When to use matching and weighting or regression in instrumental variable estimation? Evidence from college proximity and returns to college
Bibliographic record
Abstract
Abstract 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".