# Does Reliable Electricity Mean Lesser Agricultural Labor Wages? Evidence
from Indian Villages

Suryadeepto Nag  Department of Humanities and Social Sciences, Indian
Institute of Science Education and Research Pune, Pashan, Pune - 411008,
India. E-mail<EMAIL_ADDRESS>
###### Abstract

Using a panel of 1,171 villages in rural India that were surveyed in the India
Human Development Surveys, I perform a difference-in-differences analysis to
find that improvements in electricity reliability have a negative effect on
the increase in casual agricultural labor wage rates. Changes in men’s wage
rates are found to be affected more adversely than women’s, resulting in a
smaller widening of the gender wage gap. I find that better electricity
reliability reduces the time spent by women in fuel collection substantially
which could potentially increase labor supply. The demand for labor remains
unaffected by reliability, which could lead the surplus in labor supply to
cause wage rates to stunt. However, I show that electrical appliances such as
groundwater pumps considerably increase labor demand indicating that
governments could target increasing the adoption of electric pumps along with
bettering the quality of electricity to absorb the surplus labor into
agriculture.

JEL Classification: J23, J30, O13, Q40

Keywords: Electricity Reliability, Labor Wages, South Asia

Acknowledgements: I thank David I. Stern for several helpful discussions and
detailed comments.

##  1 Introduction

The question of whether advancements in electrification have effects on
agriculture has been a longstanding one (Barnes and Binswanger,, 1986). In the
late twentieth century, several countries promoted developments in electricity
infrastructure to increase the adoption of electrical farm equipment and
agricultural productivity. However, recent studies (Khandker et al.,, 2013;
Kumar and Rauniyar,, 2018; Nag and Stern,, 2023) have cast doubt on any
electricity-induced benefits for agricultural income. Does this imply
electricity access has no consequence on agriculture, or is it simply not
significantly bettering productivity and income? In this paper, I study a
less-studied dimension of electricity and agriculture – the effect of
electricity quality on agricultural labor wages.

|  | Means (standard deviation)
---|---|---
|  | 2004-2005 |  | 2011-2012 |  | $\Delta$
|  | Increase in | Non-increase in |  | Increase in | Non-increase in |  | Increase in | Non-increase in
|  | Reliability | Reliability |  | Reliability | Reliability |  | Reliability | Reliability
Women’s daily wage rate (2012 Rs.) |  | 86.71 | 85.98 |  | 133.29 | 138.93 |  | 40.44 | 47.31
|  | (40.2) | (41.72) |  | (57.2) | (69.97) |  | (46.99) | (49.97)
Men’s daily wage rate (2012 Rs.) |  | 116.5 | 119.55 |  | 167.2 | 183.32 |  | 50.52 | 62.81
|  | (51.45) | (52.21) |  | (66.76) | (83.07) |  | (56.14) | (61.62)
Average daily wage rate (2012 Rs.) |  | 104.93 | 105.42 |  | 150.5 | 161.63 |  | 44.8 | 55.45
|  | (47.08) | (46.46) |  | (59.37) | (73.87) |  | (50.16) | (54.64)

Table 1:  Casual agricultural labor wage rate statistics for villages where
reliability improves and villages where it does not. Reliability is measured
as the average number of hours of electricity available per day. Includes 1281
Indian villages. The wages have been standardized to the 2012 Rupee by
multiplying the 2005 rates by the national consumer price index (reported by
the World Bank). Averages are unweighted. The data are unweighted. Source:
IHDS I, and IHDS II surveys.

Taking a cursory glance at village-level data from the India Human Development
Surveys (IHDS) (Desai et al.,, 2005, 2012), we can observe a strange
phenomenon (Table 1). Villages where the quality of electricity improved from
2004-5 to 2011-12, have lower agricultural labor wage rates in 2012 than
villages where the quality of electricity worsened. Both types of villages
have similar average wage rates in 2004-5, and there is a general trend of
increasing wages. However, the increase is less in villages that observe an
improvement in the hours of access, when compared to villages that don’t (most
of which observe a decline). The disparity is consistent in both men’s and
women’s wages, although the difference is larger in men’s wages. After
controlling for inflation, I find that the increase in wage rates is close to
24% higher in villages where the reliability worsened or stayed at the same
level than in villages where the reliability improved. Although the
observation may merely be a correlation, a causal link between electricity and
agriculture, wherein better quality electricity drives down agricultural labor
wages, may have implications for our understanding of the role of electricity
interventions in agrarian economies.

For my study, I use data from two panels of Indian villages surveyed as part
of the India Human Development Surveys (IHDS), covering a period from 2004-5
to 2011-12. Among lower and lower-middle-income countries, India has been
among the forerunners in the expansion of energy access, and studying the
causal effects of reliability in India has consequences for several countries
of the Global South expected to ramp up their power generation and
distribution to meet electrification targets in the coming years. To
investigate if improved electricity access is causally driving down wages, I
employ a difference-in-differences design, while controlling for several
village-level variables, to suitably identify the effects of reliability on
the wage rates, and eliminate biases due to village- and time-fixed effects.
My analysis is lent further aid by the rich IHDS survey data sets, which have
data on several variables both at the level of households and villages. The
data, representative at the national level, includes several essential
variables, apart from reliability, such as the time since when a village has
electricity, and the fraction of households in the village that have access to
electricity. The surveys also have data on various other characteristics such
as the status of infrastructure in the village, proximity to banks and
markets, the number of schools in the village, etc., making it easier to
control for a large number of confounding variables.

The period of study (2004-5 to 2011-12) coincides with India’s last-mile
electrification efforts, with over 90% villages already having been
electrified prior to this period, and over 99% by the end of it. Since most
villages had already been connected to the grid, rural India provides a good
stage to study the more intricate dimensions of electricity access such as
availability and disruptions. According to data from the World Bank, in 2005,
India constituted the largest rural population of the World with villages
making up 71% of the national population. At this time, agriculture, forestry,
and fisheries made up over 15% of the Indian GDP (over 100 billion 2021 USD in
size), compared to the global average of 3.2% share of the World GDP, making
India an ideal site for investigating changes in agricultural practices and
wages.

I find that improvements in electricity quality result in a lower increase in
casual agricultural labor wage rates. The effect is especially pronounced in
men’s wage rates, which consequently results in a smaller widening of the
gender wage gap in these villages. On analyzing the time spent in fuel
collection which could be a possible labor supply channel, I find that
reliable electricity reduces the time burden of biomass collection, which
could potentially increase labor supply. On the other hand, analyzing labor
demand, I find that the demand for labor does not change with reliability.
Since disguised unemployment and saturated labor demand are existing concerns
in the Indian agricultural sector, households may not be able to reap
reliability-linked benefits to income that are found elsewhere Dang and La,
(2019); Pepino et al., (2021). Instead, a possible increase in labor market
participation could potentially hurt wage rates. However, I find that
electrical farm machinery such as groundwater pumps have a large positive
effect on the demand for labor. Therefore, policymakers could focus on greater
investments in electric pumps or other alternate avenues that could increase
labor demand and absorb the surplus labor, to help reduce what may be an
electricity-induced aggravation of disguised unemployment.

The paper is organized as follows: In the next section, I discuss some
theoretical arguments and the state of the literature. In the third section, I
discuss the institutional context and present the data. This is followed by
the empirical strategy in the fourth section, and the results in the fifth. In
the final section, I discuss the conclusions.

##  2 Theoretical Arguments and Evidence from the Literature

Although several studies have looked at the effect of electricity quality and
outages on industrial performance (Allcott et al.,, 2016; Maruyama Rentschler
et al.,, 2019; Fakih et al.,, 2020), few studies have investigated the
economic benefits of reliability on well-being, in general (Nduhuura et al.,,
2021; Hussain et al.,, 2023), and lesser still on agriculture. Studies find
mostly positive impacts of electricity quality on income including Chakravorty
et al., (2014) in India, Dang and La, (2019) in Vietnam, and Pepino et al.,
(2021) in the Philippines. Samad and Zhang, (2016), however, find that
electricity reliability reduces non-farm income in India (2% reduction for
every additional hour of power).111 Samad and Zhang, (2016) are primarily
interested in studying the effects of electricity access, and reliability is
used as more of a control variable. The authors use propensity-score-weighted-
regressions to remove selection bias in which households receive access, and
the estimates for the effect of reliability may be biased by the weights. They
also include households not connected to the grid as a control, assuming zero
reliability, but these households may not benefit the same way as households
who observe no changes in their existing reliability. Given that the main
source of non-farm income in rural India is agricultural labor, this would be
consistent with a reliability-induced fall in wage rates that we see in Table
1.

There are several ways through which the quality of electricity could impact
agricultural practices and production (Costantini and Gracceva,, 2004), which
could have an impact on the demand for labor. Studies have shown that
electricity access increases the adoption of electric pumps (Barnes and
Binswanger,, 1986; Smith and Urpelainen,, 2016). If irrigation pumps are
operated at fixed times in the day, irregularity of power could affect the
usability of pumps, and agricultural households may be forced to employ
conventional methods of manual irrigation or other machinery such as diesel
pumps (Smith and Urpelainen,, 2016). Agricultural mechanization may supplant
human labor used in farms (Baur and Iles,, 2023), and the replacement could
reduce labor demand. However, the presence of the machines may otherwise also
lead to an increased demand for workers to operate the machines or simply as a
consequence of increased productivity or farming during dry seasons. In the
Indian context, the latter is more likely. Reliability may also affect
agriculture indirectly, by affecting decision-makers and laborers, which may
alter the demand and supply of agricultural labor in villages. Better quality
electricity could help households diversify their income through
entrepreneurial options, as well as new employment opportunities that may have
opened up because of better reliability. The demand for agricultural labor
demand may also be affected by enhanced affluence, particularly from non-farm
income increases, such as the effects observed by Chakravorty et al., (2014).
This may reduce households’ reliance on agriculture, which may, in turn,
reduce the demand for agricultural labor.

In the context of labor supply, electricity access is also thought to have a
positive impact on labor market participation (Dinkelman,, 2011; Salmon and
Tanguy,, 2016; Rathi and Vermaak,, 2018). Labor supply could increase via a
reduced time burden of domestic chores, especially for women. Since reliable
electricity is essential for disruption-free electric lighting and
refrigeration, better quality electricity could relieve individuals of the
time spent in fuel collection (Samad and Zhang,, 2016; Njenga et al.,, 2021),
and may enable them to participate in the labor market, which could increase
the supply of labor. Whether there is a surplus of agricultural labor and
disguised unemployment (Robinson,, 1936) in India and other developing
countries has been a topic of interest (Wellisz,, 1968). Foster and
Rosenzweig, (2010) suggest that surplus labor already exists in Indian
agriculture. Thus additional electricity-induced participation in casual
agricultural labor markets is only likely to reduce the village-level wage
rates if there isn’t a proportional increase in the demand for labor. However,
a reduction in the time burden of domestic chores may not necessarily
encourage household members to engage in paid employment. In the case of
agricultural households, this may result in members of the household supplying
more labor to household farms, which could reduce the demand for hired labor.

The evidence on the relationship between electrification and agricultural
labor has been mixed, although most studies look at the presence of
electricity connections rather than the quality of electricity. In the context
of casual labor, Van de Walle et al., (2017) study the impact of electricity
access on labor supply and wage rates in India using a long period from
1982-1999. The authors find a negative impact on the number of days of casual
wage work supplied by men, from both household and village-level
electrification, although they do not find such effects for women. However,
using data from harvest wage rates, the authors do not find a statistically
significant impact of electrification on wage rates, either for men or women.
To study village-level electrification, though, Van de Walle et al., (2017)
use only one dimension of electricity – the time since the village was first
connected. In contrast, Emran and Shilpi, (2018) use the fraction of connected
households as a control variable in their study of the effects of agricultural
productivity on hired labor and find a statistically significant negative
effect associated with the fraction of households connected, implying that
villages with better access to electricity saw agricultural households hiring
less labor. Using solitary variables, as in the cases above, may lead to
omitted variable biases and paint an incomplete picture. Therefore in my
analysis, I complement the reliability variable with the fraction of
households connected and also use the time since the village was first
connected as a control to test for endogeneity in treatment assignment, to
appropriately arrive at unbiased estimates of the effect of reliability.

##  3 Setting and Data

###  3.1 Historical Context

At the time of India’s independence from the British Empire, in 1947, a
majority of India’s population was neither educated nor literate, with its
rural population particularly worse off. In the absence of industries, rural
India was overwhelmingly agrarian. In 1951, agriculture and allied sectors
contributed to 51.9% of India’s GDP (Wagh and Dongre,, 2016). While that
number has since fallen sharply to 17% in 2014-15, a majority of Indians
continue to remain employed in agriculture. Therefore, it comes as little
surprise that Government efforts in the second half of the 20th century toward
electrifying villages were focused on connecting farms, rather than
households.

Figure 1: Time series of the total number of Indian villages with electricity
connections in India since 1947. Source: Central Electricity Authority, (2021)
Figure 2: Time series of the total electric power consumption by the
agricultural sector in India since 1947. Source: Central Electricity
Authority, (2021)

Investments in bringing electric pumps to villages were further accelerated by
the famine in Bihar in 1966-1976, and the drought in Maharashtra in 1970-1973
(Dubhashi,, 1992). In 1969, the Ministry of Power established the Rural
Electrification Corporation to oversee village electrification, with the
specific purpose of aiding State Electricity Boards in facilitating the
adoption of electric groundwater pumps in villages to increase agricultural
productivity. The droughts and famines of the 1960s and 1970s, ultimately
along with the Green Revolution in the 1970s, led to an expeditious increase
in the rate of village electrification in India, as can be seen in Figure 1.
Despite the growth in the number of villages connected, power consumption by
the agricultural sector remained low, and consumption only crossed 10,000GWh
in the late 1970s (Figure 2), with an annual consumption of 12,028GWh in 1979,
at the end of the Fifth 5-year Plan.222Reported progress at the end of the
Indian financial year (31st March) on the final year of the Plan. By the end
of the Seventh Plan, in 1997, electricity consumption had grown sevenfold. The
growth in agricultural power consumption and the overall installed capacity
(Central Electricity Authority,, 2021) may imply that even though many
villages were connected to the grid in the mid-late 1900s, the quality and
quantity may have been subpar, or it may mean that it takes a longer time
before households can make use of their electricity connections (Nag and
Stern,, 2023).

Village electrification continued at a steady rate till the turn of the
century, and by 2002, approximately 90% villages333According to the 2001
census, India had 593,732 inhabited villages in 2001. had been connected
(Central Electricity Authority,, 2021), although a large number of households
in these villages were yet to be connected as of 2004-5 (Desai et al.,, 2005).
In fact, until the year 2003, power consumption by the agricultural sector
exceeded domestic power consumption at the national level (Central Electricity
Authority,, 2021). It is only in 2005 that the government began prioritizing
household connections, and launched the Rajiv Gandhi Grameen Vidyutikaran
Yojana (RGGVY) to connect the remaining unelectrified households. It is during
this period that my study is set.

###  3.2 Study Period and Dataset

For my analysis, I use data from two waves of the India Human Development
Survey (Desai et al.,, 2005, 2012). The first round of the survey was
conducted in 2004-2005, and the second round was conducted in 2011-2012. This
makes for a suitable period, as most villages had already been connected,
allowing for a larger sample of villages where changes in reliability can be
observed and studied. However, since a number of households would be connected
in this period, my analysis would need to control for the fraction of
households in a village that were connected to the grid. According to the
Central Electricity Authority, (2021), transmission and distribution losses
were considerably higher than average in this period (31.25% in 2004-5 which
reduced to 23.65% by 2011-12). This would have an impact on the reliability of
electricity and induce variation in the sample. This period is also roughly 15
years after the shock of economic liberalization in India, and the benefits to
economic growth had already set in, distinct from earlier work that
investigated wage rates before and after the economic liberalization of 1991
(Van de Walle et al.,, 2017).

The IHDS-I survey covered 41,554 households, and IHDS-II covered 42,152
households in total from 384 districts, 1,503 villages, and 971 urban blocks.
Since I am primarily interested in village-level quantities like labor wage
rates, I restrict my preliminary analysis to the village questionnaires and
data. In all, I construct a panel of 1406 villages from the two survey rounds.
I further use household-level data to study labor demand and supply in an
attempt to explain the phenomena observed in the village-level analysis.
Although there are differences in some of the questions asked in each survey,
most of the variables of interest are present in both rounds.

The IHDS surveys make for an excellent data set to study the impact of
electrical reliability on agricultural labor wages. The survey has detailed
data on several insightful dimensions of village-level electricity. The survey
tells me whether a village has electricity, what fraction of households in the
village have electricity, when the village was first connected to the grid,
and the average number of hours in a day that a village receives power.
Similarly, the surveys also have data on the casual agricultural wage rates
for sowing, and harvesting, for both men and women and for both major cropping
seasons in India - Kharif (summer/monsoon) and Rabi(winter). Since the surveys
also have household-level data, I can further investigate the labor demand and
supply channels contributing to the trends observed in agricultural wages, at
a more microscopic level, by studying changes in agricultural households.

###  3.3 Descriptive Statistics

Since I work with observed data, rather than an experiment, whether a village
experienced improvements or declines in reliability need not necessarily have
been determined by randomization. Therefore, it is important to check if the
assignment of villages to different treatment doses i.e., different levels of
improvement or declines in electricity reliability were influenced by other
factors. The ideal way to check whether the treatment is “As good as random”
is to see if there are parallel trends in the treatment groups in the pre-
treatment periods. However, in the absence of multiple pre-treatment periods,
the best I can do is make the observation that the pre-treatment levels of
wages are essentially indistinguishable in the pre-treatment period (Table 1).

| Means (standard deviation) |  |
---|---|---|---
| 2004-2005 |  | 2011-2012 |  |
|  |  |  |  |  |  | Correlation of
|  |  |  |  |  |  |  $\Delta$Reliability with
| Positive Treatment | Negative Treatment |  | Positive Treatment | Negative Treatment |  | Initial levels (2004-5)
Percentage of households with Electricity access (%) | 69.98 | 71.50 |  | 80.36 | 79.90 |  | 0.00
| (32.83) | (30.19) |  | (25.99) | (24.86) |  |
Presence of Metalled Roads† | 0.62 | 0.73 |  | 0.86 | 0.90 |  | -0.09∗∗
| (0.49) | (0.45) |  | (0.35) | (0.31) |  |
Distance to the nearest bank branch/credit cooperative (km) | 5.27 | 4.03 |  | 5.44 | 4.54 |  | 0.10∗∗∗
| (6.05) | (4.61) |  | (5.99) | (4.95) |  |
Distance to the closest market (km) | 2.37 | 2.38 |  | 2.43 | 2.78 |  | 0.00
| (4.69) | (4.33) |  | (5.26) | (5.50) |  |
Presence of NGO/Development Organization† | 0.11 | 0.15 |  | 0.14 | 0.14 |  | -0.07∗
| (0.31) | (0.36) |  | (0.35) | (0.35) |  |
Presence of Primary Healthcare Center† | 0.11 | 0.17 |  | 0.12 | 0.15 |  | -0.09∗∗
| (0.32) | (0.44) |  | (0.33) | (0.36) |  |
Number of Government Primary Schools† | 1.54 | 1.95 |  | 1.67 | 1.52 |  | -0.17∗∗∗
| (1.34) | (1.89) |  | (1.54) | (1.78) |  |
Number of Government Middle Schools† | 0.70 | 0.74 |  | 0.88 | 0.89 |  | -0.09∗∗
| (0.68) | (0.74) |  | (0.73) | (0.72) |  |
Number of Government Secondary Schools† | 0.28 | 0.34 |  | 0.38 | 0.42 |  | -0.05
| (0.51) | (0.53) |  | (0.66) | (0.61) |  |
Number of Government Higher Secondary Schools† | 0.11 | 0.14 |  | 0.18 | 0.17 |  | -0.03
| (0.35) | (0.37) |  | (0.44) | (0.43) |  |

Table 2: Descriptive statistics for treatment and control Villages 2004-2005
and 2011-2012. “Positive Treatment” villages refer to those villages which see
an improvement in the average number of hours of electricity received in a
day, and “Negative Treatment” villages refer to those which do not (These
include control villages where there is no change). Correlation refers to
Pearson’s correlation coefficient, with the two-sided alternate hypothesis.
Includes 1254 villages. The data are unweighted. Source: IHDS I, and IHDS II
surveys.
† Dummy variable which takes 1 for “yes” and 0 for “no”.
*Significant at the 5% level, **Significant at the 1% level, ***Significant at the 0.1% level Figure 3: Average state-wise changes in women’s casual agricultural labor wage rates (2012 Rs.) between 2004-5 and 2011-12. Averages are unweighted. Source: Desai et al., (2005) Figure 4: Average state-wise changes in men’s casual agricultural labor wage rates (2012 Rs.) between 2004-5 and 2011-12. Averages are unweighted. Source: Desai et al., (2005) Figure 5: Average state-wise changes in the gender wage gap (men’s rate - women’s rate) (2012 Rs.) between 2004-5 and 2011-12. Averages are unweighted. Source: Desai et al., (2005) Figure 6: Average state-wise changes in reliability (hours per day) between 2004-5 and 2011-12. Averages are unweighted. Source: Desai et al., (2005)

Another method through which one could eliminate the possibility of selection
bias in assignment to treatment due to covariates is by measuring the
correlations between pre-existing levels of village characteristics and the
treatment dose. Table 2 presents the mean and standard deviations of village
characteristics of villages that receive more reliable electricity access and
those that do not along with Pearson’s correlation coefficient of the change
in reliability with the 2005 levels of the variables. Overall, there are only
marginal differences between most characteristics of treatment and control
group villages, and the difference remains similar over time which encourages
a difference-in-differences approach (Wing et al.,, 2018).

Since the correlations between pre-treatment characteristics and the change in
reliability are weak, I assume that the treatment is random. This is not an
unreasonable assumption as changes in reliability, are hardly conscious
decisions made by policymakers based on village characteristics, and are
mostly a result of transmission and distribution losses. A more formal
analysis of whether the assignment to treatment is random or not is presented
in Appendix A.

In 2004-5, the average hours of electricity available to villages was 11.76
hours with a standard deviation of 7.80 hours, which increased to 13.38 hours
in 2011-12, with a standard deviation of 6.74 hours. The correlation between
household-level reliability and village-level reliability is 0.5491 and
0.6628, in 2004-05 and 2011-12, respectively, both coefficients being
exceptionally statistically significant. While there is some parity between
household responses on reliability, there are large variations as well, with
households often even reporting higher quality than that received at the
village level, which may indicate that the data at the household level for
electricity quality may not be as trustworthy.

|  | Means (standard deviation)
---|---|---
|  | 2004-2005 |  | 2011-2012 |  | $\Delta$
|  | Increase in | Non-increase in |  | Increase in | Non-increase in |  | Increase in | Non-increase in
|  | Reliability | Reliability |  | Reliability | Reliability |  | Reliability | Reliability
Women’s daily wage rate (2012 Rs.) | summer-monsoon | 86.73 | 85.94 |  | 134.2 | 138.9 |  | 41.23 | 46.53
|  | (39.8) | (42.51) |  | (59.26) | (70.89) |  | (48.73) | (50.46)
| Winter | 84.56 | 85.63 |  | 132.93 | 138.86 |  | 40.02 | 48.01
|  | (40.91) | (42.399) |  | (56.43) | (68.46) |  | (48.19) | (52.45)
Men’s daily wage rate (2012 Rs.) | summer-monsoon | 116.99 | 119.66 |  | 168.26 | 183.48 |  | 51.24 | 61.93
|  | (51.51) | (52.81) |  | (68.21) | (83.76) |  | (57.66) | (63.38)
| Winter | 115.95 | 117.95 |  | 166.76 | 184.65 |  | 49.48 | 63.51
|  | (53.04) | (51.8) |  | (66.04) | (83.15) |  | (56.72) | (61.76)
Average daily wage rate (2012 Rs.) | summer-monsoon | 105.37 | 105.46 |  | 151.52 | 161.73 |  | 45.53 | 54.75
|  | (46.86) | (46.57) |  | (61.01) | (74.74) |  | (51.71) | (56.00)
| Winter | 105.23 | 104.74 |  | 150.13 | 162.96 |  | 42.8 | 55.58
|  | (49.01) | (46.66) |  | (58.52) | (73.85) |  | (51.1) | (54.81)

Table 3:  Casual agricultural labor wage rate statistics for villages where
reliability improves and villages where it does not, segregated by gender and
cropping seasons. Reliability is measured as the average number of hours of
electricity available per day. Includes 1281 Indian villages. The wages have
been standardized to the 2012 Rupee by multiplying the 2005 rates by the
national consumer price index (reported by the World Bank). Averages are
unweighted. The data are unweighted. Source: IHDS I, and IHDS II surveys.

The IHDS surveys have data on casual agriculture wage rates in villages for
both major cropping seasons in India - the Kharif season where crops are sowed
towards the end of the summer, around June, and harvested at the end of the
monsoon around October and November, and the Rabi season in which crops are
sown at the start of winter, around mid-late November, and harvested in
Spring, in April. Rice and maize are the main Kharif crops in India, while
wheat is the main Rabi crop. I construct the wage rates for each category by
averaging the wage rates for sowing and harvesting, when available444In the
absence of one of the two, I use the wage rate for the task that is available.
Plowing wage rates were also present in the first round of the survey but I
excluded plowing wage rates from the outcome variable, as plowing was
relegated to the category of “Other agricultural work”, a category which may
include wages for other work such as winnowing or threshing.. From the data
(Table 3), I can observe that there is little seasonal variation in the wage
rates. However, the wage rates are usually higher for men than for women, with
men earning nearly 35% more than women in 2004-5. Even though the wage gap
shrinks slightly by 2011-12, there is still a considerable disparity. Figure
3-6 shows the distribution of changes in real wage rates for agricultural
labor for women (Figure 3), men (Figure 4), the gender wage gap (Figure 5),
and reliability (Figure 6). Typically, in states where reliability reduces,
the gender wage gap increases, while in states where reliability increases,
the gender wage gap reduces (Rajasthan, Punjab, and Northeast Indian states
being the only examples), with the correlation between changes in state-wise
reliability and the gender wage gap being -0.33. It is noteworthy that the
reliability-associated differences in the growth of wages discussed earlier in
Section 1 exist in seasonal wages as well. The difference, however, is less
for women’s wage rates, than men’s wage rates, suggesting that gender may play
a role in the dynamics of the effect on wage rates.

##  4 Empirical Strategy

###  4.1 Difference-in-differences specification

I begin with a two-way fixed effects specification for village $j$:

$y_{jt}=\alpha_{j}+\beta R_{jt}+\gamma
X_{jt}+\mu_{t}+\epsilon_{jt},~{}~{}~{}t=2005,2012$ (1)

where the outcome variable $y_{jt}$ can be either the log or level of the
casual agricultural labor wage rate of the village, $R_{jt}$ is the
electricity reliability, $X_{jt}$ is the vector of control variables,
$\alpha_{j}$ is the village-specific fixed effect, $\mu_{t}$ is the time-
specific fixed effect, and $\epsilon_{jt}$ is the idiosyncratic error term.
The control variables that I use are the fraction of households in the village
with electricity access, whether there are metalled roads, NGOs or development
organizations, primary healthcare centers in the village, the distances to the
closest bank or credit cooperative, and market, the number of government and
private primary, middle, secondary and higher secondary schools, and whether
the village has less than 1000 inhabitants or more than 5000 inhabitants. I am
interested in the coefficient $\beta$ which quantifies the effect of
reliability. Taking the first difference of equation 1 in time,

$\Delta y_{jt}=\Delta\mu_{t}+\beta\Delta R_{jt}+\gamma\Delta
X_{jt}+\Delta\epsilon_{jt},~{}~{}~{}t=2012$ (2)

Since I have data on wage rates, reliability, and control variables, that
allow me to generate a cross-section of differences as required for the above
model, I can estimate the coefficients using an ordinary least squares (OLS)
regression, with $\Delta\mu$ as the intercept.

At this stage, it is important to note that the reliability is constrained to
be between 0 and 24 hours a day. Therefore even if the assignment is random,
only villages with access to high-quality electricity in the pre-treatment
period would be able to experience a large negative treatment, and similarly,
only villages with poor reliability in the pre-treatment period will be able
to experience large benefits. This association poses a problem to the
identification, as it will be difficult to determine if the effects are
attributed to the changes or rather a lagged effect arising from pre-treatment
levels. Is an increase in reliability reducing wages, or are wages reducing
because the village historically has low reliability? One simple way to deal
with this problem is to include the pre-treatment level of reliability in the
regression as well, so that the regression does not suffer from omitted
variable bias, and the effects can be independently attributed to the change
in reliability and the pre-treatment level of reliability. Incorporating a
lagged effect of the pre-treatment level ($R_{jt-1}$), the model stands as
follows:

$\Delta y_{jt}=\Delta\mu_{t}+\beta_{1}\Delta
R_{jt}+\beta_{2}R_{jt-1}+\gamma\Delta
X_{jt}+\Delta\epsilon_{jt},~{}~{}~{}t=2012$ (3)

Including the pre-treatment level in the difference equation, is the same as
including a partial sum of reliability in the original two-way fixed effects
specification. Thus, the correct form of equation 1 is

$y_{jt}=\alpha_{j}+\beta_{1}R_{jt}+\beta_{2}\sum_{s=t_{0}}^{t-1}R_{js}+\gamma
X_{jt}+\mu_{t}+\epsilon_{jt},~{}~{}~{}t=2005,2012$ (4)

Clearly, the fraction of households connected, which is a control variable,
has the same problem, as values range from 0 to 1, and thus I also incorporate
a pre-treatment level of the fraction as a control variable. The coefficient
$\beta_{1}$ in Equation 3, would give an unbiased estimate of the effect of
changes in reliability.

However, there is little reason to believe that the effects of reliability
would be symmetrical, i.e., the effects of a positive change in reliability
need not quantitatively be the opposite of the effects of a negative change in
reliability. Therefore, I consider two separate natural experiments, one to
study the effect of improvements in reliability, and one to study the effect
of a decline in reliability. The advantage of splitting the sample into
villages that include only positive or negative samples is that I require
changes in reliability – the treatment – to be randomly assigned only within
each sample. For instance, there could be a selection bias in determining
which villages receive positive and negative treatment, but as long as the
magnitude of the treatment is random, the results will hold. In Appendix A, I
investigate whether the treatment is genuinely random. We find that for
villages where reliability increases, the treatment can be assumed to be as
good as random, and the results of the difference-in-differences estimates
will be unbiased. However, for villages where reliability reduces, there may
be some endogeneity in the assignment to treatment and the results may not be
as robust. Therefore, when we split the sample, as opposed to including two
treatment variables in a single regression, we are able to isolate a sample of
villages and a type of treatment that is as good as random.

###  4.2 Mechanisms: Labor Demand and Supply

Since there are no village-level variables for demand and supply, I use
household-level data to study the demand and supply of labor, although I use
village-level reliability as the treatment, as households often report
considerably different levels of electricity reliability and the household
data may not be as accurate.555Since all the electricity comes from a common
distributor in the village, the variation in quality is unlikely to be as much
as I find in the household data. While there may be losses that affect
individual households, sometimes households report levels of electricity
reliability that exceed the levels received by the village, which indicates
that household reporting may be an unreliable source of information for
electricity quality. Furthermore, even load shedding is typically carried out
for entire villages and not individual households. Moreover, the quality of
electricity available to houses may not be the same as the quality of
electricity available to farms (which will be useful in studying labor
demand), and in the absence of data on electricity reliability in farms, the
village’s reliability is the best proxy.

There is no variable in the survey that can be directly used as a measure of
labor supply. Thus, I investigate a channel through which labor supply may be
affected, as this is easier to test from the data. I postulate that the supply
of labor would be affected by electricity quality primarily through a reduced
time burden of domestic chores. In particular, the tasks that would be
especially affected by electricity disruptions are lighting and refrigeration.
4045 households in the sample (21.2%) claimed to own refrigerators in 2011-12.
Long disruptions would hamper refrigeration, and the presence of good-quality
electricity could reduce the time and fuel required for cooking, through
refrigeration. Since the survey does not have questions related to the time
spent cooking, I am constrained to restrict my analysis to fuel. About 75%
households use firewood for either cooking or lighting. Over 40% households
also use dung for cooking. A large number of households collect these fuels
from either owned or community land, good-quality electricity could
significantly reduce the time spent by members in collecting biomass. Thus, as
a supply channel, I study the effect of electricity reliability on the time
spent by men and women on fuel collection. I use a difference-in-difference
specification, similar to equation 3. For a household $i$ in village $j$, we
model the time spent in fuel collection $F_{ijt}$ by

$\Delta F_{ijt}=\Delta\mu_{t}+\beta_{1}\Delta
R_{jt}+\beta_{2}R_{jt-1}+\gamma\Delta
X_{ijt}+\Delta\epsilon_{ijt},~{}~{}~{}t=2012$ (5)

where most of the coefficients represent the effects of the same quantities as
before (on time spent in fuel collection), except $\gamma^{\prime}$ which now
includes coefficients for some household-level controls along with the
village-level controls listed about. The household-level controls are the
number of adult men and women, the presence of a source of drinking water in
the house, the presence of flush toilets, and the presence of separate
kitchens. Obviously, village-level electricity quality cannot have a direct
effect on households not connected to the grid. Hence, I only consider those
households that had electricity in both 2004-5 and 2011-12. Ideally, had there
been data for how much labor individuals in a household were willing to supply
I could have studied the effect of time saved in fuel collection on such a
variable. But in the absence of such a variable, a proper analysis of labor
supply is beyond the scope of this work.

Unlike supply, demand can be studied by considering the person-days of labor
hired by agricultural households to work on their farms. Of course, I need to
control for the labor wage rates as well, to avoid an omitted variable bias.
Not controlling for labor wage rates could be particularly problematic, as
reliability is correlated with the wage rates, and we know from theory that
wage rates themselves can affect the demand for labor. In addition to the
control variables listed above, we also include the change in the ownership of
electric pumps ($\Delta P_{ijt}$). We use the logarithm of the person-days of
hired farm labor $D_{ijt}$ as the outcome variable and formulate another
difference-in-differences equation

$\Delta D_{ijt}=\Delta\mu_{t}+\beta_{1}\Delta
R_{jt}+\beta_{2}R_{jt-1}+\delta_{m}M_{jt}+\delta_{w}W_{jt}+\phi\Delta
P_{ijt}+\gamma\Delta X_{ijt}+\Delta\epsilon_{ijt},~{}~{}~{}t=2012$ (6)

$M_{jt}$ is the log men’s casual agricultural wage rate in village $j$, and
$W_{jt}$ is the corresponding quantity for women’s wages. The coefficients
$\delta_{m}$ and $\delta_{w}$ are the elasticities of labor demand for men and
women, respectively, and tell us the fractional change in demand, for a
fractional change in wages. Since pump ownership could have an impact on
demand, it is also important to check whether reliability has an effect on
pump ownership and is not affecting demand through pump ownership. We do so
using another difference-in-difference regression:

$\Delta P_{ijt}=\Delta\mu_{t}+\beta_{1}\Delta
R_{jt}+\beta_{2}R_{jt-1}+\gamma\Delta
X_{ijt}+\Delta\epsilon_{ijt},~{}~{}~{}t=2012$ (7)

##  5 Results

###  5.1 Effect of electricity quality on wage rates

Table 4 presents the results of the difference-in-differences regressions for
the impact of a change in the electricity reliability (both positive and
negative) on the change in men’s and women’s casual agricultural labor wage
rates. Although we present the results for both positive and negative
treatments, only the positive treatments can be assumed to be as good as
random, and the estimates of the effects of negative treatment may not be as
robust. First, I find that reliability has a negative effect on both logs and
levels of casual agricultural labor wages, for both men and women, although
the effect on women’s wage rates is considerably smaller, and not
statistically significant. For every additional hour’s increase in electricity
reliability in a village, men’s casual agricultural wage rates reduce by
nearly Rs. 2 or 1.34% in relative terms. The effect on both levels and logs is
statistically significant at the 1% level. On the other hand, positive
treatments have small and statistically insignificant impacts on women’s wage
rates.

The effects of a decrease in quality, however, has consistently larger effects
on both men’s and women’s wages. For every additional hour’s reduction in
electricity availability, men’s wage rates rise by over Rs. 2 (significant at
the 1% level), although the effect on the log of wage rates is small (Rs. 0.6)
and not significant. Similarly, women’s wage rates increased by Rs. 1.2
(significant at the 5% level), although the effect on log wages is not
statistically significant. Nevertheless, this implies that the effects of
changes in reliability are asymmetrical, particularly for women’s wage rates,
and wage rates are more sensitive to reductions in reliability than increases.
But these estimates are not as robust as those for improvements in reliability
as the assignment to treatment may be biased by initial characteristics.

|  | Coefficients (standard errors)
---|---|---
|  | Positive Treatment |  | Negative Treatment
|  | Women’s Wage Rates |  | Men’s Wage Rates |  | Women’s Wage Rates |  | Men’s Wage Rates
|  | (2012 Rs.) |  | (2012 Rs.) |  | (2012 Rs.) |  | (2012 Rs.)
|  | $N=658$ |  | $N=720$ |  | $N=580$ |  | $N=637$
| Independent Variable | Levels | Logs |  | Levels | Logs |  | Levels | Logs |  | Levels | Logs
| Intercept | 29.2885∗∗∗ | -0.2267∗∗∗ |  | 51.7882∗∗∗ | -0.0721 |  | 13.2521 | -0.3864∗∗∗ |  | 29.7162∗∗∗ | -0.2218∗∗∗
|  | (6.8389) | (0.0642) |  | (8.1564) | (0.0591) |  | (9.2220) | (0.0663) |  | (9.5753) | (0.0547)
|  $\Delta$ Reliability (hours) | -0.7196 | -0.0060 |  | -1.9521∗∗∗ | -0.0134∗∗∗ |  | -1.1836** | -0.0065 |  | -2.0438∗∗∗ | -0.0061
|  | (0.5177) | (0.0044) |  | (0.6213) | (0.0042) |  | (0.5991) | (0.0045) |  | (0.7467) | (0.0049)
| Pre-treatment Reliability (hours) | -0.6470 | -0.0047 |  | -1.4216∗∗∗ | -0.0104∗∗∗ |  | -1.1436∗∗ | -0.0050 |  | -1.7940∗∗∗ | -0.0091∗∗∗
|  | (0.3962) | (0.0032) |  | (0.5042) | (0.0032) |  | (0.4914) | (0.0033) |  | (0.5267) | (0.0029)

Table 4: The impact of reliability on men’s and women’s casual agricultural
labor wage rates. Positive treatment refers to the set of villages where
reliability increased or stayed the same. Negative treatment refers to the set
of villages where reliability decreased or stayed the same. Robust standard
errors clustered at the district level.
*Significant at the 10% level, **Significant at the 5% level, ***Significant at the 1% level

Interestingly, even the pre-treatment level of reliability has a statistically
significant negative effect on men’s wages– a Rs. 1.4 or a 1.04% reduction for
every hour’s increase or a Rs. 1.8 or 0.91% increase for every hour’s
reduction (all significant at the 1% level). From this, I can conclude that
the effect of the overall level, not the change alone, is negative. However,
for women’s wages, the effect is again statistically insignificant for all
cases except on the levels of the wage rate in the negative treatment case,
where wages increase by Rs. 1.4, significant at the 5% level. An obvious
consequence of different effects on men’s and women’s wages is that better
reliability leads to a smaller wage gap. From the estimated coefficients, it
is clear that the wage gap increases over time for both men and women, after
controlling for various factors, although villages where reliability improves
would experience a smaller widening of the gender wage gap when compared to a
village where reliability declines.

The effect of reliability on wage rates, however, is difficult to explain
intuitively and will require studying the effects of reliability on labor
demand and supply.

###  5.2 Time spent in fuel collection: A potential labor supply channel

In order to study the possible effect of electricity reliability on the supply
of labor, I estimate the impact of changes in reliability on the time spent by
men and women on fuel collection. Table 5 presents the results.666We include
only villages where reliability increases as the treatment in these villages
can be assumed to be random. See Appendix A. Reliability has a negative effect
on the time spent on fuel collection. In villages where reliability improved,
every additional hour of power available reduces the time spent by women on
fuel collection by 12.6 minutes per week, which is statistically significant
at the 1% level, while men’s fuel collection time reduces by 6.1 minutes per
week, significant at the 10% level. This can amount to a very large reduction
of fuel collection time if the quality changes sizeably.

| Coefficients (standard errors)
---|---
|  $\Delta$ Women’s Fuel Collection Time |  $\Delta$ Men’s Fuel Collection Time |  |
| (minutes per week) | (minutes per week) |  |
| $N=2667$ | $N=2513$ |  |
Intercept | 63.9049 | -91.7331* |  |
| (79.3594) | (53.9066) |  |
$\Delta$ Reliability (hours) | -12.6077*** | -6.1298* |  |
| (4.2664) | (3.4914) |  |
Pre-treatment Reliability (hours) | 0.6603 | -0.3436 |  |
| (3.0550) | (2.7551) |  |

Table 5: The impact of reliability on men’s and women’s fuel collection times.
Includes villages where reliability did not reduce. Robust standard errors
clustered at the district level.
*Significant at the 10% level, **Significant at the 5% level, ***Significant at the 1% level

Clearly, women’s fuel collection times are affected more, as fuel collections
like other domestic chores are typically carried out by women. Time relieved
from household chores could substantially increase the supply of labor and has
been hypothesized to be a channel for electricity-facilitated increase in
labor participation in past studies as well (Dinkelman,, 2011). However, it
may be more complicated, especially in the Indian context, and in other
overwhelmingly agrarian rural economies. In general, time saved could increase
women’s labor market participation, which would increase the supply of labor
and may have a detrimental effect on wages, or the time could be utilized in
leisure, in which case the labor market would be virtually
unaffected.777Unfortunately, there is no variable that aptly quantifies
leisure time, and I cannot test this empirically. A third possibility is that
women take up more domestic chores that used to be performed by men, freeing
up men’s time burden of domestic work, which could increase men’s labor market
participation, which would also increase labor supply. This may explain why
men’s wage rates fall more than women’s wage rates. Although that could also
be if women are hired more in the labor market because farmers may want to
hire cheaper labor. But this possibility only works on the assumption that
men’s and women’s labor is thought of to be fungible, which is unlikely to be
the case. Another alternative is that women could provide labor on household-
owned businesses, or more likely in the case of rural agricultural households
in India, women could supply labor to farms owned by their households, a
common phenomenon in Indian farms. While this would not exactly increase labor
supply in a strict sense, since it is not paid work, it could reduce the
demand for hired labor. Thus, it is also important that I study if
reliability, or even fuel collection time, has an impact on labor demand.

###  5.3 Labor demand effects of electricity reliability

To estimate the effects of electricity quality on labor demand, I study how
changes in reliability affected the number of person-days of agricultural
labor hired by farms. Table 6 presents the estimated effects on the levels and
logs of the number of days of hired work. Both the changes in reliability and
the pre-treatment level of reliability have no statistically significant
impacts on the number of days of labor hired by farms. We also do not find any
statistically significant effects of changes in wage rates, either at the
levels or logs, on demand. This suggests that the own elasticity of labor
demand is inelastic, for both men and women.

| Coefficients (standard errors)
---|---
|  $\Delta$ Number of Man-days of Hired Farm Labor
$N=593$ | Levels | Logs
Intercept | -39.3914 | -0.9263***
| (26.9972) | (0.2527)
$\Delta$ Reliability (hours) | -0.0521 | 0.0104
| (1.0093) | (0.0160)
Pre-treatment Reliability (hours) | 0.9098 | 0.0055
| (0.8845) | (0.0127)
$\Delta$ Women’s agricultural labor Wage Rate (2012 Rs.) | 0.2541* |
| (0.1374) |
$\Delta$ Men’s agricultural labor Wage Rate (2012 Rs.) | -0.1100 |
| (0.1236) |
$\Delta$ Log Women’s agricultural labor Wage Rate (2012 Rs.) |  | 0.5450**
|  | (0.2423)
$\Delta$ Log Men’s agricultural labor Wage Rate (2012 Rs.) |  | -0.4286
|  | (0.2850)
$\Delta$ Number of Electric Pumps Owned | 56.9618*** | 0.3035**
| (15.1355) | (0.0827)

Table 6: The impact of reliability on the person-days of hired farm labor.
Includes villages where reliability did not reduce. Robust standard errors
clustered at the district level.
*Significant at the 10% level, **Significant at the 5% level, ***Significant at the 1% level

| Coefficients (standard errors)
---|---
$N=4410$ |  $\Delta$ Number of Electric Groundwater Pumps Owned
Intercept | 0.0985
| (0.0655)
$\Delta$ Reliability (hours) | -0.0050
| (0.0038)
Pre-treatment Reliability (hours) | 0.0003
| (0.0026)

Table 7: The impact of reliability on the ownership of electric pumps.
Includes villages where reliability did not reduce. Robust standard errors
clustered at the district level.
*Significant at the 10% level, **Significant at the 5% level, ***Significant at the 1% level

An increase in the number of pumps, however, has a large statistically
significant positive impact on the demand for labor. Although the distribution
of pumps may not be random thus making the sizes difficult to interpret, the
positive effect of pump ownership on log changes in the number of hired days
is significant at the 1% level. Pump ownership increases the number of person-
days of hired labor by 57 days or 30% in relative terms, which is close to two
months for one laborer. This effect does not disappear even after controlling
for baseline levels of household affluence using the 2004-5 levels of
expenditure. The positive effect, signifies, that electrical equipment such as
irrigation pumps do not supplant labor, and instead drive up the demand for
labor. This increase in demand could be because pumps may require labor to
operate, or because pumps could increase agricultural productivity,
incentivizing higher investments in agriculture. It could also arise from
pumps facilitating agriculture during the dry seasons, which is most likely.
Thus, the possibility that electricity quality reduces labor wages through the
mechanization of agriculture may be ruled out. To ensure that reliability does
not affect labor demand through increased pump ownership, I test the effect of
changes on reliability on pump ownership. Table 7 presents the results, and
clearly there is no statistically significant increase (or decrease) in pump
ownership due to increases in reliability.

##  6 Conclusion

I use village and household-level data from rural India to study how
agricultural labor wages responded to improvements in electricity quality
between 2004-5 and 2011-12. Using the India Human Development Survey which
includes a panel of 1406 villages, I employ a difference-in-differences design
to eliminate village and time-fixed effects. The major finding is that
villages where electricity quality increases substantially experience a
smaller increase in casual agricultural labor wage rates than villages where
there are smaller or no improvements in electricity reliability. The effect is
larger and statistically significant for men’s wage rates, while the effect on
women’s wage rates is statistically insignificant, although still negative. As
a result, villages with large improvements in electricity quality see a
smaller widening of the gender wage gap, both in absolute and relative terms.
This effect of electricity quality on wages also helps explain the negative
effect of reliability on non-farm income found by Samad and Zhang, (2016).

Since changes in electricity reliability could affect wage rates through labor
and supply, I also investigate such possibilities. In the absence of
appropriate variables for supply, I study a channel that could potentially
affect supply instead – the effect of reliability on the time spent by men and
women in fuel collection, hypothesizing that a smaller time burden of
household chores could encourage greater participation in labor markets. I
find that the effect of reliability on women’s fuel collection times is
statistically significant (and on men’s times to a lesser extent). Women’s
fuel collection times are reduced more for every additional hour of
electricity availability, as they already spend a greater time in fuel
collection in comparison to their male counterparts. Despite the smaller time
saved by men, the effect on men’s wages may be larger because the time saved
by women may not necessarily be spent on employment opportunities, and women
may simply displace men in other domestic chores that are not affected by
better-quality electricity. An unrelated implication of negative effects on
the time spent in collecting primitive fuels is that it suggests that better
quality electricity may facilitate households’ assent on the fuel ladder (van
der Kroon et al.,, 2013).

Since a smaller time burden need not necessarily initiate wage work, and
households may also work in household farms, thereby driving down demand, it
is also important to investigate how the demand for hired agricultural labor
responds to improvements in electricity quality. I find no effect of
electricity reliability on the number of person-days of hired work, implying
that while supply increases, the demand does not change with electricity
quality. Since demand does not increase, surplus labor aggravates the levels
of disguised unemployment and drives down wages. Thus, better quality
electricity indirectly causes an overall negative effect on the agricultural
labor market and labor wage rates.

What does, however, increase the demand for labor is the adoption of electric
groundwater pumps, as these would require more hired labor to operate, and can
lead to an increase in the number of cropping seasons driving up labor demand
during the off-season. This has serious policy implications for governments in
the Global South. Interventions such as household electricity connections or
improvements in the quality of electricity supplied that could increase labor
participation may be detrimental to wage rates due to the already saturated
labor markets with persistent disguised unemployment, but the situation may be
alleviated if improvements in electricity quality are also accompanied by
other changes that could help absorb the surplus labor. Since the labor supply
effects of electricity access and quality in rural areas are well-known,
governments should focus on simultaneously enabling farms to make good use of
that electricity through pumps and other infrastructure or invest in alternate
avenues to proportionately increase the demand for labor.

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##  Appendix A Testing the Randomness of Treatment Assumption

That the treatment is as good as random, i.e., there are no factors observable
or otherwise that bias the assignment to treatment is a key identifying
assumption for difference-in-differences analyses. However, this needs to be
tested formally. The method typically used to control for selection bias due
to covariates is propensity scores. Propensity scores measure the probability
of a unit receiving the treatment that it does based on covariates prior to
the assignment of treatment. In this case, it quantifies the probability of a
village experiencing the improvement or decline in the quality of electricity
that it did. If there are no statistically significant coefficients associated
with covariates, it means that the effect of covariates in biasing treatment
assignment is indistinguishable from zero. If coefficients are statistically
significant, it implies that the treatment may not be random.

Clearly, the change in reliability, my treatment variable is a continuous
variable potentially ranging from -24 hours to +24 hours. Therefore, I
estimate the effect of covariates on the assignment of the treatment dose,
which would be a precursor to estimating propensity scores if the treatment
were non-random. Following Hirano and Imbens, (2004), I assume a normal
distribution of the treatment ($T_{i}$) given the covariates ($X_{i}$):

$T_{i}|X_{i}=\mathcal{N}(\beta_{0}+\beta^{\prime}X_{i},\sigma^{2})$ (8)

Here, $\beta_{0}$ and $\beta^{\prime}$ can be found by estimating from the
linear model

$T_{i}=\beta_{0}+\beta^{\prime}X_{i}+\varepsilon_{i}$ (9)

and $\sigma$ is the standard deviation of the residuals $\varepsilon_{i}$. The
control variables used in the vector $X_{i}$ are the fractions of various
caste populations in the villages, the size of the villages, and the pre-
treatment levels of the presence of metalled roads, the number of government
schools, the distance from the nearest bank or credit organization, the
distance from the nearest market, the presence of NGOs or development
organizations, the presence of primary healthcare centers, the fraction of
households connected to the grid, and the years since the village was first
connected to the grid.

Table 8 presents the results of the first-stage regression used to estimate
propensity scores. The four columns show the estimates for four different
samples of villages for two different types of treatment – the first is the
sample of villages that had data for women’s wage rates for villages where
reliability improved and the second is the sample of villages where
reliability improved that had data for men’s wage rates. The third and fourth
are the corresponding estimates for villages where reliability depreciated.
The four separate estimates are necessary also because we estimate these
effects separately in our main study.

|  $\Delta$ Electricity Reliability
---|---
| Coefficient (standard error)
| Positive Treatment |  | Negative Treatment
| Sample of Women’s Wage Rates | Sample of Men’s Wage Rates |  | Sample of Women’s Wage Rates | Sample of Men’s Wage Rates
Covariates | $N=643$ | $N=705$ |  | $N=579$ | $N=636$
Intercept | 4.8532* | 3.7443 |  | 1.3095 | 0.2580
| (2.7732) | (2.7822) |  | (2.2233) | (1.4103)
Fraction of population – Brahmin (%) | -0.0184 | -0.0053 |  | -0.0356 | -0.0326
| (0.0310) | (0.0317) |  | (0.0263) | (0.0221)
Fraction of population – non-Brahmin forward castes (%) | -0.0032 | 0.0057 |  | -0.0201 | -0.0092
| (0.0284) | (0.0284) |  | (0.0228) | (0.0155)
Fraction of population – Other Backward Castes (%) | -0.0156 | -0.0135 |  | -0.0442** | -0.0336**
| (0.0272) | (0.0270) |  | (0.0219) | (0.0148)
Fraction of population – Scheduled Castes (%) | 0.0038 | 0.0229 |  | -0.0378 | -0.0179
| (0.0283) | (0.0289) |  | (0.0262) | (0.0180)
Fraction of population – Scheduled Tribes (%) | 0.0166 | 0.0238 |  | -0.0401* | -0.0309*
| (0.0294) | (0.0294) |  | (0.0239) | (0.0175)
Presence of metalled road | -0.2162 | 0.0685 |  | 0.0244 | 0.1114
| (0.4076) | (0.4263) |  | (0.4903) | (0.4470)
Number of government schools | -0.0673 | -0.0790 |  | -0.1347 | -0.1600
| (0.0836) | (0.0876) |  | (0.0950) | (0.0934)
Village population – small | 0.0114 | -0.1607 |  | -0.2980 | -0.3623
| (0.5696) | (0.5654) |  | (0.5220) | (0.4761)
Village population – big | -0.8590 | -0.6042 |  | -0.4257 | -0.2428
| (0.5351) | (0.5370) |  | (0.7113) | (0.6733)
Distance from bank/credit organization | 0.0558 | 0.0549 |  | -0.0868 | -0.0501
| (0.0369) | (0.0366) |  | (0.0651) | (0.0535)
Distance from market(km) | 0.0141 | -0.0070 |  | -0.1143** | -0.1052**
| (0.0449) | (0.0450) |  | (0.0456) | (0.0433)
Presence of NGO/development organization | 0.5932 | 0.1416 |  | -0.2576 | -0.3167
| (0.6640) | (0.6778) |  | (0.5496) | (0.5424)
Fraction of households connected (%) | -0.0114 | -0.0047 |  | 0.0102 | 0.0091
| (0.0071) | (0.0072) |  | (0.0092) | (0.0083)
Years since the village was first connected | 0.0302* | 0.0322** |  | -0.0523*** | -0.0565***
| (0.0164) | (0.0160) |  | (0.0169) | (0.0162)
Presence of primary healthcare center | 0.7551 | 0.5502 |  | -0.3163 | -0.3212
| (0.6095) | (0.6165) |  | (0.5049) | (0.4725)

Table 8: First stage results of the effect of 2004-5 levels of covariates on
the treatment dose (change in reliability). Robust standard errors clustered
at the district level.
*Significant at the 10% level, **Significant at the 5% level, ***Significant at the 1% level

From the table, we can see that in the villages that received a positive
treatment, no variable is statistically significant at the 5% level for the
sample of villages where data on women’s wage rate is available. For the
sample where data on men’s wage rates are available, only one variable is
statistically significant – the number of years since a village was connected
– that too being a borderline case ($p=0.0446)$. Since most of the villages
are common between these two samples, this effect may be due to correlations
arising from certain outliers. Furthermore, the effect is extremely small in
size - less than 2 minutes for every year that the village has been connected
for. To put this in context, one standard deviation in the years of connection
(about 15 years) makes a difference of only 27 minutes, which is about a tenth
of one standard deviation of changes in reliability in the sample. This is a
very small effect, and not even consistently significant in the two samples
receiving positive treatment. Therefore, it is fair to assume that the
positive changes in reliability (and controls) were as good as random. It is
noteworthy that contrary to expectations, I find that variables known to be
important in terms of connecting villages such as size (Burlig and Preonas,,
2016), or caste compositions (Aklin et al.,, 2021), do not affect changes in
the quality of electricity access in these villages. Interestingly, even the
fraction of households already connected to the grid has virtually no effect
on reliability.

On the other hand, negative changes in reliability may not random. Multiple
variables – the fraction of other backward caste members, distance from
markets, and the years since connection – are all statistically significant at
the 5% level or higher. This indicates that there may be some selection bias
due to covariates in determining how large the fall in quality is in these
samples, although the sizes of these effects are small and in most cases may
not make a large difference. Nevertheless, my results on negative treatments
are bound to be less robust, than the results on positive treatment. Due to
this asymmetry in the presence of biasing factors between positive and
negative treatment assignment, I slice the samples into two – one each for the
two types of treatment, so that at least estimates from two out of four
samples are robust.888I also attempted a propensity-score weighted regression
for this set but the common support assumption may not hold and some
households receive an extra-ordinarily high weights, in the order of several
hundreds while an overwhelming majority of weights are much lesser.