Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/250584 
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14923
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference"). We study visual inference within the context of regression discontinuity (RD) designs by measuring how accurately readers identify discontinuities in graphs produced from data generating processes calibrated on 11 published papers from leading economics journals. First, we assess the effects of different graphical representation methods on visual inference using randomized experiments. We find that bin widths and fit lines have the largest impacts on whether participants correctly perceive the presence or absence of a discontinuity. Incorporating the experimental results into two decision theoretical criteria adapted from the recent economics literature, we find that using small bins with no fit lines to construct RD graphs performs well and recommend it as a starting point to practitioners. Second, we compare visual inference with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error rates and complements econometric inference.
Subjects: 
graphical methods
visual inference
regression discontinuity design
expert prediction
statistical decision theory
scientific communication
JEL: 
A11
C10
C40
Document Type: 
Working Paper

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