EconBase
← All papers

A Nonresponse Bias Correction using Nonrandom Followup with an Application to the Gender Entrepreneurship Gap

Clint Harris, Jon Eckhardt, Brent Goldfarb

arXiv 26 Apr 2024 · Econometrics

arXiv:2404.17693 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We develop a nonresponse correction applicable to any setting in which multiple attempts to contact subjects affect whether researchers observe variables without affecting the variables themselves. Our procedure produces point estimates of population averages using selected samples without requiring randomized incentives or assuming selection bias cancels out for any within-respondent comparisons. Applying our correction to a 16% response rate survey of University of Wisconsin-Madison undergraduates, we estimate a 15 percentage point male-female entrepreneurial intention gap. Our estimates attribute the 20 percentage point uncorrected within-respondent gap to positive bias for men and negative bias for women, highlighting the value of within-group nonresponse corrections.

Citation extraction

35
references
65
in-text mentions
35
distinct cited
0
self-citations
7,355
main-text words

appendix boundary found by appendix_command · 80% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Imbens, Guido W and Angrist, Joshua D (1994) Identification and Estimation of Local Average Treatment Effects1.00053100%
2Dutz, Deniz and Huitfeldt, Ingrid and Lacouture, Santiago and Mogsta… (2025) Selection in Surveys: Using Randomized Incentives to Detect and Account for Nonresponse Bias0.9285480%
3Van de Ven, Wynand PMM and Van Praag, Bernard MS (1981) The demand for deductibles in private health insurance: A probit model with sample selection0.8947371%
4Heckman, James J (1979) Sample selection bias as a specification error0.8229356%
5Angrist, Joshua D and Imbens, Guido W and Rubin, Donald B (1996) Identification of causal effects using instrumental variables0.73732100%
6Behaghel, Luc and Crépon, Bruno and Gurgand, Marc and Le Barbanchon,… (2015) Please call again: Correcting nonresponse bias in treatment effect models0.64422100%
7Heckman, James J and Vytlacil, Edward (2005) Structural equations, treatment effects, and econometric policy evaluation 10.64422100%
8Heckman, James J and Vytlacil, Edward J (2007) Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation0.64422100%
9Horowitz, Joel L and Manski, Charles F (2000) Nonparametric analysis of randomized experiments with missing covariate and outcome data0.64422100%
10Lee, David S (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects0.64422100%

Showing the top 10 of 35 scored citations.