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Proxying ability by family background in returns to schooling estimations is generally a bad idea

Abstract of Working paper 2008:22

A regression model is considered where earnings are explained by schooling
and ability. It is assumed that schooling is measured with error and that there
are no data on ability. Regressing earnings on observed schooling then yields an
estimate of the return to schooling that is subject to positive omitted variable
bias (OVB) and negative measurement error bias (MEB). The effects on the OVB
and the MEB from using family background variables as proxies for ability are
investigated theoretically and empirically. The theoretical analysis demonstrates
that the impact on the OVB is uncertain, while the MEB invariably increases in
magnitude. The empirical analysis shows that the MEB generally dominates the
OVB. As the measurement error increases and/or more family background variables
are added, the total bias rapidly becomes negative, driving the estimated
return further and further away from the true value.
JEL codes: C13, C20, C52, J31
Keywords: missing data, proxy variables, measurement error, consistent estimates
of omitted variable bias and measurement error bias.

A regression model is considered where earnings are explained by schooling and ability. It is assumed that schooling is measured with error and that there are no data on ability. Regressing earnings on observed schooling then yields an estimate of the return to schooling that is subject to positive omitted variable bias (OVB) and negative measurement error bias (MEB). The effects on the OVB and the MEB from using family background variables as proxies for ability are investigated theoretically and empirically. The theoretical analysis demonstrates that the impact on the OVB is uncertain, while the MEB invariably increases in magnitude. The empirical analysis shows that the MEB generally dominates the OVB. As the measurement error increases and/or more family background variables are added, the total bias rapidly becomes negative, driving the estimated return further and further away from the true value.

Keywords: missing data, proxy variables, measurement error, consistent estimates of omitted variable bias and measurement error bias.
JEL codes: C13, C20, C52, J31


Published by:

Ifau

Changed:

9/21/2010