Igor Asanov
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Is Self-employment a Career Trap?

5/20/2026

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Asanov, I., & Mavlikeeva, M. (2026). Is Self-Employment a Career Trap? A Large-Scale Field Experiment in the Labor Market. Entrepreneurship Theory and Practice, 0(0). https://doi.org/10.1177/10422587261440312

Field Experiment

As AI displaces entry-level jobs (Brynjolfsson et al., 2025), governments will grow more tempted to push young workers into self-employment. But what happens when those workers later try to return to traditional employment? The findings from our study with Maria Mavlikeeva speak directly to this concern.

We sent 8,328 fictitious résumés to 2,150 real job vacancies across Russia — one of the largest pre-registered correspondence experiments of its kind.

Work Experience Senior Accountant Company LLC 2019 – present Junior Analyst Global Bank · 2016 – 2019 Education State University, 2016 Wage earner Work Experience Senior Accountant Self-employed 2019 – present Junior Analyst Global Bank · 2016 – 2019 Education State University, 2016 Self-employed

The key randomly assigned difference between résumés was employment status: some listed a company as employer, others listed the applicant as self-employed. Everything else — experience, education, age, gender — was identical and randomized.

We targeted six occupations across finance, IT, and public relations — applying for both managerial and associate professional positions within each industry.

Information Technology Finance Public Relations
Managers
Higher skill
IT Manager Finance Manager PR Manager
Associate Professionals
Lower skill
IT Technician Accounting Associate Event Planner

We then measured whether employers were more or less likely to call back applicants for an interview depending on their employment status. Hypotheses were locked in before data collection and the study was pre-registered. There is an overall self-employment penalty — but the asymmetry across roles is stark.

Self-employed applicants face a hiring penalty — but only in lower-skilled roles
Callback rates from 8,328 fictitious résumés sent to real Russian job vacancies, 2017.
Wage earner Self-employed
Callback rate by skill group
Key finding Associate professionals face 28% fewer callbacks when self-employed — comparable to the ethnic minority hiring penalty (~29%, Lippens et al., 2023). Managers face almost no penalty at all (−5%).
Callback rate by occupation — choose to explore
✕ Clear ↑ or click a row below
SE = Self-employed · WE = Wage earner (reference group) · Percentages reflect relative difference in callback rates. Occupation rates directly measured, not interpolated.

The pattern sharpens at the occupation level: using O*NET managerial skill ratings, we find a clean dose-response — the lower the skill content of a role, the larger the penalty. Applicant demographics do not appear to explain the penalty. Gender, age, and education were all randomized independently, and none are associated with the effect.

Two mechanisms can explain why. Self-employment builds generalist skills — useful for managers — at the cost of the specialist depth associate roles demand. Employers appear to sense this mismatch.

But it goes beyond skills. Employers might also assume self-employed workers are too independent for subordinate roles. We tested this directly: adding a team-player statement to the cover note boosted callbacks for both groups — the penalty for self-employed applicants remained.

The policy implication deserves attention. Self-employment — promoted as opportunity — can become a trap for lower-skilled workers, scarring their future employability. As AI shrinks entry-level hiring and governments reach for entrepreneurship as the answer, our evidence urges caution: promoting self-employment among less-skilled workers without supporting their path back may transfer risk onto those least able to bear it.

Asanov, I., & Mavlikeeva, M. (2026). Is Self-Employment a Career Trap? A Large-Scale Field Experiment in the Labor Market. Entrepreneurship Theory and Practice, 0(0). https://doi.org/10.1177/10422587261440312

Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), pp. 889–942.

Pre-registered at AEA RCT Registry (AEARCTR-0001308). Conducted March–August 2017 in the Russian labor market. 8,328 résumés across 6 occupations, 3 industries, and 2 ISCO skill groups in the main analysis.

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New World Bank Policy Note on Online Learning

5/6/2026

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Picture

World Bank, From Evidence to Policy series, March 2026.

The World Bank's Strategic Impact Evaluation Fund (SIEF) just published a policy note based on our PNAS paper on remote learning in Ecuador. It covers the key findings from our RCT with 45,000 high school students — what worked, what didn't, and what it means for education systems facing future disruptions.

The note is part of the World Bank's From Evidence to Policy series and is available open access.

Read the policy note →

Asanov et al. (2023). "System-, teacher-, and student-level interventions for improving participation in online learning at scale in high schools." PNAS 120(30): e2216686120.

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No evidence that non-incentivized behavioral interventions effectively mitigate climate change

5/5/2026

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Hardaker, A., Asanov, I., Bartoš, F., & Bruns, S. B. (2026). No evidence that non-incentivized behavioral interventions effectively mitigate climate change after adjusting for publication bias. PNAS Nexus. pgag150. [Link]

≈ 0.000
Bias-adjusted effect (d)
63.5
BF₀₁ for null
91
Field studies
98.4%
Pr(zero effect)
Null or counterintuitive results tend to go unpublished — so the literature can overstate the effectiveness of interventions such as nudges (Maier, Bartoš et al., 2022). We therefore reanalyzed 91 field studies on policy-appealing green behavioral interventions (Nisa et al., 2019) using Robust Bayesian Meta-Analysis (Bartoš et al., 2025)—after correcting for publication bias, the effect disappears. The data are 63.5× more consistent with zero effect than any effect at all.
Raw (unadjusted) Bias-corrected
← What did researchers report before correction? Press to find out.
Open circle = point estimate · Whiskers = 95% CI (raw) or CrI (corrected)

"After accounting for publication bias and model uncertainty, the data strongly favor a zero average effect. On average, behavioral interventions without incentives on households and individuals are unlikely to deliver material climate benefits."

Shifting climate policy toward incentives and structural interventions appears more promising than standalone behavioral nudges directed at citizens.

References

Nisa, C. F., Bélanger, J. J., Schumpe, B. M., et al. (2019). Meta-analysis of randomised controlled trials testing behavioural interventions to promote household action on climate change. Nature Communications, 10, 4545.

Maier, M., Bartoš, F., Stanley, T. D., Shanks, D. R., Harris, A. J. L., & Wagenmakers, E.-J. (2022). No evidence for nudging after adjusting for publication bias. PNAS, 119(31). https://doi.org/10.1073/pnas.2200300119

Bartoš, F., Maier, M., & Wagenmakers, E.-J. (2025). Robust Bayesian multilevel meta-analysis: Adjusting for publication bias in the presence of dependent effect sizes. PsyArXiv. https://doi.org/10.31234/osf.io/9tgp2_v1

Hardaker, A., Asanov, I., Bartoš, F., & Bruns, S. B. (2026). No evidence that non-incentivized behavioral interventions effectively mitigate climate change after adjusting for publication bias. PNAS Nexus. pgag150. [Link]

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