What Works Best and When
Accounting for Multiple Sources of Pureselection Bias in Program Evaluations
Bibliographic Data
| ID | 11684388 |
|---|---|
| Authors | Haeil Jung (0000-0002-0489-1408, Indiana University Bloomington, corresponding author), Maureen A Pirog (Indiana University Bloomington) |
| Year | 2014 |
| Volume | 33 |
| Issue | 3 |
| Pages | 752-777 |
| Publication date | 2014-04-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Policy Analysis and Management (JOURNAL) |
| Journal identifiers | ISSN: 0276-8739 • E-ISSN: 1520-6688 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/pam.21764 |
| OpenAlex | W1521964661 |
| Language | EN |
| Citations received | 3 |
| References cited | 29 |
Most evaluations are still quasi‐experimental and most recent quasi‐experimental methodological research has focused on various types of propensity score matching to minimize conventional selection bias on observables. Although these methods create better‐matched treatment and comparison groups on observables, the issue of selection on unobservables still looms large. Thus, in the absence of being able to run randomized controlled trials (RCTs) or natural experiments, it is important to understand how well different regression‐based estimators perform in terms of minimizing pure selection bias, that is, selection on unobservables. We examine the relative magnitudes of three sources of pure selection bias: heterogeneous response bias, time‐invariant individual heterogeneity (fixed effects [FEs]), and intertemporal dependence (autoregressive process of order one [AR(1)]). Because the relative magnitude of each source of pure selection bias may vary in different policy contexts, it is important to understand how well different regression‐based estimators handle each source of selection bias. Expanding simulations that have their origins in the work of Heckman, LaLonde, and Smith ( ), we find that difference‐in‐differences (DID) using equidistant pre‐ and postperiods and FEs estimators are less biased and have smaller standard errors in estimating the Treatment on the Treated (TT) than other regression‐based estimators. Our data analysis using the Job Training Partnership Act (JTPA) program replicates our simulation findings in estimating the TT
Autoregressive model · Econometrics · Estimator · Model selection · Propensity score matching · Randomized experiment · Regression · Regression analysis · Selection (genetic algorithm · Selection bias · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence
Characterizing Selection Bias Using Experimental Data
Structural Equations, Treatment Effects, and Econometric Policy Evaluation1
Choosing among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs
Does matching overcome LaLonde's critique of nonexperimental estimators?
Accountability and Flexibility in Public Schools
Dummy Endogenous Variables in a Simultaneous Equation System
Evaluating the Effect of Education on Earnings
The Economics and Econometrics of Active Labor Market Programs
Alternative Approaches to Evaluation in Empirical Microeconomics
Accounting for No-Shows in Experimental Evaluation Designs
Causal Effects in Nonexperimental Studies
Making The Most Out Of Programme Evaluations and Social Experiments
Matching As An Econometric Evaluation Estimator
Identification and Estimation of Local Average Treatment Effects
Sample Selection Bias as a Specification Error
Accounting for Dropouts in Evaluations of Social Programs
Propensity Score-Matching Methods for Nonexperimental Causal Studies
Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs
How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment
The Impact of Providing Vision Screening and Free Eyeglasses on Academic Outcomes
School vouchers and academic performance
Three conditions under which experiments and observational studies produce comparable causal estimates
Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within‐Study Comparison
Recent Developments in the Econometrics of Program Evaluation
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2017 - 2020 (4) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |