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Value Added Estimation in the Presence of Missing Data

Author

Listed:
  • Niu Gao

    (Public Policy Institute of California)

  • Anastasia Semykina

    (Department of Economics, Florida State University)

Abstract

Ignoring missing data may introduce biases into value-added estimation. We consider a model where both correlated student effects and idiosyncratic factors may cause selection biases. Moreover, we model selection as a function of school-level factors (e.g. charter proximity) that may have distinct effects on teachers in different traditional public schools. We discuss a correction procedure and study its performance using simulations. We find that correction tends to produce noisier estimates of teacher productivity, but can help to substantially reduce the bias. Using observational data from North Carolina we find that corrected estimates of teacher productivity are very similar to those produced by OLS, which appear to be due to small partial effects of school-level covariates on the probability of selection.

Suggested Citation

  • Niu Gao & Anastasia Semykina, 2017. "Value Added Estimation in the Presence of Missing Data," Working Papers wp2017_08_02, Department of Economics, Florida State University.
    • Handle: RePEc:fsu:wpaper:wp2017_08_02
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    File URL: https://coss.fsu.edu/econpapers/wpaper/wp2017_08_02.pdf
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    More about this item

    Keywords

    value-added estimation; missing data; selection correction;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I20 - Health, Education, and Welfare - - Education - - - General

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