GoKawiilMaterials
The first stage of this paper, the construction of the EUROPE-IGM-ATLAS, relies on harmonized cross-country individual-level survey data. The second stage, an application of our database in which we use the indices to investigate the relationship between intergenerational mobility and innovation, relies on aggregate datasets from several sources discussed below. These include proxies for innovation and localized controls for initial economic conditions.
To estimate intergenerational mobility of education, we use 11 waves of the ESS conducted between 2002 and 2023. The ESS is a representative cross-national survey in which 40 countries have participated in at least one round since the 2002/2003 wave. Importantly, it includes questions about the level of education and retrospective questions on parental education, therefore enabling us to measure intergenerational mobility while avoiding the bias associated with selectivity in co-residency samples49. We pool all survey waves and apply survey design weights, normalizing the weights to make them consistent across waves30. Furthermore, we restrict our sample to respondents who were at least 22 years old, and were therefore likely to have completed their education, when the survey was conducted. The analysis could be sensitive to this restriction if individuals had not yet completed their educational career. Suitable robustness checks imposing different age restrictions (for example, older than 25) yield no significant changes in the resulting estimates.
We operationalize our definition of migrants in two ways; (1) first-generation migrants, who were not born in their country of residence; and (2) second-generation migrants, those with an indirect migration background either because their parents were first-generation migrants or because they do not possess the citizenship of their country of residence. We use these definitions to construct three versions of our indices: (1) excluding first-generation migrants only; (2) excluding both first- and second-generation migrants; and (3) including migrants. As migration could be endogenously related to both human capital allocation and economic performance within regions50, in our main application, we use the version of our indices obtained by excluding first-generation migrants, giving us a total sample size of 257,919 individuals. Estimations based on the two alternative samples (that is, the sample excluding individuals with a migration background and the one including both natives and migrants) yield consistent results (Supplementary Table 10). In general, see Supplementary Table 11 for clarification on database versioning.
To compute estimates at the subnational level, we use information about the region of residence of ESS respondents (that is, their geographical location of residence in adulthood at the time the survey was conducted). Regional information in the ESS is recorded using country-specific administrative codes that correspond to varying levels of the NUTS (Nomenclature of Territorial Units for Statistics) classification system. Participating countries provide regional identifiers at different hierarchical levels, with some countries coding at NUTS 1, some at NUTS 2, and others at NUTS 3. Moreover, there have been temporal changes in NUTS boundaries, particularly at smaller spatial scales. This creates substantial heterogeneity in regional granularity both within and across survey waves.
To achieve consistent cross-national and cross-temporal comparability, we implement a harmonization procedure. We compute estimates at both the NUTS 1 and NUTS 2 levels where possible. Thus, for countries who code at the NUTS 3 level, we aggregate upward. For regions where NUTS 2 boundaries changed substantially between survey rounds, we do not provide NUTS 2 level estimates, instead further aggregating to the more stable NUTS 1 classification to preserve temporal consistency. The version of the NUTS that serves as the basis of our harmonized definition of regional unit is the 2016 version, with the exception of Poland—which is included based on the 2008 NUTS boundaries due to regional divisions in later versions of NUTS. Our definition of regional boundaries is further augmented by the addition of non-EU countries, such as Serbia, Kosovo, Montenegro and so on. A cartographical depiction is shown in Supplementary Fig. 4.
The NUTS classification system is particularly useful for cross-national and cross-regional comparability purposes due to the statistical consistency of NUTS units. However, it is important to note that NUTS regions may not always align with regional policymaking structures.
Measuring innovation
To measure regional innovation, our main outcome variable of interest, we rely on patenting as an established indicator of innovation performance51. We retrieve patenting data from the European Patent Office’s (EPO) Worldwide Patent Statistical Database (PATSTAT, version 2024a) and construct three hierarchical measures of regional innovation: (1) patent count as a proxy for the regional quantity of innovation activities regardless of their quality; (2) granted patent count as a proxy for the regional quantity of innovation that surpasses a certain legal quality threshold; and (3) citation-weighted patent count, as a proxy for the economic value of regional innovation. In constructing these measures, we closely follow relevant guidelines and the prior literature52,53.
Specifically, we focus on EPO and World Intellectual Property Office (WIPO) filings, prioritizing EPO filings over WIPO filings. We consolidate applications at the patent family level and select the earliest filing year as the year of invention. To assign patents to NUTS regions, we use information from PATSTAT on geocoded patent inventor locations, which reflect the location where the innovation activity took place. If a patent lists inventors from more than one region (for example, one from region A and another from region B), we apply fractional counting (that is, we assign 0.5 of the patent to region A and 0.5 to region B). We consider all patent applications from 1985 (the first year available with a one-year lag in our contemporary controls, see the ‘Covariates’ section) until 2020 (the last year that ensures a complete citation window for all patents when constructing citation-weighted patent count, given availability in the 2024a version of PATSTAT).
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