When to use matching and weighting or regression in instrumental variable estimation? Evidence from college proximity and returns to college
Bibliographic Data
| ID | 21542331 |
|---|---|
| Authors | Stefan Tübbicke (0000-0002-0197-9930, Institut für Arbeitsmarkt und Berufsforschung, corresponding author) |
| Year | 2023 |
| Volume | 65 |
| Issue | 6 |
| Pages | 2979-2999 |
| Publication date | 2023-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Empirical Economics (JOURNAL) |
| Journal identifiers | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-023-02441-7 |
| OpenAlex | W4380051600 |
| Language | EN |
| References cited | 32 |
Standard two-stage least squares (2SLS) regression remains dominant in instrumental variables estimation of causal effects even though the literature has shown that 2SLS may be inconsistent when effects are heterogenous and the instrument is only valid when conditioning on covariates. To show that this is not merely a hypothetical threat, this paper re-estimates the returns to college using college proximity as an instrument based on the data from Card (Aspects of labour market behavior: essays in honour of John Vanderkamp, University of Toronto Press, Toronto, 1995). The results show that 2SLS yields systematically larger estimates of the returns to college than more flexible estimators based on the instrument propensity score. In the full sample, differences amount to about 50 to 100%. This is due to the implicit conditional-variance weighting performed by 2SLS. Moreover, in line with the theoretical prediction by Sloczynski (When should we (not) interpret linear IV estimands as LATE? IZA discussion papers 14349, Institute of Labor Economics (IZA), 2021), findings suggest that the impact of the conditional-variance weighting is larger when instrument groups are not roughly the same size. Thus, it is advised to use 2SLS with caution and use estimators based on the instrument propensity score instead when groups are of different size and covariates are predictive of the instrument
Average treatment effect · Covariate · Econometrics · Economics · Estimation · Estimator · Instrumental variable · Linear regression · Multicollinearity · Propensity score matching · Regression · Statistics · Variance inflation factor · Weighting · Advanced Causal Inference Techniques · Efficiency Analysis Using DEA · Fiscal Policy and Economic Growth · Mathematics
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Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants
Semiparametric instrumental variable estimation of treatment response models
Average causal effects from nonrandomized studies
The Returns to College Admission for Academically Marginal Students
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
Causal Effects in Nonexperimental Studies
Some Practical Guidance for the Implementation of Propensity Score Matching
Estimating causal effects of treatments in randomized and nonrandomized studies.
Identification and Estimation of Local Average Treatment Effects
Covariate distribution balance via propensity scores
Efficient Covariate Balancing for the Local Average Treatment Effect
The Effect of Attending the Flagship State University on Earnings
Testing Instrument Validity for Late Identification Based on Inequality Moment Constraints
New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators
Testing Local Average Treatment Effect Assumptions
Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators
A Systematic Review of Propensity Score Methods in the Social Sciences
| Highly cited | No |
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