Agnes Norris Keiller, Aureo de Paula, John Van Reenen
arXiv 10 Jul 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2407.07988 · PDF · DOI · OpenAlex · Extracted main text
Standard methods for estimating production functions in the Olley and Pakes (1996) tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on a firm's expectations of its future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to dynamic panel methods, our proposed estimator can be implemented on very short panels (including a single cross-section), and Monte Carlo simulations show it outperforms alternative estimators when firms' material input choices are subject to optimization error. Implementing a range of production function estimators on UK data, we find our proposed estimator yields results that are either similar to or more credible than commonly-used alternatives. These differences are larger in industries where material inputs appear harder to optimize. We show that TFP implied by our proposed estimator is more strongly associated with future jobs growth than existing methods, suggesting that failing to adequately account for input endogeneity may underestimate the degree of dynamic reallocation in the economy.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Olley, G. Steven, Pakes, Ariel (1996) The Dynamics of Productivity in the Telecommunications Equipment Industry | 1.000 | 5 | 4 | 100% |
| 2 | Ackerberg, Kevin, Frazer, Garth (2015) Identification Properties of Recent Production Function Estimators | 0.920 | 9 | 6 | 78% |
| 3 | Bloom, Nicholas, Kawakubo, Takafumi, Meng, Charlotte, Mizen, Paul, R… (2021) Do Well Managed Firms Make Better Forecasts? | 0.843 | 3 | 3 | 100% |
| 4 | Blundell, Richard, Bond, Stephen (2000) GMM Estimation with persistent panel data: an application to production functions | 0.843 | 3 | 3 | 100% |
| 5 | Dominitz, Jeff, Manski, Charles F (1997) Using Expectations Data to Study Subjective Income Expectations | 0.737 | 3 | 2 | 100% |
| 6 | Pya, Natalya, Wood, Simon N (2015) Shape constrained additive models | 0.644 | 5 | 2 | 40% |
| 7 | Robinson, P. M (1988) Root-N-Consistent Semiparametric Regression | 0.644 | 4 | 1 | 100% |
| 8 | Arellano, Manuel, Attanasio, Orazio, Augsburg, Britta, Crossman, Sam… Modelling Subjective Expectations of Future Income: Evidence from Colombia and India | 0.644 | 2 | 2 | 100% |
| 9 | Levinsohn, James, Petrin, Amil (2003) Estimating Production Functions Using Inputs to Control for Unobservables | 0.644 | 2 | 2 | 100% |
| 10 | (2022) MES Management and Expectations Survey, 2016-2020: Secure Access | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model | 0.644 | 2 | 2 |
| 2 | Estimating Individual Responses when Tomorrow Matters | 0.405 | 1 | 1 |