Knowledge that Transforms

In this review, we discuss three major contributions economists have made to our understanding of the relationship between the environment and individual well-being. First, in explicitly recognizing how optimizing behavior, particularly in the form of residential sorting, can lead to nonrandom assignment of pollution, economists have employed a wide range of quasi-experimental techniques to develop causal estimates of the effect of pollution. Second, economic research has placed a considerable focus on the role of avoidance behavior, which is an important component for understanding the difference between biological and behavioral effects of pollution and for proper welfare calculations. Lastly, economic research has expanded the focus of analysis beyond traditional health outcomes to include measures of human capital, including labor supply, productivity, and cognition. Our review of the quasi-experimental evidence on this topic suggests that pollution does indeed have a wide range of effects on individual well-being, even at levels well below current regulatory standards. Given the importance of health and human capital as an engine for economic growth, these findings underscore the role of environmental conditions as an important factor of production.

Very little. A plethora of integrated assessment models (IAMs) have been constructed and used to estimate the social cost of carbon (SCC) and evaluate alternative abatement policies. These models have crucial flaws that make them close to useless as tools for policy analysis: certain inputs (e.g., the discount rate) are arbitrary, but have huge effects on the SCC estimates the models produce; the models' descriptions of the impact of climate change are completely ad hoc, with no theoretical or empirical foundation; and the models can tell us nothing about the most important driver of the SCC, the possibility of a catastrophic climate outcome. IAM-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading.

Using a unique conjoint data set drawn from 281 college students in Hong Kong, I estimate a random-coefficient discrete choice demand system for Microsoft Office from legal and various illegal sources. Counterfactual results show two things. First, most students would switch to Internet piracy even if the government eradicated street piracy. This explains why software piracy in Hong Kong remains well above 40% despite the government's successful measures to bring down street piracy. Second, the true gain from shutting off all sources of piracy is HK$48.6 (US$6) per person, only 15% of the Business Software Alliance's estimated cost of piracy.

We evaluate the effect of a change in the energy code applied to buildings using residential billing data on electricity and natural gas, combined with data on observable characteristics of each residence. The study is based on comparisons between residences constructed just before and after an increase in the stringency of Florida's energy code in 2002. We find that the code change is associated with a decrease in the consumption of electricity by 4% and natural gas by 6%. We estimate average social and private payback periods that range between 3.5 and 6.4 years.

Endogeneity between health and wealth presents a challenge for estimating causal effects of health shocks. Using a quasi-experimental design, comprising exogenous shocks sustained as bus accident injuries in India, with controls drawn from travelers on the same bus routes one year later, I present new evidence of causal effects on consumption and debt. Using primary household survey data, I find that households faced with shock-related expenditures are able to smooth consumption on food, housing, and festivals, with small reductions in educational spending. Debt was the principal mitigating mechanism households used, leading to significantly larger levels of indebtedness.

Using weekly spot and future commodity prices in Chicago and New York, we construct expected transportation rates for grain between these two cities, expected inventory levels in New York, and realized errors in the expectations of such variables. We incorporate these exogenous commodity market dynamics into Porter's (1983) structural modeling of the Joint Executive Committee Railroad Cartel. As in Porter, we model marginal cost as a parametric function of (instrumented) output, among other factors. Unlike Porter, we model pricing over marginal cost as a nonparametric function of a set of variables, which include expectations of deterministic demand cycles and cartel stability. We estimate the pricing and demand equation simultaneously and semiparametrically. Our estimated weekly markups during periods of cartel stability are shown to reflect optimal collusive pricing over deterministic business cycles, as modeled in Haltiwanger and Harrington (1991). Periods of cartel instability are proven to be triggered by realized mistakes in expectations of New York grain prices.

The authors thank Kathy Nguyen for her support in conducting this experiment and for her assistance in creating the treatment messages and ensuring the experimental design was policy relevant. The authors also thank Herb Richardson for transferring the outcome data, Merlin Hanauer and Juan Jose Miranda for work to compile the data. John List, Subrendu Pattanayak, Laura Taylor, and participants at the 2009 AEA-ASSA meetings and seminars at the Georgia Water Wise Council, the Georgia Association

This paper investigates if the R&D of an industry changes due to the R&D of an industry's suppliers and/or demanders. Using an annual industry-level panel of manufacturing R&D in the United States, I find that regressing the R&D of an industry on the lagged values of another industry's R&D suggests R&D comovement between industries with a strong supplier-demander relationship. Variance decompositions indicate that the R&D of an industry has high forecasting power over the R&D variance of another industry if the two industries share a strong supplier-demander relationship.

This paper examines the implications of a binary action, binary outcome decision problem for estimating risk. We use data on the invasiveness of biological imports to develop the first comparison of two classical methods—maximum likelihood and Bayesian—against a third, the recently developed maximum utility (MU) approach. MU estimation uniquely takes advantage of the structure of the decision problem, which depends on a local rather than global fit to the model. Extending methods to account for an endogenously stratified sample, we show that the MU approach is less sensitive to specification error and can offer significant economic gains under model uncertainty.

This paper investigates the extent of vengeful feelings and their determinants using data on more than 116,000 individuals from 66 countries. Country characteristics as well as personal attributes of the individuals influence vengeful feelings. The magnitude of vengeful feelings is greater for people in countries with low levels of education, low-income countries, and interrupted democracies. Personal education has an impact on vengeful feelings in lower-income countries. The results suggest that some puzzles about individual choice can best be explained by considering the interplay of personal and economic factors.