The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Identifying Technology Spillovers and Product Market Rivalry
Open accessAbstract
The impact of R&D on growth through spillovers has been a major topic of economic research over the last thirty years. A central problem in the literature is that firm performance is affected by two countervailing “spillovers” : a positive effect from technology (knowledge) spillovers and a negative business stealing effects from product market rivals. We develop a general framework incorporating these two types of spillovers and implement this model using measures of a firm's position in technology space and productmarket space. Using panel data on U.S. firms, we show that technology spillovers quantitatively dominate, so that the gross social returns to R&D are at least twice as high as the private returns. We identify the causal effect of R&D spillovers by using changes in federal and state tax incentives for R&D. We also find that smaller firms generate lower social returns to R&D because they operate more in technological niches. Finally, we detail the desirable properties of an ideal spillover measure and how existing approaches, including our new Mahalanobis measure, compare to these criteria.
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
Initial conditions and moment restrictions in dynamic panel data models
Blundell, R., & Bond, S. (1998) · Journal of Econometrics · doi:10.1016/s0304-4076(98)00009-8 · Cited by 22,571
Initial conditions and moment restrictions in dynamic panel data models
Blundell, R., & Bond, S. (1995) · Working Paper Series · doi:10.1920/wp.ifs.1995.9517 · Cited by 14,755
The Theory of Industrial Organization
Jean Tirole, Z. (1988) · MIT Press Books · Cited by 8,651
Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations
Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993) · The Quarterly Journal of Economics · doi:10.2307/2118401 · Cited by 7,791
“A Model of Growth: Through Creative Destruction”
Aghion, P., & Howitt, P. (1992) · Econometrica · doi:10.2307/2951599 · Cited by 7,529
Identification of Endogenous Social Effects: The Reflection Problem
Manski, C. F. (1993) · The Review of Economic Studies · doi:10.2307/2298123 · Cited by 6,209
Innovation and growth in the global economy
Broadberry, S. (1992) · International Journal of Industrial Organization · doi:10.1016/0167-7187(92)90023-r · Cited by 3,930
The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools
Hall, B., Jaffe, A., & Trajtenberg, M. (2001) · National Bureau of Economic Research · doi:10.3386/w8498 · Cited by 3,507
The Dimensions of Residential Segregation
Massey, D. S., & Denton, N. A. (1988) · Social Forces · doi:10.1093/sf/67.2.281 · Cited by 3,483
Bresnahan, T. F., Brynjolfsson, E., & Hitt, L. M. (2002) · The Quarterly Journal of Economics · doi:10.1162/003355302753399526 · Cited by 3,211
Cited by
Syverson, C. (2011) · Journal of Economic Literature · doi:10.1257/jel.49.2.326 · Cited by 2,942
Teece, D. J. (2018) · Research Policy · doi:10.1016/j.respol.2017.01.015 · Cited by 1,767
Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry
Aghion, P., Dechezleprêtre, A., Hémous, D., Martin, R., & Van Reenen, J. (2016) · Journal of Political Economy · doi:10.1086/684581 · Cited by 1,604
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
Financing Innovation: Evidence from R&D Grants
Howell, S. T. (2017) · American Economic Review · doi:10.1257/aer.20150808 · Cited by 1,138
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
Clusters, convergence, and economic performance
Delgado, M., Porter, M. E., & Stern, S. (2014) · Research Policy · doi:10.1016/j.respol.2014.05.007 · Cited by 853
Networks and the Macroeconomy: An Empirical Exploration
Acemoglu, D., Akcigit, U., & Kerr, W. (2016) · NBER Macroeconomics Annual · doi:10.1086/685961 · Cited by 452
Patents and Cumulative Innovation: Causal Evidence from the Courts *
Galasso, A., & Schankerman, M. (2015) · The Quarterly Journal of Economics · doi:10.1093/qje/qju029 · Cited by 448
Economic reforms and industrial policy in a panel of Chinese cities
Alder, S., Shao, L., & Zilibotti, F. (2016) · Journal of Economic Growth · doi:10.1007/s10887-016-9131-x · Cited by 435
The long-run effects of the scramble for Africa
Michalopoulos, S., & Papaioannou, E. (2016) · American Economic Review · doi:10.1257/aer.20131311 · Cited by 424
Who Profits from Patents? Rent-Sharing at Innovative Firms*
Kline, P., Petkova, N., Williams, H., & Zidar, O. (2019) · The Quarterly Journal of Economics · doi:10.1093/qje/qjz011 · Cited by 384
Glaeser, S. (2018) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2018.04.002 · Cited by 361
Public R&D Investments and Private-sector Patenting: Evidence from NIH Funding Rules
Azoulay, P., Graff Zivin, J. S., Li, D., & Sampat, B. N. (2019) · The Review of Economic Studies · doi:10.1093/restud/rdy034 · Cited by 349
Overlapping Ownership, R&D Spillovers, and Antitrust Policy
López, Á. L., & Vives, X. (2019) · Journal of Political Economy · doi:10.1086/701811 · Cited by 303
Disentangling the Effects of a Banking Crisis: Evidence from German Firms and Counties
Huber, K. (2018) · American Economic Review · doi:10.1257/aer.20161534 · Cited by 299
Can Innovation Help U.S. Manufacturing Firms Escape Import Competition from China?
HOMBERT, J., & MATRAY, A. (2018) · The Journal of Finance · doi:10.1111/jofi.12691 · Cited by 293
Finance and Corporate Innovation: A Survey
He, J. (., & Tian, X. (2018) · Asia-Pacific Journal of Financial Studies · doi:10.1111/ajfs.12208 · Cited by 282
Does the Gender Composition of Scientific Committees Matter?
Bagues, M., Sylos-Labini, M., & Zinovyeva, N. (2017) · American Economic Review · doi:10.1257/aer.20151211 · Cited by 281
Interfirm Relationships and Business Performance*
Cai, J., & Szeidl, A. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx049 · Cited by 280
Non-farm entrepreneurship in rural sub-Saharan Africa: New empirical evidence
Nagler, P., & Naudé, W. (2017) · Food Policy · doi:10.1016/j.foodpol.2016.09.019 · Cited by 221
Demographic structure, knowledge diffusion, and endogenous productivity growth
Davis, C., Hashimoto, K.-I., & Tabata, K. (2022) · Journal of Macroeconomics · doi:10.1016/j.jmacro.2021.103396 · Cited by 9
Metadata
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2013
- Volume
- 81
- Issue
- 4
- Pages
- 1347-1393
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2130788273
- Cited by
- 1,758
- References
- 56
- FWCI
- 144.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 973 d)
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
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.