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.
What Can Machines Learn and What Does It Mean for Occupations and the Economy?
2018298 citationsErik Brynjolfsson, Tom M. Mitchell et al.AEA Papers and Proceedingsprofile →
The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
2020228 citationsErik Brynjolfsson, Daniel Rock et al.American Economic Journal Macroeconomicsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Daniel Rock'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 Daniel Rock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Rock more than expected).
This network shows the impact of papers produced by Daniel Rock. 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 Daniel Rock. The network helps show where Daniel Rock may publish in the future.
Co-authorship network of co-authors of Daniel Rock
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Rock.
A scholar is included among the top collaborators of Daniel Rock 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 Daniel Rock. Daniel Rock is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Brynjolfsson, Erik, et al.. (2020). COVID-19 and Remote Work: An Early Look at US Data. National Bureau of Economic Research.26 indexed citations
3.
Tambe, Prasanna, Lorin M. Hitt, Daniel Rock, & Erik Brynjolfsson. (2020). Digital Capital and Superstar Firms. SSRN Electronic Journal.1 indexed citations
4.
Brynjolfsson, Erik, Daniel Rock, & Chad Syverson. (2020). The Productivity J-Curve: How Intangibles Complement General Purpose Technologies. American Economic Journal Macroeconomics. 13(1). 333–372.228 indexed citations breakdown →
5.
Brynjolfsson, Erik, Seth Benzell, & Daniel Rock. (2020). Understanding and Addressing the Modern Productivity Paradox. Chapman University Digital Commons (Chapman University).9 indexed citations
Brynjolfsson, Erik, Tom M. Mitchell, & Daniel Rock. (2018). What Can Machines Learn, and What Does It Mean for Occupations and the Economy?. DSpace@MIT (Massachusetts Institute of Technology).4 indexed citations
10.
Brynjolfsson, Erik, Tom M. Mitchell, & Daniel Rock. (2018). What Can Machines Learn and What Does It Mean for Occupations and the Economy?. AEA Papers and Proceedings. 108. 43–47.298 indexed citations breakdown →
11.
Brynjolfsson, Erik, Daniel Rock, & Chad Syverson. (2017). Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. National Bureau of Economic Research. 23–57.14 indexed citations
12.
Rock, Daniel, Sinan Aral, & Sean J. Taylor. (2016). Identification of Peer Effects in Networked Panel Data. International Conference on Information Systems.1 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.