Teacher Quality in Educational Production: Tracking Decay and Student Achievement

Working Paper: NBER ID: w14442

Authors: Jesse Rothstein

Abstract: Growing concerns over the achievement of U.S. students have led to proposals to reward good teachers and penalize (or fire) bad ones. The leading method for assessing teacher quality is "value added" modeling (VAM), which decomposes students' test scores into components attributed to student heterogeneity and to teacher quality. Implicit in the VAM approach are strong assumptions about the nature of the educational production function and the assignment of students to classrooms. In this paper, I develop falsification tests for three widely used VAM specifications, based on the idea that future teachers cannot influence students' past achievement. In data from North Carolina, each of the VAMs' exclusion restrictions are dramatically violated. In particular, these models indicate large "effects" of 5th grade teachers on 4th grade test score gains. I also find that conventional measures of individual teachers' value added fade out very quickly and are at best weakly related to long-run effects.

Keywords: teacher quality; value-added models; student achievement; educational production

JEL Codes: I21; J24; J33


Causal Claims Network Graph

Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.


Causal Claims

CauseEffect
Conventional VAMs indicating significant effects of fifth-grade teachers (C29)Teacher assignments are correlated with prior student performance (D29)
Teacher assignments are correlated with prior student performance (D29)Estimates of teacher effects are influenced by prior student performance (D29)
Estimates of teacher effects are influenced by prior student performance (D29)Short-term value-added measures may poorly predict long-term teacher effectiveness (J17)
Selection bias in teacher assignments (I24)Teacher assignments are correlated with prior student performance (D29)
Falsification tests of VAMs (C52)Assumptions required for identifying causal effects using VAMs are likely violated (C36)

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