Working capital and R&D smoothing: Evidence from the Tel Aviv stock exchange

Alkhataybeh, Ahmad Article Working capital and R&D smoothing: Evidence from the Tel Aviv stock exchange Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Alkhataybeh, Ahmad (2021) : Working capital and R&D smoothing: Evidence from the Tel Aviv stock exchange, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 24, Iss. 1, pp. 91-102, https://doi.org/10.1080/15140326.2021.1877599 This Version is available at: https://hdl.handle.net/10419/314118 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Working capital and R&D smoothing: evidence from the Tel Aviv stock exchange Ahmad Alkhataybeh To cite this article: Ahmad Alkhataybeh (2021) Working capital and R&D smoothing: evidence from the Tel Aviv stock exchange, Journal of Applied Economics, 24:1, 91-102, DOI: 10.1080/15140326.2021.1877599 To link to this article: https://doi.org/10.1080/15140326.2021.1877599 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 23 Mar 2021. Submit your article to this journal Article views: 3017 View related articles View Crossmark data Citing articles: 7 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 RESEARCH ARTICLE Working capital and R&D smoothing: evidence from the Tel Aviv stock exchange Ahmad Alkhataybeh Faculty of Economics and Administrative Sciences, Yarmouk University, Irbid, Jordan ABSTRACT This paper proposes new tests for financing constraints on R&D investment by directly examining the role played by working capital in smoothing the R&D expenditures of firms listed on the Tel Aviv stock exchange. It emphasizes the importance of working capital, not only for use but also as a source of funds. The findings offer new evidence for why levels of liquidity are important for R&Dintensive firms. Working capital alleviates the effects of transient finance shocks on the level of R&D, thereby averting the high adjustment costs that accompany changes in R&D. ARTICLE HISTORY Received 3 June 2020 Accepted 13 January 2021 KEYWORDS Working capital; financing constraints; R&D investment 1. Introduction Since the emergence of the great importance of endogenous growth theory on human welfare, recent studies in the corporate finance field have shifted towards focusing on capital market imperfection and the financing menu of R&D (Brown & Petersen, 2011; Chen & Guariglia, 2013; Guariglia & Liu, 2014; He & Wintoki, 2016; Kang, Baek, & Lee, 2017; Mina, Lahr, & Hughes, 2013). This trend has resulted from the acknowledgment that R&D is vital for the growth of companies and economies alike. Together with the employment of technological advances to create new products and services and to develop new production processes, it aims to achieve competitive advantage, thereby securing a larger market share (Cohen & Levinthal, 1989). Since R&D is of high importance for firms, certain obstacles such as the asymmetric information dilemma, low collateral value, and the uncertainty allied with its output make it more prone to financial constraints. Previous literature on the corporate levels of R&D investment has mainly focused on the availability of internally generated funds (Grabowski, 1968; Himmelberg & Petersen, 1994; Ughetto, 2008), and has evidenced that R&D levels are allied to levels of internal cash flow. However, considering the riskiness and intangibility of R&D, Switzer (1984) and Wang and Thornhill (2010) concluded that debt is not a genuine source of R&D financing. On the other hand, Carpenter and Petersen (2002) and Brown, Martinsson, and Petersen (2012) found that among the external means of funding, equity is the ideal source for this purpose. Since both internally and externally generated funds can experience high volatility, the high adjustment costs of R&D investment make it expensive for CONTACT Ahmad Alkhataybeh [email protected] Faculty of Economics and Administrative Sciences, Yarmouk University, Irbid 211-63, Jordan. JOURNAL OF APPLIED ECONOMICS 2021, VOL. 24, NO. 1, 91–102 https://doi.org/10.1080/15140326.2021.1877599 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. firms to adjust their R&D flow during transitory finance shocks. 1 Fazzari and Petersen (1993) developed the investment smoothing concept by means of working capital, measured as the difference between current assets and current liabilities on a company’s balance sheet. Considering working capital accounts, Bigelli & Sánchez-Vidal (2012) found strong evidence of the significant role of cash holdings in constrained firms. Similarly, Petersen and Rajan (1997) and Nazir and Afza (2009) point out the merits of trade credit and account receivables. Brown and Petersen (2011) and Guney, Karpuz, and Ozkan (2017) were among the first to investigate the role played by precautionary cash reserves in the R&D smoothing process, concluding that financially constrained firms draw upon their precautionary cash holdings to preserve a relatively smooth R&D path. However, there is little evidence as to whether working capital as a source of liquidity plays a role in the R&D smoothing process (see, for example, Alkhataybeh, 2018). Therefore, this paper intends to fill this gap by underlining the role played by working capital in the smoothing process, as it is regarded as a source and easily reversible store of liquidity, and to explore whether R&D financing constraints do in fact exist. The research adds to the finance literature in two ways. First, related investigation on this subject has yet to be conducted on a developed economy (in this case Israel). The comparative scarcity of evidence as to whether the means of financing is of concern for the R&D of Israeli firms remains a puzzle, raising concerns as to whether financing constraints are hindering R&D investment in this environment. Second, this research employs a more comprehensive and accurate measure for testing the existence of financing constraints on R&D investment; that is, working capital as a whole, rather than only one of its components. As a result, if financing constraints on R&D investment exist, a negative relation with changes in net working capital is expected to be noticed, suggesting that companies resort to liquidity reserves to smooth such investment, thus stressing the importance of working capital in companies’ financial policies. The rationale for studying the Israeli market is as follows. First, Israel is considered to be a prominent economic success story among innovating economies and one of the most recognized innovative hubs in the world (Trajtenberg, 2000). Second, this knowledge-based economy has outperformed many other leading countries in terms of R&D per GDP expenditure. Indeed, it has been ranked first in the world during the last decade (OECD, 2020). In addition, the priority given by the Israeli government to this type of investment, especially as seen in the R&D Law, makes it an ideal choice for investigation (Ministry of Economy, 2014). Finally, as Israel is considered to be a market-based economy, it is assumed that financing constraints on corporate investment are more pronounced than in bank-based economies. Bond, Elston, Mairesse, and Mulkay (2003) propose that market-based economies are weak in channelling desired capital towards companies with potential investment opportunities because of the arm’s-length relationship between companies and capital providers. Therefore, the Israeli financial system might result in financial constraints among its listed firms. These reasons are assumed to be sufficiently important to investigate this market, whereas this hub might be considered 1 Adjustment costs refer to recruitment and training costs for new specialists in the case of temporary cuts in the R&D process. Moreover, if these cuts result in the laying off of expert workers, the potential dissemination of firm-specific commercial secrets may harm the value of a firm’s innovation process and consequently be more costly. 92 A. ALKHATAYBEH as a listing choice for firms conducting R&D, or a market diversification choice for equity investors (i.e., it is of economic significance). Section 2 presents the preliminary analysis and overview of the key variables, and identifies the model employed for the investigation. Section 3 displays the results, while Section 4 concludes by summarizing the paper, drawing implications and providing suggestions for future research. 2. Data and methodology 2.1. Data The research sample covers all active and inactive firms listed on the Tel Aviv stock exchange between 2008 and 2016. Annual data were retrieved from the Worldscope database through Datastream. The research only targeted firms listed in the manufacturing and service industry category; in addition, as the focus of the research is on R&D performing firms, the sample excluded all non-R&D reporting ones, as they were inappropriate for the analysis. 2 Therefore, the remaining number of firms in the unbalanced panel was 114. The homogenous purposeful sampling process is summarized in Figures 1 and 2. Figure 1. Target population of firms listed on the Tel Aviv stock exchange. 2 Among the 382 firms, the actual number of firms included in the analysis is 114, with the remaining 268 firms being categorized as non-R&D reporting firms with missing data (non-zero reports). Further, although 13 out of the 114 firms reported zero R&D expenditure, there remained enough reported observations for them to be included in the sample (i.e., not full zero R&D records), suggesting that corporate engagement decisions are made in spikes, with breaks in the engagement process between periods of normal corporate activities. please refer to Appendix A1. for a comparison of descriptive statistics for reporting and non-reporting R&D firms. JOURNAL OF APPLIED ECONOMICS 93 2.2. Tel Aviv stock exchange firms Table 1 shows the operational measures of the variables used in the analysis, while Table 2 displays their summary statistics and correlation matrix. Preliminary analysis showed that the dependent variable (RD) was statistically associated with all the Table 1. Operational measures of the variables. Variable name Measure Source Research & Development it (RD) Value of R&D expenses of firm i at time t divided by the value of total assets at time t-1. Worldscope Market to Book it (MB) Market value of the total assets of firm i at time t-1 divided by the book value of total assets at time t-1. DataStream Sales Growth it (SGr) Change in net sales of firm i between time t and time t-1, divided by net sales at time t-1. Worldscope Cash Flow it (CFlow) Gross internally generated cash flow of firm i at time t divided by the value of total assets at time t-1. Gross cash flow is equal to after-tax income before extraordinary items and preferred dividend, plus depreciation, depletion and amortization expenses and research and development expenses. Worldscope Stock Issues it (Stk) Net cash raised from stock issues of firm i at time t divided by the value of total assets at time t-1. Net cash raised is equal to the issued minus the purchased common and preferred stocks. Worldscope Long-Term Debt Issues it (Dbt) Net cash raised from the long-term debt of firm i at time t divided by the value of total assets at time t-1. Net cash raised is equal to issued minus reductions in long-term debt. Worldscope Change in Net Working Capital it (NWC) Change in net working capital of firm i between time t and time t-1, divided by the value of total assets at time t-1. Net working capital is equal to current assets minus current liability. Worldscope The definitions and measurement of the variables are consistent with the literature. Figure 2. Target population of R&D reporting firms. 94 A. ALKHATAYBEH predictors. The signs of these correlations were as expected, apart from CFlow & NWC. The condition of non-multicollinearity was verified, and the correlation coefficient matrix displayed no sign of multicollinearity among the variables. 2.3. Methodology Roodman (2009) advises that the generalized method of moments (GMM) is the best econometric estimator for use in the estimation of dynamic models. It is mostly applicable to panel data analysis that is characterized by many individuals and few time periods. In addition, it is a solution for the endogeneity problem associated with the predictors, as it assumes that valid instruments are available inside the immediate dataset and depends on the lag transformation of the instrumented variables. Following Brown and Petersen (2011), the analysis is based on the following regression model: RDi;t¼β1RDi;t1þβ2RD2i;t1þβ3MBi;tþβ4SGri;tþβ5CFlowi;tþ β6CFlowi;t1þβ7Stki;tþβ8Stki;t1þβ9Dbti;tþβ10Dbti;t1þ β11NWCi;tþβ12NWCi;t1þdind þfiþdtþεi;t In accordance with the dynamic optimization “Euler condition”, the quadratic term of the lagged dependent variable in means was added to the model to control for the target level of R&D expenditure in the presence of adjustment costs. Accordingly, the expected sign of the coefficient of the lagged predetermined variable is positive and close to one, and the coefficient of the quadratic term is negative. Controlling for firms’ investment demand (as explained in Tobin’s Q and accelerator theories), the model includes the variables MB and SGr (McLean, Zhang, & Zhao, 2012 and Shapiro et al., 1986). In addition, it controls for industry-fixed effects (dind), firm-fixed effect (f i ) and year-fixed effects (dt). Finally, all the financial variables in the model are treated as endogenous. 3. Results Given the dynamic structure of the research model and the endogeneity of its predictors, typical least squares regressions led to fairly inconsistent estimates. Such inconsistency is due to the correlation between the lagged dependent variable and the unobservable fixed effects, as well as the endogenous nature of the right-hand side variables (Flannery & Hankins, 2013). Accordingly, Arellano and Bond (1991) addressed these problems by introducing the differenced-GMM estimator and taking the first difference; however, this process does not Table 2. Descriptive statistics and pairwise correlation matrix. Variable Mean S.D. Obs RD MB SGr CFlow Stk Dbt NWC RD 0.152 0.237 775 1.000 MB 2.260 5.303 904 0.0743** 1.000 SGr 0.169 0.714 641 0.2664*** −0.0220 1.000 Cflow −0.057 0.459 775 −0.5604*** 0.0656 −0.0692 1.000 Stk 0.232 0.859 760 0.6402*** 0.0407 0.1483*** −0.6780*** 1.000 Dbt 0.100 0.160 731 −0.1612*** −0.1296*** 0.0098 −0.0031 −0.0202 1.000 NWC 0.053 0.499 775 0.3172*** 0.0926** 0.0837** −0.3056*** 0.7070*** −0.0123 1.000 ***and ** denote statistical significance at the 1% and 5% levels, respectively. The financial variables are trimmed at the 1% level. JOURNAL OF APPLIED ECONOMICS 95 eliminate the correlation between the disturbances and the lagged dependent variable. Therefore, it is necessary to use instruments that are uncorrelated with the disturbances, but are associated with the explanatory variables, in order to overcome this endogeneity problem. However, as pointed out by Blundell and Bond (1998) and Bond, Hoeffler, and Temple (2001), estimates of the difference-GMM are not completely dependable in the presence of weak instruments problem because estimates tend to be downwards-biased. 3 To improve the GMM estimator, Blundell and Bond (1998) introduced the systemGMM estimator with a combination of the moment conditions for the differenced equation and those for the equation in level. When applying this estimator, the preference is to use one-step or two-step estimation. The one-step estimator assumes homoscedastic errors, while the two-step version assumes heteroscedastic ones. In this context, Flannery and Hankins (2013) concluded that the two-step estimator was asymptotically more efficient; however, its standard error estimates are typically biased downwards. This therefore encourages the use of finite-sample correction of standard errors. Accordingly, this research takes into account the application of a finite sample correction in the estimation of the two-step system-GMM. It should be noted that in the system specification of this research, the instruments used for the level equation are the lagged difference endogenous variables, and the lagged level endogenous variables (dated t-2 to t-3) for the equation in difference. Table 3 presents the two-step system-GMM estimates. The lagged and squared lagged of the dependent variable are statistically significant, with positive and negative effects on the current level of R&D expenses, respectively. This is in line with the specifications of the Euler equation and is also consistent with previous research findings; for instance, those of Bond and Meghir (1994) and Guney et al. (2017). MB as a control for investment demand is statistically insignificant, implying weak control for R&D investment demand via Tobin’s Q theory, which states that the higher the Tobin’s Q value (above unity), the higher the corporate incentives to expand investment activities, and vice versa when the Tobin’s Q value is below unity. It is argued that the optimal investment level is reached when the Tobin’s Q value is equal to one (Tobin, 1969). The insignificant power of this controller is not surprising and is consistent with the findings of Brown and Petersen (2011) and Borisova and Brown (2013), given that financial policy plays a role in the level of corporate investment decisions. Similarly, SGr is positively insignificant, thus providing weak evidence that sales growth plays a significant role in R&D investment demand, as explained by the accelerator model. The principle of this theory is based on the idea that when the corporate level of sales increases, profits are expected to increase accordingly. Therefore, firms would have an incentive to invest more to minimize the gap between the required stock of capital and the existing ones if sales are expected to last. The increased investments would result in a further increase in sales and profits, consequently causing the multiplier effect. The insignificance, however, can be explained by the fact that this ratio does not 3 According to Bond et al. (2001), inconsistent difference-GMM estimates can be detected if the coefficient of the lagged dependent variable ranges between OLS (upward-biased) and fixed-effect (downward-biased) estimates, with the coefficient being closer to the latter. Using the lagged level endogenous variables (t-2 to t-3) as instruments in the differenced equation results in the OLS and fixed-effect estimates of the lagged dependent variable serving as boundaries for the difference-GMM estimation. Particularly, since the difference-GMM estimate is closer to the fixed effect estimate, this suggests a weak instrument problem that may cause inconsistent estimates. Therefore, a system GMM estimator is used. The results of these estimations are available upon request. 96 A. ALKHATAYBEH capture other corporate investment determinants, for example, financial constraints (Brown & Floros, 2012; Wang & Thornhill, 2010). The insignificance of cash flow as an internal means of finance is inconsistent with the pecking-order theory. This means that the R&D investment of the tested firms was not determined by significant internal cash flow. This could be because the firms were still young, and so had had insufficient time to generate additional, or non-negative, cash flow (Borisova & Brown, 2013; Del Canto & Gonzalez, 1999). The coefficient of stock issues is positively and statistically significant, suggesting their importance as an ideal external means of funding to finance R&D investment. When computing the economic significance of stock issues, it is found that a one-standard deviation increase in stock issues increases R&D expenditure by 175.6%. 4 These findings support the argument that as Table 3. Estimation results of the dynamic RD model. Two-step GMM RD RD i,t-1 0.833*** (0.312) RD i,t-12 −0.669** (0.339) MB i,t 0.0006 (0.001) SGr i,t 0.0152 (0.021) CFlow i,t 0.170 (0.104) CFlow i,t-1 0.016 (0.043) Stk i,t 0.311*** (0.101) Stk i,t-1 −0.012 (0.048) Dbt i,t 0.149 (0.113) Dbt i,t-1 −0.150 (0.091) NWC i,t −0.216** (0.033) NWC i,t-1 −0.029 (0.041) Industry dummies Yes Year dummies Yes No. of observations 495 No. of firms 114 No. of instruments 89 AR (1) −1.75 (p = 0.080) AR (2) −1.28 (p = 0.201) J-test 62.89 (p = 0.444) Standard errors are reported in parentheses. *** and ** denote significant at the 1% and 5% levels, respectively. 4 In order to assess the economic magnitude, the coefficient of stock issues is multiplied by the standard deviation of stock issues (0.311×0.859 = 0.267). Thus, an increase in stock issues by one standard deviation increases R&D expenditure by 0.267. Since the mean of R&D expenditure is 0.152, a change by 0.267 represents 175.6%. JOURNAL OF APPLIED ECONOMICS 97