marginal effect interpreted in
percentage point changes of HYV coverage. Model includes year, district, and state×year fixed
effects, and the set of current and lagged temperature and precipitation variables described in
Section 4.4. Observations are weighted by 1961 gross cropped area and standard errors are
clustered by 1983 NSS region. Dashed lines represent 95% confidence intervals. Results are for
271 districts from 1956-1987.
Data: World Bank Climate and Agriculture; Geohydrological Map of India 1969
42
Figure 4: Time-Varying Effect of Groundwater on Male and Female Agricultural Laborer
Wages
Notes: Coefficients from models separately regressing log nominal male and female wages
on groundwater coverage interacted with year dummies. Both models include year, district,
month of year, and agricultural operation fixed effects. Observations are weighted by 1961
total agricultural labor workforce divided by the number of district×year observations to
correct for oversampling.
Data: Agricultural Wages in India; Geohydrological Map of India 1969; Vanneman and
Barnes (2000)
43
Figure 5: Time-Varying Effect of Groundwater on Male Laborer Wage Premium
Notes: Coefficients from a triple difference specification specified in equation (6.2) regressing
log nominal wages on the interaction of groundwater coverage, 1(male), and time dummies.
Observations are weighted by 1961 agricultural labor workforce divided by the number of
district×year observations to correct for oversampling. Model includes year, month of year,
and agricultural operation fixed effects.
Data: Agricultural Wages in India; Geohydrological Map of India 1969; Vanneman and
Barnes (2000)
44
Figure 6: Effect of Groundwater on Male/Female Wage Ratio
Notes: Results from separate models regressing nominal wage ratio on groundwater coverage
× year interval dummies with interval widths specified in the legend. Intervals are centered
such that 1966 is the left-most edge of one interval. All models include year, district, month
of year, and agricultural operation fixed effects. Observations are weighted by agricultural
labor workforce reported in the 1961 Census of India divided by count of district×year
observations. Confidence intervals shown are at the 95% and clustered by 1983 NSS region.
Data: Agricultural Wages in India; Geohydrological Map of India 1969; Vanneman and
Barnes (2000)
45
Figure 7: Division of Time Spent in Agricultural Activities by Gender
(a) All India
(b) Haryana
(c) Himachal Pradesh
(d) Karnataka
Notes: India-wise and state-wise share of total days spent engaged in each agricultural
activity for all survey respondents by survey round (left vertical axis). Values are based on
time-use reporting for seven day period preceding survey. Interview dates are not available
for Round 38.
Data: NSS Employment Unemployment Survey Rounds 38 (1983), 60 (2004)
46
Figure 8: Relationship Between Household Expenditure and Extensive Margin Labor Supply
(a) Agricultural Wage Work
(b) Self-Employed Farming
(c) Agricultural Laborer Households
(d) Self-Employed Farming Households
Notes: Row 1: Binned scatterplot results using REDS 1970, 1982 data, with dummy
dependent variable taking 1 if respondent identifies as an agricultural wage laborer (a), or
as engaged in self-employed farming (b), based on respondent’s usual activity status. In
(a) and (b), village × year fixed effects, household size, education, marital status, and total
land ownership have been partialled out. Samples are subsetted to household members
aged 15-59 who are neither pensioners nor retired. Row 2: Binned scatterplot results using
NSS 1983 data, with dummy dependent variable taking 1 if respondent’s weekly status is
recorded as attendance to domestic duties only, or coupled with free collection of goods for
household use. Sample are subsetted to female respondents aged 15-59. In (c), sample is
further restricted to only women in agricultural labor households, while (d) is restricted to
women in households that are self-employed in agricultural occupations. Expenditures in
row 1 have been deflated using state-level consumer price index for agricultural laborers
(Besley and Burgess, 2000). In both models, household size, religion marital status, years
of formal education, age, age2 , land ownership, and NSS region fixed effects have been
partialled out.
Data: REDS 1970, 1982; NSS Employment Unemployment 1983
47
Figure 9: Groundwater Coverage and Farm Equipment Capital Stock
Tractor Density
Bullock Density
Notes: Both outcomes in number per thousand hectares of cropped area.
Data: World Bank Climate and Agriculture; Geohydrological Map of India 1969
48
Figure 10: State-Level Groundwater Coverage and Farm Equipment Capital Stock
Tractor/Power
Operated
Bullock
Operated
Hand
Operated
Notes:
Data: Agricultural Input Survey 1981/82; Geohydrological Map of India 1969
49
Tables
Table 1: Summary Statistics
Mean
Min
Max
SD
N
World Bank India Agriculture and Climate (1957-1987)
Groundwater Coverage (%)
Rice Yield (t/ha)
Wheat Yield (t/ha)
% Rice Area
% Wheat Area
% HYV for Eligible Crops
0.41
1.03
0.97
0.30
0.14
0.23
0
0
0
0
0
0
1.00
24
18.0
1.00
0.73
1
0.42
0.69
0.73
0.31
0.15
0.26
271
8672
8672
8672
8672
8672
0.48
32.2
8.40
0.53
0.88
0.19
0.18
174.9
167.9
0.20
3.33
691.4
0.54
0
15
1
0
0
0
0
1
1
0
0
60.1
0
1
60
43
1
1
1
1
365
365
1
92
8268.8
1
0.50
12.9
4.39
0.50
0.32
0.39
0.38
80.7
98.0
0.40
4.99
440.8
0.50
33463
33463
33425
33463
33446
33463
33463
5911
1075
33425
33425
33033
33463
REDS 1970, 1982
1(Female)
Age
Household Size
1(Illiterate)
1(Hindu)
1(Ag Laborer)
1(Self-Employed Farmer)
Days Earning Ag Wages
Days Earning Non-Ag Wages
1(Landless)
Land Owned (ha.)
Total Expenditure Per Capita (Rs. 1973/74)
1(Groundwater)
Notes: Crop area shares are computed based on gross cropped area. The HYV share measure
is the ratio of total area planted with HYVs to area planted with all varieties for the HYVeligible crops (wheat, rice, maize, sorghum, and pearl millet).
Data: World Bank Climate and Agriculture, REDS 1970, 1982; Geohydrological Map of
India 1969; Vanneman and Barnes (2000)
50
51
0.24
8655
∗
∗∗
p < 0.05,
∗∗∗
p < 0.01
0.27
8672
3.73
8668
(1)
0.214∗∗∗
(0.0513)
-0.16
7670
-0.06
8208
Rice
(3)
0.157∗
(0.0795)
0.32
8672
Non-HYV Crops
(4)
-0.112∗∗∗
(0.0201)
Wheat
(2)
0.198∗
(0.105)
Rice
(3)
0.0206
(0.0130)
Log Revenue Ha−1
0.13
8672
Wheat
(2)
0.105∗∗∗
(0.0116)
-0.09
7563
Maize
(4)
-0.294∗∗∗
(0.102)
-0.72
7118
Sorghum
(5)
0.0439
(0.0965)
1.20
8654
Intensity
(6)
0.0665∗∗
(0.0256)
Log Yields
6.28
8654
Net
(5)
-0.0208
(0.0200)
Cropped Area
-0.85
6054
Pearl Millet
(6)
0.112
(0.110)
6.45
8409
(7)
0.590∗∗
(0.271)
Fertilizer
Notes: Sample covers 271 districts from 1956-1987. Column A3 is the share of gross cropped area planted with crops for
which no HYV option was available. Net cropped area is in log hectares (’000s) and captures the maximum area under
cultivation for a single season. Fertilizer is measured in log tonnes (’000s) of phosphorus. Cropped area intensity (col. A5) is
the ratio of gross to net cropped area and measures the number of cycles a parcel of land is cropped in a calendar year. Crops
listed in Panel B are the 5 HYV-eligible crops included in the World Bank dataset. All models include year and district fixed
effects and the set of contemporaneous and lagged weather variables. Observations are weighted by 1961 gross cropped area.
Standard errors are clustered by 1983 NSS region.
Data: World Bank Climate and Agriculture; Geohydrological Map of India 1969
p < 0.10,
Mean DV
N
≥ 1966 × Groundwater %
Panel B: Cropping Outcomes
Mean DV
N
≥ 1966 × Groundwater %
Irrigated
(1)
0.133∗∗∗
(0.0245)
% Total Cropped Area
Table 2: Effects of Groundwater Coverage on Selected Agricultural Measures
52
∗
∗∗
p < 0.05,
∗∗∗
p < 0.01
0.19
X
X
889
0.702
0.16
X
X
889
0.480
Female
(2)
-0.0500∗∗∗
(0.00867)
-0.0965∗∗∗
(0.0122)
0.39
X
X
889
0.252
% Female
(3)
-0.0336∗
(0.0188)
-0.0840∗∗∗
(0.0219)
0.58
X
X
889
0.756
Male
(4)
-0.0119
(0.0131)
0.00351
(0.0146)
0.24
X
X
889
0.695
Female
(5)
0.141∗∗∗
(0.0364)
0.0884∗∗∗
(0.0312)
0.24
X
X
889
0.743
% Female
(6)
0.0556∗∗
(0.0268)
-0.00705
(0.0227)
% Cultivators
0.76
X
X
889
0.441
Male
(7)
0.00367
(0.0217)
0.0361
(0.0272)
1.27
X
X
889
0.632
Female
(8)
-0.109∗∗∗
(0.0352)
0.0298
(0.0429)
Main and Marginal
0.54
X
X
889
0.985
Male
(9)
0.0966∗∗∗
(0.0237)
0.129∗∗∗
(0.0168)
1.04
X
X
889
0.979
Female
(10)
0.371∗∗∗
(0.0572)
0.489∗∗∗
(0.0526)
Main Only
% Non-Workers
Notes: Percentage values are constructed using the total number of rural workers (main and marginal) by occupation and the total rural population. Estimates are
relative to the omitted interaction of 1961 × groundwater. All models include district and year fixed effects, and the set of contemporaneous and lagged weather
variables. Observations are weighted by 1961 population census rural population. Standard errors are clustered by 1983 National Sample Survey regions.
Data: Vanneman and Barnes (2000); Geohydrological Map of India 1969
p < 0.10,
DV Mean
Year
District
N
Adj. R2
1981 × Groundwater %
1971 × Groundwater %
Male
(1)
0.0120
(0.0124)
0.0129
(0.0165)
% Ag Laborers
Table 3: Effect of Groundwater Access on Occupational Affiliation
53
p < 0.10,
∗∗
p < 0.05,
∗∗∗
p < 0.01
0.27
17393
0.310
(1)
-0.0801
(0.0567)
0.15
10872
0.230
(2)
-0.0843
(0.0654)
∗
∗∗
p < 0.05,
∗∗∗
p < 0.01
39.65
17393
0.221
21.46
16028
0.318
184.69
3734
0.316
158.00
2177
0.326
Female
(4)
-4.403
(22.11)
Days |t > 0
Male
(3)
-18.13
(19.39)
8.26
17393
0.108
Male
(5)
2.191
(4.154)
0.21
5102
0.398
(5)
-0.186
(0.120)
2.29
16028
0.0664
Female
(6)
0.588
(2.254)
175.52
819
0.595
Male
(7)
11.84
(32.18)
143.37
256
0.657
Female
(8)
-71.39
(59.35)
0.21
6201
0.360
(6)
-0.264∗∗∗
(0.101)
Male AgLaborer
Days |t > 0
Non-Agricultural
Days
0.11
16028
0.295
(4)
-0.0933∗
(0.0559)
HoH AgLaborer
Not HoH
Female
Notes: Weighted OLS models regressing binary outcome variables (Panel A) and number of days worked in wage-earning,
non-salaried activities (Panel B) on village-level unconsolidated groundwater coverage, using household-level sampling
probability weights. Panel B models are conditional on positive labor supply. All models include village fixed effects and
control for respondent’s age, age2 , marital status, household size, and religion. Sample restricted to respondents aged
15-60. Standard errors are clustered by village.
Data: REDS 1970, 1982; Geohydrological Map of India 1969
p < 0.10,
Mean DV
N
Adj. R2
1 (Groundwater) × 1982
Female
(2)
-4.450
(8.730)
Days
Male
(1)
-11.79
(10.99)
Agricultural
0.29
2935
0.336
(3)
0.149
(0.123)
All
1(Ag Laborer)
HoH AgLaborer
Not HoH
Male
Panel B: Days Worked as Wage-Earner by Sector
∗
Mean DV
N
Adj. R2
1 (Groundwater) × 1982
All
Panel A: Extensive Margin Outcomes
0.27
17393
0.274
(7)
0.0643
(0.0560)
Male
0.08
16028
0.203
(8)
0.0557
(0.0341)
Female
1(Self-Employed Farmer)
Table 4: Effects of Groundwater Access on Labor Outcomes
0.33
17393
0.316
(9)
-0.0508
(0.0389)
0.81
16028
0.295
(10)
0.0255
(0.0518)
Female
1(Non-Earner)
Male
54
Mean DV
N
Adj. R2
∗
∗∗
p < 0.05,
∗∗∗
.31
5488
0.312
M
(1)
-0.0617
(0.113)
.42
2373
0.417
F
(2)
-0.0286
(0.143)
Ag Wage
.14
11746
0.342
M
(3)
-0.0498
(0.0655)
.16
13426
0.315
F
(4)
-0.0290
(0.0563)
30
11746
0.0711
6.2
5488
0.402
M
(5)
-0.162∗∗
(0.0763)
.86
2373
0.276
6.1
2373
0.391
F
(6)
-0.164∗
(0.0912)
.32
11746
0.269
6.5
11746
0.477
6.4
13426
0.394
F
(8)
-0.133
(0.0963)
Others
M
(7)
-0.0641
(0.0991)
Log Exp PC
.52
5488
0.224
.66
13426
0.437
(10)
-0.271∗∗∗
(0.0771)
Others
(9)
-0.154∗∗∗
(0.0576)
1(Illiterate)
F
(8)
-0.217∗
(0.123)
Ag Wage
M
(7)
-0.214∗
(0.110)
Ag Wage
32
13426
0.0478
F
(6)
-0.917
(1.009)
Others
M
(5)
-2.887∗∗
(1.378)
Others
33
2373
0.101
F
(4)
0.847
(2.896)
1(Landless)
36
5488
0.0881
M
(3)
-2.964∗
(1.577)
Ag Wage
Age
Notes: Groundwater is a binary measure for whether village is located above any unconsolidated aquifer. Respondents are coded
as agricultural laborers if they primarily identify as an agricultural laborer or reported a positive number of days worked over the
research period, while ‘Others’ includes the remainder of the population of sampled individuals aged 15-60. Landless is defined as
households owning no land. Expenditure values in (13)-(16) are deflated to 1973/74 Indian rupees using the state-level consumer
price index for agricultural laborers (Besley and Burgess, 2000). All models include village fixed effects, household-level sampling
weights, and clustered standard errors by village.
Data: REDS 1970, 1982; Geohydrological Map of India 1969
p < 0.10,
Mean DV
N
Adj. R2
p < 0.01
.3
7861
0.0803
1 (Groundwater) × 1982
1 (Groundwater) × 1982
(2)
0.0124
(0.0237)
(1)
-0.0801∗
(0.0473)
.53
25172
0.0299
Others
1(Female)
Ag Wage
Selection Tests
Table 5: Results from Tests for Selection on Observables of Agricultural Laborers
55
-0.08
X
X
8208
-0.09
X
X
7670
Wheat
(2)
0.0130
(0.0202)
3.80
X
X
8668
(3)
0.0300
(0.0212)
Log Revenue Ha-1
-0.08
X
X
8208
-0.140∗∗∗
(0.0196)
Rice
(4)
-0.09
X
X
7670
-0.0228
(0.0141)
Wheat
(5)
Log Yields
Output Response to Rainfall Shocks
3.80
X
X
8668
-0.107∗∗∗
(0.0297)
(6)
Log Revenue Ha-1
-0.08
X
X
8208
0.0569∗∗∗
(0.00895)
Rice
(7)
-0.09
X
X
7670
0.0102
(0.00854)
Wheat
(8)
Log Yields
3.80
X
X
8668
0.0363∗∗∗
(0.0123)
(9)
Log Revenue Ha-1
Notes: Positive (negative) rainfall shocks are dummies for the top (bottom) quintile of cumulative rainfall from May through September over 1930-2014. All models
include district and year fixed effects. Models are weighted by a district’s gross cropped area in 1961. Standard errors are clustered by 1983 NSS region.
Data: World Bank Climate and Agriculture; Climate Research Unit TS 3.23 Monthly Precipitation
Mean DV
Year
District
N
Kharif Precip Z-Score
1 (− Rainfall Shock)
1 (+ Rainfall Shock)
Rice
(1)
0.0437∗∗∗
(0.00989)
Log Yields
Table 6: Effect of Rainfall Shocks on Agricultural Outcomes
Table 7: Robustness Tests of Rainfall Shocks on Male/Female Wage Ratio
Male/Female Wage Ratio Results
1(+ Rainfall Shock)
Mean DV
Village
Year
Month
State × Year
Village Trends
Year × Month
≥ July
N
(1)
0.0375∗∗
(0.0169)
(2)
0.0428∗∗∗
(0.0153)
(3)
0.0441∗∗∗
(0.0153)
(4)
0.0458∗∗∗
(0.0150)
(5)
0.0456∗∗∗
(0.0164)
1.24
X
X
X
1.24
X
1.24
X
1.24
X
1.24
X
X
X
X
X
X
X
X
X
18031
18031
X
X
X
X
9131
18034
18031
Male/Female Wage Ratio Results
1(− Rainfall Shock)
Mean DV
Village
Year
Month
State × Year
Village Trends
Year × Month
≥ July
N
(1)
-0.00524
(0.0155)
(2)
0.000572
(0.0193)
(3)
-0.00624
(0.0137)
(4)
-0.00505
(0.0140)
(5)
0.00325
(0.0109)
1.24
X
X
X
1.24
X
1.24
X
1.24
X
1.24
X
X
X
X
X
X
X
X
X
18031
18031
X
X
X
X
9131
18034
18031
Notes: All models use agricultural laborer wage data by reporting center from
1973-1990. Positive (negative) rainfall shocks are dummies for the top (bottom)
quintile of cumulative rainfall from May through September over the 1930-2014
period. In addition to the described fixed effects, all models include fixed effects
for each agricultural operation. Models are weighted by a district’s 1961 agricultural laborer workforce divided by the number of wage observations to correct for
oversampling. Standard errors are clustered by 1983 NSS region.
Data: Agricultural Wages in India; Geohydrological Map of India 1969; Vanneman and Barnes (2000)
56
57
6.2
2949
0.104
4.3
1737
0.282
S2
(2)
-2.956∗∗∗
(0.567)
4.3
1605
0.185
S3
(3)
-2.266∗∗∗
(0.681)
7.8
1549
0.105
S1
(4)
0.0553
(0.593)
Wage Labor
∗
∗∗
p < 0.05,
∗∗∗
p < 0.01
6.4
9513
0.134
6.3
8918
0.122
S2
(2)
1.119∗∗∗
(0.287)
6.2
8739
0.134
S3
(3)
1.055∗∗∗
(0.329)
2.5
1578
0.426
S1
(4)
0.994∗∗∗
(0.221)
Grinding
2.1
1709
0.103
S1
(5)
0.126
(0.226)
Fuel
.15
3391
0.103
.2
3004
0.141
1.9
2958
0.133
S1
(6)
0.175
(0.183)
.14
3429
0.145
6.7
13917
0.0391
7.5
13726
0.0489
S2
(8)
-0.396
(0.301)
Leisure
S3
(8)
-0.0487
(0.450)
S1
(7)
-0.555∗∗
(0.268)
S2
(7)
-0.849∗
(0.487)
Water
S1
(6)
-1.019∗∗
(0.449)
Agriculture
7.3
13419
0.0465
S3
(9)
-0.607∗
(0.314)
Notes: Dependent variable in all models is the number of hours, conditional on being positive, spent in an activity for a representative
day of that season. Sn in column headers denotes first, second, and third season where S1 refers to the primary agricultural season for the
respondent’s location. Total work is the sum of time spent engaged in wage labor, salaried work, and an unspecified other work category,
all expressed in hours. Other work includes crafts and services, marketing, trading and business, fishing, and others. Household work
consists of cleaning, washing, cooking, food collection, and other household work. Groundwater is a binary measure for whether village is
located above any unconsolidated aquifer. Sample consists of all female household members in survey households aged 15-59. All models
control for age, age2 , caste, religion, years of schooling, number of living children, number of respondents in household, husband’s age and
age2 , and husband’s years of schooling. Standard errors are clustered by village. Sampling weights used in all models.
Data: REDS Demographic Questionnaire 1982; Geohydrological Map of India 1969
p < 0.10,
Mean DV
N
Adj. R2
1 (Groundwater)
S1
(1)
1.260∗∗∗
(0.255)
Household Work
3.6
1340
0.145
S1
(5)
-1.147∗
(0.616)
Other Work
Panel B: Unpaid Activity Time-Use, Average Hours per Day
Mean DV
N
Adj. R2
1 (Groundwater)
S1
(1)
-1.404∗∗∗
(0.514)
Total Work
Panel A: Paid Activity Time-Use, Average Hours per Day
Table 8: Cross-Sectional Results for Female Time Use by Season
A
Appendix Tables and Figures
58
Figures
Figure A1: India-Wide Area Under Irrigation and Number of Operating Pumpsets
Notes: ‘All sources’ include canals, tanks, tubewells, other wells, and other sources.
Pumpset counts include both diesel and electric.
Data: Ministry of Agriculture; Central Water Commission; Water Resources Information
System Directorate; Ministry of Statistics and Programme Implementation
59
Tables
60
Table A1: Robustness Tests of Crop Productivity Results
Log Wheat Yield
1(t ≥ 1966) × Groundwater %
(1)
0.198∗
(0.105)
(2)
0.279∗∗∗
(0.102)
(3)
0.116∗∗
(0.0485)
(4)
0.185∗
(0.107)
(5)
0.186∗
(0.109)
(6)
0.236∗
(0.129)
(7)
0.0834∗
(0.0486)
-0.16
-0.16
X
-0.16
-0.19
-0.22
-0.30
-0.30
X
X
X
7350
7162
X
X
X
5626
X
X
X
X
5626
Mean DV
AWI Districts Only
State × Year
Minus Punjab
Minus Haryana
Minus Uttar Pradesh
N
X
7670
6259
7670
Log Rice Yield
1(t ≥ 1966) × Groundwater %
(1)
0.157∗
(0.0795)
(2)
0.222∗∗
(0.104)
(3)
0.246∗∗∗
(0.0800)
(4)
0.0804
(0.0610)
(5)
0.0600
(0.0627)
(6)
0.0500
(0.0980)
(7)
0.225∗∗
(0.0894)
-0.06
-0.05
X
-0.06
-0.08
-0.10
-0.08
-0.08
X
X
X
7888
7724
X
X
X
6188
X
X
X
X
6188
Mean DV
AWI Districts Only
State × Year
Minus Punjab
Minus Haryana
Minus Uttar Pradesh
N
X
8208
6704
8208
Log Gross Unit Hectare Revenue
1(t ≥ 1966) × Groundwater %
(1)
0.214∗∗∗
(0.0513)
(2)
0.251∗∗∗
(0.0577)
(3)
0.231∗∗∗
(0.0578)
(4)
0.174∗∗∗
(0.0449)
(5)
0.152∗∗∗
(0.0456)
(6)
0.124∗∗
(0.0555)
(7)
0.219∗∗∗
(0.0637)
3.73
3.71
X
3.73
3.71
3.70
3.65
3.65
X
X
X
8348
8156
X
X
X
6620
X
X
X
X
6620
Mean DV
AWI Districts Only
State × Year
Minus Punjab
Minus Haryana
Minus Uttar Pradesh
N
∗
p < 0.10,
∗∗
p < 0.05,
X
8668
∗∗∗
7164
8668
p < 0.01
Notes: All models include year and district fixed effects with observations weighted by 1961 gross cropped area.
Runs with AWI districts only restricts the sample to districts reporting Agricultural Wages in India. All models
control for the set of current and lagged temperature and precipitation variables described in Section 4. Standard
errors are clustered by 1983 NSS region. Full sample includes 271 districts for 1956-1987.
Data: World Bank Climate and Agriculture; Geohydrological Map of India 1969
61
62
∗
p < 0.05,
∗∗∗
p < 0.01
92760
1.15
(1)
0.140∗∗∗
(0.0382)
92760
1.15
X
(2)
0.137∗∗∗
(0.0372)
92760
X
1.15
(3)
0.136∗∗∗
(0.0365)
91141
X
1.13
(4)
0.140∗∗∗
(0.0390)
88859
X
X
1.11
(5)
0.140∗∗∗
(0.0397)
X
X
X
86754
1.11
(6)
0.134∗∗∗
(0.0430)
X
X
X
86754
1.11
X
(7)
0.134∗∗∗
(0.0415)
X
X
X
X
86754
1.11
(8)
0.137∗∗∗
(0.0406)
Notes: All models include gender, agricultural operation, month of year, year and district fixed effects with observations weighted by
1961 agricultural laborer workforce divided by the number of wage observations to correct for oversampling. All models control for the
set of current and lagged temperature and precipitation variables described in Section 4. Standard errors are clustered by 1983 NSS
region. Includes years through 1987.
Data: Agricultural Wages in India; Geohydrological Map of India 1969; Vanneman and Barnes (2000)
p < 0.10,
∗∗
Mean DV
State Trend
State × Year
Minus Punjab
Minus Haryana
Minus Uttar Pradesh
N
≥ 1966 × Groundwater % × 1 (Male)
Table A2: Robustness Tests of Effects on Laborer Wages
63
∗
∗∗
p < 0.05,
∗∗∗
p < 0.01
.75
2864
0.315
.83
4427
0.350
AWI
(2)
0.103
(0.0724)
-0.370∗∗∗
(0.0258)
0.282∗∗∗
(0.0740)
.75
2864
0.495
.83
4427
0.508
AWI
(4)
0.133∗∗
(0.0570)
-0.374∗∗∗
(0.0259)
0.124
(0.0866)
State FE
REDS
(3)
0.0603
(0.0660)
-0.376∗∗∗
(0.0619)
0.0677
(0.0590)
1970
1.8
2841
0.177
REDS
(5)
-0.0315
(0.0489)
-0.285∗∗∗
(0.0301)
0.255∗∗∗
(0.0511)
1.9
3301
0.204
AWI
(6)
0.0813
(0.0893)
-0.226∗∗∗
(0.0472)
0.322∗∗∗
(0.0952)
No State FE
1.9
48345
0.0924
NSS
(7)
0.191∗∗
(0.0890)
-0.459∗∗∗
(0.0416)
0.214∗∗
(0.0978)
1.8
2841
0.344
REDS
(8)
-0.0623
(0.0479)
-0.259∗∗∗
(0.0292)
0.121∗∗
(0.0506)
1982/1983
1.9
3301
0.367
AWI
(9)
0.0941
(0.0782)
-0.233∗∗∗
(0.0432)
0.0791
(0.106)
State FE
1.9
48345
0.152
NSS
(10)
0.191∗∗∗
(0.0696)
-0.444∗∗∗
(0.0393)
-0.0137
(0.101)
Data: REDS 1970, 1982; NSS Employment Unemployment 1983; ICRISAT Apportioned Meso Database; Geohydrological Map of India 1969
Notes: Results from weighted OLS models. Groundwater is a continuous variable in AWI and NSS models and a binary variable when used with REDS data.
REDS 1970 and NSS models use household-level sampling weights, while AWI models are weighted by 1961 district rural population. REDS 1970 models include
controls for education, marital status, relationship to household head, age, and age2 . REDS 1982 models use village-level wage data reported by agricultural
operation, with the models including dummies for each. Standard errors clustered by 1983 National Sample Survey regions.
p < 0.10,
Mean DV
N
Adj. R2
Groundwater
Female
Female × Groundwater
REDS
(1)
0.0372
(0.0724)
-0.408∗∗∗
(0.0702)
0.270∗∗∗
(0.0700)
No State FE
Log Average Daily Wage (Rs.)
Table A3: Log Average Daily Wage, Agricultural Laborers (1970, 1982/1983)