Welfare analysis of a tax reform for Germany: a comparison of the unitary and collective models of household labour supply

We compare results of a tax reform analysis obtained with the collective and unitary models of household behaviour. We simulate real world micro-data by means of a collective approach, using a compound procedure of estimation and calibration based on the 1998 wave of the German socio-economic panel. We estimate a unitary model on this ‘collective’ data set. Investigating a move from joint to individual taxation on the basis of both models, we obtain important discrepancies between predicted adjustments to labour supply and distortions in the welfare analysis of the reform on the basis of unitary estimates.      

试论艾青诗歌创作中的宗教情感

艾青诗歌中表现了“救世者”、“创世者”的宗教情感,这种情感的形成对其诗歌创作产生了积极的影响,是其社会思想、审美思想、道德情感的具体表现,使其诗歌具有世代感和使命感.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Urban ecosystem Services in Latin America: mismatch between global concepts and regional realities?

Urban Ecosystems - Latin America and the Caribbean (LAC) is one of the most urbanized and biologically diverse regions in the world but is often characterized by weak environmental governance and...     

‘Scarier than another storm’: values at risk in the mapping and insuring of US floodplains

How do people respond to the ways in which insurance mediates environmental risks? Socio‐cultural risk research has characterized and analyzed the experiential dimension of risk, but has yet to focus on insurance, which is a key institution shaping how people understand and relate to risk. Insurance not only assesses and communicates risk; it also economizes it, making the problem on the ground not just one of risk, but also of value. This article addresses these issues with an investigation of the social life of the flood insurance rate map, the central technology of the U.S. National Flood Insurance Program (NFIP), as it grafts a new landscape of ‘value at risk’ onto the physical and social world of New York City in the aftermath of Hurricane Sandy. Like other risk technologies, ubiquitous in modern societies as decision‐making and planning tools, the map disseminates information about value and risk in order to tame uncertainty and enable prudent action oriented toward the future. However, drawing together interview, ethnographic, and documentary data, I find that for its users on the ground, the map does not simply measure ‘value at risk’ in ways that produce clear strategies for protecting property values from flooding. Instead, it puts values‐beyond simply the financial worth of places‐at risk, as well as implicates past, present, and future risks beyond simply flooding. By informing and enlarging the stakes of what needs protecting, and from what, I argue that plural and interacting ‘values at risk’ shape how people live with and respond to environmental risks that are mediated by insurance technologies.     

Demographic differences in response rates for PHQ9 in a university student population

Objective: To examine differences in complete response rates for depression screening questions based on demographic characteristics. Methods: Cross-sectional study examining associations between demographic characteristics and completely responding depression-screening questions. Participants: “Healthy Minds Study” data, collected in a public University in February 2016, where 7,326 students participated. Results: women (AOR: 0.69; 95% CI =0.57–0.83) and gay/lesbian students (AOR: 0.24; 95% CI =0.10–0.60) had better complete response rates. Non-US (AOR: 1.46; 95% CI =1.03–2.07), black (AOR: 3.32; 95% CI =1.92–5.77), and Middle-Eastern students (AOR: 3.73; 95% CI =1.73–8.02) had lower complete response rates. Conclusions: Our study shows sex, gender, citizenship, and race categories have significant differences in complete response rates for the outcome. Our findings have several implications; including recognizing interventions for depression based on responders may not target those that tend to be “partial-responders”. Efforts in survey design, recruiting and completion of surveys should be maximized.