Several recent papers employ the regression discontinuity design (RDD) to estimate the causal effect of a diploma (or similar credentials) on wages. Using a simple model of asymmetric information, I show that RDD estimates the information value of a diploma. A positive information value arises if employers, unable to observe the test score that determines diploma receipt, infer that workers with a diploma have higher average productivity than those without. Crucially, a diploma can have information value regardless of whether workers' productivity is solely determined by acquisition of...  [+] Show more

Several recent papers employ the regression discontinuity design (RDD) to estimate the causal effect of a diploma (or similar credentials) on wages. Using a simple model of asymmetric information, I show that RDD estimates the information value of a diploma. A positive information value arises if employers, unable to observe the test score that determines diploma receipt, infer that workers with a diploma have higher average productivity than those without. Crucially, a diploma can have information value regardless of whether workers' productivity is solely determined by acquisition of knowledge and skills through studying (the pure human capital model) or whether studying has no effect on productivity (the pure signaling model). Thus, while RDD estimates of diploma effects are evidence for information frictions and statistical discrimination, they do not help to distinguish between human capital and signaling. However, with longitudinal data, RDD can be used to estimate the speed of employer learning, since RDD coefficients are direct estimates of (differences in) expectation errors.

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