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.
Sample Selection Bias as a Specification Error
197920.8k citationsJames J. HeckmanEconometricaprofile →
The Importance of Noncognitive Skills: Lessons from the GED Testing Program
2001931 citationsJames J. Heckman, Yona RubinsteinAmerican Economic Reviewprofile →
The Empirical Content of the Roy Model
1990435 citationsJames J. Heckman, Bo E. HonoréEconometricaprofile →
Longitudinal Analysis of Labor Market Data
1985357 citationsJames J. Heckman et al.Cambridge University Press eBooksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by James J. Heckman
Since
Specialization
Citations
This map shows the geographic impact of James J. Heckman'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 James J. Heckman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James J. Heckman more than expected).
Fields of papers citing papers by James J. Heckman
This network shows the impact of papers produced by James J. Heckman. 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 James J. Heckman. The network helps show where James J. Heckman may publish in the future.
Co-authorship network of co-authors of James J. Heckman
This figure shows the co-authorship network connecting the top 25 collaborators of James J. Heckman.
A scholar is included among the top collaborators of James J. Heckman 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 James J. Heckman. James J. Heckman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Heckman, James J.. (2007). The Economics, Technology and Neuroscience of Human Capability Formation. NBER Working Paper No. 13195.. National Bureau of Economic Research.13 indexed citations
Heckman, James J., Lance Lochner, & Petra Todd. (2003). Fifty Years of Mincer Earnings Regressions. National Bureau of Economic Research.1 indexed citations
Heckman, James J. & Yona Rubinstein. (2001). The Importance of Noncognitive Skills: Lessons from the GED Testing Program. American Economic Review. 91(2). 145–149.931 indexed citations breakdown →
11.
Heckman, James J. & Edward E. Leamer. (2001). Handbook of Econometrics Volume 5.36 indexed citations
12.
Heckman, James J., Hidehiko Ichimura, & Petra Todd. (1998). Matching as an econometric estimator. The Review of Economic Studies.22 indexed citations
13.
Heckman, James J. & Hidehiko Ichimura. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training. The Review of Economic Studies. 64(221). 605–654.8 indexed citations
14.
Heckman, James J. & Peter Siegelman. (1993). The Urban Institute Audit Studies: Their Methods and Findings.211 indexed citations
15.
Heckman, James J. & Bo E. Honoré. (1990). The Empirical Content of the Roy Model. Econometrica. 58(5). 1121–1121.435 indexed citations breakdown →
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.