Introduction
I am interested in studying the career processes of entrepreneurs, especially those who move in and out of traditional, hierarchical firms. Kacperczyk and Younkin (2021) found that having an entrepreneurial experience negatively influences workers’ prospects at the hiring stage and shows how this founding penalty varies by gender. Study 1 is a resume-based audit study that tested the effect of entrepreneurial experience on the probability of interview callbacks, and Study 2 is an experimental survey that attempted to evaluate the explanations about why there exists a penalty for ex-founders returning to wage employment. I replicate the findings from Study 2, because it is not only appropriate in scope and time but also engages with the literature on understanding why founding penalties exist.
Participants are given one of four resumes differing in founding experience and gender and asked to evaluate the given job candidate’s qualities, especially their fit and commitment. In other words, this survey experiment is a two-by-two between-subject design with participants randomly assigned to read one type of resume out of four conditions: female founder, male founder, female non-founder/employee, and male employee. The dependent variable is the respondents’ willingness to recommend the given job candidate to a future employer, and mediating variables are the extent to which the given job candidate is a good fit to a hierarchical organization and is likely to quit next job. At the end of the experiment, to control for the respondents’ characteristics, their information including age, gender, and years of work experience will be collected.
The primary challenge will be to find a sample as similar as possible to the original study: marketing managers with more than five years of work experience and at least a bachelor’s degree who come from a diverse range of industries, including manufacturing, advertising, healthcare, software development, and consulting. Because the paper does not mention the industry composition of the sample and only writes that the sample is from “across a range of industries, such as manufacturing, advertising, healthcare, software development, and consulting”, I am worried about getting a sample whose industry composition vastly differs from that of the original study sample. Another challenge will be difficulties associated with retaining attention, which would especially increase from my additional questions. The more questions and tasks respondents are asked to do, the more likely that the responses we get are answered less carefully.
Methods
Power Analysis
Original effect size, power analysis for samples to achieve 80%, 90%, 95% power to detect that effect size. Considerations of feasibility for selecting planned sample size.
Planned Sample
The planned sample of the present replication study is 425 individuals who have hiring experience. If the budget allows for further selection of the sample, it would be helpful to further restrict the sample to those who have at least 2 years of work experience.
Materials and Procedure
Here is the link for the materials: https://stanforduniversity.qualtrics.com/jfe/form/SV_6sRpi4Fvh05zJPg.
The resumes of job candidates differing in founding experience and gender used in the present study are downloaded from the original study. The procedure is also exactly the same except one additional questionnaire I ask in the replication study about the respondents’ current job position and industry, because the original study sample consists entirely of hiring managers who have extensive experience hiring for marketing positions.
Analysis Plan
“We begin by analyzing differences in means between our treatment and control conditions. Consistent with the audit results, participants gave a significantly stronger (t = 2.05, p = 0.04) interview recommendation to nonfounders (mean = 5.73) than to ex-founders (mean = 5.40), and female ex-founders received a significantly higher recommendation (t = 4.33, p = 0.01, mean = 5.89) than male ex-founders (mean = 4.86). Table 5 repeats this analysis with a regression framework and displays OLS models predicting interview recommendations as a function of treatment. Model (1) replicates the estimates from the audit study on the full sample of job candidates: the estimates are recovered, with the coefficient of Ex-Founder being negative and statistically significant at the 5% level. Model (2) reestimates this baseline specification but adds the interaction between Ex-Founder and Female: the coefficient of Ex-Founder is statistically different for female and male job seekers, as indicated by the positive and statistically significant interaction term between Ex- Founder and Female Candidate (p < 0.05). The magnitude of this effect is striking: among male candidates, the probability of being recommended for an interview decreases by 0.765 points on a seven-point Likert scale.”
The key analysis of interest is the causal mediation analysis. Moreover it is worth noting that because of the differences from the original study in sample, we aim to ask additional questions about their current employment status, for whom they work for, which allows us to learn about what sector/industry they work in, and what industry their ventures are in if they are self-employed.
Differences from Original Study
I expect there to be some differences in sample. First, the present replication study does not recruit hiring managers but rather those who have any experience in making hiring decisions. Moreover, the original study recruits those who have experience hiring for marketing positions, but the sample collected in the previous study is expected differ in their job positions. Because perceptions of ex-entrepreneurs, which is the focus of Study 2, might differ across job positions and industries, I do anticipate that these differences in sample might create a difference based on claims in the original article. Though this might be seen as ‘failing’ to replicate, new insights can be yielded from this exercise such that job positions and industries are significant drivers of the results. Otherwise, the setting, procedure, and analysis plan are strictly followed from the original study.
Methods Addendum (Post Data Collection)
Actual Sample
Sample size, demographics, data exclusions based on rules spelled out in analysis plan
Differences from pre-data collection methods plan
Any differences from what was described as the original plan
Results
Confirmatory analysis
The analyses as specified in the analysis plan.