Igor Asanov - Blog

Is Self-employment a Career Trap?

Wed, 20 May 2026 07:00:00 GMT

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

↑ 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.

New World Bank Policy Note on Online Learning

Wed, 06 May 2026 10:52:17 GMT

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.

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

Tue, 05 May 2026 07:00:00 GMT

≈ 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]

Thank you for the experience.

Sun, 02 Nov 2025 17:16:46 GMT

No evidence for effectiveness of behavioral interventions to mitigate climate change after adjusting for publication bias

Sat, 04 Oct 2025 08:48:48 GMT

"We reanalyzed the evidence of behavioral interventions on citizens. We conducted Robust Bayesian Meta-Analysis (RoBMA), averaging across a full set of publication bias-adjusted models, to the 144 effect estimates (91 studies) compiled by Nisa et al. (2019). The bias-adjusted model-averaged posterior mean standardized effect of behavioral interventions on citizens is shrunk to 0.00 (95 % credible interval 0.00; 0.00), with a Bayes factor of 66 favoring the null."

"Accordingly, the previously reported noteworthy mean benefit of -0.093 (95% confidence interval - 0.123; -0.063) of behavioral interventions, including promising light-touch interventions (nudges or social comparison), on households and individuals is an artefact of publication bias."

Find out more in the paper: No evidence for effectiveness of behavioral interventions to mitigate climate change after adjusting for publication bias, I4R Discussion Paper Series, No. 263, Institute for Replication (I4R), s.l.it. [Link]

The Economist as Plumber

Wed, 17 Sep 2025 07:56:56 GMT

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I am honored and excited to be an invited researcher at Abdul Latif Jameel Poverty Action Lab (J-PAL). Thank you for the opportunity

Mon, 25 Nov 2024 09:30:24 GMT

Read more

How to organise education online when students cannot go to school?

Tue, 30 Jan 2024 18:32:13 GMT

"How to organise education online when students cannot go to school? While massive online open courses struggle from low completion rates among voluntary learners, compulsory education at school demands new approaches. Our international research team had to search for these new approaches as we decided to help students in their final years of high school in Ecuador to finish school during the COVID-19 pandemic. When the pandemic broke out in Spring 2020, we were finalising a randomised controlled trial of educational online materials from our programme "Showing Life Opportunities", which aims to boost high-growth entrepreneurship and science, technology, engineering, and mathematics (STEM) careers in Ecuador. Under emergency conditions, we successfully scaled up our programme to cover more than 45,000 students in 1,151 schools across Ecuador. In our study we tested a set of light-touch interventions to improve students' educational process and knowledge outcomes. A recent paper published in the Proceedings of the National Academy of Sciences (PNAS) describes what we learned from experimenting with light-touch interventions."

Read more on IGL website.

Igor Asanov, Anastasiya-Mariya Asanov (Noha), Thomas Åstebro, Guido Buenstorf, Bruno Crépon, David McKenzie, Francisco Pablo Flores T., Mona Mensmann & Mathis Schulte (2023): "System-, Teacher-, and Student-level Interventions for Improving Participation in Online Learning at Scale in High Schools", Proceedings of the National Academy of Science (PNAS), 2023 Vol. 120 No. 30 e2216686120; https://doi.org/10.1073/pnas.221668612

Photo by Avi Richards on Unsplash

Do postdoc years monetary pay off outside academia?

Mon, 30 Jan 2023 14:20:57 GMT

In most cases, doctoral graduates leave universities some years after graduation. How much do doctoral graduates earn when they leave university? Do postdoc years monetary pay off outside academia? In a recent paper, Johannes König shows that postdoctoral time does not result in wage premiums but is associated with wage losses outside academia. Moreover, the later doctoral graduates leave academia, the less they earn in the private sector (in the first five years after graduation). The wage gap is sizable and rapidly grows with postdoc time (see Fig 1). The first postdoc year is associated with a wage loss of 5% compared to no postdoc experience. Leaving five years after graduation is associated with a wage loss of 18%.

Fig 1. Earnings differences in relation to retention in science. Adopted from Koenig (2022).

Johannes finds this pattern in the labor market, analyzing more than 33 000 observations within an extensive 15 years timeframe (graduates between 1994 and 2009). This pattern holds on the five largest subject fields: humanities and arts, social sciences, science and mathematics, medicine, and engineering. Moreover, these findings "can be considered representative of doctorate recipients who were employed as postdocs at universities for up to 5 years after graduation and later changed the employment sector".

Naturally, the question arises if selection plays a role: Perhaps, the most productive, ambitious postdocs stay in academia, while others leave with no wage premium or loss. Apart from including a set of control variables that can account for the difference between doctoral graduates, Johannes uses a matching approach that shall partially reduce this concern. He matches statistically comparable doctoral graduates on observable characteristics, e.g., age, citizenship, previous work experience, and compares the wages among them. Yet, the pattern remains unchanged after this procedure suggesting the robustness of the observed wage gap.

These results beg the question if one can consider the postdoctoral period as a further qualification used to justify a postdoc's relatively insecure working conditions. Why is the payoff so low if the postdoctoral period is deemed the advanced qualification phase?

Read more:
König, J., 2022. Postdoctoral employment and future non-academic career prospects. Plos one, 17(12), p.e0278091.

Can group identity explain the gender gap in the recruitment process?

Fri, 06 Jan 2023 08:00:00 GMT

Being interested in the gender gap in the labor market, we (Maria Mavlikeeva and I) were intrigued by the somewhat counterintuitive observation in the literature and the result of our meta-analysis of correspondence studies: On average, women are more likely to be invited for job interviews than men (see Fig. 1). This result is at odds with the overall gender gap in the labor market, which favors men.

Fig. 1. Funnel plot of risk ratio (left) and odds ratio (right) of callback for female compared to male. Each dot represents an estimate of this effect from each correspondence study.

To explain this counterintuitive result, we developed a hypothesis based on group identity theory: As recruiters may favor applicants of their gender, the predominance of female recruiters is responsible for a higher rate of women being invited for job interviews than men. We used data from our large-scale correspondence study to test this hypothesis. In this correspondence study, we randomly varied the gender of the applicant (male or female applicant name) on the resumes sent in response to real job openings; then, we measured the rate of callbacks for interviews.

As expected, we found that female applicants were more likely to receive callbacks for interviews. We also observed that the majority of the contact persons responsible for the recruitment process in our sample were female. But perhaps most importantly, we found that if the recruiter and applicant were of the same gender, the probability of the applicant being invited for an interview increased (see Fig. 2). These findings suggest gender-based in-group favoritism in the recruitment process.

Fig. 2 Gender preferences in callbacks (Callback rate in percent, number of resumes sent in square brackets).

The evidence of in-group favoritism in the recruitment process offers a promising avenue for addressing gender-based hiring discrimination. Ensuring that recruiters' positions at various levels are equally appealing to all genders can help to decrease gender-based bias during the selection stage of the recruitment process for other applicants. Moreover, incorporating discussions of in-group bias, regardless of gender, in diversity training could be beneficial to equalize employment opportunities.