Leveraging Lotteries for School Value-Added: Testing and Estimation
Conventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students' demographic characteristics and previous scores. This article tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement conseq...
| Principais autores: | , , , |
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| Formato: | Artigo |
| Publicado em: |
Oxford University Press (OUP)
2018
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| Acesso em linha: | http://hdl.handle.net/1721.1/113679 https://orcid.org/0000-0001-6992-8956 https://orcid.org/0000-0003-3910-1573 https://orcid.org/0000-0001-8621-3864 |
| _version_ | 1826203574555639808 |
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| author | Angrist, Joshua Hull, Peter Davenport Pathak, Parag Walters, Christopher Ross |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Angrist, Joshua Hull, Peter Davenport Pathak, Parag Walters, Christopher Ross |
| author_sort | Angrist, Joshua |
| collection | MIT |
| description | Conventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students' demographic characteristics and previous scores. This article tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement consequences of random assignment to specific schools. Test results from admissions lotteries in Boston suggest conventional VAM estimates are biased, a finding that motivates the development of a hierarchical model describing the joint distribution of school valueadded, bias, and lottery compliance. We use this model to assess the substantive importance of bias in conventional VAM estimates and to construct hybrid valueadded estimates that optimally combine ordinary least squares and lottery-based estimates of VAM parameters. The hybrid estimation strategy provides a general recipe for combining nonexperimental and quasi-experimental estimates. While still biased, hybrid school value-added estimates have lower mean squared error than conventional VAMestimates. Simulations calibrated to the Boston data show that, bias notwithstanding, policy decisions based on conventional VAMs that control for lagged achievement are likely to generate substantial achievement gains. Hybrid estimates that incorporate lotteries yield further gains. |
| first_indexed | 2024-09-23T12:39:46Z |
| format | Article |
| id | mit-1721.1/113679 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:39:46Z |
| publishDate | 2018 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1136792022-09-28T09:17:20Z Leveraging Lotteries for School Value-Added: Testing and Estimation Angrist, Joshua Hull, Peter Davenport Pathak, Parag Walters, Christopher Ross Massachusetts Institute of Technology. Department of Economics Angrist, Joshua Hull, Peter Davenport Pathak, Parag Walters, Christopher Ross Conventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students' demographic characteristics and previous scores. This article tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement consequences of random assignment to specific schools. Test results from admissions lotteries in Boston suggest conventional VAM estimates are biased, a finding that motivates the development of a hierarchical model describing the joint distribution of school valueadded, bias, and lottery compliance. We use this model to assess the substantive importance of bias in conventional VAM estimates and to construct hybrid valueadded estimates that optimally combine ordinary least squares and lottery-based estimates of VAM parameters. The hybrid estimation strategy provides a general recipe for combining nonexperimental and quasi-experimental estimates. While still biased, hybrid school value-added estimates have lower mean squared error than conventional VAMestimates. Simulations calibrated to the Boston data show that, bias notwithstanding, policy decisions based on conventional VAMs that control for lagged achievement are likely to generate substantial achievement gains. Hybrid estimates that incorporate lotteries yield further gains. 2018-02-15T14:10:36Z 2018-02-15T14:10:36Z 2017-02 2018-02-14T18:44:11Z Article http://purl.org/eprint/type/JournalArticle 0033-5533 1531-4650 http://hdl.handle.net/1721.1/113679 Angrist, Joshua D. et al. “Leveraging Lotteries for School Value-Added: Testing and Estimation.” The Quarterly Journal of Economics 132, 2 (February 1, 2017): 871–919 © 2016 The Author(s) https://orcid.org/0000-0001-6992-8956 https://orcid.org/0000-0003-3910-1573 https://orcid.org/0000-0001-8621-3864 http://dx.doi.org/10.1093/QJE/QJX001 Quarterly Journal of Economics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press (OUP) NBER |
| spellingShingle | Angrist, Joshua Hull, Peter Davenport Pathak, Parag Walters, Christopher Ross Leveraging Lotteries for School Value-Added: Testing and Estimation |
| title | Leveraging Lotteries for School Value-Added: Testing and Estimation |
| title_full | Leveraging Lotteries for School Value-Added: Testing and Estimation |
| title_fullStr | Leveraging Lotteries for School Value-Added: Testing and Estimation |
| title_full_unstemmed | Leveraging Lotteries for School Value-Added: Testing and Estimation |
| title_short | Leveraging Lotteries for School Value-Added: Testing and Estimation |
| title_sort | leveraging lotteries for school value added testing and estimation |
| url | http://hdl.handle.net/1721.1/113679 https://orcid.org/0000-0001-6992-8956 https://orcid.org/0000-0003-3910-1573 https://orcid.org/0000-0001-8621-3864 |
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