The Lost Marie Curies and Foregone Economic Growth

Layer 1: Overview

Women accounted for only 3% of U.S. inventors in 1976 and still just 14% in 2023, a pace of convergence far slower than in law (3% to 49%) or medicine (6% to 46%) over the same period. Under the natural assumption of no innate gender differences in inventive potential, this persistent underrepresentation reveals a misallocation of talent. The paper asks how costly this misallocation is for aggregate productivity and welfare.

Brouillette develops an overlapping-generations (OLG) model of semi-endogenous growth in the spirit of Jones (1995), in which individuals with heterogeneous innate inventive talent choose sequentially among three decisions: (1) whether to pursue a STEM education (the prerequisite for research), (2) whether to work in research or production, and (3) whether to have children. Three gendered barriers can deter women from their comparative advantage. First, a labor market distortion, modeled as a tax on research earnings, captures discrimination in pay and credit attribution. Second, a child penalty distortion reduces mothers’ hours in research relative to fathers, amplified by the “greedy job” nature of research (a premium on long hours). Third, an exposure distortion, modeled as a Bernoulli random variable, captures the probability of ever encountering inventive career opportunities — driven empirically by the absence of female role models.

The model is calibrated to the U.S. economy using two data sources: PatentsView (all USPTO patents since 1976, covering roughly 1.7 million inventors and 3.7 million patents, with gender inferred from first names) and the U.S. Decennial Census/ACS (demographic and occupational data). Across these sources, female inventors exhibit only marginally higher research productivity than men (consistent with modest positive selection from the earnings tax), while mothers in research work approximately 4.5% fewer hours per week than childless female researchers (fathers work 2.7% more). The small productivity gap and modest hours gap together imply that neither the earnings tax nor the child penalty is the dominant driver; the exposure distortion is inferred as the residual, calibrated to a benchmark female share in research of 23% (average of 19% from PatentsView and 27% from Census/ACS). The resulting distortion estimates are: labor market tax 3.3%, child penalty 7%, and exposure barrier 79%.

Counterfactual elimination of all three distortions raises U.S. income per person by 14.2% in the long run, compared with only 1.5% from a 30% R&D subsidy in a distortion-free economy. The gain materializes slowly, with a half-life of approximately 76 years, reflecting the semi-endogenous structure (where reallocating talent shifts the level but not the long-run growth rate of living standards) and the OLG structure (where career choices are irreversible, slowing labor reallocation). Aggregate research labor increases by 49% within the first 50 years of the transition — women’s research labor more than quadruples while men’s shrinks by about 10% — but almost all of the productivity gain operates through the intensive rather than the extensive margin: the aggregate share of inventors barely rises, because exposure barriers blocked many talented women entirely rather than only marginal ones, so lifting them introduces very high-quality new researchers who crowd out less talented men. If the underrepresentation were instead attributed entirely to selection-based barriers (labor market or child penalty), long-run consumption would rise by only 3.6%, less than a quarter of the baseline 14.2%.

Taking transition dynamics into account, eliminating all distortions is equivalent to permanently raising everyone’s consumption by 7.2% (lower than 14.2% because the transition is slow and future gains are discounted back at a rate exceeding the low projected U.S. population growth). Of this welfare gain, 95% comes from higher mean consumption; the remainder comes from reduced consumption inequality and utility from children. The distribution of gains is unequal across time and demographic groups: future cohorts experience an 8.6% permanent consumption increase versus only 1% for surviving cohorts. Among the current generation of inventors, women gain the equivalent of a 1.3% permanent consumption increase while men lose 1.7%, a distributional tension that complicates implementation when current costs are concentrated and future benefits diffuse.

Layer 2: Deep Dive

What is the identification strategy for the three distortions, and what are the main threats to it?

The three distortions are identified from three moments, each theoretically linked to a specific distortion through the model’s aggregation. The labor market distortion (earnings tax) is identified from the research productivity gender gap: positive selection under this tax implies women should be marginally more productive, and the magnitude of the observed (small) gap pins down a distortion of 3.3%. The child penalty distortion is identified from gender differences in hours worked between parent and non-parent researchers: mothers work 4.5% fewer hours than childless women while fathers work 2.7% more; after normalizing male distortions to zero, the model recovers a child penalty distortion of 7%. The exposure distortion is identified as the residual that explains remaining underrepresentation (23% female share in research) after accounting for the other two mechanisms; it is estimated at 79%. Key threats: (1) The gender productivity gap is measured from PatentsView, which uses name-based gender attribution and citation-weighted patents — both susceptible to gender bias (women are documented to receive 30% fewer citations than men with common names, and are 59% less likely to be credited with authorship on patents they contributed to), so the paper uses stock market valuation and textual similarity of patents as bias-resistant alternatives. (2) The exposure distortion is a residual and could capture other forces not in the model, including occupational preferences, gendered barriers to human capital retention, or mismeasurement of the female researcher share. (3) The model abstracts from the direction of innovation (unlike Einïo, Feng, and Jaravel 2022), so welfare effects through consumption-cost inequality across groups are not captured.

What are the main mechanisms and how are they distinguished empirically?

The three mechanisms operate through distinct theoretical channels, which allows moment-based identification. The labor market distortion works through selection on talent: if only highly talented women choose research despite earning below their marginal product, the female researcher pool should be right-shifted in the talent distribution, implying modestly higher measured productivity for women. The empirical counterpart is the gender gap in patent output (quality-weighted patents per career year), controlling for field fixed effects and team size. The child penalty works through hours worked: a higher opportunity cost of childbearing in research (amplified by greedy-job premiums) reduces mothers’ time in research. The empirical counterpart is the gender gap in hours worked between parents and non-parents in research, from the Census/ACS. The exposure distortion works through the extensive margin of talent — it is a binary probability of ever having access to research as a career path, so it can block even the most talented women, unlike the other two distortions which induce selection. It is identified as the residual after the other two are estimated. The insight that the productivity gap is small and the hours gap is modest together rule out the first two as primary drivers, placing most explanatory weight on the exposure distortion.

How does the semi-endogenous growth framework differ from an endogenous growth approach, and what are the implications for the results?

In semi-endogenous growth (Jones 1995), the long-run per-capita growth rate equals n/[(sigma-1)(1-phi)], determined by population growth and idea difficulty, not by the quantity or quality of researchers. A reallocation of inventive talent therefore cannot raise the long-run growth rate but can raise the level of per-capita consumption by shifting the cumulative stock of ideas and thus the entire trajectory of living standards upward. This stands in contrast to endogenous growth models where reallocating talent can permanently raise the growth rate. The author justifies the semi-endogenous approach on two grounds: (1) despite sustained researcher-population growth in most advanced economies, the per-capita growth rate has not trended up; (2) the framework is qualitatively and quantitatively consistent with the documented fact that ‘ideas are getting harder to find’ (Bloom et al. 2020, which estimates phi = -2.1 for the aggregate U.S. economy). The implication is that the paper finds more modest effects on productivity growth than prior endogenous-growth models, with the gain materializing entirely as a level shift with a long half-life of ~76 years. Einïo, Feng, and Jaravel (2022), using an endogenous growth model, find that barriers to female innovation reduce the growth rate by 1.4 percentage points; this paper’s semi-endogenous model finds a 14.2% level gain with no permanent growth rate effect.

What heterogeneity in the gender gap is documented empirically?

Field heterogeneity: Between the 1990 and 2020 inventor cohorts, the female share in chemistry and metallurgy rose from 13% to approximately 30%, while in fixed constructions and mechanical engineering it rose from under 5% to about 10%. Despite this, male-dominated fields accounted for about 53% of total patents granted in 2023. Importantly, when the inventive productivity gender gap is plotted against the female share across technological fields and cohorts, there is no significant relationship (the slope is -0.09 with a standard error of 0.2), implying selection-based barriers are not the primary driver of field-level disparities. Cohort heterogeneity: By cohort, the female share among new inventors rose from 7.5% (1990 cohort) to 17.6% (2020 cohort). Life-cycle heterogeneity: The inventive productivity gender gap (with women slightly ahead) is primarily a cohort effect rather than a within-career pattern; more recent cohorts show a somewhat larger productivity advantage for women at career onset, but the magnitude remains modest, which argues against gendered human capital depreciation as a leading explanation. Parental status heterogeneity: The fraction of female researchers who are mothers converged to the fraction of male researchers who are fathers over time (both around 40% by 2023, down from an 80% male vs. 40% female gap in 1960), suggesting research has become more accommodating. The child penalty in research (hours worked differential between parents and non-parents) has also narrowed over time and is smaller in research than in non-research occupations.

What robustness checks are conducted?

Five sets of robustness exercises are reported. (1) Degree of increasing returns to scale (gamma): Jones (2002) estimates gamma from 0.05 to 0.33; Peters (2021) estimates 0.6. Across this range, the long-run consumption gain from eliminating all distortions ranges from about 2% to almost 27% for gamma going from 0.05 to 0.6. (2) Talent signal shape parameter (theta_s): With theta_s raised to 2 from the baseline 1.26 (implying greater scarcity of superstar inventors, so fewer marginal researchers are displaced), the long-run gain falls to 8.7% from 14.2%. (3) Demographic parameters (retirement rate d and entry rate b): Setting d to match expected working lives of 20 and 40 years (versus baseline 30) shifts the transition half-life by roughly 6-8 years, leaving long-run income unchanged but moving welfare gains slightly (7.6% or 6.9% vs. baseline 7.2%). (4) Knowledge spillover parameter (phi): Values of 0.5 and -6.2 (lower bound of Bloom et al.) are tested with sigma adjusted to hold gamma constant; long-run income gains remain at 14.2%, while the half-life varies modestly and welfare gains shift by at most 24 basis points. (5) Patent quality metrics: Three alternative measures of patent quality are used — stock market valuation (Kogan et al. 2017), textual ‘importance’ (Kelly et al. 2021), forward citations, and unweighted counts. Results are consistent across measures, with the bias-resistant metrics (stock market valuation and textual importance) ruling out citation-based bias as a confound.

How does this paper relate to and differ from Einïo, Feng, and Jaravel (2022)?

Einïo et al. (2022) is the closest antecedent. That paper develops a two-sector endogenous growth model with heterogeneous consumer tastes and unequal access to innovation across sociodemographic groups including gender, finding that barriers to female innovation are responsible for an 18.2% difference in the cost of living between women and men and reduce the economic growth rate by 1.4 percentage points. Brouillette’s paper uses a semi-endogenous growth framework and arrives at a 14.2% long-run level gain in income per person and a 7.2% consumption-equivalent welfare gain, with no permanent effect on the growth rate. Beyond the growth framework, the paper extends the analysis to include labor market discrimination and a child penalty for female researchers, which Einïo et al. do not model. However, Brouillette’s model abstracts from the direction of innovation — the idea that women and men produce inventions differently tailored to different users’ needs — which Einïo et al. show is quantitatively important for cost-of-living inequality. The two papers are therefore treated as providing complementary insights.

What is the role-model externality extension, and how does it change the results?

In the baseline model, the exposure distortion is a fixed parameter representing the probability of ever encountering inventive career opportunities. In the extension, this probability is multiplied by a technology friction that depends on the fraction of same-gender and opposite-gender inventors in prior generations, with elasticities calibrated from Bell et al. (2018): own-gender elasticity 0.24 for girls, cross-gender elasticity approximately 0 (statistically insignificant in the underlying regression). This creates a positive externality: current inventors increase exposure probabilities for future cohorts of the same gender, but they are not compensated for this spillover, constituting a market failure. In the extended model, some of what was previously captured as the exposure distortion is now attributed to the technological friction from role model scarcity, and the residual exposure distortion is smaller. The counterfactual elimination of all distortions yields a more modest long-run income gain of 10.6% and a consumption-equivalent welfare gain of 3.8% (compared to 14.2% and 7.2% in the baseline). The role model externality also opens a rationale for temporarily gender-differentiated wage subsidies for female researchers as transitional optimal policy: a welfare-maximizing planner might accept a slightly worse talent allocation today in order to accelerate the expansion of the female role model base, reaching the efficient allocation sooner.

What are the policy implications and their scope conditions?

The paper’s central policy implication is that interventions targeting exposure to innovation for girls earlier in the pipeline — before entry into the labor market — offer far larger aggregate productivity returns than either conventional R&D subsidies or policies aimed at reducing workplace discrimination or the child penalty in isolation. A 30% R&D subsidy yields only 1.5% long-run income per capita growth versus 14.2% from full elimination of female research barriers. Within those barriers, the exposure distortion alone accounts for the bulk of the gain: if the underrepresentation were entirely due to the labor market or child penalty distortions (selection-based mechanisms), long-run gains would be only 3.6%. Scope conditions and caveats: (1) The framework is calibrated to the U.S. and to patent-based inventors plus Census-classified researchers, so generalization to other settings requires re-estimation of distortions. (2) The semi-endogenous structure implies that gains are level effects, not growth rate effects, and the half-life of ~76 years means that most gains accrue to future rather than current generations. (3) Distributional effects are asymmetric: the current generation of male inventors suffers a 1.7% consumption loss, while future cohorts broadly gain 8.6%; this temporal and demographic incidence complicates implementation. (4) The model abstracts from the direction of innovation, so welfare effects through differential cost-of-living impacts on men and women are not captured. (5) The role model externality extension suggests that affirmative action policies for female researchers may be warranted on efficiency grounds, but the exact form of optimal transitional policy is not fully characterized.

What is the ‘greedy job’ mechanism and how is it quantified?

The ‘greedy job’ concept (Goldin 2021) refers to occupations where extended, inflexible hours are compensated at a premium, making it suboptimal for couples to share labor supply equally and thus imposing a larger effective cost of parenthood on whoever reduces hours (in practice, more often women). In the model, an individual researcher’s effective labor supply is proportional to alpha^(1+delta) when they have children (where alpha = 0.93 is the fraction of time parents spend working and delta > 0 governs the additional return to hours in research). This magnifies the talent threshold required for a parent to prefer research over production. The parameter delta is estimated empirically by regressing log hourly wages on log hours worked, an indicator for research occupation, and their interaction (plus controls for age, experience, education, occupation, state, race, marital status, year, gender, and occupation-by-gender fixed effects), using the Census/ACS with over 11.8 million observations. The estimated delta for researchers is 0.004, statistically significant but modest — implying research is a ‘modestly greedy job,’ less so than law (0.011) or medicine (0.006). This small value of delta constrains the child penalty distortion’s aggregate impact and helps explain why the exposure distortion dominates empirically.

How is research productivity measured, and what biases are addressed?

Research productivity is measured as average quality-weighted patents granted per year over an inventor’s career, with experience fixed effects removed before averaging across years. Three patent quality metrics are used: (1) stock market valuation (Kogan et al. 2017), inferred from abnormal stock returns around patent grant announcements — chosen for its resistance to gender bias because it reflects market assessments rather than subjective citation choices; (2) ‘importance’ (Kelly et al. 2021), measured from textual similarity between patent pairs, rewarding novelty relative to prior patents and influence on subsequent ones, and also robust to citation bias because it would require precise paraphrase rather than mere omission; (3) forward citation counts, acknowledged as potentially biased (Jensen et al. 2018 show women with common names receive 30% fewer citations, while women with rare names receive 20% more); (4) unweighted patent counts. All metrics are adjusted for 3-digit CPC class fixed effects and co-inventorship team size. The results are consistent across all four measures, with women slightly ahead in all cases, suggesting that citation bias does not qualitatively alter the productivity comparison. A further concern is attribution bias: Ross et al. (2022) show women are 59% less likely to be credited with authorship on patents they contributed to, meaning PatentsView may undercount the true female inventor population.

What does the paper say about the STEM education gender gap specifically?

Women account for approximately 35% of employed STEM graduates aged 25 to 45 in the Census/ACS data (and less than 20% of engineering graduates). However, this STEM gap alone explains only 7% of the patenting gender gap (Hunt et al. 2013, using the 2003 NSCG which recorded patenting in the prior five years); a substantial 78% of the gap stems from differences in patenting behavior among STEM graduates themselves. Furthermore, since the early 2000s, female researchers have been more likely than male researchers to hold a college degree, ruling out educational attainment differences as the primary driver. The model addresses STEM underrepresentation not through a gendered STEM education cost but through the exposure distortion, on the grounds that: (1) exposure to role models is well-documented as influencing girls’ decisions to pursue STEM (Carrell et al. 2010; Breda et al. 2023; Bell et al. 2018); and (2) if a higher STEM cost were the primary barrier, the model would predict women to be substantially more productive than men (strong positive selection), which the data does not support.

Key Concepts

Semi-endogenous growth: A growth framework in which the long-run per-capita growth rate is determined by population growth and the difficulty of finding new ideas (the knowledge spillover parameter phi), not by the quantity or quality of researchers. Reallocating inventive talent shifts the level of living standards permanently but cannot alter the long-run growth rate; ‘ideas are getting harder to find’ (phi < 0 in the paper’s calibration, phi = -2.1) is an integral feature.

Exposure distortion: A Bernoulli random variable with mean (1 - tau_E_gk) governing whether an individual of gender g and cohort k ever encounters inventive career opportunities, regardless of their talent. In the baseline model it captures the aggregate probability of not having relevant role models or other enabling conditions during formative years; it is estimated at 79% for women (meaning only 21% of women are exposed to research as a potential career path). Unlike selection-based distortions, it blocks access to the innovation system even for the most talented women.

Labor market distortion: A proportional tax tau_L on the research earnings of female inventors, representing discrimination in compensation, credit attribution, promotions, and rent-sharing from intellectual property. It induces positive selection: under this tax, only sufficiently talented women prefer research over production, making the average female researcher marginally more productive than the average male researcher. Estimated at 3.3%.

Child penalty distortion: A proportional reduction tau_C in the effective research hours of mothers, capturing the disproportionate burden of childcare and household responsibilities on women’s research careers. Combined with the ‘greedy work’ parameter delta (the premium on long hours in research), it raises the talent threshold above which a woman who wants children will still choose a research career. Estimated at 7%.

Greedy job: An occupation, in the sense of Goldin (2021), where working long and inflexible hours is rewarded at a premium over and above what a simple proportional-hours model would predict. In the model, captured by the parameter delta > 0 in the research labor supply function. Estimated at delta = 0.004 for researchers (modest relative to lawyers at 0.011 or doctors at 0.006), implying that research is a modestly greedy job, amplifying the child penalty but not dominating the exposure distortion.

Intensive vs. extensive margin of research labor: The extensive margin refers to the number (fraction) of people who choose research careers; the intensive margin refers to the average quality (talent-weighted hours) of researchers. The paper’s key finding is that the 14.2% long-run income gain from eliminating gender barriers is achieved almost entirely on the intensive margin: the aggregate share of inventors barely rises, but average researcher quality increases substantially because exposure barriers had been blocking the most talented women entirely.

Consumption-equivalent welfare variation: The permanent proportional adjustment lambda to every person’s consumption in the distorted economy that would make utilitarian social welfare equal to that in the undistorted economy. A lambda of 1.072 (7.2% gain) means permanently raising everyone’s consumption by 7.2% would compensate for remaining in the distorted equilibrium rather than transitioning to the undistorted one. It is lower than the 14.2% long-run income gain because the slow transition and the discounting of future population growth reduce the present value of future gains.

Inventive productivity gender gap: The difference in average quality-weighted patents per year between female and male inventors, after controlling for technological field fixed effects, experience, and co-inventorship team size. Measured across multiple patent quality metrics (stock market valuation, textual importance, forward citations, unweighted counts). In the paper’s data, the gap is positive but small — women are slightly more productive — which is the key empirical moment used to identify the (small) labor market distortion and to rule out large selection-based barriers as the primary driver of underrepresentation.