Climate policies vary widely across countries, with some countries imposing stringent emissions policies and others doing very little. When climate policies vary across countries, energy-intensive industries have an incentive to relocate to places with few or no emissions restrictions, an effect known as leakage. Relocated industries would continue to pollute but would be operating in a less desirable location. We consider solutions to the leakage problem in a simple setting where one region of the world imposes a climate policy and the rest of the world is passive. We solve the model analytically and also calibrate and simulate the model. Our model and analysis imply: (1) optimal climate policies tax both the supply of fossil fuels and the demand for fossil fuels; (2) on the demand side, absent administrative costs, optimal policies would tax both the use of fossil fuels in domestic production and the domestic consumption of goods created with fossil fuels, but with the tax rate on production lower due to leakage; (3) taxing only production (on the demand side), however, would be substantially simpler and almost as effective as taxing both production and consumption, because it would avoid the need for border adjustments on imports of goods; and (4) the effectiveness of the latter strategy depends on a low foreign elasticity of energy supply, which means that forming a taxing coalition to ensure a low foreign elasticity of energy supply can act as a substitute for border adjustments on goods.
The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments’ ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.
The economic impacts of climate change are highly uncertain. Two of the most important uncertainties are the sensitivity of the climate system and the so-called damage functions, which relate climate change to economic costs and benefits. Despite broad awareness of these uncertainties, it is unclear which of them is most important, especially at the regional level. Here we construct regional damage functions, based on two different global damage functions, and apply them to two climate models with vastly different climate sensitivities. We find that uncertainty in both climate sensitivity and aggregate economic damages per degree of warming are of similar importance for the global economic impact of climate change, with the decrease in global economic productivity ranging between 4% and 24% by the end of the century under a high-emission scenario. At the regional level, however, the effects of climate change can vary even more substantially, depending both on a region's initial temperature and the amount of warming it experiences, with some regions gaining in productivity and others losing. The ranges of uncertainty are therefore potentially much larger at a regional level. For example, at the end of the century, under a high-emission scenario, we find that India's productivity decreases between 13% and 57% and Russia's increases between 24% and 74%, while Germany's change in productivity ranges from an increase of 8% to a decrease of 4%. Our findings emphasize the importance of including these uncertainties in estimates of future economic impacts, as they are vital for the resulting impacts and thus policy implications.
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.
We exploit recent molecular genetics evidence on the genetic basis of arsenic excretion and unique information on family links among respondents living in different environments from a large panel survey to uncover the hidden costs of arsenic poisoning in Bangladesh. We provide for the first time estimates of the effects of the ingestion and retention of inorganic arsenic on direct measures of cognitive and physical capabilities as well as on the schooling attainment, occupational structure, entrepreneurship, and incomes of the rural Bangladesh population. We also provide new estimates of the effects of the consumption of foods grown and cooked in arsenic-contaminated water on individual arsenic concentrations. The estimates are based on arsenic biomarkers obtained from a sample of members of rural households in Bangladesh who are participants in a long-term panel survey following respondents and their coresident household members over a period of 26 years.
I consider the aggregate impact of low intermediate input intensity in the agricultural sector of developing countries. In a dynamic general equilibrium model with idiosyncratic shocks, incomplete markets, and subsistence requirements, farmers in developing countries use fewer intermediate inputs because it limits their exposure to uninsurable shocks. The calibrated model implies that Indian agricultural productivity would increase by 16% if markets were complete, driven by quantitatively important increases in both the average real intermediate share and measured TFP through lower misallocation. I then extend the results to consider the importance of risk in other contexts. First, the introduction of insurance decreases cross-country differences in agricultural labour productivity by 14%. Second, scaling the introduction of improved seeds to decrease downside risk reduces inequality by reallocating resources from rich to poor farmers via equilibrium effects. This reallocation substantially increases aggregate productivity relative to what would be expected from extrapolating the partial equilibrium impact.
Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion. Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low.
Rural isolation can limit access to basic services and income-generating opportunities. Among some communities, rainfall induced flooding can cause increased uncertainty where first-mile transportation infrastructure is limited. In Rwanda, this challenge is apparent, where 90% of the population below the poverty line live in rural areas that are typically mountainous with frequent flooding - events that may be increasing in frequency and severity as the climate changes. To reduce these transportation barriers, the non-profit organization Bridges to Prosperity (B2P) plans to construct hundreds of trailbridges in Rwanda between 2018 and 2023. This scale of rural infrastructure services presents an opportunity for experimental investigation of the effects of these new trailbridges on economic, health, agricultural and education outcomes in rural communities. In this paper, we present a cohort study evaluating the potential community benefits of rural trailbridges - including economic, health and social outcomes for Rwandan communities experiencing environmental change. We examined households living near 12 trailbridge sites and 12 comparison sites over February 2019–March 2020. We found that labor market income increased by 25% attributable to the trailbridges. We did not observe any significant effects on agricultural income, education or health outcomes, however given the small sample and short duration of this study we anticipate observing additional outcomes within the recently started 200 site, 4 year trial.
The integration of markets may improve efficiency by lowering costs or reducing local market power. India, seeking to reduce electricity shortages, set up a new power market, in which transmission constraints sharply limit trade between regions. During congested hours, measures of market competitiveness fall and firms raise bid prices. I use confidential bidding data to estimate the costs of power supply and simulate market outcomes with more transmission capacity. Counterfactual simulations show that transmission expansion increases market surplus by 22 percent, enough to justify the investment. One-third of this gain is due to sellers' response to a more integrated grid.
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, thus reducing customers' incentives to pay. The equilibrium outcome is uneven and sporadic access that undermines growth.
We measure the impact of increasing integration between rural villages and outside labor markets. Seasonal flash floods cause exogenous and unpredictable loss of market access. We study the impact of new bridges that eliminate this risk. Identification exploits variation in riverbank characteristics that preclude bridge construction in some villages, despite similar need. We collect detailed annual household surveys over three years, and weekly telephone followups to study contemporaneous effects of flooding. Floods decrease labor market income by 18 percent when no bridge is present. Bridges eliminate this effect. The indirect effects on labor market choice, farm investment, and savings are quantitatively important and consistent with the predictions of a general equilibrium model in which farm investment is risky, and households manage labor market risk and agricultural risk simultaneously. In the calibrated model, the increase in consumption-equivalent welfare is substantially larger than the increase in income due to the ability to mitigate risk.