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.

Appendix

1.1 Construction of the data for Figs. 1, 2, 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.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.

1.2 Construction of the variables and regression samples

1.2.1 R&D stocks

The real R&D expenditures are calculated using the GDP deflator²² 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).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.

1.2.2 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.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.

1.2.3 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).

1.2.4 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.

1.2.5 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.²⁵