The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Technological Innovation, Resource Allocation, and Growth*
Abstract
We propose a new measure of the economic importance of each innovation. Our measure uses newly collected data on patents issued to U.S. firms in the 1926 to 2010 period, combined with the stock market response to news about patents. Our patent-level estimates of private economic value are positively related to the scientific value of these patents, as measured by the number of citations the patent receives in the future. Our new measure is associated with substantial growth, reallocation, and creative destruction, consistent with the predictions of Schumpeterian growth models. Aggregating our measure suggests that technological innovation accounts for significant medium-run fluctuations in aggregate economic growth and TFP. Our measure contains additional information relative to citation-weighted patent counts; the relation between our measure and firm growth is considerably stronger. Importantly, the degree of creative destruction that is associated with our measure is higher than previous estimates, confirming that it is a useful proxy for the private valuation of patents.
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
Corporate financing and investment decisions when firms have information that investors do not have
Myers, S. C., & Majluf, N. S. (1984) · Journal of Financial Economics · doi:10.1016/0304-405x(84)90023-0 · Cited by 18,957
Endogenous Technological Change
Romer, P. M. (1990) · Journal of Political Economy · doi:10.1086/261725 · Cited by 17,711
“A Model of Growth: Through Creative Destruction”
Aghion, P., & Howitt, P. (1992) · Econometrica · doi:10.2307/2951599 · Cited by 7,529
Syverson, C. (2011) · Journal of Economic Literature · doi:10.1257/jel.49.2.326 · Cited by 2,942
Measuring and Explaining Management Practices Across Firms and Countries
Bloom, N., & Van Reenen, J. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.2007.122.4.1351 · Cited by 2,373
Jovanovic, B., & Rousseau, P. L. (2005) · Handbook of Economic Growth · doi:10.1016/s1574-0684(05)01018-x · Cited by 708
Cited by
Artificial intelligence, firm growth, and product innovation
Babina, T., Fedyk, A., He, A., & Hodson, J. (2024) · Journal of Financial Economics · doi:10.1016/j.jfineco.2023.103745 · Cited by 1,460
The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
Brynjolfsson, E., Rock, D., & Syverson, C. (2021) · American Economic Journal: Macroeconomics · doi:10.1257/mac.20180386 · Cited by 1,003
Are Ideas Getting Harder to Find?
Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2020) · American Economic Review · doi:10.1257/aer.20180338 · Cited by 1,002
Are US Industries Becoming More Concentrated?
Grullon, G., Larkin, Y., & Michaely, R. (2019) · Review of Finance · doi:10.1093/rof/rfz007 · Cited by 678
Going digital: implications for firm value and performance
Chen, W., & Srinivasan, S. (2024) · Review of Accounting Studies · doi:10.1007/s11142-023-09753-0 · Cited by 412
Metadata
- Authors
- Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N.
- Venue
- The Quarterly Journal of Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-03-21
- Volume
- 132
- Issue
- 2
- Pages
- 665-712
- ISSN
- 0033-5533 (ISSN-L)
- 1531-4650
- OpenAlex
- W3122666126
- Cited by
- 2,430
- References
- 6
- FWCI
- 206.27
- 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: Technological Innovation, Resource Allocation, and Growth*. https://app.cochat.ai/papers/10.1093/qje/qjw040
- The paper
- Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N. (2017). Technological Innovation, Resource Allocation, and Growth*. The Quarterly Journal of Economics. https://doi.org/10.1093/qje/qjw040
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.
