Impact evaluations in South Korea and China
Bibliographic Data
| ID | 6233376 |
|---|---|
| Authors | Haeil Jung (0000-0002-0489-1408, Korea University, corresponding author), Ruodan Zhang (0000-0002-0702-246X, Indiana University) |
| Year | 2017 |
| Volume | 25 |
| Issue | 3 |
| Pages | 328-349 |
| Publication date | 2017-09-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Asian Journal of Political Science (JOURNAL) |
| Journal identifiers | ISSN: 0218-5377 • E-ISSN: 1750-7812 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/02185377.2017.1373685 |
| OpenAlex | W2755680977 |
| Language | EN |
| References cited | 9 |
While evidence-based policy-making is increasingly in demand, as new policies are required to bring effective results to targeted groups in South Korea and China, few studies have investigated the progress of quantitative impact evaluation that focuses on causality. This paper studies the trends of quantitative impact evaluation of public policy in South Korea and China by surveying major public administration and public policy journals in these two countries from 2000 to 2015. Among published articles in the major journals, our study pool includes research articles directly related to quantitative impact evaluation. Our study found that there has been considerable progress in impact evaluation research in South Korea and China in both data quality and empirical methods. However, empirical impact evaluation still comprises a small fraction (only one to two percent) of all research in public administration and public policy in both countries. We also found limited discussion on the selection mechanism and related bias in South Korea even in recent years, while causality and selection bias have been more commonly discussed in China. Also, advanced empirical methods are more frequently observed in journal articles in China than those in South Korea
Causality (physics · China · Development economics · Economic growth · Economics · Empirical research · Geography · Impact assessment · Political science · Public economics · Public policy · Regional science · Advanced Causal Inference Techniques · Policy Transfer and Learning · Public Administration
Does matching overcome LaLonde's critique of nonexperimental estimators?
Matching As An Econometric Evaluation Estimator
The Economics and Econometrics of Active Labor Market Programs
Causal Effects in Nonexperimental Studies
The central role of the propensity score in observational studies for causal effects
Matching As An Econometric Evaluation Estimator
Propensity Score-Matching Methods for Nonexperimental Causal Studies
Do experimental and nonexperimental evaluations give different answers about the effectiveness of government-funded training programs
| Citation velocity | historical |
|---|---|
| Highly cited | No |