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While existing literature focused on effects captured by direct spillovers with partner countries only, here we take a complex network perspective to explore whether the global embeddedness of countries in the macroeconomic multi-network may significantly impact income, net of country local characteristics such as local foreign exposure. We employ data for the period 2000\u20132020 to build a time sequence of 3-layer multi graphs, with countries as nodes and links weighted by the intensity of bilateral relations in international trade, finance and human migration. Using panel-regression techniques, we then ask if country (eigenvector) centrality in the multi network can account for parts of the observed heterogeneity in country per-capita income, both cross-sectionally and over time. Robustly across a number of alternative specifications of the empirical model, we find that being more central significantly boosts country income. This implies that income-enhancing technological spillovers are not only channeled via local exposure, but also through indirect interactions with more distant nodes.<\/jats:p>","DOI":"10.1007\/s41109-023-00584-1","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T12:02:27Z","timestamp":1693915347000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Centrality in the macroeconomic multi-network explains the spatiotemporal distribution of country per-capita income"],"prefix":"10.1007","volume":"8","author":[{"given":"Giorgio","family":"Fagiolo","sequence":"first","affiliation":[]},{"given":"Davide Samuele","family":"Luzzati","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"issue":"82","key":"584_CR1","first-page":"1","volume":"6","author":"G Abel","year":"2019","unstructured":"Abel G, Cohen JE (2019) Bilateral International Migration Flow Estimates for 200 Countries. 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