How much versus who: which social norms information is more effective?

We conduct an experiment to investigate how different types of information about social norms affect individuals’ stated contributions to a specific pro-environment program, a student ‘green fee’, in the context of a referendum. Compared to students that receive no information about peer contributions, on average, students that receive information about the dollar value range of contributions at peer institutions contribute less while students that learn about the high percentage of students voting ‘yes’ on green fee programs at peer institutions contribute more. The results are economically significant as the absolute values of both effects represent approximately 25% of average contributions. These results suggest that information about participation rates can be more effective than information about dollar amounts in encouraging contributions to environmental initiatives. Of interest to stated preference researchers, we find that results do not change when controlling for self-selection into survey completion.     

How much does schooling disutility matter?

Studies on structural education choice models are often inconsistent in choosing whether and how to include a disutility of education, especially in an environment with risk and wealth inequality. We show that adding a disutility term to the education decision, a human capital investment option, is equivalent to assuming a relationship between wealth, risk, and education. Utility gain from education is increasing in the riskiness of future consumption. A riskier environment further propels an agent to choose the human capital investment option that maximizes future income. If the degree of risk increases heterogeneously across multiple human capital investment options, risk aversion and the precautionary savings motive can compound or negate each other depending on which option has a greater increase in risk.     

How much are electric vehicles driven?

The prospect for electric vehicles as a climate change solution hinges on their ability to reduce gasoline consumption. But this depends on how many miles electric vehicles are driven and on how many miles would have otherwise been driven in gasoline-powered vehicles. Using newly-available U.S. nationally representative data, this paper finds that electric vehicles are driven considerably fewer miles per year on average than gasoline-powered vehicles. The difference is highly statistically significant and holds for both all-electric and plug-in hybrid vehicles, for both single- and multiple-vehicle households, and both inside and outside California. The paper discusses potential explanations and policy implications. Overall, the evidence suggests that today’s electric vehicles imply smaller environmental benefits than previously believed.     

How much of a threat to economic growth is a mature AIDS epidemic?

The article models the impact on economic growth of HIV/AIDS when the epidemic is in a mature phase, in contrast with previous studies focused on periods of expansion as it is typically the case in African countries. Simulations for Honduras, the epicentre of the epidemic in Central America, show that AIDS is not likely to threaten economic growth neither through labour nor capital accumulation channels. Impacts are estimated between 0.007 and 0.27% points of Gross Domestic Product (GDP) growth annually for the period 2001 to 2010. Increasing spending on prevention, higher public treatment subsidies and increasing treatment access will not jeopardize economic growth prospects. Critical factors that slash economic growth in Africa (such as human capital reductions and shifts in relative skills) are not strong in Honduras.     

How much should we trust regression-kink-design estimates?

In a regression kink (RK) design with a finite sample, a confounding smooth nonlinear relationship between an assignment variable and an outcome variable around a threshold can be spuriously picked up as a kink and results in a biased estimate. In order to investigate how well RK designs handle such confounding nonlinearity, I firstly implement Monte Carlo (MC) simulations and then study the effect of fiscal equalization grants on local expenditure in Japan using an RK design. Results in both the MC simulations and the empirical application suggest that RK estimation without covariates can be easily biased, and this problem can be mitigated by adding observed covariates to the regressors. On the other hand, a smaller bandwidth or a higher-order polynomial, even a quadratic polynomial, tends to result in imprecise estimates although they may be able to reduce estimation bias. In sum, RK estimation with confounding nonlinearity often suffers from bias or imprecision, and estimates are credible only when relevant covariates are controlled for. I also examine how placebo RK estimation can effectively address these issues.