A double-differences approach to inference in regression discontinuity designs with a discrete running variable

Georg Graetz and Mattias Nordin

No 26269, RFBerlin Discussion Paper Series from ROCKWOOL Foundation Berlin (RFBerlin)

Abstract: We present a new approach to nonparametric inference in regression discontinuity designs where the running variable is discrete and may have few support points. The approach is based on an intuitive and comparatively weak identification assumption: In the absence of treatment, the difference in population mean outcomes at the cutoff is not the largest nor the smallest among differences at any two adjacent support points. Test statistic and confidence interval are constructed from a set of t-statistics based on double differences of the outcome variable, eliminating the need to estimate a conditional expectation function. Simulation evidence suggests that statistical power compares favorably to existing methods. We illustrate our approach by revisiting two studies from the economics of education literature.

Keywords: Regression discontinuity design; causal inference; policy evaluation; economics of education (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 I21 (search for similar items in EconPapers)
Date: 2026-10
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