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Costly external finance, reallocation, and aggregate productivity

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Abstract

Empirical studies document that resource reallocation across production units plays an important role in accounting for aggregate productivity growth in the US manufacturing. Financial market frictions could distort the reallocation process and hence may hinder aggregate productivity growth. This paper studies the quantitative impact of costly external finance on aggregate productivity through resource reallocation across firms with idiosyncratic productivity shocks. A partial equilibrium model calibrated to the US manufacturing data shows that costly external finance causes inefficient output reallocation from high productivity firms to low productivity firms and as a result leads to a 1 percent loss in aggregate TFP.

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Notes

  1. Petrin and Levinsohn (2005) argue that the popular measurement of aggregate productivity growth adds a “reallocation” term to the traditional growth accounting measure and fails to use the correct weights in the aggregation so that they call into question the literature’s interpretation of “reallocation” as productivity growth. Instead, they propose a new method for separating real productivity growth within plants from reallocation effects and find that such reallocation effects are reasonably stable within industries and almost always positively impact aggregate productivity growth.

  2. Baily et al. (1992) find that reallocation of output shares to more productive plants within stayers accounts for nearly half of the TFP growth for the 1972–1977 period and about one third of the rapid productivity growth in the 1980s. Foster et al. (2001) find that reallocation within continuing plants accounts for 26 percent of overall multifactor productivity growth in US manufacturing for the 1977–1987 period.

  3. The Compustat data record the year a firm is deleted from the file and the reason for deletion. Among the reasons for deletion, bankruptcy and liquidation are regarded as closely related to firm exit from operation. During the period 1989–2003, which is the sample period of the data set we use to calibrate the model, firm deletion rate due to bankruptcy and liquidation is about 0.5 percent.

  4. A disadvantage of using Compustat data is it represents only about 1/3 of employment in the US, see Davis et al. (2006). Another disadvantage is the lack of young and small firms in Compustat data. As documented in empirical studies, young and small firms play an important role in reallocation.

  5. Currently, the Center for Economic Studies of the Bureau of the Census is linking the LRD to many other data sets, including public financial databases.

  6. Agency cost models, such as Bernanke et al. (1996), suggest that the external finance premium is countercyclical since it is inversely related to firms’ net worth which is procyclical. Bloom et al. (2009) document a significant rise in both micro and macro uncertainty during recessions.

  7. This does not necessarily imply decreasing return to scale in the underlying production function. Alternatively, as in Cooper and Ejarque (2003) and Bloom (2009), if a firm has constant return to scale production function and faces iso-elastic demand curve, its profit function would exhibit decreasing return to scale. So α is also referred as the revenue return to scale; a lower α indicates higher mark-up or market power.

  8. Most empirical research on productivity using business microdata, including the studies we cited in the Introduction, had to measure output using revenue data so that their productivity measures embody price differences which may reflect idiosyncratic demand shifts or variations in market power. Foster et al. (2008), using a new data set with price observations, is one of the few studies that are able to compute physical productivity directly.

  9. There is belief that high external finance ratios for small firms as shown in Table 1 are due to the fact that a lot of small firms in Compustat are young high-tech firms which are recently publicly listed and have very high equity financing. Since firm age information is not available in Compustat, we are not able to re-examine this relationship by controlling for firm age. But we re-calculate the external finance ratios by asset class for each of the 20 manufacturing industries and find that the negative relationship between external finance ratio and firm asset size holds for most industries and is particularly remarkable for some high-tech industries such as Chemicals & Allied Products (SIC code 2800), Industrial and Commercial Machinery and Computer Equipment (SIC code 3500), Electric and Electronic Equipment and Exchange Components (SIC code 3600), Measurement Instrument, Photo Goods and Watches (SIC code 3800). When we exclude these industries from the data sample, the negative relationship between firm size and external finance ratio still holds but is less remarkable than shown in Table 1.

  10. For a detailed description of the data sample, see Appendix A.1 of the working paper: http://www.economics.unimelb.edu.au/research/wp/wp08/1044.pdf.

  11. A more detailed description can be found in Appendix A.3 of the working paper: http://www.economics.unimelb.edu.au/research/wp/wp08/1044.pdf.

  12. According to a widely used decomposition methodology of aggregate productivity growth, the change in aggregate productivity can be decomposed into several parts characterizing the relative contributions of within establishments productivity growth, reallocation, and net entry. See Baily et al. (1992) for an application of this decomposition method, and Foster et al. (2001) for a discussion of various problems it’s subject to and other decomposition methods.

  13. In fact, we proved in the working paper that the output-weighted aggregate productivity with costless external finance is independent of the price of capital.

  14. In Cooper and Ejarque (2003), the data moments also include autocorrelations and standard deviations of investment rates which take similar values as in our study.

  15. There is evidence that firm exits are related to low productivity, and also impacted by external financing issues. Some recent literature on firm dynamics has explicitly modeled these links, see Hopenhayn (1992) and Clementi and Hopenhayn (2006) for examples. Modeling these issues is beyond the scope of the paper.

  16. A more realistic way to model firm entry is to let new firms’ productivity follow some distribution. We abstract from this complication since results from the two simple extreme cases would somehow provide a range for the quantitative impact of costly external finance in an environment where firm exit is exogenous and new entry firms’ productivities range from the lowest to the highest level, and in our view this is sufficient to provide some insights on the sensitivity of the results to firm entry and exit.

  17. The estimation routine finds that there is a tension in the two moments: standard deviation of investment rates and external finance ratio. Since we put more emphasis on external finance ratio, as we do for the baseline model, the estimation yields a low standard deviation of investment rates than in the data.

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Acknowledgments

I thank Prof. Russell Cooper and Dean Corbae for their valuable guidance in the early stage of this project. I also thank Professor John Haltiwanger and two anonymous referees for their insightful comments. All remaining errors are my own.

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Correspondence to Shuyun May Li.

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Li, S.M. Costly external finance, reallocation, and aggregate productivity. J Prod Anal 35, 181–195 (2011). https://doi.org/10.1007/s11123-010-0197-8

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