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James McNerney

2024
A journey through time: the story behind ‘eight decades of changes in occupational tasks, computerization and the gender pay gap’
2024. A journey through time: the story behind ‘eight decades of changes in occupational tasks, computerization and the gender pay gap’. Industry and Innovation , 31 (4). Publisher's VersionAbstract

In this interview article, we embark on a fascinating journey through time alongside the winners of the 2023 DRUID Best Paper Award. DRUID, an annual research conference renowned as the hub of cutting-edge research on innovation and the dynamics of structural, institutional, and geographic change, bestows this award on the most innovative and exceptional conference submission. As longstanding allies of DRUID, Industry and Innovation offers an exclusive peek behind the curtains, unveiling the untold stories that underlie award-winning research.

In 2023, this coveted DRUID prize was awarded to a paper by Ljubica Nedelkoska, Shreyas Gadgin Matha, James McNerney, Andre Assumpcao, Dario Diodato, and Frank Neffke. Their work stands out through an impressive data collection effort and the exploration of a compelling and urgent research question – how technological change has impacted the gender pay gap. Throughout this interview, the author team takes us down memory lane, retelling the story behind their research project. On this journey through time, we trace the genesis of the authors’ innovative ideas and the intricate pathways they navigated in their quest to understand the past as a means of unravelling the future of work and its implications for gender inequality in the labour market. This journey not only takes us back in time but also points to potential avenues for future research and open questions that lie ahead.

2023
Nedelkoska, L., et al., 2023. Eight Decades of Changes in Occupational Tasks, Computerization and the Gender Pay Gap.Abstract
We build a new longitudinal dataset of job tasks and technologies by transforming the U.S. Dictionary of Occupational Titles (DOT, 1939 -1991) and four books documenting occupational use of tools and technologies in the 1940s, into a database akin to, and comparable with its digital successor, the O*NET (1998 -today). After creating a single occupational classification stretching between 1939 and 2019, we connect all DOT waves and the decennial O*NET databases into a single dataset, and we connect these with the U.S. Decennial Census data at the level of 585 occupational groups. We use the new dataset to study how technology changed the gender pay gap in the United States since the 1940s. We find that computerization had two counteracting effects on the pay gap -it simultaneously reduced it by attracting more women into better-paying occupations, and increased it through higher returns to computer use among men. The first effect closed the pay gap by 3.3 pp, but the second increased it by 5.8 pp, leading to a net widening of the pay gap.
2023-06-cid-fellows-wp-151-occupational-tasks.pdf
2022
How production networks amplify economic growth
McNerney, J., et al., 2022. How production networks amplify economic growth. Proceedings of the National Academy of Sciences of the United States of America (PNAS) , 119 (1). Publisher's VersionAbstract

Technological improvement is the most important cause of long-term economic growth. In standard growth models, technology is treated in the aggregate, but an economy can also be viewed as a network in which producers buy goods, convert them to new goods, and sell the production to households or other producers. We develop predictions for how this network amplifies the effects of technological improvements as they propagate along chains of production, showing that longer production chains for an industry bias it toward faster price reduction and that longer production chains for a country bias it toward faster growth. These predictions are in good agreement with data from the World Input Output Database and improve with the passage of time. The results show that production chains play a major role in shaping the long-term evolution of prices, output growth, and structural change.

Media release: New study finds economic progress is aided by longer supply chains and deeper networks

2021
McNerney, J., et al., 2021. Bridging the short-term and long-term dynamics of economic structural change.Abstract
In the short-term, economies shift preferentially into new activities that are related to ones they currently do. Such a tendency should have implications for the nature of an economy’s long-term development as well. We explore these implications using a dynamical network model of an economy’s movement into new activities. First, we theoretically derive a pair of coordinates that summarize long-term structural change. One coordinate captures overall ability across activities, the other captures an economy’s composition. Second, we show empirically how these two measures intuitively summarize a variety of facts of long-term economic development. Third, we observe that our measures resemble complexity metrics, though our route to these metrics differs significantly from previous ones. In total, our framework represents a dynamical approach that bridges short-and long-term descriptions of structural change, and suggests how different branches of economic complexity analysis could potentially fit together in one framework.
2021-10-cid-wp-133-bridging-dynamics-of-economic-structural-change.pdf
2020
Coscia, M., et al., 2020. The Node Vector Distance Problem in Complex Networks. ACM Computing Surveys , 53 (6). Publisher's VersionAbstract

We describe a problem in complex networks we call the Node Vector Distance (NVD) problem, and we survey algorithms currently able to address it. Complex networks are a useful tool to map a non-trivial set of relationships among connected entities, or nodes. An agent—e.g., a disease—can occupy multiple nodes at the same time and can spread through the edges. The node vector distance problem is to estimate the distance traveled by the agent between two moments in time. This is closely related to the Optimal Transportation Problem (OTP), which has received attention in fields such as computer vision. OTP solutions can be used to solve the node vector distance problem, but they are not the only valid approaches. Here, we examine four classes of solutions, showing their differences and similarities both on synthetic networks and real world network data. The NVD problem has a much wider applicability than computer vision, being related to problems in economics, epidemiology, viral marketing, and sociology, to cite a few. We show how solutions to the NVD problem have a wide range of applications, and we provide a roadmap to general and computationally tractable solutions. We have implemented all methods presented in this article in a publicly available open source library, which can be used for result replication.