ResearchPad - economic-sciences https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Impact of <i>Xylella fastidiosa</i> subspecies <i>pauca</i> in European olives]]> https://www.researchpad.co/article/N35024fc2-4751-4bfa-b381-3fd461109092 Xylella fastidiosa is the causal agent of plant diseases that cause massive economic damage. In 2013, a strain of the bacterium was, for the first time, detected in the European territory (Italy), causing the Olive Quick Decline Syndrome. We simulate future spread of the disease based on climatic-suitability modeling and radial expansion of the invaded territory. An economic model is developed to compute impact based on discounted foregone profits and losses in investment. The model projects impact for Italy, Greece, and Spain, as these countries account for around 95% of the European olive oil production. Climatic suitability modeling indicates that, depending on the suitability threshold, 95.5 to 98.9%, 99.2 to 99.8%, and 84.6 to 99.1% of the national areas of production fall into suitable territory in Italy, Greece, and Spain, respectively. For Italy, across the considered rates of radial range expansion the potential economic impact over 50 y ranges from 1.9 billion to 5.2 billion Euros for the economic worst-case scenario, in which production ceases after orchards die off. If replanting with resistant varieties is feasible, the impact ranges from 0.6 billion to 1.6 billion Euros. Depending on whether replanting is feasible, between 0.5 billion and 1.3 billion Euros can be saved over the course of 50 y if disease spread is reduced from 5.18 to 1.1 km per year. The analysis stresses the necessity to strengthen the ongoing research on cultivar resistance traits and application of phytosanitary measures, including vector control and inoculum suppression, by removing host plants.

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<![CDATA[Delayed negative effects of prosocial spending on happiness]]> https://www.researchpad.co/article/N6991889e-db1b-4a5b-90fc-496587c29b4d

Significance

Governments around the world increasingly acknowledge the role of happiness as a societal objective and implement policies that target national wellbeing levels. Knowledge about the determinants of happiness, however, is still limited. A longstanding candidate is prosocial behavior. Our study empirically investigates the causal effect of prosocial behavior on happiness in a high-stakes decision experiment. While we confirm previous findings of a positive effect in the short term, our findings distinctly show that this effect is short lived and even reverses after some time. This study documents that prosocial behavior does not unequivocally increase happiness because prosocial spending naturally requires giving up something else, which may decrease happiness in its own right.

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<![CDATA[Common power laws for cities and spatial fractal structures]]> https://www.researchpad.co/article/N299a082f-68ac-4717-9c9d-9d3ac211643a

Significance

Socioeconomic attributes of cities (e.g., wages, education, industrial diversity, and crime) exhibit strong correlations with city size, as measured by population. It has thus been a major research objective to characterize and explain city-size distributions. Whereas city-size distributions are known to exhibit power laws at the country level, we find that they exhibit strikingly similar power laws when examined along a spatial hierarchy of regions within a country. Such a high degree of similarity could not be obtained if city sizes were generated by a random (growth) process. The fact that this regularity emerges along spatial dimensions in a recursive manner suggests the existence of spatial fractal structures. However, such estimated common power laws differ markedly across countries.

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<![CDATA[Predicting high-risk opioid prescriptions before they are given]]> https://www.researchpad.co/article/Nee6a2aa2-0321-49a2-8dc5-ab3aaedb6da9

Significance

We describe a hypothetical preventative policy solution to address the opioid crisis using an integrated administrative database developed in collaboration with the State of Rhode Island. Machine learning algorithms trained on observations of past opioid prescription accurately predict adverse opioid-related outcomes among Medicaid recipients even before their initial opioid prescription is written. Although these models are limited to individuals who have been selected for opioid prescription, they suggest a feasible path forward for using administrative data to inform prescription risk. Under the assumption that the cost of diverting individuals from opioid therapy to an alternative therapy is homogenous across individuals, we simulate a hypothetical policy for restricting opioid prescriptions based on risk that is likely net-beneficial given current cost estimates.

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<![CDATA[Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm]]> https://www.researchpad.co/article/5c26b51dd5eed0c484764bce

Significance

In the present study, we applied reinforcement learning models that are not classically used in experimental economics to a multistep exchange task of the emergence of money derived from a classic search-theoretic paradigm for the emergence of money. This method allowed us to highlight the importance of counterfactual feedback processing of opportunity costs in the learning process of speculative use of money and the predictive power of reinforcement learning models for multistep economic tasks. Those results constitute a step toward understanding the learning processes at work in multistep economic decision-making and the cognitive microfoundations of the use of money.

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