Using Heteroscedasticity to Estimate the Returns to Education

Working Paper: NBER ID: w9145

Authors: Vincent Hogan; Roberto Rigobon

Abstract: We apply a new estimator to the measurement of the economic returns to education. We control for endogenous education, unobserved ability and measurement error using only the natural heteroscedasticty of wages and education attainment. Our prefered estimate, 6.07%, is closer to the OLS estimate but smaller (and more precise) than the estimates typically reported by studies that use IV. Our results indicate that the biases generated by unobserved ability and measurement error tend to cancel each other out as suggested by Griliches (1977). We also present Monte Carlo evidence to show that the finite sample bias our estimator is small.

Keywords: Returns to Education; Heteroscedasticity; Endogeneity; Measurement Error

JEL Codes: C30; I20; J31


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
heteroscedasticity (C21)structural parameters (C23)
unobserved ability (D29)OLS estimates (L00)
measurement error (C20)OLS estimates (L00)
unobserved ability + measurement error (C29)OLS estimates (L00)
OLS estimates (L00)true return to education (I26)
OLS estimate (0.68) (C29)IV estimate (0.52) (C26)
return to education (I26)OLS estimates (L00)
IV estimates (C26)true return to education (I26)

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