From Products to Capabilities: Constructing a Genotypic Product Space
Economic development is a path-dependent process in which countries accumulate capabilities that allow them to move into more complex products and industries. Inspired by a theory of capabilities that explains which countries produce which products, these diversification dynamics have been studied in great detail in the literature on economic complexity analysis. However, so far, these capabilities have remained latent and inference is drawn from product spaces that reflect economic outcomes: which products are often exported in tandem. Borrowing a metaphor from biology, such analysis remains phenotypic in nature. In this paper we develop a methodology that allows economic complexity analysis to use capabilities directly. To do so, we interpret the capability requirements of industries as a genetic code that shows how capabilities map onto products. We apply this framework to construct a genotypic product space and to infer countries’ capability bases. These constructs can be used to determine which capabilities a country would still need to acquire if it were to diversify into a given industry. We show that this information is not just valuable in predicting future diversification paths and to advance our understanding of economic development, but also to design more concrete policy interventions that go beyond targeting products by identifying the underlying capability requirements.
Eight Decades of Changes in Occupational Tasks, Computerization and the Gender Pay Gap
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.
The impact of return migration on employment and wages in Mexican cities
How does return migration from the US to Mexico affect local workers? Return migrants increase the local labor supply, potentially hurting local workers. However, having been exposed to a more advanced U.S. economy, they may also carry human capital that benefits non-migrants. Using an instrument based on involuntary return migration, we find that, whereas workers who share returnees’ occupations experience a fall in wages, workers in other occupations see their wages rise. These effects are, however, transitory and restricted to the city-industry receiving the returnees. In contrast, returnees permanently alter a city’s long-run industrial composition, by raising employment levels in the local industries that hire them.
A Simple Theory of Economic Development at the Extensive Industry Margin
We revisit the well-known fact that richer countries tend to produce a larger variety of goods and analyze economic development through (export) diversifcation. We show that countries are more likely to enter ‘nearby’ industries, i.e., industries that require fewer new occupations. To rationalize this finding, we develop a small open economy (SOE) model of economic development at the extensive industry margin. In our model, industries differ in their input requirements of non-tradeable occupations or tasks. The SOE grows if profit maximizing frms decide to enter new, more advanced industries, which requires training workers in all occupations that are new to the economy. As a consequence, the SOE is more likely to enter nearby industries in line with our motivating fact. We provide indirect evidence in support of our main mechanism and then discuss implications: We show that there may be multiple equilibria along the development path, with some equilibria leading on a pathway to prosperity while others resulting in an income trap, and discuss implications for industrial policy. We finally show that the rise of China has a non-monotonic effect on the growth prospects of other developing countries, and provide suggestive evidence for this theoretical prediction.
Is Our Human Capital General Enough to Withstand the Current Wave of Technological Change?
The degree to which modern technologies are able to substitute for groups of job tasks has renewed fears of near-future technological unemployment. We argue that our knowledge, skills and abilities (KSA) go beyond the specific tasks we do at the job, making us potentially more adaptable to technological change than feared. The disruptiveness of new technologies depends on the relationships between the job tasks susceptible to automation and our KSA. Here we first demonstrate that KSA are general human capital features while job tasks are not, suggesting that human capital is more transferrable across occupations than what job tasks would predict. In spite of this, we document a worrying pattern where automation is not randomly distributed across the KSA space – it is concentrated among occupations that share similar KSA. As a result, workers in these occupations are making longer skill transitions when changing occupations and have higher probability of unemployment.
Why do Industries Coagglomerate? How Marshallian Externalities Differ by Industry and Have Evolved Over Time
The fact that firms benefit from close proximity to other firms with which they can exchange inputs, skilled labor or know-how helps explain why many industrial clusters are so successful. Studying the evolution of coagglomeration patterns, we show that which type of agglomeration benefits firms has drastically changed over the course of a century and differs markedly across industries. Whereas, at the beginning of the twentieth century, industries tended to colocate with their value chain partners, in more recent decades the importance of this channels has declined and colocation seems to be driven more by similarities industries’ skill requirements. By calculating industry-specific Marshallian agglomeration forces, we are able to show that, nowadays, skill-sharing is the most salient motive in location choices of services, whereas value chain linkages still explain much of the colocation patterns in manufacturing. Moreover, the estimated degrees to which labor and input-output linkages are reflected in an industry’s coagglomeration patterns help improve predictions of city-industry employment growth.
Original version of this paper was published in 2016.
Agglomeration Economies: The Heterogeneous Contribution of Human Capital and Value Chains
**Updated version of this paper published here**
We document the heterogeneity across sectors in the impact labor and input-output links have on industry agglomeration. Exploiting the available degrees of freedom in coagglomeration patterns, we estimate the industry-specic benefits of sharing labor needs and supply links with local firms. On aggregate, coagglomeration patterns of services are at least as strongly driven by input-output linkages as those of manufacturing, whereas labor linkages are much more potent drivers of coagglomeration in services than in manufacturing. Moreover, the degree to which labor and input-output linkages are reflected in an industry’s coagglomeration patterns is relevant for predicting patterns of city-industry employment growth.