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.
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
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 geocodedfor 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.
Andrea Morrison, Sergio Petralia, and Dario Diodato (2018), Papers in Evolutionary Economic Geography n.18.35
More than 30 million people migrated to the US between the 1850s and 1920s. In the order of thousands became inventors and patentees. Drawing on an original dataset of immigrant inventors to the US, we assess the city-level impact of immigrants patenting and their potential crowding out effects on US native inventors. Our study contributes to the different strands of literature in economics, innovation studies and economic geography on the role of immigrants as carriers of knowledge. Our results show that immigrants’ patenting is positively associated with total patenting. We find also that immigrant inventors crowd-in US inventors. The growth in US inventors’ productivity can be explained also in terms of knowledge spill-overs generated by immigrants. Our findings are robust to several checks and to the implementation of an instrumental variable strategy.
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.