Impact of COVID-19 exposure on working capital management: The moderating effect of investment opportunities and government incentives.

This study examines the impact of the COVID-19 pandemic on firms' working capital management (WCM) covering 2,542 US-publicly traded firms for the period 2019Q1-2021Q2. Proxying WCM as cash conversion cycle (CCC), I find that COVID-19-exposed firms operate with higher levels of CCC. I show that firms with more investment opportunities and firms that receive government incentives (deferred taxes and investment tax credit (DT_ITC)) operate with lower levels of CCC. Overall, I provide evidence of the significant adverse impact of COVID-19 on WCM and show that the effect could be mitigated with an increase in investment opportunities and government incentives.

Introduction

The motivation of this paper stem from the point that the COVID-19 pandemic is purported to undermine the working capital management (WCM) of firms. However, empirical evidence about the relation between WCM and COVID-19-exposed firms remains scanty. Ernst and Young reported that various firms struggle to maintain decent control over short-term cash flows and the working capital (WC). More practically, Ernst and Young's 2019 WC report stated that if the 2000 leading companies in the United States and Europe effectively manage their WC, they can extract up to US$ 1 trillion.1 Nevertheless, the COVID-19 pandemic, being unique with its challenges, make improvement even more complicated.2 PwC reported that US business activities are decelerating as millions exercise social distancing to curtail the spread of COVID-19. As such, firms are either presently facing or foreseeing substantial limitations on cash and WC, involving a probable liquidity upheaval.3 Following this, Barclays released a report indicating that the COVID-19 pandemic has had an intense effect on businesses, particularly, their cash conversion cycle (CCC).4 Business Development Company (BDC) also stated that the ability of firms to perceive and reduce their CCC will help keep them afloat.5 The Department of Defense, further received $500 million for a WC fund to respond to the pandemic as reported by Government Accountability Office (GAO).6 These anecdotal pieces of evidence require empirical investigation.

Interruptions to economic activities because of the COVID-19 pandemic have led to a lower value of firms’ assets (Almaghrabi, 2021; Hassan et al., 2020) thus impacting the firm's short-term capital requirement, rendering them ineffective in their WCM. Actions taken by the government to slow the spread of the pandemic resulted in consumer service businesses being shutdown or operated at reduced capacities, demand for goods and services tumbled (Ke, 2021), a buildup of inventories, and delays in collecting receivables. The effect of this is the tied up of firms’ cash for the goals of their short-term operation. The goal of this study is to examine the effect of COVID-19 exposure on CCC and avenues through which the effect could be mitigated.

WCM is connected to the short-term capital needed to fund operational needs, which represent a noteworthy share of a firm's balance sheet (Le, 2019). The theory of Jensen and Meckling (1976) states that WC metrics are associated with the firm's operating cycle metrics (CCC) (Barros et al., 2021). A higher CCC can increase sales and profits (Ukaegbu, 2014) which supports a relationship between firms leaning to the long term. However, higher CCC also implies inefficient WCM, tied up of cash, and the likelihood of resorting to external financing leading to higher financing costs (Altaf and Ahmad, 2019). Deloof (2003) posits that profitability is improved for firms working with lower inventories and receivables days, though profitability is lesser for firms that delay in paying suppliers. This study examines the effect of the COVID-19 on the combined effect of receivables, inventories, and payables (CCC).

The extant literature propagates that over-diversification leads to value loss. For instance, Jensen (1986) and Stulz (1990) argued that cash-rich firms may overinvest in business with poor investment opportunities leading to value loss. Szewczyk et al. (1996) added that firms with comparatively more growth opportunities are less likely to have free cash. Billett and Mauer (2003) supported this reasoning and argued that diversified firms allocate capital inefficiently which decreases their value. As increases in CCC increases the number of days cash gets tied up, firms with more investment opportunities tend to be efficient at WCM (Ujah et al., 2020) to buffer against cash constraint, thus enhancing the value of firms. Further, firms receive investment subsidies through investment tax credit (ITC) (Sen and Turnovsky, 1990) as a way of government incentive. Meyer et al. (1993) stated that ITC can be introduced momentarily to encourage investment as part of a countercyclical fiscal policy, or perpetually as part of a scheme to speed longer-term economic growth. Since firms are more likely to apply for deferred taxes (DT) and increases in ITC during the pandemic period, Smith (2020) posits that increasing ITC during the pandemic will enable firms to boost the US economy financially. I argue that granting tax deferrals and increases in investment tax credit (DT_ITC) will help firms to effectively manage their WC by mitigating the effect of the pandemic.

I summarize the preceding argument and conjecture that exposure to COVID-19 leads to an increase in CCC (i.e., negatively affecting WCM). Additionally, I expect the impact of COVID-19 to be mitigated among firms with more investment opportunities and DT_ITC. The study contributes to the ongoing literature in three folds: (1) the results contribute to theory. That is the theory of the firm; (2) the findings contributes to the literature that exposure to the COVID-19 pandemic leads firms to operate at higher levels of CCC, thus deteriorating their WCM; (3) the findings contributes to the literature that the detrimental effect of the pandemic could be mitigated among firms with high investment opportunities and increases in government incentives in the form of applying for an increase in deferred taxes and investment tax credit. Hence, by increasing investment opportunities and assessing government incentives, firms could enhance their WCM.

Research design and methodology

This paper attempts to answer the following research questions: “Does COVID-19 exposure impede firms effective WCM? Does increases in investment opportunities and government incentives mitigate the impact of COVID-19 exposure on WCM?”. To address these questions, I collected financial data from Compustat quarterly file and COVID-19 exposure data from Hassan et al. (2020). I begin the sample period from 2019Q1 to 2021Q2 where data is available for COVID-19 exposure. After dropping financial and utility firms (SIC 6000–6999 and 4900–4999), the resulting sample includes 2542 unique US firms over a total of 18,990 firm-quarter observations. The data is further split into two: “PreCOVID”- 2019Q1 to 2020Q1 and “COVID”- 2020Q2 to 2021Q2. This will enable me to address the research questions before and within the period COVID-19 was declared a global pandemic.

CCC is used as the dependent variable to assess the number of days a firm's cash is tied up. It measures how long (in days) it takes firms to convert resource entries into cash holdings. CCC is a commonly used metric as indicated by Barros et al. (2021) and in other studies (e.g., Baños-Caballero et al., 2011; Boisjoly et al., 2020; Burney et al., 2021; Deloof, 2003; Mättö and Niskanen, 2020; Singh et al., 2017; Ujah et al., 2020) given its peculiar features. For instance, CCC seeks to measure liquidity with a time feature (Afrifa and Padachi, 2016; Richards and Laughlin, 1980; Wang, 2002) given its dynamism based on firms’ operations relating data from the balance sheet and the income statement (Barros et al., 2021). As a result, I relied on CCC as a measure of WCM in this paper. Following previous literature (Barros et al., 2021; Burney et al., 2021), I used working capital requirement (WCR) as an alternative measure of CCC. It is calculated as (receivables + inventories – payables) as a percentage of sales. WCR is an inclusive metric since it shows how operational assets and liabilities are handled by a firm (Burney et al., 2021). Thus, a positive CCC and WCR imply that a company requires more days to recoup their investment, hence the need for cash.

The independent variable in this study is COVID-19 exposure (COVID_Exposure). It is defined as the firm's exposure to COVID-19, constructed on the proximity of COVID-19 associated words to either positive or negative tone in quarterly-earnings conference calls divided by the number of sentences in the transcript (Almaghrabi, 2021; Hassan et al., 2020). For robustness check, I used negative sentiment related to COVID-19 (Covid_Neg_Sentiment) obtained from Hassan et al. (2020). I expect a positive effect of COVID_Exposure and Covid_Neg_Sentiment on CCC. To ensure that my results are not spuriously driven, I utilize a similar pandemic outbreak exposure proxy called SARS_Exposure which is measured similarly to the COVID_Exposure measure which is obtained from Hassan et al. (2020). I expect the effect of SARS_Exposure on CCC to be positive but insignificant.

To examine the moderating effect of investment opportunities and COVID-Exposure on CCC, I used Tobin'sQ as a measure of a firm's investment opportunities (Ujah et al., 2020). Tobin'sQ has been used to explicate several diverse corporate phenomena, such as cross-sectional variations in decisions regarding investment and diversification (Chung and Pruitt, 1994). Ujah et al. (2020) indicated that Tobin'sQ reduces the CCC by an average of 15 days. Therefore, I explored whether Tobin'sQ reduces the effect of COVID_Exposure on CCC. I expect a negative moderating effect. Further, I explored the moderating effect of DT_ITC and COVID_Exposure on CCC. Smith (2020) argued that extending the ITC and production tax credit would allow firms to hire more workers and inject billions into the US economy. Also, Efficio Ltd.’s 2020 report suggested that applying for tax deferrals could help improve CCC.7 I posit an effective WCM with more DT_ITC. Thus, a negative relation between DT_ITC and CCC is expected. I also expect the negative effect of COVID-19 to be mitigated by the moderation between DT_ITC and COVID_Exposure. I addressed the research questions using the following fixed-effect model:where CCC is defined as 365*(receivables/cogs + inventories/sales – payables/cogs, cogs = cost of goods sold, COVID_Exposure is as defined before, Tobin'sQ is measured as the lag of the ratio of the firm's market value to its replacement cost of assets, DT_ITC is measured as the ratio of deferred taxes and investment tax credit to total assets, Controls are the set of variables noted to affect CCC. I used ROA (return on assets = net income to total assets), LEV (financial leverage = debt to asset ratio), SIZE (the natural logarithm of firm's asset), Tangibility (ratio of property plant and equipment to total assets), Profitability (operating income before depreciation and amortization to total assets), Growth (quarter-on-quarter percent change in assets), Capital Exp. (capital expenditure to total assets), and M2B (market to book ratio), I = firm effect, T = quarter effect, and μ = error term. All variables are winsorized at the 1st and 99th percentile to rule out the effect of outliers. A Hausman test was performed for model selection, and a fixed effect with clustered standard errors (firm and quarter) was deemed fit to model the specification in equation (1). Clustered standard errors account for both serial and cross-sectional correlations (Kwon et al., 2007; Petersen, 2009). Other model diagnostics were duly performed.

Results and discussion

Panel A of Table 1 presents the summary statistics of all variables. For the sample period, the mean (Std.Dev) of CCC is 425.689 (411.418). The statistics on Covid_Exposure is 0.734 (0.985). These statistics show higher levels of CCC and increasing COVID_Exposure. Statistics on other variables are expected. Panel B shows a test of means for all variables before and within the COVID-19 period. The mean difference of CCC is significant, indicating a change in WCM for the period under consideration. It is worth noting that the mean of CCC is higher during the COVID-19 period.

Table 1

Panel A Descriptive statistics.

	N	Mean	Std. Dev.	P25	Median	P75
CCC	18,990	425.689	411.418	163.103	309.491	540.797
WCR	18,990	0.926	0.700	0.486	0.777	1.146
Covid_Exposure	18,990	0.734	0.985	0.000	0.212	1.223
Covid_Neg_Sentiment	18,990	0.284	0.439	0.000	0.000	0.439
SARS_Exposure	18,990	0.002	0.050	0.000	0.000	0.000
Tobin's Q	18,990	2.295	1.877	1.126	1.597	2.680
DT_ITC	18,990	0.123	0.275	0.000	0.016	0.119
ROA	18,990	−0.003	0.045	−0.011	0.006	0.018
LEV	18,990	0.328	0.223	0.153	0.314	0.462
SIZE	18,990	7.356	1.843	6.151	7.398	8.563
Tangibility	18,990	0.256	0.222	0.092	0.175	0.360
Profitability	18,990	0.077	0.050	0.044	0.067	0.101
Growth	18,990	0.029	0.118	−0.019	0.009	0.045
Capital Exp.	18,990	0.019	0.023	0.005	0.011	0.023
M2B	18,990	3.939	7.087	1.134	2.300	4.680
Panel B Test of Means
Variable	PreCOVID	COVID	Diff	se	t
CCC	419.188	432.229	13.040	5.971	2.184
WCR	0.932	0.919	−0.013	0.010	−1.274
Covid_Exposure	0.099	1.372	1.273	0.011	116.424
Covid_Neg_Sentiment	0.031	0.539	0.508	0.005	97.558
SARS_Exposure	0.001	0.003	0.001	0.001	1.670
Tobin's Q	2.455	2.137	−0.318	0.027	−11.722
DT_ITC	0.121	0.126	0.006	0.004	1.449
ROA	−0.001	−0.005	−0.003	0.001	−5.150
LEV	0.326	0.330	0.004	0.003	1.211
SIZE	7.371	7.341	−0.030	0.027	−1.132
Tangibility	0.250	0.262	0.012	0.003	3.612
Profitability	0.075	0.078	0.003	0.001	3.923
Growth	0.030	0.028	−0.002	0.002	−1.279
Capital Exp.	0.017	0.020	0.002	0.000	6.814
M2B	4.378	3.502	−0.876	0.103	−8.526
Observation	9523	9467

This table presents the summary statistics and test of means for all variables. Variable definitions are as before. Sample runs from 2019Q1–2020Q2. I obtain financial data from Compustat quarterly file, while COVID-19 related data are from Hassan et al. (2020). Statistics are as expected.

Table 2 presents the Pearson correlation matrix. It is evident that COVID_Exposure is positively associated with CCC.

Table 2

Pearson Correlation Matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) CCC	1.000
(2) WCR	0.862*	1.000
(3) Covid_Exposure	0.074*	0.045*	1.000
(4) Covid_Neg_Sentiment	0.070*	0.058*	0.842*	1.000
(5) SARS_Exposure	0.017	0.008	0.051*	0.034*	1.000
(6) Tobin's Q	−0.013*	−0.049*	0.029*	−0.032*	0.016*	1.000
(7) DT_ITC	−0.018*	0.015*	−0.009	−0.001	0.002	−0.137*	1.000
(8) ROA	−0.132*	−0.150*	−0.026*	−0.030*	0.017*	0.051*	0.037*
(9) LEV	−0.092*	−0.100*	0.001	0.004	−0.008	−0.148*	0.067*
(10) SIZE	−0.123*	−0.127*	−0.059*	−0.068*	0.007	−0.115*	0.213*
(11) Tangibility	−0.220*	−0.257*	−0.078*	−0.037*	−0.003	−0.267*	0.147*
(12) Profitability	−0.100*	−0.212*	−0.022*	−0.043*	0.026*	0.388*	−0.251*
(13) Growth	0.003	−0.004	0.003	−0.019*	0.032*	0.188*	−0.024*
(14) Capital Exp.	−0.111*	−0.136*	−0.073*	−0.057*	0.007	−0.017*	0.056*
(15) M2B	−0.046*	−0.069*	0.008	−0.027*	0.001	0.575*	−0.077*
	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
(8) ROA	1.000
(9) LEV	−0.115*	1.000
(10) SIZE	0.315*	0.227*	1.000
(11) Tangibility	−0.013	0.263*	0.153*	1.000
(12) Profitability	0.272*	−0.137*	−0.248*	−0.234*	1.000
(13) Growth	0.144*	−0.049*	−0.034*	−0.092*	0.066*	1.000
(14) Capital Exp.	−0.009	0.028*	0.060*	0.473*	−0.044*	−0.058*	1.000
(15) M2B	0.064*	−0.082*	−0.002	−0.150*	0.225*	0.123*	−0.015*	1.000

This table presents the Pearson correlation matrix of all variables included in the study. Variables retain their definitions as before. * p<.1.

Main results

Table 3 presents the main results. The results show that an increase in COVID_Exposure is associated with an increase in CCC during the COVID-19 period but has no effect before the COVID-19 period. The result is interpreted to mean that for a unit increase in COVID_Exposure, it takes on average 0.020 standard deviation increase in CCC as indicated in Column 2. That is exposed firms’ cash is tied up for at least 8 days.8 Test of equality of coefficient for COVID_Exposure for the two groups (PreCOVID and COVID) was conducted and the coefficients were significantly different from each other. The effect intensifies when there are negative sentiments related to COVID-19 as presented in Column 4. The evidence holds when I use WCR as a dependent variable presented in Columns 5–8. These findings suggest that firms are inefficient in managing their WC when exposed to COVID-19.

Table 3

Effect of COVID-19 on Working Capital Management (WCM).

	PreCOVID	COVID	PreCOVID	COVID	PreCOVID	COVID	PreCOVID	COVID
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Variable	CCC	CCC	CCC	CCC	WCR	WCR	WCR	WCR
Covid_Exposure	0.021	0.020***††			0.009	0.017***
	(0.016)	(0.006)			(0.007)	(0.004)
Covid_Neg_Sentiment			0.044	0.052***			0.014	0.036***
			(0.028)	(0.011)			(0.018)	(0.008)
ROA	0.384	−0.739***	0.383**	−0.722***	−0.089	−0.604***	−0.090	−0.592***
	(0.302)	(0.190)	(0.162)	(0.190)	(0.107)	(0.133)	(0.107)	(0.133)
LEV	0.365**	0.313***	0.371***	0.308***	0.133**	0.256***	0.136**	0.257***
	(0.153)	(0.085)	(0.086)	(0.085)	(0.057)	(0.059)	(0.057)	(0.059)
SIZE	−0.178**	−0.037	−0.175***	−0.034	−0.072***	−0.058**	−0.070***	−0.059**
	(0.085)	(0.034)	(0.040)	(0.034)	(0.026)	(0.024)	(0.026)	(0.024)
Tangibility	−0.146	0.746***	−0.142	0.737***	−0.103	0.428***	−0.101	0.426***
	(0.242)	(0.189)	(0.162)	(0.189)	(0.107)	(0.133)	(0.107)	(0.133)
Profitability	−5.502***	−5.807***	−5.521***	−5.789***	−4.909***	−5.318***	−4.919***	−5.322***
	(0.530)	(0.255)	(0.255)	(0.254)	(0.168)	(0.179)	(0.168)	(0.178)
Growth	0.076	0.075*	0.073*	0.074*	0.064***	0.073**	0.063**	0.074**
	(0.061)	(0.043)	(0.038)	(0.043)	(0.025)	(0.030)	(0.025)	(0.030)
Capital Exp.	−0.166	−0.883***	−0.200	−0.867***	−0.244	0.015	−0.263*	0.039
	(0.483)	(0.333)	(0.243)	(0.333)	(0.161)	(0.234)	(0.160)	(0.233)
M2B	−0.003*	−0.002*	−0.003**	−0.002**	−0.000	−0.001	−0.000	−0.001
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Constant	1.651***	0.428	1.630***	0.412	1.816***	1.548***	1.806***	1.562***
	(0.618)	(0.275)	(0.301)	(0.273)	(0.199)	(0.192)	(0.199)	(0.191)
Observations	9523	9467	9523	9467	9523	9467	9523	9467
R-squared	0.082	0.113	0.082	0.115	0.143	0.170	0.143	0.171
Number of Firms	2278	2293	2278	2293	2278	2293	2278	2293
Effect:
Firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Quarter (Qtr)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr

This table presents the results of my main model. I standardized CCC to have a mean (Std.Dev) of 0(1) in all models. The results hold when the raw values are used. Robust standard errors are presented in parentheses. *** p<.01, ** p<.05, * p<.1. †† indicates that a test of equality of coefficients for COVID_Exposure in Columns (1) and (2) are significant at p<.05.

Table 4 presents the moderating effect of Tobin'sQ and COVID_Exposure and DT_ITC and COVID_Exposure on CCC. The results indicate that Tobin'sQ and the moderation between Tobin'sQ and COVID_Exposure reduce CCC, thus reducing the days cash remain tied up during the COVID-19 period as presented in Columns 1–4. The evidence amplifies when there are negative sentiments related to the pandemic. In Columns 5–8, I present the evidence using DT_ITC and DT_ITC*COVID_Exposure. The evidence suggests that increases in DT_ITC and its moderating effect reduces CCC. The results suggests that firms receiving more DT_ITC have the potential of managing their WC effectively. This implies that government incentives in the form of deferred taxes and investment tax credit can help firms mitigate the impact effect of the pandemic on their WCM. The results are consistent with WCR as the dependent variable (not reported) as well as employing COVID_Neg_Sentiment measures.

Table 4

Does Investment Opportunities and Government Incentives Moderate the Impact of COVID-19 on WCM?.

	PreCOVID	COVID	PreCOVID	COVID	PreCOVID	COVID	PreCOVID	COVID
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Variable	CCC	CCC	CCC	CCC	CCC	CCC	CCC	CCC
Covid_Exposure	0.029	0.031***			0.027	0.023***
	(0.019)	(0.010)			(0.018)	(0.007)
Covid_Neg_Sentiment			0.036	0.071***			0.062	0.072***
			(0.048)	(0.019)			(0.045)	(0.013)
Tobin'sQ	−0.011	−0.036***	−0.012	−0.038***
	(0.010)	(0.009)	(0.010)	(0.009)
Tobin'sQ*Covid_Exposure	−0.004	−0.007**
	(0.009)	(0.003)
Tobin'sQ*Covid_Neg_Sentiment			0.007	−0.011*
			(0.023)	(0.007)
DT_ITC					−0.517***	−0.886***	−0.518***	−0.690***
					(0.125)	(0.053)	(0.126)	(0.045)
DT_ITC*Covid_Exposure					−0.037	−0.049**
					(0.080)	(0.023)
DT_ITC*Covid_Neg_Sentiment							−0.141	−0.083**
							(0.164)	(0.041)
ROA	0.422**	−0.859***	0.418**	−0.846***	0.419	−0.772***	0.419	−1.109***
	(0.178)	(0.209)	(0.178)	(0.209)	(0.327)	(0.204)	(0.327)	(0.195)
LEV	0.395***	0.294***	0.402***	0.294***	0.455***	0.374***	0.463***	0.107
	(0.095)	(0.094)	(0.095)	(0.094)	(0.168)	(0.091)	(0.169)	(0.069)
SIZE	−0.207***	−0.072*	−0.208***	−0.062	−0.264***	−0.112***	−0.260***	−0.123***
	(0.046)	(0.038)	(0.046)	(0.038)	(0.095)	(0.037)	(0.095)	(0.012)
Tangibility	−0.148	0.796***	−0.146	0.789***	−0.301	0.595***	−0.299	−0.908***
	(0.179)	(0.208)	(0.179)	(0.208)	(0.262)	(0.204)	(0.261)	(0.091)
Profitability	−6.025***	−6.148***	−6.046***	−6.129***	−5.734***	−5.863***	−5.755***	−5.204***
	(0.284)	(0.286)	(0.284)	(0.285)	(0.571)	(0.276)	(0.567)	(0.242)
Growth	0.088**	0.090*	0.085**	0.086*	0.087	0.072	0.083	0.029
	(0.042)	(0.047)	(0.042)	(0.047)	(0.067)	(0.046)	(0.067)	(0.044)
Capital Exp.	−0.169	−1.010***	−0.203	−1.001***	−0.086	−0.820**	−0.125	−0.636*
	(0.270)	(0.367)	(0.268)	(0.367)	(0.532)	(0.359)	(0.525)	(0.347)
M2B	−0.003**	−0.001	−0.003**	−0.001	−0.003*	−0.002*	−0.003*	−0.002**
	(0.001)	(0.001)	(0.001)	(0.001)	(0.002)	(0.001)	(0.002)	(0.001)
Constant	2.011***	0.891***	2.019***	0.818***	2.334***	0.989***	2.305***	1.458***
	(0.351)	(0.310)	(0.350)	(0.307)	(0.685)	(0.297)	(0.683)	(0.094)
Observations	9523	9467	9523	9467	9523	9467	9523	9467
R-squared	0.082	0.117	0.082	0.118	0.097	0.156	0.097	0.011
Number of Firms	2278	2293	2278	2293	2278	2293	2278	2293
Effect
Firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Quarter (Qtr)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr	Firm & Qtr

This table presents the moderating effect of Tobin'sQ and DT_ITC on the effect of COVID_Exposure on CCC. I standardized CCC to have a mean (Std.Dev) of 0(1) in all models. The results hold when the raw values are used. Robust standard errors are presented in parentheses. *** p<.01, ** p<.05, * p<.1.

Table 5 presents robustness checks on my main results. I modeled the effect of SARS_Exposure on CCC and find positive but insignificant results for both before and within the COVID-19 period. The evidence is presented in Columns 1–2. This finding suggests that the evidence of COVID_Exposure on CCC is not spuriously driven. I performed an Arellano-Bond linear dynamic panel-data estimation (Column 3–4) which allows me to fathom potential endogeneity issues. Overall, these analyses validate my main findings. Finally, I utilized a quantile regression to study the effect of COVID_Exposure at different levels of CCC. The results (Column 5–7) suggest that the effect of COVID_Exposure on CCC is higher at the 75th percentile as compared to 25th and 50th percentile. The evidence suggests that the effect of COVID-19 differs considerably having a strong effect on CCC at higher levels (CCC@75%). This implies that firms that keep more inventories and/or delay in collecting their sales during the COVID-19 period will be cash-constrained which will affect their short-term operations. Test of equality of coefficient for COVID_Exposure at the different percentiles was conducted and the coefficients were significantly different from each other. Evidence for PreCOVID is not reported for the quantile regression, but the findings remain unchanged. It is evident that after controlling for potential endogeneity and utilizing different estimation techniques, evidence holds that firms exposed to COVID-19 operate with higher CCC and WCR, and that the impact could be mitigated with investment opportunities and government incentives.

Table 5

Robustness Test.

			GMM DPD		Quantile Regression (COVID)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Variable	CCC-PreCOVID	CCC—COVID	CCC-PreCOVID	CCC—OVID	CCC@25%	CCC@50%	CCC@75%
SARS_Exposure	0.061	0.057
	(0.177)	(0.079)
LagCCC			0.106	0.011***
			(0.164)	(0.004)
Covid_Exposure			0.073	0.022***	0.026***	0.054***	0.093***
			(0.064)	(0.007)	(0.006)	(0.007)	(0.019)
ROA	0.375**	−0.729***	6.987***	−0.039	0.014	−0.523	−2.142***
	(0.162)	(0.190)	(1.329)	(0.876)	(0.133)	(0.341)	(0.418)
LEV	0.380***	0.340***	2.362**	−0.033	−0.200***	−0.357***	−0.436***
	(0.086)	(0.085)	(1.009)	(0.303)	(0.022)	(0.030)	(0.067)
SIZE	−0.174***	−0.055	−0.699**	−0.023	−0.024***	−0.047***	−0.087***
	(0.040)	(0.034)	(0.287)	(0.085)	(0.003)	(0.006)	(0.009)
Tangibility	−0.138	0.772***	−0.990	0.426	−0.433***	−0.713***	−1.164***
	(0.162)	(0.189)	(1.296)	(0.769)	(0.023)	(0.043)	(0.064)
Profitability	−5.553***	−5.920***	−8.319***	−8.511***	−1.143***	−1.973***	−3.091***
	(0.254)	(0.254)	(1.942)	(0.931)	(0.108)	(0.211)	(0.379)
Growth	0.069*	0.080*	0.056	0.341**	−0.033	0.033	−0.007
	(0.037)	(0.043)	(0.339)	(0.133)	(0.067)	(0.095)	(0.133)
Capital Exp.	−0.260	−0.798**	1.361**	−0.511*	−0.192	−0.296	−0.136
	(0.239)	(0.333)	(0.572)	(0.294)	(0.146)	(0.307)	(0.488)
M2B	−0.003**	−0.002**	−0.003	−0.006	−0.003***	−0.005***	−0.008***
	(0.001)	(0.001)	(0.008)	(0.004)	(0.000)	(0.001)	(0.002)
Constant	1.622***	0.584**	5.224***	0.617	−0.182***	0.503***	1.532***
	(0.301)	(0.271)	(1.976)	(0.733)	(0.031)	(0.046)	(0.099)
Observations	9523	9467	7138	8813	9467	9467	9467
R-squared	0.081	0.112			0.039	0.056	0.087
Number of Firms	2278	2293	2038	2096	2293	2293	2293
Effect:
Firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Quarter (Qtr)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster	Firm & Qtr	Firm & Qtr	No	No	No	No	No

This table presents evidence to robust my main findings. I standardized CCC to have a mean (Std.Dev) of 0(1) in all models. The results hold when the raw values are used. Robust standard errors are presented in parentheses. *** p<.01, ** p<.05, * p<.1.

Conclusion

The severe conditions presented by the COVID-19 pandemic have considerably rendered firms inefficient in managing the WC. As such, this study assesses the effect of COVID-19 exposure on CCC and the moderating effect of investment opportunities and government incentives. The study is novel in finding evidence that COVID-19-exposed firms operate with higher CCC levels. Furthermore, it asserts that firms with higher investment opportunities and firms that obtain more DT_ITC could operate with lower CCC. The findings contribute to the ongoing discussions on the impact of the pandemic on corporate decisions and outcomes. Future research could explore other government subsidies that could facilitate efficient WCM among exposed firms. Additionally, further studies could examine the speed of adjustment towards WCM during the pandemic.

Author's Contribution Statement

Augustine Tarkom - being the single and the corresponding author has performed all of the following tasks in completing the research article for publication.

1. Conception of the research idea

2. Designing the research framework

3. Data collection and preparation

4. Data analysis and interpretation

5. Writing the article

6. Revision of the article

Declaration of Competing Interest

None.