Mark Thissen, Maureen Lankhuizen, Frank van Oort, Bart Los, Dario Diodato (2018), “EUREGIO: The construction of a global IO database with regional detail for Europe for 2000-2010.” Tinbergen Institute Discussion Paper TI 2018-084/VI
Dario Diodato (2018), Papers in Evolutionary Economic Geography #18.43
A frequent problem in research is the harmonization of data to a common classification, whether that is in terms of — to name a few examples — industries, commodities, occupations, or geographical areas. Statistical offices often provide concordance tables, to match data through time or with different classifications, but these concordance tables alone are often not sufficient to define a clear methodology on how the matching should be performed. In fact, the concordance tables have, in numerous occasions, a many-to-many mapping of classifications. The issue is exacerbated when two or more concordance tables are concatenated.
In this Jupyter notebook, I discuss a network-based abstraction of this problem and propose, as a general solution, a method that identifies the network components (or the network communities) to make data converge to a new classification. The method simplifies the issue and reduces greatly conversion errors.
Mark Thissen, Maureen Lankhuizen, Frank van Oort, Bart Los, Dario Diodato (2018), Tinbergen Institute Discussion Paper TI 2018-084/VI
This paper introduces the EUREGIO database: the first time-series (annual, 2000-2010) of global IO tables with regional detail for the entire large trading bloc of the European Union. The construction of this database, which allows for regional analysis at the level of so-called NUTS2 regions, is presented in detail for its methodology and applications. The tables merge data from WIOD (the 2013 release) with, regional economic accounts, and interregional trade estimates developed by PBL Netherlands Environmental Assessment Agency, complemented with survey-based regional input-output data for a limited number of countries.
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.
Pierre-Alexandre Balland (Utrecht University & Collective Learning Group, MIT Media Lab), Tom Broekel (Utrecht University), Dario Diodato (CID Harvard), Ricardo Hausmann (CID Harvard), Neave O’Clery (Oxford), and David Rigby (University of California, Los Angeles)
Elisa Giuliani (University of Pisa)
Economic complexity has emerged as a powerful paradigm to understand key issues in economics, geography, innovation studies, and other social sciences. Owing its popularity, in part, to its cross-disciplinary reach, the concept has shed new light on the variation in standards of living across nations (Hidalgo and Hausmann, 2009), differences in sophistication of technologies (Fleming and Sorenson, 2001), and the heterogeneous distribution of knowledge in space (Balland and Rigby, 2017). This excitement is not limited to academia. A host of policy institutions, ranging from international organizations such as the World Bank, World Economic Forum, European Commission, and OECD to national and local actors, have embedded both the methodology and conceptual framework of complexity into their core toolbox. Hence, as economic complexity moves from the periphery to the core of economic thinking and development policy, this special issue attempts to both reflect on past success and look forward to new research frontiers.
The complexity perspective posits that the knowledge content of a country or a city cannot be found at the intensive margin: knowledge grows not by accumulating more of the same, but by adding new and different elements to existing capabilities. It is this evolutionary, combinatorial process that drives many economic phenomena. While this description of knowledge accumulation is often in direct contrast with leading models of economic growth and development – where technology is typically a homogenous good – the theoretical roots of complexity can be found in both traditional and heterodox economics, from Smith’s division of labor (Hausmann et al, 2011) to information theory (Antonelli, 2011), from Jacob’s externalities (Jacobs, 1969) to urban scaling (Bettencourt et al. 2007, Balland et al., 2018), from agglomeration effects (Glaeser et al. 1992) to network theory (Hidalgo et al., 2007).
Important questions to address include the micro-foundations of economic complexity (how and at what scale is it created? What are its ingredients and where do they reside?), and its relation to traditional concepts such as tacit knowledge, radical innovation, agglomeration, and production networks?
The special issue is organized around four main themes:
- Micro/theoretical foundations of complexity theory, possibly connecting it to established schools of economic thought or other kinds of literature such as biology or physics
- New empirical applications of complexity to key issues in economics, geography, and human development
- Novel approaches to measuring complexity, and studying its evolution over time, organizations and space
- Implications for policy and firm strategy
We welcome full manuscripts of up to 8,000 words maximum (excluding references and appendices). Articles should be submitted online via the Research Policy web-portal. Each paper will be reviewed by two or three referees. We aim to complete the review process with a maximum of two drafts (i.e., a single ‘revise and resubmit’) before a final decision is made — unless special circumstances call for an additional revision round.
March 1, 2019: updated submission deadline for full manuscript
June 1, 2019: decisions and comments sent to authors
October 1, 2019: deadline for final draft
Feb 1, 2020: expected publication
Antonelli, Cristiano, ed. Handbook on the economic complexity of technological change. Edward Elgar Publishing (2011).
Balland, Pierre-Alexandre, and David Rigby. “The geography of complex knowledge.” Economic Geography 93, no. 1 (2017): 1-23.
Balland, P.A., Jara-Figueroa, C., Petralia, S., Steijn, M., Rigby, D., and Hidalgo, C. “Complex Economic Activities Concentrate in Large Cities.” Papers in Evolutionary Economic Geography, no 18 (2018): 1-10.
Bettencourt, Luís MA, José Lobo, Dirk Helbing, Christian Kühnert, and Geoffrey B. West. “Growth, innovation, scaling, and the pace of life in cities.” Proceedings of the national academy of sciences 104, no. 17 (2007): 7301-7306.
Fleming, Lee, and Olav Sorenson. “Technology as a complex adaptive system: evidence from patent data.” Research Policy 30, no. 7 (2001): 1019-1039.
Glaeser, Edward L., Hedi D. Kallal, Jose A. Scheinkman, and Andrei Shleifer. “Growth in cities.” Journal of Political Economy 100, no. 6 (1992): 1126-1152.
Hidalgo, César A., and Ricardo Hausmann. “The building blocks of economic complexity.” Proceedings of the national academy of sciences 106, no. 26 (2009): 10570-10575.
Hidalgo, César A., Bailey Klinger, A-L. Barabási, and Ricardo Hausmann. “The product space conditions the development of nations.” Science 317, no. 5837 (2007): 482-487.
Jacobs, Jane. The economy of cities. Random House (1969).
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.