Replication data for: Using Lagged Outcomes to Evaluate Bias in Value-Added Models
Principal Investigator(s): View help for Principal Investigator(s) Raj Chetty; John N. Friedman; Jonah Rockoff
Version: View help for Version V1
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| P2016_1081_data | 10/12/2019 11:09:AM | ||
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text/plain | 14.6 KB | 10/12/2019 07:09:AM |
Project Citation:
Chetty, Raj, Friedman, John N., and Rockoff, Jonah. Replication data for: Using Lagged Outcomes to Evaluate Bias in Value-Added Models. Nashville, TN: American Economic Association [publisher], 2016. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113472V1
Project Description
Summary:
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Value-added (VA) models measure agents' productivity based on the outcomes they produce. The utility of VA models for performance evaluation depends on the extent to which VA estimates are biased by selection. One common method of evaluating bias in VA is to test for balance in lagged values of the outcome. We show that such balance tests do not yield robust information about bias in value-added models using Monte Carlo simulations. Even unbiased VA estimates can be correlated with lagged outcomes. More generally, tests using lagged outcomes are uninformative about the degree of bias in misspecified VA models. The source of these results is that VA is itself estimated using historical data, leading to non-transparent correlations between VA and lagged outcomes.
Scope of Project
JEL Classification:
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D24 Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
I21 Analysis of Education
J24 Human Capital; Skills; Occupational Choice; Labor Productivity
D24 Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
I21 Analysis of Education
J24 Human Capital; Skills; Occupational Choice; Labor Productivity
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