Department of Economics and International Business Working Paper No

Department of Economics and International Business Working Paper No. 17-02 January 2017

ELECTRIFICATION AND WELFARE OF POOR HOUSEHOLDS IN RURAL INDIA

Aditi Bhattacharyya Department of Economics and International Business Sam Houston State University Huntsville, TX, USA [email protected]

Daisy Das Cotton College State University Guwahati, Assam, India [email protected]

Arkadipta Ghosh Mathematica Policy Research Inc. Princeton, NJ, USA [email protected] Abstract We examine the impact of electrification on the welfare of poor households in rural India. We use two rounds of survey data (2004-2005 and 2011-2012) from the National Sample Survey Organization, and examine household welfare as captured by monthly and annual expenditures on multiple categories of goods and services among the poorest households. Using inverse propensity score weighting and difference-in-differences estimation in two separate analyses for households across all states and in eight backward states, we find significant evidence for improved welfare from electrification. This includes higher monthly expenditures in total as well as on food, fuel, entertainment, nonfood, education, and durable goods across all states. We also find evidence for higher expenditures in selected categories of goods like fuel, entertainment, nonfood, and education items after electrification in backward states. Additionally, we find that the poorest rural households in backward states experienced reduced medical expenditures after electrification.

Keywords: Rural electriﬁcation, household welfare, poor households. JEL Codes: O13, I30 I. Introduction

Access to modern energy sources like electricity is widely recognized as necessary for stimulating economic development and improving the quality of life. Energy poverty is a major obstacle to India’s economic development, preventing the modernization and efficiency-enhancing transformation of labor-intensive industries, while reinforcing income poverty and backwardness

(Rud, 2012). Compared to traditional sources of energy, modern and superior sources like electricity can enable people with low incomes ascend to a higher income group (Pachauri et al.,

2012). This paper aims to study how the use of electricity affects economic welfare of poor, rural households in India, as captured by household expenditures.

The relationship between the use of modern energy sources and economic welfare is well documented, although causality can run in both directions. In particular, electricity offer numerous benefits – from allowing the use of electric appliances providing comfort and efficiency in daily life to creating opportunities for more productive use of time. Household electrification can foster small home-based enterprises, encourage longer working and study hours, improve access to knowledge and information, and limit pollution from the use of traditional fuels for indoor lighting

– improving household income, health, and education. Several studies have found positive effects of electrification on education, income, and employment, and negative effects on poverty and fertility (Energy Sector Management Assistance Program, 2002; Independent Evaluation group,

2008; Lipscomb et al., 2013; Cabraal et al., 2005). Also, Barkat et al., (2002) found significant positive effects of electrification on political empowerment, human development, and expansion of commercial activities in Bangladesh. Electrification has been found to have favorable effects on employment at the intensive margin, that is, on hours worked, for women, and on household enterprise (Grogan and Sadanand, 2013; Dinkelman, 2011; Deininger et al., 2007; Gibson and

Olivia, 2010). Other studies have confirmed that electrification can increase the non-farm income of households and help diversify livelihoods (Rao, 2012; Bastakoti, 2006). The inability to use a regular source of lighting beyond daylight hours can negatively impact income, education, business related activities, and health (Chakrabarty and Chakrabarty, 2000, Sharif and Marufa,

2012; Diouf et al., 2013; Mondal et al., 2011; Mala et al., 2008).

Several studies have examined welfare effects of household energy use in India

(Chakravorty et al., 2014; Khandker et.al, 2012a; Walle et.al, 2013; Ahmed et.al, 2014, Torero,

2014, Sehjpal et al., 2014, Rao and Reddy, 2007, Pachauri, 2004, Khandker et al, 2012b,

Balachandra, 2011a, 2011b, Rao, 2012). Although these studies examine welfare effects of electrification by looking at various socioeconomic outcomes, most do not analyze different categories of household expenditures, or changes in such expenditures, which are more reliable indicators of household income and poverty, and also fail to capture changes in household welfare over time. Although Walle et.al (2013) use a panel data set from 1982 to 1999 to identify changes in welfare indicators at the household level over time in rural India, the data used by the authors is old, not allowing an examination of the effects of recent advances in electrification.

Our paper contributes to the existing literature in several ways. First, to address concerns about selection bias, we use an inverse propensity score weighting approach together with a difference-in-differences estimation strategy to estimate effects of electrification on household welfare. Second, we use nationwide data from the National Sample Surveys (NSS) and focus on a more recent period: 2004-2005 to 2011-2012, to not only examine the effect of electrification at a single point in time, but also investigate likely gains in the welfare-enhancing effects of electrification over time. Benefits from electrification could increase over time due to complementary investments in other rural infrastructure projects like roads, schools, and health

3 centers. Also, as more households obtain access to electricity, there is likely to be increased demand for services and opportunities to take advantage of electricity supply, such as, non-farm enterprises, entertainment, and telecommunication.

Third, we analyze changes in household welfare as captured by total per capita expenditures, as well as expenditures broken down by several categories of goods and services.

Household expenditures are widely used in the development literature to construct poverty and welfare measures (Gao et al., 2006; Davis, 2005; Kaushal et al., 2007; Meyer & Sullivan, 2008;

Wong& Yu, 2002, Gregg et al., 2006; Blundell and Etheridge, 2010; Guha and Ghosh, 2007).

Actual expenditures of a household during a certain period on food and non-food items, fuel, entertainment, durable goods, as well as for medical and educational purposes is likely to offer a comprehensive measure of its standard of living, and thereby capture household welfare. Our findings can therefore be used to gauge the likely welfare impacts of recent government initiatives and policies to expand rural electrification. Finally, several studies in the existing literature report that government subsidies promoting use of modern energy sources often benefit the rich and middle income groups more than the poor (Lahoti et al., 2012; Rao, 2012; Srivastava and Rehman,

2006; Viswanathan and Kavi Kumar, 2005). Establishing a clear link between the use of modern energy sources and household welfare among poor, rural households can therefore provide justification for evidence-based policy reforms—targeting such subsidies towards the rural poor.

In the absence of random assignment, selection bias is always a concern in estimating the effect of electrification or other household-level interventions on welfare. For instance, if relatively richer or better-off households are more likely to obtain an electricity connection, then the estimated effect of electrification on household welfare is likely to be upward biased. Given such concerns about the potential endogeneity of electrification with respect to household

4 characteristics, and the two-way relationship between energy consumption and economic growth

(see Payne 2010), several studies have used instrumental variables to estimate the effects of electrification (Dinkleman, 2011; Khandker et al., 2012a, 2012b, 2013; Chakravorty, 2014, Rao,

2012; Lipscomb et al., 2013). In our analysis, we directly address the potential endogeneity of electrification in two ways. First, we restrict our analysis to the poorest households belonging to the lowest quartile of monthly per capita expenditure distribution, which limits the extent of variation in household income or wealth, and therefore, in the possibility of selection bias. Second, we employ inverse propensity score weighting to ensure balance in observed characteristics across households with and without electricity, and use a difference-in-differences estimation approach to examine effects of electrification on various expenditure categories. Further, in a subgroup analysis, we examine if effects of electrification differ for the eight most backward states in India.

The remainder of the paper is organized as follows. The next section provides background information on electrification in India. Data and estimation methods are discussed in Section III.

Section IV presents results, and Section V concludes with a discussion of the key findings, their implications, as well as limitations of the study.

II. Background

Among developing countries in general, and emerging economies in particular, India’s record on improving rural infrastructure, including access to electricity, is quite dismal. According to the World Bank’s Global Electrification Database, India has the lowest electrification rate among BRICS nations.4 About 79 percent of the Indian population had access to electricity in

4 http://data.worldbank.org/indicator/EG.ELC.ACCS.ZS

2012, whereas electrification rates during the same period were 99.5 percent in Brazil, 100 percent in Russia and China, and over 85 percent in South Africa. Prior to the late 1960s, India’s growth in rural electrification was extremely slow. In 1969, the Rural Electrification Corporation was established to accelerate the pace of rural electrification. The Minimum Needs Programme was introduced in1974 to facilitate loans for states with lower electrification rates, and the Kutir Jyoti

Scheme was launched in 1988 to provide single point connection to electricity in below poverty line (BPL) households.5 Rural electrification schemes were also accorded high priority during

2000-2006 when eight schemes were introduced to help the rural poor. Some major policies implemented during this period were: Pradhan Mantri Gramadoya Yojana (2000), National

Electricity Policy (2005), Rajiv Gandhi Grameen Vidyutikaran Yojana (2005) and Rural

Electrification Policy (2006). In a further push towards universal electrification, in the recent

Union Budget presented in February 2016, the Indian government has committed to achieving 100 percent village electrification by May 1st, 2018, by scaling up public investment in rural infrastructure.6

Figure 1 presents the percentage of households with electricity at two different points in time: 2004-2005 and 2011-2012, based on NSS data from the 61st and 68th rounds. The use of electricity for lighting has increased between 2004-2005 and 2011-2012 in both rural and urban areas, especially in the former, with the overall share increasing from 74 to 87 percent, driven by improvements in electricity coverage in rural areas. The NSS data also show that almost all remaining households rely on the use of kerosene for lighting. However, with increasing electricity coverage for rural and urban households, the percentage of households using kerosene for lighting

5 Households with less than Rupees 816 (may vary from state to state) per capita monthly income in rural India were identified as BPL in 2011-12 (http://planningcommission.nic.in/news/pre_pov2307.pdf). 6 http://www.hindustantimes.com/union-budget/live-union-budget-2016-highlights-of-jaitley-s-budget-updates-on- taxes-reforms/story-Mswh4d37kIjA5bo7dHKPDP.html

6 has nearly halved in rural areas. Although these data show marked improvements in rural areas in terms of modern energy use, around a fifth still did not have electricity in 2011-2012. This, together with the recent policy declarations and new schemes launched by the Indian government to achieve universal electrification are likely to make the analysis and findings in this paper quite timely and policy relevant.

III. Data and Methods

A. Data

The study is based on household level data from a comprehensive survey carried out by the National Sample Survey Organization (NSSO). The NSS is a quinquennial survey conducted since 1950-1951 in India that collects data on consumer expenditures. The NSSO collects information on levels and patterns of consumer expenditures, household consumption of various goods and services, and household characteristics including energy sources for cooking and lighting (National Sample Survey Organization, 2013). The information in the NSSO is used by the government to generate estimates of monthly per capita consumption expenditure (MPCE) for households, assess the standard of living for various sections of the population as well as their nutritional levels, and calculate the budget shares of different commodities for constructing the weighted consumer price indices (CPIs). One key advantage of using the NSS data for examining household expenditures is that it assigns monetary value to all goods, including those obtained from household production and not purchased from the market (Gundimeda and Kohlin, 2008).

The NSS covers both rural and urban India. We use two waves of the NSS corresponding to the periods 2004-2005 (61st round of the survey) and 2011-2012 (68th round of the survey). The

68th round was conducted during July 2011 to June 2012 covering all states of India, except the

7 interior and inaccessible villages of Nagaland and villages in the Andaman and Nicobar Islands which were inaccessible during that time. The 61st round excluded Leh (Laddakh and Kargil districts of Jammu and Kashmir) as well as the areas not surveyed in the 68th round. The NSS uses a stratified multi-stage survey design and samples are selected randomly. Our sample consists of

34,736 poor households in rural India, of which 19,817 households are from the 61st round, and another 14,919 households from the 68th round.7

The outcome variables of interest in our analysis are per capita household expenditures on various goods and services that are meant to capture household welfare. Use of consumption expenditures as the main indicator of economic welfare is a comparatively new approach and relies on utilizing detailed information on household expenditures for various items (Guha and Ghosh,

2007). Existing studies suggest that information on consumption can yield a more accurate estimate of living standard than income, especially among poor families (Gao et al., 2006; Davis,

2005; Kaushal et al., 2007; Meyer and Sullivan, 2008; Wong and Yu, 2002, Gregg et al., 2006;

Blundell and Etheridge, 2010; Guha and Ghosh, 2007). Since income tends to be reported with error, consumption may provide a more consistent and reliable measure of well-being (Meyer and

Sullivan, 2008). Moreover, the psychological law of consumption propounded by Keynes (1936) states that consumption increases with income up to a certain limit. This is especially likely to be true for poor households in a developing country like India that are operating at subsistence or below-subsistence levels. Also, studies on poverty analysis routinely focus on changes in

7 Total number of rural households surveyed in these two rounds were 79,306 and 59,695 respectively. After the 61st and 66th quinquennial survey rounds, the National Statistical Commission in India decided to implement the 68th round within 2 years of the 66th round. The sample size in the 68th round was smaller than that in the 61st round and similar to that in the 66th round. Further details and reports about the surveys are available on the website of Ministry of Statistics and Programme Implementation in India (http://mail.mospi.gov.in/index.php/catalog/CEXP).

8 expenditure patterns resulting from welfare reform to examine changes in household well-being over time (Kaushal et al., 2007).

The NSS dataset uses a reference or recall period of 30 days for household expenditures, with an additional reference or recall period of 365 days, or a year, for selected items. Expenditures on the following items are available with a 30-day recall: all food items or edible goods (including intoxicants), goods for fuel and lighting, entertainment, miscellaneous non-food items (like clothing, bedding, and footwear), education, health care, and durable goods. Additionally, expenditures on non-food items, expenditures for education, medical expenditures, and expenditures on durable goods are available with a 365-day recall. We examine food, fuel, and entertainment expenditures that account for more frequent household spending using the outcome variables with a 30-day recall. We also use information on total expenditures over the previous 30 days. However, to examine expenditures on non-food items, health care, education, and durable goods, we use expenditure outcomes with a 365-day recall. Given the infrequent and lumpy nature of these purchases, expenditure on non-food items, durable goods, health care and education may not necessarily occur in every 30-day period, and annual estimates of expenditures are likely to be more reliable for such outcomes.

The NSS data also provides information on the socio-demographic and economic condition of households, including marital status, sex and age of the household head, family size, presence of any salaried person, home ownership, land ownership, membership in specific social groups or caste, religion, and occupation (whether an agricultural household). We use these household characteristics as control variables in our analysis described below.

B. Methods

The main threat to the identification of the effect of electrification is selection bias, or the potential endogeneity of electrification with respect to household characteristics. Since households were not randomly assigned to receiving an electricity connection, household characteristics - both measured and unmeasured - could be correlated with electrification status and with outcomes. In particular, a household’s location and its income are likely to be crucial determinants of having an electricity connection. Therefore, in the absence of exogenous variation, an ordinary least squares model simply controlling for household characteristics is likely to give us biased estimates of the effects of electrification.

In our analysis, we seek to address endogeneity concerns in the following ways. First, we focus exclusively on households that are identified as poor based on the distribution of total per capita expenditures over a 30-day period. Specifically, our sample consists of households in the bottom quartile of the distribution of 30-day total per capita expenditures. This strategy of using the empirical distribution to identify poor households is likely to be superior to using the indicator for below-poverty line (BPL) households based on government criteria. This is because there is significant scope for errors, misidentification, and corruption in the application of the government criteria. Our sample restriction limits the scope of variation in household income by specifically ensuring that all households in our estimation sample are similarly situated in terms of low levels of expenditures (and income), and are therefore economically disadvantaged and likely to have poor health, education, and other socioeconomic outcomes. This strategy also allows us to estimate the effect of electrification for an especially vulnerable group of low-income households.

We used the 25th percentile of the distribution of monthly per capita total household expenditures in the rural sample for each of the two NSS waves to define the cutoff for identifying

10 poor households. Table 1 presents means, standard deviations, and quartiles of total per capita monthly expenditures among rural households, by NSS wave. Across all states, monthly per capita total expenditure of rural households increased by nearly Rs.1,000 from Rs.696 in 2004-2005 to

Rs.1,656 in 2011-12. The median increased from Rs.548 to Rs.1,269, and the 25th and 75th percentiles also more than doubled. Based on the value of the 25th percentile, the cut-offs for identifying households in the bottom quartile of the monthly per capita expenditures were Rs.399 and Rs.914 in 2004-2005 and 2011-2012, respectively.

Second, to address potential endogeneity concerns or differences in characteristics across households with and without electricity, we employ inverse probability (or, propensity score) weighting. Inverse probability weighting (IPW) belongs to the class of methods referred to as the potential outcome model or the Rubin causal model that are used to estimate treatment effects in observational data (Rubin, 1973; Imbens, 2000 and 2004; Imbens and Wooldridge, 2009;

Wooldridge 2007 and 2010). Specifically, IPW estimates treatment effects by using weighted means of outcomes across treated and comparison units, where the weights are based on the propensity score or the probability of receiving the treatment - predicted using observed characteristics. With the weight being the inverse of the probability of being in a specific treatment condition, IPW estimates the counterfactual or potential outcomes by giving a higher weight to units that receive an unlikely treatment. Similar to other treatment effect estimators under the potential outcomes framework, the IPW approach relies on the standard assumption of independent and identically distributed (i.i.d) sampling, as well as two other well-known assumptions: conditional independence (CI) or unconfoundedness, and overlap. The CI assumption requires that conditional on the observed covariates (or, the propensity score), treatment assignment is

11 independent of potential outcomes. Overlap requires each observation or sample member to have a positive probability of being in a treatment condition.

Under IPW and other treatment effect estimators, weights can be defined to either estimate the average treatment effect (ATE) or the average effect of treatment on the treated (ATT). Since we are interested in estimating the welfare effects of electrification among poor, rural households that are using electricity in each survey wave, and in also estimating the likely gains in such effects over time among households with electricity, we focus on the ATT in our analysis. Focusing on the ATT has the added advantage of requiring less restrictive or weaker versions of the CI and overlap assumptions (see Wooldridge 2010 for details). In estimating the ATT, treated households or those with electricity receive a weight of one, and comparison households or ones without electricity receive a weight of , where is the estimated propensity score or the predicted 𝑝𝑝𝑖𝑖 1−𝑝𝑝𝑖𝑖 𝑝𝑝𝑖𝑖 probability of having an electricity connection for household i. Therefore, comparison or non- electrified households with high predicted probabilities or high likelihood of having electricity receive larger weights than those with low predicted probabilities in estimating the counterfactual or potential outcome for treated households.

To implement this approach, we use households in the bottom quartile of the expenditures distribution in each of the two NSS waves, and model treatment status as a function of observed household characteristics - including household size, age, gender, and marital status of household head, caste, religion, land ownership, and BPL status. Based on the estimated model, we obtain the predicted probabilities (or, the propensity score) of receiving treatment or having electricity connection for all households in the analysis sample in each wave. We estimate this model separately for each of the two NSS rounds using a logistic regression (under the “teffects” routine together with the “ipw” option in Stata [StataMP 13]).

Using the inverse probability weights under the weighting approach described above, we can calculate the weighted potential outcome mean and subtract it from the actual treatment group mean to obtain estimates of the ATT for any outcome, in each survey wave. However, given that the IPW estimator only models the probability of receiving treatment and does not specify an outcome model, it relies heavily on the model specified for estimating the propensity score and ignores any remaining imbalance (after weighting) in household characteristics across different levels of treatment. IPW estimators tend to be less reliable if the overlap assumption is even close to being violated. Further, estimating the IPW treatment effect separately in each wave of data does not allow us to simultaneously exploit information from both rounds to account for temporal variation or secular changes in outcomes or household welfare over time. Therefore, apart from obtaining IPW estimates of treatment effects separately within each wave, we combine data from both waves to additionally estimate multivariate regressions, with observations from each wave weighted by their respective inverse probability weights. By modeling both the probability of treatment and outcome, this approach allows us to estimate inverse-probability-weighted regression-adjustment (IPWRA) treatment effects, which are “doubly robust”, since only one of the two models needs to be correctly specified (Wooldridge 2007 and 2010). In the outcome regressions, we control for the same set of household characteristics that were used in the propensity score regression to address any remaining imbalance in observed characteristics, and also include district fixed effects. The district fixed effects account for geographic location including local area characteristics, and proximity to sources of electricity connection. In other words, the inclusion of the time-invariant district characteristics address concerns about local area variation in the availability and reliability of electricity connection, and effectiveness of

13 government welfare programs that can affect both the electrification status of a household and its income or expenditures.

We also control for secular changes over time in household expenditures due to economic growth, rise in per capita income, and inflation, by including an indicator for time period or for the second (versus the first) survey wave in our analysis. Effectively, therefore, our estimation framework is based on a difference-in-differences (DD) analysis approach with repeated cross sections of households that controls for district and time fixed effects as well as household characteristics, and additionally employs inverse probability weights from estimating the propensity score or probability of treatment in each wave. We operationalize our regression framework by estimating the following model:

= . + . + + . + . . + + (1)

𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖 𝛽𝛽 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 𝜸𝜸 𝑬𝑬𝒊𝒊𝒊𝒊𝒊𝒊 𝜃𝜃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝛿𝛿 𝑬𝑬𝒊𝒊𝒊𝒊𝒊𝒊 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝝁𝝁𝑑𝑑 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 where i indexes a household, d denotes district, and t time period. is an outcome of interest,

𝑖𝑖𝑖𝑖𝑖𝑖 such as per capita household expenditures on food or fuel. X is a vector𝑦𝑦 of household-level control variables, E is a binary indicator for household electrification, Post is an indicator for the time period or survey wave corresponding to 2011-12 versus 2004-05, and is the idiosyncratic error term. The district fixed effects are denoted by . The main parameter 𝜀𝜀of interest in Equation (1)

𝑑𝑑 is the difference-in-differences estimate captured𝝁𝝁 by , which estimates the differential increases in expenditures experienced by households with electricity𝛿𝛿 versus households without electricity between the two survey waves.

The estimation approach could be further strengthened by using panel data on households, that is, by following the same households over time. This would allow us to more directly address endogeneity concerns by differencing out time-invariant household-specific characteristics or

14 estimating the effect of a within-household change in electrification status. However, in the absence of a household-level panel, we use data on cross-sections of households from two different points in time, controlling for household- and local area-level characteristics, as well as secular changes over time. We estimate Equation (1) for each expenditure outcome across all households in the bottom quartile of the total per capita expenditures distribution, using inverse probability weights. Standard errors are clustered at the district level in all regressions.

To explore potential variation in the effects of electrification across states, we re-estimate the outcome regressions described in Equation (1) by adding an indicator and its interaction with electrification status—identifying a subgroup of eight backward states (Bihar, Chattisgarh,

Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttarakhand, and Uttar Pradesh) that have historically experienced low levels of development, low income, and poor health, education and other socio-economic outcomes. This additional analysis allows us to check for heterogeneity in the effects of electrification on household welfare for economically disadvantaged households, depending on the backwardness or level of development in a state. For instance, it is possible that poor households in backward states derive fewer benefits from electrification due to limited opportunities for employment and income generation or for obtaining education and health services, as well as due to the unreliability of electricity supply.

IV. Results

A. Overlap in propensity score distribution and balance in household characteristics

Of the three assumptions associated with implementing a treatment effects estimator like the IPW - i.i.d., CI, and overlap - it is relatively straightforward to check for the overlap assumption that requires each observation or sample member to have a positive probability of being in a

15 treatment condition. Also, IPW estimators tend to become unstable if the overlap assumption is even close to being violated, since it can lead to extremely high values of weights. Therefore, we begin by providing evidence of overlap in our analysis sample. Since we implemented IPW separately for each wave of data, Figures 2 and 3 present the distribution of estimated propensity scores for both households with and without electricity in the analysis sample, in 2004-2005 and

2011-2012 respectively. These figures demonstrate that in both waves of data, the distribution of propensity scores across households with and without electricity were quite similar. More importantly, none of the households in either treatment condition had extreme values of propensity scores at zero or one, which can lead to a breakdown of the overlap assumption. Since the proportion of households with electricity increased from 40 percent to 62 percent (Appendix Table

A.1) between 2004-2005 and 2011-2012 in the analysis sample of poor, rural households, the distribution of propensity scores shows a marked rightward shift going from Figure 2 to Figure 3.

IPW and other propensity score-based estimation strategies rely on accurately estimating the counterfactual or potential outcomes, by achieving balance or similarity across treatment conditions on observed characteristics that predict treatment status and are likely to be correlated with outcomes. Therefore, in Table 2, we check for balance in observed characteristics that were used in estimating the propensity score and calculating the inverse probability weights, by survey wave. We present variable means for households with and without electricity in each wave, including both unweighted and weighted means for the latter group of households—to demonstrate how the weights improved balance. As can be seen, differences in household characteristics across households with and without electricity before applying the inverse probability weights were almost completely eliminated, after weighting. This is true for both waves, and the high degree of balance is amply demonstrated by the small values of the standardized differences in weighted

16 means—with the absolute value of all standardized differences less than 0.1, and in fact, equal to or less than 0.01 in most cases.

Table 2 also shows how household characteristics changed between the two waves among households with electricity. We notice an increase in the proportion of Antodaya and/or BPL households from 44 to 54 percent, which could be driven by better identification or targeting of poor households as well as increasing awareness and popularity of government schemes benefitting poor households.8 Between the two survey waves, there was a marginal decline in household size (as captured by the decline in the log of households size), and a sharp decrease by around 25 percent in the proportion of agricultural households—from 54 to 41 percent—signifying increasing diversification in rural occupations. Although the proportion of households that use clean cooking fuel (for example, liquefied petroleum gas, kerosene, or gobar gas) more than doubled, it was still very low at 7 percent in 2011-2012, among households with electricity. For detailed distribution of household characteristics across all poor, rural households, by wave, see

Appendix Table A.1.

B. Estimated Effects of Electrification from Inverse Probability Weighting

Table 3 presents estimated effects of electrification on household expenditures by estimating potential outcomes using the IPW approach, by wave. The estimates of ATT for the first wave or 2004-2005 suggest statistically significant increases in household welfare from electrification. For expenditure outcomes with a 30-day recall, total per capita expenditures

8 BPL households are identified based on government criteria or the official poverty line. State specific poverty lines (in Rupees per capita per month) ranged from Rs.293 to Rs.430 in rural India in 2004-2005, with an average of Rs.356. The poverty line values increased in 2011-2012 and were between Rs.738 and Rs.1,301 across states, with an all-India rural average of Rs.816. Among BPL households, those that are poorest without any stable income are identified as Antodaya.

17 increased by Rs.17 or 5 percent, and expenditures on food, fuel, and entertainment increased by

Rs.2, Rs.7, and Rs.1 respectively, with all estimates significant at the 5 percent level. While entertainment expenditures more than doubled, the percentage increase was somewhat small for food expenditures (1 percent), but relatively large (17 percent) for fuel expenditures. Among expenditures outcomes with a 365-day recall, non-food, medical, and education expenditures increased significantly by Rs.52, Rs.12, and Rs.18 respectively, or 15, 37, and 26 percent.

However, there was a decline in durable goods expenditures by approximately 10 percent, which is unexpected, and suggests a possible underestimation of the effect of electrification on durable goods expenditures under the IPW approach, as discussed in greater detail below.

Similarly, in 2011-2012, estimates of ATT suggest significant gains from electrification in household welfare. Specifically, total 30-day expenditures increased by Rs.44 or 6 percent, and expenditures on food, fuel, and entertainment increased by Rs.20, Rs.12, and Rs.5 respectively, with all estimates significant at the 1 percent level. This amounts to a quadrupling of entertainment expenditures, and increases of 5 and 13 percent in food and fuel expenditures, respectively. Among per capita expenditures outcomes with a 365-day recall, the increase in medical expenditures was not statistically significant, although non-food, education, and durable goods expenditures increased significantly by Rs.90, Rs.38, and Rs.28 respectively, or by 11 to 18 percent.

Since there was an expected secular increase in income and expenditure levels over time among all households (for instance, total 30-day per capita expenditures more than doubled from

2004-2005 to 2011-2012 among households with and without electricity), the increase in the absolute magnitude (in terms of Rupees per capita) of the effects of electrification between the two waves is not unexpected. Therefore, to examine how the welfare gains from electrification might have changed over time, it is more useful to look at the impacts in relative or percentage terms.

Although not uniformly true across all expenditure categories, in general, the impact of electrification in percentage terms increased over time for expenditure outcomes measured over

30 days, and declined for the 365-day expenditure outcomes. For example, among per capita expenditures with a 30-day recall, there was a small increase in percentage impact for total 30-day expenditures, as well as large increases in percentage impacts for food and entertainment expenditures, although the percentage impact for fuel expenditures declined. For per capita expenditures with a 365-day recall, percentage impacts decreased over time for three out of the four expenditure categories—non-food, medical, and education—with an especially steep decline in the percentage impact on medical expenditures, which was no longer statistically significant.

However, in contrast to the negative effect in 2004-2005, the effect of electrification on durable goods expenditures in 2011-2012 was positive, statistically significant, and sizeable at 11 percent.

C. Results from Difference-in-Differences Analysis with Inverse Probability Weighting

For a fuller exploration of changes in welfare gains from electrification over time, we now turn to the results from the DD regression analysis with inverse probability weighting. Table 4 presents regression results for per capita monthly and annual expenditures in poor households— specifically, the coefficients on the key variables of interest (full regression results are presented in Appendix Table A.2).

Focusing on the monthly expenditure variables first, we find that electrification was associated with significantly higher monthly total and fuel expenditures at baseline (2004-2005).

Households with electricity spent approximately an additional Rs. 13 per capita per month in total expenditures, and Rs. 12 more for fuel, but spent slightly less on monthly entertainment expenditures, as compared to households without electricity in 2004-2005. In percentage terms,

19 households with electricity experienced approximately 4 percent and 31 percent higher expenditures in total and fuel expenditures per capita, as compared to the average expenditures of households without electricity in 2004-2005. Both total and category-specific expenditures on food, fuel and entertainment increased significantly over time, that is, between 2004-2005 and

2011-2012, as captured by the coefficients on the “post” indicator. While total per capita monthly expenditures increased by Rs. 376, food, fuel, and entertainment expenditures also increased significantly by Rs. 216, Rs. 44, and Rs. 2, respectively. Further, households with electricity had significantly greater increases in all categories of monthly total spending between 2004-2005 and

2011-2012, as shown by the DD estimates or the coefficients on electrification interacted with the post indicator. Specifically, increase in the monthly total expenditure was higher by Rs.34 among households with electricity, which translates to 5 percent of the average monthly expenditures among households without electricity in 2011-2012. Similarly, the differential increase in monthly expenditures on food was Rs.19 (4 percent). Although increases in monthly expenditures were higher for both fuel and entertainment by the same absolute amount of approximately Rs.5 in households with electricity in 2011-2012, in percentage terms, this change was much larger for entertainment expenditures (233 percent) as compared to the changes for fuel expenditures (5 percent).

Based on these estimates, the overall increase in expenditures for households with electricity in 2011-2012 can be estimated as the sum of coefficients on electrification and electrification interacted with post, or ( + ) from Equation (1). We find that households with electricity experienced overall increases𝛾𝛾 in total𝛿𝛿 expenditures as well as in expenditures on food, fuel, and entertainment by approximately Rs. 47, Rs. 17, Rs. 17, and Rs. 5, respectively, with all four estimates being statistically significant. Thus, as percentage of the potential outcome means

20 for the same expenditure categories in 2011-2012 (reported in Table 3), monthly total per capita expenditures and expenditures for food, fuel and entertainment were higher by approximately 7,

4, 19, and 217 percent, respectively, in households with electricity as compared to households without electricity in 2011-2012. Estimated values for ( + ) from Equation (1) and associated p-values are included in Table 4. 𝛾𝛾 𝛿𝛿

Table 4 also shows the effects of electrification on annual expenditures for nonfood items, medical needs, education, and durable goods that capture the long-term quality of life. Household electrification was associated with significantly higher nonfood and education expenditures in

2004-2005 by Rs. 30 (8 percent) and Rs. 27 (40 percent). As expected, expenditures on all annual expenditure categories were significantly higher in 2011-2012 than in 2004-2005, with increases of Rs. 446, Rs. 106, Rs. 130, and Rs. 138 for nonfood, medical, education, and durable goods, respectively. The differential impact of electrification, as captured by the coefficient on the electrification dummy interacted with the post indicator, reveals that increases in annual nonfood, education, and durable goods expenditures were significantly higher by Rs. 60, Rs. 30, and Rs. 54, respectively, in households with electricity compared to households without electricity in 2011-

2012. However, the differential increase in medical expenditures was small and not statistically significant. As percentages of the mean expenditures in households without electricity in 2011-

2012, these additional increases represented 7, 14, and 21 percent of annual expenditures on nonfood items, education, and durable goods, respectively.

Adding up and from equation (1), we obtained estimates of the overall increase in the annual expenditures𝛾𝛾 for households𝛿𝛿 with electricity in 2011-2012, as shown in Table 4. These suggest statistically significant overall increases in per capita annual expenditures of Rs. 90 for nonfood items, and Rs. 57 for both education and durable goods, in households with electricity in

2011-2012. Thus, as percentage of the potential outcome means for the same expenditure categories in 2011-2012 (reported in Table 3), per capita annual expenditures for nonfood, education, and durable items were higher by 11, 27, and 22 percent, respectively, in households with electricity as compared to households without electricity in 2011-2012. However, electrification did not have any statistically significant effect on medical expenditures in 2011-

2012, as shown by the insignificant overall increase of Rs.13 in medical expenditures among households with electricity.

Comparing the ATT estimates for households with electricity in wave 2 from Table 3 against the estimated increases in expenditures for households with electricity in wave 2 from

Table 4—by combining the coefficients on electrification and post*electrification—we find that most, but not all, of the estimated increases are quite similar across the two estimation approaches.

For instance, while Table 3 suggests estimated increases of Rs.44, Rs.20, Rs.12, and Rs.5 in per capita total, food, fuel, and entertainment expenditures measured over 30 days in 2011-2012, the estimates in Table 4 suggest increases of Rs.47, Rs.17, Rs.17, and Rs.5 for the same four expenditure outcomes. Similarly, estimated ATTs of Rs.90, Rs.14, Rs.38, and Rs.28 for nonfood, medical, education and durable goods in Table 3 for 2011-2012, are quite similar with two of the four corresponding estimates of Rs.90, Rs.13, and Rs.57 (for both education and durable goods expenditures).

Given the doubly robust property of the IPWRA estimates and therefore their higher reliability, we attach greater weight to the findings in Table 4 compared to those in Table 3.

Comparing the two sets of estimates, it appears that the favorable effects of electrification on household welfare are underestimated with IPW alone—at least for some of the expenditure categories like fuel, education, and durable goods. This is especially true for durable goods

22 expenditures, since the IPW estimate for wave 1 in Table 3 suggests a significant decrease of Rs.10 versus a small and insignificant increase under IPWRA in Table 4, and the IPW estimate for wave

2 in Table 3 (Rs.28) is half the IPWRA estimate in Table 4 (Rs.57).

Next, we look into the potential heterogeneous effects of electrification, based on the backwardness of a state, by adding an indicator for the eight backward states along with its interactions with the electrification dummy, post indicator, and the electrification*post interaction term. Coefficient estimates for key variables of interest in this analysis are presented in Table 5

(full regression results with all variables are included in the Appendix Table A.3). As before, the

DD estimate for non-backward states, as captured by the estimated coefficient on the electrification*post interaction term, shows significant differential increases in all categories of per capita monthly expenditures as well as in annual expenditures for nonfood and durable goods in households with electricity during 2011-2012.

Turning to the estimates for monthly expenditure variables in backward states, we find that the generally low levels of development and per capita income in backward states is confirmed by the coefficient estimates for the backward state indicator. Specifically, per capita total monthly expenditures as well as monthly expenditures on food and fuel were significantly lower in backward states at baseline (2004-2005) by Rs. 103, Rs. 81, and Rs. 28, respectively. Interestingly, however, per capita monthly expenditures on entertainment in those states were higher by approximately Rs. 4 during 2004-2005. Electrification was associated with significantly higher total monthly expenditures in the backward states during 2004-2005 by Rs.23, and also higher expenditures on food, fuel, and entertainment by Rs.15, Rs.3, and less than Rupee 1 respectively.

In line with the lower levels of expenditures in backward states, the secular increase in per capita total monthly expenditures over time was lower among households in backward states than for

23 households in non-backward states, as shown by the negative and significant coefficient on the post*backward state interaction for total and food expenditures. Finally, the coefficient estimates for the triple interaction term (electrification*post*backward state) show that poor households in backward states experienced smaller differential increases in monthly expenditures from electrification than those in non-backward states during 2011-2012. Specifically, compared to households in non-backward states, households with electricity in backward states experienced lower differential increases in per capita total monthly expenditures, and monthly expenditures on food and entertainment by approximately Rs. 21, Rs. 10, and Rs. 5, respectively. In other words, although the overall increases in monthly expenditures were still positive for households with electricity in backward states during 2011-2012, as estimated by the sum of coefficients on electrification, post*electrification, and electrification*post*backward state, these were smaller than increases experienced by households in non-backward states. Specifically, the estimated overall increases in per capita total monthly expenditures and monthly expenditures on fuel and entertainment in backward states were: Rs. 21, Rs. 15, and Rs. 2, respectively—all significant at the 1 percent level. Therefore, when compared to households without electricity in eight backward states in 2011-2012, per capita monthly total expenditures as well as expenditures for fuel and entertainment were higher by 3, 17, and 96 percent, respectively, in households with electricity in these states.9 However, the overall increase in food expenditures during 2011-2012 among households with electricity in backward states was close to zero and not significant.

Turning to findings for annual expenditures, given lower per capita incomes in general, households in backwards states spent approximately Rs. 466, Rs. 55, and Rs. 144 less on annual nonfood, medical, and education expenditures respectively during 2004-2005. Only exception was

9 Average (weighted) per capita monthly household expenditures in households without electricity in backward states were Rs. 675 in total, and Rs. 86 and Rs. 2 for fuel and entertainment respectively, in 2011-2012.

24 the per capita annual expenditure on durable goods, which was higher by Rs. 39 in the backward states during that time period. The coefficients for the electrification*backward states term clearly show that although electrification was positively associated with higher per capita annual medical expenditures during 2004-2005, it did not have any significant differential impact on annual nonfood, education, and durable goods expenditures in these states during that same time period.

Also, annual per capital household expenditures did not experience any significant differential change in backward states between 2004-2005 and 2011-2012, as reflected through the coefficients for post*backward states interaction term. Finally, the differential impact of electrification for backward states in 2011-2012, as captured by coefficients for the triple interaction term

(electrification*post*backward state), were not statistically significant for any annual expenditure categories, except for annual medical expenditures. Although the overall effect of electrification in backwards states during 2011-2012—as captured by the sum of coefficients on electrification, post*electrification, and electrification*post*backward state—still showed a significant increase in per capita annual nonfood and education expenditures by Rs. 83 (10 percent) and Rs. 33 (15 percent) respectively, these were smaller in magnitude than the increases in non-backward states.10

The overall effect of electrification—an increase of Rs. 36—was not statistically significant for household expenditures on durable goods in backward states.

Also, the coefficient estimates for per capita annual medical expenditures point towards an interesting pattern in backwards states. Although electrification was associated with higher medical expenditures in backward states during 2004-2005 by an additional Rs. 37, compared to non-backward states, this pattern was reversed over time, such that the differential effect of electrification in backward versus non-backward states was a reduction of Rs.100 in per capita

10 Average (weighted) per capita annual household expenditures in households without electricity in backward states were Rs. 804 for nonfood items, and Rs. 214 for education in 2011-2012.

25 annual medical expenditures in 2011-2012. The overall effect of electrification in backward states during 2011-2012, therefore, suggests a reduction in per capita annual medical expenditure by Rs.

52 or 37 percent.11 The lower medical costs could be driven by possible improvements in health outcomes among households with electricity in backward states, as discussed below.

V. Discussion

In this paper we examined the impact of electricity on welfare of poor households in rural

India. In two separate analyses, we examined the welfare effects for poor, rural households across all states and in eight backward states. We captured potential welfare improvements by examining changes in a range of household expenditures variables between two national surveys fielded in

2004-2005 and 2011-2012. We focused on households in the bottom quartile of the total monthly per capita expenditure distribution in the rural sample for each of the two NSS waves. To identify effects of electrification in these households, we employed inverse probability weighting and estimated treatment effects separately within each wave. Further, we implemented a doubly robust estimation strategy by combining data from both waves to estimate difference-in-differences regressions while using the inverse probability weights. Our results indicate significant positive effects of electrification on several categories of household expenditures, suggesting improvements in household welfare. The results of this paper are in line with findings from existing studies that also identify productivity and income-enhancing effects of electrification.

We found that after accounting for the potential endogeneity of household electrification status, poor households with electricity had higher total monthly expenditures in general, as well

11 Average (weighted) per capita annual medical expenditures in households without electricity in backward states was Rs. 139 during 2011-2012.

26 as higher monthly expenditures on items like food, fuel, and entertainment. These results point towards the beneficial effects of electrification on poor households, in terms of an increase in income generation opportunities, higher productivity, and higher actual earnings. The impact of electrification on poor households was especially large for monthly entertainment expenditure, possibly due to the electricity-induced demand for modern electrical gadgets like televisions and video players. This result is in line with existing literature that finds a strong correlation between electrification and television ownership across countries over time (Independent Evaluation group,

2008), and identifies television as one of the major sources of electricity consumption in rural India

(Kohlin et. al., 2011).

We also found that poor households with electricity spent more on nonfood, education, and durable items on an annual basis, indicating a strong association of electrification with long-term welfare improvements. However, poor households in rural India did not experience any significant change in annual medical expenditures as a result of household electrification. This can probably be explained by two simultaneous yet opposing effects operating on medical expenses from the use of electricity. On the one hand, electrification can potentially improve health through an increase in income, and therefore, in nutritional status, leading to a reduction in medical costs.

However, with greater productivity and earnings, households are less likely to forego needed medical care, especially if there is a pent-up demand for costly treatments and interventions that households can only afford with greater incomes, thereby increasing medical expenditures.

Expectedly, the impacts of electrification on poor households were markedly different in backward states that suffer from low levels of development and income, and inferior quality of infrastructure. Poor households in backward states experienced limited welfare improvements from electrification, or lower increases in monthly expenditures than those in non-backward states,

27 possibly reflecting the limited economic opportunities and facilities in such states. However, the overall effect of electrification was still positive and statistically significant for total monthly expenditures, as well as for monthly expenditures on fuel and entertainment, and for annual nonfood and education expenditures in backward states. This suggests that although limited and smaller in magnitude compared to other states, households in backward states do experience certain welfare-enhancing impacts of electrification both in the short run and in the long run.

However, household electrification in backward states does not lead to a significant increase in annual expenditures on durable goods among poor households—possibly reflecting both tighter income constraints and limited income-generating potential of long-term investments in such states.

Another interesting difference in finding for backward states was the change in direction for the effect of electrification on medical expenditures over time leading to a significant decrease in annual medical expenditures among poor households in backward states during 2011-2012 by

Rs. 52 or 37 percent, versus the statistically insignificant effect on medical expenditures across all states. As discussed above, electrification can have two opposing effects on medical expenditures.

It is possible that limited increases in income from opportunities associated with electrification in backward states initially leads to a rise in medical spending among poor households, especially in the presence of pent-up demand for medical treatment. However, with more households gaining electricity over time and greater increases in incomes and improvements in nutrition and health, the reduction in medical spending due to avoidable reasons outweighs possible increases in medical spending from higher incomes, leading to an overall decline in medical spending among poor households in backward states.

Our findings clearly point towards the welfare-enhancing potential of electrification for poor households in a developing country. These findings, however, need to be interpreted carefully, given underlying threats to identifying the causal effects of electrification on household welfare in an observational study like ours. Although we aim to minimize concerns with potential endogeneity of household electrification status by focusing on a subset of the poorest households and by using econometric techniques like inverse probability weighting and difference-in- differences estimation, our methods do not allow us to rule out such concerns entirely. We believe that the finding for medical expenditures in backward states boosts the credibility of our estimates as being truly “causal” instead of being driven merely by a positive correlation between higher household incomes and likelihood of having an electricity connection on the one hand, and between income and spending on the other. But our methodological approach can be further improved in future research by using panel data on households or by using carefully chosen instrumental variables to address endogeneity concerns.

FIGURES AND TABLES

Figure 1: Percentage of Households Using Electricity for Lighting in India During 2004- 2005 and 2011-2012

100 96 91 87 81 80 74 64 60

Electrification Rate (%) 20

0 Rural Urban India 2004-2005 2011-2012

Source: Prepared by authors from NSS rounds 61 and 68. Notes: Majority of the remaining households used kerosene for lighting and negligible proportion of households used other sources of energy like solar panels, candles.

Figure 2: Distribution of Propensity Score by Electrification Status among Poor, Rural Households in 2004-2005

Households without electricity 5 4 3 2 1 0 Households with electricity 5 Density 4 3 2 1 0 0 .2 .4 .6 .8 1 Propensity score for household electrification

Figure 3: Distribution of Propensity Score by Electrification Status among Poor, Rural Households in 2011-2012

Households without electricity 5 4 3 2 1 0 Households with electricity 5 Density 4 3 2 1 0 0 .2 .4 .6 .8 1 Propensity score for household electrification

Table 1: Monthly Per Capita Total Expenditures (in Rupees) of Rural Households in India, by NSS wave

Variable 2004-2005 2011-2012 Monthly Per Mean 25th 50th 75th 25th 50th 75th Capita Total (S.D.) Percentile Percentile Percentile Mean (S.D.) Percentile Percentile Percentile Expenditures 696 1,656 399 548 782 914 1,269 1,827 (Rupees) (974) (4,026) Number of Observations 79,298 59,695 Source: Prepared by authors using data from the 61st and 68th rounds of the NSS. Notes: Values are rounded to the nearest Rupees. NSS = National Sample Survey; SD = Standard deviation.

Table 2: Characteristics of Poor Households in Rural India, By NSS Wave and Electrification Status

2004-2005 2011-2012 Households Households Household With Households Without With Households Without Characteristic Electricity Electricity Standardized Electricity Electricity Standardized Mean (Un- Mean Difference Mean Mean Difference Mean weighted ) (Weighted) (weighted) Mean (Un- (Weighte (weighted) Log of Household Size 1.68 1.61 1.68 0.00 1.63 1.58 1.63 -0.01 Home ownership 0.94 0.96 0.94 0.02 0.96 0.98 0.96 0.00 Uses clean cooking fuel 0.03 0.00 0.03 0.00 0.07 0.01 0.08 -0.04 Salaried person in household 0.10 0.05 0.11 -0.01 0.12 0.06 0.14 -0.06 Age of household head in years 44.94 43.48 45.02 -0.01 46.34 44.74 46.43 -0.01 Household head is male 0.91 0.89 0.91 0.00 0.90 0.89 0.90 0.01 Household head is currently married 0.88 0.87 0.87 0.02 0.88 0.87 0.88 0.00 Agricultural household 0.54 0.55 0.55 -0.01 0.41 0.40 0.41 0.00 Muslim 0.08 0.13 0.08 0.00 0.11 0.13 0.11 0.00 Christian 0.02 0.02 0.02 0.00 0.05 0.04 0.05 -0.03 Other religion 0.03 0.02 0.03 0.00 0.02 0.02 0.02 -0.01 Scheduled Tribe 0.15 0.21 0.15 -0.01 0.23 0.19 0.23 0.01 Scheduled Caste 0.24 0.26 0.24 0.00 0.21 0.26 0.21 0.00 Other Backward Class 0.43 0.37 0.43 0.00 0.38 0.40 0.38 0.00 Ownership of homestead land only 0.38 0.37 0.38 0.00 0.39 0.39 0.38 0.01 In top quartile of land holding 0.30 0.22 0.30 0.00 0.28 0.20 0.29 -0.01 Antyodaya or BPL 0.44 0.39 0.44 0.00 0.54 0.55 0.53 0.03 Number of observations 7,979 11,838 9,262 5,657 34

Notes: Values are rounded to two decimal places. Weighted means were calculated using inverse probability weights based on propensity score estimation for household electrification status, by wave. Clean cooking fuels include liquefied petroleum gas (LPG), gobar gas, kerosene, and electricity. NSS = National Sample Survey; BPL = Below Poverty Line.

Table 3: Estimated ATT of Electrification on Per Capita Expenditures (in Rupees) among Poor Households in Rural India, by NSS Wave

2004-2005 2011-2012 Without Without Expenditure With Electricity With Electricity ATT ATT Categories Electricity (Potential ATT p-value Electricity (Potential ATT p-value (Percentage) (Percentage) (Mean) Outcome (Mean) Outcome Mean) Mean)

30-day Expenditures Per Capita (Rupees) Total 324 308 17 5% 0.00 732 687 44 6% 0.00 Food 217 215 2 1% 0.02 451 431 20 5% 0.00 Fuel 45 38 7 17% 0.00 98 86 12 13% 0.00 Entertainment 1 0.5 1 182% 0.00 8 2 5 250% 0.00 365-day Expenditures Per Capita (Rupees) Non-food 404 352 52 15% 0.00 893 803 90 11% 0.00 Medical 43 32 12 37% 0.05 137 122 14 12% 0.38 Education 85 68 18 26% 0.00 252 214 38 18% 0.00 Durable Goods 87 97 -10 -10% 0.02 280 252 28 11% 0.03 Number of Observations 7,979 11,838 9,262 5,657 Notes: Values are rounded to the nearest Rupees. All ATTs were estimated using inverse probability weighting based on propensity score estimation for household electrification status, by wave. ATT = Average effect of treatment on the treated.

Table 4: DD Regression Results for Per Capita Expenditures (in Rupees) among Poor Households in Rural India

Variables Monthly Expenditures Annual Expenditures Total Food Fuel Entertainment Nonfood Medical Education Durable Electrification 13.10*** -1.55 11.88*** -0.34** 30.20*** 7.23 27.31*** 2.56 [2.27] [1.65] [0.73] [0.17] [7.46] [10.86] [7.43] [10.93] Post 375.50*** 216.10*** 43.61*** 2.01*** 445.90*** 105.80*** 130.50*** 137.90*** [3.61] [2.93] [1.18] [0.22] [12.71] [14.63] [9.60] [11.32] Post*Electrification 34.16*** 18.99*** 4.59*** 5.11*** 59.97*** 5.98 29.66** 54.15*** [1.86] [1.46] [0.48] [0.15] [5.98] [10.49] [6.04] [7.79] Constant 368.10*** 249.90*** 47.49*** -2.94*** 519.20*** 37.09 20.89 -41.22 [13.67] [11.78] [3.72] [0.75] [43.36] [61.94] [105.9] [45.54] Electrification + 47.26*** 17.44*** 16.47*** 4.78*** 90.17*** 13.20 56.97*** 56.71*** (Post*Electrification) [3.60] [2.79] [1.08] [0.35] [11.67] [16.42] [15.66] [13.85] Number of Observations 34,736 34,736 34,736 34,736 34,736 34,736 34,736 34,736 R-squared 0.81 0.72 0.60 0.21 0.49 0.03 0.14 0.06 Notes: Robust standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1. Values are rounded to two decimal places. Control variables include log of family size, home ownership, use of clean cooking fuels, presence of any salaried person, individual characteristics of household head like marital status, sex, and age, whether an agricultural household, membership in specific social groups or caste, religion, land ownership type and status, and whether belongs to Antodaya or BPL group. All regressions incorporate inverse probability weights based on propensity score estimation for household electrification status, by wave. BPL = Below Poverty Line.

Table 5: DD Regression Results for Per Capita Expenditures (in Rupees) among Poor, Rural Households with Backward States Interaction

Monthly Expenditures Annual Expenditures Variables Entertain- Total Food Fuel ment Nonfood Medical Education Durable 5.63** -4.74** 10.22*** -0.25 35.73*** -6.784 23.39** -10.02 Electrification [2.40] [1.97] [0.81] [0.24] [10.00] [11.70] [10.45] [15.96] 393.90*** 238.90*** 42.79*** 2.11*** 475.10*** 93.64*** 131.00*** 141.10*** Post [5.96] [4.56] [1.97] [0.42] [20.52] [35.24] [26.79] [24.15] 36.71*** 15.14*** 4.78** 7.25*** 52.20** 54.84 45.40 57.00* Post*Electrification [5.26] [4.20] [1.97] [0.51] [21.36] [37.48] [30.28] [32.79] -103.20*** -81.29*** -27.94*** 3.91*** -466.30*** -55.41* -144.20*** 38.58* Backward states [5.60] [4.19] [1.98] [0.48] [22.14] [30.79] [18.93] [22.57] 23.26*** 15.17*** 2.78** 0.59* 0.612 36.65* 12.76 25.85 Electrification*Backward states [4.09] [2.89] [1.35] [0.34] [14.00] [21.96] [10.81] [18.62] -25.13*** -31.36*** 1.21 -0.13 -40.74 17.91 -0.40 -3.80 Post*Backward states [7.49] [5.71] [2.46] [0.49] [26.56] [38.89] [28.89] [27.55] Electrification*Post*Backward -21.42*** -10.08* -0.07 -4.73*** -5.106 -99.81** -35.55 -10.88 states [1.86] [1.45] [0.48] [0.14] [5.975] [10.59] [6.01] [7.81] 360.90*** 239.90*** 49.47*** -4.43*** 499.0*** 27.73 14.31 -33.42 Constant [13.25] [11.35] [3.83] [0.82] [43.14] [65.10] [103.50] [44.45] Electrification + (Post*Electrification) + (Electrification*Post*Backward 20.93*** 0.32 14.94*** 2.26*** 82.82*** -51.76** 33.23** 36.10 states) [6.26] [4.77] [1.91] [0.45] [20.10] [25.14] [14.94] [23.87] Number of Observations 34,736 34,736 34,736 34,736 34,736 34,736 34,736 34,736 R-squared 0.807 0.724 0.598 0.222 0.490 0.030 0.140 0.063 Notes: Robust standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1. Values are rounded to two decimal places. Control variables include log of family size, home ownership, use of clean cooking fuels, presence of any salaried person, personal characteristics of household head like marital status, sex and age, whether an agricultural household, membership in specific social groups or caste,

38 religion, land ownership type and status, and whether belongs to Antodaya or BPL group. All regressions incorporate inverse probability weights based on propensity score estimation for household electrification status, by wave. BPL = Below Poverty Line.

APPENDIX Table A.1: Characteristics of Poor Households in Rural India, by NSS Wave

2004-2005 2011-2012 Characteristic Mean Std. Dev Mean Std. Dev Household has electricity 0.40 0.49 0.62 0.49 Household size 5.72 2.60 5.49 2.33 Home ownership 0.96 0.20 0.97 0.18 Uses clean cooking fuel 0.01 0.12 0.05 0.21 Salaried person in household 0.07 0.26 0.10 0.30 Age of household head in years 44.07 13.36 45.74 13.11 Household head is male 0.90 0.31 0.90 0.30 Household head is currently married 0.87 0.33 0.88 0.33 Agricultural household 0.54 0.50 0.40 0.49 Muslim 0.11 0.31 0.12 0.32 Christian 0.02 0.15 0.04 0.20 Other religion 0.02 0.15 0.02 0.14 Scheduled Tribe 0.19 0.39 0.22 0.41 Scheduled Caste 0.25 0.43 0.23 0.42 Other Backward Class 0.40 0.49 0.38 0.49 Ownership of homestead land only 0.38 0.48 0.39 0.49 In top quartile of land holding 0.25 0.43 0.25 0.43 Antyodaya or BPL 0.41 0.49 0.55 0.50 Number of observations 19,817 14,919 Notes: Values are rounded to two decimal places. Clean cooking fuels include liquefied petroleum gas (LPG), gobar gas, kerosene, and electricity. NSS = National Sample Survey; BPL = Below Poverty Line.

Table A.2: Detailed DD Regression Results for Per Capita Expenditures (in Rupees) among Poor Households in Rural India

Variables Monthly Expenditures Annual Expenditures Total Food Fuel Entertainment Nonfood Medical Education Durable Electrification 13.10*** -1.55 11.88*** -0.34** 30.20*** 7.23 27.31*** 2.56 [2.27] [1.65] [0.73] [0.17] [7.46] [10.86] [7.43] [10.93] Post 375.50*** 216.10*** 43.61*** 2.01*** 445.90*** 105.80*** 130.50*** 137.90*** [3.61] [2.93] [1.18] [0.22] [12.71] [14.63] [9.60] [11.32] Post*Electrification 34.16*** 18.99*** 4.59*** 5.11*** 59.97*** 5.98 29.66** 54.15*** [1.86] [1.46] [0.48] [0.15] [5.98] [10.49] [6.04] [7.79] log (Household size) -40.48*** -17.09*** -30.19*** 0.72*** -47.50*** 33.76*** 58.86*** -29.21*** [2.84] [1.99] [0.85] [0.14] [7.55] [12.34] [9.03] [9.67] Home ownership 25.54*** 22.44*** 10.29*** 0.83** 23.08 -12.02 -32.28 74.04*** [4.73] [4.19] [1.41] [0.34] [17.92] [19.79] [19.95] [16.18] Clean Cooking Fuel 30.31*** 8.49 2.90 3.29*** 62.93** -38.50 116.10*** 35.02 [7.29] [5.51] [2.07] [0.72] [29.93] [23.43] [23.10] [25.04] Salaried 19.92*** 8.18*** 0.27 0.69*** 67.03*** -0.40 69.09*** 42.13*** [2.90] [2.34] [0.76] [0.25] [10.95] [13.50] [15.21] [14.03] Age of household 2.83*** 2.24*** 0.95*** -0.03 2.59** -5.52*** 4.47 -0.28 head in years [0.41] [0.33] [0.11] [0.03] [1.29] [2.03] [3.36] [1.77] Age of households -0.03*** -0.02*** -0.01*** 0.00 -0.03** 0.06*** -0.06* 0.00 head squared [0.00] [0.00] [0.00] [0.00] [0.01] [0.02] [0.03] [0.02] Households head is 15.40*** 15.31*** -1.66* 0.30 32.05*** -2.94 0.97 4.24 male [3.90] [3.23] [0.99] [0.23] [10.20] [19.21] [17.59] [14.18] Households head is 17.11*** 8.69*** 3.29*** 0.36* 17.14** 18.66 -22.56 13.43 currently married [3.33] [2.53] [0.83] [0.19] [8.72] [16.05] [18.08] [11.23] Agricultural -3.89** -1.72 0.29 -0.25** -7.91* 3.06 -6.46 -18.90* Household [1.74] [1.29] [0.40] [0.11] [4.77] [8.54] [5.82] [10.15] Muslim -6.61** 1.27 -1.56** -1.11*** -14.58 -10.81 -45.08*** -41.03*** [2.89] [2.34] [0.77] [0.23] [11.49] [35.84] [8.83] [13.14] Christian -3.03 -5.75 -3.86* 1.08* 53.04** -14.36 49.96 12.35 [6.88] [5.66] [2.03] [0.58] [21.37] [20.58] [37.99] [25.39]

Table A.2 Continued

Variables Monthly Expenditures Annual Expenditures Total Food Fuel Entertainment Nonfood Medical Education Durable Other religion 2.62 0.80 1.57 0.64 4.62 41.53 106.70 14.71 [6.25] [3.77] [2.26] [0.55] [31.18] [47.00] [87.06] [29.96] Scheduled Tribe -32.24*** -15.74*** -3.53*** -0.62** -64.12*** -45.29** -48.05*** -41.42*** [4.22] [2.78] [1.11] [0.31] [13.47] [20.01] [9.10] [14.53] Scheduled Caste -16.34*** -10.37*** -0.95 0.045 -44.66*** -28.54 -33.54*** -32.83*** [3.07] [2.30] [0.81] [0.22] [10.57] [21.09] [7.59] [11.65] Other Backward -6.42** -5.92*** 0.05 0.20 -29.92*** -17.79 -7.02 3.83 Class [2.75] [2.15] [0.75] [0.20] [9.50] [20.53] [7.45] [12.63] Ownership of -13.18*** -7.79*** -0.44 0.031 -20.38*** -23.84** -14.60*** -44.54*** homestead land only [2.06] [1.56] [0.55] [0.15] [6.80] [10.20] [5.39] [9.19] In top quartile of land 17.40*** 11.28*** -0.12 0.49*** 39.41*** 8.84 29.02*** 37.60*** holding [2.60] [1.88] [0.62] [0.16] [7.41] [11.98] [10.78] [12.37] Antyodaya or BPL -29.40*** -29.66*** 0.04 -0.24 -17.77*** 20.45* -12.52** -6.12 [1.86] [1.46] [0.48] [0.15] [5.98] [10.49] [6.04] [7.79] Constant 368.10*** 249.90*** 47.49*** -2.94*** 519.20*** 37.09 20.89 -41.22 [13.67] [11.78] [3.72] [0.75] [43.36] [61.94] [105.90] [45.54] Electrification + 47.26*** 17.44*** 16.47*** 4.78*** 90.17*** 13.20 56.97*** 56.71*** (Post*Electrification) [3.60] [2.79] [1.08] [0.35] [11.67] [16.42] [15.66] [13.85] Number of Observations 34,736 34,736 34,736 34,736 34,736 34,736 34,736 34,736 R-squared 0.81 0.72 0.60 0.21 0.49 0.03 0.14 0.06 Notes: Robust standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1. Values are rounded to two decimal places. Control variables include log of family size, home ownership, use of clean cooking fuels, presence of any salaried person, personal characteristics of household head like marital status, sex and age, whether an agricultural household, membership in specific social groups or caste, religion, land ownership type and status, and whether belongs to Antodaya or BPL group. All regressions incorporate inverse probability weights based on propensity score estimation for household electrification status, by wave. BPL = Below Poverty Line.

Table A.3: Detailed DD Regression Results for Per Capita Expenditures (in Rupees) among Poor, Rural Households, with Backward States Interaction

Monthly Expenditures Annual Expenditures Entertain- Variables Total Food Fuel ment Nonfood Medical Education Durable 5.63** -4.74** 10.22*** -0.25 35.73*** -6.78 23.39** -10.02 Electrification [2.40] [1.97] [0.81] [0.24] [10.00] [11.70] [10.45] [15.96] 393.90*** 238.90*** 42.79*** 2.11*** 475.10*** 93.64*** 131.00*** 141.10*** Post [5.96] [4.56] [1.98] [0.42] [20.52] [35.24] [26.79] [24.15] 36.71*** 15.14*** 4.78** 7.25*** 52.20** 54.84 45.40 57.00* Post*Electrification [5.26] [4.20] [1.97] [0.51] [21.36] [37.48] [30.28] [32.79] -103.20*** -81.29*** -27.94*** 3.91*** -466.30*** -55.41* -144.20*** 38.58* Backward states [5.60] [4.19] [1.98] [0.49] [22.14] [30.79] [18.93] [22.57] Electrification* 23.26*** 15.17*** 2.78** 0.59* 0.61 36.65* 12.76 25.85 Backward states [4.09] [2.89] [1.35] [0.34] [14.00] [21.96] [10.81] [18.62] -25.13*** -31.36*** 1.21 -0.13 -40.74 17.91 -0.40 -3.80 Post*Backward states [7.49] [5.71] [2.46] [0.49] [26.56] [38.89] [28.89] [27.55] Electrification*Post* -21.42*** -10.08* -0.07 -4.73*** -5.11 -99.81** -35.55 -10.88 Backward states [7.85] [6.00] [2.63] [0.63] [27.84] [43.99] [33.02] [37.35] -40.80*** -17.35*** -30.18*** 0.68*** -47.78*** 33.07*** 58.57*** -29.31*** log (Household size) [2.79] [1.95] [0.85] [0.14] [7.51] [12.38] [9.09] [9.73] 24.87*** 21.69*** 10.39*** 0.72** 21.85 -13.10 -32.86 74.27*** Home ownership [4.64] [4.11] [1.39] [0.33] [17.83] [19.80] [20.07] [15.96] 28.83*** 7.27 2.95 3.07*** 61.53** -42.14* 114.60*** 34.66 Clean Cooking Fuel [7.18] [5.45] [2.06] [0.69] [29.84] [23.69] [23.12] [25.13] 19.85*** 8.14*** 0.25 0.70*** 67.07*** -0.34 69.12*** 41.99*** Salaried [2.87] [2.31] [0.76] [0.25] [10.95] [13.52] [15.19] [14.03] 2.91*** 2.33*** 0.95*** -0.03 2.68** -5.50*** 4.49 -0.25 Age of household head in years [0.41] [0.32] [0.11] [0.03] [1.28] [2.04] [3.40] [1.76]

Table A.3 Continued

Monthly Expenditures Annual Expenditures Entertain- Variables Total Food Fuel ment Nonfood Medical Education Durable 16.09*** 16.03*** -1.65* 0.31 32.76*** -2.91 1.09 4.61 Households head is male [3.87] [3.20] [0.98] [0.23] [10.12] [19.39] [17.30] [14.23] Households head is currently 16.80*** 8.34*** 3.29*** 0.36* 16.74* 18.82 -22.57 13.30 married [3.30] [2.49] [0.83] [0.19] [8.67] [16.09] [17.90] [11.23] -4.16** -1.99 0.29 -0.25** -8.19* 2.94 -6.55 -19.03* Agricultural Household [1.72] [1.27] [0.40] [0.11] [4.74] [8.65] [5.77] [10.21] -6.75** 1.03 -1.61** -1.02*** -14.69 -9.43 -44.61*** -41.33*** Muslim [2.86] [2.30] [0.77] [0.23] [11.47] [36.45] [8.86] [12.93] -3.95 -6.64 -3.88* 1.04* 52.18** -14.94 49.62 11.88 Christian [6.96] [5.71] [2.03] [0.57] [21.45] [20.54] [37.89] [25.37] 2.92 1.35 1.45 0.69 5.80 41.30 106.80 14.19 Other religion [6.22] [3.72] [2.24] [0.52] [30.85] [47.60] [86.92] [29.80] -32.56*** -16.19*** -3.46*** -0.65** -64.93*** -45.30** -48.18*** -41.19*** Scheduled Tribe [4.12] [2.71] [1.11] [0.30] [13.26] [20.12] [9.03] [14.47] -16.34*** -10.48*** -0.90 0.03 -45.01*** -28.34 -33.53*** -32.58*** Scheduled Caste [3.05] [2.29] [0.81] [0.21] [10.51] [21.09] [7.57] [11.63] -6.82** -6.40*** 0.09 0.17 -30.66*** -18.02 -7.22 3.92 Other Backward Class [2.71] [2.11] [0.75] [0.20] [9.39] [20.57] [7.43] [12.52] Ownership of homestead land -13.47*** -8.10*** -0.43 0.02 -20.74*** -23.91** -14.68*** -44.65*** only [2.04] [1.56] [0.55] [0.15] [6.77] [10.11] [5.41] [9.21] 17.01*** 10.83*** -0.09 0.47*** 38.81*** 8.71 28.88*** 37.57*** In top quartile of land holding [2.58] [1.84] [0.62] [0.16]] [7.37] [11.98] [10.85] [12.22] -29.38*** -29.59*** 0.05 -0.26* -17.71*** 20.03* -12.65** -6.08 Antyodaya or BPL [1.86] [1.45] [0.48] [0.14] [5.98] [10.59] [6.02] [7.81] 360.90*** 239.90*** 49.47*** -4.43*** 499.00*** 27.73 14.31 -33.42 Constant [13.25] [11.35] [3.83] [0.82] [43.14] [65.10] [103.50] [44.45]

Table A.3 Continued

Monthly Expenditures Annual Expenditures Entertain- Variables Total Food Fuel ment Nonfood Medical Education Durable Electrification + (Post*Electrification) + (Electrification*Post*Backward 20.93*** 0.32 14.94*** 2.26*** 82.82*** -51.76** 33.23** 36.10 states) [6.26] [4.77] [1.91] [0.45] [20.10] [25.14] [14.94] [23.87] Number of Observations 34,736 34,736 34,736 34,736 34,736 34,736 34,736 34,736 R-squared 0.81 0.72 0.60 0.22 0.49 0.03 0.14 0.06 Notes: Robust standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1. Values are rounded to two decimal places. Control variables include log of family size, home ownership, use of clean cooking fuels, presence of any salaried person, personal characteristics of household head like marital status, sex and age, whether an agricultural household, membership in specific social groups or caste, religion, land ownership type and status, and whether belongs to Antodaya or BPL group. All regressions incorporate inverse probability weights based on propensity score estimation for household electrification status, by wave. BPL = Below Poverty Line.

REFERENCES

Ahmad, S., Mathai, M.V. and Parayil, G. (2014) Household electricity access, availability, and human well-being: Evidence from India, Energy Policy, 69, pp. 308–315

Balachandra, P. (2011a) Dynamics of rural energy access in India: An assessment, Energy, 36, pp. 5556-5567.

Balachandra, P. (2011b) Modern energy access to all in rural India. An integrated implementation strategy, Energy Policy, 39, pp. 7803-7814.

Barkat, A., Khan, S. H., Rahman, M., Zaman, S., Poddar, S., Halim, S., Ratna, N. N., Majid, M., Maksud, A. K. M., Karim, A. and Islam, S. (2002) Economic and social impact evaluation study of the rural electrification program in Bangladesh. Retrieved from www.hdrc-bd.com.

Bastakoti, B. P. (2006) The electricity-livelihood nexus: Some highlights from the Andhikhola Hydroelectric and Rural Electrification Centre (AHREC), Energy for Sustainable Development, 10(3), pp. 26-35.

Blundell, R. and Etheridge, B. (2010) Consumption, income and earnings inequality in Britain, Review of Economic Dynamics, doi:10.1016/j.red. 2009.10.004

Cabraal, R.A., Barnes, D. F. and Agarwal, S. G. (2005) Productive uses of energy for rural development. Annual Review of Environment and Resources, 30, pp. 117–144.

Chakrabarti, S. and Chakrabarti, S. (2000) Rural electrification programme with solar energy in remote region–a case study in an island, Energy Policy, 30, pp. 33–42

Chakravorty, U., Pelli, M., Marchand, B. U. (2014) Does quality of electricity matter? Evidence from rural India, Journal of Economic Behaviour and Organization, 107, pp. 228-247.

Davis, D. (2005) Urban consumer culture, The China Quarterly, 183, pp. 677–694.

Deininger, K. (2007) Sri Lanka’s rural non-farm economy: removing constraints to pro-poor growth. World Development, 35(12), pp. 2056–2078.

Dinkelman, T. (2011) The effects of rural electrification on employment: new evidence from South Africa, American Economic Review, 101, pp. 3078–3108.

Diouf, B., Pode, R. and Osei, R. (2013) Initiative for 100% rural electrification in developing countries: Case study of Senegal, Energy Policy, 59, pp. 926–930.

Energy Sector Management Assistance Program (2002) Energy strategies for rural India: Evidence from six states. Retrieved from https://www.esmap.org/node/1030.

Gao, Q., Zhai, F., Yang, S. and Li, S. (2006) Does Welfare Enable Family Expenditures on Human Capital? Evidence from China, World Development, 64, pp. 219–231.

Gibson, J. and Olivia, S. (2010) The effect of infrastructure access and quality on non-farm enterprises in rural Indonesia, World Development, 38 (5), pp. 717-726.

Gregg, P., Waldfogel, J. and Washbrook, E. (2006) Family expenditures post-welfare reform in the UK: Are low-income families starting to catch up? Labour Economics, 13, pp. 721-746.

Grogan, L. and Sadanand, A. (2013) Rural electrification and employment in poor countries: Evidence from Nicaragua, World Development, 43, pp. 252-265.

Guha, I. and Ghosh, S. (2007) Does tourism contribute to local livelihoods? A case study of tourism, poverty and conservation in the Indian Sundarbans, SANDEE Working Paper 26-27.

Gundimeda, H. and Kohlin, G. (2008) Fuel demand elasticities for energy and environmental poilicies. Indian sample survey evidence. Energy Economics, 30, pp. 517-546.

Independent Evaluation group (2008) The welfare impact of rural electrification: a reassessment of the costs and benefits. An IEG Impact Evaluation. Retrieved from http://siteresources.worldbank.org.

Imbens, G.W. (2000) The role of propensity score in estimating dose-response functions, Biometrika, 87 (3), pp. 706-710. Imbens, G.W. (2004) Nonparametric estimation of average treatment effects under exogeneity: A review, The Review of Economics and Statistics, 86 (1), pp. 4-29 Imbens, G.W. and Wooldridge, J. M. (2009) Recent developments in the econometrics of program evaluation, Journal of Economic Literature, 47, pp. 5-86. Kaushal, N., Gao, Q. and Waldfogel, F. (2007) Welfare reform and family expenditures: How are single mothers adapting to the new welfare and work regime? Social Service Review, 9, pp. 369-396.

Khandker, S., Samad, H. A. and Ali, R. (2012a) Who benefits most from rural electrification? Evidence in India. Paper prepared for presentation at the Agricultural & Applied Economics Association’s 2012 AAEA Annual Meeting, Seattle, Washington. Retrieved from http://ageconsearch.umn.edu.

Khandker, Shahidur R. & Barnes, Douglas F. and Samad, Hussain A. (2012b). Are the energy poor also income poor? Evidence from India," Energy Policy, Elsevier, 47, pp. 1-12.

Khandker, S.R., Barnes, D.F. and Samad, H.A. (2013) Welfare impacts of rural electrification: A panel data analysis from Vietnam, Economic Development and Cultural Change, 61(3), pp. 659- 692.

Kohlin, G., Sills, E.O., Pattanayak, S.K. and Wilfong, C. (2011) Energy, gender and development: What are the linkages? Where is the evidence? Retrieved from www.worldbank.org/socialdevelopment

Keynes, J. M. (1936) The General Theory of Employment, Interest, and Money, London: Macmillan (reprinted 2007)

Lahoti, R., Goutam, P. and Suchitra, J.Y. (2012) Subsidies for whom? The case of LPG in India, Economic and Political Weekly, 47(44), pp. 16-18.

Lipscomb, M., Mobarak, A. M. and Barham, T. (2013) Development effects of electrification: Evidence from the topographic placement of hydropower plants in Brazil, American Economic Journal: Applied Economics, 5(2), pp. 200–231.

Mala, K., Schlapfer, A. and Pryor, T. (2008) Better or worse? The role of solar photovoltaic (PV) systems in sustainable development: Case studies of remote Atoll communities in Kiribati, Renewable Energy, 34, pp. 358–361.

Meyer, B. D. and Sullivan, J. X. (2008) Changes in the consumption, income, and well-being of single mother headed families, American Economic Review, 98(5), pp. 2221–2241.

Mondal, A. H., Linda, M. K. and Pachova, N. I. (2011) Drivers, barriers, and strategies for implementation of renewable energy technologies in rural areas in Bangladesh -An innovation system analysis, Energy Policy, 38, pp. 4626–4634.

National Sample Survey Organization (2013) Key Indicators of Household Consumer Expenditure in India, NSS 68th round. Retrieved from mospi.nic.in/Mospi_New/upload/KI-68th- HCE.pdf.

Pachauri, S. (2004) An analysis of cross-sectional variations in total household energy requirements in India using micro survey data, Energy Policy, 32, pp. 1723–1735

Pachauri, S., Brew-Hammond, A., Barnes, D.F., Bouille, S. Gitonga, V. Modi, G. Prasad, A. Rath and H. Zerriffi, (2012) Energy access for development. Retrieved from http://www.iiasa.ac.at/.

Payne, J.E. (2010). A survey of the electricity consumption-growth literature, Applied Energy, 87(3), pp. 723-731

Rao, M.N. and Reddy, B.S. (2007) Variation in energy use by Indian households: An analysis of micro level data, Energy, 32, pp. 143-153

Rao, N. D. (2012) Does (better) electricity supply increase household enterprise income in India? Energy Policy, 57, pp. 532-541.

Rubin, D.B. (1973) Matching to remove bias in observational studies, Biometrics, 29, pp. 159-183

Rud, J. P. (2012) Electricity provision and industrial development: evidence from India, J. Dev. Econ. 97 (2), pp. 352–367.

Sehjpal, R., Ramji, A., Soni, A. and Kumar, A. (2014) Going beyond incomes: Dimensions of cooking energy transitions in rural India, Energy, 68, pp. 470-477.

Sharif, I. and Marufa, M. (2012) Rural electrification using PV: The success story of Bangladesh, Energy Procedia, 33, pp. 343–354.

Srivastava, L. and Rehman, I. H. (2006) Energy for sustainable development in India: Linkages and strategic direction, Energy Policy, 34, pp. 643-654.

Torero, M. (2014) The impact of rural electrification: Challenges and ways forward. Retrieved from http://www.afd.fr/.

Viswanathan, B. and Kavi Kumar, K. S. (2005) Cooking fuel use patterns in India: 1983-2000, Energy Policy, 33, 1021-1036.

Walle, D., Ravallion, M., Mendiratta, V. and Koolwal, G. (2013) Long-term impacts of household electrification in rural India. Working paper series 6527. Retrieved from www.elibrary.worldbank.org/.

Wong, G. K. M. and Yu, L. (2002) Income and social inequality in China: Impact on consumption and shopping patterns. International Journal of Social Economics, 29, 370-384.

Wooldridge, J. M. (2007) Inverse probability weighted estimation for general missing data problem, Journal of Econometrics, 141, pp.1281-1301 Wooldridge, J. M. (2010) Econometric analysis of cross section and panel data, Second Edition. Cambridge, MA: MIT Press