What Defines Quality of Life? The Gap Between Public Policies and Locally Defined Indicators Among Residents of Kodagu, Karnataka (India)

Improving quality of life (QoL) is one of the main goals of many public policies. A useful tool to measure QoL needs to get a good balance between indicators guided by theories (top-down approach) and indicators defined by local people (bottom-up approach). However, QoL measurement tools often neglect to include elements that define the standard of living at local level. In this paper, we analyse the correspondence between human development index, as an indicator adopted by governments to assess QoL, and the elements defined by local people as important in their QoL, called here local means. Using a free-listing technique, we collected information from 114 individuals from Kodagu, Kartanataka (India), to capture local means defining QoL. We then compared local means with the indicators used by Human development report (HDR) of Karnataka, the main measurement tool of QoL in Kodagu. The list of local means included access to basic facilities and many issues related to agriculture and natural resources management as elements locally defining QoL. We also found that HDR does not capture the means defined by people as indicators of QoL. Our findings suggest an important gap between current QoL’s indicators considered by public policies and the means of QoL defined by people. Our study provides insights for a set of plausible local indicators useful to achieve a balance between top-down and bottom-up approaches for the local public policies.     

The exponentiated Weibull distribution: a survey

A review is given of the exponentiated Weibull distribution, the first generalization of the two-parameter Weibull distribution to accommodate nonmonotone hazard rates. The properties reviewed include: moments, order statistics, characterizations, generalizations and related distributions, transformations, graphical estimation, maximum likelihood estimation, Bayes estimation, other estimation, discrimination, goodness of fit tests, regression models, applications, multivariate generalizations, and computer software. Some of the results given are new and hitherto unknown. It is hoped that this review could serve as an important reference and encourage developments of further generalizations of the two-parameter Weibull distribution.     

A modified DEA model to estimate the importance of objectives with an application to agricultural economics

This paper demonstrates a connection between data envelopment analysis (DEA) and a non-interactive elicitation method to estimate the weights of objectives for decision-makers in a multiple attribute approach. This connection gives rise to a modified DEA model that allows us to estimate not only efficiency measures but also preference weights by radially projecting each unit onto a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of multiple attribute decision analysis the basic contribution of this paper is a new interpretation in terms of efficiency of the non-interactive methodology employed to estimate weights in a multicriteria approach. We also propose a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures that does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.     

Ecological,Groundwater, and Human Health Risk Assessment in a Mining Region of Nicaragua

The objective of the present study was to integrate the relative risk from mercury exposure to stream biota, groundwater, and humans in the Río Artiguas (Sucio) river basin, Nicaragua, where local gold mining occurs. A hazard quotient was used as a common exchange rate in probabilistic estimations of exposure and effects by means of Monte Carlo simulations. The endpoint for stream organisms was the lethal no‐observed‐effect concentration (NOECs), for groundwater the WHO guideline and the inhibitory Hg concentrations in bacteria (IC), and for humans the tolerable daily intake (TDI) and the benchmark dose level with an uncertainty factor of 10 (BMDLs_0.1). Macroinvertebrates and fish in the contaminated river are faced with a higher risk to suffer from exposure to Hg than humans eating contaminated fish and bacteria living in the groundwater. The river sediment is the most hazardous source for the macroinvertebrates, and macroinvertebrates make up the highest risk for fish. The distribution of body concentrations of Hg in fish in the mining areas of the basin may exceed the distribution of endpoint values with close to 100% probability. Similarly, the Hg concentration in cord blood of humans feeding on fish from the river was predicted to exceed the BMDLs_0.1 with about 10% probability. Most of the risk to the groundwater quality is confined to the vicinity of the gold refining plants and along the river, with a probability of about 20% to exceed the guideline value.     

Disease Spread Models to Estimate Highly Uncertain Emerging Diseases Losses for Animal Agriculture Insurance Policies: An Application to the U.S. Farm‐Raised Catfish Industry

Emerging diseases (ED) can have devastating effects on agriculture. Consequently, agricultural insurance for ED can develop if basic insurability criteria are met, including the capability to estimate the severity of ED outbreaks with associated uncertainty. The U.S. farm‐raised channel catfish (Ictalurus punctatus) industry was used to evaluate the feasibility of using a disease spread simulation modeling framework to estimate the potential losses from new ED for agricultural insurance purposes. Two stochastic models were used to simulate the spread of ED between and within channel catfish ponds in Mississippi (MS) under high, medium, and low disease impact scenarios. The mean (95% prediction interval (PI)) proportion of ponds infected within disease‐impacted farms was 7.6% (3.8%, 22.8%), 24.5% (3.8%, 72.0%), and 45.6% (4.0%, 92.3%), and the mean (95% PI) proportion of fish mortalities in ponds affected by the disease was 9.8% (1.4%, 26.7%), 49.2% (4.7%, 60.7%), and 88.3% (85.9%, 90.5%) for the low, medium, and high impact scenarios, respectively. The farm‐level mortality losses from an ED were up to 40.3% of the total farm inventory and can be used for insurance premium rate development. Disease spread modeling provides a systematic way to organize the current knowledge on the ED perils and, ultimately, use this information to help develop actuarially sound agricultural insurance policies and premiums. However, the estimates obtained will include a large amount of uncertainty driven by the stochastic nature of disease outbreaks, by the uncertainty in the frequency of future ED occurrences, and by the often sparse data available from past outbreaks.     

THE PRIVATE AND FISCAL RETURNS TO SCHOOLING IN THE EUROPEAN UNION

We present estimates of the private and fiscal returns to schooling in 14 European Union countries. Estimates of the private returns to post‐compulsory formal education take into account the effects of schooling on wages and employment probabilities and allow for academic failure rates, the direct and opportunity costs of education, and the impact of personal taxes, social security contributions, and unemployment and pension benefits on lifetime earnings. Estimates of fiscal returns capture the long‐term effects of a marginal increase in average educational attainment on public finances under conditions that approximate general equilibrium. (JEL: I20, J31, H60).     

A GLOBAL VIEW OF CROSS‐BORDER MIGRATION

This paper evaluates the global welfare impact of observed levels of migration using a quantitative multi‐sector model of the world economy calibrated to aggregate and firm‐level data. Our framework features cross‐country labor productivity differences, international trade, remittances, and a heterogeneous workforce. We compare welfare under the observed levels of migration to a no‐migration counterfactual. In the long run, natives in countries that received a lot of migration—such as Canada or Australia—are better off due to greater product variety available in consumption and as intermediate inputs. In the short run, the impact of migration on average welfare in these countries is close to zero, while the skilled and unskilled natives tend to experience welfare changes of opposite signs. The remaining natives in countries with large emigration flows—such as Jamaica or El Salvador—are also better off due to migration, but for a different reason: remittances. The welfare impact of observed levels of migration is substantial, at about 5% to 10% for the main receiving countries and about 10% in countries with large incoming remittances.