india

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:

ddvsrd

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!

 

WWP: Rain, rain, go away, you're hurting my human capital development

Greetings from String Matching Land! Since I seem to be trapped in an endless loop of making small edits to code and then waiting 25' for it to run (break), I'm going to break out for a little bit and tell you about a cool new paper that I just finished reading. Also, it's Wednesday, so my WWP acronym remains intact. Nice.  

Manisha Shah (a Berkeley ARE graduate!) at UCLA and Bryce Millett Steinberg, a Harvard PhD currently doing a postdoc at Brown who is on the job market this year (hire her! this profession needs more women!), have a new paper that I really like. I thought of this same idea (with slightly different data) recently, and then realized that this is forthcoming in the JPE (damn)- and it's excellently done. The writing is clear, the set-up is interesting, the data are cool, the empirics are credible, and the results are intuitive. Did I mention it's forthcoming in the JPE? 

In this paper, Shah and Steinberg tackle a prominent strand of development economics: what do economic shocks do to children at various stages of growth? There's a long literature on this, including the canonical Maccini and Yang paper (2009 AER), who find that good rainfall shocks in early life dramatically improve outcomes for women as adults (in Indonesia). This paper does a great job of documenting a treatment effect (if you haven't read it yet, metaphorically put down this blog and go read that instead), but less to say about the mechanisms behind it.

Steinberg and Shah take seriously the idea that rainfall shocks might affect human capital through multiple channels: good rain shocks could mean more income, and therefore consumption and human capital, or good rain shocks might mean a higher opportunity cost of schooling, leading to less education and human capital development. They put together a very simple but elegant model of human capital decisions, and test its implications using a large dataset including some simple math and verbal tests from India.  They show that good rain shocks are beneficial for human capital (as proxied by test scores) early in life, but lead to a decrease in human capital later in life. They demonstrate that children are in fact substituting labor for schooling in good harvesting years, and show that rainfall experienced in childhood matters for total years of schooling as well, which could help explain the Maccini and Yang result, though they don't find differential effects by gender. 

In the authors' own words (from the abstract):

Higher wages are generally thought to increase human capital production, particularly in the developing world. We introduce a simple model of human capital production in which investments and time allocation differ by age. Using data on test scores and schooling from rural India, we show that higher wages increase human capital investment in early life (in utero to age 2) but decrease human capital from ages 5-16. Positive rainfall shocks increase wages by 2% and decrease math test scores by 2-5% of a standard deviation, school attendance by 2 percentage points, and the probability that a child is enrolled in school by 1 percentage point. These results are long-lasting; adults complete 0.2 fewer total years of schooling for each year of exposure to a positive rainfall shock from ages 11-13. We show that children are switching out of school enrollment into productive work when rainfall is higher. These results suggest that the opportunity cost of schooling, even for fairly young children, is an important factor in determining overall human capital investment.
Obligatory stock photo of Indian school kids during the rainy season. Obviously not my own photo.

Obligatory stock photo of Indian school kids during the rainy season. Obviously not my own photo.

A few nitpicky points: I could've missed this, but the data are a repeated cross-section rather than a panel of students, so I wanted a little more discussion of whether selection into the dataset was driving the empirics. Also, when they start splitting things by age group, I'm surprised that they still have enough variation in test performance among 11-16-year-olds to estimate effects. I would've expected these students to max out the test metrics, given that the exam being administered is incredibly basic numeracy and literacy skills. But maybe not. Finally, since I'm teaching my 1st-year econometrics students about figures soon, these graphs convey the message but aren't the sexiest. Personal gripe. All in all, though, this is a really nice paper - I urge you to go read it. 

A final caveat: this is of course context-specific. I don't at all mean to suggest (and nor do the authors), for instance, that these results should have Californians glad that we're done with the rain and back to sunny weather. As much as I enjoy sunrise runs (n = 1) and sitting outside reading papers, I'd be happy with a little more of what El Nino's got to offer the West Coast.

What do autorickshaws and blood diamonds have in common?

One of my favorite things about urban India is the wide range of transportation options. You can travel by foot, (in Jaipur: camel or elephant), bike, old-school rickshaw, scooter, motorcycle, autorickshaw, car, bus, or metro. 

As you might have guessed, though, there's a cost to all of these amazing methods for getting around: local air pollution is through the roof. Delhi was recently named the most polluted city in the world, with PM 2.5 concentrations one-and-a-half times higher than Beijing last week (Beijing, mind you, issued its first-ever air pollution "red alert" on Monday, bringing the city to a standstill). Bangalore, where we currently are, feels noticeably better than Delhi, but has its own pollution problem as well.

We've been doing the majority of our getting around using autorickshaws, amazing motorized three-wheeled contraptions which can weave between buses, fit two comfortably on the back seat, and are (to a Westerner, at least) dirt cheap. Plus they're fun and exciting, but don't come with the terror of being on the back of a motorcycle. 

Bangalore's autorickshaw fleet is composed of two types of vehicles: those with two-stroke (petrol) motors, and those with four-stroke motors (LPG). They're conveniently color-coded: the dirty ones are painted black, whereas the clean(er) ones are a beautiful green color. Oh, the symbolism! The good they provide, however, is identical. Fares are the same across rickshaws, and the ride quality/speed/etc is basically indistinguishable as well. The dirty ones are supposedly being phased out over time - newly purchased rickshaws have to be the clean variety - but there are still plenty in the fleet. Because we're good little environmentalists, Louis and I have been trying to take the LPG ones whenever possible.

Ready to be hired!

Ready to be hired!

...Except that all that we're getting by avoiding black ricks is green glow. The rickshaw situation reminds Louis (who is "finishing [his] PhD and doesn't have time to [guest post on my] blog") and I (therefore taking the credit) of blood diamonds, thanks to Catherine, Jim Bushnell, and Carla Peterman

Humor me for a second here. Suppose we have a world with 5 potential rickshaw riders (diamond buyers), and 5 potential rickshaw rides (diamonds). Initially, transportation services (diamonds) are seen as homogenous, but in reality, there are 2 rides available in dirty rickshaws (blood diamonds). Louis and I decide to boycott these dirty rides (blood diamonds), and commit to only riding in the 3 remaining clean rickshaws (buying conflict-free diamonds). The other three consumers in the market are indifferent - so long as they get from point A to point B (have a diamond), they're happy. The end result? We ride only in green rickshaws (buy clean diamonds) and get our green glow, but pollution (diamond-driven conflict) hasn't actually changed, because we just swap rickshaws (diamonds) with the remaining 3 buyers in the market.

If we wanted to actually effect change, the share of clean-only riders in the market would have to be larger than the fraction of green rickshaws. To see this, suppose there are still 3 clean rickshaws, but now we've got 4 green-only riders. In this scenario, the total number of rides consumed drops from 5 to 4 (one green-only rider will be unable to find a clean rickshaw and will refuse to ride), and the ride that isn't taken is a dirty ride. Cool. But unlikely to be the situation in urban India, where people seem to be pretty indifferent about their rickshaw colors.

Jim Bushnell 's neat figure illustrating the successful-boycott case. Borrowed from a set of slides on reshuffling.

Jim Bushnell's neat figure illustrating the successful-boycott case. Borrowed from a set of slides on reshuffling.

Looks like we're probably stuck with just green (rickshaw) glow for now. But on the off chance we might end up on the margin (and because, let's admit it, green glow feels good!), we'll keep riding the LPG rickshaws if we've got the choice. 

Officially SSMART!

I've been a bad blogger over the past month or so, something I'm hoping to remedy over the coming weeks. (Somewhere out there, a behavioral economist is grumbling about me being present-biased and naive about it. Whatever, grumbly behavioral economist.) I'm writing this from SFO, about to head off to Bangalore (via Seattle and Paris, where I'll meet my coauthor/adventure buddy Louis), thanks to USAID and Berkeley's Development Impact Lab. We're hoping to study the effects of the smartgrid in urban India, as well as to learn more about what energy consumption looks like in Bangalore in general. There is a small but quickly-growing body of evidence on energy use in developing countries (see Gertler, Shelef, Wolfram, and Fuchs -- forthcoming AER, and one of my favorite of Catherine's papers! -- and Jack and Smith -- AER P&P on pre-paid metering in South Africa -- for a couple of recent examples). Still, there's a lot that we don't know, and, of course, a lot more that we don't know that we don't know. Thanks a lot, Rumsfeld.

Feeling SSMARTer already!

Feeling SSMARTer already!

In other exciting news, the Berkeley Initiative for Transparency in the Social Sciences (BITSS) has released its SSMART grant awardees - and my new project (with Matt and Louis, and overseen by Catherine) on improving power calculations and making sure researchers get their standard errors right has been funded! Very exciting. Check out the official announcement here, and our page on the Open Science Framework here. Since this is a grant explicitly about transparency, we'll be making our results public as we go through the process. Our money is officially for this coming summer, so look for an update / more details in a few months.

Where we are currently: there are theoretical reasons to be handling standard errors differently than we currently are in a lot of empirical applications, and there are also theoretical reasons that existing formula-based power calculations might be ending up under powered. In progress: how badly wrong are we when we use current methods? 

My flight is boarding, so I'll leave you with that lovely teaser!

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!