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Sick and Tell: A Field Experiment Analyzing the Effects of an Illness-Related Employment Gap on the Callback Rate
Last registered on March 21, 2017


Trial Information
General Information
Sick and Tell: A Field Experiment Analyzing the Effects of an Illness-Related Employment Gap on the Callback Rate
Initial registration date
March 20, 2017
Last updated
March 21, 2017 10:51 AM EDT
Primary Investigator
Kansas State University
Other Primary Investigator(s)
PI Affiliation
Kansas State University
PI Affiliation
Kansas State University
Additional Trial Information
Start date
End date
Secondary IDs
Using a résumé-based correspondence test, we compare the employment consequences of an illness-related employment gap to those of an unexplained employment gap. Previous research shows that employment gaps, in general, have adverse effects on the probability of getting hired. It is not clear, however, if employers view spells of joblessness due to health issues distinctly. To shed light on this, we present a model in which employers use information on employment gaps as a signal of unobserved productivity and healthcare costs. We investigate the empirical implications of the model by sending three types of fictitious r´esum´es to real vacancies advertised online. One résumé indicates that the applicant is newly unemployed. The other résumés indicate employment gaps which are either unexplained or
explained as being related to an illness. The results of the experiment show that while the callback rate of applicants with an illness-related employment gap is lower than that of the newly unemployed, applicants with illness-related employment gaps are more likely to receive
a callback than identical applicants who provide no explanation for the gap. Our research provides evidence that employers use information on employment gaps as additional signals about workers’ unobserved productivity.
External Link(s)
Registration Citation
Blankenau, William, Sheryll Namingit and Benjamin Schwab. 2017. "Sick and Tell: A Field Experiment Analyzing the Effects of an Illness-Related Employment Gap on the Callback Rate." AEA RCT Registry. March 21. https://doi.org/10.1257/rct.2117-1.0.
Former Citation
Blankenau, William et al. 2017. "Sick and Tell: A Field Experiment Analyzing the Effects of an Illness-Related Employment Gap on the Callback Rate." AEA RCT Registry. March 21. http://www.socialscienceregistry.org/trials/2117/history/15246.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Callback from employers
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
In our field experiment, carefully prepared résumé s and corresponding cover letters were sent to employers who advertised vacancies in online job boards. For each vacancy, we sent three types of résumés. One résumé contained an explained illness-related employment gap while another contained an unexplained employment gap. These were in contrast to a third résumé where the applicant was newly unemployed (no gap). For illness-related and unexplained employment gaps, the résumé showed no employment over the previous seven months or more. To signal an illness-related employment gap, a phrase in the cover letter explained that the employment gap was due to a physical illness followed by a full recovery. An additional signal on medical history was sent via information in the résumé that indicates involvement in a cancer recovery support group. The corresponding cover letters of résumés with unexplained gaps did not provide any explanation for the gap. For the résumé of newly unemployed applicants, the length of the gap is limited to less than two months. Based on the literature, this is too short a gap to bring about adverse effects. The corresponding cover letter of newly unemployed applicants notes that the applicant left the last job because her family had to move from another state and that she is currently looking for a new job. We chose this control as our "no gap" group because applicants who are currently working tend to have fewer callbacks.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Individual résumé
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
3,771 résumés
Sample size: planned number of observations
4000 résumés
Sample size (or number of clusters) by treatment arms
1257 for each treatment arms (no gap, illness-related gap, non-illness related gap)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Study has received IRB approval. Details not available.
IRB Approval Date
Details not available
IRB Approval Number
Details not available
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)