Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Medium-Term Business Cycles
2006478 citationsDiego Comín, Mark Gertlerprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Diego Comín's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Diego Comín with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Comín more than expected).
This network shows the impact of papers produced by Diego Comín. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Diego Comín. The network helps show where Diego Comín may publish in the future.
Co-authorship network of co-authors of Diego Comín
This figure shows the co-authorship network connecting the top 25 collaborators of Diego Comín.
A scholar is included among the top collaborators of Diego Comín based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Diego Comín. Diego Comín is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cirera, Xavier, Diego Comín, Márcio Cruz, & Kyung Min Lee. (2020). Anatomy of Technology in the Firm. SSRN Electronic Journal.1 indexed citations
6.
Bianchi, Francesco, et al.. (2019). Slow recoveries through fiscal austerity: New insights in the effects of fiscal austerity. Econstor (Econstor).3 indexed citations
Anzoategui, Diego, Diego Comín, Mark Gertler, & Joseba Martinez. (2016). Endogenous Technology Adoption and R&D as Sources of Business Cycle Persistence. London Business School Research Online (London Business School).71 indexed citations
Hobijn, Bart & Diego Comín. (2012). How Early Adoption Has Increased Wealth--Until Now. Harvard business review. 90(3). 34–35.2 indexed citations
11.
Comín, Diego, Mikhail Dmitriev, & Esteban Rossi‐Hansberg. (2012). The Spatial Diffusion of Technology. SSRN Electronic Journal.2 indexed citations
12.
Comín, Diego, et al.. (2011). Fraunhofer: Innovation in Germany (TN). 409–444.
13.
Comín, Diego, et al.. (2010). Technology Diffusion and Postwar Growth. Federal Reserve Bank of San Francisco, Working Paper Series. 1.000–62.000.12 indexed citations
14.
Comín, Diego & Bart Hobijn. (2009). Lobbies and Technology Diffusion. The Review of Economics and Statistics. 91(2). 229–244.61 indexed citations
15.
Comín, Diego & Bart Hobijn. (2009). The CHAT Dataset. SSRN Electronic Journal.12 indexed citations
Comín, Diego, et al.. (2008). Technology usage lags. Journal of Economic Growth. 13(4). 237–256.52 indexed citations
18.
Comín, Diego, et al.. (2005). A Theory of Growth and Volatility at the Aggregate and Firm Level. SSRN Electronic Journal.9 indexed citations
19.
Hobijn, Bart & Diego Comín. (2005). Lobbying and Technology Diffusion. RePEc: Research Papers in Economics.1 indexed citations
20.
Comín, Diego & Bart Hobijn. (2004). Neoclassical Growth and the Adoption of Technologies. National Bureau of Economic Research.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.