Research

Developing-country governments routinely attempt to collect revenue using threats they cannot systematically enforce. We study how citizens assess the credibility of such empty threats in the context of payment for electricity in Madhya Pradesh, India, where state-run utilities recovered only 60 cents per dollar of power supplied. Using two field experiments covering 30,000 households with high arrears, we show that the household response to a threat depends on the state's choice of messenger. Legal threats delivered by local linesmen — state agents with a history of ignoring non-payment — have no effect. Yet identical notices sent by registered mail, bypassing linesmen, reduce arrears by 11.4 percent among recipients, a 241 percent return on investment. A second experiment a year later shows these effects are dynamic: consumers originally visited by a linesman do not respond to future notices, while those never exposed to linesmen do. Low-credibility state agents can render threats ineffective.

Over 2 billion people lack clean drinking water. Existing solutions face high costs (piped water) or low demand (point-of-use chlorine). Using a 60,000-household cluster-randomized experiment, we test an alternative approach: decentralized treatment and home-delivery of clean water to the rural poor. At low prices, take-up exceeds 90 percent, sustained throughout the experiment. High prices reduce take-up but are privately profitable. We experimentally recover revealed-preference measures of valuation: willingness-to-pay is several times higher than prior indirect estimates, and willingness-to-accept is larger and exceeds marginal cost. Self-reported health measures improve accordingly. On a cost-per-DALY basis, free water-delivery regimes appear highly cost-effective.

Market-based environmental regulations are seldom used in low-income countries, where pollution is highest but state capacity is often low. We collaborated with the Gujarat Pollution Control Board to design and experimentally evaluate the world's first particulate-matter emissions market, covering industrial plants in a large Indian city. There are three main findings. First, the market functioned well: treatment plants, randomly assigned to the market, traded permits to become significant net sellers or buyers, and after trading held enough permits to cover their emissions 99% of the time, compared to just 66% compliance under the command-and-control status quo. Second, treatment plants reduced emissions, relative to control plants, by 20% to 30%. Third, the market reduced abatement costs by an estimated 11%, holding emissions constant. Together, the pollution reductions and low costs imply mortality benefits that exceed the market's costs by at least twenty-five times.

Scientific evidence has documented that we are undergoing a mass extinction of species, caused by human activity. However, allocating conservation resources is difficult due to scarce evidence on the damages from losing individual species. This paper studies the collapse of vultures in India, triggered by the expiry of a patent on a painkiller. Our results suggest the functional extinction of vultures — efficient scavengers that removed carcasses from the environment — increased human mortality by over 4 percent because of a large negative shock to sanitation. We quantify damages at $69.4 billion per year. These results suggest high returns to conserving keystone species such as vultures.

The global electrification frontier is the set of places where households are getting electricity for the first time. The advent of off-grid solar means many households now choose between competing electricity sources. This paper estimates the demand for electricity, over all available sources, to understand how poor households make this choice and to value electrification. The setting is Bihar, India, where an experiment introduced solar microgrids and varied their price across 100 village-level markets for two and a half years. Combining the experiment with detailed survey data, we model household choices over a four-year period in which electrification rates leapt from 27% to 64%. Household surplus from electrification tripled, with gains due nearly as much to off-grid solar as to the subsidized grid; the surplus from electrification is 3–5× greater than from any single source. Nonetheless, we project future electrification will come mainly from the grid, as households prefer it as they grow wealthier.

The use of air quality monitoring networks to inform urban policies is critical, especially where populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference-grade monitors at scale — only 33 reference-grade monitors serve all of Delhi (1,500 sq km, 15 million residents). We describe a high-precision spatio-temporal prediction model that derives fine-grained pollution maps from two years of data from a low-cost network of 28 custom-designed portable sensors. Combining message-passing recurrent neural networks with conventional spatio-temporal geostatistics, the model achieves high accuracy despite high data variability and intermittent availability, predicting within 1-hour windows at 9.4–10.5% mean absolute percentage error. These fine-grained maps offer a path to citizen-driven low-cost monitoring of hazardous urban air quality.

Common resources may be managed with inefficient policies for the sake of equity. We study how rationing the commons shapes the efficiency and equity of resource use, in the context of agricultural groundwater use in Rajasthan, India. We find that rationing binds on input use, such that farmers, despite trivial prices for water extraction, use roughly the socially optimal amount of water on average. The rationing regime is still grossly inefficient, because it misallocates water across farmers, lowering productivity. Pigouvian reform would increase agricultural surplus by 12% of household income, yet fall well short of a Pareto improvement over rationing.

Domestic mobility restrictions to control the spread of COVID-19 are widespread in developing countries, and have trapped millions of migrant workers in hotspot cities. In this paper we show that bans can increase cumulative infections relative to a counterfactual without restrictions. A SEIR model shows that bans' impacts are nonlinear in duration. We test this hypothesis empirically using a natural experiment in India as well as data from China, Indonesia, the Philippines, South Africa, and Kenya. Although very short and very long restrictions limit the spread of disease, moderately lengthy restrictions substantially increase infections. This underscores the importance of considering duration in mobility-restricting policy decisions in developing countries.

Hotter years are associated with lower economic output in developing countries. We show that the effect of temperature on labor is an important part of the explanation. Using microdata from selected firms in India, we estimate reduced worker productivity and increased absenteeism on hot days. Climate control significantly mitigates productivity losses. In a national panel of Indian factories, annual plant output falls by about 2% per degree Celsius. This response appears to be driven by a reduction in the output elasticity of labor. Our estimates are large enough to explain previously observed output losses in cross-country panels.

This paper seeks to explain why billions of people in developing countries either have no access to electricity or lack a reliable supply. We present evidence that these shortfalls are a consequence of electricity being treated as a right and that this sets off a vicious four-step circle. In step 1, because a social norm has developed that all deserve power independent of payment, subsidies, theft, and nonpayment are widely tolerated. In step 2, electricity distribution companies lose money with each unit of electricity sold and in total lose large sums of money. In step 3, government-owned distribution companies ration supply to limit losses by restricting access and hours of supply. In step 4, power supply is no longer governed by market forces and the link between payment and supply is severed, reducing customers' incentives to pay. The equilibrium outcome is uneven and sporadic access that undermines growth.

This paper provides field evidence from India examining changes in electricity consumption in response to various behavioral interventions. I study the impact of (i) weekly reports with peer comparisons of electricity use; (ii) reports augmented with monetary incentives to reduce consumption; and (iii) price variation. I estimate consumption changes using a randomized controlled trial in conjunction with a quasi-experiment. Households provided reports alone reduced summer-season consumption by 7 percent. The price elasticity identified from cross-sectional and time-series variation was about −0.56; against this benchmark, the impact of peer comparisons alone was equivalent to raising tariffs by about 12.5 percent. Counter-intuitively, when weekly reports were augmented with monetary incentives rewarding conservation, households no longer reduced consumption. Households receiving reports also show higher price elasticity relative to controls.

India's population is exposed to dangerously high levels of air pollution. Using a combination of ground-level in-situ measurements and satellite-based remote sensing data, this paper estimates that 660 million people — over half of India's population — live in areas that exceed the Indian National Ambient Air Quality Standard for fine particulate pollution. Reducing pollution in these areas to achieve the standard would, we estimate, increase life expectancy for these Indians by 3.2 years on average, for a total of 2.1 billion life-years. We outline directions for environmental policy to start achieving these gains.

Regulatory regimes that have increased household energy efficiency are of widespread interest to policymakers today. A prominent example is California, where residential electricity intensities have stayed near constant since the 1970s, in sharp contrast to nationwide trends in the United States. A structural model of residential energy consumption is used to show that using energy intensities alone to evaluate the success of California's efficiency programs is misleading and glosses over important policy-independent factors. We quantify the effects of price, climate conditions and demographic characteristics on energy consumption in California, and provide evidence of split-incentive considerations in residential energy consumption patterns. We conclude that while state policy may have had some effect on efficiency, caution needs to be exercised in using the California example to inform expectations from similar measures in other regions.

Real-time information feedback delivered via technology has been reported to produce up to 20 percent declines in residential energy consumption, though estimates vary widely. In this study we conduct a field experiment to estimate the impact of a real-time feedback technology (Google PowerMeter). Access to feedback leads to an average reduction in household electricity consumption of 5.7 percent, and significant declines persist for up to four weeks. Examining time-of-day effects, the largest reductions are observed initially at all times of day, but as time passes the morning and evening intervals show larger reductions. We find no convincing evidence that household characteristics explain heterogeneity in our treatment effects.

This analysis traces U.S. transport CO2 emissions in combustion by mode for 1960–2008. Changes in emissions are divided into components related to overall population and economic growth, transport mode shift, changes in the ratio of fuel used to passenger- or tonne-km of activity, and changes in the CO2 content of fuels. Where data permit we show how changes in vehicle utilization affected CO2 emissions. A Log-Mean Divisia Index and Laspeyres decompositions of the 1960–2008 changes are calculated. From this decomposition we speculate to what extent the factors associated with increases in CO2 emissions since 1960 would be important in the future, and what other factors could reduce emissions.

Fertility decline, driven by the one-child policy, and son preference have contributed to an alarming difference in the number of live male and female births in China. We present a quantitative model in which people choose to sex-select because they perceive that married sons are more valuable than married daughters. Due to the predominant patrilocal kinship system in China, daughters-in-law provide valuable emotional and financial support, enhancing the perceived present value of married sons. We argue that inter-generational transfer data will help ascertain the extent to which economic schemes (such as pension plans for families with no sons) can curtail the increasing sex ratio at birth.

In this article we apply the independent component analysis technique for mining spatio-temporal data. The technique is applied to mine for patterns in weather data using the North Atlantic Oscillation (NAO) as a specific example. We find that the strongest independent components match the observed synoptic weather patterns corresponding to the NAO. We also validate our results by matching the independent component activities with the NAO index.