Water is an essential good for human life but there is controversy on whether it should be allocated using markets. In 1966, the irrigation community in Mula (Murcia, Spain) switched from a market institution, an auction which had been in place in the town for over 700 years, to a system of xed quotas with a ban on trading to allocate water from the town's river. We present a model in which farmers face liquidity constraints (LC) to explain why the new, non-market institution is more ecient. We show that farmers underestimate water demand in the presence of LC. We use a dynamic demand model and data from the auction period to estimate both farmers' demand for water and their nancial constraints, thus obtaining unbiased estimates. In our model, markets achieve the rst-best allocation only in the absence of LC. In contrast, quotas achieve the rst-best allocation only if farmers are homogeneous in productivity. We compute welfare under both types of institutions using the estimated parameters. We nd that the quota is more ecient than the market. This result implies that one should be cautious in advocating for water markets, especially in developing areas where LC might be a concern.JEL Codes: D02, D53, G14, Q25
We find that perceptions of crime and individual experience with crime (crime victimization) are positively correlated with left-wing beliefs within countries, controlling for income and other correlates of ideology, in a sample for Latin American countries in the mid-1990's.JEL: P16, K42, E62, Keywords: beliefs, crime, fairness, legitimacy of market.
This paper develops a structural model of urban travel to estimate long-run price elasticities. A dynamic discrete choice demand model with switching costs is estimated, using a panel dataset with public market-level data on automobile and public transit use for Chicago. The estimated model shows that long-run own-(automobile) and cross-(transit) price elasticities are more elastic than short-run elasticities, and that elasticity estimates from static and myopic models are downward biased. The estimated model is used to evaluate the response to a gasoline tax. Static and myopic models mismeasure long-run substitution patterns, and could lead to incorrect policy decisions.
We investigate the role of punishment progressivity and individual characteristics in the determination of crime. To analyze welfare implications we model individuals' response to judges' optimal punishment in a dynamic setting. We introduce two distinctive features motivated by our empirical setting. First, judges rarely imposes maximum punishment for first time offenders. Instead, we observe low fines (or just a warning) even when crime detection technology is efficient and punishment is not costly. We account for this by allowing an unobservable (to the judge) individual state to be correlated with a public signal (the environment). This generates an optimal punishment that is conditional on individual observables. Second, judges punishments follow a progressive system: conditioning on type, recidivists are punished harsher than first-time offenders for the same crime. We account for these dynamics by introducing a persistent unobservable (to the judge) component. Judges update their beliefs about individuals depending on whether they committed a crime in the previous period; this gives rise to progressivity in the optimal punishment system. For the empirical analysis we examine a novel trial data set from a self-governed community of farmers in Southern Spain. We find that judges vary the degree of imposed punishments based on individual characteristics-such as when victims or accused have a Don honorific title indicating they are wealthy. Recidivists are punished harsher than first time offenders.JEL Codes: D44, C13, L14, K14, K42
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.