Out of the Darkness and Into the Light?:Development Effects of Rural Electrification in India

Fiona Burlig and Louis Preonas∗

October 25, 2015

PRELIMINARY AND INCOMPLETE. PLEASE DO NOT CITE.

Abstract

Over 1 billion people still lack access to basic electricity infrastructure, and global development organizationsregularly claim that improving energy services is crucial to reducing poverty in low-income countries. Despite thehigh correlation between electricity access and economic success, however, the causal effect of electrification ondevelopment remains poorly understood. This paper investigates India’s RGGVY national rural electrificationprogram, which aims at expanding transmission and distribution capacity and grid connections in over 400,000villages. We exploit a population-based program eligibility threshold to identify the medium-run economic effectsof electrification in over 30,000 villages. Using a regression discontinuity approach in conjunction with Indianadministrative data and high-resolution spatial images, we find that being eligible for RGGVY led to an increasein nighttime brightness measured from space that is comparable with other ground-truthed estimates of theincrease in brightness that comes with electrification; placebo and falsification tests provide further evidencethat these gains were caused by RGGVY. Despite these increases in electrification, we find only modest effectsof eligibility for RGGVY on a wide range of development outcomes. We find that electrification caused smallincreases in the percentage of men employed outside of the home in the non-agricultural sector, and a smalldecline in male employment in the agricultural sector. We find no corresponding changes for women. We areable to reject even relatively small changes in asset ownership, the housing stock, and public goods in RGGVYvillages. Taken together, our results suggest that while RGGVY caused a meaningful change in electrificationrates in rural India, we find little evidence to suggest that this led to large effects on economic development intreated villages.JEL Codes: O10, O18, Q40

1 Introduction

As of 2014, there were still 1.3 billion people around the world without access to electricity. These people areoverwhelmingly rural, and live almost exclusively in sub-Saharan Africa and Asia (International Energy Agency

∗University of California, Berkeley: Department of Agricultural and Resource Economics and Energy Institute at Haas. Burlig:fiona.burlig@berkeley.edu. Preonas: lpreonas@berkeley.edu. We thank Michael Anderson, Max Auffhammer, Steve Cicala, LucasDavis, Meredith Fowlie, Michael Greenstone, Sol Hsiang, Katrina Jessoe, Shaun McRae, Ted Miguel, Erich Muehlegger, Paul Novosad,Nicholas Ryan, Elisabeth Sadoulet, Jake Shapiro, Catherine Wolfram, and seminar participants at the University of California, Berkeley,the University of Michigan, and the Energy Institute at Haas for valuable advice. We benefited from conversations with employees of theIndian Ministry of Power, the Rural Electrification Corporation, and the JVVNL Distribution Company in Jaipur, as well as KendonBell, Joshua Blonz, Susanna Berkouwer, Fenella Carpena, James Gillan, Andrew Stevens, and Matt Woerman. George Fullerton andPuja Singhal assisted us in acquiring the data that made this project possible. We thank the Department of Agricultural and ResourceEconomics at the University of California, Berkeley, for providing travel funds. Burlig is generously supported by the National ScienceFoundation’s Graduate Research Fellowship Program. All remaining errors are our own.

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(2015)). Countries are increasingly making large investments to change this: the International Energy Agencycalculated that in 2009, approximately $9.1 billion was spent to extend modern energy access, projected to riseto $14 billion per year by 2030 (International Energy Agency (2011)). Access to electricity is highly correlatedwith economic development; the Western world boasts nearly universal electrification, while the world’s poorestnations’ citizens are largely unelectrified (World Bank (2015a)). Ascribing a causal interpretation to this correlationis problematic, however: it could easily be the case that development causes electrification, and it could just aseasily be true that electrification causes development. Even within countries, electricity infrastructure requireslarge-scale investments; economic and political considerations likely both influence the timing and location ofelectrification projects. This poses a challenge for the econometric identification of the effects of electrification oneconomic development. At the same time, however, energy access is being touted as necessary for economic progress(International Energy Agency (2015), UNDP (2015), World Bank (2015b)). Universal electricity access is a loftyand expensive goal. It is crucial that we understand well both the benefits and the costs of electrification, especiallywhen the opportunity cost of an electrification project in a developing country could be funding directed towardshealth, education, roads, or other public goods. In this paper, we make progress on these important questions byexploiting a population cutoff in a massive national rural electrification program in India to identify the causalimpact of electrification on development.

Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY), or the Prime Minister’s Rural Electrification Program,was launched in 2005 to expand electricity access in over 400,000 rural Indian villages. This program was startedunder India’s 10th Five Year Plan. In order to keep costs manageable, only villages with constituent habitationslarger than 300 people were eligible for the scheme.1 With the program’s subsequent renewal under the 11th and12th Plans, the population cutoff was lowered to 100. As of 2015, the program covers all villages.

In order to measure the effects of rural electrification, we combine several large administrative datasets withextremely detailed geospatial information to construct a panel of Indian villages. We draw our administrativedata from the 2001 and 2011 waves of the Indian Census, including the Primary Census Abstract, the VillageDirectory, and the Houselisting Primary Census Abstract. We combine this with implementation data from theRGGVY scheme and sub-village-level data from the Ministry of Water Resources from 2003 and 2009. We generateour spatial data by combining village, district, and state boundary data from ML InfoMap, Ltd. with NOAA’sOLS-DMSP satellite images of Earth at night.

We advance the literature on infrastructure and energy in the developing world by using a fuzzy regression disconti-nuity design to estimate the effects of electrification on development in a large national program. Because RGGVYis a village-level program, we are able to identify the effects of electricity infrastructure provision on community-level outcomes. Measuring outcomes that extend beyond the individual household is essential to understandingthe effects of energy access in the developing world.

Our results suggest that RGGVY had a statistically significant and meaningful effect on village electrification.Despite these gains, however, we find only modest effects of eligibility for the program on a wide range of develop-ment outcomes. We do find that electrification caused 0.3 percentage-point increase in male employment outsideof the home in non-agricultural occupations, and a 0.7 percentage-point decline in male employment in the agri-cultural sector, suggesting that electrification leads men to move out of agrarian work and into other employment.In contrast with the previous literature, we find no corresponding effects for women. We are able to reject evenrelatively small changes in asset ownership, the housing stock, and village-level public goods provision. Together

1As of 2001, the village was the lowest-level administrative unit in the Indian Census. In reality, villages can be further broken upinto habitations (these are sometimes called “hamlets”), which are the inhabited areas of a village. Typical South Asian villages haveone or more inhabited regions surrounded by agricultural land. This is in contrast to the Eastern African context, in which farmerslive interspersed with their fields. In some Indian states, the majority of villages have only one habitation; in other states, manyhabitations make up a single village. There are approximately 1.6 million habitations in India as of 2009. The 2011 Census containssome information about habitations, but coverage is far from universal.

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these results suggest that while RGGVY did in fact increase access to electricity in small rural villages, it did notyield the level of economic benefits typically associated with large-scale development programs.

The remainder of the paper proceeds as follows: Section 2 discusses the existing literature on electrification in thedeveloping world. Section 3 provides a description of the history of rural electrification in India and details theRGGVY scheme. Section 4 describes our empirical strategy. Section 5 discusses the data used in our analysis.Section 6 presents our findings. Section 7 concludes.

2 Existing literature

Energy access is hailed as a necessary component for poverty reduction by important global policy actors includingthe World Bank (2015b), International Energy Agency (2015), and UNDP (2015). According to the 2011 WorldEnergy Outlook, $9.1 billion was invested globally in increasing access to energy services in 2009 (InternationalEnergy Agency 2011). Meanwhile, the International Energy Agency estimates that providing universal, reliableelectricity access by 2030 will cost $770 billion. Infrastructure represents a large proportion of development spend-ing, both by national governments and by outside funding agencies. In light of this, there is a large literature whichlooks at the effects of infrastructure provision in the developing world.2 There is also a large and growing literaturethat focuses on energy in the developing world.3 Despite the size of these overlapping literatures, surprisingly fewempirical papers address the effect of electrification on economic development outcomes.

Most of the papers that do study the causal relationship between energy and development use instrumental variablesstrategies to identify the economic impacts of electrification. Dinkelman (2011) finds that female employmentincreased in the wake of a rural electrification program in post-Apartheid South Africa. She instruments fortreatment status under the program with land gradient, which is positively correlated with the cost of installingtransmission infrastructure. Rud (2012) uses pre-Green Revolution groundwater availability as an instrument forelectrification in India, and finds large increases in state-level manufacturing output due to increased electricityaccess. Lipscomb, Mobarak, and Barham (2013) instrument for the placement and timing of hydroelectric dams inBrazil with the predictions from an engineering model, and show county-level increases in housing values and theHuman Development Index. Van de Walle et al. (2013) use historical electrification rates and geographic variablesto instrument for electrification in India. They find long-run increases in consumption, earnings, and femaleschooling at the household level. The authors also show positive spillover effects of village-level electrification tonon-electrified households.

Several recent and ongoing studies have traded off geographic scale in favor of experimental variation by imple-menting randomized controlled trials to test the effects of electrification at the household level.4 Barron and Torero(2014) and Barron and Torero (2015) study the effects of household electrification on time use and air pollution

2Influential papers on infrastructure and development include Donaldson (2010) on the effects of railroads on trade costs and welfarein India; Banerjee, Duflo, and Qian (2012) and Faber (2014) on roads in China; and Duflo and Pande (2007) on dams in India. Theclosest non-electricity paper to our own is Asher and Novosad (2014), which looks at India’s rural road building program, PMGSY.The authors exploit a similar population cutoff in a regression discontinuity design, showing that roads lead to an increase in ruralemployment in treated villages.

3Wolfram, Shelef, and Gertler (2012), Gertler et al. (2013), and Davis and Gertler (2015) investigate the effects of income growth onenergy demand. Ryan (2014) demonstrates the potential for reducing market power in the Indian electricity sector with transmissioninvestments. Several papers study the effects of power quality on firms and individuals in developing countries, including Foster andSteinbuks (2009), McRae (2010), Alby, Dethier, and Straub (2011), Andersen and Dalgaard (2012), Fisher-Vanden, Mansur, and Wang(2012), Alam (2013), Fetzer, Pardo, and Shanghavi (2013), Rao (2013), Allcott, Collard-Wexler, and O’Connell (2014) Burlando (2014),and Chakravorty, Pelli, and Ural Marchand (2014). McRae (2013) and Jack and Smith (2015) provide evidence on different electricitypayment schemes in Colombia and South Africa, respectively. Baskaran, Min, and Uppal (2015) investigates the relationship betweenpolitics and power quality in India.

4We are excited to be able to cite pre-analysis plans for ongoing experimental work here.

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in El Salvador, respectively. Bernard and Torero (2015) explore spillovers in grid connections among householdsin Ethiopia. Lee et al. (2014) present baseline data on an ongoing field experiment subsidizing grid connectionsfor rural Kenyan households. In Miguel et al. (2014), the same researchers are using experimental variation inthese subsidies to trace out a demand curve for electricity access, while also measuring the development effects onhouseholds that purchase a connection. We know of two other ongoing experiments in this area, both in India.Brewer et al. (2012) are installing solar microgrids in rural Rajasthan, while also providing energy efficient appli-ances to some households. Ryan et al. (2014) are similarly offering solar microgrid connections at different pricesto unconnected households in Bihar.

Our paper makes several key contributions to this existing body of work. First, we bridge the gap between thepapers that study large geographic regions and those that employ experimental identification. Our regressiondiscontinuity design invokes less demanding identifying assumptions than the instrumental variables strategies ofprevious papers. At the same time, we study an electrification program that has affected more than 400,000 villagesacross 27 states in India.5 Second, we look at a wide range of development outcomes, which allows us to commenton the specific channels through which electrification leads to development (our analysis of non-labor outcomes isstill in progress). Third, our treatment and outcomes variables are at the village level. This allows us to estimatecommunity-level treatment effects, including both the between-village and within-village economic spillovers wemight expect from infrastructure improvements.6

3 Rural electrification in India

At the time of its independence in 1947, only 1,500 of India’s villages had access to electricity (Tsujita (2014)). ByMarch 2014, that number has risen to 576,554 out of 597,464 total villages (Central Electricity Authority (2015)).This massive technological achievement is largely attributable to a series of national electrification programs, thefirst of which began in the 1950s.7 The modern Indian electricity sector is governed by the Electricity Act, 2003,which lays out comprehensive regulations for the generation, transmission and distribution, trading, and use ofelectricity in India. Importantly, the Act declared that the government “shall endeavour to supply electricity toall areas including villages and hamlets” (Ministry of Law and Justice (2003)), spurring a resurgence of ruralelectrification in India.

Prior to 2004, a village had been considered electrified if any electricity was used within the village boundary forany purpose.8 The Indian government adopted a more stringent definition of electrification in 2004. A villageis now officially considered electrified only if basic infrastructure, including transformers and distribution wiring,exists in that village and in its associated habitations; if public locations such as schools, government offices, andhealth centers have electricity; and if at least 10% of the village’s households are electrified (Ministry of Power(2004)).9 This new definition classified approximately 125,000 (21%) of rural villages as unelectrified. Just threeyears earlier, the 2001 Census estimated that 56.5% of all rural households lacked access to electricity, with 30%of these unelectrified households falling below the poverty line (Ministry of Power (2013)). This suggests that asof 2004, substantial opportunities remained to connect more households to the grid, even among villages classifiedas electrified.

5Even though our regression discontinuity design ultimately recovers a local average treatment effect, we include over 30,000 villagesin our sample, making this the largest study to date on the effects of rural electrification.

6Previous studies have conducted their empirical analyses either at the individual/household level or at the county/district/statelevel. Our analysis at the village level will capture spillovers that are often ignored in the household-level literature, while also exploitingrich variation across villages. To the best of our knowledge, none of the previous papers on rural electrification has done this.

7See our online appendix for further detail. For now, this is only available from the authors upon request.8To paraphrase one government official: if a village contained a single lit bulb, it was legally considered electrified.9This definition was issued by the Ministry of Power in a letter dated February 5, 2004. It went into effect in 2005.

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3.1 RGGVY

The flagship program of the modern electrification efforts is Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY),or the Prime Minister’s Rural Electrification Plan.10 RGGVY subsumed the preceding electrification programswhen it launched in 2005 with the goal of electrifying the over 100,000 remaining unelectrified villages across 27Indian states.11 The program also set out to provide free grid connections to 23.4 million rural households livingbelow the poverty line (Ministry of Power (2015a)).

RGGVY has a mandate to create or upgrade at least one 33/11 kV (or 66/11 kV) distribution substation along withassociated power lines in each Census block, such that this infrastructure is capable of handling the added load fromnewly electrified households. In addition, each village is supposed to receive at least one distribution transformer,as well as low tension distribution lines and free connections for all below poverty line (BPL) households. Inregions where extending the grid is not feasible or cost effective, RGGVY installs a decentralized distribution cumgeneration (DDG) system instead of extending grid lines.12 Funding for the program comes almost entirely from thenational government. The Rural Electrification Corporation (REC), a public-private financial institution overseenby the Ministry of Power, provides states with 90% of the capital for RGGVY implementation and loans them theremaining 10% (Ministry of Power (2013)). The government authorizes these funds through its Five Year Plans.The 10th Plan (2002-2007), 11th Plan (2007-2012), and 12th Plan (2012-2017) have all financed stages of RGGVYimplementation, and the program is expected to end after the 13th Plan. Figure 1 shows the spatial distributionof RGGVY implementation, and Figure 2 presents a timeline.

In order for a state to receive funding under RGGVY, its electric utilities must submit district-level implementationproposals, or Detailed Project Report (DPRs), to the REC. These DPRs are based on surveys carried out in everyvillage within a district, for both unelectrified villages and partially electrified villages in need of “intensive electrifi-cation”.13 They enumerate each village’s electrification status, population, number of households (above/below thepoverty line, and with/without electricity), and number of public places (with/without electricity). Each DPR thenproposes a village-by-village RGGVY implementation plan, which includes details on new electricity infrastructureto be installed and the number of households and public places to be connected (Ministry of Power (2014)).14

The REC then reviews DPR proposals, approves projects, and disburses funds to the states. In most cases, stateutilities serve as the agencies in charge of RGGVY implementation (Ministry of Power (2008b)).

As of August 31, 2014, RGGVY had slated 112,075 unelectrified villages for electrification and 374,239 previouslyelectrified villages for intensive electrification. Thus far, the program reports 97% and 83% electrification of thesetwo groups of villages, respectively. The REC also reports that 80% of the BPL households in these villages havereceived connections. Implementation in the remaining villages is still ongoing.

RGGVY program guidelines establish cost norms for village-level electrification, based on village status and terrain.They recommend spending 1.3 (1.8) million rupees for each unelectrified village in normal (hilly) terrain, and 0.4(0.6) milion rupees for each previous electrified village in normal (hilly) terrain. They also recommend spending2,200 rupees for each BPL household connection (Ministry of Power (2008a)). In 2005, the full program was

10The program has since been subsumed into Deendayal Upadhyaya Gram Jyoti Yojana, or the Deendayal Upadhyaya Village LightPlan (DDUGJY). We continue to refer to the program as RGGVY in this paper, since that was its name during the implementationphases we study.

11The state of Goa was excluded from RGGVY along with all 7 union territories, because 100% of their villages were electrified priorto 2005 (Ministry of Power (2012)). We treat Telangana as part of Andhra Pradesh, since its 2014 split from Andhra Pradesh occurredafter our period of analysis.

12We ignore these DDG systems in our analysis, as they affect a very small subset of treated villages.13In most cases, states will submit separate DPRs for each of their constituent districts to be covered under the program.14DPRs are supposed to report total population, as well as separately reporting scheduled caste and scheduled tribe populations. A

sample DPR form can be found here: http://www.rggvy.gov.in/rggvy/rggvyportal/link_files/guide1.pdf

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Figure 1: Indian Districts by RGGVY Coverage

Notes: This map shows 2001 district boundaries, shaded by RGGVY coverage status. Blue districts arecovered under the 10th Plan, green districts are covered under the 11th Plan, cross-hatched districts werecovered under both the 10th and 11th Plans, and white districts are not covered by RGGVY. As of 2001,India had 584 districts across its 28 states and 7 Union Territories. RGGVY covered 530 total districts in27 states (neither Goa nor the Union Territories were eligible), with 30 districts split between both 10thand 11th Plans.

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Figure 2: RGGVY Timeline

Notes: This figure shows the timing of the Indian decennial census, the habitation census, the 10th and11th Five-Year Plans, and RGGVY under these Plans.

expected to cost 634.2 billion rupees, or approximately 10.4 billion US dollars.15 RGGVY progress reports suggestthat by 2014, the program had allocated roughly half that amount.

As a means of reducing program costs, the Ministry of Power restricted RGGVY eligibility to villages and habi-tations (i.e. neighborhood units within villages) above a certain size. Under the 10th Plan, only villages withhabitations larger than 300 people were eligible for RGGVY. This threshold was reduced to 100 people under the11th and 12th Plans. DDUGJY, RGGVY’s successor program, covers all villages and habitations in the country,regardless of population (Ministry of Power (2015b)).

4 Empirical strategy

In an ideal experiment, we would helicopter-drop electricity infrastructure to randomly selected villages acrossIndia.16 Such an intervention will likely never happen in the context of grid-scale electricity: energy infrastructureprojects are large and expensive, and governments allocate time and money to ensure that they target certainregions or groups of people. Unfortunately for the econometrician, this makes it challenging to identify the causaleffect of electrification on development. Governments likely allocate electricity infrastructure based on observed orunobserved economic and political factors, which can bias econometric analyses of these projects. In particular, ifa program allocates infrastructure to places that are already (or are projected to be) experiencing relatively higheconomic growth, an OLS regression of electrification on development will be biased towards finding a positiveeffect. In light of these endogeneity issues, we apply a regression discontinuity design to estimate the causal effectof electrification on development.

Under the RGGVY program rules for the 10th Plan, villages are eligible for treatment if they contain habitationswith populations of 300 or above. Importantly, our analysis sample consists of only villages whose districts received

15This conversion uses the 2014 exchange rate of 0.016 dollars to the rupee.16This new electricity infrastructure would also have to work. It is hard to imagine building one lone distribution line without

transmission around it, which makes such a thought experiment even less feasible.

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funding under the 10th Plan.

This provides us with the opportunity to implement a regression discontinuity (RD) design to estimate treatmenteffects. In this design, the probability of treatment changes discontinuously as village population (our “runningvariable”) crosses the 300 person threshold17, allowing us to identify the effect of eligibility for RGGVY on a varietyof outcomes.

This design necessitates two main identifying assumptions. First, we must assume continuity across the RDthreshold for all village covariates and unobservables that might be correlated with our outcome variables. Whilethis assumption is fundamentally untestable, we can support it with graphical evidence from several key villagecharacteristics.18 In addition, we know of no other Indian social program with a 300-person eligibility threshold.19

Second, we assume that the running variable is not manipulable around the threshold.20 Because our runningvariable, 2001 Census population, predates the announcement of RGGVY in 2005, we are confident that reportedpopulations were not influenced by the presence of RGGVY.

Given these assumptions, our RD design provides a consistent estimate of the effect of eligibility for treatment onoutcomes of interest. Formally, we estimate:

Y 2011vs = β0 + β1Zvs + β2(Pvs − c) + β3(Pvs − c) · Zvs + β4Y

2001vs + ηs + εvs for c− h ≤ Pvs ≤ c+ h (1)

where

Zvs ≡ 1[Pvs ≥ c].

Y 2011vs represents the outcome of interest in village v in state s in 2011, Pvs is the village population, c is the 300-

person RD cutoff, Zvs is the RD instrument indicating RGGVY eligibility above the cutoff, h is the RD bandwidth,Y 2001vs is the 2001 value of the outcome variable, ηs is a state fixed effect21, and εvs is an idiosyncratic error term.

Our preferred specification allows for arbitrary dependence between the error terms of villages within the samedistrict by clustering at the district level. For all of the results below, we perform a variety of sensitivity analyses,falsification tests, and placebo tests, which are described in further detail in the online appendix.

5 Data

Our empirical analysis uses data from three main sources. First, we use publicly available administrative datafrom the RGGVY program to determine the timing of program rollout across states and districts. Next, we linksatellite images of nighttime brightness to GIS maps of village boundaries, assigning a brightness metric to eachvillage-year. We then leverage several village-level datasets published by the Census of India, which provide uswith village populations, covariates, and a wide range of economic outcomes.

17See Cook (2008), Imbens and Lemieux (2008), and Lee and Lemieux (2009) for more information about regression discontinuity ineconomics.

18These results are available in the online appendix. We find no evidence to suggest that covariates change discontinuously acrossthe 300-person cutoff in the pre-period.

19Other programs use population-based eligibility thresholds, including the road construction program PMGSY. This program’sinitial cutoff was 1000 people (beginning in 2003), which shrank to 500 in 2007.

20See Figure 421Neither the 2001 value of the outcome variable nor the fixed effects are necessary for identification, but they improve efficiency.

See Lee and Lemieux (2009) for more detail.

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5.1 RGGVY program data

The Rural Electrification Corporation maintains an online database of documents and data pertaining to theRGGVY program.22 We use district-level progress reports to determine the RGGVY implementing agency foreach district, as well as the approximate timing of implementation. The progress reports also assign each districtto a specific Five Year Plan, allowing us to separate districts funded by the 10th Plan from those funded by the11th Plan. This distinction is crucial for the validity of our empirical strategy for two reasons. First, the 10th Planused a 300-person population cutoff to determine program eligibility, whereas the 11th Plan used a 100-personcutoff. Our fuzzy regression discontinuity design relies on identifying village-level electrification around the correctpopulation cutoff. Second, we use outcome data collected in 2011, which are likely to reflect medium-run impactsof electrification in 10th Plan villages. Table 1 summarizes these progress reports at the district level.

This online database also publishes RGGVY implementation details at the village level, including the number ofhousehold connections completed and the capacity of transformers installed. Ideally, we would utilize these village-level outcomes in our program impact evaluation. Unfortunately, the quality of these data is quite poor; RGGVYprogram outcomes are frequently missing, internally inconsistent, or conflicting with proposed implementationdetails. Moreover, RGGVY village-level outcomes are often difficult to reconcile with other village-level datasets.For these reasons, we do not use RGGVY village-level data in our subsequent analysis. Our online appendixaddresses these data irregularities in further detail.

Table 1: Summary of RGGVY Implementation by District

Type ofImplementing Agency States Districts Financial

Award DatesFunds Awarded(Billion Rupees)

UnelectrifiedVillages

ElectrifiedVillages

A. 10th PlanPublic Sector Undertakings 11 57 Mar 2005 – Oct 2007 49.95 32638 20126State Departments of Power 5 10 Jan 2007 – Mar 2010 3.87 542 1088State Electricity Boards 3 6 Dec 2006 – May 2007 10.77 4482 2604Distribution Companies 14 156 Jun 2005 – Sep 2008 55.56 26429 76495Total 25 229 120.15 64091 100313

B. 11th PlanPublic Sector Undertakings 9 78 Mar 2008 – Mar 2011 78.68 30298 62705State Departments of Power 5 33 Apr 2008 – Mar 2010 15.13 2710 3850State Electricity Boards 3 34 Aug 2008 – Mar 2011 3.72 80 18166Distribution Companies 15 183 Mar 2008 – Dec 2010 77.32 13118 135038Rural Electricity Coops 2 5 Dec 2008 – Feb 2011 0.38 0 755Total 25 331 175.23 46206 220514

Notes: Data summarize district-level progress reports, available at http://rggvy.gov.in. Public sector undertakingsinclude government-owned generating companies, such as Power Grid Corporation of India and National HydroelectricPower Corporation. Funds are shown in nominal rupees, which traded at approximately 45 rupees per U.S. dollar from2005–2011. Villages classified as “electrified” had basic electricity infrastructure with at least 10% of householdselectrified prior to RGGVY implementation. 23 (of 27) states contain both 10th and 11th Plan districts, while 30 (of530) individual districts were targeted under both Plans.

22Even though RGGVY has been subsumed into DDUGJY, the web address remains http://rggvy.gov.in.

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5.2 Nighttime lights data

In the absence of reliable village-level program data, we use changes in satellite-measured nighttime brightness asan indicator of electrification under RGGVY. The National Oceanic and Atmospheric Administration’s DefenseMeteorological Satellite Program–Operational Line Scan (DMSP–OLS) Nighttime Lights data collect images fromU.S. Air Force satellites, which photograph the earth daily between 8:30pm and 10:00pm local time. After cleaningand processing these images, NOAA averages them across each year and distributes yearly images online.23 Eachhigh-resolution image reports light intensity for each 30 arc-second pixel (approximately 1 km2) on a 0–63 scale,which is proportional to the average observed luminosity.24 Figure 3 shows the increase in brightness of Indiabetween 2001 and 2011.25

Figure 3: Nighttime Lights in India

Notes: This figure shows the DMSP-OLS nighttime brightness data for India. The left panel shows theaverage brightness for 2001, and the right panel the average brightness for 2011. The ≈1km2 pixels in thisimage range in brightness from 0 to 63.

Economists have frequently used these nighttime lights data as proxies for economic activity, including recentwork by Henderson, Storeygard, and Weil (2011, 2012), Bleakley and Lin (2012), Michalopoulos and Papaioannou(2013), and Storeygard (2013). These papers typically argue that nighttime brightness is highly correlated withGDP, because brightness is a direct indicator of outdoor and indoor lighting, which is highly correlated with theconsumption of nearly all goods and services in the evening. Because we are studying an electrification program,we do not take nighttime brightness as an indicator of economic activity; rather, we exploit the fact that there isa physical relationship between the consumption of energy services and nighttime brightness, making brightnessa good proxy for electrification. Existing work demonstrates that nighttime brightness can be used to detectelectrification, even at small spatial scales: Min et al. (2013) find evidence of a statistically detectable relationshipbetween NOAA DMSP–OLS brightness and the electrification status of rural villages in Senegal and Mali. Min andGaba (2014) show that a similar correlation between electrification and nighttime brightness also exists in ruralVietnam. Chand et al. (2009) show a direct relationship between nighttime lights and electric power consumptionin India, while Min (2011) finds a strong correlation between brightness and district-level electricity consumption

23This cleaning removes any sunlit hours, glare, cloud cover, forest fires, the aurora phenomena, and other irregularities. Nighttimelights data are available for download at http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html.

24Chen and Nordhaus (2011) detail the relationship between physical luminosity and brightness in the nighttime lights images.25Note that there are potential issues when using only time-series variation in these nightlights data. The time series of nightlights

used in this paper was collected by four different satellites, and the satellite sensors degrade over time. There is no on-board calibrationof the sensors. This has been adjusted for in other economics papers by using satellite or year fixed effects; our empirical strategy relieson both time-series and cross-sectional variation, shielding us from this concern.

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in Uttar Pradesh.

Even if the RGGVY village-level administrative data were of better quality, we still might expect NOAA DMSP–OLS data to provide a more reliable measurement of electrification. Manipulation of administrative data is afrequent concern for program impact evaluations in developing countries, particularly with large decentralizedprograms such as RGGVY. Asher and Novosad (2014) find widespread evidence of data manipulation in theiranalysis of India’s PMGSY road-building program, whereby administrative records were adjusted to increase thelikelihood of treatment under the program. RGGVY implementing agencies might also have an incentive to adjustprogram data in order to overstate the extent of electrification. By using satellite images instead of program data,we are able to measure the actual physical effects of increased electrification.

Nighttime lights will also allow us to better observe the timing and effectiveness of village-level electrification.Whereas the RGGVY data do not report project implementation dates at the village level, annual satellite imagescan reveal the timing of increased energy access. In addition, examining brightness allows us to learn about theamount and quality of power that is provided to a village: if villages A and B are treated under RGGVY but villageB receives very low voltage and experiences frequent power outages, nighttime brightness should increase weaklymore for village A. Using nighttime lights allows us to quantify the increase in on-the-ground energy access thatwas caused by RGGVY, rather than the effects of infrastructure upgrades that appear only in government ledgers.

We construct a village-level panel of nighttime lights values by combining our village boundary data with the nightlights data in ArcGIS. Indian villages have official boundaries, which are recorded by the Census Organization ofIndia. Every square meter in India (excluding bodies of water) is contained in a city, town, or village. We keepall night lights pixels whose centroids are contained within a village boundary, in order to calculate the mean andmaximum DN value for each village.26 In certain years, NOAA had two satellites operating DMSP–OLS equipment.For these years, we calculate the mean and maximum lights values for each satellite separately, and then we takean unweighted average across satellites to obtain village-year DN statistics. Our online appendix provides furtherdetail on the nighttime lights data. In performing this calculation, we are forced to drop 10 states from our sample,due to missing and poor-quality shapefiles. We exclude 5 states because we do not have shapefiles for them, buttogether, these states make up fewer than 3% of the total villages covered by RGGVY.27 We also exclude 5 statesbecause we believe these shapefiles to be of extremely low quality: the correlation between the village area impliedby the shapefiles and village area as recorded by the Indian Census is below 0.35.28 Importantly, we do not needto make this restriction for any other variables.

5.3 Census of India

We combine several datasets published by the Census of India from the 2001 and 2011 decennial Censuses, which arereleased online at different levels of aggregation.29 The Primary Census Abstract contains basic village populationdata, along with a detailed breakdown of labor allocation within each village, by gender and job type. TheVillage Directory reports on a wide range of village-level amenities, including schools, health facilities, bankingfacilities, source of drinking water, and transportation infrastructure. Finally, the Houselisting Primary CensusAbstract provides extensive data on living conditions, household size, physical household characteristics, and asset

26This is performed using the the standard Zonal Statistics as Table operation in ArcGIS. For villages too small to contain apixel’s centroid, we assign the value of the pixel at the village centroid as both the mean and maximum lights value. All of the villagesthat did not contain a pixel centroid only overlapped with one pixel, so this is the correct operation for these very small villages.

27These states are Arunachal Pradesh, Meghalaya, Mizoram, Nagaland, and Sikkim.28These states are Assam, Himachal Pradesh, Jammu and Kashmir, Uttar Pradesh, and Uttarakhand. Previous papers have used

similar restrictions to focus their analysis on Indian states where indicators were consistent across datasets (see, for example, Asherand Novosad (2014)).

29These data are all publicly available, and they can be found at http://www.censusindia.gov.in.

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ownership. We link these three datasets within each census year using village codes, and then we use a 2001–2011village concordance to link them across census years, creating a two-period panel.30 All six datasets are availableat the village level, except for the 2001 Houselisting Primary Census Abstract, which only exists at the block level.

This two-period panel dataset contains the village-level economic outcomes that we use to test the impact ofelectrification under the RGGVY program. It also contains many baseline village characteristics, allowing us tocontrol for heterogeneity across villages. Conveniently, the 2001 Census predates the announcement of RGGVYin 2005, while most projects sanctioned under the 10th Plan were likely implemented before collection of the 2011Census. Since many 11th Plan projects were implemented after 2011, we focus our empirical analysis on the subsetof districts with 10th Plan RGGVY projects.

The 2001 Primary Census Abstract reports the official 2001 population of each village, which we use as the runningvariable in our fuzzy regression discontinuity design. However, RGGVY implementing agencies were instructed todetermine eligibility based on 2001 habitation populations. There exist virtually no nationwide habitation-leveldatasets, with the exception of a habitation census conducted by the National Rural Drinking Water Program.31

We use a fuzzy matching algorithm to link this habitation census to our village-level Census panel, which allows usidentify the 50% of villages with exactly one habitation (our online appendix describes this matching algorithm indetail). For these single-habitation villages, habitation populations are equivalent to village populations—meaningthat 2001 village population should exactly correspond to the population that determined RGGVY eligibility forthis subset of villages.

In constructing our final analysis dataset, we merge the 2001–2011 Census panel with the village-year panel ofnighttime brightness described above, by using the village codes embedded in the village boundary shapefiles. Weadd RGGVY program implementation details at the district level, along with a count of habitations per village asindicated by the fuzzy merge to the habitation census. As each merge between different data sources is imperfect,our analysis includes only those villages that we successfully match to all datasets. We use only single-habitationvillages in RGGVY 10th Plan districts in order to ensure the internal validity of our research design. Table 2 showsthe number of villages present at each step in this merging process.

Table 2: Count of Villages by Merged Dataset

Number of Villages Total RGGVY 10thPlan Districts

RGGVY 10th Plan,Single-Habitation

Raw Census datasets (village-level) >593,0002001–2011 Census panel 580,643 290,0672001–2011 Census panel + habitations 499,799 249,648 129,0062001–2011 Census panel + habitations + lights 480,025 247,086 127,114

Notes: All village counts exclude Goa and the 7 Union Territories, which were not covered under RGGVY. These villagecounts are preliminary, since we are still waiting on the release of the 2011 Village Directory.

Notably, our panel dataset of single-habitation, 10th-Plan villages includes only 22% of Indian villages. Becauseonly 52% of villages were eligible under RGGVY’s 10th Plan, and only half of villages have one habitation, ourresearch design forces us consider this smaller sample of the population of villages. Despite these dropped villages,we achieve comparable merge rates to others working with these data (see Asher and Novosad (2014)), and oursample includes villages from across the country. When we restrict our data to villages with between 150 and

30We re-aggregate villages that split between 2001 and 2011, and we drop the few villages that merged between 2001 and 2011.31Administered by the Ministry of Drinking Water and Sanitation, this census of habitations was collected in 2003 and 2009.

It is available for download at http://indiawater.gov.in. This census of habitations is the recommended reference for RGGVYimplementing agencies (Ministry of Power (2014)).

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450 people, we end up with a sample of 33,728 villages. Figure 4 displays the reduction in sample size fromthese restrictions, and specifically demonstrates that these restrictions do not change discontinuously at the RDthreshold. This figure also shows that our running variable, 2001 village population, is smooth across the RDthreshold. Figure 5 shows the full range of Indian village populations for 2001 and 2011; our 150–450 RD windowlies in the area of support with the highest density of villages.

Figure 4: Running variable – 2001 population

Notes: This figure shows the proportion of total villages in India that are in our final sample: 10th Planvillages with one habitation (navy), relative to all Indian villages (white) and all villages in 10th Plandistricts (blue). This also demonstrates that the running variable is not discontinuous at the 300-personcutoff.

Table 3 shows key indicators for three sets of villages with populations between 150 and 450: all Indian villages,all villages in 10th plan districts, and all villages in 10th plan districts that have only one habitation. Villages in10th Plan districts are, on average, geographically smaller and less electrified than the national average, but aresimilar across a range of other 2001 covariates. 10th Plan villages with only one habitation look nearly identicalto their non-singleton counterparts.

6 Results

As discussed above, we are concerned that the likely endogeneity of electrification will bias any OLS estimates.To recover a consistent estimate of the effects of RGGGY eligibility on outcomes of interest, we use the regressiondiscontinuity design described in Section 4 to estimate the causal effect of RGGVY. We will first provide evidence

13

Figure 5: Histogram of Indian Village Populations

Notes: This figure displays the population distribution of villages in India in 2001 (solid navy) and 2011(hollow blue). We will use villages with 2001 populations between 150 and 450 in our analysis.

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Table 3: Summary Statistics – Villages with population between 150 and 450

2001 Village Characteristics All Districts 10th-Plan Districts 10th-Plan DistrictsSingle-Habitation

Village area (hectares) 199.74 177.97 176.40(462.39) (561.29) (636.05)

Share of area irrigated 0.27 0.33 0.38(0.34) (0.35) (0.36)

Agricultural workers / all workers 0.39 0.37 0.37(0.16) (0.16) (0.15)

Household workers / all workers 0.01 0.01 0.01(0.04) (0.04) (0.04)

Other workers / all workers 0.06 0.06 0.06(0.08) (0.08) (0.08)

Literacy rate 0.45 0.44 0.45(0.18) (0.17) (0.17)

Education facilities (0/1) 0.66 0.58 0.58(0.47) (0.49) (0.49)

Medical facilities (0/1) 0.13 0.12 0.12(0.34) (0.32) (0.32)

Banking facilities (0/1) 0.01 0.01 0.01(0.11) (0.11) (0.10)

Agricultural credit societies (0/1) 0.03 0.03 0.03(0.18) (0.16) (0.16)

Electric power (0/1) 0.86 0.76 0.74(0.35) (0.42) (0.44)

Share of households with indoor water 0.21 0.21 0.24(0.17) (0.17) (0.19)

Share of households with thatched roofs 0.27 0.27 0.28(0.27) (0.24) (0.24)

Share of households with mud floors 0.78 0.79 0.78(0.17) (0.16) (0.17)

Average household size 5.36 5.53 5.55(0.58) (0.61) (0.60)

Number of villages 129439 62639 33728

Notes: This table shows village-level summary statistics from the 2001 Census, for three sets of villages with 2001 populationsbetween 150 and 450: all villages, villages in 10th-Plan districts, and single-habitation villages in 10th-Plan districts. This third groupcorresponds to the sample of villages used in our analysis. Standard deviations in parentheses.

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that RGGVY caused changes in electrification. We then show the effects of RGGVY eligibility on a wide range ofdevelopment outcomes. Finally, we discuss how to scale these results to determine the overall effects of electrificationon development in the Indian context.

6.1 Electrification

In order to demonstrate that RGGVY had an economically meaningful effect on electrification in eligible villages,we examine the effects of eligibility for RGGVY on nighttime brightness. We estimate the effect of having a 2001population above the RGGVY cutoff on the brightness of the brightest pixel in a village in 2011.32 More discussionof this variable is available in the online appendix.

Importantly, as discussed above, we remove states with missing or low-quality shapefiles from this estimation,leaving us with 12 states for regressions using nighttime lights. Our preferred specification uses an RD bandwidth of150 people, which includes all 21,060 single-habitation villages in RGGVY 10th Plan districts with 2001 populationsbetween 150 and 450 in states with usable shapefiles.33

We estimate the effects of RGGVY eligibility on nighttime brightness using the same specification as Equation 1)above:

L2011vs = γ0 + γ1Zvs + γ2(Pvs − c) + γ3(Pvs − c) · Zvs + γ4L

2001vs + ηs + υvs for c− h ≤ Pvs ≤ c+ h (2)

where

Zvs ≡ 1[Pvs ≥ c]

where L2011vs represents the brightness of the brightest pixel in 2011 in village v in state s, Pvs is the village

population, c is the 300-person RD cutoff, Zvs is the RD instrument indicating RGGVY eligibility above thecutoff, h is the RD bandwidth, L2001

vs is the brightness of the brightest pixel in 2001, ηs is a state fixed effect, andυvs is an idiosyncratic error term. We cluster our standard errors at the district level.34

Figure 6 presents the results from this regression graphically, with 25-person population bins and regression linesfit to the village microdata. We find that 2011 nighttime brightness increases discontinuously at the 300-personthreshold by 0.12 units of brightness, on a baseline of 6.21. This jump is statistically significant, with a t-statisticof 2.22, with standard errors clustered by district. Table 4 presents these results in numerical form.

These results demonstrate that eligibility for electrification under RGGVY did in fact lead to a noticeable increasein brightness for barely-eligible villages, as compared to barely-ineligible villages. The observed effect is consistentwith those found by Min et al. (2013) in Senegal and Mali and Min and Gaba (2014) in Vietnam. Our onlineappendix provides more extensive sensitivity analysis, including alternative specifications, functional forms, band-widths, standard errors, placebo tests, and falsification tests. Our results are robust to a variety of checks. Sincenighttime lights are a good proxy for energy consumption, our results suggest that being eligible for RGGVYcaused electrification in Indian villages.

As further evidence that this increase in electrification is attributable to RGGVY, we perform a placebo test anda a falsification exercise. First, we employ a placebo test in which we randomly draw 10,000 “thresholds” from a

32We use the brightest pixel because Indian villages are typically organized such that there are centralized populated areas surroundedby fields. We are trying to measure electrification that is specifically targeted at populated parts of the village, so averaging overagricultural land is not intuitive here. That said, our results remain largely unchanged if we use the mean lights value rather than themaximum value.

33Our results are robust to a variety of bandwidth choices (see our online appendix for details).34Our results are robust to a variety of clustering approaches, including a Conley HAC method. See the online appendix for further

details.

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Figure 6: Regression Discontinuity – Nighttime Brightness

Notes: This figure shows regression discontinuity results using maximum 2011 nighttime brightness as adependent variable, as reported in Table 4. Blue dots show average residuals from regressing the 2011maximum nighttime brightness on 2001 maximum nighttime brightness and state fixed effects. Each dotcontains approximately 1800 villages, averaged in 25-person population bins. Lines are estimatedseparately on each side of the 300-person threshold, for all 21,060 single-habitation villages between150–450 people, in 10th-Plan districts, for the 12 states with available village shapefiles that correspond toCensus village areas (with a correlation above 0.35). The discontinuity shows an increase in brightness of0.12, which is statistically significant at the 5% level.

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Table 4: Regression Discontinuity, Nighttime Brightness

2011 village brightness1[2001 pop ≥ 300] 0.1169**

(0.0526)

2001 population −0.0004(0.0006)

1[2001 pop ≥ 300] × 2001 pop 0.0004(0.0007)

2001 village brightness 1.3221***(0.0507)

Constant .3384(0.2149)

State FEs YesRD bandwidth 150Number of observations 21060Number of districts 128Mean of dependent variable 6.4712R2 0.775

Notes: This table shows results from estimating Equation (1), which corresponds to Figure 6. We define village brightnessbased on the brightest pixel contained within the village boundary. Each regression includes all single-habitation villagesin 10th-Plan districts with 2001 populations in the RD bandwidth (a 150-person bandwidth includes villages with 2001populations between 150 and 450), for the 12 states with available village shapefiles that match to Census village areaswith a correlation above 0.35. Standard errors are clustered at the district level. Significance: *** p < 0.01, ** p < 0.05,* p < 0.10.

uniform distribution U ∼ [100, 275]⋃

[325, 1000].35 For each threshold, we re-run the regression discontinuityestimate and save the coefficients. We plot these coefficients in Figure 7; the true coefficient is above the 95thpercentile of the placebo coefficients, supporting the notion that this effect is directly caused by RGGVY. Next,as a falsification exercise, we exploit the details of RGGVY’s implementation. Because the eligibility rule wasapplied at the habitation level, and because the population cutoff was lowered to 100 people under the 11th Plan,we can compare our single-habitation-village, 10th Plan estimate to three other possibilities: multiple-habitationvillages covered under the 10th Plan, single-habitation villages covered under the 11th Plan, and multiple-habitationvillages covered under the 11th Plan. None of these alternative models should display results using the 300-personcutoff, and indeed, we do not find effects for any of these alternatives (figures and tables are available in the onlineappendix).

Taken together, these results suggest that RGGVY caused a substantial increase in electrification in eligible villages.We now turn to estimates of the effects of RGGVY eligibility on a variety of development outcomes.

6.2 Development effects

We test for the effects of electrification on development across four broad categories: labor, assets, the housingstock, and public goods. In each of these categories, we estimate the same type of reduced form regression as

35Note that we leave out thresholds between 275 and 325 to avoid possible contamination of the placebo results with the real effect.

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Figure 7: Placebo Test – Nighttime Brightness

Notes: This figure shows the results of a placebo test, where we ran Equation (2) on 10,000 placebo RDcutoffs between 150 and 1000. This blue bars show the placebo distribution of coefficients on the RDdiscontinuity, while the red line indicates our actual estimated coefficient in Table 4. Our RD estimate atthe 300-person cutoff falls above the 95th percentile of the placebo distribution.

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above:

Y 2011vs = β0 + β1Zvs + β2(Pvs − 300) + β3(Pvs − 300) · Zvs + β4Y

2001vs + ηs + υvs for 150 ≤ Pvs ≤ 450 (3)

Here, Y tvs represents an outcome of interest for village v in state s in Census year t.36 Pvs is the 2001 village

population, Zvs is an RD dummy indicating RGGVY program eligibility, and ηs are state fixed effects.

Because prior work has found evidence that electrification (Dinkelman (2011)) and road construction (Asher andNovosad (2014)) leads to large changes in labor force participation, we begin with employment information fromthe 2001 and 2011 Primary Census Abstract (PCA). The PCA reports the number of men and women thatare working as “cultivators” (on their own land) or “agricultural laborers” (on others’ land); “household industryworkers” (engaged in informal production of goods within the home); and “other workers” that engage in all othertypes of work. Examples of “other workers” include government servants, municipal employees, teachers, factoryworkers, and those engaged in trade, commerce, or business. We pool cultivators and agricultural laborers into one“agriculture” category. We then estimate Equation (3) using the total number of male (female) workers in a givencategory divided by the total male (female) population of a village as the dependent variable.

Figure 8 summarizes the workforce results graphically, with reduced-form estimates of Equation (3) for each laborcategory for both male and female workers. We find that eligibility for RGGVY caused a visible 0.7 percentagepoint decrease in the share of men in agricultural labor, on a mean of 42 percent. In contrast, the percentage of menin non-agricultural, non-household labor increased by 0.3 percentage points on a mean of 10 percent. Importantly,these results are quite small, especially when compared to the existing literature, and, because our results aretightly estimated, we can rule out large shifts in labor allocation as a result of RGGVY eligibility. We also find nostatistically or economically significant effects of electrification on female employment.37

The Houselisting Primary Census Abstract (HPCA) provides us with village-level information on the share ofhouseholds with a variety of different assets, as well as on the housing stock in each village. We use variables fromthis dataset as outcome variables, and estimate Equation (3) as above. Figure 9 depicts regression discontinuityresults using the percent of households who own a telephone, percent of households who own a television, percentof households who own a motorcycle, percent of households who have kerosene lighting, percent of households withmud floors, and percent of households that are described as “dilapidated” by the Census telephone as dependentvariables. We see no strong graphical evidence to suggest discontinuous changes in any of these variables at the300-person cutoff.

Table 5 presents these results, as well as evidence on the total (male, female) population; the male and femaleworkforce; the share of households with radios, bicycles, and without assets; the share of households cooking withelectricity or gas; the share of households with thatched roofs; and the share of households with electric lighting.These results support the graphical evidence: we do not see evidence that RGGVY led to economically meaningfulchanges in labor force participation or asset ownership. Table 5 also displays regression results for public goods,in the form of mobile phone coverage, the presence of a post office or telegraph, banking, and credit societiesin a village. In no cases are these results statistically significant, and even the upper bounds on the 95 percentconfidence intervals represent economically insignificant changes in these outcomes. Table 5 also presents evidenceon electricity. We find that electricity provision did increase as a result of RGGVY, in particular in the commercialsector. This is consistent with the evidence on nighttime brightness shown above.

Importantly, even in cases where we are unable to reject the null hypothesis of no effect, our 95 percent confidence36This specification uses the 2011 Census outcomes and includes 2001 levels of the dependent variable as controls. These results are

robust to a variety of functional form assumptions. For further details, see the online appendix.37This is different from Dinkelman (2011), who finds that electrification leads to very large increases in female employment in the

South African context.

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Figure 8: RD – Reduced Form: Labor Outcomes

Notes: This figure shows the reduced form of our preferred RD specification (Equation (3)). Blue dotsshow average residuals from regressing the 2011 percentage of the male/female population classified in eachlabor category on the corresponding 2001 percentage and state fixed effects. Each dot containsapproximately 1700 villages, averaged in 15-person population bins. Lines are estimated separately on eachside of the 300-person threshold, for all 33,728 single-habitation villages between 150 and 450 people, in10th-Plan districts. Table 5 reports regression results for each outcome variable.

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Figure 9: RD – Reduced Form: Housing and Asset Ownership

Notes: This figure shows the reduced form of our preferred RD specification (Equation (3)). Blue dotsshow average residuals from regressing the 2011 percentage of households owning each asset (or with eachcharacteristic) on the corresponding 2001 percentage and state fixed effects. Each dot containsapproximately 1700 villages, averaged in 15-person population bins. Lines are estimated separately on eachside of the 300-person threshold, for all 33,728 single-habitation villages between 150 and 450 people, in10th-Plan districts. Table 5 reports regression results for each outcome variable.

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Table 5: Regression Discontinuity, Reduced-Form Results

2011 Outcome Variable RDCoefficient 95% C.I. Mean of

Dep VarTotal population −0.2492 [−4.8672, 4.3688] 356.8095Male population −0.1081 [−2.5928, 2.3766] 182.9128Female population −0.1411 [−2.3810, 2.0989] 173.8967All male workers / male population −0.0047∗∗ [−0.008,−0.001] 0.5341All female workers / female population −0.0085∗∗ [−0.016,−0.001] 0.3415Male agricultural workers / male population −0.0069∗∗∗ [−0.012,−0.002] 0.4243Female agricultural workers / female population −0.0055 [−0.013, 0.002] 0.2797Male household workers / male population −0.0008 [−0.002, 0.000] 0.0100Female household workers / female population −0.0015 [−0.003, 0.001] 0.0131Male other workers / male population 0.0035∗ [−0.000, 0.007] 0.0997Female other workers / female population −0.0016 [−0.005, 0.002] 0.0487Share of households with telephone 0.0025 [−0.007, 0.012] 0.5381Share of households with radio 0.0042 [−0.004, 0.012] 0.1891Share of households with TV 0.0014 [−0.006, 0.009] 0.2473Share of households with bicycle 0.0007 [−0.007, 0.008] 0.5102Share of households with motorcycle −0.0008 [−0.006, 0.004] 0.1304Share of households without assets 0.0016 [−0.006, 0.009] 0.2238Share of households with electric/gas cooking 0.0008 [−0.004, 0.005] 0.0568Share of households with thatched roof −0.0037 [−0.013, 0.005] 0.2286Share of households with mud floors 0.0056 [−0.002, 0.013] 0.7415Share of households dilapidated −0.0024 [−0.008, 0.003] 0.0690Share of households with kerosene lighting 0.0023 [−0.009, 0.014] 0.4968Share of households with electric lighting −0.0020 [−0.014, 0.010] 0.49861/0 Electricity (All End Uses) 0.0199∗ [−0.003, 0.043] 0.48531/0 Electricity (Commercial) 0.0223∗∗ [0.002, 0.042] 0.49671/0 Electricity (Domestic) −0.0044 [−0.014, 0.005] 0.90231/0 Electricity (Agriculture) −0.0151 [−0.033, 0.003] 0.66111/0 Mobile phone coverage in village 0.0050 [−0.016, 0.026] 0.74501/0 Post office/telegraph in village 0.0006 [−0.004, 0.005] 0.00941/0 Banking in village 0.0002 [−0.005, 0.005] 0.01481/0 Credit societies 0.0005 [−0.006, 0.007] 0.0252

Notes: Each row is generated from a separate regression of the outcome variable on the RD variables, stateFEs and the 2001 level of the outcome variable (when possible). The RD bandwidth includes 33728villages with 2001 populations between 150 and 450, across 221 districts. The second column shows thepoint estimate for the RD discontinuity, for each separate regression. Standard errors are clustered at thedistrict level. Significance: *** p < 0.01, ** p < 0.05, * p < 0.10.

23

intervals are small. We can clearly reject large effects of RGGVY eligibility on the labor force, asset ownership,the housing stock, and village-level public goods.

6.3 Scaling

Because we do not have access to a binary treatment indicator, we cannot use a standard 2SLS fuzzy regressiondiscontinuity approach to scale our reduced form results by the proportion of villages that “complied” with treat-ment. Our above estimates are intent-to-treat estimates, in that we have estimated the effect of being eligible forRGGVY on our outcomes of interest, rather than the effect of being treated under RGGVY on our outcomes ofinterest. In light of this, it is important to consider how we should scale our estimates such that we recover theeffect of electrification on development.

We propose two methods of scaling our estimates here: first, we consider scaling our outcomes based on theproportion of villages within our bandwidth that RGGVY claims to have treated. This is akin to the scale factorwe would apply with a traditional 2SLS estimate. Using RGGVY’s administrative data suggests that 67 percent ofvillages with populations between 300 and 450 were treated.38 This implies that our estimates should be inflatedby a factor of 3/2 in order to recover the causal effect of electrification on outcomes.

Another approach to scaling our estimates uses the nighttime lights: Min et al. (2013) suggest that when villagesin Senegal and Mali were electrified, they experienced increases of approximately 0.4 nighttime brightness points.We recover a nighttime brightness effect of 0.12. This implies that our estimates should be inflated by a factor of4 to recover the causal effect of electrification on outcomes.

Scaling the point estimates reported in Table 5 does not yield adjusted estimates that are economically meaningful.Even when we scale the upper (lower) bound of the 95 percent confidence interval by these factors for outcomeswith positive (negative) effects, we still see only small changes in the labor force, in asset ownership, in villagepublic goods, and in the housing stock.

7 Conclusion

Our preliminary results indicate that the RGGVY electrification program contributed to differential nighttimebrightness for barely eligible villages relative to barely ineligible villages. However, we have yet to find evidence tosuggest that program eligibility had economically significant impacts on a wide range of development outcomes.While we demonstrate that the program shifted male workers out of the agricultural sector, the magnitude of thisis effect is quite small. Moreover, we see no evidence of increases in asset ownership or improvements in the housingstock, which we would expect to have occurred if incomes had risen as a result of eligibility for the program. Theseresults hold when scaling our reduced form estimates to account for non-compliance among villages that wereeligible for treatment.39

While our datasets do not contain every possible measure of economic development, we provide results for severalbroad classes of outcomes which, taken together, suggest that increased electrification under RGGVY did not leadto large-scale economic transformation in the short-to-medium term. It is also important to highlight that theseestimates come from villages with relatively low populations; though we include 22% of India’s villages in our

38These data are of poor quality, and we are unable to use specific program outcomes (as reported in these village-level data) direcltyin our analysis. However, village counts by district correspond to the number of villages in RGGVY’s aggregated district-level progressreports (see Table 1). These village counts represent 67% of total villages within our 300–450 eligible bandwidth.

39To further assuage concerns that our results might be driven by low-quality program provision, in the online appendix, we showthat the results are quantitatively similar when we use the states that showed the largest changes in nighttime brightness.

24

regressions, our data cannot speak to whether or not this type of electrification would be beneficial in larger orwealthier villages.

In ongoing work, we are continuing to add to our range of outcomes. We are also in the process of using district-level wage data in conjunction with program expenditure information to perform a back-of-the-envelope cost-benefitanalysis.

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