Dana Scott

Dana Scott

@danascott.bsky.social

PhD student at Yale Economics, on the JM in 2025/26. Mostly labor. Spare time: cat 1 road bike racer + 2:44 marathoner. dana-scott.com

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These findings suggest that we can’t simply regulate our way to a future with more women-friendly workplaces. Thinking about ways to reduce coordination costs is more likely to stick—but certainly a lot more challenging to imagine.

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On the firm side, I find that the productivity cost of flexibility varies sharply by skill level. High-skill occupations in competitive markets (e.g., finance jobs in Paris) face 2x the penalty of mid-skill jobs. This explains why high-paying jobs often bundle good wages with demanding schedules.

Using the model, I can ask what would happen if we required firms to provide a minimum level of flexibility. While this would increase amenity provision, it would *increase* the gender pay gap by 1.8pp because it would deepen gendered sorting across jobs.

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On the labor supply side, I identify labor markets nonparametrically using E:E transitions, estimate rich substitution patterns across occupations and jobs, and recover job-specific labor supply elasticities WRT wages and amenities for men and women.

Women's higher WTP for flexibility (30% higher than men's) combined with lower labor supply elasticities (25% lower) creates a double burden. Preference differences explain 20% of the gender wage gap—a substantial share, but certainly not everything!

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I document three empirical patterns: (1) flexibility co-varies within firms across establishments and occupations; (2) some flexibility dimensions correlate positively and others negatively with pay; and (3) women sort into jobs with better flexibility within occupations.

These suggest that there are important roles for both firms’ technological constraints and workers’ preferences. To explain these patterns, I estimate a model where firms choose wages and flexibility amenities subject to BOTH worker preferences AND productivity costs of providing the amenity.

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Importantly, compensating differentials are *equilibrium* trade-offs between wages and amenities. Worker preferences are one side of the equation. But we know less about the firm’s side. With flexibility in particular, we might be concerned that flexibility may affect workers’ output productivity.

Studying amenities is hard in part because they are difficult to observe. I link French matched employer-employee data to large-scale surveys to observe a range of amenities related to workplace flexibility. This addresses the measurement challenge and helps us earn about firms' endogenous choices.

I study these questions by focusing on workplace flexibility—what Goldin (2014) defines as the “temporal aspects of work”: when, where, and how flexibly one works. Flexibility is especially important for gender inequality, because we know that women have a higher WTP for it than men do.

Suppose I have an instrument Z for some endogenous variable X. Z does a much better job predicting X for some X values than others. E.g. major better predicts occupation for nurses than for management consultants. What problems does this introduce? Is there a go-to reference for this issue?

Hello #EconSky 📉📈 I'm Dana! I'm a 4th-year economics PhD student at Yale. I study labor markets and dabble elsewhere: public, household finance, (aspirationally) applied econometrics. I race marathons and bikes in my spare time. Excited to see what is happening over here!

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