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Res Int Bus Finance. 2023 Oct; 66: 102014.
Published online 2023 Jun 3. doi:10.1016/j.ribaf.2023.102014
PMCID: PMC10239288
PMID: 37293527
Thi Hong An Thai,a Thi Thuy Anh Vo,a,⁎ and Mieszko Mazurb
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Abstract
This study investigates investment–cash flow sensitivity during the COVID-19 economic crisis. Using an international sample of publicly listed firms, we find that the sensitivity of capital expenditures to cash flows is significantly reduced during the crisis. When we split the sample into strongly and weakly affected countries, we find that firms in countries affected more seriously by COVID-19 exhibit lower investment responsiveness to cash flows. We further find that investment–cash flow sensitivity is diminished when government aid is greater, firms have more cash on hand, and investment opportunities decline. Our results survive a host of robustness checks. This study contributes to the discussion on the impact of COVID-19 on corporate policies within an international framework.
Keywords: Capital expenditures, COVID-19, Crisis, Investment, Investment–cash flow sensitivity, Government aid
1. Introduction
Investment decisions are one of the most important corporate decisions because of the uncertainty of future payoffs and irreversibility of investments (Pindyck, 1993). In their seminal analysis, Fazzari et al. (1988) propose an empirical investment model that estimates investment–cash flow sensitivity and find a positive relation between capital expenditure levels and internally generated cash flows. More recent studies argue that investment–cash flow sensitivity can be interpreted as a proxy for the quality of investment opportunities (Lin et al., 2011) or reliance on external financing (Custódio and Metzger, 2014); therefore, the sensitivity of investment to cash flow may not necessarily be a proxy for financial constraints. Along these lines, Chen and Chen (2012) show that investment–cash flow sensitivity disappears despite financial constraints being the first-order concern of firms.
This study investigates investment–cash flow sensitivity during the COVID-19 economic crisis. Studying the investment behavior of firms during the economic crisis is particularly important because COVID-19 represents an unexpected exogenous shock to firms worldwide. The shock induced by COVID-19 is noteworthy because it affects firms differently depending on the severity of the pandemic observed in a specific geographic location and the subsequent government reaction to it. Using data on close to 30,000 publicly listed firms worldwide, we find that following the COVID-19 breakout, firms exhibit weaker investment–cash flow sensitivity. Next, when we split the sample into strongly and weakly affected countries, we find that firms domiciled in countries affected more seriously by COVID-19 exhibit lower investment responsiveness to cash flows.
We then investigate possible explanations of why investment–cash flow sensitivity is significantly lower during the COVID-19 crisis. Generally, we provide empirical evidence suggesting that other financing sources, including government aid and accumulated cash on hand, are responsible for significantly lower investment–cash flow sensitivity during COVID-19. Moreover, we find evidence of a decline in investment opportunities during COVID-19 that may be responsible for the reduced sensitivity of investment expenditures to the internally generated cash flows. The argument of the quality of investment opportunities in explaining low investment–cash flow sensitivity in normal times has some support in the literature (e.g., Lin et al., 2011). Also, we find that lower cash flows per se may be partially responsible for lower investment–cash flow sensitivity. When comparing the results obtained for the COVID-19-induced crisis with those observed for the financial crisis of 2008–09, we find that during the great recession of 2008–09, investment–cash flow sensitivity remained at virtually the same level as before the crisis.
Our study extends the literature in several ways. First, we add to the emerging literature on COVID-19 and corporate finance (Cejnek et al., 2021, Amore et al., 2022, Vo et al., 2022) by documenting significantly lower investment–cash flow sensitivity for a cross-country sample of firms during the COVID-19 economic crisis. This effect is strongest in the subsample of firms domiciled in countries highly affected by COVID-19. Second, we extend the literature on the sensitivity of investment to cash flows by providing evidence about the relationship between investment and cash flows during times of crisis induced by a severe revenue shock (as opposed to the 2008 credit crunch) (Duchin et al., 2010, Campello et al., 2010). In contrast to the 2008–09 great recession, the COVID-19 crisis is characterized by the sound financial conditions of firms ex ante. When crises are associated with limited access to credit, firms may exhibit higher investment–cash flow sensitivity because they depend more on internal cash flow for contemporaneous investment. Third, the paper complements studies that focus on the effect of COVID-19 on distinct aspects of the economy within the international context (e.g., Augustin et al., 2022).
The remainder of this study is structured as follows. In Section 2, we review the literature and develop hypotheses. Section 3 presents the data and discusses our empirical strategy. Section 4 presents the results, while Section 5 concludes the paper.
2. Literature overview and hypothesis development
As argued in the seminal paper by Fazzari et al. (1988), if internal and external capital markets are perfect substitutes with no cost advantage, then we should see no relationship between investment and internally generated cash flows, as any level of investment could be financed by a mix of internal and external funds with no preference for any given type of financing. It follows that in the empirical investment model, the coefficient estimate on the cash flow variable should be insignificant and the sign of the coefficient random, either positive or negative, with little economic and statistical meaning. However, Fazzari et al. (1988) find that corporate investment is positively associated with fluctuations in cash flows, implying that investment intensity significantly depends on internally generated cash. This positive and statistically significant relationship holds for various firms, industries, and periods.
The existing literature generally finds a similar result, a positive and statistically significant association between corporate investment and cash flows (Allayannis and Mozumdar, 2004, Ağca and Mozumdar, 2008, Brown and Petersen, 2009, Lewellen and Lewellen, 2016, Moshirian et al., 2017, Larkin et al., 2018). Moreover, many empirical studies focus on various specific aspects of the investment–cash flow relationship. For example, Brown and Petersen (2009) find that the sensitivity of investment to cash flows has decreased over time due to the transformation of investment expenditures from tangible assets toward research and development (R&D) and reductions in financial constraints over time (i.e., publicly listed equity becomes more available and accessible). Allayannis and Mozumdar (2004) compare the magnitude of the investment–cash flow sensitivity of the 1977–1986 period with that of 1987–1996 and find that investment–cash flow sensitivity is lower for the latter interval.
Naturally, the question arises as to the extent to which exogenous shocks to the economy affect the investment–cash flow sensitivity of firms. Studies that examine the impact of exogenous shocks on the investment–cash flow relationship are scant, and most of them focus on the 2008–09 financial crisis (Campello et al., 2010, Drobetz et al., 2016). One of the exceptions is Chowdhury et al. (2016) who investigate the effect of regulatory shocks on the investment-cash flow sensitivity. They find a significant increase in investment-cash flow sensitivities for firms affected by industry deregulation in the 1970s. On the other hand, they document a decline in the investment-cash flow sensitivities for firms following the implementation of the Sarbanes-Oxley Act.
Based on the above discussion, we forumlate a set of alternative research questions. On the one hand, we might see reduced investment–cash flow sensitivity for firms affected by COVID-19 due to the significant decline in revenues and, consequently, cash flows. Therefore, companies affected by COVID-19 should rely less on internally generated cash flows to finance investment. If this is the case, investment–cash flow sensitivity during the COVID-19 crisis should be significantly lower than during the preceding period. We refer to this as Hypothesis 1:
Hypothesis 1
Firms exhibit lower responsiveness of investment to cash flow during the COVID-19 crisis.
A variant of this hypothesis suggests that lower investment–cash flow sensitivity might be due to the government financial aid for COVID-19-affected firms. Government assistance may reduce firms' reliance on their current internal cash flows for investment vis-à-vis government funding. Thus, we should see reduced investment–cash flow sensitivity, if government financial help to firms is more pronounced (Hypothesis 1a). Similarly, for cash holdings, accumulated cash should reduce firms’ reliance on internal cash flows, as companies can use cash on hand to pay for investment expenditures (Hypothesis 1b). Finally, the hypothesized lower investment–cash flow sensitivity may result from lower quality or amount of investment opportunities in a given period, in our case, during the COVID-19 crisis (Hypothesis 1c):
Hypothesis 1a
: During the COVID-19 crisis, investment–cash flow sensitivity diminishes if firms receive greater government aid.
Hypothesis 1b
: During the COVID-19 crisis, investment–cash flow sensitivity diminishes if firms have more cash on hand.
Hypothesis 1c
: During the COVID-19 crisis, investment–cash flow sensitivity diminishes if investment opportunities decline.
On the other hand, an alternative hypothesis predicts that due to liquidity constraints in financial markets and, therefore, reduced external financing availability, investment–cash flow sensitivity will rise. In other words, firms will become more reliant on internally generated cash flows, when external financing sources are limited. We refer to this prediction as Hypothesis 2:
Hypothesis 2
Firms exhibit higher responsiveness of investment to cash flow during the COVID-19 crisis.
It should be stressed that the sub-hypotheses developed above (Hypothesis 1a–1c) still hold within the alternative framework set by Hypothesis 2. For example, during liquidity crises, government financial aid should reduce investment–cash flow sensitivity, as firms become less reliant on internal cash flows to finance investment.
3. Methodology
3.1. Data
Our study uses an international sample of 27,944 publicly listed firms domiciled in 78 countries. The data used to calculate variables at the firm level are extracted from Compustat Global Fundamentals Quarterly, whereas data on macroeconomic activity come from the World Development Indicators database. Investor protection (ADRI) is the Spamann (2010) ADRI or the Djankov et al. (2008) revised ADRI if it is not available in Spamann (2010). We have ADRI values for 61 countries out of the 78 countries in the sample. Information on government effectiveness is from the Worldwide Governance Indicators of the World Bank. COVID-19 data have been collected from the ourworldindata.org website. To eliminate outliers, all firm-specific variables are winsorized at the 1% level. Variable definitions and data sources are provided in Appendix A.
3.2. Empirical model
To investigate the relationship between investment and cash flow, we use the formula below:
INVijt = α0 + α1CFijt + ∑Control_Variablesijt + ∑Quater + ∑Industry + εijt,
(1)
where INVijt is the investment of firm i in country j during quarter t, measured by dividing capital expenditures by the book value of total assets at the beginning of the quarter. CFijt denotes cash flows, defined as the sum of earnings before extraordinary items and depreciation, scaled by the book value of total assets at the beginning of the quarter. Control variables include firm size, Tobin’s Q, asset tangibility, net debt, GDP growth rate, and inflation rate.
To gage the change in investment–cash flow sensitivity after the breakout of the COVID-19 economic crisis, we estimate Eq. (1) for two subperiods: Q1 2011–Q4 2019 and Q1 2020–Q4 2020. As an additional robustness check, we employ the difference-in-differences (DID) approach.
We proxy for the severity of the COVID-19 economic crisis by the logarithm of the total cases per million inhabitants and the new cases per million inhabitants for each country in our sample measured quarterly. Based on the above indicators, we split the sample into strongly and weakly affected, with the median being the boundary between the two.
3.3. Descriptive statistics
Table 1 presents descriptive statistics of all variables used in the study. The average investment ratio before COVID-19 is 1.32% per quarter, and the average level of cash flows is 0.88%. Following the COVID-19 breakout, the investment rate drops to 0.91%, while the average cash flow falls to 0.48%. Moreover, net debt increases from 5.87% pre-COVID-19 to 6.36% during the crisis.
Table 1
Descriptive statistics.
| Panel A: Firm-level variables | ||||||
|---|---|---|---|---|---|---|
| Variable | Pre-COVID-19 | COVID-19 | ||||
| Observations (Firm-quarter) | Mean | Std. Dev. | Observations (Firm-quarter) | Mean | Std. Dev. | |
| INV | 358,630 | 0.0132 | 0.0231 | 33,682 | 0.0091 | 0.0180 |
| CF | 358,630 | 0.0088 | 0.0552 | 33,682 | 0.0048 | 0.0544 |
| Size | 358,630 | 19.7293 | 2.1470 | 33,682 | 19.9150 | 2.1467 |
| Q | 358,630 | 1.6388 | 1.6753 | 33,682 | 1.6710 | 1.8247 |
| Tang | 358,630 | 0.8594 | 0.1886 | 33,682 | 0.8531 | 0.1872 |
| Netdebt | 358,630 | 0.0587 | 0.3145 | 33,682 | 0.0636 | 0.3202 |
| CH | 358,630 | 0.1713 | 0.1982 | 33,682 | 0.1958 | 0.2049 |
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| Panel B: Macroeconomic variables | |||
|---|---|---|---|
| Variable | Number of observations for available data | Mean | Std. Dev. |
| GDP | 853 | 3.2147 | 2.9181 |
| Inf | 853 | 3.1979 | 3.7232 |
| SMC | 831 | 70.7875 | 124.7917 |
| GE | 643 | 0.6902 | 0.9052 |
| ADRI | 61 | 3.8443 | 1.0023 |
| Gov_aid_GDP | 78 | 0.0711 | 0.0685 |
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| Panel C: COVID-19 variables | |||
|---|---|---|---|
| Variable | Observations (Country-quarter) | Mean | Std. Dev. |
| new_cases | 291 | 6.6904 | 2.4796 |
| accum_cases | 291 | 7.1285 | 2.5149 |
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The table presents descriptive statistics for the entire sample between Q1 2011 and Q4 2020 as well as the two subsamples including the period of COVID-19 (i.e., Q1 2020–Q4 2020) and the preceding period (Pre-COVID-19). In Panel B, the number of observations are at the country-year level except for ADRI and Gov_aid_GDP measured at the country level. Variable definitions and data sources can be found in Appendix A.
Pairwise comparisons for all firm-specific variables used in our regression are shown inTable 2. Following Kennedy (2008), we argue that multicollinearity between explanatory variables is not a concern in our setup because all correlation coefficients between explanatory variables are lower than 0.8. As can be seen in Table 2 the investments and cash flows are positively related. Finally, the correlation matrix shows a positive association between investment and all other firm-level variables, excluding cash holdings.
Table 2
Correlation matrix.
| INV | CF | Size | Q | Tang | Netdebt | CH | |
|---|---|---|---|---|---|---|---|
| INV | 1 | ||||||
| CF | 0.0737 | 1 | |||||
| Size | 0.0277 | 0.4234 | 1 | ||||
| Q | 0.0276 | −0.2895 | −0.2381 | 1 | |||
| Tang | 0.1520 | −0.1156 | −0.1649 | 0.0214 | 1 | ||
| Netdebt | 0.0567 | 0.3718 | 0.3755 | −0.3697 | −0.1927 | 1 | |
| CH | −0.0703 | −0.4454 | −0.2943 | 0.4068 | 0.2093 | −0.8536 | 1 |
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The table reports pairwise correlation matrix of all firm-level variables used in our regressions. Variable definitions and the data sources are provided in Appendix A.
4. Empirical results
4.1. COVID-19 and investment–cash flow sensitivity
Table 3 presents our baseline results for all firms (Columns 1–3) as well as for the non-China subsample (Columns 4–6) and non-US subsample (Columns 7–9). In each study sample, we run Eq. (1) for the period before and during COVID-19. We then employ the DID method to verify our findings. As shown in Table 3, the coefficient estimates on the cash flow variable are lower following the breakout of the COVID-19. For example, for the entire sample, the estimate of cash flow sensitivity is 0.0285 during the COVID-19 crisis, compared with 0.0428 for the pre-COVID-19 period (Columns 1–2, Table 3). When the variable After is introduced to the model, the interaction term After*CF has a negative and significant coefficient estimate of −0.0105, reinforcing our baseline finding. For the non-China and non-US subsamples, we also find that the magnitude of investment–cash flow sensitivity is significantly reduced after the breakout of COVID-19. Generally, these results are consistent with Hypothesis 1, suggesting that companies affected by COVID-19 have significantly lower investment–cash flow sensitivity. Consequently, we do not find any support for Hypothesis 2.
Table 3
Baseline regressions.
| Variable | All firms | Non-CN | Non-US | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| After | −0.0041*** | −0.0042*** | −0.0046*** | ||||||
| (0.0005) | (0.0005) | (0.0011) | |||||||
| After*CF | −0.0105*** | −0.0117*** | −0.0120** | ||||||
| (0.0027) | (0.0026) | (0.0050) | |||||||
| CF | 0.0428*** | 0.0285*** | 0.0424*** | 0.0371*** | 0.0226*** | 0.0367*** | 0.0626*** | 0.0385*** | 0.0614*** |
| (0.0019) | (0.0029) | (0.0019) | (0.0020) | (0.0029) | (0.0019) | (0.0030) | (0.0050) | (0.0030) | |
| Size | 0.0002*** | 0.00001 | 0.0002*** | 0.0003*** | 0.0001 | 0.0003*** | 0.00001 | −0.00001 | 0.00001 |
| (0.00001) | (0.0001) | (0.00001) | (0.0001) | (0.0001) | (0.00001) | (0.0001) | (0.0001) | (0.0001) | |
| Q | 0.0012*** | 0.0009*** | 0.0012*** | 0.0013*** | 0.0007*** | 0.0012*** | 0.0014*** | 0.0012*** | 0.0014*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Tang | 0.0164*** | 0.0120*** | 0.0161*** | 0.0170*** | 0.0123*** | 0.0166*** | 0.0155*** | 0.0115*** | 0.0151*** |
| (0.0004) | (0.0005) | (0.0004) | (0.0004) | (0.0005) | (0.0004) | (0.0005) | (0.0007) | (0.0005) | |
| Netdebt | 0.0060*** | 0.0055*** | 0.0060*** | 0.0068*** | 0.0051*** | 0.0066*** | 0.0046*** | 0.0054*** | 0.0047*** |
| (0.0003) | (0.0004) | (0.0003) | (0.0003) | (0.0004) | (0.0003) | (0.0004) | (0.0006) | (0.0004) | |
| GDP | 0.0004*** | 0.0006*** | 0.0005*** | 0.0005*** | 0.0001 | 0.0005*** | 0.0004*** | 0.0005*** | 0.0004*** |
| (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | |
| Inf | 0.0001* | −0.0001** | 0.00001 | 0.00001 | −0.0001** | 0.00001 | 0.0001* | −0.0002*** | 0.00001 |
| (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | |
| Constant | −0.0127*** | −0.0019 | −0.0118*** | −0.0143*** | −0.0006 | −0.0133*** | −0.0067*** | −0.0004 | −0.0059*** |
| (0.0014) | (0.0024) | (0.0014) | (0.0015) | (0.0023) | (0.0014) | (0.0018) | (0.0027) | (0.0018) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 358,630 | 33,682 | 392,312 | 297,961 | 27,823 | 325,784 | 255,687 | 25,299 | 280,986 |
| R2 | 0.1043 | 0.0713 | 0.1038 | 0.1177 | 0.0799 | 0.1168 | 0.0854 | 0.0612 | 0.0854 |
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The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flows (CF) as well as a set of controls including industry- and time-fixed effects. COVID-19 is the period from Q1 through Q4 2020, and Pre-COVID-19 is from Q1 2011 through Q4 2019. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.
In the following analysis, we measure the severity of the COVID-19 crisis by looking at the number of new and aggregate COVID-19 cases per million inhabitants at the country level. We then divide the sample into strongly and weakly affected along the median values of the aforementioned variables. The results are reported inTable 4. The table shows that firms domiciled in countries affected more seriously by COVID-19 have significantly lower investment–cash flow sensitivity (e.g., Columns 1–3 and 7–9). This result is more robust than the one obtained in Table 3 because it is more directly related to COVID-19 severity and its effect on the real economy. These findings further reinforce our earlier results that during COVID-19, investment–cash flow sensitivity is significantly reduced. Hence, we again we find evidence supporting Hypothesis 1.
Table 4
Impact of COVID-19 on investment–cash flow sensitivity. Split sample analysis.
| Variable | New cases | Cumulative cases | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Strongly affected | Weakly affected | Strongly affected | Weakly affected | |||||||||
| Pre-COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | |
| After | −0.0018*** | 0.0264** | −0.0019*** | 0.0267** | ||||||||
| (0.0007) | (0.0125) | (0.0007) | (0.0130) | |||||||||
| After*CF | −0.0294*** | −0.0148 | −0.0275*** | −0.0242 | ||||||||
| (0.0052) | (0.0189) | (0.0052) | (0.0188) | |||||||||
| CF | 0.0490*** | 0.0207*** | 0.0492*** | 0.0689*** | 0.0547*** | 0.0711*** | 0.0464*** | 0.0204*** | 0.0467*** | 0.0768*** | 0.0559*** | 0.0800*** |
| (0.0056) | (0.0030) | (0.0054) | (0.0180) | (0.0089) | (0.0179) | (0.0056) | (0.0030) | (0.0054) | (0.0177) | (0.0091) | (0.0177) | |
| Size | 0.0003*** | 0.0001 | 0.0002*** | −0.0002 | −0.000003 | −0.00004 | 0.0003*** | 0.0001 | 0.0002*** | −0.00003 | −0.0001 | 0.0001 |
| (0.0001) | (0.0001) | (0.0001) | (0.0002) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0002) | (0.0002) | (0.0001) | |
| Q | 0.0008*** | 0.0007*** | 0.0008*** | 0.0019*** | 0.0013*** | 0.0017*** | 0.0008*** | 0.0007*** | 0.0008*** | 0.0022*** | 0.0013*** | 0.0018*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0005) | (0.0002) | (0.0003) | (0.0001) | (0.0001) | (0.0001) | (0.0004) | (0.0002) | (0.0003) | |
| Tang | 0.0178*** | 0.0109*** | 0.0163*** | 0.0213*** | 0.0094*** | 0.0173*** | 0.0181*** | 0.0108*** | 0.0165*** | 0.0197*** | 0.0100*** | 0.0170*** |
| (0.0009) | (0.0005) | (0.0008) | (0.0018) | (0.0014) | (0.0014) | (0.0010) | (0.0005) | (0.0008) | (0.0016) | (0.0014) | (0.0013) | |
| Netdebt | 0.0064*** | 0.0050*** | 0.0060*** | 0.0080*** | 0.0066*** | 0.0073*** | 0.0067*** | 0.0050*** | 0.0062*** | 0.0083*** | 0.0067*** | 0.0075*** |
| (0.0008) | (0.0004) | (0.0006) | (0.0016) | (0.0009) | (0.0010) | (0.0009) | (0.0004) | (0.0006) | (0.0015) | (0.0009) | (0.0010) | |
| GDP | 0.0003*** | −0.0003*** | 0.0001* | 0.0011*** | 0.0025*** | 0.0015*** | 0.0003*** | −0.0003*** | 0.0002** | 0.0005*** | 0.0028*** | 0.0010*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0003) | (0.0002) | (0.0002) | (0.0001) | (0.0001) | (0.0001) | (0.0002) | (0.0003) | (0.0002) | |
| Inf | 0.0001 | −0.0001 | 0.0001 | 0.0002 | −0.0024*** | −0.0007** | 0.0001 | −0.0001 | 0.00004 | 0.0004 | −0.0028*** | −0.0003 |
| (0.0001) | (0.0001) | (0.0001) | (0.0004) | (0.0004) | (0.0003) | (0.0001) | (0.0001) | (0.0001) | (0.0004) | (0.0004) | (0.0003) | |
| Constant | −0.0187*** | 0.0011 | −0.0145*** | −0.0363*** | 0.0075 | −0.0365*** | −0.0192*** | 0.0005 | −0.0146*** | −0.0378*** | 0.0104** | −0.0389*** |
| (0.0027) | (0.0025) | (0.0024) | (0.0129) | (0.0046) | (0.0127) | (0.0029) | (0.0025) | (0.0026) | (0.0134) | (0.0049) | (0.0133) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 73,576 | 23,096 | 96,672 | 14,381 | 9,973 | 24,354 | 69,155 | 23,251 | 92,406 | 18,338 | 9,685 | 28,023 |
| R2 | 0.1335 | 0.0883 | 0.1256 | 0.1243 | 0.0865 | 0.1146 | 0.1392 | 0.0884 | 0.1297 | 0.1103 | 0.0881 | 0.1028 |
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The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flows (CF) as well as a set of controls including industry- and time-fixed effects. The sample is divided into strongly and weakly affected countries. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.
Table 5 reports a variant of the above test, where instead of splitting the sample, we use interaction terms. As the table shows, the interaction term CF*new_cases is −0.008 and highly statistically significant (Column 1). Similarly, the interaction term CF*cum_cases is −0.0072 and highly significant. The results are similar for non-China and non-US subsamples; all estimated coefficients of interest are statistically significant at the 1% level.
Table 5
Impact of COVID-19 on investment–cash flow sensitivity. Additional controls.
| Variable | All firms | Non-CN | Non-US | |||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| CF | 0.0821*** | 0.0798*** | 0.0485*** | 0.0515*** | 0.0886*** | 0.0901*** |
| (0.0120) | (0.0127) | (0.0124) | (0.0127) | (0.0174) | (0.0219) | |
| CF*new_cases | −0.0080*** | −0.0039*** | −0.0082*** | |||
| (0.0014) | (0.0015) | (0.0025) | ||||
| new_cases | −0.0007*** | −0.0005*** | −0.0010*** | |||
| (0.0001) | (0.0001) | (0.0001) | ||||
| CF*cum_cases | −0.0072*** | −0.0040*** | −0.0076*** | |||
| (0.0015) | (0.0015) | (0.0029) | ||||
| cum_cases | −0.0009*** | −0.0005*** | −0.0011*** | |||
| (0.0001) | (0.0001) | (0.0001) | ||||
| Size | 0.0001* | 0.0001* | 0.0002*** | 0.0001** | −0.00002 | 0.000005 |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Q | 0.0009*** | 0.0009*** | 0.0007*** | 0.0007*** | 0.0011*** | 0.0012*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Tang | 0.0106*** | 0.0106*** | 0.0113*** | 0.0114*** | 0.0101*** | 0.0099*** |
| (0.0005) | (0.0005) | (0.0005) | (0.0005) | (0.0007) | (0.0007) | |
| Netdebt | 0.0057*** | 0.0055*** | 0.0050*** | 0.0051*** | 0.0058*** | 0.0057*** |
| (0.0004) | (0.0004) | (0.0004) | (0.0004) | (0.0006) | (0.0006) | |
| GDP | −0.0003*** | −0.0001 | −0.00004 | −0.0001 | −0.0004*** | −0.0002** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Inf | −0.0001 | −0.0001* | −0.0001 | −0.0001 | −0.0001 | −0.0001 |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Constant | 0.0037 | 0.0036 | 0.0014 | 0.0016 | 0.0083*** | 0.0078*** |
| (0.0024) | (0.0024) | (0.0024) | (0.0024) | (0.0028) | (0.0028) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 32,936 | 32,936 | 27,077 | 27,077 | 24,553 | 24,553 |
| R2 | 0.0815 | 0.0782 | 0.0836 | 0.0829 | 0.0717 | 0.0676 |
Open in a separate window
The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flow (CF) and a set of controls, including industry- and time-fixed effects. The new and total COVID-19 case-related variables are included to examine the direct effect of COVID-19 on investment–CF sensitivity. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
4.2. Additional analyses
4.2.1. Government financial aid and investment–cash flow sensitivity
The decline in investment–cash flow sensitivity during the COVID-19-induced economic crisis may be explained by government financial aid. Government-sponsored funds can act as an additional funding source for corporate investment during the crisis. We describe this prediction as Hypothesis 1a in the section above. If government funding plays a role in corporate investment during the COVID-19 crisis, we should see a diminished effect of cash flows on investment. To test this possibility, we introduce two new variables: Gov_aid_GDP, which measures government aid scaled by a country’s GDP, and High_aid, which is a dummy variable equal to 1 if government aid exceeds the median amount of aid in the cross section of firms in our sample. The results are presented inTable 6.
Table 6
The role of government aid.
| Variable | (1) | (2) |
|---|---|---|
| CF | 0.0843*** | 0.0993*** |
| (0.0086) | (0.0094) | |
| CF*Gov_aid_GDP | −0.3184*** | |
| (0.0394) | ||
| Gov_aid_GDP | −0.0013 | |
| (0.0012) | ||
| CF*High_aid | −0.0850*** | |
| (0.0100) | ||
| High_aid | 0.0006** | |
| (0.0003) | ||
| Size | 0.00001 | 0.00001 |
| (0.0001) | (0.0001) | |
| Q | 0.0009*** | 0.0009*** |
| (0.0001) | (0.0001) | |
| Tang | 0.0113*** | 0.0117*** |
| (0.0005) | (0.0005) | |
| Netdebt | 0.0061*** | 0.0062*** |
| (0.0004) | (0.0004) | |
| GDP | 0.0006*** | 0.0006*** |
| (0.0001) | (0.0001) | |
| Inf | −0.0002*** | −0.0002*** |
| (0.0001) | (0.0001) | |
| Constant | −0.0021 | −0.0027 |
| (0.0024) | (0.0023) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter |
| Obs. | 33,682 | 33,682 |
| R2 | 0.0765 | 0.0783 |
Open in a separate window
The table reports the investment–cash flow sensitivity obtained by regressing investment (INV) on cash flows (CF) as well as a set of controls including time and industry fixed effects. Gov_aid_GDP is the ratio between government aid and GDP. High_aid is a dummy variable which is equal to 1 if the firm is located in a country with government aid over GDP higher than the sample median, and 0 otherwise. Variable definitions of other control variables and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.
As the table shows (Column 1), the interaction term CF*Gov_aid_GDP is negative and statistically significant at the 1% level, implying that the higher the government aid (holding other factors constant), the lower the investment - cash flow sensitivity. A similar picture emerges in Column 2, which suggests that firms in countries that provide relatively high government aid have significantly lower investment–cash flow sensitivity. Overall, these findings seem to support Hypothesis 1a.
4.2.2. The role of cash holdings during COVID-19
Cash holdings are one of the most important corporate factors determining firm survival. Thus, cash holdings can be seen as precautionary savings for various purposes, including investment (Almeida et al., 2014, Bates et al., 2009, Dao et al., 2023, Dao et al., 2023, El Ghoul et al., 2023, Jiang and Wu, 2022, Machokoto et al., 2022). During the COVID-19 crisis, besides government financial support, we conjecture that stockpiled cash holdings could be used to finance investments, thereby reducing investment–cash flow sensitivity. We refer to this prediction as Hypothesis 1b.
To test this possibility, we run additional tests using cash holdings as a variable of interest in our investment model (variable CH). As expected, cash holdings decrease investment sensitivity to cash flows, with a greater reduction during the COVID-19 crisis (Columns 1–2,Table 7). This implies that cash holdings during the COVID-19 crisis are a more important source of funding compared with the pre-COVID-19 period. The results hold when interacting with CF and CH (Columns 4–5, Table 7). Here again, the decline in investment–cash flow sensitivity is more pronounced during COVID-19 for companies with greater cash holdings (the interaction term is negative and statistically significant at the 1% level). Generally, the above evidence seems to be consistent with Hypothesis 1b.
Table 7
Role of cash holdings.
| Variable | Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID |
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| After | −0.0040*** | −0.0039*** | ||||
| (0.0005) | (0.0005) | |||||
| After*CF | −0.0109*** | −0.0088*** | ||||
| (0.0027) | (0.0026) | |||||
| CF | 0.0404*** | 0.0251*** | 0.0400*** | 0.0607*** | 0.0390*** | 0.0598*** |
| (0.0020) | (0.0032) | (0.0020) | (0.0035) | (0.0060) | (0.0034) | |
| CH | −0.0040*** | −0.0046*** | −0.0040*** | −0.0063*** | −0.0060*** | −0.0063*** |
| (0.0009) | (0.0012) | (0.0008) | (0.0009) | (0.0012) | (0.0008) | |
| CF*CH | −0.0517*** | −0.0321*** | −0.0506*** | |||
| (0.0053) | (0.0092) | (0.0051) | ||||
| Size | 0.0002*** | 0.0001 | 0.0002*** | 0.0002*** | 0.00004 | 0.0002*** |
| (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | |
| Q | 0.0013*** | 0.0009*** | 0.0012*** | 0.0012*** | 0.0009*** | 0.0012*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Tang | 0.0168*** | 0.0125*** | 0.0164*** | 0.0169*** | 0.0126*** | 0.0165*** |
| (0.0004) | (0.0005) | (0.0004) | (0.0004) | (0.0005) | (0.0004) | |
| Netdebt | 0.0041*** | 0.0032*** | 0.0040*** | 0.0036*** | 0.0031*** | 0.0036*** |
| (0.0006) | (0.0007) | (0.0005) | (0.0006) | (0.0007) | (0.0005) | |
| GDP | 0.0004*** | 0.0005*** | 0.0004*** | 0.0005*** | 0.0005*** | 0.0005*** |
| (0.0000) | (0.0001) | (0.0000) | (0.0000) | (0.0001) | (0.0000) | |
| Inf | 0.0001* | −0.0001** | 0.00003 | 0.0001 | −0.0002*** | 0.00003 |
| (0.0000) | (0.0001) | (0.0000) | (0.0000) | (0.0001) | (0.0000) | |
| Constant | −0.0129*** | −0.0022 | −0.0120*** | −0.0121*** | −0.0018 | −0.0112*** |
| (0.0014) | (0.0024) | (0.0014) | (0.0014) | (0.0024) | (0.0014) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 358,630 | 33,682 | 392,312 | 358,630 | 33,682 | 392,312 |
| R2 | 0.1045 | 0.0719 | 0.1041 | 0.1058 | 0.0726 | 0.1053 |
Open in a separate window
The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flow (CF) as well as a set of controls industry- and time-fixed effects. A new variable of cash-holding (CH) is included to examine the role of cash in investment–CF sensitivity. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.
4.2.3. Evolution of investment opportunities during COVID-19
Another possible explanation for lower investment–cash flow sensitivity during the COVID crisis could relate to the lower quality or amount of investment opportunities induced by the pandemic. We test this conjecture by employing sales growth as a proxy for investment opportunities. To this end, we split the sample into two subsets based on whether the company experiences growth versus decline in sales during COVID-19. As shown inTable 8, both groups experience a reduction in the investment–cash flow sensitivity during COVID-19; however, companies that have declining revenues during that period have much lower investment–cash flow sensitivity as compared to companies that report an increase in sales during COVID-19 or, alternatively, compared with precrisis times.
Table 8
Role of investment opportunities.
| Variable | Sales revenue increase | Sales revenue decrease | Full sample | |||
|---|---|---|---|---|---|---|
| Pre- COVID-19 | COVID-19 | Pre- COVID-19 | COVID-19 | Pre- COVID-19 | COVID-19 | |
| (1) | (2) | (3) | (4) | (5) | (6) | |
| CF | 0.0482*** | 0.0386*** | 0.0381*** | 0.0140*** | 0.0501*** | 0.0391*** |
| (0.0028) | (0.0051) | (0.0023) | (0.0040) | (0.0028) | (0.0050) | |
| Sales_ decrease*CF | -0.0140*** | -0.0258*** | ||||
| (0.0027) | (0.0060) | |||||
| Sales_decrease | -0.0022*** | -0.0023*** | ||||
| (0.0001) | (0.0002) | |||||
| Size | 0.0003*** | 0.0004*** | 0.00001 | 0.0002** | 0.0002*** | 0.0003*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.00004) | (0.0001) | |
| Q | 0.0014*** | 0.0009*** | 0.0010*** | 0.0008*** | 0.0012*** | 0.0009*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Tang | 0.0177*** | 0.0134*** | 0.0162*** | 0.0112*** | 0.0171*** | 0.0124*** |
| (0.0005) | (0.0009) | (0.0005) | (0.0007) | (0.0004) | (0.0006) | |
| Netdebt | 0.0063*** | 0.0055*** | 0.0052*** | 0.0049*** | 0.0058*** | 0.0052*** |
| (0.0004) | (0.0007) | (0.0003) | (0.0005) | (0.0003) | (0.0005) | |
| GDP | 0.0004*** | 0.0008*** | 0.0005*** | 0.0003*** | 0.0005*** | 0.0005*** |
| (0.00004) | (0.0001) | (0.00004) | (0.0001) | (0.00003) | (0.0001) | |
| Inf | 0.0002*** | -0.0003*** | −0.0001** | −0.0002* | 0.0001 | -0.0002*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Constant | -0.0164*** | -0.0097*** | −0.0094*** | −0.0047 | -0.0128*** | -0.0062** |
| (0.0019) | (0.0037) | (0.0017) | (0.0031) | (0.0015) | (0.0027) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 173,164 | 14,314 | 136,316 | 14,169 | 309,480 | 28,483 |
| R2 | 0.1205 | 0.0780 | 0.1037 | 0.0747 | 0.1144 | 0.0760 |
Open in a separate window
The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flows (CF) as well as a set of controls including industry- and time-fixed effects. The sample is divided into revenue-increasing and -decreasing firms (Columns 1–4). In Columns 5–6, Sales_ decrease dummy variable which takes the value of 1 if firms experience a decrease in sales revenue and 0 otherwise is added. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
As a robustness check, we create a new dummy variable Sales_decrease equal to one if the firm experiences a decline in sales and zero otherwise. Then, we interact this dummy variable with the variable representing the level of firm’s cash-flows (CF). We find that the coefficient estimate on the interaction term Sales_decrease*CF is negative and highly statistically significant, implying that a firm that exhibits a decline in sales has also a significantly lower sensitivity of capital expenditures to cash-flows (see Columns 5 and 6 of Table 8). Together, the results obtained from the above analyses are consistent with Hypothesis 1c.
4.2.4. COVID-19 versus the Great Recession
In this section, we compare the changes in investment–cash flow sensitivity during COVID-19 with the changes in investment–cash flow sensitivity observed during the 2008–09 financial crisis. We collect additional data going back to 2003. We select the beginning of 2003, as this is the year in which the S&P 500 reached a local minimum following the recession of 2001 and began its ascent that ended in the global financial crisis (GFC). As can be noticed inTable 9, investment–cash flow sensitivity is nearly identical during pre-GFC and GFC periods (Columns 1–2). As a robustness check, we use a DID approach. As shown in Column 3, the coefficient estimate for the interaction term Crisis*CF is insignificantly different from zero, implying no statistical difference in investment–cash flow sensitivity during the great recession and before. It is worth noticing that the magnitude of this coefficient is very close to zero, pointing to a near-zero difference in investment–cash flow sensitivity between precrisis and crisis times.
Table 9
COVID-19 versus the Great Recession.
| Variable | Pre-GFC (2003–2007) | GFC (2008–2009) | DID (2003–2009) |
|---|---|---|---|
| (1) | (2) | (3) | |
| Crisis | −0.0010 | ||
| (0.0017) | |||
| Crisis*CF | 0.0087 | ||
| (0.0074) | |||
| CF | 0.0466*** | 0.0450*** | 0.0440*** |
| (0.0054) | (0.0068) | (0.0052) | |
| Size | −0.0009** | 0.0016*** | 0.0001 |
| (0.0004) | (0.0004) | (0.0003) | |
| Q | 0.0116*** | 0.0094*** | 0.0115*** |
| (0.0007) | (0.0009) | (0.0006) | |
| Tang | 0.0733*** | 0.0644*** | 0.0684*** |
| (0.0037) | (0.0033) | (0.0029) | |
| Netdebt | 0.0319*** | 0.0290*** | 0.0301*** |
| (0.0040) | (0.0038) | (0.0031) | |
| GDP | 0.0009*** | 0.0013*** | 0.0011*** |
| (0.0002) | (0.0002) | (0.0002) | |
| Inf | 0.0056*** | 0.0030*** | 0.0039*** |
| (0.0004) | (0.0003) | (0.0002) | |
| Constant | −0.0362*** | −0.0452*** | −0.0443*** |
| (0.0114) | (0.0111) | (0.0092) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 41,048 | 28,206 | 69,254 |
| R2 | 0.2284 | 0.1938 | 0.2119 |
Open in a separate window
The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flow (CF) as well as a set of controls industry- and time-fixed effects. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
4.3. Further robustness checks
In this section, we consider additional factors that may impact investment–cash flow sensitivity. For example, we control for stock market capitalization as the percentage of GDP based on the World Development Indicators database (variable SMC). Further, we control for country governance related to government effectiveness on the World Governance Indicators (variable GE). Several studies (e.g., Rajan and Zingales, 1998; Wurgler, 2000) show that investor protection has a significant impact on investment allocation; thus, we add the Spamann (2010) investor protection variable (i.e., ADRI) to our investment model.
The regression results are presented inTable 10 and are quite similar to our baseline findings presented in Table 3. Consequently, these and the earlier results reported above provide evidence in favor of Hypothesis 1 and against Hypothesis 2.
Table 10
Controlling for additional factors.
| Variable | All firms | Non-CN | Non-US | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| After | −0.0040*** | −0.0040*** | −0.0045*** | ||||||
| (0.0005) | (0.0005) | (0.0011) | |||||||
| After*CF | −0.0099*** | −0.0112*** | −0.0101* | ||||||
| (0.0027) | (0.0027) | (0.0054) | |||||||
| CF | 0.0424*** | 0.0280*** | 0.0420*** | 0.0363*** | 0.0212*** | 0.0359*** | 0.0630*** | 0.0410*** | 0.0619*** |
| (0.0019) | (0.0031) | (0.0019) | (0.0020) | (0.0031) | (0.0020) | (0.0032) | (0.0055) | (0.0031) | |
| Size | 0.0002*** | 0.00001 | 0.0002*** | 0.0003*** | 0.0001** | 0.0003*** | −0.00001 | −0.0001 | −0.00001 |
| (0.00001) | (0.0001) | (0.00001) | (0.0001) | (0.0001) | (0.00001) | (0.0001) | (0.0001) | (0.0001) | |
| Q | 0.0012*** | 0.0009*** | 0.0012*** | 0.0012*** | 0.0007*** | 0.0012*** | 0.0014*** | 0.0012*** | 0.0013*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Tang | 0.0167*** | 0.0117*** | 0.0164*** | 0.0173*** | 0.0121*** | 0.0169*** | 0.0160*** | 0.0114*** | 0.0157*** |
| (0.0005) | (0.0006) | (0.0004) | (0.0005) | (0.0006) | (0.0005) | (0.0006) | (0.0008) | (0.0006) | |
| Netdebt | 0.0062*** | 0.0054*** | 0.0061*** | 0.0070*** | 0.0050*** | 0.0068*** | 0.0047*** | 0.0056*** | 0.0048*** |
| (0.0003) | (0.0004) | (0.0003) | (0.0004) | (0.0004) | (0.0003) | (0.0004) | (0.0006) | (0.0004) | |
| GDP | 0.0005*** | 0.0006*** | 0.0005*** | 0.0005*** | 0.0003 | 0.0005*** | 0.0004*** | 0.0003** | 0.0004*** |
| (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0002) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | |
| Inf | 0.0001** | −0.0002** | 0.0001 | 0.0001 | −0.0001 | 0.00001 | 0.0001** | −0.0002** | 0.0001* |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| ADRI | −0.00001 | 0.0001 | −0.00001 | −0.0002 | 0.0003*** | −0.0001 | 0.00001 | −0.0004** | −0.00001 |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0002) | (0.0001) | |
| SMC | −0.00001** | −0.00001*** | −0.00001** | −0.00001*** | −0.00001*** | −0.00001*** | −0.00001*** | −0.00001** | −0.00001*** |
| (0.00001) | (0.00001) | (0.00001) | (0.00001) | (0.00001) | (0.00001) | (0.00001) | (0.00001) | (0.00001) | |
| GE | 0.0004** | 0.0002 | 0.0004** | 0.0001 | 0.0007** | 0.0002 | 0.0006** | 0.0003 | 0.0006*** |
| (0.0002) | (0.0003) | (0.0002) | (0.0002) | (0.0003) | (0.0002) | (0.0002) | (0.0003) | (0.0002) | |
| Constant | −0.0134*** | −0.0030 | −0.0126*** | −0.0141*** | −0.0042 | −0.0134*** | −0.0078*** | 0.0016 | −0.0069*** |
| (0.0015) | (0.0027) | (0.0015) | (0.0016) | (0.0027) | (0.0016) | (0.0021) | (0.0033) | (0.0020) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 344,957 | 31,153 | 376,110 | 284,288 | 25,294 | 309,582 | 242,014 | 22,770 | 264,784 |
| R2 | 0.1051 | 0.0702 | 0.1043 | 0.1194 | 0.0797 | 0.1183 | 0.0852 | 0.0582 | 0.0848 |
Open in a separate window
The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flow (CF) as well as a set of controls including industry- and time-fixed effects. COVID-19 is the period from Q1 through Q4 2020, and Pre-COVID-19 is from Q1 2011 through Q4 2019. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
In our next robustness test, we use the two-step system generalized method of GMM. This approach neutralizes the problem of endogeneity between investments and other explanatory variables in the investment model (e.g., Ağca and Mozumdar, 2008). The GMM results are reported inTable 11. As indicated in the table, our findings are very similar to our baseline results in Table 3. The result of this test reinforces our conclusion about significantly lower investment–cash flow sensitivity during the COVID-19 economic crisis.
Table 11
GMM results.
| Variable | All firms | Non-CN | Non-US | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | Pre- COVID-19 | COVID-19 | DID | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| After | 0.00001 | −0.0164*** | −0.0024 | ||||||
| (0.00001) | (0.0017) | (0.0016) | |||||||
| After*CF | −0.9328*** | −0.2796*** | −0.4289*** | ||||||
| (0.1793) | (0.1052) | (0.1546) | |||||||
| CF | 0.0417*** | 0.0226*** | 0.1214*** | 0.0351*** | 0.0177*** | 0.0574*** | 0.0631*** | 0.0318*** | 0.1006*** |
| (0.0018) | (0.0027) | (0.0153) | (0.0019) | (0.0027) | (0.0091) | (0.0029) | (0.0047) | (0.0142) | |
| Size | 0.0003*** | 0.0001 | 0.0001** | 0.0004*** | 0.0001** | 0.0003*** | 0.0001 | 0.00001 | 0.00001 |
| (0.00001) | (0.0001) | (0.0001) | (0.00001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Q | 0.0012*** | 0.0007*** | 0.0013*** | 0.0012*** | 0.0006*** | 0.0011*** | 0.0013*** | 0.0010*** | 0.0012*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Tang | 0.0147*** | 0.0110*** | 0.0143*** | 0.0155*** | 0.0116*** | 0.0151*** | 0.0135*** | 0.0098*** | 0.0132*** |
| (0.0004) | (0.0005) | (0.0004) | (0.0004) | (0.0005) | (0.0004) | (0.0005) | (0.0007) | (0.0005) | |
| Netdebt | 0.0055*** | 0.0053*** | 0.0068*** | 0.0065*** | 0.0051*** | 0.0067*** | 0.0041*** | 0.0050*** | 0.0045*** |
| (0.0003) | (0.0004) | (0.0004) | (0.0003) | (0.0004) | (0.0003) | (0.0004) | (0.0005) | (0.0004) | |
| GDP | 0.0004*** | 0.0005*** | 0.0004*** | 0.0004*** | 0.0001 | 0.0004*** | 0.0004*** | 0.0005*** | 0.0004*** |
| (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | |
| Inf | 0.0001* | −0.0001 | 0.0001*** | 0.00001 | −0.0001 | 0.00001 | 0.0001* | −0.0001* | 0.0001* |
| (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | (0.00001) | (0.0001) | (0.00001) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 346,699 | 32,436 | 379,135 | 286,030 | 26,577 | 312,607 | 243,756 | 24,053 | 267,809 |
| The number of firms | 18,753 | 11,050 | 19,216 | 15,818 | 8,904 | 16,191 | 13,695 | 8,498 | 14,051 |
Open in a separate window
The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flow (CF) as well as a set of controls industry- and time-fixed effects. COVID-19 is the period from Q1 through Q4 2020, and Pre-COVID-19 is from Q1 2011 through Q4 2019. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.
Finally, we verify whether and how the results change depending on whether the firm reports an increase or decrease in cash flows, or alternatively, if cash flows turn from positive to negative during COVID-19. We split our sample into groups and run the baseline investment model on different subsamples. The results inTable 12 point to greater investment–cash flow sensitivity for companies that experience a decline in the level of cash flows both in the pre-COVID period and during the COVID crisis. Further, when we split the sample into negative and positive cash flow companies, we find that, as expected, negative cash flows are irrelevant for investments during COVID-19. The above results are consistent with our main findings.
Table 12
Cash flow dynamics before and during COVID-19.
| Variable | Cash flow | Cash flow | ||||||
|---|---|---|---|---|---|---|---|---|
| Increase | Decrease | Positive | Negative | |||||
| Pre- COVID-19 | COVID-19 | Pre- COVID-19 | COVID-19 | Pre- COVID-19 | COVID-19 | Pre- COVID-19 | COVID-19 | |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| CF | 0.0406*** | 0.0275*** | 0.0493*** | 0.0302*** | 0.1726*** | 0.1370*** | −0.0124*** | −0.0040 |
| (0.0020) | (0.0038) | (0.0025) | (0.0040) | (0.0069) | (0.0113) | (0.0026) | (0.0045) | |
| Size | 0.0002*** | 0.0001 | 0.0002*** | −0.00001 | 0.0001 | 0.0001** | 0.0012*** | 0.0002** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Q | 0.0012*** | 0.0007*** | 0.0012*** | 0.0010*** | 0.0006*** | 0.0006*** | 0.0007*** | 0.0005*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Tang | 0.0158*** | 0.0117*** | 0.0171*** | 0.0123*** | 0.0181*** | 0.0135*** | 0.0104*** | 0.0090*** |
| (0.0005) | (0.0007) | (0.0005) | (0.0006) | (0.0005) | (0.0006) | (0.0006) | (0.0010) | |
| Netdebt | 0.0055*** | 0.0044*** | 0.0066*** | 0.0064*** | 0.0078*** | 0.0070*** | 0.0068*** | 0.0061*** |
| (0.0004) | (0.0006) | (0.0003) | (0.0005) | (0.0004) | (0.0006) | (0.0004) | (0.0006) | |
| GDP | 0.0005*** | 0.0006*** | 0.0004*** | 0.0005*** | 0.0007*** | 0.0009*** | 0.0002*** | 0.0003* |
| (0.0000) | (0.0001) | (0.0000) | (0.0001) | (0.0000) | (0.0001) | (0.0001) | (0.0001) | |
| Inf | 0.0002*** | −0.0001* | −0.0001 | −0.0002* | 0.0000 | −0.0002*** | −0.0003*** | −0.0003*** |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| Constant | −0.0131*** | −0.0030 | −0.0129*** | −0.0012 | −0.0163*** | −0.0086*** | −0.0229*** | −0.0033 |
| (0.0017) | (0.0032) | (0.0017) | (0.0026) | (0.0016) | (0.0027) | (0.0024) | (0.0042) | |
| Fixed Effect | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter | Industry-Quarter |
| Obs. | 169,116 | 14,733 | 189,487 | 18,948 | 275,504 | 24,310 | 82,934 | 9,363 |
| R2 | 0.1129 | 0.0727 | 0.0986 | 0.0729 | 0.1338 | 0.0964 | 0.0897 | 0.0617 |
Open in a separate window
The table reports investment–cash flow sensitivity obtained by regressing investment (INV) on cash flows (CF) as well as a set of controls including industry- and time-fixed effects. Variable definitions and data sources can be found in Appendix A. Standard errors are adjusted for heteroskedasticity and clustered at the country level. Superscripts *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.
5. Conclusion
COVID-19 has caused an unprecedented shock to corporate operating, financing, and investment activities around the globe. In this study, we examine whether the investment–cash flow sensitivity of firms is affected by the COVID-19 economic crisis, and if so, what is the direction and magnitude of this effect. Using an international sample of close to 30,000 publicly listed firms around the globe, we show that during the crisis, firms exhibit a weaker sensitivity of investment to cash flows. Furthermore, we find that investment by firms in countries strongly affected by COVID-19 is less sensitive to cash flows than it is for firms in weakly affected countries. We also show that investment–cash flow sensitivity is significantly lower for firms in countries that provide increased government aid. Finally, we find that firms that hold more cash on hand during COVID-19 and those with better investment opportunities report higher investment–cash flow sensitivity. All documented effects are highly statistically significant and economically meaningful.
This study improves our understanding of corporate investment decisions in response to an exogenous shock that has adversely affected world economies on an unprecedented scale. A natural extension of this study would be to examine corporate divestments during COVID-19. This remains a fertile area for future research.
Declaration of Competing Interest
All authors declare that they have no conflicts of interest.
Footnotes
☆We thank the editor, John W. Goodell, and an anonymous referee for their valuable insights. We also thank the members of the UE-UD’s Finance Teaching and Research Team in Corporate Finance and Asset Pricing (TRT-CFAP), as well as Vietnam International Academic Network - Economics, Business, and Policy (VIAN-EBP) for their helpful comments. All errors are our own.
☆☆This research is funded byFunds for Science and Technology Development of the University of Danangunder project numberB2021-DN04-01.
Appendix A. Variable definitions
This appendix reports the variable names, definitions, and data sources.
| Acronym | Variable | Definition | Source |
|---|---|---|---|
| INV | Investment rate | Value of capital expenditures scaled by lagged book value of total assets | Compustat Global Fundamentals Quarterly |
| CF | Cash flow | Sum of earnings before extraordinary items and depreciation scaled by lagged book value of total assets | |
| Size | Firm size | Natural logarithm of total assets | |
| Q | Tobin Q | Book value of assets minus book value of equity plus market value of equity divided by the book value of total assets | |
| Tang | Tangibility | Tangible assets (property, plant, and equipment) divided by the book value of total assets | |
| Netdebt | Net debt | Total debt minus cash and cash equivalents divided by the book value of total assets | |
| CH | Cash holdings | Cash and cash equivalents divided by the book value of total assets | |
| GDP | GDP growth rate | Rate of change in the gross domestic product | World Bank |
| Inf | Inflation rate | Rate of change in the consumer price index | |
| SMC | Stock market capitalization | Stock market capitalization divided by the gross domestic product | |
| GE | Government effectiveness | Public opinion on the quality of government services, the civil service's quality and independence from political pressure, the quality of policy implementation, and the government's commitment to these policies | |
| ADRI | Anti-Director Rights Index | Investor protection index as inSpamann (2010). For countries whereSpamann (2010) ADRI is not available, we replace it by theDjankov et al. (2008) revised Anti-Self-Dealing Index | Spamann (2010) andDjankov et al. (2008) |
| cum_cases | Cumulative cases | Natural logarithm of the total number of COVID-19 cases divided by the total population in the country and rescaled by multiplying by 1 million. | https://ourworldindata.org/covid-cases |
| new_cases | New cases | Natural logarithm of the number of new COVID-19 cases divided by the total population in the country and rescaled by multiplying by 1 million. | |
| Acronym | Variable | Definition | Source |
| Gov_aid_GDP | Government aid | All liquidity support measures, capital grants, credit guarantees, and other fiscal benefits adopted by the governments divided by the amount of the gross domestic product | The International Monetary Fund (IMF) |
| High_aid | Government aid dummy | Equal to 1 if the firm is located in a country with government financial aid greater than the median, and 0 otherwise. Amount of government aid is scaled by GDP. | |
| Sales_ decrease | Sale decrease dummy | Equal to 1 if firms experience a decrease in sales and 0 otherwise | Compustat Global Fundamentals Quarterly |
Open in a separate window
Appendix B. Cross-country distribution of publicly listed firms in the sample
See Appendix B section here.
| Count | ISO2 | Country | The number of firms | Perc. | Count | ISO2 | Country | The number of firms | Perc. |
|---|---|---|---|---|---|---|---|---|---|
| 1 | AE | UAE | 48 | 0.17% | 40 | KY | Cayman Islands | 1,158 | 4.14% |
| 2 | AT | Austria | 56 | 0.20% | 41 | KZ | Kazakhstan | 19 | 0.07% |
| 3 | AU | Australia | 1,542 | 5.52% | 42 | LK | Sri Lanka | 190 | 0.68% |
| 4 | BD | Bangladesh | 184 | 0.66% | 43 | LT | Lithuania | 39 | 0.14% |
| 5 | BE | Belgium | 99 | 0.35% | 44 | LU | Luxembourg | 58 | 0.21% |
| 6 | BG | Bulgaria | 56 | 0.20% | 45 | LV | Latvia | 24 | 0.09% |
| 7 | BH | Bahrain | 15 | 0.05% | 46 | MA | Morocco | 24 | 0.09% |
| 8 | BR | Brazil | 272 | 0.97% | 47 | MT | Malta | 18 | 0.06% |
| 9 | CA | Canada | 1,841 | 6.59% | 48 | MU | Mauritius | 5 | 0.02% |
| 10 | CH | Switzerland | 198 | 0.71% | 49 | MX | Mexico | 107 | 0.38% |
| 11 | CL | Chile | 150 | 0.54% | 50 | MY | Malaysia | 786 | 2.81% |
| 12 | CN | China | 3,266 | 11.69% | 51 | NG | Nigeria | 67 | 0.24% |
| 13 | CO | Colombia | 34 | 0.12% | 52 | NL | Netherlands | 171 | 0.61% |
| 14 | CY | Cyprus | 68 | 0.24% | 53 | NO | Norway | 201 | 0.72% |
| 15 | DE | Germany | 533 | 1.91% | 54 | NZ | New Zealand | 132 | 0.47% |
| 16 | DK | Denmark | 107 | 0.38% | 55 | OM | Oman | 50 | 0.18% |
| 17 | EE | Estonia | 14 | 0.05% | 56 | PE | Peru | 89 | 0.32% |
| 18 | EG | Egypt | 106 | 0.38% | 57 | PH | Philippines | 157 | 0.56% |
| 19 | ES | Spain | 113 | 0.40% | 58 | PK | Pakistan | 310 | 1.11% |
| 20 | FI | Finland | 147 | 0.53% | 59 | PL | Poland | 550 | 1.97% |
| 21 | FR | France | 643 | 2.30% | 60 | PS | Palestinian Territory | 15 | 0.05% |
| 22 | GB | UK | 1,107 | 3.96% | 61 | PT | Portugal | 41 | 0.15% |
| 23 | GH | Ghana | 9 | 0.03% | 62 | QA | Qatar | 17 | 0.06% |
| 24 | GR | Greece | 206 | 0.74% | 63 | RO | Romania | 56 | 0.20% |
| 25 | HK | Hong Kong | 244 | 0.87% | 64 | RS | Republic of Serbia | 16 | 0.06% |
| 26 | HR | Croatia | 79 | 0.28% | 65 | RU | Russia Federation | 173 | 0.62% |
| 27 | HU | Hungary | 24 | 0.09% | 66 | SA | Saudi Arabia | 126 | 0.45% |
| 28 | ID | Indonesia | 465 | 1.66% | 67 | SE | Sweden | 544 | 1.95% |
| 29 | IE | Ireland | 86 | 0.31% | 68 | SG | Singapore | 614 | 2.20% |
| 30 | IL | Israel | 410 | 1.47% | 69 | SI | Slovenia | 23 | 0.08% |
| 31 | IN | India | 453 | 1.62% | 70 | TH | Thailand | 589 | 2.11% |
| 32 | IS | Iceland | 15 | 0.05% | 71 | TN | Tunisia | 34 | 0.12% |
| 33 | IT | Italy | 345 | 1.23% | 72 | TR | Turkey | 310 | 1.11% |
| 34 | JM | Jamaica | 25 | 0.09% | 73 | TT | Trinidad and Tobago | 5 | 0.02% |
| 35 | JO | Jordan | 86 | 0.31% | 74 | UA | Ukraine | 9 | 0.03% |
| 36 | JP | Japan | 36 | 0.13% | 75 | US | United States | 5,721 | 20.47% |
| 37 | KE | Kenya | 32 | 0.11% | 76 | VN | Vietnam | 401 | 1.44% |
| 38 | KR | Korea | 1,687 | 6.04% | 77 | ZA | South Africa | 189 | 0.68% |
| 39 | KW | Kuwait | 81 | 0.29% | 78 | ZW | Zimbabwe | 24 | 0.09% |
| Total | 27,944 | 100% | |||||||
Open in a separate window
Data Availability
Data will be made available on request.
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