arXiv 3 Sep 2021 · Econometrics
arXiv:2109.01741 · PDF · DOI · OpenAlex · Extracted main text
As increasingly popular metrics of worker and institutional quality, estimated value-added (VA) measures are now widely used as dependent or explanatory variables in regressions. For example, VA is used as an explanatory variable when examining the relationship between teacher VA and students' long-run outcomes. Due to the multi-step nature of VA estimation, the standard errors (SEs) researchers routinely use when including VA measures in OLS regressions are incorrect. In this paper, I show that the assumptions underpinning VA models naturally lead to a generalized method of moments (GMM) framework. Using this insight, I construct correct SEs' for regressions that use VA as an explanatory variable and for regressions where VA is the outcome. In addition, I identify the causes of incorrect SEs when using OLS, discuss the need to adjust SEs under different sets of assumptions, and propose a more efficient estimator for using VA as an explanatory variable. Finally, I illustrate my results using data from North Carolina, and show that correcting SEs results in an increase that is larger than the impact of clustering SEs.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rothstein, J (2017) Measuring the impacts of teachers: Comment | 0.693 | 7 | 1 | 100% |
| 2 | Chetty, R., Friedman, J. N., and Rockoff, J. E (2014) Measuring the impacts of teachers ii: Teacher value-added and student outcomes in adulthood | 0.693 | 5 | 1 | 100% |
| 3 | Chetty, R., Friedman, J. N., and Rockoff, J. E (2014) Measuring the impacts of teachers i: Evaluating bias in teacher value-added estimates | 0.667 | 9 | 1 | 89% |
| 4 | Canaan, S., Deeb, A., and Mouganie, P (2021) Advisor value-added and student outcomes: Evidence from randomly assigned college advisors self | 0.585 | 3 | 1 | 100% |
| 5 | Chetty, R., Friedman, J. N., and Rockoff, J. E (2017) Measuring the impacts of teachers: Reply | 0.511 | 2 | 1 | 100% |
| 6 | Jackson, C. K (2018) What do test scores miss? the importance of teacher effects on non–test score outcomes | 0.511 | 2 | 1 | 100% |
| 7 | Opper, I. M (2019) Does helping john help sue? evidence of spillovers in education | 0.511 | 2 | 1 | 100% |
| 8 | Newey, K. and McFadden, D (1994) Large sample estimation and hypothesis | 0.500 | 8 | 1 | 38% |
| 9 | Angrist, J. D., Hull, P. D., Pathak, P. A., and Walters, C. R (2017) Leveraging lotteries for school value-added: Testing and estimation | 0.405 | 1 | 1 | 100% |
| 10 | Arcidiacono, P., Kinsler, J., and Price, J (2017) Productivity spillovers in team production: Evidence from professional basketball | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.
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