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Frank Partnoy, UC Berkeley: The Ratio Problem
November 19, 2019 @ 11:00 am - 12:30 pm
ABSTRACT: We describe two problems – omitted variable bias and measurement error – that arise when a ratio is the dependent variable in a linear regression. First, we show how bias can arise from the omission of two variables based on a ratio’s denominator, and we describe tests for the degree of bias. As an example, we show that the familiar “inverse U” relationship between managerial ownership and Tobin’s Q is reversed when omitted variables are included. Second, we show how measurement error in the ratio denominator can lead to bias. We urge caution about using ratios as dependent variables.