The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Gibrat's Law for (All) Cities
Abstract
Two empirical regularities concerning the size distribution of cities have repeatedly been established: Zipf's law holds (the upper tail is Pareto), and city growth is proportionate. Census 2000 data are used covering the entire size distribution, not just the upper tail. The nontruncated distribution is shown to be lognormal, rather than Pareto. This provides a simple justification for the coexistence of proportionate growth and the resulting lognormal distribution. An equilibrium theory of local externalities that can explain the empirical size distribution of cities is proposed. The driving force is a random productivity process of local economies and the perfect mobility of workers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Knife-Edge or Plateau: When Do Market Models Tip?
Ellison, G., & Fudenberg, D. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552793 · Cited by 156
Cited by
Power Laws in Economics and Finance
Gabaix, X. (2009) · Annual Review of Economics · doi:10.1146/annurev.economics.050708.142940 · Cited by 907
Power Laws in Economics: An Introduction
Gabaix, X. (2016) · Journal of Economic Perspectives · doi:10.1257/jep.30.1.185 · Cited by 435
Alessandretti, L., Aslak, U., & Lehmann, S. (2020) · Nature · doi:10.1038/s41586-020-2909-1 · Cited by 361
There is More than a Power Law in Zipf
Cristelli, M., Batty, M., & Pietronero, L. (2012) · Scientific Reports · doi:10.1038/srep00812 · Cited by 143
Power Law Distributions in Information Retrieval
Petersen, C., Simonsen, J. G., & Lioma, C. (2016) · ACM Transactions on Information Systems · doi:10.1145/2816815 · Cited by 46
Metadata
- Authors
- Eeckhout, J.
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2004-12-01
- Volume
- 94
- Issue
- 5
- Pages
- 1429-1451
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W1985672722
- Cited by
- 778
- References
- 1
- FWCI
- 30.03
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Gibrat's Law for (All) Cities. https://app.cochat.ai/papers/10.1257/0002828043052303
- The paper
- Eeckhout, J. (2004). Gibrat's Law for (All) Cities. American Economic Review. https://doi.org/10.1257/0002828043052303
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
