Compensating Wage Differentials with Unemployment: Evidence from China

We estimate the economic value of mortality risk in China using the compensating-wage-differential method. We find a positive and statistically significant correlation between wages and occupational fatality risk. The estimated effect is largest for unskilled workers. Unemployment reduces compensation for risk, which suggests that some of the assumptions under which compensating wage differentials can be interpreted as measures of workers’ preferences for risk and income are invalid when unemployment is high. Workers may be unwilling to quit high-risk jobs when alternative employment is difficult to obtain, violating the assumption of perfect mobility, or some workers (e.g., new migrants) may be poorly informed about between-job differences in risk, violating the assumption of perfect information. These factors suggest our estimates of the value per statistical life (VSL) in China, which range from approximately US$30,000 to US$100,000, may be biased downward. Alternative estimates adjust for heterogeneity of risk within industry by assuming that risk is concentrated among low-skill workers. These estimates, which are likely to be biased downward, range from US$7,000 to US$20,000.      

Tourist Perception of Crowding and Management Approaches at Tourism Sites in Xi'an

Tourism crowding management is an important part of sustainable development at tourist attractions. This study seeks to recognize visitors' perceptions of crowding and then identify tourism crowding management approaches for tourism sites in Xi'an, western China. Three kinds of study, which were designed using quantitative and qualitative techniques, were undertaken in Xi'an. In the first two studies the actual numbers of visitors at component parts of five tourist attractions were recorded. Next, visitor responses to these kinds of levels of crowding were assessed. In the third study senior managers were interviewed and asked to explain their approaches to crowding management. Taken together, the information collected from the three studies helped build a model that plots the driving forces shaping tourism crowding at tourism sites.