Is the U.S. Aggregate Production Function Cobb-Douglas? New Estimates of the Elasticity of Substitution
By Pol Antràs. 2004
501 citations recorded by the source corpus.
Abstract I present new estimates of the elasticity of substitution between capital and labor using data from the private sector of the U.S. economy for the period 1948-1998. I first adopt Berndt's (1976) specification, which assumes that technological change is Hicks neutral. Consistently with his results, I estimate elasticities of substitution that are not significantly different from one. I next show, however, that restricting the analysis to Hicks-neutral technological change necessarily biases the estimates of the elasticity towards one. When I modify the econometric specification to allow for biased technical change, I obtain significantly lower estimates of the elasticity of substitution. I conclude that the U.S. economy is not well described by a Cobb-Douglas aggregate production function. I present estimates based on both classical regression analysis and time series analysis. In the process, I deal with issues related to the nonsphericality of the disturbances, the endogeneity of the regressors, and the nonstationarity of the series involved in the estimation.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/11996f2f51e04622498e40c8296261142877094d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- David H. Autor
- John Van Reenen
- Charles I. Jones
- Markus Poschke
- Thomas Piketty
- Daron Acemoglu
- G. Zucman
- Peter Mcadam
Citations in the corpus
- The Normalized CES Production Function: Theory and Empirics (2011, unknown): “Special cases of (27) are the specifications used by Rowthorn (1999), Bentolila and Saint-Paul (2003) or Acemoglu (2002), where N0 = K0 = Y0 = 1 is implicitly assumed, or by Antràs (2004) who sets N0 = K0 = 1.”
- Labor Hiring, Investment, and Stock Return Predictability in the Cross Section (2014, unknown): “The elasticity of substitution between capital and labor stock is set to be f5 0:5, consistent with estimates in Antràs ð2004Þ ðsee also Chirinko ½2008 for a 12 Because firms are all-equity financed in the model but use both debt and equity in the real data, we leverage up all returns generated in…”
- The Decline of the U.S. Labor Share (2013, background): “By contrast, Antras (2004) and Chirinko (2008) suggest estimates of sigma in the range 0.4 to 0.6.”
- Labor Share Decline and Intellectual Property Products Capital (2020, background): “The empirical literature almost invariably avoids this impossibility by assuming an ad-hoc shape (e.g., a linear trend in log) for Ct (Antràs, 2004, Chirinko et al., 2011).”
- Factor Substitution and Factor Augmenting Technical Progress in the Us: A Normalized Supply-Side System Approach (2004, unknown): “First, following e.g. Krueger (1999) and Antras (2004) we add two-thirds of proprietors' income to the private sector compensation to employees.”
- Identifying the Elasticity of Substitution with Biased Technical Change (2010, methodology): “Antràs (2004), for instance, suggested that the popular assumption of Hicks-neutral technical progress, coupled with relatively stable factor shares and rising capital deepening biases results towards Cobb-Douglas.”
- Capital Deepening and Non-Balanced Economic Growth (2008, unknown): “Using more recent data, and various different specifications, Krusell, Ohanian, Rios-Rull, and Violante (2000) and Antras (2001) also find estimates of the elasticity significantly less than 1.”
- The Rise of Market Power and the Macroeconomic Implications* (2020, unknown): “While Karabarbounis and Neiman (2013) claim that this elasticity is 1.25, the overwhelming majority of several decades of empirical studies (Antràs (2004), among many others) find that this elasticity is much lower than 1.”
- Explaining Movements in the Labor Share (2003, background): “Antràs (2003) provides a survey and points out that ignoring the possibility of technological change biases the estimate towards 1.”