Understanding US firm efficiency and its asset pricing implications

Abstract

We investigate the links between firm-level total factor productivity (TFP) growth and technical efficiency change, and their implications on firm-level stock returns. We estimate TFP growth of US firms between 1966 and 2015 and decompose TFP growth into returns to scale, technical progress, and technical efficiency change components. We show that most of the variation in TFP growth is explained by variation in technical efficiency change. Moreover, we examine the effects of important macro- and micro-level factors on inefficiency as well as its asset pricing implications. We find that low-efficiency firms are more vulnerable to a wide class of aggregate economic shocks, and the well-known five stock return anomalies (Fama and French in J Financ Econ 116(1):1–22, 2015) are more pronounced among those firms. Our results also emphasize the role of macroeconomic determinants of efficiency, and the stability effects of many useful policy targets on firm-level TFP.

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Notes

  1. 1.

    We explain this decomposition in Sect. 2.2. The TFP growth decomposition that we employ is a conventional method and further details can be found in Kumbhakar and Lovell (2003).

  2. 2.

    That is, when we decompose the TFP growth into these three components, the component with highest variance is the technical efficiency change component. This does not necessarily imply that technical efficiency change constitutes a larger portion of TFP growth.

  3. 3.

    For a book-length survey on stochastic frontier models, see Kumbhakar and Lovell (2003).

  4. 4.

    See Wang (2002) for properties of this model. In particular, Wang (2002) emphasizes the potential non-monotonicity of efficiency for this model.

  5. 5.

    In the neoclassical production context where the inefficiency is not present, it is common to control for potential endogeneity of inputs using variations of methods developed by Olley and Pakes (1996) and Levinsohn and Petrin (2003). Both of these methods assume full efficiency. Hence, they cannot be directly applied to stochastic frontier setting. Moreover, both methods assume that the shock proxy must be monotonically increasing with respect to the true shock. Also, variable inputs (e.g., labor and materials) must respond immediately to a shock, while state variables (e.g., capital) must respond after some lag. These assumptions are not necessarily weak and our method does not require them. However, a resulting caveat is that our parameter estimates may suffer from a potential endogeneity problems.

  6. 6.

    See, for example, Kumbhakar and Lovell (2003).

  7. 7.

    Due to the availability of the national average wage index that is required to compute labor input, our sample ranges up to 2015. Following Imrohoroglu and Tuzel (2014), we remove regulated and financial firms from the sample. Also to be included in our analysis, the firms need to have non-missing and positive data on sales, total assets, number of employees, gross property, plant and equipment, depreciation, accumulated depreciation, and capital expenditures.

  8. 8.

    Since the environmental variables include both micro- and macro-level variables and the macro-variables are not bank specific, a possible alternative to our model is to introduce a stochastic frontier model that allows such hierarchy. We, however, follow the standard practice in the finance literature and use a conventional stochastic frontier model.

  9. 9.

    For the case without environmental variables, we simply regress the technical efficiency estimates on the constant and time trend to test existence and direction of trend.

  10. 10.

    The marginal effects that we analyze are the median marginal effects.

  11. 11.

    See https://research.stlouisfed.org/econ/mccracken/fred-databases/.

  12. 12.

    We transform the raw data according to the transformation code of each variable provided in the FRED database before extracting the macro-shocks.

  13. 13.

    The data for the factors are obtained from Kenneth French’s website.

  14. 14.

    Our results are quantitatively similar using different controls, such as only including the market factor.

  15. 15.

    We use the exponential decaying weights, with the half-life of weights to be around 60 months. Again, the results are robust under the alternative decaying rate.

  16. 16.

    See also the discussions in Gorodnichenko and Weber (2016) and Hong and Sraer (2016).

  17. 17.

    Our results are of similar or even stronger magnitude if we use the estimated efficiency measure discussed in the previous sections.

  18. 18.

    We only report the results for the highest and lowest efficiency-sorted portfolio. For most of macro-factors considered, the sorted portfolios show monotonically decreasing sensitivities from low- to high-efficiency portfolios.

  19. 19.

    Following the standard approach in the literature, we match accounting data for fiscal year-ends in calendar year t − 1 to monthly returns from July t to June t + 1.

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Correspondence to Levent Kutlu.

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Calice, G., Kutlu, L. & Zeng, M. Understanding US firm efficiency and its asset pricing implications. Empir Econ 60, 803–827 (2021). https://doi.org/10.1007/s00181-019-01775-5

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Keywords

  • Asset prices
  • Efficiency
  • Frictions
  • Stock return anomalies
  • Total factor productivity

JEL Classification

  • D22
  • D24
  • G12