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This paper explores the application of machine learning to enhance our understanding of water accessibility issues in underserved communities called Colonias located along the northern part of the United States - Mexico border. We analyzed more than 2000 such communities using data from the Rural Community Assistance Partnership (RCAP) and applied hierarchical clustering and the adaptive affinity propagation algorithm to automatically group Colonias into clusters with different water access conditions. The Gower distance was introduced to make the algorithm capable of processing complex datasets containing both categorical and numerical attributes. To better understand and explain the clustering results derived from the machine learning process, we further applied a decision tree analysis algorithm to associate the input data with the derived clusters, to identify and rank the importance of factors that characterize different water access conditions in each cluster. Our results complement experts' priority rankings of water infrastructure needs, providing a more in-depth view of the water insecurity challenges that the Colonias suffer from. As an automated and reproducible workflow combining a series of tools, the proposed machine learning pipeline represents an operationalized solution for conducting data-driven analysis to understand water access inequality. This pipeline can be adapted to analyze different datasets and decision scenarios., 26 pages, 7 figures, accepted by Computers, Environment and Urban Systems (CEUS)

This paper evaluates own-price dynamics in taxing environmental externalities. We exploit a natural experiment that exposed some firms to a large and persistent price increase for groundwater, a setting characterized by incomplete markets. Using five years of post-treatment data on farm-level water use, we find that water conservation doubles between the first and fifth year of the tax. Failure to account for dynamics in policies designed to manage groundwater will mischaracterize the price elasticity of demand and introduce efficiency costs.

Presentation18344

In California, droughts are likely to become more frequent, longer, and more intense in the future, posing increasing challenges for water management, and raising the stakes for effective drought response. This project aims to help state water governance and decision-making structures adapt to the changing climatic reality. In a companion report in this volume, we analyzed the strategies the State Water Resources Control Board (Board) used for water rights administration and oversight during the last four major statewide droughts. Our findings suggest that more proactive planning and preparation, enabling reduced reliance on in-drought improvisation, would improve the Board’s future drought responses. This report builds on that retrospective analysis with specific recommendations. Our vision is simple: During droughts, California’s limited water supplies should be allocated among different human and environmental water uses transparently, efficiently, and predictably, in accordance with the priorities that flow from state and federal law. We suggest a structured means of implementing this vision that emphasizes proactive drought preparations. At the core is a contingency-based framework designed to support more timely and effective drought decision making. A suite of complementary actions aims to reduce uncertainty and lay the groundwork for improved water rights administration and oversight in future droughts. These actions include making key policy decisions that affect drought response in advance, strategically improving decision-related information, maximizing learning from droughts, prioritizing water rights enforcement between droughts, and capitalizing on the many synergies that exist between the Board’s drought and non-drought work to achieve better water management outcomes, greater clarity for water users, and more efficient use of state resources. We view these actions as crucial components of effective climate adaption for California and encourage the Board to begin implementing them now, so that it is better prepared to face the challenges the next drought brings.

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This paper uses a finite mixture logit (FML) model to investigate the heterogeneity of preferences of beach users for water quality at beaches in Southern California. The results are compared with conventional approaches based conditional logit (CL) and random parameters logit (RPL). The FML approach captures variation in preferences by modeling individual recreator preferences as a mixture of several distinct preference groups, where group membership is a function of individual characteristic and seasonal variables. The FML parameter estimates are used to calculate welfare measures for improvements in beach quality through a reduction of water pollution. These bound the traditional CL and RPL mean welfare estimates, and have the advantage of highlighting the distribution of the population sample's preferences. The data indicate the existence of four representative preference groups. As a result, willingness to pay measures for improvements in water quality can be weighted across individuals to calculate the distribution of individual welfare measures. One of the groups is people who go to the beach with small children. An interesting finding is that these people have a lower mean WTP for improving water quality than people who go without a small child. This may well be an example of cognitive dissonance: parents find they go to the beach more often than others who don't have small children, since that keeps the children occupied and happy, and they adapt their perception of the water quality to be consistent with their behavior. Previous environmental and resource economic applications of the FML have been limited to applications with small choice sets (6) and group membership variables (4). This paper extends the FML model through the estimation of a large (51) choice set with 9 membership variables. This application is the first to incorporate seasonal variables into the group membership function to capture seasonal heterogeneity.

Economists have for decades advocated transferable property rights, in which the total annual allowable catch is divided into shares, which are then tradable, as a market mechanism to decrease overcapacity. Tradable property rights, or individual transferable quotas (ITQs), are a significant departure from command-and-control regulations that impose combinations of gear restrictions, season limitations, license limitations and trip limits. While in theory tradable property rights have clear economic advantages over command and control, Federal fisheries managers face the challenge of evaluating and ranking regulatory options in a world of complexity and uncertainty. The goal of this project is to describe the changes in the composition of the Mid-Atlantic surf clam fishery, which has been regulated with first command and control and then tradable property rights. The data analysis indicates that while there were changes in the structure of the clam industry, the introduction of tradable property rights did not lead to a redistribution of welfare across different types of participants in the industry.

ObjectiveTo determine the relative importance members of the US public place on different patient attributes in triage decisions about who should receive the last available intensive care unit (ICU) bed.MethodsA discrete choice experiment was conducted with a nationally representative sample of 2000 respondents from the YouGov internet panel of US households. Respondents chose which of three hypothetical patients with COVID-19 should receive an ICU bed if only one were available. The three patients differed in age, gender, Alzheimer’s-like disability and probability of survival if the patient received the ICU bed. An experimental design varied the values of the four attributes of the three hypothetical patients with COVID-19 that a respondent saw in four choice tasks.ResultsThe most important patient attribute to respondents was the probability the patient survives COVID-19 if they get the ICU bed (OR CI: 4.41 to 6.91). There was heterogeneity among different age groups of respondents about how much age of the patient mattered. Respondents under 30 years of age were more likely to choose young patients and old patients, and less likely to select patients aged 40–60 years old. For respondents in the age group 30–49 years old, as the age of the patient declined, their preference for saving the patient declined modestly in a linear fashion.ConclusionsRespondents favoured giving the last ICU bed available to the patient with the highest probability of surviving COVID-19. Public opinion suggests a simple guideline for physician choices based on likelihood of survival as opposed to the number of life-years saved. There was heterogeneity among respondents of different age groups for allocating the last ICU bed, as well as to the importance of the patient having an Alzheimer’s-like disability (where religion of the respondent is important) and the gender of the patient (where the gender and racial identity are important).

The evaluation of health care programmes is commonly approached with stated preference methods such as contingent valuation or discrete choice experiments. These methods provide useful information for policy decisions involving health regulations and infrastructures for health care. However, econometric modelling of these data usually relies on a number of maintained assumptions, such as the use of the compensatory or random utility maximization rule. On the other hand, health policy issues can raise emotional concerns among individuals, which might induce other types of choice behaviour. In this paper we consider potential deviations from the general compensatory rule, and how these deviations might be explained by the emotional state of the subject. We utilized a mixture econometric model which allows for various potential decisions rules within the sample, such as the complete ignorance, conjunctive rule and satisfactory rules. The results show that deviations from the full linear compensatory decision rule are predominant, but they are significantly less observed for those subjects with a medium emotional state about the issue of caring for the health state of the elderly. The implication is that the emotional impact of health policy issues should be taken into account when making assumptions of individual choice behaviour in health valuation methods.