Suchergebnisse

AbstractThis paper investigates theoretically and experimentally the social benefits and cost to have an endogenous punishment-enforcing authority in public goods game. An authority is chosen among members of a society via an imperfectly discriminating contest prior to a public goods game. Once chosen the authority has a large degree of discretion to inflict punishment. Our theoretical result shows that an efficiency gain from having the endogenous authority always comes with a social cost from competing for being the authority. The larger the society is, however, the bigger the efficiency gain and the smaller the rent dissipation. The completely efficient outcome can be approximated as the size of society tends to infinity. The experimental results confirm that the presence of endogenous authority for a given group size increases the public goods contributions and the efficiency gain is significantly bigger in a larger group.

AbstractIn this paper, we estimate the effect of status incentives on charitable giving by exploiting a natural experiment during China's political reform in 2002 that significantly improved the status of private entrepreneurs and their enterprises. We find that exogenous changes in social, political and economic status of private entrepreneurs led to a significant increase in the corporate giving of their privately owned firms. The findings are robust when we use different samples of the data and when we control for a large set of entrepreneurs and firm characteristics in the regression analysis.

AbstractIn this paper, we study the behavioural impact of religious priming by showing participants religious words in a scrambled sentence task before a dictator game and a joy‐of‐destruction game. We also elicited data on individual religiosity and religious affiliation using a questionnaire. Priming religious words significantly increased prosocial behaviour in the dictator game, and the effect was especially striking among those reporting no religion, atheists and agnostics. The religious prime has no significant effect in mitigating destructive behaviour or own expectations of the other's destruction choice, but both destructive behaviour and expectations correlate positively with the multi‐dimensional religiosity measure.

AbstractThis paper documents the changes in China's Hukou reform before and after 2014 based on a unique data set of Hukou policy documents from Chinese cities between 2000 and 2016. We construct a Hukou registration index to measure the stringency of local Hukou qualification in Chinese cities. There are four main channels for migrants to get local urban Hukou: investment, home purchase, talent program, and employment. The requirements of the four channels have large variations across different tiers of cities between the two periods of 2000–2013 and 2014–2016. First‐tier and some second‐tier cities set high criteria for local Hukou registration that have become more stringent over time, while other cities have much lower requirements. The point account system for Hukou registration shows that cities have different preferences over workers eligible for local urban Hukou. The quantitative measures developed in this paper can be used to study a variety of topics on the social and economic consequences of labor mobility barriers.

Abstract
Age-related macular degeneration (AMD) is the principal cause of blindness in developed countries, and its prevalence will increase to 288 million people in 2040. Therefore, automated grading and prediction methods can be highly beneficial for recognizing susceptible subjects to late-AMD and enabling clinicians to start preventive actions for them. Clinically, AMD severity is quantified by Color Fundus Photographs (CFP) of the retina, and many machine-learning-based methods are proposed for grading AMD severity. However, few models were developed to predict the longitudinal progression status, i.e. predicting future late-AMD risk based on the current CFP, which is more clinically interesting. In this paper, we propose a new deep-learning-based classification model (LONGL-Net) that can simultaneously grade the current CFP and predict the longitudinal outcome, i.e. whether the subject will be in late-AMD in the future time-point. We design a new temporal-correlation-structure-guided Generative Adversarial Network model that learns the interrelations of temporal changes in CFPs in consecutive time-points and provides interpretability for the classifier's decisions by forecasting AMD symptoms in the future CFPs. We used about 30,000 CFP images from 4,628 participants in the Age-Related Eye Disease Study. Our classifier showed average 0.905 (95% CI: 0.886–0.922) AUC and 0.762 (95% CI: 0.733–0.792) accuracy on the 3-class classification problem of simultaneously grading current time-point's AMD condition and predicting late AMD progression of subjects in the future time-point. We further validated our model on the UK Biobank dataset, where our model showed average 0.905 accuracy and 0.797 sensitivity in grading 300 CFP images.