Research

WORKING PAPER

How did China’s New Rural Pension Scheme affect the aged rural population? An empirical evaluation with the CLHLS data, with Nathan Johnson, Danyang Zhao, Accepted at Journal of Population Ageing

China initiated a new rural pension scheme targeting the large rural population in 2009. This new scheme was claimed to be a huge improvement of the previous welfare institution and a strong defense to rural people’s elderly life. Using a panel data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we applied the PSM-DID approach to identify causal relationships between the pension and multiple covariates at individual level. We found that the pension significantly reduced systolic and diastolic, as well as improved overall health and life quality of participants. We acknowledged the positive influences of China’s new rural pension on elderly life of the rural population, and discussed potential directions for future research.

A GMM Approach for Non-monotone Missingness on Both Treatment and Outcome Variables (New draft coming soon)

I examine the common problem of multiple missing variables, which we refer to as multiple missingness, with non-monotone missing pattern and is usually caused by sub-sampling and a combination of different data sets. One example of this is missingness in both the endogenous treatment and outcome when two variables are collected via different stages of follow-up surveys. Two types of dependence assumptions for multiple missingness are proposed to identify the missing mechanism. The identified missing mechanisms are used later in an Augmented Inverse Propensity Weighted moment function, based on which a two-step semiparametric GMM estimator of the coefficients in the primary model is proposed. This estimator is consistent and more efficient than the previously used estimation methods because it includes incomplete observations. We demonstrate that robustness and asymptotic variances differ under two sets of identification assumptions, and we determine sufficient conditions when the proposed estimator can achieve the semiparametric efficiency bound. This method is applied to the Oregon Health Insurance Experiment and shows the significant effects of enrolling in the Oregon Health Plan on improving health-related outcomes and reducing out-of-pocket costs for medical care. The method proposed in this paper provides unbiased and more efficient estimates. There is evidence that simply dropping the incomplete data creates downward biases for some of the chosen outcome variables. Moreover, the estimator proposed in this paper reduced standard errors by 6-24% of the estimated effects of the Oregon Health Plan.

Partial Identification on Treatment Effect on Transitions and Its Empirical Application (New draft coming soon)

This paper gives sharp partial identification bounds of dynamic treatment effect on conditional transition probabilities when the treatment is randomly assigned. Then it relaxes randomization assumption and gives partial identification bounds, under a conditional mean independence assumption. Using MTR and MTS assumptions, this bound is further tightened. These bounds are used on estimating labor market return of college degree, with data from NLSY79.