Voting on Growth Control Measures: Preferences and Strategies

Citizens of many California cities and counties have sought to restrict the rate of population growth in their localities. In 1988, Citizens for Limited Growth used the initiative process to place a pair of growth control measures on the ballot in the City and County of San Diego, respectively. The City Council and Board of Supervisors responded by placing less stringent, competing measures on the same ballot. This paper analyzes voting data from this election to examine the nature of support for such measures. We find strong support for the hypotheses that whites, homeowners, liberal/environmentalists, and those exposed to high levels of traffic congestion are more likely to favor growth controls. This paper also investigates the behavior of voters when they confront competing propositions concerning the same issue on the same ballot, and finds strong evidence of strategic voting

Voting on Growth Control Measures: Preferences and Strategies

Citizens of many California cities and counties have sought to restrict the rate of population growth in their localities. In 1988, Citizens for Limited Growth used the initiative process to place a pair of growth control measures on the ballot in the City and County of San Diego, respectively. The City Council and Board of Supervisors responded by placing less stringent, competing measures on the same ballot. This paper analyzes voting data from this election to examine the nature of support for such measures. We find strong support for the hypotheses that whites, homeowners, liberal/environmentalists, and those exposed to high levels of traffic congestion are more likely to favor growth controls. This paper also investigates the behavior of voters when they confront competing propositions concerning the same issue on the same ballot, and finds strong evidence of strategic voting

Heterogeneity of Beliefs and Trade in Experimental Asset Markets

We investigate the relationship between traders' expectations and market outcomes with experimental asset market data. The data show that those who have high price expectations buy more frequently and submit higher bids, and those who hold low price expectations sell more frequently and submit lower bids. Traders who have more accurate expectations achieve greater earnings. Simulations using only belief data reproduce the pricing patterns observed in the market well, indicating that the heterogeneity of expectations is a key to explaining market activity.National Research Fund of Luxembourg [F2R-368 LSF-PMA-13SYSB]12 month embargo; published online: 18 December 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Combining ‘‘real effort’’ with induced effort costs: the ball-catching task

We introduce the “ball-catching task”, a novel computerized task, which combines a tangible action (“catching balls”) with induced material cost of effort. The central feature of the ball-catching task is that it allows researchers to manipulate the cost of effort function as well as the production function, which permits quantitative predictions on effort provision. In an experiment with piece-rate incentives we find that the comparative static and the point predictions on effort provision are remarkably accurate. We also present experimental findings from three classic experiments, namely, team production, gift exchange and tournament, using the task. All of the results are closely in line with the stylized facts from experiments using purely induced values. We conclude that the ball-catching task combines the advantages of real effort tasks with the use of induced values, which is useful for theory-testing purposes as well as for applications

The emergence of altruism as a social norm

Expectations, exerting influence through social norms, are a very strong candidate to explain how complex societies function. In the Dictator game (DG), people expect generous behavior from others even when they cannot enforce any sharing of the pie. Here we assume that people donate following their expectations, and that they update their expectation after playing a DG by reinforcement learning to construct a model that explains the main experimental results in the DG. Full agreement with the experimental results is reached when some degree of mismatch between expectations and donations is added into the model. These results are robust against the presence of envious agents, but affected if we introduce selfish agents that do not update their expectations. Our results point to social norms being on the basis of the generous behavior observed in the DG and also to the wide applicability of reinforcement learning to explain many strategic interactions