A simple theory of economic development at the extensive industry margin

Dario Diodato, Ricardo Hausmann, Ulrich Schetter (2022), CID Faculty Working Paper No.416

We revisit the well-known fact that richer countries tend to produce a larger variety of goods and analyze economic development through (export) diversification. 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 firms 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.

Eight decades of changes in occupational tasks, computerization and the gender pay gap

Ljubica Nedelkoska, Shreyas Gadgin Matha, James McNerney, Andre Assumpcao, Dario Diodato, Frank Neffke (2021), Unpublished draft

We build a new longitudinal dataset of job tasks and technologies by transforming the US 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 US 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 from the U.S. on employment and wages in Mexican cities

Dario Diodato, Ricardo Hausmann, and Frank Neffke (2020), Papers in Evolutionary Economic Geography n.20.12

We study the effect of return migration from the U.S. to Mexico on the economies of Mexican cities. In principle, returnees increase the local labor supply and therefore put pressure on wages and employment rates of locals. However, having worked in the technologically more advanced US economy, they may also possess skills that complement the skills of local workers or even bring in new organizational and technological know-how that leads to productivity improvements in Mexico. Using an instrument based on involuntary return migration due to deportation by US authorities, we find evidence in support of both effects. Returnees affect wages of locals in different ways: whereas workers who share the returnees’ occupations experience a fall in wages, workers in other occupations see their wages rise. However, the latter, positive, effect is easily overlooked, because it is highly localized: it only affects coworkers within the same city-industry cell. Moreover, both, positive and negative, wage effects are transitory and eventually disappear. In contrast, by raising the employment levels of the industry in which they find jobs, returnees permanently alter a city’s industry composition.


Structural accounting: An empirical assessment of cross-country differences in productivity

Dario Diodato (2020), Papers in Evolutionary Economic Geography n.20.20

This paper proposes a method to decompose cross-country differences in productivity (TFP) into a technological component – depending on the overall productivity of a country – and an allocation component, which depends on whether factors of productions are allocated to productive or unproductive industries. Using a sample of over 2 million firms from 30 countries, the analysis estimates that 1/4 of inequality between countries is due to the Composition effect, while 3/4 to the Place effect. Moreover, once accounting for heterogeneity at the subnational level, I find that the Composition effect may be as high as 50%.


Technological regimes and the geography of innovation: a long-run perspective on US inventions

Dario Diodato and Andrea Morrison (2019), Papers in Evolutionary Economic Geography n.19.24

The geographical distribution of innovative activities is an emerging subject, but still poorly understood. While previous efforts highlighted that different technologies exhibit different spatial patterns, in this paper we analyse the geography of innovation in the very long run. Using a US patent dataset geocoded for the years 1836-2010, we observe that – while it is true that differences in technologies are strong determinant of spatial patterns – changes within a technology over time is at least as important. In particular, we find that regional entry follows the technology life cycle. Subsequently, innovation becomes less geographical concentrated in the first half of the life cycle, to then re-concentrate in the second half.


Is our human capital general enough to withstand the current wave of technological change?

Ljubica Nedelkoska, Dario Diodato, and Frank Neffke (2018), CID Research Fellow and Graduate Student Working Paper n.93

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.