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Human Capital Policies in Education: Further Research on Teachers and Principals

Human Capital Policies in Education: Further Research on Teachers and Principals 5 rd Annual CALDER Conference January 27 th , 2012 . Estimating the Effect of Leaders on Public Sector Productivity: The Case of School Principals. Gregory Branch, Eric Hanushek, a nd Steven Rivkin,

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Human Capital Policies in Education: Further Research on Teachers and Principals

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  1. Human Capital Policies in Education: Further Research on Teachers and Principals 5rd Annual CALDER Conference January 27th, 2012

  2. Estimating the Effect of Leaders on Public Sector Productivity: The Case of School Principals Gregory Branch, Eric Hanushek, and Steven Rivkin, January 2012

  3. Questions • Is there substantial variation in principal effectiveness? • Does the variation in principal effectiveness differ by the share of low income students in a school? • Do more effective principals make better personnel decisions? • Are “effective” principals more likely to leave high poverty schools?

  4. UTD Texas Schools Project • Stacked panels of students and staff • Annual student testing • Student demographic characteristics • Information on staff • Follow principals, teachers, and students in Texas public schools • Very large samples: 7,420 unique principals and 28,147 principal-year observations in 1995-2001

  5. Principals Change Frequently

  6. Estimation of Variation in Principal Quality • Non-random selection of principals and students • Control for observed student characteristics and prior achievement • Make principals comparable in terms of tenure

  7. No Simple Solutions Alternative approaches to estimation 1. Fixed effects for principals 2. Fixed effects for principals and schools 3. Direct estimation of quality variation 4. Validation with teacher turnover analysis

  8. Alternative Value-Added Estimates Principal Spell Fixed Effects Regress math score on lagged math score, student demographic variables, principal-by-spell fixed effects

  9. Alternative Value-Added Estimates Principal Spell Fixed Effects Regress math score on lagged math score, student demographic variables, principal-by-spell fixed effects Principal Spell and School Fixed Effects

  10. Fixed Effects Estimates (s.d.)(without school fixed effects)

  11. Test Measurement Issues • Random measurement error • Use Bayesian shrinkage estimator • Basic Skills Tests • Reweight to allow for initial achievement

  12. Measurement Effects (s.d.)

  13. Alternative Fixed Effects Estimates (s.d.)

  14. Alternative Fixed Effects Estimates (s.d.)

  15. Why is variance higher in high poverty schools? • Larger variation in underlying principal skills in high poverty schools • Or • Principal quality differences translate into larger differences in test scores in high poverty schools

  16. Direct Estimates of Variance If principal changes and if principal effects outcomes, pattern of student growth should change If other school factors are uncorrelated with principal change (partially testable), can obtain lower bound estimate of principal effectiveness.

  17. Direct Lower Bound Estimates

  18. Range of Alternative Estimates

  19. Added Analysis – Principal Quality Better principals => better teacher transitions High mobility of both best and worst in most disadvantaged schools Substantial number of worst principals become principal elsewhere.

  20. Summary • Purposeful sorting complicates estimates of principal quality and quality of leavers • Substantial variation in estimates of principal quality (fixed effects and direct) • Higher variance in high poverty schools • Not due to test measurement complications • Effects are large

  21. www.utdallas.edu/research/tsp-erc

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