Department of Agricultural and Resource Economics

The quality and inequality of US drinking water investments have gained attention after recent environmental disasters in Flint, Michigan, and elsewhere. We compare the formula-based targeting of subsidized loans provided under the Safe Drinking Water Act with the targeting of congressional drinking water earmarks (``pork barrel'' spending).  Earmarks are often critiqued for potentially privileging wealthier and more politically connected communities. We find that earmarks target Black, Hispanic, and low-income communities, partly due to targeting water systems serving large populations. Earmark and loan targeting differ significantly across all the demographics we analyze. Compared to Safe Drinking Water Act loans, earmarks disproportionately target Hispanic communities but not Black or low-income communities.

This paper proposes that strong financial, judicial, and labor market institutions provide comparative advantage in clean industries, and thereby improve a country’s environmental quality. Five complementary tests support this hypothesis. First, industries that depend on institutions are disproportionately clean. Second, strong institutions increase relative exports in clean industries, even conditional on environmental regulation and factor endowments. Third, an industry’s complexity helps explain the link between institutions and clean goods. Fourth, a quantitative general equilibrium model indicates that strengthening a country’s institutions decreases its pollution through relocating dirty industries abroad, though increases pollution in other countries. Fifth, cross-country differences in the composition of output between clean and dirty industries explain more of the global distribution of emissions than differences in the techniques used for production do. The comparative advantage that strong institutions provide in clean industries gives one under-explored reason why developing countries have relatively high pollution levels.

Background: Identifying effective treatments and policies early in a pandemic is challenging because only limited and noisy data are available, and biological processes are unknown or uncertain. Consequently, classical statistical procedures may not work or require strong structural assumptions. An information-theoretic approach can overcome these problems and identify effective treatments and policies. The efficacy of this approach is illustrated using a study conducted at the beginning of the COVID-19 pandemic.

Methods: An information-theoretic inferential approach with and without prior information was applied to the limited data available in the second month (April 24, 2020) of the COVID-19 pandemic. For comparison, a second statistical analysis used a large sample with millions of observations available at the end of the pandemic’s pre-vaccination period (mid-December 2020).

Results: Even with limited data, the information-theoretic estimates performed well in identifying influential factors and helped explain why death rates varied across nations. Later experiments and statistical analyses based on more recent, richer data confirm that these factors contribute to survival.

Conclusions: An information-theoretic statistical technique is a robust method that can overcome the challenges of under-identified estimation problems in the early stages of medical emergencies. It can easily incorporate prior information from theory, logic, or previously observed emergencies.

We study the regulation of open-access resources under long implementation horizons. Our theoretical model clarifies when and how future regulation creates either an anticipatory decline or perverse incentives to accelerate extraction (a "Green Paradox'"). Then, we evaluate the early effects of a major groundwater regulation in California that does not yet bind. We assemble new data and compare within pairs of neighboring agencies that face varying restrictions on extraction. Differences in future regulation do not affect measures of water-intensive investments or groundwater extraction today. The absence of anticipatory response in either direction can be explained by high private discount rates.

This paper evaluates own-price dynamics in taxing environmental externalities. We exploit a natural experiment that exposed some firms to a large and persistent price increase for groundwater, a setting characterized by incomplete markets. Using five years of post-treatment data on farm-level water use, we find that water conservation doubles between the first and fifth year of the tax. Failure to account for dynamics in policies designed to manage groundwater will mischaracterize the price elasticity of demand and introduce efficiency costs.

The hallmark of full risk sharing is that agents' marginal utilities of expenditure (MUEs) have a simple factor structure; a Pareto weight is divided by an aggregate price. Take logarithms and full risk-sharing can be easily tested using panel data with two-way fixed effects. The catch is that we don't directly observe MUEs, and must infer these using data on consumption expenditures. The standard approach to this inference problem is to assume some form of homothetic utility, in which case the MUE is a function of total expenditures and a single price index, and all demands have unit price elasticities. This approach works well when the shocks being tested affect agents' budgets without changing prices; i.e., when the shocks are idiosyncratic. But "covariate" shocks may change relative prices, in which case the standard risk-sharing tests which assume that no demands are inelastic will deliver apparently perverse results.

What is the class of utility structures that allow one to test risk-sharing using only panel data on expenditures and two-way fixed effects, and does this class included non-homothetic preferences which are consistent with more realistic demand responses to changes in relative prices? We obtain this class, which happens to be semi-parametric and nests the usual homothetic specification, but which allows for highly flexible Engel curves, with $n$ parameters corresponding to the income elasticities of $n$ goods. We provide a simple algorithm to infer both these parameters and the agents' MUEs. We compute these using panel data from Uganda, and show that risk-sharing tests of covariate shocks using our computed MUEs deliver sensible results while the standard tests do not.

Concerns around reliability in China’s electricity sector have rekindled interest in a traditional solution: building more coal-fired generation. However, over the past decade China’s electricity sector has seen significant changes in supply costs, demand patterns, and regulation and markets over the past decade, with falling costs for renewable and storage generation, “peakier” demand, and the creation of initial wholesale markets. These changes suggest that traditional approaches to evaluating the economics of different supply options may be outdated. This paper illustrates how a net capacity cost metric – fixed costs minus net market revenues – might be better suited to evaluating supply options in China. Using a simplified example with recent resource cost data, the paper illustrates how, with a net capacity cost metric, solar PV and electricity storage may be a more cost-effective option for meeting demand growth than coal-fired generation.