Oliver Vanden

Eynde Liam

Wren Lewis

Oliver Vanden Eynde
email: o.vandeneynde@psemail.eu.

Liam Wren-Lewis
email: liam.wren-lewis@psemail.eu.

Complementarities in Infrastructure: Evidence from Indian Agriculture

Keywords: JEL Classification: O13, O18, Q15

Complementarities between infrastructure projects have been understudied. This paper examines interactions in the impacts of large-scale road construction, electrification, and mobile phone coverage programs in rural India. We find strong evidence of complementary impacts between roads and electricity on agricultural production: dry season cropping increases significantly when villages receive both, but not when they receive one without the other. These complementarities are associated with a shift of cropping patterns towards market crops and with improved economic conditions. In contrast, we find no consistent evidence of complementarities for the mobile coverage program.

Introduction

Rural infrastructure is central to the development agenda of most low and middleincome countries. Governments often bundle large-scale infrastructure programs in the belief that complementarities between these interventions are important (Sanchez et al., 2007; Moneke, 2020). However, concentrating infrastructure investments geographically worsens spatial inequalities (Kanbur and Venables, 2005). Evaluating the size of any complementarities is therefore important, but evidence is sparse because infrastructures' non-random placement makes it difficult to credibly identify interactions. In particular, in many contexts the size of the overlap between infrastructure programs makes it hard to consistently identify both the individual and combined impacts of different programs.

This paper evaluates complementarities in infrastructure programs in rural India.

We exploit the independent roll-out of three major programs across the country between 2005 and 2014: PMGSY (roads), RGGVY (electrification), and USOF (mobile telecommunications). These programs were part of India's "Bharat Nirman" campaign of rural infrastructure development. Their management and deployment were independent, however, allowing us to analyze potential complementarities. Because of the almost unprecedented scale of these programs, which covered a substantial share of India's more than 600,000 villages, we are able to provide plausibly causal estimates of the separate and joint provision of different infrastructure types.

Our main outcome of interest is dry season agricultural production. While Indian agricultural productivity rose sharply during the Green Revolution, it has stalled in recent years. Improved dry season cropping is an important potential source of agricultural productivity growth (Saksena et al., 2020; Jain et al., 2021). To the extent that it goes hand in hand with crop diversification, dry season production could also help mitigate climate change impacts (Lal et al., 2017; Sambasivam et al., 2020).

Two additional features make dry season cropping particularly well-suited to serve as the main outcome of our current study. First, it is measured at high frequency, so we can use plausible exogeneity in the timing of infrastructure projects to estimate their effect. Second, it benefits from a combination of inputs and market integration, meaning complementarities could be important. Dry season cropping requires water-management which is facilitated by the use of electric pumps. At the same time, access to input markets (in particular, high quality seeds of varieties well suited to the dry season) and output markets boost the returns to irrigation investments (Saksena et al., 2020). Both road access and mobile phone coverage could strengthen market integration and hence could spur dry season cropping through this channel.

To estimate the effect of the separate and joint provision of infrastructure, we rely on various data sets and approaches.

First of all, we exploit the staggered introduction of the programs in an event-study design in order to estimate effects on satellite-based measures of dry-season cropping.

We find that the joint provision of an electricity and road connection boosts dry season cropping beyond the effect of each type of infrastructure on its own. These two types of infrastructure are hence complementary in driving rural development. For mobile phone coverage, we do not find consistent evidence of complementarities with either electrification or road access. We support our finding on the complementarity between roads and electrification through a large set of robustness checks including (i) the inclusion of time-varying effects of control variables, (ii) the inclusion of district-year effects, (iii) the separate estimation of impacts depending on the order of construction, (iv) allowing for heterogeneous treatment effects, and (v) allowing for standard errors to be clustered at different levels.

The complementarities we find between roads and electricity are consistent with the hypothesis that the joint provision of these infrastructures is necessary to make dry season cropping profitable. Electrification in isolation may not provide the market access needed to grow market crops, while road connectivity on its own may not allow for improved water management.

To explore the welfare impacts and mechanisms of the observed complementarity between electrification and road access, we rely on a second empirical exercise. We use cross-sectional variation from India's population Census and the Socio-Economic Caste Census. Using this approach, we find that the combined provision of electricity and road access increases market crop production and raises asset ownership. While road construction on its own reduces agricultural employment -a finding that is line with the literature -this relationship is not significantly changed with electrification. recent papers study complementarities explicitly. 4 Interestingly, in our cross-sectional analysis, the separate effects of these three infrastructure types are broadly in line with the existing literature, but only the joint provision of electrification and road access is associated with increased asset ownership.

A very small and recent literature has started exploring complementarities in infrastructure provision. Moneke (2020) is one existing study that causally estimates interactions between electrification and road construction. He shows that, in Ethiopia, the combined provision of electricity and road connections boosted industrialisation. This impact of joint provision is substantially different from the one of road connections on their own. One limitation of this study, however, is that it cannot estimate the impact of electrification on its own. Another difference from our paper is that Moneke (2020) focuses on industrialisation, whereas we focus on rural development. Sharing our focus on rural development, Gebresilasse (2023) looks at agricultural productivity in Ethiopia, and finds evidence of complementarities between road construction and extension services, but this author does not investigate complementarities between infrastructure types. Another very recent paper that explicitly looks at complementarities between road access and electrification is Abbasi et al. (2022). Focusing on Sub-Saharan Africa, these authors find that the joint provision of these infrastructures contributes to a shift of low-skilled to high-skilled jobs. Our study is highly complementary to this work, exploiting an alternative identification strategy at a different spatial and temporal scale with a focus on agricultural productivity in the Indian context of rapid structural transformation. 5 Finally, Chaurey and Le (2022) implicitly point at complementarities in rural infrastructure development in India. They find 4 Asher and Novosad (2020) and Burlig and Preonas (2022) use regression discontinuity approaches for identification. However, the overlap between the RD samples in PMGSY and RGGVY is too small to identify complementarities between these programs. Instead, we will rely on a event study design and use high-frequency outcomes.

5 Abbasi et al. (2022) conduct their analysis at the individual level and focus on employment status (regardless of location). Hence they do not investigate explicitly whether infrastructure transforms occupations within rural villages or shifts employment to urban areas. They also do not analyze welfare effects directly. Our focus on agricultural technology adoption in rural villages speaks more directly to the geographical inequalities that are generated by the structural transformation process, and we are able to use direct welfare measures. In addition to these differences in approach, it should be borne in mind that India's rural population is larger than those of African countries at comparable levels of development.

that an infrastructure maintenance program (aimed at so-called 'backward' villages) at the district level increased non-agricultural employment and consumption, and more so in villages with prior paved roads and electricity connections. However, their study does not focus on the causal identification of interactions between different types of infrastructure at the village level.

Improving agricultural productivity is a key part of structural change, and infrastructure development is at the heart of rural development policies. Our paper is one of the first to explore this important topic, and its results suggest that the simultaneous provision of electricity and roads is necessary to maximize their economic impacts.

Theoretical Framework

We consider a rural economy that is predominantly agricultural. Broadly, we can think of infrastructure as impacting economic development in two ways: Providing a direct input in an agricultural production process (e.g. energy, water), or facilitating access to markets (e.g. transport, telecommunications). The direct welfare effects of both types of infrastructure are likely to be positive.

When it comes to the interactions between these two types of infrastructure, one can distinguish two mechanisms. First, there may be direct complementarities in farmers' profit functions: a farmer may, for instance, require the productive input infrastructure to produce a crop and also require the market access infrastructure to sell the crop. 6 For this reason, policy makers are understandably keen on bundling infrastructure. Second, in a context of structural transformation, one of the most important impacts of market access infrastructure may be to reallocate production across space.

For rural locations, market access may encourage labour to move out as local production becomes less necessary and urban opportunities become more accessible. If this is the case, interactions in infrastructure impacts may be determined by labour reallocation. On the one hand, the impact of production enhancing infrastructure could be lowered by providing market access infrastructure, because of the resulting reduction in labour supply (assuming that labour is complementary to other inputs). On the other hand, providing production enhancing infrastructure to a location may reduce the reallocation induced by market access infrastructure if it reduces the productivity gap between rural and urban areas.

Understanding how infrastructure impacts interact at a fine spatial scale is crucial to guide policy makers decisions about where to target rural programs. Beyond efficiency motivations, understanding interactions is important for policy makers who are concerned about rural areas being 'left behind' in a process of structural transformation. If productive infrastructure interacts positively with market access infrastructure, rolling out both programs simultaneously can be a way to mitigate local negative impacts of labour moving out of rural areas. If the interaction is negative, it suggests that policy makers must develop alternative strategies for dealing with areas 'left behind' by the development process.

The Indian rural infrastructure programs we focus on provide an ideal setting to estimate infrastructure interactions within this conceptual framework. Our main outcome of interest will be a key component of agricultural production that we expect to be sensitive to infrastructure provision: dry season cropping. In many areas of India, production in the dry season is often impossible without irrigation and electricity access greatly reduces the cost of irrigation. Although irrigation is likely to be beneficial even in villages with low market access, there are also good reasons to hypothesize direct complementarities -in particular, for crops sold on markets or crops requiring inputs. At the same time, the context we study is characterized by rapid structural transformation, and several studies have shown that an important impact of the roads program has been to facilitate the movement of labour (Aggarwal, 2018; Asher and Novosad, 2020; Shamdasani, 2021), in some cases out of agriculture. Overall, therefore, the net interactive impacts of roads and electricity on agricultural output are ex ante unclear. This is also the case for the interactions with mobile phones. While we expect electricity to mainly enhance productivity and roads to mainly increase market access, the effect of mobile phones could in principle be on either dimension. It could facilitate the adoption of new technologies (Gupta et al., 2020), while information on market conditions could contribute to effective market integration (Jensen, 2007). At the same time, the mobile phones program we study (USOF) may not have a first order impact on the adoption of irrigation technologies in the way roads and electricity access do. Hence, we do not have clear expectations for interactions between mobile phone coverage and the other infrastructure types, but we present our main results for these interactions for comparison and completeness.

Background and data

Our paper focuses on the period 2005-2014, when India invested heavily in rural infrastructure provision. We study three flagship schemes on which we collected detailed implementation data, and for which complementarities could be important: electrification, road construction, and mobile phone coverage. In this section, we provide background details on each of these programs, we introduce our measures of dry season cropping, and we outline the other data sources used in our paper. Details of the timing of the infrastructure programs are given in Figure 1.

Rural Electrification: Rajiv Gandhi Grameen Vidyutikaran Yojna (RGGVY)

Since independence, the Indian Government has launched multiple village electrification schemes, but full electrification was still a far-off goal in the early 2000s. To accelerate the pace of electrification, the government launched an ambitious flagship scheme -RGGVY -in 2005. The aim of the program was to secure an electricity supply to all un-electrified villages as well as to provide free electricity connections to all below poverty line (BPL) households. All existing rural electrification programs were merged into RGGVY in 2005. The program targeted more than 360,000 villages.

The office overseeing the program was the Rural Electrification Corporation (REC), while the implementation was delegated to the State Utility Providers. We obtained administrative data on the implementation of the program directly from the REC. This information is matched to 2001 census villages. As our main measure of electrification, we use the completion date at the village level. Villages for which we do not have a completion date are not included in the sample. 7

Rural Road Construction: Pradhan Mantri Gram Sadak Yojana (PMGSY)

The government launched PMGSY in 2000 with the goal of connecting unconnected villages to the road network through an all-weather road. 8 The proposed network of roads was determined in 2001, and the subsequent implementation of PMGSY has consisted of the gradual realisation of this "Core Network". Habitations with larger populations were prioritised. The program has been described as "unprecedented in its scale and scope" (Aggarwal, 2018), with roadwork for over 125,000 habitations completed by 2016. The program was coordinated by the National Rural Roads Development Agency (NRRDA), even if most implementation decisions were taken at the state level. In contrast to RGGVY, which relied entirely on public sector providers, the road construction under PMGSY was carried out by private contractors. Our information on PMGSY implementation comes from the SHRUG dataset, which provides data at the level of 2001 Census villages. 9 As our main measure of road connectivity, we use the road completion date at the village level.

Mobile Phone Coverage: Universal Service Obligation Fund (USOF)

The National Telecom Policy (1999) introduced USOF as a national program to increase telecom services in rural India (Noll and Wallsten, 2006). USOF consists of different programs, but we focus on Phase I of the Shared Mobile Infrastructure Scheme, which encouraged private mobile infrastructure development and infrastructure shar- 7 Our results go through when we impute electrification dates for villages with missing information, as shown in Figure A9. 8 In principle, all-weather roads may not be essential for market access in the dry season. However, our analysis is not driven by roads that were merely hard to access in the rainy season. The upgrade of existing fair-weather roads were marked as "upgrades" in the administrative records, and following Adukia et al. (2020), we exclude villages with such upgraded roads from our sample (since it is unclear how substantial an improvement such upgrades made). 9 The Socioeconomic High-resolution Rural-Urban Geographic Platform (SHRUG) was constructed by Asher et al. (2021). Although we collected data from administrative sources on PMGSY independently, we use the SHRUG data for consistency with the recent literature.

ing in rural areas. Under this scheme, infrastructure providers were subsidized to construct telecom towers in specified multi-village clusters. The infrastructure could be shared among providers to install their own equipment to provide mobile service to the cluster. The Department of Telecommunications proposed tower locations according to population eligibility criteria. Towers were approximately grouped by district, and for each group, providers could bid for annual subsidies. Once awarded the subsidy for a group, providers were allowed to shift the location and number of the towers. Shifts in location were considered justified if coverage was already available at the selected location, construction was too difficult given the soil or geography, or coverage could be improved at a nearby location. Providers were required to ensure towers were not built within three kilometers of any existing cellular tower.

Overall, 7,874 towers were proposed, and 7,353 were actually built under phase one, with the median proposed tower site finally having a tower built 3km away. We collected administrative data on the implementation of the program directly from the Department of Telecommunication. As the implementation date, we use the date at which actual towers were commissioned. 10 We then calculate the distance of villages to proposed and constructed towers to villages based on their coordinates.

Dry season cropping in India

As in many developing countries, dry season cropping is one of the most important margins through which increases in agricultural production can be achieved. India has two main cropping seasons: (i) Kharif, the monsoon season from July to October; and (ii) Rabi, the dry season from October to March.11 Only about 35% of total agricultural land in India is irrigated and two thirds of cultivated land is entirely dependent on rainfall.

Dry season agricultural production can be measured from satellite imagery based on the observed vegetation ("greenness"). We use such measures from three sources.

The first is the annual Land Use Land Cover maps produced by the Indian Space Research Organisation (ISRO). This uses ISRO's Resourcesat satellites and gives an estimation of land use at a 56m resolution. 12 For this and other gridded data, we calculate measures at the village level by overlapping with Thiessen polygons constructed using the 2001 Census village coordinates. 13 The second source is a measure of winter cropping produced by Jain et al. (2017). This uses the Enhanced Vegetation Index from the MODIS satellites to estimate the share of each 1km pixel that is cropped during the winter. The third source is from Asher and Novosad (2020), who compute the change in the Normalized Difference Vegetation Index in the Rabi season, also from MODIS. Since each of these sources is attempting to measure the same latent variable -i.e. dry season cropping -our preferred outcome measure is an inverse covariance weighted index which combines the information from each source (Anderson, 2008). In the online appendix, we validate this approach -Table A4 shows that each measure is partially correlated with the extent of dry season cropping in the Indian Health and Development Survey (IHDS) and, moreover, the index has a higher R 2 than each one of the individual measures.

Additional data sources

We use information from the 2001 Census to construct control variables in robustness checks. The 2011 population census and the 2011 socio-economic and caste census (SECC) are used for additional validation and mechanism results. We also use measures of nightlight emissions at the village level to validate our electrification measures. All of these variables are taken from the SHRUG database or, if not available in SHRUG, from the replication data of Asher and Novosad (2020) or from our own copy of the 2001 Census. As additional control variables, we also use the characteristics of the state-level legislators that represent the assembly constituency that a 12 The LULC 1:250K data from Bhuvan is described by the Indian Space Research Organisation (2010) and here: https://bhuvan.nrsc.gov.in/bhuvan_links.php 13 The village coordinate data is based on the India Place Finder website (http://india.csis.

u-tokyo.ac.jp/,accessed in July 2014), which we complement with data from ML InfoMap (for villages that are not covered by India Place Finder). The Thiessen polygons are constructed per district, so that district boundaries are respected. 3 Empirical strategy and results for dry season cropping

Empirical strategy

To estimate complementarities in the programs, we rely on the plausible exogeneity of their timing. 16 While each of the programs we study had rules that linked eligibility to population thresholds, exploiting these thresholds would not be well suited to our analysis since adherence to the rules was low and the overlap between the windows around eligibility cut-offs was small. By using variation in implementation rather than eligibility, we have enough statistical power to estimate complementarities between each pair of infrastructure programs. The overlap between all three programs is, however, still too small for meaningful inference about potential three-way interactions.

Our general estimating equation for identifying the impacts of two infrastructure types P and Q is: 14 We assign villages to Assembly Constituencies based on their geographical coordinates. Maps of assembly constituencies come from ML Infomap, and electoral data are from Bhavnani (2014). Statelevel government coalition data is from Asher and Novosad (2017).

15 This data source is described here: https://www.collinsbartholomew.com/ mobile-coverage-maps/mobile-coverage-explorer/ 16 A similar approach is used to estimate the impacts of the PMGSY program on education and deforestation in Adukia et al. (2020) and Asher et al. (2020).

y i,s,t = ∑ τ≥-5,τ =-1 ζ τ (1(t = t P i,s + τ))+ ∑ κ≥-5,κ =-1 ζ κ (1(t = t Q i,s + κ))+ ∑ δ≥-5,δ =-1 ζ δ (1(t = t P×Q i,s + δ)) + γ s,t + η i + i,s,t (1)
In this event study style equation, the term t P i,s is the time at which village i in state s first got access to infrastructure P. The outcome y i,s,t is our measure of dry season cropping at time t. In addition to village fixed effects, our main specification includes state-specific year fixed effects, to control for the large differences in agricultural practices between Indian states. We cluster standard errors at the sub-district level to account for spatial correlation between locations in the same administrative unit.

We cannot identify linear pre-trends in this event study design because of the staggered nature of our treatment (which is why we need to define two reference periods), but we can identify a trend break at the time of treatment. We estimate dynamic effects and plot these in the figures. We also calculate the corresponding average treatment effect by taking a weighted average of the post-treatment coefficients, with weights given by the share of observations in a given time-to-treatment group.

Our reference points are the year just prior to treatment and all periods more than five years before treatment.17

The main identification assumption in this design is that the villages in each treatment group would have followed parallel trends in the absence of treatment. We believe that the context supports this assumption: village-level implementation was the result of a multitude of local conditions and higher-level administrative factors which are unlikely to have been correlated with sudden future improvements in agricultural productivity. For PMGSY alone, our design is identical to the one in Adukia et al. (2020). 18 We will present important robustness checks in which we control ex-plicitly for time-varying effects of village-level characteristics. For example, the roads and electrification programs prioritised (in principle) larger villages within a given district. We do not think that this prioritisation rule leads to an obvious violation of the parallel trends assumption our identification relies on, especially because our findings are almost identical when we allow for time-varying impacts of population size (in column 3 of Table 2). Table A8 shows baseline differences between villages that received each program early versus late, while Table 2 shows the robustness of our findings to adding additional controls including time-varying impacts of a large set of observables. However, our strongest evidence in support of our main assumption will come from the absence of pre-trends and the immediate impact of the programs -we will describe these results in detail below.

Our identification strategy uses the staggered introduction of different types of infrastructure. As highlighted by de Chaisemartin and D'Haultfoeuille (2020), classical difference-in-difference estimators can be problematic when treatment effects are heterogeneous since it may give negative weights to some of the underlying treatment effects. In our main approach, we use event-study specifications, which do allow for heterogeneity in treatment effects over time, but which do not allow for full heterogeneity of effects between the treatment groups composed of all villages treated at the same time. Allowing for full treatment heterogeneity would raise interpretational challenges in our context, as the notion of complementarities requires the comparison of treatment effects across groups. Still, we assess the robustness of our findings to relaxing assumptions about the heterogeneity of treatment effects in two ways. First, in Figure A6, we use the method proposed by de Chaisemartin and D'Haultfoeuille (2020), allowing for fully heterogenous effects in the interaction between electrification and road access, while controlling for dynamic effects for the construction of roads and electrification. Second, we use the method of de Chaisemartin and D'Haultfoeuille (2020) to estimate fully heterogeneous, sequential treatment effects, which depend on the order in which villages are treated. 19 We then calculate the implied interaction terms by comparing the estimate for the joint provision to those obtained for the provision of only one type of infrastructure. These results are reported in table A11. power for the implementation time.

These two alternative estimation strategies yield qualitatively similar results: they consistently show positive additional impacts of having both electricity and roads beyond the combined estimated effects of the programs individually.

Sample construction

In identifying the impacts of electricity and roads, we only consider villages that were included in the relevant program at some point within the time period we study. To study the interaction of roads and electricity, we therefore restrict to villages which received both programs. Note that this is the minority of villages which received either program -of the roughly 600,000 villages in India, we have electrification dates for around 185,000 and new road construction dates for around 38,000, with only 14,000 receiving both programs.

When considering mobile tower construction, an important limitation of the event study design is that most towers were built around the same time (in 2008 or 2009).

We therefore use an additional source of identifying variation, following Gupta et al. (2020), by including villages that were supposed to be covered by a tower (i.e., within 5 kilometers of the proposed tower location) but in the end were not, which amounts to around 150,000 villages. In other words, within the population of villages which were proposed to be covered by the program, we compare villages that were actually covered to those that were not. We then use the construction date of the actual tower to estimate time-to-treatment dummies. 20 We include provider-by-year fixed effects to account flexibly for time patterns that are specific to the broader areas that are covered by a particular provider. 21 To estimate complementarities of mobile connectivity with (i) PMGSY and (ii) RGGVY, the sample is restricted respectively to: (i) villages covered by proposed towers and by PMGSY, and (ii) villages covered by proposed towers and by RGGVY. Again, this amounts to a small share of the villages that received either program, with around 40,000 villages in the sample focusing on electricity and mobile phones, and around 10,000 in the sample focusing on roads an mobile phones.

As before, our identification strategy relies on a common trend assumption. It is unlikely that tower locations were changed in a way that correlate with trends in agricultural production (Gupta et al., 2020), and our event-studies will offer support for this assumption.

Details of the timing of the infrastructure programs and the number of treated villages are given in Figure 1 and summary statistics are given in Table A2. From Panel A of the figure, we can see that while most villages that got both electricity and roads received roads first, a substantial number received electricity first. Moreover, some villages had received both programs as early as 2005, and in general the number of villages that received both increased gradually over time. These patterns are repeated across the other panels, meaning that we have enough variation to identify the individual impacts of both programs and their interaction. Table 1 shows the average impact of the separate and joint provision of the infrastructure programs, which is calculated as the sum of the event study coefficients weighted by the size of each post-treatment group. For our preferred outcome of dry season cropping, the inverse covariance weighted index, the average effect of joint provision (additional to the effects of separate provision) is 0.07 standard deviations.

Results

For the constituent measures, which are in log points, coefficients correspond to an threat to our identification of the post-treatment impacts. increase in dry season cropping of between 6% and 12%. There is no consistent evidence of a similar impact on dry season cropping from the separate provision of infrastructure, or the joint provision of mobile phone coverage with either road access or electricity supply. There is a weakly negative effect of the interaction of road and mobile phone coverage (column 4 of Table 1, Panel C). In the light of our conceptual framework, such a negative effect could be the result of migration and diversification out of agriculture. We expect road access to facilitate the movement of labour out of agriculture, and this process could be strengthened as mobile coverage improves access to information about opportunities outside of the village. This effect should not be over-interpreted though, because (i) it is not consistent across dry season cropping measures and only marginally significant for our preferred outcome in column ( 4);

(ii) it is driven by time periods that are far away from the treatment (see Panel C of Figure 1); and (iii) it does not survive robustness checks (e.g. Table A6 or Table A10).

We also do not find a significant interactions with mobile phone coverage when we use a sample that is similar to the one of Panel A. 23 Finally, in Table A5 we present the combined effects of joint program provision, rather than the additional effects. These results are presented for completeness -based on our conceptual framework, in which complementarities could have different signs depending on the mechanism at work, our primary interest is in the additional effects. 23 In Table A6, we estimate the impacts of the mobile phone program and its interactions with the other two programs while we weight observations by the number of villages in the district in the roads plus electricity sample. The positive and significant effects of the separate provision of electricity (column 1) and roads (column 2) are consistent with these effects including any complementarities between the programs and existing roads/electricity. 1, Panel A -model specification is as described in the notes to that table unless stated otherwise. The dependent variable is the (standardized) inverse correlation weighted index of dry season cropping. Column (1) includes the terms relating to electricity and road program timing interacted with year fixed effects. The reported coefficients for these terms are then weighted according to the number of villages within each state that contribute to the estimation of each term. Column (2) uses the same method as column (1) only replaces year fixed effects with state fixed effects. The control set interacted with year fixed effects in column (3) is from the 2001 census and includes: the log of the village population, the log of the number of households in the village, the log of the village area, the scheduled caste share, the scheduled tribe share, the literate share, indicators of power supply (general, agricultural, and domestic), the presence of a dirt road, the presence of a primary/secondary/senior secondary school or college, the presence of a health center or primary health centre, a drinking water indicator, the presence of a post office, the availability of a landline phone connection, and the log of the distance to settlements with at least 10k, 50k, 100k or 500k people. In column (4) we include we include MLA party fixed effects and a dummy indicating whether their party is in power at the state level, each interacted with dummies measuring the time since the last election. In column (5), we drop districts that are affected by India's Maoist conflict. In column (6), the treatment date for road access is based on the planned completion date rather than the actual completion date. Column ( 7) is as the baseline regression but with district-year fixed effects instead of state-year fixed effects. In columns (1) to ( 7), standard errors are clustered at the sub-district level; in column (8) standard errors are clustered per district; in column (9) we allow for auto-correlation of up to 10 years and spatial clustering amongst villages up to 100km away using a uniform spatial weighting kernel. *** p < 0.01, ** p < 0.05, * p < 0.1.

Robustness

In the online appendix we present results from several important robustness checks.

One potential concern is that the estimated complementarity between roads and electricity is picking up heterogeneous effects of one of the programs along a dimension that is correlated with the other program's provision. An obvious such dimension is time, since villages are more likely to have receive both programs in later years.

To control for this, an extended version of our model allows for the effects of both programs to have variable effects depending on the year (in addition to how long the village has had the program). Results are reported in column (1) of A4).

Another concern is that our results may be biased if program timing is correlated with an omitted variable that causes differential trends in dry season cropping.

To mitigate this concern, in column (3) of Table 2 we include year-effects of a large set of control variables. Coefficients remain stable. Similarly, in column (4) we include some potentially relevant time-varying controls relating to the local Member of the Legislative Assembly (MLA). In particular, we include party fixed effects and a dummy indicating whether their party is in power at the state level, each interacted with dummies measuring the time since the last election (the sample size falls since we only have this data up to 2012). One other omitted variable we may worry about is conflict, since both project completion and dry season cropping may be boosted by reductions in violent conflict. However, the results are similar if we restrict to a subsample of districts that are unaffected by conflict (column 5 of Table 2). 24 One could similarly be concerned that actual project completion responds to unexpected shocks that also drive sudden shifts in dry-season cropping. In column (6) we therefore use the planned completion date of the road (as agreed in the construction contract) instead of the actual completion date. The complementarity result goes through in this specification, suggesting that deviations from the planned schedule are not driving our effect. Finally, to remove the possibility of any district-level omitted variables, in column ( 7) we include district-by-year fixed effects (instead of state-by-year fixed effects). This is a demanding specification given that a large amount of variation in program timing is at the district level, but we still find a significant positive coefficient on the complementarity result. This flexible specification absorbs a large set of potential confounding factors including ones related to the administrative level, as the district administration plays an important role in the implementation of projects.

A range of further tests show that the complementarity result is robust to other changes in specification. We already allow for some spatial correlation by clustering our standard-errors at the sub-district level in our main results. In columns ( 8) and

(9) of Table 2, we show that standard errors are similar when clustering at the district level, or with Conley standard errors (Conley, 1999). Table A7 then investigates the sensitivity of the complementarity between roads and electricity access to the logarithmic transformation we use for our dry season cropping measures. When we don't use such a transformation, the coefficients are estimated imprecisely for certain outcomes, but the coefficient remains large and significant for an index of the level measures (column 4). Finally, we investigate whether the result is driven by a particular ordering of the two programs by splitting our sample in three according to program ordering.

Table A9 then shows results are consistent with our main specification in all three of these subsamples.

One potential issue in interpreting the results is that differences in program impacts may stem from differences in how effective the programs were in providing infrastructure rather than differential impacts of infrastructure provision. 25 In the online appendix we therefore validate that the infrastructure programs did consistently lead to substantive increases in infrastructure provision. We validate the electrification treatment in Figure A8 using the event study strategy on night light data. In both samples, electrification is followed by a sharp increase in night light emissions.

Importantly, however, there is no evidence of interaction effects -i.e. the combined programs do not increase nightlights beyond the sum of the two direct impacts. In columns (1) and ( 2) of Table A12 we show that villages that received roads before 2011 according to the administrative completion measure are more likely to report a road connection in the 2011 census. The probability of having a road in the census is not higher for villages that are both electrified and covered by the PMGSY scheme. 26 In columns ( 3) and ( 4) we show that villages coded as having received mobile coverage from the USOF program by 2011 are more likely to report coverage in 2012 based on the Mobile Coverage Explorer data.

Additional outcomes

The combination of the electricity and roads programs has a large impact on dry season cropping. In this subsection, we analyze whether there are impacts of this combination of programs on other related outcomes. Unfortunately, few outcomes are measured annually at the village-level, which prevents us from using a pure event study approach for many outcomes of interest.

Of course, we can easily use our approach to measure impacts on production in the rainy season (Kharif), which we show in figure A7. The event study graph suggests there is no significant effect of the interaction between electricity and roads on Kharif season output. 27 . This result is in line with our expectation that the increase in dry season cropping is mostly coming about through an increase in double-cropping rather than a shifting of agricultural production across seasons. In addition, the fact that output remains stable in the Kharif season is consistent with the idea that there are improvements in total agricultural output across seasons. This is important for 26 There is a positive relationship between electrification and road connectivity as measured by the census, and a negative relationship with the interaction of electricity and roads. These coefficients are significant but small in size compared to the coefficient on road access. Further investigation reveals that the effects of electrification disappears when we allow for the roads program to have heterogeneous effects by state (results not reported), which our main results are robust to (column 1 of Table 2). Moreover, the negative effect on the interaction term would work against the positive effects we find on dry season cropping. 27 We do find a positive effect of roads on Kharif season production. This is consistent with the results of Aggarwal (2018) and Shamdasani (2021) who show positive impacts of road connections on agricultural inputs. The average effect we estimate, however, is small in size compared to the effect of the interaction between roads and electricity in Figure 2. Notes: Regressions at the village level. Results in the 'Electricity' and 'Road' rows are the coefficients on dummy variables indicating whether the village has received the program by the beginning of 2011 -the 'Elec and road' row reports the coefficient on the interaction of these two terms. The sums of these three sets of coefficients can be found in Table A17. The outcome in column ( 1 the welfare implications of infrastructure provision, and we get at these more directly when we analyse impacts on assets and consumption -as we will discuss below.

To consider impacts on a broader set of outcomes, we can take advantage of two censuses -the 2011 Census of India and the Socio-economic caste census (SECC).

Since these censuses were undertaken in 2011/2012, when some (but not all) of our villages had received the infrastructure programs, we can compare outcomes in villages that happen to have received the program before 2011 to those that received them afterwards.

In Table 3 we look at a range of outcomes using this cross-sectional approach. To be consistent with the event study, we keep the same sample of villages and use only state fixed effects as controls. Column (1) confirms this approach give similar results

to our event study -the joint provision of electricity and road access is associated with an increase in our dry season cropping index (relative to their isolated provision).

Roads could encourage dry season cropping because lower transport costs increase the profitability of selling crops at market. We lack data on the proportion of a village's agricultural output that is sold on the market, but we can use the IHDS to identify categories of crops which are both grown in the dry season and predominantly sold rather than directly consumed. Three types of crops fit these criteriafruits, vegetables, and spices (see Table A13 for the share of each crop category produced and sold in the rabi season). In column (2) of Table 3 we then indeed find a significant positive correlation between the interaction of electricity and roads and the reporting of growing crops in these categories.

In columns ( 3)-( 5), we focus on important outcomes studied by Asher and Novosad (2020), who analyze the impact of road access through PMGSY. Our results for PMGSY on its own are consistent with theirs -in column (3) road access is associated with a reduction in the share of households whose main source of income is cultivation, but there is no significant association of road access on its own with asset holdings or the poverty rate (columns 4 and 5). 28 Similarly, there is no significant association between assets and electrification on its own, which is consistent with Burlig and Preonas (2022). We do, however, find that the joint provision of electrification and road access is associated with higher asset levels and lower poverty. Combined with our earlier findings, the positive coefficient on the interaction term suggests that complementarities boosted incomes through higher agricultural productivity.

In the appendix, we show the equivalent tables for the provision of mobile coverage and electrification (Table A15), and the provision of mobile coverage and road access (Table A16). Again, there is no evidence of complementarities with mobile phone coverage. These results for Census outcomes should be interpreted with some caution as they are based on a cross-sectional comparison of villages. However, in An additional data source that allows us to exploit temporal variation is the National Sample Survey. We were able to match a large majority of villages in ten rounds of NSS data from 2004 to 2015, based on village population numbers included in the data or deduced from the survey weights. Hence, the NSS data gives us a repeated annual cross-section of villages. Unfortunately, the number of NSS villages that see new road construction is too small for meaningful inference on the implementation of PMGSY road projects. Instead, we look at the effects of electrification and the interaction with the presence of a road -either at baseline (from the 2001 Census) or as a result of the PMGSY program. In Table 4, we first confirm that the electrification program is associated with increased household electricity consumption (column 1). In line with our earlier findings based on census data, we do not find effects on employment outcomes of electrification or its combination with road access (column 2). However, we do confirm that the combined provision of electricity and road ac-cess improves consumption (column 3). This result on consumption measured from the NSS complements the asset-based outcomes from the Census we reported in Table 3. In columns ( 4)-( 5), we split up consumption between respondents who were interviewed after the Rabi harvest and those who were interviewed after the Kharif harvest. The effect is driven by those interviewed after the Rabi harvest. Figure A10 shows the impact on post-Rabi harvest consumption using an event study approach.

This result provides further evidence both of welfare improvements stemming from complementarities and that dry-season cropping is a likely underlying channel.

Finally, we also use four waves of the agricultural census to analyze impacts of the electrification program at the sub-district level. The results support our cross-sectional analysis by suggesting that electrification increased the share of land dedicated to fruits, vegetables, and spices (see section A of the online appendix and, in particular, Table A1).

Our paper does not provide a full welfare analysis. A key concern in our context is that dry season cropping could go hand-in-hand with fires or ground-water depletion. 30 In the online appendix Table A18, we show, however, that the joint provision of electricity and roads does not appear to significantly increase fires. Our result on complementarities also holds for villages that are characterised by low ground water depletion, and for villages that have access to non-well irrigation. These results suggest that the observed increase in dry season cropping does not necessarily come at the expense of sustainability, even if a full analysis of the welfare impacts is beyond the scope of this paper.

So far, the additional evidence presented in this section rationalises the complementarities for roads and electrification, but ex ante we could have expected similar impacts from the combined provision of mobile phone coverage and one of the other programs. The null results for mobile phone coverage may be in part because the impact of the public program we study was short-lived, as private cell phone networks developed quickly during our period of study. 31 Alternatively, mobile coverage may not be as important in the decision on whether to crop during the dry season, even if it may encourage other agricultural investments.

Overall, the additional results presented in this section confirm that complementarities in infrastructure provision operate in line with the theoretical framework we set out: productivity increases through irrigation require a combination of improved market access and reduced costs of irrigation. Our results also point at real welfare benefits of the combined provision of roads and electricity.

Conclusion

We find strong evidence of complementarities between electricity provision and road access: dry season cropping increases when villages receive both a new road and electrification. It does not increase when villages receive either piece of infrastructure alone. This complementary effect is consistent with the hypothesis that dry season agricultural production only becomes profitable when improved water management is combined with better market access. In line with this idea, joint provision of electricity and roads is associated with a shift of cropping patterns towards market crops and an improvement in living conditions.

Of course, the impacts we document and their underlying mechanisms could be specific to Indian agriculture as well as the types of infrastructure we focus on. Therefore, there is ample scope for future research to examine complementarities in alternative settings. Our paper is able to leverage the unparalleled scale of India's rural development efforts, but credible identification of complementarities in other settings may require strictly implemented eligibility rules or deliberate experimenting by policy-makers. In spite of these challenges, the careful study of complementarities could have large returns. Infrastructure development is central to the development agenda of any low and middle-income country. Our results suggest that, when governments plan the implementation of major infrastructure projects, the simultaneous provision of different types of infrastructure could be essential to generate tangible economic benefits.

y i,s,t = ∑ τ ∈ 1995, 2000, 2010
1(t = τ) β τ s 2006-2010 i,s + λ τ s ALL i,s + γ s,t + η i + i,s,t (2)
Here y i,s,t is the outcome variable in subdistrict i of state s in year t, s ALL i,s is the share of villages in the subdistrict covered by the program, and s While we therefore preserve the essence of the identification strategy for the impact of electrification, we cannot use the same strategy to identify the impact of the roads program at the subdistrict level. This is because in the large majority of subdistricts, only a small share of villages were included in the roads program, meaning that we would lack the power to detect relevant impacts. To assess evidence of complementarities between electricity and roads, we therefore split the sample between sub-districts that have a share of road connections above or below the median in 2005, based on the PMGSY program and roads reported in the 2001 census.

The results of this exercise are reported in Table A1. In spite of the different level of analysis and empirical approach, it confirms our main findings. First, to test for the consistency of this empirical strategy, in columns ( 1) and ( 2) we show the effect of electrification on our main index of dry-season cropping at the sub-district level. 2 Electrification boosts dry-season cropping, but only if the share of villages with road access is sufficiently high. In columns (3) and (4), we show that electrification boosts irrigation (as measured in the agricultural census). Finally, in columns ( 5) and ( 6) we also see an increase in the share of land used for fruits, vegetables, and spices. As shown in Table A13, these are the crop categories for which most of the production takes place in the rabi season and is sold rather than directly consumed. Notes: Regressions at the sub-district level. The outcome in columns (1) and ( 2) is the (standardized) inverse correlation weighted dry-season cropping index based on remote sensing sources. The outcomes in columns (3)-( 6) are based on the agricultural census. Coefficients for the 2005 year are omitted since we include sub-district fixed effects. The first row after the coefficients reports the p-value of the test that the relevant coefficients in the first row are equal -i.e. whether there is a significant difference in these results between the below-median roads sample and the above-median roads sample. Stateyear fixed effects are also included in all columns. Standard errors are clustered at the sub-district level; *** p < 0.01, ** p < 0.05, * p < 0.1. A3). The sample for these graphs is the union of the 3 samples for the pair-wise combinations of the programs. Panel D shows the distribution of the inverse covariance weighted index calculated on this union. 1. Each point comes from a separate regression, which adds to the baseline regression two interaction terms involving the relevant control variable: one interacted with a dummy for whether the village has received the electricity program, and one for whether it has received the roads program. In this way, we allow for heterogeneous impacts of the program according to these baseline characteristics. 95% confidence intervals are included. Since their estimation strategy is not designed to estimate the interaction of multiple treatments, we estimate the interacted effect on the full sample while controlling for dynamic (but homogenous across groups) impacts of electricity and roads. We allow for state-specific year effects and cluster standard errors at the sub-district level. Notes: Regressions at the village level; the specification is analogous to Table 3. The outcomes in columns (1) and ( 2 3 only with additional control variables included -please see notes to that table for details. The control variables included differ across columns and are selected by post-double selection lasso from the following set of variables from the 2001 Census: the log of the village population, the log of the number of households in the village, the log of the village area, the scheduled caste share, the scheduled tribe share, the literate share, indicators of power supply (general, agricultural, and domestic), the presence of a dirt road, the presence of a primary/secondary/senior secondary school or college, the presence of a health center or primary health centre, a drinking water indicator, the presence of a post office, the availability of a landline phone connection, and the log of the distance to settlements with at least 10k, 50k, 100k or 500k people. Results in the 'Electricity' and 'Road' rows are the coefficients on dummy variables indicating whether the village has received the program by the beginning of 2011 -the 'Elec and road' row reports the coefficient on the interaction of these two terms. The sums of these three sets of coefficients can be found in Table A17. The model includes state fixed effects. Standard errors are clustered at the sub-district level in columns (1)-(4); they are bootstrapped in column (5) to account for the construction of the poverty measure. *** p < 0.01, ** p < 0.05, * p < 0.1.

B Additional Figures

C Additional Tables

Figure 2

 2 Figure2shows event study results for our main outcome of interest, the index of dry season cropping. Panel A shows strong evidence of complementarities between road connectivity and electrification. The share of land under dry season cropping jumps by up to 0.1 standard deviations in the five years following the joint provision of road access and electricity supply. Panels B and C suggest that the separate or joint provision of mobile phone connectivity did not spur dry season cropping. The event studies also lend support to our main identification assumption: the pre-trends for joint or separate provision are flat for all types of infrastructure in all samples.22

Figure 1 :

 1 Figure 1: Timing of programs in each sample Panel A: Electricity and roads

Figure 2 :

 2 Figure 2: Impact of infrastructure programs on dry season cropping Panel A: Electricity and roads

  ) is the change between 2005 and the average between 2010 and 2012 of the standardized inverse covariance weighted index of dry-season cropping. The outcomes in columns (2)-(5) are based on the 2011 Indian Population Census or the 2011 Socio-economic caste census (SECC). The sample only includes villages that were covered by both programs. The model includes state fixed effects. Standard errors are clustered at the sub-district level; they are bootstrapped in column (5) to account for the construction of the poverty measure; *** p < 0.01, ** p < 0.05, * p < 0.1.

  subdistrict which were covered by the program between the beginning of 2006 and the end of 2010. Since we are including subdistrict fixed effects, we are essentially doing a diff-in-diff, looking at the change of the outcome between 2005 and the reference year. In this sense, we should interpret the coefficient β 2010 as the impact of village electrification, and the coefficients β 1995 and β 2000 as placebos which test for pre-trends.

Figure

  Figure A1: Sample -share of villages per district

Figure A2 :

 A2 Figure A2: Distribution of raw dependent variables and index of logs Panel A: Dry season cropping (ISRO) Panel B: Dry season cropping (Jain et al.)

Figure A3 :

 A3 Figure A3: Impact of programs on night lights Panel A: Electricity and Roads Panel B: Electricity and Mobile Coverage

Figure A4 :

 A4 Figure A4: Robustness to heterogeneity of infrastructure impacts by baseline characteristics

Figure A5 :

 A5 Figure A5: Impact of infrastructure programs on dry season cropping -semi-dynamic model Panel A: Electricity and Roads

Figure A6 :

 A6 Figure A6: Estimates using Chaisemartin and d'Hautefeuille estimation approach

Figure A7 :

 A7 Figure A7: Impacts of electricity and roads on Kharif season production

Figure A8 :

 A8 Figure A8: Impact of programs on night lights Panel A: Electricity and Roads Panel B: Electricity and Mobile Coverage

Figure A9 :

 A9 Figure A9: Robustness of impact of electricity and roads -using imputed electrification dates

Figure A10 :

 A10 Figure A10: Impacts of electricity on consumption after the first Rabi harvest

  ) are based on the village amenities in the 2011 Indian Population Census. The outcome in columns (3) and (4) are from the Mobile Coverage Explorer data. Details of infrastructure programs are provided in the main text. The sample includes: villages that were covered by both the electricity and roads programs in column (1); those covered by a proposed telecommunications tower and the roads program (columns 2 and 4); and those covered by a proposed tower and the electrification program (column 3). Results in the individual infrastructure program rows are the coefficients on dummy variables indicating whether the village has received the program by the beginning of 2011the following rows reports the coefficients on the interaction of the relevant terms. The model includes state fixed effects and, in columns (2) to (4), mobile provider fixed effects. Standard errors are clustered at the sub-district level in column 1 and (two-way) at the sub-district and proposed tower level in columns (2) to (4). *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 1 :

 1 Impacts of infrastructure programs on dry season cropping Coefficients are an average of post-treatment coefficients in equation 1, weighted by the number of observations in each treatment bin. The joint coefficients (e.g. 'Elec and road') should be interpreted as the additional impact of having both programs above the estimated individual impacts of each program. Combined effects of the three programs are given in TableA5. The sample in Panel A includes

		Log of 1 + percentage	Log of ∆ NDVI	Dry season
		land cropped during	in dry season	cropping
		dry season	(Asher &	index
		(ISRO)	(Jain et al.)	Novosad)
		(1)	(2)	(3)	(4)
	Panel A: Electricity and roads
	Electricity	-0.026	0.022	0.11*	0.015
		(0.038)	(0.033)	(0.066)	(0.019)
	Road	-0.036	-0.001	-0.082	-0.025
		(0.039)	(0.029)	(0.075)	(0.018)
	Elec and road	0.1***	0.058**	0.12*	0.067***
		(0.036)	(0.029)	(0.068)	(0.017)
	Observations	140590	111530	137690	140910
	Sub-districts	1364	1236	1339	1365
	Mean of dep. var.	2.49	1.01	2.58	0
	Panel B: Electricity and mobile phones
	Electricity	-0.009	0.05**	-0.016	0.009
		(0.037)	(0.024)	(0.053)	(0.015)
	Mobile	0.016	-0.009	0.014	-0.004
		(0.031)	(0.019)	(0.052)	(0.013)
	Elec and mobile	0.002	0.030	0.004	0.014
		(0.031)	(0.021)	(0.053)	(0.014)
	Observations	403540	320990	398950	404460
	Sub-districts	6033	5494	5949	6037
	Mean of dep. var.	2.62	1.18	2.68	0
	Panel C: Roads and mobile phones
	Road	0.032	0.053*	0.066	0.023
		(0.025)	(0.028)	(0.061)	(0.014)
	Mobile	0.021	0.030	0.16**	0.028
		(0.032)	(0.035)	(0.075)	(0.018)
	Road and mobile	-0.004	-0.044	-0.18**	-0.03*
		(0.029)	(0.033)	(0.072)	(0.016)
	Observations	97220	72570	95900	97460
	Sub-districts	3855	3083	3786	3857
	Mean of dep. var.	2.81	1.62	3.49	0
	Notes:

villages that were covered by both electricity and road programs; in Panel B by a proposed tower and the electrification program; and in Panel C by a proposed tower and the roads program. The dry season cropping index is the (standardized) inverse correlation weighted index of the measures of dry season cropping presented in the first three columns. The model includes village fixed effects, state-by-year fixed effects, and (in panels B and C only) provider-by-year fixed effects. Standard errors are clustered at the sub-district level in panel A and (two-way) at the sub-district and proposed tower level in panels B and C. *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 2 :

 2 Robustness -Electricity and roads

		R + E het by year	R + E het by state	Controls by year FEs	Political controls	Only districts without conflict	Using planned road date	District by year FEs	District clustered standard errors	Spatially clustered standard errors
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Electricity	0.032	0.025	0.012	0.004	0.013	0.030	-0.010	0.015	0.015
		(0.021)	(0.021)	(0.017)	(0.019)	(0.037)	(0.021)	(0.015)	(0.028)	(0.02)
	Road	-0.017	-0.018	-0.027	-0.022	-0.035	-0.001	-0.001	-0.025	-0.025*
		(0.011)	(0.026)	(0.017)	(0.017)	(0.03)	(0.02)	(0.014)	(0.02)	(0.015)
	Elec and road	0.068***	0.068***	0.067***	0.066***	0.06**	0.045**	0.027**	0.067***	0.067***
		(0.021)	(0.017)	(0.016)	(0.019)	(0.028)	(0.019)	(0.013)	(0.019)	(0.016)
	Observations	140910	140910	140910	112304	81470	133960	140720	140910	140910
	Sub-districts	1365	1365	1365	1359	642	1304	1346	242	N/A

Notes: This table provides a number of alternative specifications to the model for which results are presented in column (4) of Table

  Table 2 -coefficients are similar to the baseline specification. This is also the case when we allow for heterogeneous effects by state -column (2) of Table 2 -or a range of variables from the 2001 Census (Figure

Table 3 :

 3 Census outcomes -Electricity and roads

			Reports
			growing	Share of
		∆ Dry season cropping	fruit, vegetable, or spice	households main inc. source is cultivation	Asset Index	Poverty rate
		index	(Census)	(SECC)	(SECC)	(SECC)
		(1)	(2)	(3)	(4)	(5)
	Electricity	-.0051	-.0018	-.016	.022	-.01
		(.022)	(.014)	(.014)	(.029)	(.0099)
	Road	-.018	-.017	-.022 *	.017	-.012
		(.019)	(.012)	(.013)	(.027)	(.0075)
	Elec and road	.047 *	.027 *	-.021	.11 * *	-.02 *
		(.027)	(.014)	(.017)	(.044)	(.011)
	R 2	.1	.097	.13	.11	.094
	Observations	13972	8353	13893	10585	13956
	Sub-districts	1359	1073	1334	1189	1355
	Dep. var. mean	.21	.083	.4	-.22	.45

Table A14

 A14 of the online appendix we show that the results of Table3are generally robust to controlling for a long list of village-level characteristics.29

Table 4 :

 4 Impacts of electricity on outcomes from the National Sample Survey Outcomes in this table come from multiple rounds of the National Sample Survey between 2004 and 2015. Column 1 uses rounds60, 61, 62, 63, 64, 66, and 68, while columns 2-5 additionally use rounds 69, 71, and 72. Column 4 uses only surveys undertaken in the months following the first Kharif harvest but prior to Rabi harvest, namely September-January. Column 5 contains observations made in the following months. Coefficients are an average of post-treatment coefficients from estimating an event study equation similar to that equation (1) of the paper, with coefficients weighted by the number of observations in each treatment bin. The joint coefficient 'Elec and road' should be interpreted as the additional impact of having electricity when the village has a road. All regressions include district-year fixed effects and a set of controls including household size dummies, logged village population, logged dwelling area, measures of land possessed, religion, and social group. All controls and fixed effects are interacted with the road dummy, which takes the value one if the village is recorded as having a tar road in the 2001 census or has a road constructed under the PMGSY program in or prior to the year of survey. Standard errors are clustered at the sub-district level.

		Elec cons. as share of	Hhd head works in	Log of hhd.	Log of hhd. consumption,	Log of hhd. consumption,
		total cons.	agriculture	consumption	post-Kharif	post-Rabi
		(1)	(2)	(3)	(4)	(5)
	Electricity	.0041***	-0.004	-0.038	-0.009	-0.036
		(0.0016)	(0.029)	(0.032)	(0.048)	(0.046)
	Elec and road	-0.003	0.003	.069*	-0.008	.11**
		(0.0018)	(0.032)	(0.036)	(0.055)	(0.052)
	Observations	121326	324042	324037	128057	195936
	Villages	12731	17548	17548	7093	10684
	NSS Rounds	7	10	10	10	10
	Mean of dep. var.	.023	.489	8.27	8.27	8.27
	Notes:

*** p < 0.01, ** p < 0.05, * p < 0.1.

Table A1 :

 A1 Agricultural census outcomes

						Log of 1+
				Log of 1+	percent of land
		Dry season	percent of land	fruits, spices,
		cropping index	irrigated	or vegetables
		Below	Above	Below	Above	Below	Above
		median	median	median	median	median	median
		roads	roads	roads	roads	roads	roads
		(1)	(2)	(3)	(4)	(5)	(6)
	Share electrified	-.053	.17 * * *	.1	.086 *	.088	.22 * * *
	× Year = 2010	(.047)	(.032)	(.091)	(.045)	(.093)	(.064)
	Share electrified
	× Year = 2005	.	.	.	.	.	.
	Share electrified			-.12	.042	-.027	.074
	× Year = 2000			(.078)	(.053)	(.098)	(.066)
	Share electrified			-.047	-.13	-.044	.066
	× Year = 1995			(.14)	(.1)	(.14)	(.13)
	P-val, 2010 coefs equal	.000091	.89		.25
	Observations	3338	3474	5450	5419	5450	5419
	Sub-districts	1669	1737	1669	1737	1669	1737
	Dep. var. mean	-.12	.11	2.9	3.5	1.6	1.7

Table A2 :

 A2 Summary statistics Village level observations in the samples for each pair-wise combination of infrastructure programs. For the 'Electricity & Mobile' sample, four villages are not contributing to the estimation since all the villages in one sub-district have no variation in dry-season cropping; they are dropped from the reported regression output in Table1, but are part of our sample.

		Electricity & Roads	Electricity & Mobile	Roads & Mobile
		N = 14091		N = 40450	N = 9746
		Mean	S.d.	Mean	S.d.	Mean	S.d.
		(1)	(2)	(3)	(4)	(5)	(6)
	Electrified	.49	.5	.61	.49	.54	.5
	Dirt road	.92	.28	.82	.38	.94	.24
	Tar road	0	0	.43	.49	0	0
	Population	896	813	1015	4058	1030	929
	Area (hectares)	448	627	378	656	393	529
	Scheduled caste (share)	.16	.2	.19	.21	.15	.18
	Literate (share)	.43	.16	.46	.17	.43	.16
	Primary school	.86	.35	.74	.44	.87	.34
	Received other program	.23	.42	.08	.27	.33	.47
	Notes:

Table A3 :

 A3 Observations of dependent variables ) reports the number of villages with observations for each of the three measures of dry season cropping. The sample consists of the union of the 3 samples for the pair-wise combinations of the programs.

		Number of villages	Number of villages with
		with observations	zero or negative values
			in 2005	in 2014
		(1)	(2)	(3)
	Land cropped during rabi season (ISRO)	57697	5210	8741
	Land cropped during rabi season (Jain et al.)	45447	24763	23039
	Change in NDVI during rabi season (Asher & Novosad)	56870	37715	30377
	Notes: Column (1

Table A4 :

 A4 Validation of dependent variables using IHDS The dependent variable is the log of 1 + the percentage of land cultivated during the rabi season from IHDS. Observations are weighted by the number of villages in at least one of our samples in the state. Observations are at the village level among villages in IHDS I which we are able to match to our remote sensing data. In column (4), we replace missing values of the remote sensing measures with zeros and include dummy variables indicating whether each measure is missing. The dry season cropping index used in column 5 is the standardized inverse covariance weighted index calculated on this sample of villages.

		(1)	(2)	(3)	(4)	(5)
	Log of land cropped	.12 * * *			.066 * * *
	during rabi season (ISRO)	(.02)			(.022)
	Log of land cropped		.044 * * *		.035 * * *
	during rabi season (Jain et al.)		(.014)		(.013)
	Log of ∆ NDVI			.051 * * *	.036 * * *
	during rabi season (Asher & Novosad)			(.0066)	(.007)
	Dry season cropping					.18 * * *
	index					(.023)
	Observations	617	465	595	618	618
	Adjusted R 2	0.10	0.02	0.12	0.15	0.13
	Notes:

Table A6 :

 A6 Mobile interactions in similar sample to electricity and road sample The dependent variable is the (standardized) inverse-correlation weighted index of dry season cropping. This table reports results similar to column (4) of Panels B and C of Table1(i.e. the model given in equation 1) only with observations weighted by the number of villages in the district in the roads and electricity sample to make the results more comparable to Panel A of Table1. The sample in column (1) includes villages that were covered by a proposed mobile phone tower and the electrification program; and in column (2) by a proposed mobile phone tower and the roads program. The model includes village fixed effects, state-by-year fixed effects, and tower provider-by-year fixed effects. Standard errors are (two-way) clustered at the sub-district and proposed tower level. *** p < 0.01, ** p < 0.05, * p < 0.1.

		Electricity and mobile	Roads and mobile
	Electricity	0.049**
		(0.025)
	Road		0.044**
			(0.022)
	Mobile	0.023	0.017
		(0.024)	(0.029)
	Elec and mobile	-0.004
		(0.024)
	Road and mobile		0.011
			(0.028)
	Observations	327230	69880
	Sub-districts	1649	1109
	Mean of dep. var.	-.251	-.395
	Notes:

Table A8 :

 A8 Correlation of program timing with predetermined observables Regressions at the village level; the specification is analogous to Table3. All variables are mesured in the 2001 census, except for the first two variables which refer to the situation in 2004 (i.e. the last year before the programs began). Standard errors are clustered at the sub-district level.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
			Electricity and roads		Electricity and mobiles		Roads and mobiles
		Elec	Road	Elec &	Elec	Mobile	Elec &	Road	Mobile	Road &
				road			mobile			mobile
	MLA party aligns w/ state	-0.020	0.049*	0.014	0.038	-0.012	0.029	0.024	0.0077	-0.0012
		(0.031)	(0.028)	(0.040)	(0.031)	(0.023)	(0.027)	(0.027)	(0.026)	(0.030)
	MLA election year	0.024	0.030	-0.024	0.0048	-0.0058	0.0056	0.00017	-0.010	0.0073
		(0.018)	(0.021)	(0.018)	(0.0045)	(0.0047)	(0.0049)	(0.0063)	(0.013)	(0.016)
	Primary school	-0.035**	-0.0040	0.024	-0.038***	0.0038	-0.0029	0.017	0.0084	-0.0024
		(0.015)	(0.012)	(0.018)	(0.011)	(0.0086)	(0.012)	(0.013)	(0.013)	(0.016)
	Middle school	0.0052	0.11***	-0.0072	-0.020*	0.014	-0.016	0.080***	0.011	0.0032
		(0.013)	(0.013)	(0.015)	(0.010)	(0.0087)	(0.011)	(0.014)	(0.013)	(0.016)
	Secondary school	0.0092	0.057***	-0.016*	-0.0086	0.012*	-0.016**	0.025***	0.0030	0.0050
		(0.0072)	(0.0075)	(0.0083)	(0.0071)	(0.0063)	(0.0075)	(0.0084)	(0.0076)	(0.010)
	Senior secondary school	0.0042*	0.0056***	-0.0036	0.000031	-0.0025	-0.0037	0.0040	0.0024	-0.0014
		(0.0025)	(0.0021)	(0.0030)	(0.0033)	(0.0033)	(0.0039)	(0.0033)	(0.0028)	(0.0041)
	College	0.0023	0.0023**	-0.0028	-7.8e-06	-0.00099	-0.0015	-0.0035	-0.0029	0.0037
		(0.0015)	(0.00090)	(0.0019)	(0.0013)	(0.0014)	(0.0015)	(0.0023)	(0.0025)	(0.0026)
	Log of number of households	-0.13***	0.20***	0.072*	-0.15***	0.020	-0.024	0.29***	0.068**	-0.071*
		(0.036)	(0.032)	(0.043)	(0.035)	(0.029)	(0.036)	(0.035)	(0.032)	(0.040)
	Log of population	-0.12***	0.23***	0.055	-0.14***	0.023	-0.025	0.30***	0.062*	-0.073*
		(0.037)	(0.031)	(0.042)	(0.036)	(0.029)	(0.036)	(0.035)	(0.032)	(0.039)
	Scheduled caste share	0.0091	0.013	-0.0059	0.028***	0.021***	-0.020***	0.010	0.0095	-0.0022
		(0.0099)	(0.0083)	(0.010)	(0.0073)	(0.0053)	(0.0066)	(0.0078)	(0.0077)	(0.0090)
	Scheduled tribe share	-0.038*	-0.099***	0.034	-0.051***	-0.037**	0.017	-0.058***	-0.041**	0.016
		(0.022)	(0.017)	(0.022)	(0.019)	(0.014)	(0.015)	(0.019)	(0.017)	(0.020)
	Literacy rate	0.0065	0.025***	0.0038	0.015**	0.0037	0.0036	0.0040	0.0093	-0.0024
		(0.0090)	(0.0068)	(0.0091)	(0.0071)	(0.0050)	(0.0060)	(0.0075)	(0.0069)	(0.0080)
	Electricity for agriculture	0.016	0.015	-0.035	-0.032*	-0.0018	-0.011	0.015	-0.018	-0.012
		(0.019)	(0.015)	(0.027)	(0.018)	(0.012)	(0.019)	(0.016)	(0.015)	(0.020)
	Electricity for domestic use	-0.13***	-0.046**	0.043*	-0.10***	0.025**	-0.021	-0.011	-0.017	-0.011
		(0.026)	(0.019)	(0.025)	(0.020)	(0.012)	(0.017)	(0.020)	(0.021)	(0.024)
	Electricity for all purposes	-0.056**	-0.028	0.043	-0.029***	0.0015	-0.0072	0.0076	0.00076	0.0038
		(0.025)	(0.023)	(0.030)	(0.0100)	(0.0056)	(0.0100)	(0.016)	(0.014)	(0.019)
	Dirt road	0.0056	-0.0034	0.0068	0.000011	0.0015	0.010	0.0060	0.0025	-0.0044
		(0.0085)	(0.0070)	(0.013)	(0.010)	(0.010)	(0.011)	(0.0087)	(0.0091)	(0.011)
	Tar road				-0.023	-0.0084	0.0095
					(0.015)	(0.013)	(0.015)
	Log of village area	-0.14***	0.10***	-0.047	-0.10***	0.044	-0.070*	0.12***	-0.042	0.052
		(0.038)	(0.040)	(0.062)	(0.036)	(0.028)	(0.039)	(0.043)	(0.039)	(0.047)
	Log of km to town (10k)	-0.017	0.020	-0.14**	-0.014	-0.033	0.034	-0.052*	-0.0039	0.011
		(0.037)	(0.039)	(0.063)	(0.029)	(0.024)	(0.027)	(0.027)	(0.025)	(0.029)
	Log of km to town (50k)	0.057	0.027	-0.14***	-0.071**	-0.055**	0.056*	-0.029	-0.015	-0.014
		(0.036)	(0.030)	(0.046)	(0.033)	(0.024)	(0.029)	(0.029)	(0.026)	(0.032)
	Log of km to town (100k)	-0.034	0.035	-0.23***	-0.13***	-0.066**	0.069**	-0.041	-0.039	0.031
		(0.040)	(0.044)	(0.071)	(0.036)	(0.028)	(0.031)	(0.030)	(0.028)	(0.032)
	Log of km to town (500k)	0.0041	0.083**	-0.21***	-0.12***	-0.071***	0.055**	-0.0084	-0.019	0.016
		(0.042)	(0.036)	(0.046)	(0.032)	(0.022)	(0.026)	(0.027)	(0.027)	(0.030)
	Log of irrigation share	0.028	-0.014	0.030	0.035***	0.019*	-0.014	0.021	0.031**	-0.026
		(0.017)	(0.015)	(0.022)	(0.013)	(0.010)	(0.012)	(0.015)	(0.014)	(0.017)
	Health centre	-0.0016	0.00017	0.0012	0.00063	0.0012	-0.0034	0.00084	0.0026	0.00031
		(0.0019)	(0.0018)	(0.0025)	(0.0020)	(0.0016)	(0.0022)	(0.0020)	(0.0023)	(0.0029)
	Primary health centre	0.0016	0.0072**	-0.00073	0.0019	-0.0019	-0.0032	0.0029	0.0012	0.0037
		(0.0035)	(0.0033)	(0.0043)	(0.0035)	(0.0031)	(0.0041)	(0.0038)	(0.0042)	(0.0053)
	Drinking water	0.00096	0.0024	-0.0043	0.0011	0.0026**	-0.0018	0.00053	0.0029	-0.0026
		(0.0020)	(0.0019)	(0.0027)	(0.0015)	(0.0012)	(0.0018)	(0.0027)	(0.0023)	(0.0029)
	Post office	-0.0082	0.080***	-0.011	-0.023***	0.0084	-0.016*	0.051***	-0.0055	0.020
		(0.011)	(0.010)	(0.014)	(0.0088)	(0.0076)	(0.0096)	(0.013)	(0.012)	(0.016)
	Landline phone	0.0091	0.034***	-0.0056	-0.0036	0.014*	-0.0060	0.029**	0.017	-0.0021
		(0.013)	(0.012)	(0.016)	(0.010)	(0.0080)	(0.0095)	(0.014)	(0.011)	(0.015)
	Notes:

Table A10 :

 A10 Impacts of infrastructure programs on dry season cropping -semidynamic estimation Notes: The regressions are identical to the ones in Table 1, except that pre-treatment coefficients are not estimated here. See notes to Table 1 for details.

		Log of 1 + percentage	Log of ∆ NDVI	Dry season
		land cropped during	in dry season	cropping
		dry season	(Asher &	index
		(ISRO)	(Jain et al.)	Novosad)
		(1)	(2)	(3)	(4)
	Panel A: Electricity and roads
	Electricity	-0.063	0.032	0.100	0.006
		(0.04)	(0.032)	(0.073)	(0.019)
	Road	-0.062*	0.013	-0.055	-0.024
		(0.033)	(0.028)	(0.072)	(0.016)
	Elec and road	0.1***	0.045*	0.070	0.056***
		(0.035)	(0.026)	(0.068)	(0.016)
	Observations	140590	111530	137690	140910
	Sub-districts	1364	1236	1339	1365
	Mean of dep. var.	2.49	1.01	2.58	0
	Panel B: Electricity and mobile phones
	Electricity	-0.022	0.058**	-0.040	0.003
		(0.037)	(0.024)	(0.052)	(0.015)
	Mobile	0.023	-0.004	0.040	0.005
		(0.027)	(0.016)	(0.047)	(0.011)
	Elec and mobile	0.001	0.023	-0.016	0.010
		(0.03)	(0.02)	(0.053)	(0.014)
	Observations	403540	320990	398950	404460
	Sub-districts	2147	1989	2110	2148
	Mean of dep. var.	2.62	1.18	2.68	0
	Panel C: Roads and mobile phones
	Road	0.011	0.037	0.048	0.011
		(0.025)	(0.028)	(0.058)	(0.014)
	Mobile	0.029	0.030	0.14**	0.03*
		(0.028)	(0.029)	(0.065)	(0.016)
	Road and mobile	-0.004	-0.037	-0.12*	-0.024
		(0.029)	(0.031)	(0.066)	(0.016)
	Observations	97220	72570	95900	97460
	Sub-districts	1602	1269	1556	1604
	Mean of dep. var.	2.81	1.62	3.49	0

Table A12 :

 A12 Validation of road and mobile programs

				Share of village
		Reports tar road	with mobile coverage
		in 2011 census	in 2012
		(1)	(2)	(3)	(4)
	Road	.3 * * *	.3 * * *		.011
		(.017)	(.021)		(.018)
	Mobile		.0087	.067 * * *	.093 * * *
			(.018)	(.015)	(.017)
	Electricity	.05 * * *		.025
		(.019)		(.017)
	Elec & Road	-.06 * * *
		(.021)
	Elec & Mobile			-.0036
				(.016)
	Road & Mobile		-.00085		-.0023
			(.023)		(.02)
	Observations	14070	9737	40357	9722
	Sub-districts	1361	1603	2147	1602
	Dep. var. mean	.47	.57	.83	.81

Table A13 :

 A13 Estimated share of crop produced and sold in rabi season Statistics are calculated from IHDS, with crop categories defined to be consistent with the Agricultural Census. Crop production is aggregated by season across all farmers in the survey, weighted by the number of villages in the district in our main sample of analysis (i.e. villages covered by both the roads program and the electricity program).

				Share of total production
		Share of total production	Share of rabi	produced in rabi season
		grown in rabi season	production sold	and sold
	Fruits	0.71	0.98	0.69
	Spices	0.83	0.73	0.60
	Vegetables	0.64	0.94	0.60
	Wheat	1.00	0.39	0.39
	Pulses	0.68	0.56	0.38
	Oilseeds	0.48	0.75	0.36
	Sugarcane	0.35	0.89	0.31
	Maize	0.04	0.90	0.03
	Rice	0.05	0.54	0.03
	Other Cereals	0.04	0.37	0.01
	Plantation	0.01	0.94	0.01
	Fibres	0.00	0.40	0.00
	Notes:

Table A14 :

 A14 Census outcomes -Electricity and roads, with post double selection controls This table presents results of the same model as displayed in Table

			Reports
			growing	Share of
		∆ Dry season cropping	fruit, vegetable, or spice	households main inc. source is cultivation	Asset Index	Poverty rate
		index	(Census)	(SECC)	(SECC)	(SECC)
		(1)	(2)	(3)	(4)	(5)
	Electricity	-.0018	-.0023	-.019	-.00045	.00024
		(.021)	(.013)	(.014)	(.023)	(.0099)
	Road	-.016	-.018	-.022 * *	-.02	.0064
		(.018)	(.012)	(.011)	(.02)	(.0075)
	Elec and road	.057 * *	.028 * *	-.0073	.057 *	-.012
		(.026)	(.014)	(.015)	(.033)	(.011)
	R 2	.12	.1	.17	.34	.32
	Observations	13972	8353	13893	10585	13956
	Sub-districts	1359	1073	1334	1189	1355
	Dep. var. mean	.21	.083	.4	-.22	.45
	Notes:

Table A18 :

 A18 Results related to negative externalities Fire measures are based on the Fire Information for Resource Management System (FIRMS) dataset and the burnt area is from MODIS Burned Area Pixel product, version 5.1. Villages included in column (3) are those where most of the irrigated area is recorded as coming from canals, tanks, lakes, or rivers in the 2001 census. Villages included in column (4) are those with a base water depletion score of "low" or "low-medium" according to the WRI Aqueduct 3.0. Details of the infrastructure programs are provided in the main text. Regressions at the village-year level(2005-2014). Coefficients are estimated as the sum of post-treatment coefficients in equation 1, weighted by the number of observations in each treatment bin. The model includes village fixed effects and state-by-year fixed effects. Standard errors are clustered at the sub-district level. *** p < 0.01, ** p < 0.05, * p < 0.1.

				Dry season cropping	Dry season cropping
		Fires in	Log of burnt area	index in villages with	index in districts with
		winter months	in winter months	non-well irrigation	low water depletion
		(1)	(2)	(3)	(4)
	Electricity	0.022	-0.015	0.002	-0.014
		(0.027)	(0.015)	(0.032)	(0.019)
	Road	0.054	-0.020	0.001	-0.009
		(0.048)	(0.016)	(0.037)	(0.019)
	Elec and road	-0.060	0.015	0.057*	0.046***
		(0.05)	(0.013)	(0.032)	(0.017)
	Observations	140590	140590	26500	79330
	Sub-districts	1364	1364	713	897
	Mean of dep. var.	.13	.047	.171	-.186
	Notes:

Labour market access may also implicitly insure farmers and hence allow them to invest in risky inputs.

When the commissioning date is missing, we use the earliest date a tower was commissioned in the sub-district if available, and otherwise we use the earliest date in the district or state.

The Zaid agricultural season runs from April to June, but is characterised by much lower cultivation due to its hot and dry conditions. A brief introduction to Indian agricultural and its sensitivity to climate change can be found here: https://ccafs.cgiar.org/regions/south-asia/india.

For consistency, we use equation 1 as our main specification for all event study graphs and tables. In FigureA5and TableA10, we show the main results for a semi-dynamic model.

These authors find that, apart from population size, village characteristics had low explanatory

We thank Xavier D'Haultfoeuille for his suggestions on this topic.

For the mobile phone coverage results, we cluster standard errors two-way at the sub-district and proposed tower level, to account for correlation between locations covered by the same tower. In terms of the estimating equation, villages that are covered by a proposed tower but not by an actual tower, are treated as having a time to treatment more negative than -5, so that they enter the control group. A very small number of towers were completed in 2011 and 2012. Villages near these towers are not included in our sample since we are unsure what caused these abnormal delays in completion.

As described in section 2.3, providers bid for larger areas and have influence over the final tower locations. The set of providers includes both private firms and India's public telecoms provider, and the bidding and location strategies could differ by provider. As program implementation and the characteristics of coverage areas are likely to differ by provider, the fixed effects ensure that such differences are not driving our findings.

For each of the infrastructures and their interactions in each of the samples, we undertake an F-test that the pre-construction coefficients are all equal to zero. From these tests, we obtain a p-value greater than 0.1 for each set of terms except those on "electricity and mobile" in Panel B. Indeed, examining this panel we can see that both the -2 and -3 terms are significantly different from zero. Since the signs are different, however, we believe these are more likely to be the result of chance than evidence of a

For this check, we exclude Jammu and Kashmir, all North-Eastern states, and all Maoist-affected districts (from[START_REF] Ghatak | Economic determinants of the Maoist conflict in India[END_REF].

On roads, for instance,[START_REF] Lehne | Building connections: Political corruption and road construction in India[END_REF] show that PMGSY implementation suffered from political corruption affecting program completion. On electricity, the supply of electricity to rural communities is frequently rationed, even when there is a physical network[START_REF] Burgess | The consequences of treating electricity as a right[END_REF][START_REF] Ryan | Rationing the Commons[END_REF].

The asset index comes fromAsher and Novosad (2020). The poverty rate comes from SHRUG and is estimated using the same asset base in the SECC.

We use post-double selection lasso to determine which control variables enter the model.

[START_REF] Blakeslee | Way down in the hole: Adaptation to long-term water loss in rural India[END_REF] document the growing water scarcity in India and the adaptation strategies of farmers.

TableA12supports this idea: the effect of USOF coverage on measured mobile phone coverage in 2012 is less than a third of the effect of PMGSY coverage on road access in the 2011 census. However, the event studies of Figure2show no impact even in the immediate aftermath of tower construction.

These data were downloaded from http://agcensus.nic.in/ between January

and Decem-

ber 2020. We also considered using crop price data, as in[START_REF] Aggarwal | Do rural roads create pathways out of poverty? Evidence from India[END_REF], but the number of market centers per district is much lower than the number of sub-districts, which means that we cannot follow our empirical approach (which relies on granular variation in infrastructure completion) at this level.

Our remote sensing measures are not available for the two earliest agricultural census years(1995- 96 and 2000-01), and therefore for this outcome we cannot estimate the placebo coefficients β 1995 and β 2000 .

* This project has benefited from support by EUR grant ANR-17-EURE-0001, ANR grant COOPCONFLICT, the International Growth Center, and CEPREMAP. Oliver Vanden Eynde started working on this project when he was visiting Princeton University. Jacob Shapiro contributed to the collection of data that are used in this project. Jamie-Hansen Lewis and Nitin Bharti provided excellent research assistance for this project.

Appendix to "Complementarities in Infrastructure: Evidence from Rural India" For Online Publication January 27, 2023

A Results based on the Agricultural Census

We complement our analysis by using the Indian Agricultural Census at the subdistrict level. 1 We combine data from 4 waves (1995-96, 2000-01, 2005-06, and 2010-11) to construct a panel of agricultural outcomes. The agricultural census provides more precise information on cropping patterns than the population census: the former measures the areas cropped, whereas the latter just lists the three main crops. Moreover, the agricultural census aims to provide consistent data over time going back to 1995, whereas the population census only provides data on crops grown in 2011.

One important limitation of the agricultural census, however, is that it does not provide information at the village level; the lowest administrative unit available is the sub-district. This means we adapt our identification strategy in the following way.

To estimate the impact of the electrification program, we continue to exploit variation in the timing at which villages were electrified by regressing outcomes on the share of villages in the sub-district which were electrified between 2006 and 2010 while controlling for the share of villages that were electrified at any point in time. In this way, we are again only using variation in the timing of the program's roll-out within a sub-district, and not the ultimate share of the sub-district which was covered by the program. In other words, we estimate the following equation: Notes: This table presents results of the same models as displayed in Tables 3 andA14 , only in this case looking at the interaction of electricity and mobile towers rather than the interaction of electricity and roads. The sample includes villages that were covered by the electricity program and were proposed to be covered by the mobile program. Results in the 'Electricity' and 'Mobile' rows are the coefficients on dummy variables indicating whether the village has received the program by the beginning of 2011 -the following row reports the coefficient on the interaction of these two terms. The sum of these three sets of coefficients can be found in Table A17. The model includes state fixed effects and mobile provider fixed effects . Standard errors are (two-way) clustered at the sub-district and proposed-tower levels in columns ( 1)-(4); they are bootstrapped at the sub-district level in column (5) to account for the construction of the poverty measure; *** p < 0.01, ** p < 0.05, * p < 0.1.