Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

COVID-19 and Precautionary Corporate Cash Holdings:
Evidence from Japan*
Tomohito Honda **Institute of Economic Research, Hitotsubashi University
E-mail: thshhonda@gmail.com

Iichiro Uesugi
Institute of Economic Research, Hitotsubashi University
E-mail: iuesugi@ier.hit-u.ac.jp
ABSTRACTThis study examines how listed firms have managed their cash holdings since the outbreak of the
COVID-19 crisis, using quarterly data on publicly-traded firms in Japan. After providing an
overview of developments in cash holdings since the start of the crisis, we focus on the
precautionary motive for corporate cash holdings and examine the role of firms’ cash flow andvolatility therein in firms’ cash holdings to find the following: (1) corporate cash holdings haveincreased rather than decreased since the start of the crisis; (2) an increase in firms’ cash flow
has a positive impact on their cash holdings during normal times, and the sensitivity of cash
holdings to cash flows was more pronounced during the first three months of the crisis; (3) firms
facing higher sales volatility held more cash in the second three-month period following the startof the crisis; and (4) the cash flow sensitivity of financially constrained firms’ cash holdings
during the crisis period increased more than that of unconstrained firms. Overall, even though the
COVID-19 crisis is different from the previous financial crises such as the Global Financial
Crisis in that it was not originated in the financial sector, the pandemic crisis has had asubstantial impact on corporate cash management strategies and the results are consistent withthe precautionary motive theory for cash holdings.
Key words: Cash holdings, Precautionary motive, COVID-19
JEL Classification: G31; G32*We are grateful to an anonymous referee of the journal and Ralph Paprzycki for valuable and useful comments.
**Corresponding author

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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

1. Introduction
Cash holdings play a critical role in corporate liquidity management and have been the focus
of many previous studies. A number of researchers have studied the determinants of firms’ cash
holdings and have identified two major factors: the transaction motive and the precautionary
motive1. The transaction motive refers to firms’ desire to hold a sufficient amount of cash to pay
for transactions without having to incur the cost of converting fixed assets into liquid ones.
Meanwhile, the precautionary motive refers to the desire to hold sufficient cash for unexpected
contingencies. Firms tend to hold substantial amounts of cash for unexpected funding demands(see, e.g., Almeida et al., 2004; Riddick and Whited, 2009; Duchin et al., 2010). Numerous
studies examine situations in which the precautionary motive for cash holdings plays animportant role. Opler et al. (1999), for example, argue that the precautionary motive becomes
more important when firms’ cash flow is subject to greater risk or firms have limited access toexternal financing. Similarly, modeling firms’ demand for liquidity, Almeida et al. (2004) showthat financially constrained firms are likely to save a larger amount of their cash flow forprecautionary cash holdings than unconstrained firms.
The precautionary motive for corporate cash holdings becomes even more important during
times of financial or economic crisis, such as the current crisis brought about by the COVID-19
pandemic. Several studies examine whether the propensity to build up precautionary cash
reserves is greater during financial crises than during normal times and find that this is indeed thecase. For instance, Sun and Wang (2015), focusing on the impact of the global financial crisis in
2008, find that the cash flow sensitivity of cash was significantly greater during the crisis period.
Similarly, investigating the long-term effect of the Asian financial crisis on corporate cash
holdings in eight East Asian countries, Song and Lee (2012) find that firms in these countries
built up cash holdings following the crisis by decreasing investment. They also show that firms’
increased sensitivity to cash flow volatility was one of the main factors for their higher level ofcash holdings. Meanwhile, examining the 2008 European financial crisis, Lozano and Yaman(2020) find that the crisis had a positive impact on corporate cash holdings for three years
following the crisis. Further, investigating the link between cash flow volatility and cash
holdings for constrained firms, they observe that the positive correlation was larger during thecrisis than before the crisis.
While these studies focus on the impact of financial crises on corporate cash holdings, to thebest of the authors’ knowledge, there are no studies to date that examine the impact of theoutbreak of the COVID-19 pandemic on precautionary corporate cash holdings2,3. The
COVID-19 crisis differs from the financial crises such as the Global Financial Crisis in several
unique ways and warrants further research on cash holdings. First, in contrast with periods of©Japan Society of Monetary Economics 2022

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financial crisis in the past, the financial sector has remained quite stable since the emergence ofthe current crisis. For example, in Japan, credit spreads on corporate bonds, which had jumped
during the global financial crisis, increased only marginally at the onset of the crisis and haveleveled off since then (Bank of Japan, 2020: Chart II-2-11). Various policy measures introducedby the Japanese government and the central bank have contributed to the stability of the financial
system thus far. Second, the shock to the real economy has been unprecedented not only in its
sheer size but also in the way industries are affected. The initial drop in aggregate output inJapan, for example, was the largest in the past 70 years. Moreover, due to the nature of the shock,the damage was distributed unevenly across industries. Some industries, such as transportation,accommodations and restaurants, and services for individuals incurred massive losses due to thestate of emergency declared by the Japanese government made people refrain from going out andeating out, while for other industries, such as construction, telecommunications, and businessservices, the damage was relatively minor4. Third, the degree of uncertainty in a variety of areas
has risen sharply since the outbreak of the pandemic. For example, looking at various measuresfor economic uncertainty for Japan, we find that the macroeconomic uncertainty index and theeconomic policy uncertainty index have reached the highest value in two decades5. This increasein economic uncertainty potentially may have led to an increase in demand for precautionary
corporate cash holdings.
Against this background, this study examines how the precautionary demand for cash has been
affected by the outbreak of the pandemic by focusing on the period from January to June 2020.
Given that the outbreak of COVID-19 and government restrictions to restrain it represent a
massive external shock to the economy, the pandemic provides an excellent natural experiment
to examine whether and how the precautionary motive affects corporate cash holdings.
For our analysis, we employ a sample of 1,773 listed Japanese firms for the period up to theend of the second quarter of 2020. Our observation period includes not only the quarter from
April to June, when the economy was massively affected by the state of emergency declared by
the government for about two months, but also the preceding quarter from January to March6.
Using quarterly data allows us to identify in a timely manner how the shock affected corporate
cash holdings.
We obtain the following four findings. First, corporate cash holdings have increased rather
than decreased since the onset of the COVID-19 crisis. Second, an increase in firms’ cash flow
has a positive impact on their cash holdings during normal times, and this positive cash flow
sensitivity of cash was more pronounced in January–March 2020. Third, firms facing higher
sales volatility held more cash in April–June 2020. Fourth, the increase in the cash flow
sensitivity of cash during the crisis was larger for financially constrained firms.

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Overall, our findings suggest that the precautionary motive is the primary cause for theincrease in Japanese publicly traded firms’ cash holdings during the COVID-19 crisis, and theincrease in precautionary cash holdings is more pronounced for firms that are likely to befinancially constrained. Our study differs from extant research on corporate cash holdings during
times of crisis in that it focuses on the COVID-19 crisis, which differs substantially in naturefrom the financial crises involving an increase in firms’ external financing costs that previous
studies focus on. In contrast, the current crisis caused direct damage to the real economy rather
than via the financial sector. Our findings show that despite the absence of a rise in externalfinancing costs, a significant increase in corporate cash holdings can be observed, which is a
novel finding in the literature.
The remainder of the study proceeds as follows. Section 2 provides a more detailed overview
of the related literature and posits our empirical hypotheses. Section 3 then explains the data we
use and our empirical approach. Next, Section 4 presents the results, while Section 5 offers
concluding remarks.
2. Related Literature and Hypotheses
Previous research has highlighted four motives for firms to hold cash: the transaction motive,
the tax motive, the agency motive, and the precautionary motive. Among these, the motive thathas received the most research attention is the precautionary motive. Several studies provide
theoretical models that explain the role of the precautionary motive for corporate cash holdings
and present supportive empirical evidence. Studies on the precautionary motive for cash holdings
can be divided into two broad strands, which differ in terms of the variables they employ to
examine to what extent cash holding is precautionary.
The first strand of the literature focuses on the level of firms’ cash flow. For instance, Almeida
et al. (2004) construct a model for the precautionary demand for cash and posit that financiallyconstrained firms save cash out of their cash flow, while the cash savings of unconstrained firms
should not be systematically related to cash flows. Almeida et al. (2004) regard firms’ cash flow
sensitivity of cash, that is, the propensity to save cash flow for cash, as an indicator of the extent
to which firms are financially constrained and hence save for precautionary reasons. Estimating
cash ratio equations, they find that the correlation between cash and cash flow is indeed positive.
Another study focusing on firms’ cash flow but employing a different theoretical setup from thatof Almeida et al. (2004) is Acharya et al. (2007). Specifically, in their setup, firms have only alimited capacity to hedge future investment opportunities against income shortfalls. In theirmodel, firms expecting a large number of investment opportunities are more likely to save cashfrom their cash flow than to reduce their current debt. In their empirical analysis based on this©Japan Society of Monetary Economics 2022

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model, the coefficients on firms’ cash flow are significantly positive, which is consistent with
their theoretical prediction.
A number of studies have followed up on these studies employing the methodology introduced
by Almeida et al. (2004) and Acharya et al. (2007). A notable example is the study by Sun andWang (2015), who examine precautionary corporate savings during the 2008 global financial
crisis to find that the cash flow sensitivity of cash was significantly larger during the crisis thannormal times. In Japan, Hosono et al. (2019) investigate the evolution of corporate cash holdings
during the period 1994–2016 to find an increase in the cash flow sensitivity of cash since the late
2000s.
Based on these studies, we posit our empirical hypothesis for corporate cash holdings during
the COVID-19 crisis. Specifically, we argue that firms’ cash flow is an important determinant of
their cash holdings in normal times, since firms require a precautionary cash buffer for their dayto-day activities. In addition, the COVID-19 pandemic likely increased firms’ desire forprecautionary cash holdings. Our first empirical hypothesis, therefore, is as follows:
Hypothesis 1: The cash flow sensitivity of cash is positive in normal times and became more
pronounced during the COVID-19 crisis.
The second strand of the literature focuses on the volatility of firms’ cash flow as adeterminant of precautionary cash holdings. In the literature, the volatility of a firm’s cash flowis regarded as reflecting the degree of uncertainty regarding its future income and affects theamount of cash it holds. For instance, Opler et al. (1999) find that firms that face greater cash
flow uncertainty hold a larger amount of cash than those with less uncertainty. Similarly, Han
and Qiu (2007) theoretically show that financially constrained firms with higher cash flow
volatility tend to hold a larger amount of cash for precautionary purposes. Bates et al. (2009)
report that the average amount of cash held by firms in the US increased during the period 1980–
2006 and conclude that the precautionary motive plays an important role in explaining theincrease in the cash ratio. Finally, Riddick and Whited (2009) find that income uncertainty
affects cash holdings more than do external finance constraints.
Among the follow-up studies employing the methodology introduced in this strand of theliterature, several studies investigate the impact of financial crises on precautionary corporate
cash holdings. For instance, Song and Lee (2012) investigate the long-term effect of the Asianfinancial crisis on corporate cash holdings in eight East Asian countries to show an increased
sensitivity to cash flow volatility. They observe that this is one of the main factors explaining thehigher level of firms’ cash holdings after the crisis. Meanwhile, examining the 2008 European

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financial crisis, Lozano and Yaman (2020) find that for financially constrained firms the cash
sensitivity to cash flow volatility was higher in the three years after the onset of the crisis than
before the crisis.
Based on the above literature, we now posit our empirical hypothesis about the impact offirms’ cash flow volatility on their cash holdings during the COVID-19 crisis. Specifically, we
assume that firms’ cash flow volatility is an important determinant of their cash holdings and thatthe crisis caused by the pandemic further increased the relevance of this determinant. Therefore,
our second hypothesis regarding corporate cash holdings is as follows:
Hypothesis 2: There is a positive link between firms’ cash flow volatility and their cash holdings
in normal times, and this link became more pronounced during the COVID-19 crisis.
In the following section, we first describe the data and empirical strategy employed in our
analysis and then examine the overall developments in firms’ cash holdings before and since theoutbreak of the pandemic, before empirically testing our hypotheses in Section 4.
3. Data and Empirical Strategy
3.1. Data and sample selection
For the analysis, we employ firm-level data from Nikkei NEEDS Financial QUEST provided
by Nikkei Incorporated. Our sample comprises publicly-traded non-financial firms in Japan
during the period March 2019–June 2020 and is limited to firms whose fiscal year ends in
March. We drop firms that newly listed in 2020, leaving us with a final sample of 10,638 firmquarter observations for 1,773 firms. All the variables used in our analysis are winsorized at the
1st and 99th percentiles to avoid problems caused by extreme outliers.
3.2. Empirical approach
To examine the two empirical hypotheses on the impact of cash flows and their volatility on
cash holdings, we employ the following the conventional specification in the empirical literatureon the determinants of cash holdings:
Y i, t = α + β1EBIT DAi, t + β2EBIT DAi, t*Crisist + β3Sales V olatilit yi, t+ β4Sales V olatilit yi, t*Crisist + β5Crisist + βX + δi + εi, t(1)

As dependent variable Yi,t we employ four different variables. The first is Cash, which isdefined as the sum of cash and deposits outstanding divided by book assets. The second,
Liquidity, is calculated as the sum of cash, deposits, and marketable securities outstanding©Japan Society of Monetary Economics 2022

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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

divided by book assets. The third, ∆Cash, is defined as the quarter-on-quarter change in Cash.
Finally, the fourth, ∆Liquidity, is the quarter-on-quarter change in Liquidity.
Turning to the explanatory variables, EBITDA, a proxy of cash flow, is the ratio of earnings
before interest, taxes, and depreciation and amortization to book assets. Based on Hypothesis 1,
we expect the coefficient on EBITDA to be positive. Sales Volatility is the standard deviation of afirm’s sales over the five years preceding the current period standardized by the average amountof assets during the same five years. The reason that we employ the standard deviation of sales
rather than that of EBITDA for cash flow volatility is that a firm’s cash flow consists of sales andcosts, and firms likely regard volatility in sales as more exogenous and difficult to control thanvolatility in costs. Hypothesis 2 expects that firms with higher sales volatility are likely to be
more at risk of becoming financially distressed and therefore have a greater demand forprecautionary cash holdings. We therefore expect the coefficient on Sales Volatility to be positive
as well. Next, Crisis represents the period of the coronavirus crisis. Specifically, we use twodummies: Crisis I is a dummy for the first quarter (January–March) of 2020, while Crisis II is adummy for the second quarter, i.e., April–June 2020. The purpose of using these two different
dummies is to capture how Japanese firms’ cash management changed in each of two periods.
We are also interested in the interaction terms between EBITDA or Sales Volatility and Crisis,
since both hypotheses predict that the coefficients on EBITDA and Sales Volatility should belarger during the COVID-19 crisis. We therefore expect the coefficients on these interaction
terms, i.e., EBITDA×Crisis and Sales Volatility×Crisis, to be positive.
We add various other explanatory variables as controls. Size is the natural logarithm of a firm’sbook assets. The larger firms are, the more easily they can access external finance, and thesmaller their demand for cash will be due to the smaller information asymmetry between the firmand lenders. We therefore expect the coefficient on Size to be negative. Next, Q is the ratio of afirm’s market to book value of assets. Firms with more growth opportunities prefer cash toexternal finance because of the greater extent of information asymmetry for high growth firms.
Consequently, we expect a positive coefficient on Q. Further, Capex is the ratio of the sum of thequarterly change in tangible assets, depreciation, and amortization to the book value of assets.
We expect the coefficient on Capex to be negative because capital investment increases a firm’s
stock of collateralizable assets and enhances its debt capacity, which leads to a smaller demandfor cash. Meanwhile, NWC is the ratio of net working capital to book assets, and we predict a
negative coefficient because firms use working capital as an alternative source of cash. Finally,
Debt is the ratio of the sum of short- and long-term debt to book assets. Different theories yield
different predictions, so the sign of the coefficient on Debt could be positive or negative. On the
one hand, theories suggesting that debt and cash act as substitutes in terms of firms’ funding

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sources suggest that the sign should be negative (Opler et al., 1999; Kim et al., 1998; Bates et al.,2009). On the other hand, if firms are limited in their hedging capacity and debt and cash areimperfect substitutes, as suggested by the theoretical models in Acharya et al. (2007) and Guney
et al. (2007), the sign will be positive.
3.3. Summary Statistics
In this subsection, we describe the characteristics of the variables that we employ in our
analysis. We start with descriptive statistics, which are provided in Table 1.
The mean of Cash is 0.200 and that of Liquidity is 0.208, indicating that in Japan, firms’ cash
holdings amount to about 20% of their total assets. This is above the average cash holding ratioof 16.8% reported by De Vito and Gomez (2020) for 26 mostly developed countries in 2018. The
averages of ∆Cash and ∆Liquidity are identical at 0.004, indicating that the cash ratio and theliquidity ratio slightly increased during the period.
Next, we examine how the means of these variables differ across subperiods, that is, the period
before and the period after the outbreak of the COVID-19 pandemic. Table 2 shows the meansfor various variables in each subperiod. We also test the statistical significance of differences
between these subperiods. There are four notable findings. First, the cash and liquidity ratios

Table 1. Descriptive statistics for the variables employed in the estimations
Variables

N

Mean

Sd

Min

MedianMax

Cash10,637

0.200

0.147

0.013

0.164

0.749

Liquidity

10,637

0.208

0.152

0.013

0.171

0.767

ΔCash

10,519

0.004

0.033

−0.097

0.002

0.126

ΔLiquidity

10,519

0.004

0.034

−0.100

0.002

0.126

EBITDA

9,771

0.019

0.022

−0.076

0.018

0.088

Sales Volatility

10,438

0.045

0.040

0.004

0.032

0.215

Size

10,637444,3392,079,013

284

58,04655,900,000

Q

10,637

0.840

1.154

0.072

0.460

7.914

Capex

9,756

0.009

0.013

−0.029

0.007

0.064

NWC

10,056

0.138

0.145

−0.205

0.133

0.598

Debt

10,637

0.146

0.151

0.000

0.102

0.630

This table reports summary statistics for the sample. Definitions of variables are provided in Section 3.2. The unit for Size ismillion yen.

Table 2. Means of variables for different subperiods

Table 3.

PeriodCash

Liquidity

ΔCash

ΔLiquidity

EBITDA

Sales Volatility

Pre Crisis

0.195

0.204

0.000

−0.001

0.021

0.045

Difference between Crisis I
p1

Crisis I

0.202

0.211

0.013

0.013

0.017

0.044

Cash

Crisis II

0.214

0.222

0.011

0.011

0.011

0.046

Liquidity−0.112Difference (Crisis I - Pre Crisis)0.007*

0.007*

0.013***

0.013***−0.003***−0.001

EBITDA−0.074

Difference (Crisis II - Pre Crisis)0.019***

0.018***

0.012***

0.012***−0.010***0.001

Difference between Crisis II
p1−0.114This table reports the averages of Cash, Liquidity, Δ Cash, Δ Liquidity, EBITDA, and Sales Volatility for subperiods and measuresdifferences between periods. Definitions of variables are provided in Section 3.2. Pre Crisis is from 2019Q1 to 2019Q4. Crisis I is 2020Q1 Cashand Crisis II is 2020Q2. *** and * indicate that the difference is statistically significant at the 1% or 10% level, respectively.
Liquidity−0.122

EBITDA−0.094©Japan Society of Monetary Economics 2022

26

−0.131This table reports percentile
between Crisis II and Pre C
2019Q1 to 2019Q4, Crisis I t

Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

increased significantly after the outbreak of the pandemic. Cash increased from 0.195 in the precrisis period to 0.202 in the first quarter of 2020 and 0.214 in the second quarter of 2020.
Liquidity increased to a similar extent. The increase in these variables between the periods isstatistically significant. Second, the growth in the cash and liquidity variables accelerated during
the crisis period. While both ∆Cash and ∆Liquidity were around zero before the crisis, they
increased to around 0.011 to 0.013 during the crisis. Third, due to the deterioration in businessconditions during the crisis period, EBITDA, our measure of cash flow, decreased significantly
after the outbreak of the crisis. While the profit rate was 0.021 before the crisis, it fell to 0.017 in
the first quarter of 2020 and 0.011 in the second quarter of 2020. Fourth, there was little change
in Sales Volatility after the start of the crisis. This is mostly due to the way we construct thevariable, since we calculate the standard deviation of a firm’s sales over the preceding five years.
Further, we examine detailed information on the distribution of differences in variables
between periods. Specifically, for Cash, Liquidity, and EBITDA, we produce percentile statistics
for the differences between Crisis I or Crisis II on the one hand and Pre Crisis on the other.
These statistics allow us to observe the share of firms that experienced a drop in theirprofitability and the share of firms that saw an increase or decrease in their cash balance.
Table 3 shows the results. The distributions of Cash and Liquidity shift toward the right,
indicating that these variables increased in the crisis period, while that of EBITDA moves toward
the left, meaning that firms’ profitability substantially dropped during the crisis. The increase in
the cash holding ratio and the decline in profitability became more pronounced as the crisis
deepened from the first quarter to the second quarter of 2020. It should be noted that in thesecond quarter of 2020, the cash holding ratio of the majority of firms increased even though
three-quarters of the firms experienced a decline in profitability.
To summarize, there was a substantial increase in corporate cash holdings during the first halfof 2020, when the economy was affected by the COVID-19 shock and firms’ profitability

Table 3. Percentile statistics on the differences between the pre-crisis and crisis periods
Difference between Crisis I and Pre Crisis
p1
p5
p10

p25

p50

p75

p90

p99

−0.114−0.049−0.032−0.009

0.006

0.023

0.046

0.143

Liquidity−0.112−0.051−0.033−0.010

0.006

0.024

0.045

0.149

EBITDA−0.074−0.040−0.024−0.011−0.002

0.005

0.017

0.063

Cash

Difference between Crisis II and Pre Crisis
p1
p5
p10

p25

p50

p75

p90

p99

Cash

−0.122−0.049−0.028−0.004

0.014

0.040

0.075

0.176

Liquidity−0.131−0.053−0.030−0.004

0.013

0.040

0.074

0.176

EBITDA−0.094−0.048−0.032−0.017−0.007

0.000

0.009

0.040

This table reports percentile statistics of differences for Cash, Liquidity, and EBITDA between Crisis I and Pre Crisis andbetween Crisis II and Pre Crisis. Definitions of variables are provided in Section 3.2. Pre Crisis refers to the period from
2019Q1 to 2019Q4, Crisis I to 2020Q1, and Crisis II to 2020Q2.

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substantially dropped. Moreover, the increase in the average cash holding ratio was due not to anincrease in the cash ratio of a small number of large firms but reflects an increase in the cash
ratio for the majority of firms.
4. Results
In the summary statistics in the previous section, we observed an increase in corporate cash
holdings and a decrease in cash flows in 2020 when the COVID-19 crisis unfolded. We also
found that there was little change in sales volatility during the observation period. However, thedescriptive statistics tell us little about the link between cash holdings and cash flows and,moreover, do not control for other factors. Therefore, in this section, we first present ourestimation results controlling for other factors that are important determinants of cash holdings
as well as firm fixed effects. Next, given that financial constraints have been highlighted as a key
reason for precautionary cash holdings, we conduct various subsample analyses to examine how
financial constraints affect corporate cash holdings.
4.1. Baseline Results
Table 4 presents our baseline results on the determinants of cash holdings and changes therein.
Columns (1) and (2) show the results when we employ the level of cash and liquid asset holdings
as the dependent variable. There are several notable findings. First, the coefficients on EBITDA
are positive and significant. This result indicates that the cash flow sensitivity of cash is positivein normal times. Turning to the interaction terms between cash flow and the crisis dummies, thecoefficient on the interaction term between EBITDA and Crisis I is positive and significant incolumn (2), while it is positive but insignificant in column (1). On the other hand, thecoefficients on EBITDA*Crisis II are insignificant in both columns. These results indicate thatthe cash flow sensitivity of cash was more pronounced at the onset of the crisis in the firstquarter of 2020, but this was not necessarily the case later in the crisis (in the second quarter of2020), when firms’ cash flow declined substantially. A possible explanation for the insignificant
coefficients on EBITDA*Crisis II is that EBITDA declined substantially in the Crisis II period, sothat firms may not have been able to afford to save cash out of their cash flow.
Second, we find that the coefficients on Sales Volatility in columns (1) and (2) areinsignificant. This indicates that in normal times firms do not hoard cash in response to higher
sales volatility. In contrast, the coefficients on the interaction terms between sales volatility andthe crisis dummies turn significantly positive in the second quarter of 2020, i.e., a few months
into the crisis, while this is not the case for the first quarter of 2020, the onset of the crisis. These
results suggest that firms that faced higher sales volatility started to prepare for the liquidity
shortage once they realized the substantial impact of the crisis and began to expect that it would©Japan Society of Monetary Economics 2022

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Table 4. Baseline results

Table 5.

(1)

(2)

(3)

(4)

(1)

Dep. Var.

Cash

Liquidity

ΔCash

ΔLiquidity(A) S

EBITDA

0.116***

0.096**

0.117***

0.120***

Dep. Var.

Cas(0.044)(0.043)(0.040)(0.039)

EBITDA

0.131

0.109

0.134**

0.106**

0.114**(0.070)(0.062)(0.053)(0.051)

0.094

0.127

0.006

−0.002(0.086)(0.081)(0.065)(0.066)

0.016

0.009

−0.059−0.069

EBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility(0.144)(0.145)(0.100)(0.102)

Sales Volatility*Crisis I−0.002

0.006

0.029

0.046

(0.029)(0.028)(0.028)(0.029)

Sales Volatility*Crisis II

0.071**

0.085***

0.070**

0.079**(0.032)(0.032)(0.034)(0.034)

Crisis I

0.005***

0.004***

0.008***

0.008***(0.002)(0.002)(0.001)(0.001)

Crisis II

0.010***

0.008***

0.002

0.002

(0.002)(0.002)(0.002)(0.002)Size
Q−0.026−0.033*

0.001

0.002

(0.019)(0.019)(0.014)(0.014)

0.007

0.007

−0.002−0.001(0.05
EBITDA*Crisis I

0.12

(0.10
EBITDA*Crisis II

0.08

(0.11
Sales Volatility

0.03

(0.19
Sales Volatility*Crisis I

−0.0

(0.04
Sales Volatility*Crisis II

0.08

(0.04
Crisis I

0.005

Crisis II

0.010

(0.00

(0.00
Size

−0.0

(0.03

(0.005)(0.004)(0.002)(0.002)Capex−0.340***−0.349***−0.558***−0.593***(0.036)(0.036)(0.039)(0.039)NWC−0.666***−0.690***−0.543***−0.561***(0.030)(0.030)(0.023)(0.023)0.225***

0.236***

0.289***

0.298***(0.047)(0.047)(0.030)(0.031)

Firm FE

Firm FE

Firm FE

Firm FE(0.06

Observations

9,723

9,723

9,723

9,723

Firm

R2

0.379

0.397

0.269

0.286

Debt

Q

0.00

(0.00
Capex

−0.394NWC−0.672

(0.05

(0.04
Debt

Observations

0.156

5,04

0.35
This table shows the estimation results for cash and liquidity holdings. The dependent variables are Cash, R2
Liquidity, ΔCash, and ΔLiquidity. All estimations include a constant term and firm fixed effects. Definitions ofvariables are provided in Section 3.2. The estimations employ standard errors that are clustered by firms andThis table shows the estimation results for cdependent variables are Cash and Liquidity.
reported in parentheses. *** and ** denote statistical significance at the 1% and 5% level, respectively.
while columns (3) and (4) show the results
term and firm fixed effects. Definitions of v
are clustered by firms and reported in parent
respectively.

last for a long time.
Third, the coefficients on the crisis dummies indicate that firms began to hoard more cash as
the crisis deepened. The result in column (1) indicates that in the first quarter of 2020 firms’ cash
ratio was 0.5 percentage points higher than in the same quarter of 2019. The year-on-yearincrease was even larger in the second quarter, reaching 1.0 percentage point. In column (2), we
find a similar pattern for the liquid asset ratio.
Fourth, there are several other control variables whose coefficients are statistically significant.
Capex has negative coefficients, presumably because capital investment increases firms’
collateralizable assets and debt capacity, thus reducing the need for them to hold cash. NWC also
has negative coefficients, which suggests that working capital substitutes for cash. The

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coefficients on Debt are positive, indicating that cash and debt are imperfect substitutes, aspredicted by Acharya et al. (2007).
Next, we turn to the results in columns (3) and (4), where we employ the changes in cash andliquid asset holdings as the dependent variable. While the results are by and large similar to thosein columns (1) and (2), there are a few things to note. First, the coefficients on EBITDA and its
interaction terms with the crisis period dummies have generally the same sign as in columns (1)and (2), but some coefficients are more statistically significant. In particular, the coefficients on
the interaction term between EBITDA and Crisis I are positive and significant in both columns(3) and (4). Second, the coefficients on the crisis dummies are positive and significant for the
first quarter of 2020 but insignificant for the second quarter of 2020.
Overall, these results in Table 4 indicate that the increase in the cash ratio was larger at theonset of the crisis in the first quarter of 2020 and then leveled off in the second quarter of 2020,
and the propensity to save cash out of cash flow was more pronounced during the crisis, which isconsistent with our hypotheses.
4.2. Results for financially constrained firms
In this subsection, we conduct a set of subsample analyses and examine how financiallyconstrained firms manage their cash holdings. As discussed in Section 2, previous theoreticalstudies on the precautionary motive for corporate cash holdings suggest that financiallyconstrained firms are more likely to hoard precautionary cash than financially unconstrained
firms.
Against this background, we expect the predictions of the two empirical hypotheses to apply
more to financially constrained firms than to unconstrained ones. We therefore employ several
variables to identify financially constrained firms, and by comparing them with unconstrained
firms, we examine if financially constrained firms tended to save cash more during the crisis.
The variables we use for defining constrained firms include firms’ size, payout ratio, leverage,
cash ratio, access to the bond market, and access to credit lines. Additionally, we take into
account of the existence of the government support to businesses that suffered from substantial
losses during the pandemic. We focus on firms in damaged industries that are more dependent on
the government support than are other industries. Assuming that the amount of support issufficient, we regard firms in these industries as financially unconstrained and those in industries
that are less dependent on the government support as financially constrained.
Employing each of these variables in turn, we divide the total sample of firms into two groups(for example, small firms and large firms), define one group (small firms in this example) as
financially constrained, and compare the estimation results with those for unconstrainted firms(large firms in this example). Note that in the analysis that follows, we limit the dependent©Japan Society of Monetary Economics 2022

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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

variables to Cash and Liquidity, that is, variables that represent levels rather than changes. The
reason is that the baseline estimation results for the coefficients on EBITDA, Sales Volatility, andtheir interaction terms with the Crisis dummies in Section 4.1. were qualitatively similar
regardless of whether we used the dependent variables in levels or we used changes.
4.2.1. Small versus large firms
We start by using firms’ asset size to identify financially constrained firms, based on Almeida
et al. (2004) and Acharya et al.’s (2007) argument that the degree of firms’ external financial
frictions is related to their size. Specifically, we divide the sample into two groups based on the
average amount of book assets throughout the year 2019 (from the first to the fourth quarter ofthe year). We then regard firms with assets below the median as financially constrained and thosewith assets above the median as unconstrained.
Table 5 shows the results. The two columns under (A) are for small, constrained firms, while
those under (B) are for large, unconstrained firms. The coefficients on EBITDA in the Cash andLiquidity estimations are significantly positive only for small firms. Moreover, the coefficientson the interaction terms between EBITDA and the Crisis dummies are marginally significant in
the Liquidity estimation for small firms but insignificant in all the other estimations. In sum, wefind a positive cash flow sensitivity of cash only for small firms. Moreover, the Liquidity
estimation for small firms suggests that the extent of the cash flow sensitivity of cash increased,albeit marginally, during the onset of the crisis in the first quarter of 2020.
Meanwhile, the results for Sales Volatility show no substantial differences between small andlarge firms in the way their cash holdings responded to sales volatility not only during normal
times but also during the crisis period. Specifically, for both small and large firms, thecoefficients on Sales Volatility and the interaction term between Sales Volatility and Crisis I are
insignificant, while those on the interaction term between Sales Volatility and Crisis II have thesame positive sign and are of a similar magnitude.
4.2.2. Low versus high payout firms
Next, based on Fazzari et al. (1988) and Almeida et al.’s (2004) argument that firms facing
high external financing costs have an incentive to reserve cash instead of paying out cash flows
to shareholders, we divide the sample into two groups based on firms’ payout ratio (annual
payouts/total assets) in March 2020. We then regard firms below the median as constrained andthose above as unconstrained.
Table 6 presents the results. They show that, on the one hand, the coefficients on EBITDA are
insignificant for low payout firms, while they are significant and positive for high payout firms.
On the other hand, the coefficients on the interaction terms between EBITDA and the Crisis
dummies are positive and significant for low payout firms, while they are insignificant for their

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Table 5. Regression results for small versus large firms
(1)

(2)

(A) Small Firms (constrained)

(3)

(4)

(B) Large Firms (unconstrained)

Dep. Var.

Cash

EBITDA

0.131**

0.106*

0.071

0.060

(0.058)(0.057)(0.046)(0.046)

EBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size

Liquidity

CashLiquidity

0.123

0.161*

0.078

0.077

(0.101)(0.088)(0.065)(0.064)

0.080

0.123

0.120

0.141

(0.114)(0.104)(0.085)(0.089)

0.031

0.030

−0.038−0.073(0.197)(0.199)(0.144)(0.144)−0.000

0.013

0.021

0.023

(0.043)(0.042)(0.033)(0.032)0.083*

0.104**

0.082*

0.085*(0.047)(0.047)(0.048)(0.048)0.005**

0.004

0.004**

0.003(0.003)(0.002)(0.002)(0.002)0.010***

0.008***

0.008***

0.006**(0.003)(0.003)(0.003)(0.003)−0.034−0.047−0.014−0.016(0.034)(0.033)(0.014)(0.014)0.007

0.008*−0.001−0.004(0.005)(0.005)(0.006)(0.004)−0.394***−0.394***−0.272***−0.296***(0.056)(0.056)(0.035)(0.036)−0.672***−0.685***−0.671***−0.716***(0.042)(0.041)(0.029)(0.027)0.156**

0.167**

0.345***

0.363***(0.069)(0.069)(0.045)(0.045)

Firm FE

Firm FE

Firm FE

Firm FE

Observations

5,045

5,045

4,678

4,678

R2

0.351

0.362

0.472

0.508

Q
Capex
NWC
Debt

This table shows the estimation results for cash and liquidity holdings by firms’ size (measured in terms of their assets). The
dependent variables are Cash and Liquidity. Columns (1) and (2) show the results for small and financially constrained firms,
while columns (3) and (4) show the results for large and financially unconstrained firms. All estimations include a constantterm and firm fixed effects. Definitions of variables are provided in Section 3.2. The estimations employ standard errors thatare clustered by firms and reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level,
respectively.

high payout counterparts. The results imply that for constrained firms the cash flow sensitivity of
cash was insignificant during normal times but became positive in the first quarter of 2020 andstayed positive in the second quarter of 2020. In contrast, for unconstrained firms, the cash flow
sensitivity of cash was already positive in normal times and the pandemic did not significantly
change this sensitivity. Thus, Hypothesis 1, which predicts a larger cash flow sensitivity of cash
during the crisis period, applies more to low payout (constrained) firms than to firms with a highpayout ratio (unconstrained firms).
Meanwhile, the results for Sales Volatility show substantial differences between low and highpayout firms. Specifically, while Sales Volatility did not affect corporate cash holdings (i.e., Cash
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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

Table 6. Regression results for low versus high payout firms
(1)

(2)

(A) Low Payout Firms (constrained)(3)

Table 7(4)(B) High Payout Firms (unconstrained)

Dep. Var.

Cash

Liquidity

CashLiquidity

EBITDA

0.053

0.066

0.170***

0.132**(0.067)(0.063)(0.056)(0.056)

EBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size
Q
Capex
NWC
Debt

0.224*

0.203*

0.024

0.071

(0.114)(0.113)(0.087)(0.077)0.289**

0.262**−0.0320.029(0.129)(0.125)(0.105)(0.098)

0.108

0.082

−0.021−0.018(0.163)(0.165)(0.191)(0.192)

0.021

0.032

−0.018−0.008(0.042)(0.039)(0.040)(0.040)0.121***

0.128***

0.024

0.046

(0.036)(0.035)(0.051)(0.050)

0.004

0.004

0.006***

0.004**(0.003)(0.003)(0.002)(0.002)0.008***

0.007**

0.011***

0.007***(0.003)(0.003)(0.003)(0.003)−0.061**−0.069***−0.013−0.021(0.024)(0.025)(0.025)(0.024)

0.009

0.011

0.004

0.005

(0.009)(0.008)(0.004)(0.004)−0.385***−0.396***−0.303***−0.311***(0.057)(0.058)(0.045)(0.045)−0.707***−0.735***−0.631***−0.651***(0.033)(0.033)(0.049)(0.048)0.213**

0.230**

0.252***

0.260***(0.089)(0.092)(0.048)(0.047)

Firm FE

Firm FE

Firm FE

Firm FE

Observations

4,236

4,236

5,487

5,487

R2

0.465

0.486

0.322

0.335

Dep. Var.
EBITDAEBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size
Q
Capex
NWC
Debt

Observations

R2
This table shows the estimation results for cash and liquidity holdings by firms’ payout ratio. The dependent variables are
Cash and Liquidity. Columns (1) and (2) show the results for firms with a low payout ratio (i.e., financially constrained firms),This table shows the estimation resu
while columns (3) and (4) show the results for firms with a high payout ratio (i.e., financially unconstrained firms). AllCash and Liquidity. Columns (1) an
estimations include a constant term and firm fixed effects. Definitions of variables are provided in Section 3.2. The estimations while columns (3) and (4) show th
employ standard errors that are clustered by firms and reported in parentheses. ***, **, and * denote statistical significance atestimations include a constant term athe 1%, 5%, and 10% level, respectively.
employ standard errors that are clust
the 1%, 5%, and 10% level, respecti

and Liquidity) during normal times, the significant positive coefficient on the interaction term
between Sales Volatility and Crisis II for low payout firms indicates that cash holdings did
increase for constrained firms in the second quarter of 2020. This finding suggests that
Hypothesis 2, which expects firms to respond more to volatility in their performance during acrisis than in normal times, holds for constrained firms but not for unconstrained ones.
4.2.3. Low versus high cash holding firms
Further, we use firms’ amount of cash holdings to identify constrained firms, based on Duchin
et al.’s (2010) finding that during the global financial crisis firms with low cash reserves reduced
capital investment more than firms with high cash reserves. Specifically, we divide the sample

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Table 7. Estimation results for low versus high cash holding firms
(1)

(2)

(A) Firms with Low Cash Holdings(constrained)

(3)

(4)

(B) Firms with High Cash Holdings(unconstrained)

Dep. Var.

Cash

Liquidity

Cash

EBITDA

0.036

0.044

0.130**

0.099*(0.035)(0.034)(0.057)(0.056)0.224***

0.232***

0.086

0.118

(0.051)(0.051)(0.099)(0.087)

EBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size

Liquidity

0.067

0.109

0.102

0.138

(0.083)(0.085)(0.111)(0.102)−0.092−0.080

0.028

0.016

(0.114)(0.115)(0.181)(0.182)

0.045

0.030

−0.0140.005(0.031)(0.031)(0.039)(0.038)0.120***

0.140***

0.076*

0.094**(0.042)(0.041)(0.044)(0.044)0.003*

0.002

0.006**

0.004*(0.001)(0.001)(0.002)(0.002)0.009***

0.007***

0.009***

0.006**(0.002)(0.002)(0.003)(0.003)0.001−0.002−0.040−0.051(0.015)(0.014)(0.032)(0.031)

0.006

0.006

0.005

0.006

(0.004)(0.004)(0.005)(0.005)−0.239***−0.270***−0.417***−0.412***(0.031)(0.033)(0.056)(0.056)NWC−0.528***−0.573***−0.719***−0.737***(0.030)(0.029)(0.036)(0.036)Debt

0.325***

0.345***

0.164**

0.176***

Q
Capex(0.031)(0.030)(0.068)(0.068)Firm FE

Firm FE

Firm FE

Firm FE

Observations

3,974

3,974

5,749

5,749

R2

0.484

0.513

0.378

0.392

This table shows the estimation results for cash and liquidity holdings by firms’ cash holdings. The dependent variables are
Cash and Liquidity. Columns (1) and (2) show the results for firms with a low cash ratio (i.e., financially constrained firms),
while columns (3) and (4) show the results for firms with a high cash ratio (i.e., financially unconstrained firms). All
estimations include a constant term and firm fixed effects. Definitions of variables are provided in Section 3.2. The estimations
employ standard errors that are clustered by firms and reported in parentheses. ***, **, and * denote statistical significance atthe 1%, 5%, and 10% level, respectively.

into two groups based on firms’ average cash ratio from the first to the fourth quarter of 2019.
We regard firms with an average cash ratio below the median as constrained and those above as
unconstrained.
Table 7 reports the results. Similar to the results in Table 6, the coefficients on EBITDA are
insignificant but those on the interaction term between EBITDA and Crisis I are positive andsignificant for constrained firms. For unconstrained firms, the coefficients on EBITDA are
significant but those on the interaction terms are insignificant. Hence, Hypothesis 1 applies more
to firms with a low cash ratio than those with a high cash ratio.
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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

The results for Sales Volatility show no substantial differences between low and high cash
ratio firms in terms of how their cash holdings respond to sales volatility. That is, for firms inboth subsamples, the coefficients on the interaction term between Sales Volatility and Crisis II
are positive and significant. Note, however, that the size of the coefficients is somewhat larger
for constrained than for unconstrained firms, suggesting that our Hypothesis 2 applies more to
low than high cash ratio firms.
4.2.4. High versus low leverage firms
Next, we focus on firms’ leverage. Highly levered firms are often regarded as financiallyconstrained. This is due to the debt overhang problem, which means that firms loaded with alarge amount of debt are unable to find new funding sources. We therefore divide the sample into
two groups based on firms’ leverage, which we define as the average ratio of a firm’s book valueof liabilities to the total assets from the first to the fourth quarter of 2019. We regard firms thatare above the median leverage as constrained and those that are below the median as
unconstrained.
Table 8 shows the results. Similar to the results in Tables 6 and 7, the coefficients on EBITDA
are insignificant for constrained firms, while they are significant for unconstrained firms. The
coefficients on the interaction terms between EBITDA and the Crisis dummies are positive andsignificant for high leverage firms, while none of the coefficients on the interaction terms are
significant for low leverage firms. Therefore, we can say that our Hypothesis 1 applies more tohigh leverage than low leverage firms.
However, the results for the interaction terms between Sales Volatility and the Crisis dummies
are not in line with our hypothesis that the cash holdings of financially constrained firms werelikely to have increased more during the crisis than those of unconstrained firms. Specifically, we
find that the coefficients on Sales Volatility*Crisis II are no larger for constrained firms than for
unconstrained firms.
4.2.5. Firms without versus firms with access to the bond market
In addition, we follow the literature focusing on bond market access in order to identify
financially constrained firms. Firms that have access to the bond market are more creditworthy
and face lower external financing costs (Almeida et al., 2004; Acharya et al., 2007). In contrast,
firms that do not have access to the bond market are unable to tap this important source forfinancing and consequently need to rely on banks for funding. We therefore regard these firms as
firms without bond market access and consider them to be more financially constrained thanfirms that have access to the bond market. To identify whether firms have access to the bondmarket, we employ the method introduced in Iwaki (2019). Specifically, we look at corporatebond (CB) and corporate paper (CP) issuance records as well as firms’ balance sheet information

35©Japan Society of Monetary Economics 2022

Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

Table 8. Estimation results for high versus low leverage firms
(1)

(2)

(A) High Leverage Firms (constrained)(3)

Table 9. Estimat(4)(B) Low Leverage Firms (unconstrained)

Dep. Var.

Cash

Liquidity

CashLiquidity

EBITDA

0.071

0.054

0.200***

0.182***(0.053)(0.056)(0.067)(0.064)0.223***

0.235***−0.0100.034(0.062)(0.063)(0.137)(0.120)0.199*

0.225**

0.004

0.058

(0.111)(0.113)(0.133)(0.116)

EBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size
Q
Capex
NWC
Debt

0.192

0.195

−0.184−0.206(0.192)(0.192)(0.199)(0.199)

0.048

0.048

−0.048−0.019(0.033)(0.033)(0.053)(0.052)0.070

0.075*

0.087*

0.116**(0.045)(0.045)(0.049)(0.048)

0.001

0.000

0.011***

0.008***(0.002)(0.002)(0.003)(0.003)0.010***

0.009***

0.012***

0.007**(0.002)(0.003)(0.004)(0.004)−0.016−0.015−0.060−0.091**(0.021)(0.021)(0.042)(0.037)

0.006

0.005

0.006

0.007

(0.004)(0.004)(0.007)(0.006)−0.286***−0.292***−0.437***−0.454***(0.042)(0.042)(0.064)(0.064)−0.569***−0.583***−0.804***−0.841***(0.045)(0.046)(0.035)(0.034)0.229***

0.226***

0.173

0.212*(0.047)(0.048)(0.108)(0.108)Firm FE

Firm FE

Firm FE

Firm FE

Observations

5,170

5,170

4,553

4,553

R2

0.386

0.388

0.402

0.438

(A
Dep. Var.
EBITDAEBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size
Q
Capex
NWC
Debt

Observations

R2
This table shows the estimation results for cash and liquidity holdings by firms’ leverage. The dependent variables are Cashand Liquidity. Columns (1) and (2) show the results for firms with a high leverage (i.e., financially constrained firms), whileThis table shows the estimation resu
columns (3) and (4) show the results for firms with a low leverage (i.e., financially unconstrained firms). All estimationsThe dependent variables are Cash a
include a constant term and firm fixed effects. Definitions of variables are provided in Section 3.2. The estimations employmarket (i.e., financially constrained
standard errors that are clustered by firms and reported in parentheses. ***, **, and * denote statistical significance at the 1%,market (i.e., financially unconstrain
5%, and 10% level, respectively.
variables are provided in Section 3
parentheses. ***, **, and * denote st

on CBs and CP outstanding spanning the period from 2000 to 2019. We regard firms that issued
CBs or CP or had a non-zero amount of CBs or CP outstanding on their balance sheet at least
once during the period as firms with access to the bond market. On the other hand, we define
firms that have not issued any bonds or CP as firms without access to the bond market.
Table 9 reports the estimation results for the two groups of firms. The coefficients on EBITDA
are positive and significant in all estimations. In contrast, the coefficients on the interaction term
between EBITDA and Crisis I are positive for firms without access to the bond market but not for
those with. It can therefore be said that our Hypothesis 1 applies more to firms without access tothe bond market than to firms with bond market access.
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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

Table 9. Estimation results for firms without versus firms with access to the bond market
(1)

(2)

(3)

(A) Firms without Bond Market Access(constrained)(4)(B) Firms with Bond Market
Access (unconstrained)

Dep. Var.

Cash

Liquidity

CashLiquidity

EBITDA

0.116**

0.093**

0.184*

0.208**(0.046)(0.045)(0.099)(0.096)0.129*

0.156**−0.183−0.193(0.075)(0.067)(0.118)(0.117)

EBITDA*Crisis I

0.110

0.145*−0.167−0.115(0.091)(0.085)(0.185)(0.178)0.004−0.003

0.186

0.185

(0.152)(0.153)(0.300)(0.310)−0.002

0.006

0.045

0.020

(0.030)(0.030)(0.070)(0.071)0.075**

0.090***

0.240***

0.211**(0.034)(0.034)(0.091)(0.095)

Crisis I

0.005***

0.004**

0.008***

0.006**(0.002)(0.002)(0.003)(0.003)

Crisis II

0.009***

0.007***

0.010**

0.008**EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II(0.002)(0.002)(0.004)(0.004)−0.033−0.041**0.101***

0.101***(0.020)(0.020)(0.025)(0.025)0.007

0.008*−0.011−0.018(0.005)(0.005)(0.013)(0.013)−0.350***−0.356***−0.317***−0.348***(0.040)(0.040)(0.050)(0.051)NWC−0.673***−0.696***−0.563***−0.611***(0.032)(0.031)(0.049)(0.045)Debt

0.217***

0.232***

0.325***

0.303***

Size
Q
Capex

(0.051)(0.051)(0.044)(0.039)Firm FE

Firm FE

Firm FE

Firm FE

Observations

8,405

8,405

1,318

1,318

R2

0.380

0.398

0.464

0.488

This table shows the estimation results for cash and liquidity holdings for firms with and without access to the bond market.
The dependent variables are Cash and Liquidity. Columns (1) and (2) show the results for firms without access to the bondmarket (i.e., financially constrained firms), while columns (3) and (4) show the results for firms with access to the bondmarket (i.e., financially unconstrained firms). All estimations include a constant term and firm fixed effects. Definitions ofvariables are provided in Section 3.2. The estimations employ standard errors that are clustered by firms and reported inparentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.

However, the results for the interaction terms between Sales Volatility and the Crisis dummies
are not in line with our hypothesis that the cash holdings of financially constrained firms werelikely to have increased more during the crisis than those of unconstrained firms. Morespecifically, the size of the coefficients on Sales Volatility*Crisis II for firms without access tothe bond market is substantially smaller than that for firms with bond market access.
4.2.6. Firms that have access to credit lines and firms that do not
Further, we focus on whether firms have access to credit lines and use this information to
identify constrained firms. Studies examining firm financing during the 2007–2008 global

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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

Table 10. Estimation results for firms that have access to credit lines and firms that do not
(1)

(2)

(3)

Table 11.

(A(A) Firms without Credit Lines(constrained)(B) Firms with Credit Lines(unconstrained)

Dep. Var.

Cash

Liquidity

CashLiquidity

Dep. Var:

EBITDA

0.104**

0.091**

0.202**

0.119

EBITDA(0.048)(0.046)(0.101)(0.127)0.099

0.128*

0.083

0.104

(0.080)(0.071)(0.124)(0.133)

EBITDA*Crisis I

0.101

0.132

−0.097−0.034(0.095)(0.088)(0.189)(0.195)

0.086

0.072

−0.305−0.284(0.174)(0.175)(0.230)(0.232)−0.014−0.013

0.046

0.054

(0.034)(0.033)(0.048)(0.050)0.056

0.075**

0.131

0.135

(0.034)(0.034)(0.118)(0.116)0.006***

0.005***

0.005*

0.003(0.002)(0.002)(0.003)(0.003)0.010***

0.008***

0.015**

0.012(0.002)(0.002)(0.007)(0.007)−0.017−0.026−0.047−0.043(0.020)(0.019)(0.033)(0.034)0.008

0.008*

0.002

0.001

(0.005)(0.005)(0.008)(0.008)−0.331***−0.339***−0.302***−0.298***(0.040)(0.041)(0.083)(0.086)NWC−0.687***−0.709***−0.601***−0.635***(0.031)(0.030)(0.040)(0.042)Debt

0.225***

0.238***

0.213**

0.209**EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size
Q
Capex

Estima(4)

EBITDA*Crisis I
EBITDA*Crisis II
Sales Volatility
Sales Volatility*Crisis I
Sales Volatility*Crisis II
Crisis I
Crisis II
Size
Q
Capex
NWC
Debt

(0.054)(0.054)(0.087)(0.089)Firm FE

Firm FE

Firm FE

Firm FE

Observations

8,338

8,338

1,385

1,385

Observations

R2

0.381

0.399

0.452

0.456

R2

This table shows the estimation results of cash and liquidity holdings for firms without and with access to credit lines. TheThis table shows the estimation res
dependent variables are Cash and Liquidity. Columns (1) and (2) show the results for firms with no access to credit lines (i.e.,and more likely to use the governme
financially constrained firms), while columns (3) and (4) show the results for firms with access to credit lines (i.e., financiallyto use the programs. To the former g
unconstrained firms). All estimations include a constant term and firm fixed effects. Definitions of variables are provided inof the industries belong to the latter.
Section 3.2. The estimations employ standard errors that are clustered by firms and reported in parentheses. ***, **, and *firms that belong to industries less d(3) and (4) show the results for firm
denote statistical significance at the 1%, 5%, and 10% level, respectively.
firms). All estimations include a con
estimations employ standard errors
significance at the 1%, 5%, and 10%

financial crisis found that large firms in the United States massively drew down credit lines (e.g.,
Ivashina and Scharfstein, 2010). This underlines the importance of credit lines as a means forfirms to access emergency funding during crisis times. In Japan, the use of credit lines has beenon the rise since legal reforms at the end of the 1990s. Therefore, to take credit lines into
account, we use information on credit lines as of the end of March 2020 to identify firms withcredit lines, which we regard as unconstrained firms, and those without, which we regard asconstrained firms7.
Table 10 shows the results for both groups of firms. For firms with no credit lines, the©Japan Society of Monetary Economics 2022

38

Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

coefficients on EBITDA are positive and significant in the estimations, while for firms that havecredit lines that they can draw down if necessary the coefficient is positive and significant in theestimation for Cash but insignificant in the estimation for Liquidity. Further, the coefficient on
the interaction term between EBITDA and Crisis II is positive and significant for firms without
credit lines in the Liquidity estimation but insignificant in all the other estimations. These results
provide another piece of evidence that Hypothesis 1 applies more to firms that are morefinancially constrained than to less constrained firms. In contrast, the coefficients on theinteraction terms between Sales Volatility and the Crisis dummies are insignificant in all
estimations, meaning that these estimations do not provide any insights with regard to
Hypothesis 2.
4.2.7. Firms that belong to industries intensively supported by the government and those that donotFinally, we examine whether the dependence on government subsidies or financial support
ease firms’ motivation for the precautionary saving. As the Bank of Japan’s Tankan Survey hasreported in September 2020, several industries such as services and transportations faced
substantially worse business conditions than other industries. Further, Tokyo Shoko Research(2020) reports that a number of firms in retail, transportation, and service industries applied forthe employment adjustment subsidies after the outbreak of the pandemic in 20208. These indicatethat firms in heavily affected industries tend to use government support more often than those inother industries. If the support by the government was financially sufficient, firms in thesedamaged industries will be less likely to hold cash precautionary. To identify firms that are likely
to receive government support programs, we refer the results of Bank of Japan Tankan Survey
and Tokyo Shoko Research and separate firms that belong to service, retail, and transportationindustries from firms in other industries. Then we implement estimations for each subsample offirms.
Table 11 shows results for the two subsamples. In line with our prediction, the presence of thegovernment support appears to reduce the firms’ motives for precautionary cash holdings. On the
one hand, coefficients on the interaction terms between EBITDA and Crisis I and between Sales
Volatility and Crisis II are positive and significant for firms in other industries. On the other
hand, these coefficients for firms in the damaged industries are not statistically different fromzero. These results are consistent with the conjecture that government business support programs
supplied sufficient amount of funds to firms in the damaged industries and reduced theirmotivation to hold cash for precautionary reasons.
In the above analyses, we used various definitions for financially constrained firms andexamined if our Hypotheses 1 and 2 apply more to such constrained firms than unconstrained

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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

Table 11.

Estimation results for firms that receive government subsidies and firms do not
(1)

(2)

(A) Firms in industries less dependent on
the government support (constrained)

(3)

(4)

(B) Firms in industries more dependent on
the government support (unconstrained)

Dep. Var:

Cash

Liquidity

CashLiquidity

EBITDA

0.156***

0.126**

0.057

0.069

(0.051)(0.051)(0.081)(0.079)0.104

0.159***

0.125

0.089

(0.073)(0.056)(0.161)(0.160)EBITDA*Crisis I
EBITDA*Crisis II

0.069

0.121

0.034

0.015

(0.093)(0.081)(0.178)(0.174)−0.004−0.013

0.088

0.073

(0.161)(0.162)(0.298)(0.296)

Sales Volatility*Crisis I−0.004

0.001

0.095

0.124

(0.028)(0.028)(0.089)(0.087)

Sales Volatility*Crisis II

0.088***

0.100***

0.087

0.121

(0.030)(0.030)(0.097)(0.097)0.006***

0.004***−0.002−0.003(0.002)(0.001)(0.004)(0.004)0.009***

0.006***

0.012**

0.012**(0.002)(0.002)(0.005)(0.006)−0.077***−0.091***

0.045

0.048

(0.025)(0.023)(0.044)(0.044)

0.004

0.004

0.011

0.014

(0.004)(0.003)(0.011)(0.010)−0.309***−0.316***−0.424***−0.437***(0.036)(0.036)(0.089)(0.090)NWC−0.701***−0.733***−0.589***−0.585***(0.030)(0.029)(0.072)(0.073)Debt

0.311***

0.332***

0.093

0.086

Sales Volatility

Crisis I
Crisis II
Size
Q
Capex

(0.038)(0.036)(0.111)(0.112)

Firm FE

Firm FE

Firm FE

Firm FE

Observations

7,930

7,930

1,793

1,793

R2

0.472

0.499

0.210

0.218

This table shows the estimation results of cash and liquidity holdings for firms in industries that are more severely damage
and more likely to use the government support programs and those in industries that are less severely damaged and less likely
to use the programs. To the former group belong firms in the services, retail, and transportation industries and firms in the restof the industries belong to the latter. The dependent variables are Cash and Liquidity. Columns (1) and (2) show the results forfirms that belong to industries less dependent on the government support (i.e., financially constrained firms), while columns(3) and (4) show the results for firms that belong to industries more dependent on the support (i.e., financially unconstrained
firms). All estimations include a constant term and firm fixed effects. Definitions of variables are provided in Section 3.2. The
estimations employ standard errors that are clustered by firms and reported in parentheses. ***, **, and * denote statisticalsignificance at the 1%, 5%, and 10% level, respectively.

firms. Hypothesis 1, which predicts a substantial increase in the cash flow sensitivity of cash
during the crisis, holds more for constrained firms of all types than for their unconstrained
counterparts. For Hypothesis 2, which predicts a substantial increase in the response of cash
holdings to volatility in cash flows for constrained firms, our findings are less clear-cut. The
hypothesis holds for some definitions of constrained firms (namely, firms with a low payout ratio
and firms with a low cash ratio) but not for others.

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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

5. Concluding Remarks
COVID-19 started spreading around the world at the beginning of 2020 and has caused severe
damage to the Japanese economy. This study focused on the firm sector in Japan and examined
how firms’ cash holdings have been affected during the crisis. We found the following: (1)corporate cash holdings have increased rather than decreased since the onset of the crisis; (2) anincrease in firms’ cash flow had a positive impact on their cash holdings during normal times,
and the positive cash flow sensitivity of cash was more pronounced during the first three monthsof the crisis; (3) firms facing higher sales volatility than other firms held more cash as the crisis
unfolded; and (4) the increase in the cash flow sensitivity of cash during the crisis was larger for
financially constrained firms.
Overall, the COVID-19 crisis has had a substantial impact on corporate cash management andthe results are consistent with the precautionary motive theory for cash holdings. However, thepresent study only represents a first attempt at examining these issues using the COVID-19 crisis
as an experiment. The observation period in our analysis covers only the first six months of 2020and our sample consists only of listed firms. A task for the future, therefore, is to extend theobservation period as more data become available and to expand the analysis to smaller, unlisted
firms.

NOTES
1.

In the literature on corporate finance, there are other motives such as the tax motive and theagency motive. See Bates et al. (2009), who describe these motives in more detail.

2.

There are studies that focus on the difference between the Global Financial Crisis and thecurrent COVID-19 crisis to study how firm financing were affected by each of these crises.
See Fahlenbrach et al. (2021) for example.

3.

While there are no studies so far focusing on the impact of the pandemic on precautionary
cash holdings, there already are a considerable number of studies on the impact of the
COVID-19 pandemic on various other aspects of corporate finance. For example, Francis etal. (2020) examine the impact on firms’ capital structure across 31 countries, Acharya andSteffen (2020) examine firm financing through the corporate bond market and existing
credit lines, Li et al. (2020) analyze firms’ demand for bank liquidity and banks’ capacity tosupply the liquidity in the first four months of 2020, and De Vito and Gomez (2020)
examine how the COVID-19 health crisis could affect the liquidity of listed firms across 26
countries. In Japan, Aono and Hori (2021) measure the value of corporate cash holdings
during the COVID-19 outbreak.

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Japanese Journal of Monetary and Financial Economics Vol. 10, pp. 19-43, 2022

4.

See, for example, the result of the Bank of Japan’s September 2020 Tankan Survey for theheterogeneous impact on business conditions across industries.

5.

Other uncertainty measures include the economic surprise index and the market volatility
index. For developments in each of these indices, see Shinohara et al. (2020).

6.

The declaration asked people to refrain from going out, but it was not legally enforceable.
In addition, the Japanese government required elementary school to close temporarily on
February 27. See, e.g., Watanabe and Yabu (2020) for more details.

7.

Information on credit lines is taken from Nikkei NEEDS Financial QUEST. However, sincesome of the necessary information is missing in the database, we supplement it withinformation from another database called “eol” provided by PRONEXUS Incorporated.
Meanwhile, the reason we focus on information at year-end regarding firms’ credit lines isthat firms usually disclose credit line information on an annual rather than a quarterly basis.

8.

See, https://www.tsr-net.co.jp/news/analysis/20201225_01.html.

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