Research

Despite decades of progress, labor market participation for women remains persistently below their male counterparts. More concerning, progress seems to have stalled in recent decades. The goal of this paper is to explore how childcare responsibilities contribute to this gap. It does this by exploiting random shocks in childcare needs resulting from the COVID-19 pandemic. In doing so, the evidence suggests maternal labor force participation falls by 3.4 percent and 6.1 percent when schools and childcare facilities close, respectively. Young mothers are also hit particularly hard, as are women with multiple or young children. Finally, evidence suggests that the source of these labor market outcomes is intrahousehold bargaining rather than from labor market discrimination alone.

Previous research suggests creative ability peaks at ages between the mid 30s and early 40s, but has not focused on the role of age-related changes in cognitive abilities in this pattern. Cognitive processes show aging-related increases in experience-based knowledge (pragmatics or crystallized abilities) and decreases in the ability to process novel information quickly and efficiently (mechanics or fluid abilities). We explore the role of these age-related changes in the invention process, using a new database created by combining the publicly available patent data with information on inventor ages scraped from directory websites for approximately 1.2 million U.S.-resident inventors patenting between 1976 and 2017. We have made these data publicly available on the Harvard Dataverse and full documentation can be found in Kaltenberg et al. (2021) In the current paper, we present some descriptive statistics, and explore changing patterns of invention as inventor's age. For solo inventors, backward citations and originality increase with age, consistent with their being connected to crystallized intelligence. Forward citations, number of claims, and generality measures, as well as a citation-based measure of disruptiveness decline with inventor age, consistent with a connection to fluid intelligence. A similar pattern was found for performance in teams based on the average age of inventors in the team. Exploration of age diversity showed that teams with a wider age range had patents that are slightly more important (i.e., with more forward citations). Merging of these new data with other data that capture diverse aspects of inventors' environment and incentives offers rich potential for new research on invention.

This paper aims to show that the level of inequality increases via the human capital channel with credit market imperfections generating negative effects on economic growth. We expand the model presented by Galor and Zeira (1993) to represent the fact that the economy benefits from endogenous technological progress and that the government provides financial aid to reduce the financial hurdles for human capital accumulation. We use Korean data from 1998 to 2008 to empirically confirm that education plays a significant role in the divergence of household wealth over time and that the government's financial aid package in the form of the new student loans programme positively influences equality and short-run economic growth by promoting the number of skilled workers.

Larger industries are known to pay more, but are these premiums simply a reflection of industry size or are they an expression of increased knowledge diversity? Firms that manage diverse knowledge specializations that are relatively dissimilar from one another have higher coordination costs. Firms reduce burdensome costs by hiring individuals who are effective communicators and offering a wage premium. As a result, workers with higher communication skills sort into industries with a high diversity of knowledge. I test this theory using industry-level data from the United States, where I can empirically observe varying degrees of specialization. I develop a novel way to approximate the variety of specializations in an industry which captures relative knowledge distances between two occupations. Using fixed effects and matched differences regressions, my results show that workers in industries with high knowledge diversity receive a wage premium, especially for jobs with higher social, communication and interpersonal skills.

Why do workers in some occupations earn substantially more than others with similar individual characteristics? We argue that within-occupation skill complementarity — the tendency for the skills of an occupation to be mutually reinforcing rather than redundant — provides a combinatorial wage premium. We define a measure of skill complementarity, WOSC7, among the seven most closely related co-used skills. Using a matched employer–employee panel of Portuguese private-sector workers (Portuguese Quadros de Pessoal, N = 12,917,956 observations, 2010–2018), we estimate the effects of skill complementarity on wages using a two-way fixed-effects model with worker and firm fixed effects. We find that a one-standard-deviation increase in WOSC7 (higher complementarity) is associated with a 1.02% wage premium—comparable in magnitude to two additional years of schooling. WOSC7 also predicts a 10.46% rise in the combinatorial value of skill bundles and finds increasing within-occupation wage inequality over 2010–2018. Together, these findings document individual-level evidence consistent with within-occupation skill complementarity, providing wage returns with implications for the distributional consequences of automation in rigid labor markets.

The reverse order system of the NFL (draft) is structured to provide teams that performed poorly in the prior season to have a higher likelihood of restructuring their team and bring competitive balance to the NFL. Teams observe college players to scout and recruit potential incoming draft picks and thus, prior performance of college players are signals to NFL recruiters. However, college success may not always be the best predictor of NFL individual player success. We explore if being a first-round pick compared to other rounds can accurately predict individual success in the professional league. Using propensity score matching to best compare first round players vs. other players with their college statistics, we estimate the likelihood of first round draft picks succeeding in the NFL and if they achieve success faster. We find that while being drafted in round 1 does not improve increase the chance of overall professional success, if a candidate is successful and they are drafted in round 1 the time in which it takes them to succeed is slightly shorter than a candidate drafted in subsequent.

Female invention participation has steadily grown in the US over the past few decades, but the gender innovation gap remains substantial. This growth in participation corresponds with an overall increase of female labor force participation and changes in maternity leave policies. Using inventor data from patents from the US Patent and Trademark Office, this paper seeks to evaluate the impact of unpaid maternity leave (FMLA) of female inventors of child-bearing age on their exit decisions, productivity, and innovative activity. Our findings suggest that unpaid maternity leave policies promote retention of female inventors, but these policies have little impact on increasing productivity.

This paper overviews the data collection procedures and resulting data for inventor ages and associated death dates. We use information about inventors from patents (name and location) and search for age and date of death information from publicly available online web directories and build a scoring system to indicate the quality of information that we collect. After applying a variety of heuristics and robustness checks, we are confident of 1,508,676 inventor ages associated with patents granted between 1976 and 2018. We also find the death dates of 206,589 inventors, though we are not as confident of the accuracy of the death information. The datasets and associated replication files are freely available at: https://doi.org/10.7910/DVN/YRLSKU This is an accompanying paper to Invention and Lifecourse for data documentation details.

Existing research suggests that automation has the potential to impact inequality through two channels, either by changing the relative wage returns for different sets of tasks or by changing the composition of employment. This paper measures the relative importance of these two channels for a sample of European countries by decomposing the effects of a set of characteristics along these two dimensions using the structure of earnings survey (SES) and data for 2002 and 2014 Firpo et al. (2018). The approach isolates changes in the earnings distribution to identify the component that is due to changes in composition and to changes in the wage structure. We find that the risk of automation has the largest impact on inequality in our sample of European countries. The composition effect explains a large part of automation related inequality in all of the countries, but the wage effect is also relevant in half of the countries. These results confirm that the way in which technology is increasing inequality is largely due to the fact that there is a growing wage dispersion between jobs that are resilient to automation and those that are not.

Social stratification is determined not only by income, education, race, and gender, but also by an individual's job characteristics and their position in the industrial structure. Utilizing a dataset of 76.6 million Brazilian workers and methods from network science, we map the Brazilian Industry- Occupation Space (BIOS). The BIOS measures the extent to which 600 occupations co-appear in 585 industries, resulting in a complex network that shows how industrial-occupational communities provide important information on the network segmentation of society. Gender, race, education, and income are concentrated unevenly across the core-periphery structure of the BIOS. Moreover, we identify 28 industrial occupational communities from the BIOS network structure and report their contribution to total income inequality in Brazil. Finally, we quantify the relative poverty within these communities. In sum, the BIOS reveals how the coupling of industries and occupations contributes to mapping social stratification.

We analyze mismatches between local labor markets and higher education enrollments in 22 educational fields across 137 mesoregions in Brazil between 2009 and 2016 using a two-stage regression model. The results show that local labor markets have a significant effect on enrollment rates by field in higher education institutions and need to be considered by educational policies to prevent skill mismatches. Moreover, a large share of public universities, urban population and low unemployment rates are associated with low mismatches. Public universities address skills shortages, particularly in poor regions and natural sciences, better than private universities that contribute to oversupply in certain fields, e.g. in business or law. This is an important shortcoming in countries like Brazil where almost 90% of universities are private.

Recently we uploaded to the arxiv a paper entitled: Improving the Economic Complexity Index. There, we compared three metrics of the knowledge intensity of an economy, the original metric we published in 2009 (the Economic Complexity Index or ECI), a variation of the metric proposed in 2012, and a variation we called ECI+. It was brought to our attention that the definition of ECI+ was equivalent to the variation of the metric proposed in 2012. We have verified this claim, and found that while the equations are not exactly the same, they are similar enough to be our own oversight. More importantly, we now ask: how many variations of the original ECI work? In this paper we provide a simple unifying framework to explore multiple variations of ECI, including both the original 2009 ECI and the 2012 variation. We found that a large fraction of variations have a similar predictive power, indicating that the chance of finding a variation of ECI that works, after the seminal 2009 measure, are surprisingly high. In fact, more than 28 percent of these variations have a predictive power that is within 90 percent of the maximum for any variation. These findings show that, once the idea of measuring economic complexity was out, creating a variation with a similar predictive power (like the ones proposed in 2012) was trivial (a 1 in 3 shot). More importantly, the result show that using exports data to measure the knowledge intensity of an economy is a robust phenomenon that works for multiple functional forms. Moreover, the fact that multiple variations of the 2009 ECI perform close to the maximum, tells us that no variation of ECI will have a performance that is substantially better. This suggests that research efforts should focus on uncovering the mechanisms that contribute to the diffusion and accumulation of productive knowledge instead of on exploring small variations to existing measures.

How much knowledge is there in an economy? In recent years, data on the mix of products that countries export has been used to construct measures of economic complexity that estimate the knowledge available in an economy and predict future economic growth. Here we introduce a new and simpler metric of economic complexity (ECI+) that measures the total exports of an economy corrected by how difficult it is to export each product. We use data from 1973 to 2013 to compare the ability of ECI+, the Economic Complexity Index (ECI), and Fitness complexity, to predict future economic growth using 5, 10, and 20-year panels in a pooled OLS, a random effects model, and a fixed effects model. We find that ECI+ outperforms ECI and Fitness in its ability to predict economic growth and in the consistency of its estimators across most econometric specifications. On average, one standard deviation increase in ECI+ is associated with an increase in annualized growth of about 4% to 5%. We then combine ECI+ with measures of physical capital, human capital, and institutions, to find a robust model of economic growth. The ability of ECI+ to predict growth, and the value of its coefficient, is robust to these controls. Also, we find that human capital, political stability, and control of corruption; are positively associated with future economic growth, and that income is negatively associated with growth, in agreement with the traditional growth literature. Finally, we use ECI+ to generate economic growth predictions for the next 20 years and compare these predictions with the ones obtained using ECI and Fitness. These findings improve the methods available to estimate the knowledge intensity of economies and predict future economic growth.

Social stratification is determined not only by income, education, race, and gender, but also by an individual’s job characteristics and their position in the industrial structure. Utilizing a dataset of 76.6 million Brazilian workers and methods from network science, we map the Brazilian Industry-Occupation Space (BIOS). The BIOS measures the extent to which 600 occupations co-appear in 585 industries, resulting in a complex network that shows how industrial-occupational communities provide important information on the network segmentation of society. Gender, race, education, and income are concentrated unevenly across the core-periphery structure of the BIOS. Moreover, we identify 28 industrial occupational communities from the BIOS network structure and report their contribution to total income inequality in Brazil. Finally, we quantify the relative poverty within these communities. In sum, the BIOS reveals how the coupling of industries and occupations contributes to mapping social stratification and the network structure of inequality.

This paper aims to understand the role that technology plays, particularly in structural change, as a driver of economic growth. Noting the exceptional few countries that succeeded in becoming a developed state and the accelerated period of globalisation (1995 - 2009), we analyse growth patterns at the product, sectoral and macroeconomic levels. Utilising trade data, we detail the type of complex products exported as a reflection of a nation's latent capabilities. At the sectoral level, technological shifts are observed through a TFP analysis to understand how countries use technological progress at varying development stages to realise economic growth. Finally, we use input-output analysis for a macroeconomic perspective on the impact of globalisation on production by sector and on demand patterns of foreign and domestic markets, both globally and regionally