The cyclical behavior of R&D investment during the Great Recession

Abstract

This paper investigates the cyclicality of research and development (R&D) activities during the Great Recession period by incorporating the role of credit constraints, using the Great Recession period as a natural case study. Recession period is a good setting in which to identify cyclicality and eliminate endogeneity issues that have been discussed in the literature. Using firm-level data on non-federally funded, high-technology firms in the USA, this paper shows that firms without bond ratings had more procyclical R&D investments than those firms with bond ratings. I also test whether capital or inventory investments of firms that also do R&D are procyclical. I find that firms without bond ratings adjust their inventories more rapidly compared to capital and R&D investments, when they are hit by a bad shock.

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Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    See Hall and Lerner (2010) for a detailed discussion of financing of R&D and a review of the literature related to financial constraints on R&D investment.

  2. 2.

    Using investment-cash flow sensitivity to test for the presence of financial constraints started with Fazzari et al. (1988). See Kaplan and Zingales (1997) for possible endogeneity issues in this approach.

  3. 3.

    See Erickson and Whited (2000) on measurement error problems in Tobin’s Q.

  4. 4.

    Highly cited papers are Brown et al. (2009) and Brown and Petersen (2009, 2011) for R&D investment in the USA and Brown et al. (2012) in Europe.

  5. 5.

    For this analysis, I construct capital stock series for each firm, using the perpetual inventory method and capital expenditures data. Inventory stock data are available on the Compustat database.

  6. 6.

    See Arvanitis and Woerter (2013) for a good summary of theories in this literature.

  7. 7.

    Erickson and Whited (2000), Almeida and Campello (2007), and Hennessy and Whited (2007) emphasize the importance of finding exogenous proxies in these estimations.

  8. 8.

    The steps that I followed to construct the datasets for Figs. 1, 2 and 3 are explained in detail in “Appendix”. The gray bars indicate recession periods.

  9. 9.

    These results are in line with Brown et al. (2009).

  10. 10.

    Using the data from National Science Foundation, Anzoategui et al. (2016) show that R&D expenditure per capita of the US corporations experienced a sharper contraction before and during the 2001–2002 crisis than the Great Recession period. Bianchi et al. (2014) find during the Great Recession, there was a significant drop in technology adoption and utilization rates, but small change in accumulation of knowledge. In contrast, during 2001 there was a modest change in the adoption rate of technology. They conclude that the aforementioned characteristics of the Great Recession resulted in severe contractions in the short-term, but had less effect on the trend growth.

  11. 11.

    The list of non-federally funded, high-tech industries was obtained from Brown et al. (2009).

  12. 12.

    Early papers that use the existence of bond ratings to identify potentially constrained firms are Fazzari et al. (1988) and Whited (1992) for capital investments and Kashyap et al. (1994) for inventory investments.

  13. 13.

    This fact is well documented by Brown et al. (2009).

  14. 14.

    The steps that I take in constructing the regression sample are explained in detail in “Appendix”.

  15. 15.

    This specification is similar to the one used by Aghion et al. (2012) in which they test the cyclicality of R&D investments for constrained firms in France, and the one by Kashyap et al. (1994), in which they test for bank dependence in inventory investments of the firms.

  16. 16.

    See Gilchrist and Himmelberg (1995) and Erickson and Whited (2000) for detailed discussions of the control variables and of Tobin’s Q. Alti (2003) shows that Tobin’s Q can be a noisy measure for investment opportunities for young firms.

  17. 17.

    See Hall and Lerner (2010) for a discussion of why there is often a large wedge between internal and external sources of finance for R&D investments compared to other types of investments.

  18. 18.

    Kashyap et al. (1994) apply this step before they test for the liquidity constraints on the inventory investment of the firms.

  19. 19.

    I apply this rule because due to small sample size there are very few firms without access to bond markets, and the estimation does not have enough statistical power with the previous definitions of \(B=1\) and \(B=0\).

  20. 20.

    Eberly et al. (2008) is an example of research that uses double-declining balance.

  21. 21.

    I used the FRED (Federal Reserve Economic Data) database from the Federal Reserve Bank of St. Louis to assemble the data used herein; see the references for information on the specific series.

  22. 22.

    I used the FRED (Federal Reserve Economic Data) database from the Federal Reserve Bank of St. Louis to assemble the data used herein; see the references for information on the specific series.

  23. 23.

    Some studies in the literature suggest taking a constant growth rate that applies to all firms, which is around 5 or 8% (Hall 1990, 1993; Hall and Mairesse 1995). Hall and Mairesse (1995) point out that the choice of growth rate has an effect on the initial stock, but it declines in importance as time passes. More recent studies choose growth rates that differ at the firm or industry level (Parisi et al. 2002; Lyandres and Palazzo 2012). In this paper, the main results are obtained by using different growth rates at the industry level. The results, obtained by using a fixed growth rate, are also reported as a robustness check.

  24. 24.

    I used the FRED (Federal Reserve Economic Data) database from the Federal Reserve Bank of St. Louis to assemble the data used herein; see the references for information on the specific series.

  25. 25.

    I used the Organization for Economic Co-operation and Development (OECD) database to assemble the data used herein; see the references for information on the specific series.

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Acknowledgements

I am grateful for helpful comments from Daniele Coen-Pirani, Marla Ripoll, Frederik-Paul Schlingemann, Sewon Hur, James Cassing, Kristle Romero-Cortés and seminar participants in the University of Pittsburgh. I also would like to thank the editor, Bertrand Candelon, and the anonymous reviewer for their constructive remarks on this version.

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Correspondence to Zeynep Kabukcuoglu.

Appendix

Appendix

Construction of the data for Figs. 12, and 3

  • Use the annual frequency data from the Compustat database for the years 1995–2015 and keep firms that have their headquarters located in the USA.

  • Keep firms that report a stock price and firms that have employment data. These steps improve consistency within the regression sample.

  • Keep firms that report positive R&D expenditure (XRD, data item #46) data. Convert the data into real values using the GDP deflator.Footnote 21

  • Classify firms with the following three-digit SIC codes as non-funded, high-tech firms: 283, 357, 366, 367, 382, 384 and 737.

  • Determine whether firms have access to bond markets using the existence of a bond rating by Standard and Poor’s.

  • Liquidity is defined as the ratio of cash and short-term investments as a fraction of total assets.

  • R&D-to asset ratio is defined as the ratio of R&D expenditures to total assets.

  • Winsorize variables at 1% from both tails.

  • Convert the data into quarterly units using linear interpolation.

Construction of the variables and regression samples

R&D stocks

The real R&D expenditures are calculated using the GDP deflatorFootnote 22 and R&D expenditure data from the Compustat database (XRD, data item #46). Real R&D capital stock is computed by a perpetual inventory method at the firm level by using the following equation:

$$\begin{aligned} \mathrm{RD}_{i,t}= (1-\delta )\ \mathrm{RD}_{i,t-1} +\mathrm{XRD}_{i,t}. \end{aligned}$$
(2)

where \(\mathrm{RD}_{i,t}\) represents the R&D stock; \(\mathrm{XRD}_{i,t}\) represents the real R&D expenditures of firm i at time t; and \(\delta \) is the depreciation rate. In order to obtain the initial R&D stock, the first observation of the real R&D expenditure is divided by a constant rate of depreciation (\(\delta \)) plus a growth rate (g).Footnote 23 Following Hall et al. (2005), I use 15% as the constant rate of depreciation.

The average growth rate of the R&D expenditure is calculated for each industry in the sample. For a firm that has the first R&D expenditure data at year t, g is the average growth rate of R&D expenditures in the industry that the firms belong to in the period between the first year the data are observed at the industry level and the year t. This procedure generates different growth rates for firms that belong to different industries. Also, I remove the firms that have their first observation of the R&D expenditures after 2006.

Before I apply the perpetual inventory method, I eliminate firms with zero or missing R&D expenditures for more than two consecutive years. The missing data points are obtained using linear interpolation. Firms that exit or have missing control variables between 2006Q4 and 2008Q4 are also eliminated. Since this sample is financially stronger, it might lead to underestimation of financing constraints. The sample only consists of firms that have their head quarters in the USA; therefore, the results cannot be generalized to other economies.

Capital stocks

Compustat reports the book value of capital (PPEGT, data item #7) and capital expenditures (CAPX, data item #145); however, for this analysis, the replacement value of capital stock is relevant. Following Salinger and Summers (1983), Fazzari et al. (1988) and Eberly et al. (2008), the replacement value of capital stock is computed by using the following recursion:

$$\begin{aligned} K_{i,t}=\left( K_{i,t-1}\frac{P_{K,t}}{P_{K,t-1}}+ \mathrm{CAPX}_{it}\right) \left( 1-\frac{1}{L_j}\right) . \end{aligned}$$
(3)

The initial value for \(K_i\) is set to the first observation in the PPEGT series for firm, i. \(P_{K,t}\) refers to the price of capital and is the implicit price deflator for nonresidential investment obtained from FRED.Footnote 24\(L_j\) refers to the useful life of capital goods in industry j. The useful life of capital goods is calculated as

$$\begin{aligned} L_j=\frac{1}{N_j}\sum _{i \in j} \frac{\mathrm{PPEGT}_{i,t-1} + \mathrm{DP}_{i,t-1}+\mathrm{CAPX}_{i,t}}{\mathrm{DP}_{it}} . \end{aligned}$$
(4)

\(\mathrm{DP}_{it}\) refers to the depreciation and amortization (Compustat Data Item #14) for firm i at year t. \(N_j\) refers to the number of firms in industry j.

Other variables

  • Tobin’s Q (Market-to-book ratio of firm’s assets) is defined following Brown and Petersen (2011):

    $$\begin{aligned} Q=\frac{(\mathrm{CSHO}\cdot \mathrm{PRCCF})+ \mathrm{AT-CEQ}}{\mathrm{AT}_{-1}} \ , \end{aligned}$$

    where the first variable in the numerator is the market value of equity, which is equal to common shares outstanding (CSHO, data item # 25) times price close (PRCCF, data item #199). Then, total assets (AT, data item #120) net of common equity (CEQ, data item # 60) are added.

  • Sales growth is the log difference of net sales (SALE, data item #117) and denoted by \(\Delta \log (\mathrm{SALE})\).

  • Liquidity is denoted by LIQ and defined as cash and short-term investments (CHE, data item #1) divided by total assets (AT, data item #120).

Firm age is computed based on the year in which price close data (PRCCF) are first observed in the Compustat database. If the firm has data for less than 15 years after the first observation of PRCCF, it is listed as young; otherwise, it is considered mature. Firm size is computed based on its number of employees (EMP, data item #29). If the firm’s number of employees is below (above) the 75th percentile of the whole sample of firms, then it is listed as small (large).

R&D regression sample

This dataset is obtained from the Compustat database between the years 2006 and 2008 and is in quarterly frequency. I choose firms that are in the manufacturing sector and have no missing data on 2006Q4, 2007Q4 and 2008Q4. I keep firms that have their headquarters in the USA (based on Compustat variable, LOC). I remove firms that have gone through mergers and acquisitions during this period (i.e., for these firms, DSLRN is equal to one). Firms without any employment data, R&D stock data, or stock price data are also removed.

R&D, capital and inventory regression sample

I applied steps similar to those of the construction of the R&D sample. Besides the R&D stock, firms should also have capital stock, and real inventory data for 2006Q4, 2007Q4 and 2008Q4. The inventory data (INVT, data item # 3 ) are deflated using the CPI.Footnote 25

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Kabukcuoglu, Z. The cyclical behavior of R&D investment during the Great Recession. Empir Econ 56, 301–323 (2019). https://doi.org/10.1007/s00181-017-1358-7

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Keywords

  • Business cycles
  • R&D
  • Investment-liquidity sensitivity
  • Great Recession