The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Measuring aggregate productivity growth using plant‐level data
Abstract
We define aggregate productivity growth (APG) as the change in aggregate final demand minus the change in the aggregate expenditures on labor and capital. We show how to aggregate plant‐level data to this quantity and how to decompose APG into technical efficiency and reallocation components. This requires us to confront the “non‐neoclassical” features that impact plant‐level data, including plant‐level heterogeneity, the entry and exit of goods, adjustment costs, fixed and sunk costs, and market power. The APG decomposition includes one term per plant related to technical efficiency and one term for each input at each plant that is a function of the value of marginal product ‐ input price gap and that relates the reallocation of inputs to growth. We compare APG to several competing variants of productivity growth that are based only on plant‐level technical efficiency. Two simple theoretical examples illustrate that technical‐efficiency reallocation can be negatively correlated with actual APG reallocation because technical efficiency is a production concept and need not have any relation with the APG reallocation gaps. We illustrate this point empirically using panel data from manufacturing industries in Chile, where we show technical‐efficiency reallocation differs substantially from measured reallocation based on our definition of APG.
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
The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity
Melitz, M. J. (2003) · Econometrica · doi:10.1111/1468-0262.00467 · Cited by 12,309
Technical Change and the Aggregate Production Function
Solow, R. M. (1957) · The Review of Economics and Statistics · doi:10.2307/1926047 · Cited by 12,259
“A Model of Growth: Through Creative Destruction”
Aghion, P., & Howitt, P. (1992) · Econometrica · doi:10.2307/2951599 · Cited by 7,529
The Dynamics of Productivity in the Telecommunications Equipment Industry
Olley, G. S., & Pakes, A. (1996) · Econometrica · doi:10.2307/2171831 · Cited by 7,112
Estimating Production Functions Using Inputs to Control for Unobservables
Levinsohn, J., & Petrin, A. (2003) · Review of Economic Studies · doi:10.1111/1467-937x.00246 · Cited by 6,496
Self-citationA Model of Growth Through Creative Destruction
Aghion, P., & Howitt, P. (1990) · National Bureau of Economic Research · doi:10.3386/w3223 · Cited by 5,634
Misallocation and Manufacturing TFP in China and India*
Hsieh, C.-T., & Klenow, P. J. (2009) · Quarterly Journal of Economics · doi:10.1162/qjec.2009.124.4.1403 · Cited by 4,175
Job Creation and Job Destruction in the Theory of Unemployment
Mortensen, D. T., & Pissarides, C. A. (1994) · The Review of Economic Studies · doi:10.2307/2297896 · Cited by 3,840
Syverson, C. (2011) · Journal of Economic Literature · doi:10.1257/jel.49.2.326 · Cited by 2,942
Markov-Perfect Industry Dynamics: A Framework for Empirical Work
Ericson, R., & Pakes, A. (1995) · The Review of Economic Studies · doi:10.2307/2297841 · Cited by 2,325
Cited by
Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing
Brandt, L., Van Biesebroeck, J., & Zhang, Y. (2012) · Journal of Development Economics · doi:10.1016/j.jdeveco.2011.02.002 · Cited by 2,303
WTO Accession and Performance of Chinese Manufacturing Firms
Brandt, L., Van Biesebroeck, J., Wang, L., & Zhang, Y. (2017) · American Economic Review · doi:10.1257/aer.20121266 · Cited by 856
“Productivity and Misallocation in General Equilibrium”
Baqaee, D. R., & Farhi, E. (2020) · The Quarterly Journal of Economics · doi:10.1093/qje/qjz030 · Cited by 648
“The Cyclicality of Productivity Dispersion”
Kehrig, M. (2011) · doi:10.2139/ssrn.1787761 · Cited by 107
Metadata
- Authors
- Petrin, A., & Levinsohn, J.
- Venue
- The RAND Journal of Economics
- Publisher
- Wiley
- Published
- 2012-12
- Volume
- 43
- Issue
- 4
- Pages
- 705-725
- ISSN
- 0741-6261 (ISSN-L)
- 1756-2171
- OpenAlex
- W2028834455
- Cited by
- 278
- References
- 51
- FWCI
- 40.15
- 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: Measuring aggregate productivity growth using plant‐level data. https://app.cochat.ai/papers/10.1111/1756-2171.12005
- The paper
- Petrin, A., & Levinsohn, J. (2012). Measuring aggregate productivity growth using plant‐level data. The RAND Journal of Economics. https://doi.org/10.1111/1756-2171.12005
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.