What if the ＂Gold Standard＂ Can＇t Be Realized？ Propensity Scoring to Adjust for Attrition in Random Experiments

When it comes to evaluating the effectiveness of interventions, the random experiment is considered the ＂gold standard＂. Randomization is considered the gold standard because it provides a way of decreasing the chance that systematic differences, other than type of intervention, will be obtained between treatment and control groups. What has received little attention in the literature, however, is the fact that even with random assignment researchers may end up facing problems similar to those faced with data from a study that did not use randomization. This is because attrition may result in the values of potentially confounding variables no longer being ＂balanced＂ between （or among） the groups under investigation. This means that in order to estimate the effect of the treatment, one must find some way of adjusting for these potential confounders. Although multiple regression modeling is the way social science researchers typically control for the effects of potentially confounding variables, this paper argues that a modification of multiple regression modeling that uses propensity scores, under some conditions, may provide more parsimonious and better fitting models.

Impacts of information about COVID-19 on pig farmers'production willingness and behavior:Evidence from China

This paper examines the impacts of information about COVID-19 on pig farmers'production willingness by using endorsement experiments and follow-up surveys conducted in 2020 and 2021 in China.Our results show that,first,farmers were less willing to scale up production when they received information about COVID-19.The information in 2020 that the second wave of COVID-19 might occur without a vaccine reduced farmers'willingness to scale up by 13.4%,while the information in 2021 that COVID-19 might continue to spread despite the introduction of vaccine reduced farmers'willingness by 4.4%.Second,farmers whose production was affected by COVID-19 were considerably less willing to scale up,given the access to COVID-19 information.Third,farmers'production willingness can predict their actual production behavior.

Derivation of Gaussian Probability Distribution: A New Approach

The famous de Moivre’s Laplace limit theorem proved the probability density function of Gaussian distribution from binomial probability mass function under specified conditions. De Moivre’s Laplace approach is cumbersome as it relies heavily on many lemmas and theorems. This paper invented an alternative and less rigorous method of deriving Gaussian distribution from basic random experiment conditional on some assumptions.

Economic resources,childcare services,and son preference:a conjoint analysis of fertility potential in China

Using a randomized experiment that we designed for the 2021 Chinese General Social Survey,this study conducts a conjoint analysis to explore the fertility potential among Chinese adults.We examine the separate impacts of different factors and their heterogeneity across social groups.The results suggest that fertility potential is context-dependent.In addition,greater economic resources and availability of childcare significantly increase fertility potential.However,preferences in terms of the care provider,family versus the market,vary by gender,educational level,and desired number of children.Notably,we also find that son preference no longer has a significant effect on fertility potential in China.In light of China’s current low fertility,our study suggests that childbearing behavior will become increasingly differentiated by socioeconomic status.Fertility-enhancing policies should focus on supplementing economic resources and improving access to childcare.