Mary Riddel

Candidate eco-labeling and senate campaign contributions

Abstract This paper analyzes campaign contributions from environmental political action committees (E-PACs) to Senate candidates. A two-stage empirical model of candidate donations and the likelihood of winning the election is developed that allows for endogeneity between donations and the expected election outcome. The results reveal that E-PACs choose to donate to candidates that are both likely to win the election and to advocate environmental positions once elected. Funding strategies differ for challengers and incumbents. Incumbent funding typically supports maintaining the existing level of environmental advocacy in the Senate by offering contributions for continued pro-environmental votes and other political support. Challenger funding is targeted at unseating vulnerable hostile incumbents. The results also suggest that E-PACs—such as the Sierra Club and the League of Conservation Voters—have been successful in influencing Senate election outcomes and promoting environmental advocacy in the Senate. E-PAC contributions act to “eco-label” candidates and contribute to their election success. The results also show that although moderate levels of environmental advocacy are beneficial to a candidate, voters reject what they perceive to be excessively “green” candidates.

Housing Market Dynamics Under Stochastic Growth: An Application to the Housing Market in Boulder, Colorado

Recent housing-market studies have modeled slow stock and price adjustment with some success. However, the empirical procedures used in these models break down if housing stocks or prices are driven by stochastic growth. In this paper I suggest an error-correction model for analyzing housing supply and demand under conditions of stochastic growth for a regional housing market. The model is applied to the housing market in Boulder, Colorado from 1981 through 1995—a period of rapid growth in housing values in the area. Long-run housing supply and demand are shown to be inelastic with respect to changes in the price of housing. The results indicate that developers respond more accurately to housing-market disequilibrium attributable to supply-side disturbances than to disturbances generated by changes in the demand for housing. On the other hand, price appreciation is driven primarily by demand disturbances. Copyright 2000 Blackwell Publishers

Valuing Environmental Changes in the Presence of Risk: A Review and Discussion of Some Empirical Issues

The theory of ex-ante welfare measures is well establlished and has been addressed extensively in papers relating to the valuation of environmental resources when environmental variables have a random component. However, there have been many new developments in incorporating risks and uncertainty into economic models, and perhaps more importantly, there seems to be remaining confusion as to how to empirically implement such models. To date, a variety of estimation techniques have been utilized, with varying degrees of success in deriving an ex-ante welfare measure under risk. This manuscript assesses the state of the art by discussing the sources of risk, uncertainty, and error in utility models that incorporate risk. We are most interested in how to incorporate these ideas into empirical models and we examine how econometric estimation methods can best be used to obtain ex-ante welfare measures. We also present the current thinking on endogenous versus exogenous risks as well as subjective versus “expert” risk measures, and discuss some of the advantages and disadvantages likely to be encountered when using subjective-based risk estimates in empirical applications based on alternatives to the expected utility models.

Willingness to pay to avoid arsenic-related risks: a special regressor approach

Subjective probabilities often better explain behaviours than science-based risks. Incorporating them into a behavioural model has important ramifications for environmental and health policy, but if these risks are measured with error or endogenous, then doing so leads to possible troubles in interpretation of results. Our application is to mortality risks associated with arsenic found in drinking water in various spots in the United States. The behaviour of interest is a yes/no response to a willingness to pay (WTP) question to reduce these risks. We apply the special regressor approach to handle the endogeneity and measurement error in the WTP model and find that doing so leads to different implications for policy that could be pursued to reduce the risks.

Finite sample properties of nonstationary binary response models: A Monte Carlo analysis

This paper investigates the finite sample distributions of maximum likelihood estimators for nonstationary probit models. This model relates an I (1) variable x_t to a latent I (1) variable y_t^\ast . The binary variable, y_t , is coded as a one if y_t^\ast exceeds some threshold, \alpha , and as a zero otherwise. We find that, analogous to standard OLS models, commonly used tests statistics almost always reject the null hypothesis of no relationship between x_t , and a latent y_t , even when they are, in fact, generated by independent random walks. However, if x_t and y_t^\ast are cointegrated, the sampling distributions of the parameter estimates are better behaved and standard Z and Wald test statistics are consistent.

RISK MISPERCEPTIONS AND SELECTION IN INSURANCE MARKETS: AN APPLICATION TO DEMAND FOR CANCER INSURANCE

We test for the influence of optimism on selection in a hypothetical cancer‐insurance market using a survey of 474 subjects. We elicit perceptions of baseline cancer risk and control efficacy and combine these with subject‐specific cancer risks predicted by the Harvard Cancer Risk Index to develop measures of baseline and control optimism. Following Fang, Keane, and Silverman ([Fang, H., 2008]), we hypothesize that a variable may lead to advantageous selection if it is positively correlated with both prevention effort and demand for insurance. We find evidence of selection from both baseline and control optimism, but little evidence of selection from cognitive ability and risk aversion. We then estimate a model that allows us to classify subjects according to their excess risk‐reducing effort and excess insurance uptake that occurs solely because of baseline and control optimism. We find subjects who overinsure relative to a subject with accurate risk beliefs are likely to have lower expected treatment costs, ceteris paribus. This indicates that on net, baseline and control optimism leads to advantageous selection in our sample.

How Do Long-Shot Outcomes Affect Preferences for Climate-Change Mitigation?

This article takes a fresh look at how public uncertainty influences willingness to pay (WTP) for climate-change mitigation programs. I elicit subjective distribution functions over future global mean temperatures and WTP for mitigation. I find, not surprisingly, that subjects, on average, view climate change as a “bad,” and the higher the expected temperature increase they perceive, the more they are willing to pay for mitigation. Subjects are generally risk averse, so that they are WTP more for mitigation programs when the outcome of the program is more certain. The skew of the subjective climate-change distribution also affects preferences, so that aversion to catastrophic outcomes is an important component of WTP for mitigation. I find that models that ignore skew are likely to underestimate the influence of uncertainty on willingness to pay for climate-change mitigation.