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Editors’ note: Vox has launched a new section – JMP Vox – that features short Vox columns written by PhD candidates on their Job Market Papers. The main goal is to provide a platform for excellent research that will not appear in journals or major discussion paper series for years. It is also a means for established economists to more easily track the research of the youngest members of the profession.

Disparities in evaluations are widespread – in hiring decisions, loan approvals, and even criminal justice proceedings (e.g. Bertrand and Duflo 2017, Lang and Spitzer 2020). A common policy proposed to remedy such disparities is to conceal candidate names during evaluations, known as ‘blinding’. If evaluators are using candidate identity information to discriminate, then shouldn’t hiding this information improve outcomes?

Whether blinding is a successful policy or not remains contested, with two fundamental questions unanswered. First, does blinding cause evaluators to change who receives favourable evaluations without sacrificing the ability to screen for quality? Past work has largely focused on how blinding affects representation (e.g. Goldin and Rouse 2000, Blank 1991), but whether blinding induces a representation-quality tradeoff is ambiguous because it depends on how evaluators use the information conveyed by names. If evaluators use the information to cater to biased preferences (Becker 1957), then blinding can simultaneously increase representation and improve the selection of high-quality candidates. On the other hand, if evaluators use names as informative signals of quality (Phelps 1972, Arrow 1973, Aigner and Cain 1977), then blinding can shift representation at the expense of quality. The possibility of these different mechanisms leads to the second question: why do disparities in evaluations exist in the first place?

In my paper (Uchida 2025), I answer these questions by running two field experiments in the review process of a major international academic conference in computational neuroscience. The setting is policy-relevant: whether blinding should be used in academia continues to be debated, and gaps persist in important outcomes such as opportunities to present work, journal acceptances, and tenure decisions across gender, institution prestige, and career stage (e.g. Blank 1991, Sarsons 2017, Doleac et al. 2021).

I begin by asking how blinding changes reviewer decisions. The main challenge in answering this question is that in most settings, reviewers and applicants choose whether to participate in a blind process, so that differences across blind and non-blind regimes may reflect differences in who chooses to take part rather than the effect of blinding itself. I overcome this by using two stages of randomisation. First, each of the 245 reviewers was randomly assigned to see author lists (‘non-blind’) or not (‘blind’). This ensures that blind and non-blind reviewers have similar characteristics on average. Second, each of the 657 submitted papers was randomly assigned to two blind and two non-blind reviewers. This allows me to compare how the same paper is judged under blind versus non-blind review.

Hiding author names helps early-career applicants and applicants from non-top-20 ranked institutions

For each assigned submission, reviewers receive a title, 300-word abstract, and 2-page summary, and give a score from 1 to 10. To understand who benefits from blinding, I test how scores of blind versus non-blind reviewers differ by applicants’ student status (students, post-PhD), affiliated institution rank (top-20, non-top-20), and gender (male, female). The applicant is the individual who submits the work and would present it if accepted. Because co-authorship is common in computational neuroscience, I also show the effects by co-author characteristics in my paper.

Figure 1 shows the average scores by applicant traits and whether the reviewer received author lists or not. Among non-blind reviewers (i.e. those who see author names), applicants who are post-PhD and from top-20 ranked universities receive significantly higher scores than applicants who are students or affiliated with lower-ranked institutions. When the same submissions are scored by reviewers who do not receive author lists, score gaps by student status and institution rank shrink.

Figure 1 Reviewer scores and effects of blinding

These changes in score gaps lead to changes in acceptances by student status. Blinding essentially eliminates the difference in acceptance rate between students and more senior applicants. Acceptance-rate gaps by institution rank are significantly reduced when the conference accepts a small fraction of papers. Interestingly, I do not find a significant change by gender. Overall, these effects of blinding on score gaps show that reviewers do use author names when the information is provided to them.

Hiding author names changes representation while preserving the evaluation’s ability to screen on quality

A key policy concern is that blinding shifts representation at the cost of quality. If author names convey information that is predictive of underlying paper quality, then hiding them may remove useful information. For instance, names could convey career stage, and applicants further along in their careers may be more likely to produce high-quality research. The main challenge in answering this question is that in most evaluation settings, underlying candidate quality is not observed by the researcher.

To test whether blinding interferes with the ability to select high-quality submissions, I track each submission for five years after the conference, collecting proxy measures of its quality: citation and publication outcomes.

Figure 2 plots the relationship between a paper’s percentile rank in quality and its percentile rank in blind and non-blind scores. I find that a paper’s blind score is as good a predictor of its citation and publication outcomes as its non-blind score. Papers that would be accepted under blind review have comparable citation and publication outcomes as those that would be accepted under non-blind review.

Figure 2 Effects of blinding on quality

The nature and extent of discrimination can determine how blinding affects representation and quality

How can blinding change representation of selected applicants without changing quality? And what underlying forces drive the gaps in evaluations in the first place?

To answer these questions, I build on past work examining sources of disparities (e.g. Canay et al. 2024) and develop a model of how non-blind reviewers assign scores using the content of the submission and author traits. I use my model to decompose disparities into four distinct forms of discrimination:

1. Accurate statistical discrimination (Phelps 1972, Arrow 1973, Aigner and Cain 1977), where reviewers use author group memberships to accurately update beliefs on underlying paper quality.
2. Inaccurate statistical discrimination (Bordalo et al. 2019, Bohren et al. 2019, Coffman et al. 2021), where reviewers rely on inaccurate beliefs about paper quality
3. Pursuit of alternative objectives beyond paper quality, such as favouring applicants whose acceptance would benefit others the most.
4. All other determinants of disparities, including taste-based discrimination and animus (Becker 1957).

The question is then: across submissions with comparable content, to what extent can differences in reviewer scores across author traits be attributed to differences in true quality, differences in reviewers’ misbeliefs about quality, or reviewers’ beliefs over alternative objectives?

Estimating this model requires overcoming two main challenges. The first is that distinguishing between actual and perceived quality differences requires observing reviewer beliefs. I therefore run a second experiment with the same conference that directly elicits reviewers’ beliefs during the review process about submission outcomes (future citation and publication status) and alternative objectives (for example, how much the applicant’s acceptance would benefit others).

The second challenge is accounting for submission content. This is a longstanding issue because oftentimes evaluators may base decisions on aspects of a submission’s content that researchers do not have data on. Failing to account for submission content implies that differences in outcomes, such as quality beliefs across groups, may be driven by differences in submission content rather than discrimination based on author traits. I address this comparability issue by using blind scores as a proxy for submission content, given that blind reviewers assign scores using only submission content without receiving author names.

Figure 3 presents the decomposition results. I find that the underlying forms of discrimination driving disparities in reviewer scores differ across traits. The entirety of the score gap by student status can be explained by two channels: reviewers hold overly pessimistic beliefs about the quality of papers submitted by students and value alternative objectives such as talk quality, which they believe is worse for students than for more senior applicants. In contrast, the score gap by institution rank is not explained by these channels and is instead consistent with a preference for applicants from top-ranked institutions (or animus against those from non-top-20 ranked institutions).

Figure 3 Decomposing non-blind score gaps

In sum, the efficacy of blinding depends on why disparities exist in the first place. My experiments show that blinding can shift representation, particularly by career stage and institution rank, without compromising on the ability to screen on quality. My model decomposition helps explain why: the mechanisms that generate changes in representation can offset each other in their effects on quality. More broadly, the decomposition demonstrates how data from blind evaluations can be leveraged to learn more about the sources of disparities that exist in the absence of blinding.

These insights, and the methodology developed in my paper, extend beyond academic review processes. Many policy-relevant evaluation settings, including job hiring, grant allocation (Li 2017), and social insurance receipt (Low and Pistaferri 2025), face potential trade-offs between information, representation, and quality. Understanding the mechanisms driving disparities therefore remains essential for designing fair and effective evaluation systems.

References

Aigner, D J, and G G Cain (1977), “Statistical theories of discrimination in labor markets”, ILR Review 30(2): 175–187.

Arrow, K J (1973), The theory of discrimination, Princeton University Press.

Becker, G S (1957), The economics of discrimination, University of Chicago Press.

Bertrand, M, and E Duflo (2004), “Field experiments on discrimination”, in Handbook of economic field experiments, Volume 1, Elsevier.

Blank, R M (1991), “The effects of double-blind versus single-blind reviewing: Experimental evidence from the American Economic Review”, American Economic Review 1041–67.

Bohren, J A, A Imas, and M Rosenberg (2019), “The dynamics of discrimination: Theory and evidence”, American Economic Review 109(10): 3395–436.

Bordalo, P, K Coffman, N Gennaioli, and A Shleifer (2019), “Beliefs about gender”, American Economic Review 109(3): 739–73.

Canay, I A, M Mogstad, and J Mountjoy (2024), “On the use of outcome tests for detecting bias in decision making”, Review of Economic Studies 91(4): 2135–67.

Coffman, K B, C L Exley, and M Niederle (2021), “The role of beliefs in driving gender discrimination”, Management Science 67(6): 3551–69.

Doleac, J L, E Hengel, and E Pancotti (2021), “Diversity in economics seminars: Who gives invited talks?”, AEA Papers and Proceedings (111): 55–59.

Goldin, C, and C Rouse (2000), “Orchestrating impartiality: The impact of ‘blind’ auditions on female musicians”, American Economic Review 90(4): 715–41.

Lang, K, and A K L Spitzer (2020), “Race discrimination: An economic perspective”, Journal of Economic Perspectives 34(2): 68–89.

Li, D (2017), “Expertise versus bias in evaluation: Evidence from the NIH”, American Economic Journal: Applied Economics 9(2): 60–92.

Low, H, and L Pistaferri (2025), “Disability insurance: Error rates and gender differences”, Journal of Political Economy 133(9): 2962–3018.

Phelps, E S (1972), “The statistical theory of racism and sexism”, American Economic Review 62(4): 659–61.

Sarsons, H (2017), “Recognition for group work: Gender differences in academia”, American Economic Review 107(5): 141–5.

Uchida, H (2025) “What do blind evaluations reveal? How discrimination shapes representation and quality”, SSRN Working Paper.

Standard, perfectly competitive models of dual-sector economies predict that wage floors should induce sizeable reallocation from formal to informal employment.
At the same time, recent evidence suggests that strict segmentation is a poor characterisation of formal versus informal employment and that gradients of informality may be a more accurate description of labour markets (e.g. Meghir et al. 2015, Ulyssea 2018, 2020 and VoxDev Talk here, Haanwinckel and Soares 2021, Feinmann et al. 2024).

In our new research (Derenoncourt et al. 2025), we show that minimum wage policy can positively affect living standards for workers thought beyond the reach of labour law, with limited reallocation effects towards the informal sector.

We study large increases in the national minimum wage that occurred in Brazil between 2000 and 2009. These increases moved the country from a regime of low minimum wage bite (a minimum-to-median wage ratio of 0.34) to one of high bite (a minimum-to-median wage ratio of 0.58) in ten years. About 46% of all workers employed in the Brazilian private sector are informal. Within this group of workers, there is substantial heterogeneity in their type of employment: about half of informal workers work in formal firms (intensive margin of informality), while the other half are employed in informal firms (extensive margin of informality). These features of the Brazilian labour market, combined with the availability of rich data capturing formality status, make Brazil an ideal context for analysing the effects of minimum wages on informality.

The bite of the minimum wage among informal workers

We start by analysing the monthly earnings distributions for workers in all types of employment. Our sample of interest is comprised of prime-age workers (25 to 54 years old) working full-time. To follow informal workers’ margin of informality in years where this information is not observed, we combine data on industry and the margin of informality available in the labour force survey since 2011 and in the establishment survey (ECINF) conducted in 1997 and 2003. Specifically, we show that working in agriculture, domestic services, and construction is a strong proxy for working in the extensive margin of informality (working at an informal firm), while informal workers in other industries can be reasonably assigned to the intensive margin (working at a formal firm). Doing so allows us to construct a database of informal workers by their margin of informality from 1995 to 2015. We make this database, along with all our other data and programs, available on our websites.   

We first show that there was a large mass of workers paid at the minimum wage in 1999, before the minimum wage increases. Among informal employees in formal firms, 8.1% of them were paid exactly at the minimum wage, with 7.3% paid strictly below. Among informal employees in informal firms, these respective shares were 10.8% and 23.5%. For comparison, among formal workers, there was almost perfect compliance, with only 0.7% paid strictly below the minimum wage and 12.7% of workers at exactly the minimum wage.

Why was there such a large mass of informal workers at the minimum wage? First, it may have been due to the relatively high penalties associated with violating labour laws. In 1999, the penalty associated with paying below the minimum wage was larger than the penalty associated with not registering informal workers and evading social security contributions. Second, the minimum wage could serve as an important benchmark for ‘fair’ remuneration – a phenomenon that could also explain the spike at the minimum wage among informal employees in informal firms (Maloney and Mendez 2004). Third, competitive mechanisms might have been at play. This hypothesis is consistent with the fact that informal employees working in formal firms at the minimum wage resemble formal workers in terms of their observable characteristics.

Interestingly, we show that the monthly earnings distributions of formal workers and informal workers in formal firms strongly track the minimum wage throughout the period of large increases in the 2000s – therefore upending the notion that the formal and informal sectors operate in different, segmented labour markets.

Figure 1 Monthly earnings distributions in the informal sector before the minimum wage increases

a) Informal employees: Intensive margin

b) Informal employees: Extensive margin

The wage effects of the minimum wage

To quantify the magnitude of the wage effect among formal employees, we use a difference-in-differences design exploiting variation in the bite of the reform across states and industries. We measure the bite of the reform for the formal sector as the fraction of formal employees paid at or below the 2009 minimum wage in 1999, across 279 state and industry cells defined pre-reform. We find that a one standard deviation increase in the share of affected workers is associated with a 13.2 log point increase in average monthly wages (see Figure 2a).

Using a similar research design, we find large and immediate wage increases among informal employees working in formal firms (see Figure 2b). These effects are concentrated at the level of the minimum wage for formal and informal employees in formal firms, with no impacts higher in the wage distribution.

We find an immediate and substantial passthrough of the minimum wage of 0.88 for informal workers working in formal firms – meaning, for the same increase in the share of affected workers as in the formal sector, wages increase by 88% of the increase in the formal sector.

For informal workers employed in informal firms, the passthrough is smaller (59%), and takes several years to materialise. We do not find any evidence of passthrough to the self-employed.

Interestingly, because the magnitude of the wage effects are similar within ~280 state-by-industry cells and ~6,500+ microregion-by-industry cells, we conclude that this suggests that the spillover effects of the minimum wage to the informal sector happen within finer local labour markets.

Figure 2 Wage effects of the minimum wage in 2009

a) For formal employees

b) For informal employees in formal firms

The effects of the minimum wage on the allocation of employment

In a setting of a low- or middle-income country, the potential for minimum wages to benefit workers and reduce poverty hinges not only on the policy’s effect on wages, but also on its effects on formalisation, specifically the reallocation of employment away from the formal sector.

Using a linear probability model, we estimate an own-wage reallocation elasticity of -0.28 in 2009 – that is, for a 10% increase in average wages as a result of the policy, there is a 2.8 percent shift into other modes of employment out of the formal salaried sector. We think of this elasticity as ‘small’ – if we follow Dube’s (2019) benchmark used to make sense of the more extensively studied ‘own-wage employment elasticities’ in high-income countries.

Finally, we ask: what would have been the formal share in the economy in the absence of the 2000s minimum wage increases? Calculations based on simple assumptions motivated by our findings (no overall disemployment effects, shifts out of the formal labour force absorbed by informal salaried employment, and no general equilibrium effects of the minimum wage), we find that the formal share would have been 64.3% in 2009 instead of the actual share of 62.1% (see Figure 3). Absent the minimum wage increases, the formalisation process would have sped up by one year for the entire private workforce of salaried workers.

Figure 3 Evolution of the formal versus informal sector, and counterfactual shares

Conclusion

Our findings raise the possibility that the minimum wage may be an effective tool to improve living standards for workers in developing economies typically considered beyond the reach of labour law. They also shed light on the historical record of other countries such as the US, which experienced strong increases in its minimum wage in the 1950s and 1960s while formalising and whose GDP per capita then was comparable to that of Brazil in the 2000s. Our results also have implications for the study of non-compliance in high-income countries today (Clemens 2021, Clemens and Strain 2022, Stansbury 2024).

Authors’ note: For more about economic research on the informal sector, see the VoxDevLit on informality; for more on minimum wages, see the various columns on VoxDev.

References

Clemens, J (2021), “How Do Firms Respond to Minimum Wage Increases? Understanding the Relevance of Non-employment Margins”, Journal of Economic Perspectives 35(1): 51-72.

Clemens, J and M Strain (2022), “Understanding “Wage Theft”: Evasion and Avoidance Responses to Minimum Wage Increases”, Labour Economics 79.

Derenoncourt, E, F Gerard, L Lagos and C Montialoux (2025), “Minimum Wages and Informality”, NBER Working Paper No. 34445.

Feinmann, J, M Lauletta and R H Roch (2024), “Payments Under the Table: Employer-employee collusion in Brazil”, Working Paper.

Fields, G S (1990), “Labor Market Modelling and the Urban Informal Sector: Theory and Evidence”, in D Turnham, B Salome and A Schwarz (eds), The Informal Sector Revisited, OECD.

La Porta, R and A Shleifer (2014), “Informality and Development”, Journal of Economic Perspectives 28(3): 109–26.

Haanwinckel, D and R R Soares (2021), “Workforce Composition, Productivity, and Labour Regulations in a Compensating Differentials Theory of Informality”, The Review of Economic Studies 88(6): 2970–3010.

Meghir, C, R Narita and J-M Robin (2015), “Wages and Informality in Developing Countries”, American Economic Review 105(4): 1509–1546.

Peattie, L (1987), “An Idea in Good Currency and How It Grew: The Informal Sector”, World Development 15(7): 851–860.

Portes, R and R Schauffler (1993), “Competing Perspective on the Latin American Informal Sector”, Population and Development Review 19(1): 33–59.

Stansbury, A (2024), “Incentives to Comply with the Minimum Wage in the US and UK”, ILR Review.

Tokman, V E (1992), Beyond Regulation, The Informal Economy in Latin America, Lynne Rienner Publishers.

Turnham, D and D Erocal (1990), “Unemployment in Developing Countries, New Light on an Old Problem”, OECD Development Centre Working Paper.

Ulyssea, G (2018), “Firms, Informality, and Development: Theory and Evidence from Brazil”, American Economic Review 108(8): 2015–2047.

Ulyssea, G (2020), “Informality: Causes and Consequences for Development”, Annual Review of Economics 12(1): 525–545.