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

At long last, Louis and I have a working paper version of our paper, "Out of the Darkness and Into the Light? Development Effects of Rural Electrification in India" (Appendix here - warning: it's a pretty big file). The paper has, as you might expect, a lot of gory details in it, and the appendix even more so, so I'll try to provide a less technical overview here. 

The basic motivation behind the paper is the following: there are still over a billion people around the world without access to electricity, most of whom are poor, rural, and live in South Asia or Sub-Saharan Africa. Developing country governments and NGOs are pouring billions of dollars into rural electrification programs with the explicit goal of bringing people out of poverty - one of the new UN Sustainable Development Goals is to "Ensure Access to Affordable, Reliable, Sustainable and Modern Energy for All."

In light of the big pushes towards universal energy access (pun only half intended), it's important that we understand what these investment dollars are going towards - even more so when we're talking about developing country dollars, which have opportunity costs like schools and health clinics and roads. It turns out, though, that we know surprisingly little about the actual effects of electrification on economic development. There is a strong positive correlation between GDP per capita and electricity consumption, with rich countries like the USA and Japan consuming much more energy per capita than poorer places like Nigeria and Bangladesh:

Data source: World Bank

Data source: World Bank

As we all know, though, correlation does not equal causation (thanks, XKCD!). Figuring out the causality here is definitely not trivial: at the country level, there are lots of things that drive income differences between Japan and Bangladesh that have nothing to do with electricity. It turns out that these omitted variable bias problems plague sub-national-level studies as well. If we just regressed incomes (or better yet, welfare!) on electricity infrastructure at the village level in a developing country (say, India), we'd likely end up with a number that was way too high. Why? Energy infrastructure projects are large and expensive, and (correctly) not randomly placed: governments think long and hard about where to put the electricity grid. In practice, this usually means that places that are already doing well economically, or places that are expected to do well in the future, are the first to get access to electricity. In more technical terms, that regression I described above is subject to a ton of omitted variable bias. 

In our new paper, Louis and I try to shed light on the following question: What does electrification do to rural economies? To do this, we take advantage of a natural experiment built into India's national rural electrification program, RGGVY, which would eventually expand electricity access in over 400,000 villages across 27 states. In order to keep costs down, in the first phase of the program, only villages with neighborhoods of 300+ people were eligible for electrification. This means that we can compare villages with 299-person neighborhoods to those with 301-person neighborhoods to look at the effect of electrification. The idea is that, in the absence of RGGVY, these villages would be virtually indistinguishable - and indeed, in the paper, we show that prior to the program, villages just above and just below the 300-person cutoff look the same. Having a cutoff of this kind built into the program is really nice from an evaluation standpoint, because it allows us to mimic a randomized experiment couched in the natural (large-scale) rollout of RGGVY. 

Having identified this cutoff, we get to bring a bunch of cool data to bear on the problem. The first thing we need to do is to demonstrate that RGGVY actually led to increases in electricity access for eligible villages. We're concerned that a simple 1/0 electrification indicator doesn't actually capture power availability and use - it would mask variation in power quality or in the number of households that have access to the grid, for example. Instead, we turn to remote sensing. We got access to boundary shapefiles (outlines) of [almost] every village in India, and superimposed them on top of NOAA's DMSP-OLS nighttime lights dataset - this is a satellite-based measure of nighttime brightness. We combine these to datasets and "cookie-cutter" out the nighttime lights values for each village, which allows us to create statistics like the average or maximum brightness in each location.

Yum, village cookies!

Yum, village cookies!

We pair nighttime brightness data from 2011, 6 years after the announcement of the program, with population information from the 2001 Census (the official population of record for RGGVY), and look at the effects of eligibility for RGGVY on night lights. Another advantage of the cutoff-based ("regression discontinuity", for those in the know) analysis? It lends itself naturally to blogging, because you should be able to see the difference between ineligible villages and eligible villages very clearly in a figure. Et voila:

Effects of eligibility for RGGVY on nighttime brightness. Each dot contains ~1,800 villages.

Effects of eligibility for RGGVY on nighttime brightness. Each dot contains ~1,800 villages.

We find that RGGVY eligibility (aka crossing the 300-person threshold) led to a sharp increase in nighttime brightness, as visible from space - remember that we're looking at villages of around 300 people, so we're pretty impressed that we can detect this. It helps that we're studying India, where there are upwards of 30,000 villages in our main estimation sample. It's a little hard to interpret this effect directly, since it's in units of brightness points, but previous papers that have gone out and groundtruthed the relationship between nighttime lights and electrification suggest that this is consistent with about a 50-percent increase in the household electrification rate in our sample. We do a bunch of work in the paper and in the appendix to show that these changes are actually attributable to RGGVY - if you're curious, check it out!


Okay, great: it looks like RGGVY brought electricity to Indian villages - but what about the effects of electricity access on things we care about, like the workforce, asset ownership, housing stock characteristics, village-wide outcomes, and education? We gathered a bunch of village-level data from the Indian Census of 2011 and the District Information System for Education, and look, among other things, at the effects of electrification on outcomes like men and women working in agriculture vs. the more formal sector; ownership of assets like TVs and motorcycles; whether households are classified as "dilapidated" or have mud floors, and other housing stock outcomes; the presence of mobile phone coverage, agricultural credit societies, and other village-wide services; and the number of children enrolled in primary and upper-primary school. Here's a snapshot of (some of) our results:

For all of the outcomes I've shown here, we see very little evidence of sharp discontinuities at the 300-person threshold. We do see clear evidence of men shifting from agricultural to non-agricultural employment (see p. 20 in the paper), but little else. The graphical evidence is borne out by the regression estimates as well: in almost no cases can we reject the null hypothesis of zero effect, but more importantly, we can reject even modest effects in nearly all outcomes. (The exception to this is education: we again see no visual evidence of effects on education, but our sample size is much smaller for these outcomes, since not all villages actually contain schools, so we have less precision with which to rule out effects; the schools effects are also sensitive to specification choices, bandwidths, etc in a way that the other outcomes are not.) To reiterate: in the medium term, we find that eligibility for RGGVY caused a substantial increase in electricity use, but can reject small effects on labor markets, asset ownership, housing characteristics, and village-wide outcomes; we do not find robust evidence that RGGVY led to changes in education. We were pretty surprised by these results, so we threw a bunch of checks at them (see the Appendix) - but they seem to hold up. (Turns out that our results are also consistent with new evidence from our Berkeley colleagues Ken Lee, Ted Miguel, and Catherine Wolfram in Kenya - see the abstract here).

A couple of these tests in particular are worth highlighting:

You might be concerned that we're not finding much because the program wasn't implemented well, or because we're lumping a bunch of villages where electrification did a lot in with villages where electrification didn't do anything, so things are averaging out to zero. When we cherry-pick the states that saw the largest increase in nighttime brightness as a result of the program, however, we don't see evidence of this. Among this selected sample, the nighttime lights effect approximately doubles - but the effects on the other outcomes stay the same. 

You might also be concerned that villages with around 300 people are unlikely to see big effects - they might be too poor, too credit-constrained, etc, etc, etc. A couple of responses to this: first, if we care about electrification from a poverty-reduction standpoint, then we should be worried about people being too poor to take advantage of electrification. But that's more speculative than data-driven, so we do a more formal test to think about effects of electrification for the rest of the villages in India. Rather than relying on our nice cutoff, we instead do a difference-in-differences (DD) analysis: we compare villages electrified in the first wave of the program (like a "treatment" group), before and after electrification, to villages electrified in the second wave of the program (like a "control" group). When we do this, and calculate different effects by population groups, here's what we find:


There are a couple important takeaways from this figure: first, our cutoff-based estimate (navy dots) line up remarkably well with the DD point estimates for the appropriately sized villages; and second, the brightness effect is increasing in population, but the other outcomes (proportion of men working in agriculture shown here) are not, suggesting that our original estimates might actually generalize to the rest of the population. 

So what does it all mean? We present well-identified quasi-experimental evidence from the world's largest unelectrified population. Taken together, our results suggest that rural electrification may not be as beneficial as previously thought. Does our paper say that we shouldn't be implementing these kinds of electrification programs, or that electricity isn't making people better off? No - we explicitly don't make any statements about overall welfare in the paper, because we don't have the data to support these types of claims. In fact, we visited some villages in Karnataka in December, which made it pretty clear that people like having access to power (so do I!). But we can say that it's difficult to find evidence in the data that electrification is dramatically transforming rural India after 5 years or so. We think this is a case where highlighting a null result is really important - take that, file drawer! At the end of the day, in the medium term, rural electrification just doesn't appear to be a silver bullet for development. In typical Ivory Tower fashion, we think more research is needed to understand where and when power can transform economies - maybe we should be targeting electricity infrastructure upgrades to urban areas, for instance. We've got a couple projects in the works to look at some of these questions - so check back with us in a few years, and hopefully we'll have some more answers!


Conference recap: IGC's Energy and Growth

Last week, I was lucky to have been invited to the IGC’s first annual (we hope!) Energy and Growth conference in London. Organized by heavy-hitter energy economists Michael Greenstone and Nick Ryan, this cool conference brought together economists who work on energy and development with policymakers from a range of international organizations and governments. The IGC seems to have facilitated many useful collaborations between researchers and the ``real world,’’ and this was an interesting venue to highlight some of that work (and some other work as well). A few highlights:

  • Not to plug my advisor again, but I can’t help it: Catherine’s work with Ted Miguel and Ken Lee (fellow ARE PhD student) on an RCT they’re conducting in Kenya might have been the hit of the conference. First, they noticed that a large number of households were “under-grid”, or had electricity infrastructure nearby, but not in, their homes. This is an example of the last mile (last centimeter?) problem. They’ve randomly assigned subsidies for grid connections to households in rural villages, and have varied the subsidy level across villages, letting them estimate a demand curve for electrification among these households. They also have cost information from the Kenyan Rural Electrification Authority, so that they can estimate a supply curve as well. The surprising punchline? The supply curve sits above the demand curve basically everywhere: households aren’t interested in taking up connections at the cost at which the Kenyan government can provide them. Also some cool stuff about credit constraints. I’d be interested in learning more about what’s driving this effect: is it concerns about reliability? Lack of information about the power of electricity (eheheh)? Something else? They’re also conducting a follow-up survey which will let them understand the effects of electrification in these regions, which will also hopefully shed some light on what’s going on here. Fascinating stuff.
  • Kelsey Jack has some really cool new work with Grant Smith (PhD student at UCT – one of my favorite places) on the consequences of pre-paid electricity metering in Cape Town, also using an RCT. When these meters are installed, some households do reduce their energy consumption – but many also dramatically increase their electricity-related transactions costs, by going to the shop to fill their meters multiple times a week. It turns out that pre-paid meters don’t substantially reduce consumption of the households that are being subsidized, and do reduce consumption among the subsidizers, so they don’t seem to be an effective tactic for additional revenue recovery, either. Again, lots more to be learned about pre-paid metering. I’m hopeful that they’ll be able to expand their study area as well.

·      Koichiro Ito has new work looking at willingness to pay for air quality in China, exploiting the Huai River discontinuity. While I’ve always been highly skeptical of a certain previous paper using this result (sorry, MG), this one is definitely more convincing, in particular because it measures PM10 which is a fairly local pollutant, because it includes longitude controls, and, most importantly, because the effect is only visible in the winter. Combining this with scanner data on retail outlet purchases of air purifiers, Koichiro is able to give us what’s probably a lower bound on the Chinese WTP for air quality (and in turn, quite a low VSL – though not so low as Kremer et al found using clean water in Kenya). I want to see more about getting from this number to an implied VSL, and the discount rates or lack of information that would have to be required to rationalize a VSL closer to the one we use in the US, but that should be doable in a fairly simple back-of-the-envelope calculation. Neat!

  • Rohini Pande made some insightful comments as part of a panel discussion about the importance of government oversight and regulatory strength in keeping local and global pollutants managed in the context of growing energy demand in the developing world. It sounds like she’s also got some interesting new work about the placement of energy infrastructure: should it be near people (ala China) or near coal (ala India)? These have vastly different distributional consequences, which we know from some of her earlier work (“Dams” with Esther Duflo) can be very important. Looking forward to seeing what she does. One of my favorite researchers.

Lots of other interesting work was also presented (check the program for some details); I’m inspired to do more work in this area. There are obviously a number of smart people working in this space – but luckily it’s a vast and important research space, so hopefully there’s room for another grad student or two.


Stay tuned to this blog for the next few weeks for a couple of exciting announcements about upcoming work and new results! 

WWP: Power-thirsty

The Weekend Working Paper series returns! The fantastic Katrina Jessoe is visiting the Energy Institute from Davis this semester (and - bonus - mentioned that she's looked at this blog...correlated with my choice to highlight her paper this week? I'll let the reader decide), so this weekend, I read her cool new working paper, joint with Reena Badiani. Like me, Katrina and Reena have been thinking about what electricity does to the Indian agricultural sector and to the environment. Unlike me, they have their acts together and have a cool paper draft! 

As a bit of context: electricity in India['s agriculture sector] is in a challenging political economy situation. For historical and social equality reasons, India has a long tradition of providing subsidized electricity to its farmers. For many states, this means that agricultural electricity users are billed (next to) nothing for energy...and on top of that, bill payment rates are quite low. Energy use among farmers is also often unmetered, and with relatively unsophisticated grid-level monitoring systems, it's just really hard to know how much electricity is even going to the ag sector - but with power costs next to nothing, you can imagine that farmers aren't exactly conserving. The economics 101 response would be to price electricity at the social marginal cost, likely with a fixed charge to enable the natural monopoly utility to stay in business (Severin's blog post would be thrilled to tell you all about fixed charges in California). But the Indian equilibrium is pretty far from this (especially in agriculture), and because farmers have a great deal of bargaining power, and because utilities are individually administered by state power boards, enacting change towards economic efficiency is extremely difficult.

An irrigation pump in Punjab.  Source .

An irrigation pump in Punjab. Source.

So, given that India's cheap power to farmers probably isn't going away any time soon, it's useful to know what kinds of costs are associated with the energy price subsidies. Here's where Reena and Katrina come in. Energy in India's agricultural sector is largely used for pumping groundwater. You can imagine that subsidized energy prices might cause extra groundwater depletion, relative to marginal-cost electricity prices. But the magnitude of these effects is really important for policy, especially given the current state of India's groundwater.

Reena and Katrina take advantage of variation in state electricity pricing over time to estimate the effect of electricity subsidies on groundwater pumping and yields. They find that lower energy prices cause substantially more groundwater use, which in turn changes the land being used for crops and the water intensity of crops. The bad news is that it looks like these subsidies have also increased the likelihood that groundwater sources are at risk of over exploitation. The good news is that these subsidies seem to be a decent way of transferring government money to farmers. In the authors' own words:

In this paper we estimate the causal effect of agricultural electricity subsidies in India on groundwater extraction and agricultural output. Our empirical approach exploits changes in state electricity prices over time controlling for aggregate annual shocks and fixed district unobservables. Electricity subsidies meaningfully increase groundwater extraction, where the implied price elasticity is -0.18. This subsidy-induced change in groundwater extraction im- pacted both agricultural output and crop composition, increasing the value of water-intensive output and the area on which water-intensive crops are grown. These subsidies also increase the probability of groundwater exploitation, suggesting that they may come at a real and long-term environmental cost.

I'm looking forward to seeing the final version of the paper - and hopefully several more like it, doing further work on these important economic and environmental issues.

PS: For those of you keeping track at home, our heatwave has thankfully passed. Today even showed glimmers of rain!

Worth a thousand words

National Geographic has a pretty striking photoset out this week, made up of user submissions. The 18 photo gallery includes shots from around the world, depicting various aspects of life in the absence of cheap, abundant energy. Several of the photos seem a little tenuously related to energy itself (though all have captions that try to make the link), but there are many where the connection is clear. It can be hard to imagine, sitting in a (well, not actually air conditioned) office in the US, where the lights, fridge, phones, and computers just work that this is what the lives of over a billion people look like. Photography is a pretty amazing thing. 

One of my favorites. Note the clearly electric fans - and of course the candle. Classic juxtaposition in places where power is available, but not reliable. 

One of my favorites. Note the clearly electric fans - and of course the candle. Classic juxtaposition in places where power is available, but not reliable. 

All of the submissions to the photoset can be found here. Well worth a look.

Powering development?

NPR's All Things Considered has a great radio piece today on some work being done by economists at Berkeley. The audio clip, as well as the accompanying post from Goats and Soda (one of my favorite development blogs), features development economics superstar Ted Miguel taking a hard look at the types of energy access that are necessary to spur development. 

The money quote from Ted is: "No industrial country has ever powered its economic growth with solar lanterns." This point echoes comments made by Catherine Wolfram, Ted's co-PI on the Kenya electrification project (and much less auspiciously, my advisor) on the Energy Institute's blog a few months ago. The basic idea is this: providing small-scale home solar energy systems that can run a lightbulb and a cell phone charger might be sexier than grid hookups, but we should think carefully about how and why electrification might drive development. It's hard to come up with stories where big changes in standards of living result from a couple of electric lights attached to home solar systems - whereas access to the grid can and does support refrigerators, irrigation pumps, and appliances that can be used in cottage industry. It's also worth noting that once a household has access to the grid, it can start small and scale up - but a solar hookup will always be limited to a few low-wattage items.

All that said, though, the effects of electrification in the developing world are (for the most part) a big question mark at this point. The empirical jury is still largely out on how distributed and grid energy access affect the lives of the rural poor in developing countries, and perhaps more importantly, on how energy access can lead to larger-scale structural transformation and economic growth. 

There is lots of ongoing work (my own included - stay tuned!) to try and address these types of questions, and I'm encouraged to see that people are evaluating both small-scale solar projects and access to the grid. I'm personally generally of the opinion that improved access to energy services is important for development - but that we might think that providing a single lightbulb can change one household's life, while providing grid hookups in a region with a little bit of capital to utilize them might be able to transform a community. When thinking about a policymaker's limited budgets, we want to get the most bang for our buck - and intuitively, that seems like it might be grid connections. I'm looking forward to being able to base my opinions on solid empirical work in the future, rather than just wild speculation (but wild speculation is half the fun of blogging, so...).