IMES DISCUSSION PAPER SERIESA Global Look into Corporate Cashafter the Global Financial Crisis

Kei-Ichiro Inaba
Discussion Paper No. 2020-E-7

INSTITUTE FOR MONETARY AND ECONOMIC STUDIESBANK OF JAPAN
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IMES Discussion Paper Series 2020-E-7
June 2020

A Global Look into Corporate Cashafter the Global Financial Crisis
Kei-Ichiro Inaba*
Abstract
This article investigates the determinants of cash holdings by publicly-traded firms
for 20 advanced and emerging countries over the last decade, with a focus on ratiosof the firms’ aggregate cash to their total assets. Panel-data regressions find thathigher cash ratios were associated with fewer non-cash current assets, smaller costsof carry, larger contemporaneous cash inflows, fewer interest-bearing liabilities,
greater expected investment opportunities, including research and development
projects, greater uncertainty, and the state of corporate governance. Regarding thelast result, higher cash ratios were associated with managers with worse ethicalbehavior, lower accountability to investors and board members, weaker investor
protection, harsher auditing and reporting standards, and greater potential to faceholdup problems by lending banks. The agency motive was greater than the
precautionary and transaction-costs motives in terms of marginal impact while being
limited in terms of explanatory power over total variation in the cash ratios.
Keywords: Corporate finance; Cash holdings; Precautionary saving motive;
Corporate governance; Agency problem.
JEL classification: C23, E41, G32, G34, H32* Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan(E-mail: keiichirou.inaba@boj.or.jp)
The author is grateful to Kaoru Hosono, Daisuke Miyakawa, Tatsuyoshi Okimoto, Toshitaka
Sekine, Toshiaki Matsui, Atsushi Tanida, Jan Toporowski, Alexis Stenfors, Etsuro Shioji, andseminar participants in the 7th Paris Financial Management Conference 2019 and the 13th
International Conference on Computational and Financial Econometrics for their helpfulcomments on earlier drafts. Views expressed in the paper are those of the author and do notnecessarily reflect those of the Bank of Japan. Any possible errors in this article are the exclusive
responsibility of the author.

1. Introduction
Why do firms hold more cash than they used to? This question has provided financial
economists with suitable opportunities to develop theories and test their predictions. To add tothe literature, I attempt to provide global evidence for the relevance of corporate governance tocorporate cash holdings since the subprime loan crisis in 2007 and the subsequent collapse of
Lehman Brothers in 2008. To do so, I improve the coverage of sample countries and years
compared to existing studies, as well as expand the variety of factors reflecting the state ofcorporate governance, while abstracting from firm-level heterogeneity by focusing on firms’
cash-to-assets ratios (CARs) at an aggregate level.

This abstraction enables me to use

institutional indicators for country-specific levels of corporate governance to examine theirrelevance to country-specific CARs from a global perspective.
Managers hoard money in preparation of financing needs in the future insofar as they havepoor access to bank loans and the corporate bond market. Such a precautionary motive is causedby asymmetric information (Myers and Majluf, 1984; Almeida, Campello, and Weisbach,2004). Under asymmetric information, publicly-traded companies should have lower CARs
because, presumably, they are informationally transparent and have better access to outside
debt. As publicly-traded companies tend to be large, they may enjoy economies of scale inmanaging transaction costs that are attributable to market imperfections and involved inconverting non-cash assets into cash for payment; that is, publicly-traded companies are in anadvantageous position of saving cash reserves (Miller and Orr, 1966; Mulligan, 1997).
Although these companies can be supposed not to have strong precautionary and transactioncosts motives to hold cash, they have increased their CARs in recent years in a number ofcountries, including the three largest economies – the United States (U.S.), China, and Japan.
Furthermore, they continued to do so even long after the turmoil and subsequent depression
caused by the global financial crisis ended.
Burgeoning corporate cash balances have also drawn much attention from the popular press
as well as skepticism from workers, shareholders, and policy makers who regard a large part ofthese cash holdings as inefficient or idle, with negative implications for the national macro
economy through excessive money demand, under-investment, and low payments toshareholders and workers. In fact, how to encourage large companies to spend their growing
profits on increasing business investments and wage payments rather than stockpiling them as

1cash reserves was one of the central campaign issues in the Japanese general election of October
2017.1 It is thus obviously important to understand the drivers of aggregate cash holdings.
I take a global look into the importance of this phenomenon by investigating the driving
forces behind publicly-traded companies’ CARs for 20 advanced and emerging countries overthe period 2007–2017. The focus on CARs at an aggregate level across countries differentiates
this investigation from previous studies, which generally share an approach of using firm-level
data for a specific country, especially the U.S.
A recent seminal study using this approach is Bates, Kahle, and Stulz (2009), who analyze
individual listed firms’ CARs in the U.S. over the period 1980–2006. They find that theincrease in CARs is associated with a decrease in inventories and receivables, an increase in
cash flow volatility, and an increased need to execute research and development (R&D) projects.
The first association represents substitution between cash and non-cash current assets while thelast two are in line with the above-mentioned precautionary motive. They argue that this motive
has an important role in explaining the increase in CARs. In spite of Jensen’s (1986) argumentthat the level of cash holdings can be affected by the conflict between shareholders andmanagers, they do not investigate directly whether or not proxies for the agency motive are
related to CARs.2
A similar approach is employed for Japanese firm-level CARs by Hori, Ando, and Saito(2010) and Hosono, Miyakawa, and Takizawa (2019). Both of these studies place little focus
on the agency motive.

Hosono, Miyakawa, and Takizawa (2019), specifically, analyze

Japanese firm-level data covering around 400,000 firms, including unlisted small companies,
over the period 1994–2016.3 They report that firms with better financial positions and business1

For example, Asahi Shimbun, one of the five nation-wide newspapers in Japan, argued in an editorial on 7 September
2017 that 210 trillion yen of cash held by Japanese companies as of March 2017 needed to be reduced by raising wages,
so as to promote private consumption. In the 2017 election, the Party of Hope, a newly-created opposition party,
promoted a similar way of thinking and promised to tax firms’ retained profits. The party captured 50 seats and became
the second largest opposition party.
2
Bates, Kahle, and Stulz (2009) conduct three indirect tests to evaluate whether the agency motive can explain increases
in CARs in their sample, and conclude that there is no consistent evidence that the agency motive affects CARs. Oneof their tests abstracts from firm-level heterogeneity and shows that the average CAR tends to be smaller for companies
with greater managerial entrenchment, contrary to what the agency motive suggests.
3
Hori, Ando, and Saito (2010) analyze firm-level CARs of Japanese listed firms over the period 1982–2005. Over theperiods 1986–1990 and 2001–2006, when CARs at an aggregate level were on an upward trend, stable determinants of
CARs for both manufacturing and non-manufacturing firms included the size of receivables and the size of investments.
It appears that, during these periods, cash was a substitute for the former while being the cheapest funding source for thelatter.

2

conditions tended to have larger cash holdings while the precautionary motive was also
effective, and stress that motives behind corporate cash holdings are heterogeneous among
sample companies.
From the perspective of investigating the determinants of time-series CARs at an aggregate
level, this article is related to Graham and Leary (G&L, 2018). They do so for U.S. listedcompanies over the period 1926–2014. They find that listed firms’ CARs were explained wellby (i) characteristics of corporate balance sheets, (ii) macroeconomic proxies for investment
opportunities, and (iii) the size of contemporaneous cash inflows and investment expenditures,the last of which had the most explanatory power. 4 Their findings are consistent with the
precautionary and transaction-costs motives to hold cash. Nevertheless, they do not consider
the relevance of the agency motive for U.S. CARs at an aggregate level.
From the perspective of investigating the relevance of the agency motive for CARs, thisarticle is related to Harford, Mansi, and Maxwell (2008), Dittmar, Mahrt-Smith, and Servaes(2003), and Ferreira and Vilela (2004). All of these studies analyze firm-level CARs. They
tend to have limited coverage of sample countries and years as well as only a few corporate
governance factors, possibly because it is practically difficult to collect data and compute thesame indicators for the state of corporate governance of a huge number of individual companies.
Harford, Mansi, and Maxwell (2008) examine the relevance of the agency motive for U.S. firmlevel CARs only and for just seven years at irregular intervals over the period 1990–2004. Theyfind that U.S. listed firms with weaker corporate governance tended to have larger cash reserves,by considering two corporate governance factors: insider ownership and shareholder rights.
Dittmar, Mahrt-Smith, and Servaes (2003) examine the agency motive behind corporate cash
holdings at a global level using firm-level data covering listed companies in no less than 45
countries. However, they do so for 1998 only. They consider two corporate governance factors:
the strength of shareholder protection and the depth of indirect debt finance. They agree withthe common conjecture that investors with less shareholder protection are less able to force
managers to disgorge excessive cash. Ferreira and Vilela (2004) come to a similar conclusion
by analyzing firm-level data in European Economic and Monetary Union countries over theperiod 1987–2000.
4

They also find that the large increase in CARs since 2000 reflected increasing foreign cash holdings in order to avoid
repatriation taxes.

3Abstracting from firm-level heterogeneity by focusing on CARs at an aggregate level
enables me to look at 20 countries in the last ten years and to use six types of institutional
indicators for country-specific levels of corporate governance. To my knowledge, this article
is the first to provide global evidence on the relevance of the agency motive for country-specific
CARs by conducting international panel-data regressions.
In line with the above-mentioned existing studies, I bring forward global evidence to showthat the levels of country-specific CARs have been associated with not only the precautionary
and transaction-costs motives, but also with the agency motive. On the first two motives, higher
national CARs have been associated over the last decade with (i) fewer non-cash current assets,(ii) a smaller cost of carry, (iii) larger contemporaneous cash inflows, (iv) fewer interest-bearing
liabilities, (v) greater expected investment opportunities, and (vi) greater uncertainty. Larger
contemporaneous cash inflows relate to the transaction-costs motive, fewer interest-bearing
liabilities can relate to both the precautionary and agency motives, and greater expected
investment opportunities and greater uncertainty relate to the precautionary motive.
My findings also support G&L’s (2018) argument at a global level – that macroeconomic
indicators are more important explanatory variables than firm-characteristic indicators for U.S.
CARs. Important macroeconomic indicators are related to the level of productivity and thesizes of contemporaneous corporate profits and investments in the case of G&L (2018) while
being related to the cost of carry, expenditure on R&D projects, business climate, and stock
return volatility in my case. In both cases, these indicators represent either the precautionary
motive or the transaction-costs motive.
My new findings on the relevance of the agency motive for national CARs are the following.
First, the impact of this motive was greater than the other two while having limited explanatory
power over the total variation in national CARs. Second, I use six indicators for corporate
governance to illustrate the global relevance of the agency motive for national CARs. Higher
CARs are associated with managers with (i) worse business ethics, (ii) lower accountability toinvestors and board members, (iii) weaker investor protection, (iv) harsher auditing andreporting standards, and (v) greater potential of facing holdup problems by lending banks. Allof these associations, except for the third, refer to corporate governance factors which theliterature has not examined very much yet in relation to CARs. Last, the agency motive is

4interacted with the other motives. That is, the first three factors make managers less sensitive
to the precautionary and transaction-costs motives.
I also report that national CARs were on an upward trend in a majority of the sample
countries over the last decade. The U.S. CAR reached 15–20 per cent, a level also achieved by
Chinese and Japanese CARs. My international panel-data regression shows that all of the
motives mentioned above explained the annual changes in national CARs, and that the mostdominant driver was the reduction in interest-bearing liabilities over total assets. What this
implies is unclear since it can be related to both the precautionary and agency motives.
This article proceeds as follows. Section 2 shows how large the cash holdings of publiclytraded companies have been in the 20 sample countries over the last decade, after giving a brief
account of the data used. Section 3 constructs a panel-data regression model. Section 4 shows
the result of estimating the baseline model, checks its robustness in four ways, and makes four
extensions. Section 5 concludes with future research suggestions.

2. Data
I use an unbalanced panel dataset covering 20 advanced and emerging countries.

Inalphabetical order, these countries are Australia (AUS), Canada (CAN), China (CHN), Finland(FIN), France (FRA), Germany (DEU), Hong Kong (HKG), Indonesia (IDN), Ireland (IRL),
Italy (ITA), Japan (JPN), Norway (NOR), Portugal (PRT), South Africa (ZAF), South Korea(KOR), Spain (ESP), Sweden (SWE), Thailand (THA), the United Kingdom (GBR), and theUnited States (USA).5 The frequency of data is annual.
My dataset can be grouped into two. The first group is composed of country-specificindicators for the state of corporate finance. In addition to relevant macroeconomic variables,
I gather corporate financial indicators for listed companies, which are at an aggregate level andscaled by total assets. I calculate these indicators from original indicators calculated byBloomberg with respect to national stock market indices and their subcomponent indices byindustry type. According to Bloomberg, stock market indices with such sub-indices exist onlyin the 20 sample countries used in this study. Following convention in the literature, I do notconsider banking and finance, real estate, and regulated businesses, the last of which includes
The sum of these sample countries’ Gross Domestic Products (GDPs) accounted for 71 per cent of world GDP quoted
in current U.S. dollars in 2017.
55

utilities and telecommunication industries. I also exclude industry sub-indices with limited
historical data or with disconnected data. I synthesize each country’s market-wide stock index
consisting of every other sub-index. I construct indicators for corporate finance by dividing“per-share” values by “per-share” total assets for all of the sub-indices. A specific corporate
financial indicator (e.g., CAR) for a country’s synthesized stock index is defined as the market
capitalization weighted average of member industries’ indicators (e.g., their industry-level
CARs). Appendix A shows the names of national market indices used, the numbers of industry
sub-indices by country, and the number of companies aggregated, the last of which increased
from 3,827 in 2003 to 5,604 in 2018. Appendix B explains all corporate finance indicators.
The other group consists of country-specific institutional indicators of the state of corporate
governance, all of which are detailed in Appendix B. The factors considered include the levelof managers’ ethical behavior, the accountability of managers to investors and board members,
the strength of investor protection, the strength of auditing and reporting standards, and thedevelopment of debt finance. All factors, except for the last one, are based upon grades madethrough corporate questionnaire studies or expert appraisals, taken from Trade andCompetitiveness Data 360, managed by the World Bank Group. The grades take specific ranges
in which the minimum and maximum mean the worst and best status of corporate governance.
I extend the selection of corporate governance indicators by Dittmar, Mahrt-Smith, and Servaes(2003) who consider only two factors: the strength of investor protection and the developmentof banking. To gauge the development of debt finance, I use commonly used proxies forcountries’ financial depth, taken from databases of international organizations.
Figure 1 plots the aggregate-level CARs defined as the ratio of aggregate cash and cash
equivalents to total assets for publicly-traded non-financial lightly-regulated companies in 20
countries over the period 2003–2018. Four observations arise. First, the level of CARs differs
by country. Second, the time-series variation looks broadly similar across countries; in many
countries, CARs dropped in 2007 or 2008 and increased thereafter. This can be said to a global
average CAR – the average of national CARs. Third, the global average CAR and a majorityof national CARs seem to have upward trends. National CARs appear to have no trends for
CAN, IDN, and ZAF while having a downward trend for SWE. Last, national CARs were very
high over the last five years in the U.S., China, and Japan, reaching 15–20 per cent.

63. A Model of Cash Holdings
To construct a panel-data regression model, I refer to G&L (2018) to control for the state ofcorporate finance while adding a number of regressors to control for the state of corporate
governance. My baseline regression equation is:
CARi,τ = h0 + h1TT + h2TT2 + h3TT3 + IEi + εi,τ+ h4Unce1i,τ+ h5INVE_Op1i,τ + h6RTAi,τ+ h7CF_Avi,τ + h8INVE_Avi,τ
+ h9IBLi,τ + h10CAi,τ–1 + h11CLi,τ + h12CFi,τ + h13INVEi,τ
+ h14CC1i,τ + h15CC2i,τ + h16Unce2i,τ + h17Unce3i,τ + h18INVE_Op2i,τ + h19INVE_Op3i,τ
+ h20CG1i,τ + h21CG2i,τ + h22CG3i,τ + h23CG4i,τ + h24CG5i,τ + h25CG6i,τ,(1)

where CAR is the national CAR defined above, i stands for an individual sample country, τ
stands for the year, hs are coefficients, TT is a time-trend, IE stands for country i’s individual
effect, and ε is the residual. Notes are as follows. First, following G&L (2018), I add asregressors TT, TT2, and TT3 so as to hedge the risk of a spurious regression generated by thefact that aggregate level-variables tend to be persistent over time. Second, IE is heterogeneity
attributable to omitted variables and unobservable factors.6 Third, depending on the nature of
IE, Eq. (1) can take one of three potential forms: a pooling model represented by dropping IEfrom Eq. (1); a fixed-effects model, in which IE is a country-specific constant; and, a randomeffects model, in which IE is a country-specific stochastic variable.7 Last, I ignore time effects
common to all sample countries (is) in individual sample years (τs) so as to both preserve space
and improve the degree of freedom. I will return to verification of this point later.
I follow G&L (2018) in selecting the 16 regressors in the second, third, and fourth lines ofEq. (1). These are related mainly to the precautionary and transaction-costs motives to holdcash. The regressors that are stock variables are year-end values.
The 10 regressors in the second and third lines are corporate financial indicators, all of which
are ratios to total assets, except for RTA. Unce1 is a proxy for the uncertainty of future financing
needs, represented by the volatility of cash flows, and is calculated as the standard deviation of

6

One omitted variable could be country-specific industrial structure; that is, the degree of dominance of industries thatneed to hoard large amounts of cash differs by country. Such differences are assumed to be included in IE.
7When either a fixed-effects model or a random-effects model is selected, four potential characteristics of the residuals(ε) need to be addressed to obtain asymptotically consistent estimates (ĥs). These four characteristics are cross-section
heteroscedasticity, period heteroscedasticity, contemporaneously correlation, and serial correlation. These can reduce
the reliability of t-tests of the estimates.

7

cash flows for the three years from τ–3 to τ–1. Cash flows are calculated as net income minus
expected dividends, where expected dividends at τ are assumed to be the same as those at τ–1.
Because volatile cash flows may increase the precautionary motive, then estimate ĥ4 should bepositive, as G&L (2018) find for U.S. firms’ aggregate-level CARs.
INVE_Op1 is the so-called average q, calculated by dividing the market value of assets by
the book value of total assets. The market value of assets is the sum of market capitalizationand the book value of total liabilities. The average q is a common representation of investment
opportunities evaluated in the stock market. If the precautionary motive is strong, then estimate
ĥ5 should be positive, as in G&L (2018).
RTA is the per-company average of total real assets. I adjust for domestic inflation andconvert the figures to U.S. dollars for international comparability. If there are economies ofscale in cash management, as predicted by the transaction-costs motive, then estimate ĥ6 shouldbe negative, as in G&L (2018).
CF_Av is the average of cash flow for the three years from τ–3 to τ–1. When one regards it
as a projection of cash flows for τ, a larger value signals a weaker precautionary motive;
therefore, estimate ĥ7 should be negative. In G&L (2018), however, an estimate for CF_Av is
statistically significantly positive, implying an intuitive result: profitable companies are cashrich.
INVE_Av is the average of investment expenditures for the three years from τ–3 to τ–1.
When one regards it as a projection of investment expenditures for τ, then a larger value signals
a greater precautionary motive; therefore, estimate ĥ8 should be positive. In G&L (2018),
however, estimate ĥ8 is statistically insignificant. A negative ĥ8, instead, would imply thatmanagers who are keen to make business investments hold less cash. This sounds reasonable
not only because internal funds are disposable for the managers but also because converting
non-cash assets into cash to cover investment expenditures involves transaction costs.
IBL is interest-bearing liabilities. As argued by Ferreira and Vilea (2004), more highly
leveraged firms are likely to increase their cash reserves because they run greater risks of default
due to the pressure caused by rigid amortization plans on firms’ cash management. If this is
the case, then estimate ĥ9 should be positive. G&L (2018), alternatively, infer that firms with
better access to outside debt tend to have less precautionary motive to hold cash. If thisreasoning is the case, then estimate ĥ9 should be negative. Notably, such a negative association8

can be related to the agency motive too. The more indebted a firm is, the more efficiently
managers may conduct cash management in order to raise funds for repayment (Ozkan andOzkan, 2004). By splitting IBL into short-term and long-term debt in terms of remaining
maturity, G&L (2018) find that the estimate is positive for long-term debt but negative for shortterm debt. Data availability does not allow me to split IBL into these two components.8
CA is current assets other than cash. If cash holdings and other current assets look similar
to managers, then there will be a high degree of substitution between the two, suggesting that
estimate ĥ10 should be negative, as in G&L (2018). On the other hand, by definition, cash is
unique in that it is perfectly liquid and free of transaction costs. When managers value these
aspects of cash, they may convert part of increasing non-cash current assets into cash. Such a
positive association between the two appears to be in line with the transaction-costs motive.
Meanwhile, to hedge the risk of endogeneity, I use CA at time τ–1.
CL is current liabilities.

This variable controls for a potential maturity-matching patternthat offsets the change in current liabilities by changing the level of cash reserves. If this is thecase, then estimate ĥ11 will be positive, as in G&L (2018).
CF and INVE are contemporaneous cash flows and investment expenditures, respectively.
These can drive variations in cash reserves. Managers allow their CARs to change over timewithin an optimal range when they find it costly to adjust cash balances (Bolton, Chen, andWang, 2011). Such a straightforward accumulation of profits and use of cash holdings would
be based upon the transaction-costs motive. If that is the case, then estimated coefficients ĥ12and ĥ13 will be positive and negative, respectively, as in G&L (2018).
The six regressors in the fourth line of Eq. (1) are macroeconomic variables. CC1 and CC2
control for the cost of holding cash. G&L (2018) control for this cost by using as regressors (i)ex-post real short-term interest rates (hypothetical interest income in real terms accruing fromcash reserves) and (ii) corporate tax costs. They find these factors to be insignificant when in
the case where they control for CF and INVE separately. Because this finding for (i) above isinconsistent with other empirical studies (e.g., Azar, Kagy, and Schmalz, 2016), I improve theanalysis by taking into account the opportunity cost of lost investment. CC1 is the difference

8

I also abstract from another subtle issue for managers: how should managers select a specific type of debt among bankloans and corporate bonds, both of which have various trade terms? As reported by Rauh and Sufi (2010), the selectionof debt type can be complicated by a variety of factors, including simultaneous decisions and credit ratings.

9

between the expected rate of return minus the ex-post real one-year interest rate. The expected
rate of return is measured by the 10-year average annual real GDP growth rate. CC2 is thecorporate tax rate. As larger CCs mean that hoarding cash is costlier, their estimates (ĥ14 andĥ15) should be negative. Such a negative association appears to be reasonable, regardless of the
precautionary, transaction-costs, and agency motives to hold cash.
Unce2 and Unce3 are proxies for the uncertainty of future financing needs at the macro level.
Unce2 is the realized volatility of stock market returns – the standard deviation of weekly
percentage changes in the price of country i’s national stock market index during year τ. G&L(2018) do not find stock price volatility a significant factor. I add Unce3, a World Bank
indicator for countries’ political stability, as an additional proxy for uncertainty. Because I
interpret a larger CC1 and a smaller CC2 as implying larger uncertainty, their estimates (ĥ16and ĥ17) should be positive and negative, respectively, based upon the precautionary motive.
INVE_Op2 and INVE_Op3 are proxies for investment opportunities at the macro level.
INVE_Op2 is the expected one-year ahead GDP growth rate. As argued by G&L (2018),
business-cycle fluctuations may affect the value of investment opportunities. INVE_Op3 is theratio of R&D expenditures to nominal GDP. G&L (2018) do not explicitly control for
expenditure on R&D projects, despite the findings by previous studies, such as Almeida andCampello (2007) and Bates, Kahle, and Stulz (2009).

These studies suggest that the

precautionary motive should be acute for financing R&D projects since intangible R&D assets
can often not be used as collateral. Because I interpret a larger INVE_Op2 and a larger
INVE_Op3 as implying greater investment opportunities, their estimates (ĥ18 and ĥ19) should bepositive, based upon the precautionary motive.
The six regressors in the fifth line of Eq. (1), or CG1–CG6, are country-specific institutional
factors representing the state of corporate governance, which is related to the agency motive.
Because managerial discretion is often accompanied by agency costs, managers’ incentives will
not necessarily be aligned with shareholders’ incentives (Jensen, 1986). Managers with weaker
governance may more easily keep idle money balances for their future private consumption orneglect to spend funds on good investment opportunities, at the expense of shareholders’ wealth.
CG1–CG6 stand for the following: the level of managers’ ethical behavior (CG1), theaccountability of managers to investors and board members (CG2), the strength of investor

10

protection (CG3), the strength of auditing and reporting standards (CG4), the depth of indirect
debt finance (CG5), and the depth of direct debt finance (CG6).
Because it is reasonable and in line with the literature to interpret larger CG1–CG3 as
implying better governance, their estimates (ĥ20, ĥ21, and ĥ22) should be negative.9 Specifically,in the case of CG3, Dittmar, Mahrt-Smith, and Servaes (2003) argue that well-governedmanagers will work for the sake of shareholders without reducing dividend payments to them.
The sign of CG4 appears to reflect inefficient behavior that managers with weak governanceengage in with respect to their cash reserves. If laxer auditing and reporting standards allow
them to shirk – neglecting business investments and skimping on payments to shareholders andworkers – its estimate (ĥ23) will be negative. Alternatively, if laxer auditing and reporting
standards encourage them to use cash reserves or raise off-the-book funds for their own private
consumption, its estimate (ĥ23) will be positive. Thus, the impact of CG4 could be ambivalent,
depending on the relative dominance of the shirking and private consumption of managers.
CG5 and CG6 are indicators widely used to represent the stage of countries’ financial
development with respect to debt finance. CG5 represents the development of the banking
sector, or the outstanding amount of bank loans to the private sector over nominal GDP. CG6
represents the development of the corporate bond market, or the outstanding amount ofcorporate bonds over nominal GDP. As long as IBL separately controls for the level of debt
issued by listed companies, CG5 and CG6 make it possible to specify how differences in thelevel of debt finance development across countries and over time can influence the level ofnational CARs.10 In particular, I would like to home in on how differently two managers in
different countries with different CG5s but the same level of IBL will behave.

It may be appropriate not to handle CG1 (the level of managers’ ethical behavior) on the same footing as CG2 –CG4. The latter three concern the relationship between shareholders (principals) and managers (agents), in which
it is necessary for shareholders to set up devices that discourage their managers from shirking and incentivize them
to work efficiently for shareholders, such as incentive structures, monitoring mechanisms, and governing structures(Eisenhardt, 1989). Managers who these devices incentivize adequately are likely to behave ethically with respect
to their shareholders. Still, CG1 can be interpreted more broadly because business ethics would be essential for
managers to obtain necessary resources and stakeholder support so as to stay in business (Freeman and McVea,
2005). Presumably, to obtain these, a firm may hold cash less as a consequence of making more payments not
only to shareholders but also to workers and trading partners as well as paying additional costs for integratingsocial and environment issues in its business model and internal controls.
10
IBL may be positively correlated with CG5 and CG6. To investigate the risk of multicollinearity caused by this,I calculate the variance-inflation factors (VIFs) between IBL and CG5 and between IBL and CG6 over the sample
period for all sample countries. VIF is defined as 1/{1 − (correlation coefficients) 2}. Suffice it here to report that
911

These differences can relate to the modality of conflict between mangers and their lendingbanks. In relationship banking, a bank generally monitors informationally opaque borrowers
to gather and process private information about them. This activity has the potential to help theborrowers economize their cash holdings through two channels. One is to reduce monitoring
costs to be added to loan rates of interest (James, 1987). The other is to improve theirgovernance; that is, bank loans are useful for keeping borrowers of lower observable quality
from causing post-lending moral hazard (Diamond, 1991; Boot and Thaker, 1997). These
positive consequences should be accompanied by a negative estimated coefficient on CG5 (ĥ24).
Monitoring activity, on the other hand, may enable the monitoring bank to extract
informational rents from the borrower, or to hold up the borrower (Mayer, 1988; Petersen andRajan, 1995; Rajan, 1992; von Thadden, 2004). According to this line of thought, managers
can be viewed as running the risk of being forced by their own informed banks to borrow
excessively and at unfavorable rates in a country in which financing sources other than banks
are few. Managers who invest in cash would be a particularly safe debtor for an informed
creditor who is basically risk-averse (Yosha, 1995). Such information-rent extraction by banks
should be accompanied by a positive estimated coefficient on CG5 (ĥ24).11
In a bank-based financial system, managers might prefer issuing bonds to save on monitoring
costs as well as to avoid the holdup risk. If such a preference for unmonitored debt accompanies
managers’ reducing cash reserves in a country with a large corporate bond market, then estimate
ĥ25 should be negative.
Finally, all of the dependent and independent variables other than the constant and timetrend terms are standardized by dividing their deviations by their standard deviations. This
allows one to compare their estimated marginal impacts, even though they are of different units.

4. Driving Forces behind Cash Holdings
4.1 Baseline Estimation Result

all of the VIFs are much smaller than 10, the criterion proposed by Snee and Marquardt (1984) as defining anegligible risk of multicollinearity.
11
Japanese banks were accused of such information rent extraction (e.g., Wu and Yao, 2012; Inaba, 2016).

12

As detailed in Table I, I follow the conventional procedure and select the fixed-effects model.12
The result of estimating Eq. (1) with the weighted generalized least squares (weighted GLS)
method is shown in the middle column of the table and summarized below:
CAR = –1.12 + 0.60***TT – 0.09***TT2 + 0.01***TT3+ 0.11Unce1 + 0.22**INVE_Op1 – 0.04RTA + 0.07CF_Av – 0.09INVE_Av– 0.53***IBL – 0.33***CA–1 – 0.02CL + 0.05*CF – 0.06INVE – 0.14***CC1 + 0.06CC2+ 0.09***Unce2 – 0.07Unce3 + 0.14*INVE_Op2 + 0.45***INVE_Op3– 0.67***CG1 – 0.18**CG2 – 0.25***CG3 + 0.46***CG4 + 0.27**CG5 – 0.12CG6(# of observations = 153, Radj2 = 0.94),
where the superscripts ***, **, and * stand for one per cent, five per cent, and ten per centlevels of statistical significance, respectively, and the p-values used are the averages of twocases in which I adjust for ε’s cross-section heteroskedasticity, period heteroscedasticity,
contemporaneously correlation, and serial correlation separately. The fixed-effects estimates
which are statistically significant are INVE_Op1 (+), IBL (–), CA–1 (–), CF (+), CC1 (–), Unce2(+), INVE_Op2 (+), INVE_Op3 (+), CG1 (–), CG2 (–), CG3 (–), CG4 (+), and CG5 (+). The
signs in parentheses are those of the corresponding ĥs.
[Table I near here]
The signs of these 13 effective regressors are as expected from the discussion above, save
IBL, CA–1, CG4, and CG5. As argued above, the coefficients of these four regressors can bepositive or negative, depending on the motive behind the cash holdings. Here, the negative
estimated coefficient on CA–1 suggests substitution between cash and non-cash current assets.
The positive estimated coefficient on CG4 suggests that strict auditing and reporting standards
should discourage managers from using cash reserves or raising off-the-book funds for theirown private consumption. The positive estimated coefficient on CG5 suggests that managers
are likely to be concerned more with their banks’ information-rent extraction in countries whose
financial systems are based more heavily upon banks. Meanwhile, what the negative estimatedcoefficient on IBL implies is elusive in the sense that it suggests two possibilities: one is thatmanagers with better access to outside debt should have less incentive to hold cash for

12

Random-effects estimation is not feasible because the number of sample countries is less than the number ofregressors for the between estimator, which is necessary to estimate the random effects innovation variance.

13

precautionary motives; the other is that more indebted managers should manage cash more
efficiently for repayment.13
Because all variables are standardized, the marginal impacts of the statistically significant
regressors on CAR can be compared, simply by looking at the absolute values of their ĥs. With
regard to the precautionary motive to hold cash for investment opportunities, the impact is 0.81.
This is the sum of three estimates: 0.22 for INVE_Op1, 0.14 for CC1, and 0.45 for INVE_Op3.
Regarding the precautionary motive to hold cash for the availability of outside debt, the impact
is 0.53: IBL’s estimated coefficient. With respect to the precautionary motive to hold cash for
uncertainty, it is 0.09: Unce2’s estimated coefficient.

That is, the total impact of the

precautionary motive is 1.43 (= 0.81 + 0.53 + 0.09) in the extreme case where I arbitrarily
attribute IBL to only the precautionary motive. For the transaction-costs motive to hold cash,
the impact is 0.32: CF’s estimated coefficient. For the agency motive to hold cash, the impact
is 1.83, which is larger than the other two. This impact consists of the sum of five estimates:
0.67 for CG1, 0.18 for CG2, 0.25 for CG3, 0.46 for CG4, and 0.27 for CG5.
I check the robustness of the baseline estimation result in four ways. The first is to verify
the fixed-effects model. To increase the degrees of freedom, I drop insignificant regressors
from Eq. (1) and compare the fixed-effects model with the random-effects model by testing thenull hypothesis that the IEs are uncorrelated with the regressors.14 A Hausman test statistic
rejects the null hypothesis at the one per cent level.
Second, to investigate whether time effects common to all is in each τ are necessary or not,I add to Eq. (1) year-dummies that take one in the given year and otherwise zero and find thatnone of them is statistically significant. I judge that the time effects are not needed.
Third, I address the risk of spurious regressions as mentioned above.

The baseline

estimation hedges this risk by using as regressors linear, quadratic, and cubic trend terms (TT,TT2, and TT3), all of which are estimated to have statistically significant coefficients. For thesame purpose, I conduct a panel co-integration analysis. I drop the constant term and quadratic
and cubic trend terms as well as statistically insignificant variables from Eq. (1). I also infer

Managers hoarding more cash could find it less necessary to issue debt. Following G&L’s (2018) specification,
I ignore this risk of endogeneity. When I use the lagged value (IBL–1) as an instrumental variable for IBL in
estimating Eq. (3), the coefficient on the instrumental variable is not statistically significant.
14
Random-effects estimators depend on the Swamy-Arora method which uses residuals gained in the within(fixed-effects) and between-means regressions.
13

14that the orders of integration of time-series data of CAR and the 13 effective regressors allow
these variables to be co-integrated with each other. 15 By running the pooled auxiliary
regression with Kao’s (1999) method, I find that the residual is stationary.16 This suggests that,at a global level, these regressors are likely to have had a long-term stable relationship, not a
spurious relationship, with CAR over the last decade.
The last robustness-check is to address the risk of multicollinearity among CG1–CG4. For
example, CG2–CG4 have the potential to affect GC1 according to Eccles, Ioannou, andSerafeim (2014), who extensively study the effect of corporate sustainability on organizational
processes and performance for U.S. companies. I calculate the VIFs (variance-inflation factors)
for all pairs of CGs for all sample countries, following Snee and Marquardt (1984). Looking
at the VIFs in Appendix C, I judge that all of them are too small to cause multicollinearity.17

4.2 Four Extensions
To get a better understanding of the baseline estimation result, I make four extensions. First, I
analyze the explanatory power of the statistically significant regressors. Following G&L’s(2018) approach, I compare Radj2s obtained by estimating regression equations that increase the15

I conduct panel unit-root tests by pegging one for the order of lag and considering both individual effects andindividual trend effects. The results are as follows: CAR (0***, 0**), Unce1 (0***, 1***), INVE_Op1 (1***, 0***), RTA(0***, 0***), CF_Av (0***, 1***), INVE_Av (0***, 0***), IBL (0***, 0***), CA (0***, 0**), CL (0***, 0***), CF (0***, 0***),INVE (0***, 0***), CC1 (0***, 0***), CC2 (0***, 0***), Unce2 (0***, 0***), Unce3 (0***, 0***), INVE_Op2 (0***, 0***),
INVE_Op3 (0***, 0***), CG1 (0***, 1***), CG2 (0***, 1***), CG3 (0***, 1***), CG4 (0***, 1***), CG5 (0***, 0***), andCG6 (0***, 0***). The first and last numbers in parentheses are the orders of integration of the corresponding
variables in (i) one case where, for a specific variable, a common unit root process is assumed for all sample
countries and (ii) the other case where, for a specific variable, different unit root processes are assumed for different
sample countries, respectively. The superscripts ***, **, and * stand for one per cent, five per cent, and ten percent levels of statistical significance, respectively. I judge their statistical significance with reference to the LevinLin-Chu bias-adjusted t statistics and the Pearson-Fisher Chi-Square statistics in the first and last cases,
respectively. I also conduct panel unit-root tests by pegging one for the order of lag and considering individual
effects but not individual trend effects. The results on the degree of the integration are the same as above, except
for IBL (0***, 1***), Unce2 (0***, 1***), Unce3 (1***, 0***), INVE_Op3 (0***, 0***), CG4 (0***, 0***), and CG6 (0***,1***).
16
The order of lag(s) is zero, as selected by the Schwarz criterion. The number of observations is 220. The ADF
t-statistic is –5.10 (p-value: 0.00). The null hypothesis that there is no co-integration can be rejected. When
dropping the linear time trend, the ADF t-statistic is –5.83 (p-value: 0.00).
17
I also consider a dynamic adjustment of national CARs by adding as a regressor its lagged-value (CAR–1) to Eq.
(1). Appendix D details the result of 1-step GMM estimation of this version of Eq. (1). Suffice it here to report
the following three. First, CAR–1 is estimated to have a statistically significant coefficient, suggesting a global
trend that the present values of national CARs depend on their past values. Second, the signs of statisticallysignificant regressors are broadly the same as in the baseline estimation. Last, standard 2-step GMM estimation
is not computable.

15

number of regressors one by one until I have the full model of Eq. (1). I start with an equationin which national CARs are regressed on a constant term and fixed IEs (individual effects). The
Radj2 obtained by estimating this simple equation is 82.5 per cent. Strikingly, country fixed
effects have great explanatory power. When estimating an expanded equation which includes
linear, quadratic, and cubic trend terms (TT, TT2, and TT3, respectively), the Radj2 is 82.8 percent. The increase in the explanatory power due to the trend terms is tiny. Next, I add the 9insignificant regressors, resulting in a Radj2 of 87.6 per cent. In the final stage, I add each of the
13 effective regressors one-by-one (while keeping the previously-added regressors). Beginningwith adding INVE_Op1, I obtain a Radj2 of 88.9 per cent. The increase of the Radj2 in responseto this addition is 1.3 (= 88.9 – 87.6) percentage points (pp). I add other regressors in thefollowing order: INVE_Op1 (+1.3pp), IBL (+0.3pp), CA–1 (+0.7pp), CF (–0.6pp), CC1 (+4.2pp),
Unce2 (–4.1pp), INVE_Op2 (–0.1pp), INVE_Op3 (+3.9pp), CG1 (+0.2pp), CG2 (+0.3pp), CG3(–0.4pp), CG4 (+0.3pp), and CG5 (+0.6pp). The figures in parentheses indicate the increase in
Radj2 in response to adding the given regressor. For details, see Table II. Apart from country
fixed effects, CC1 (the cost of carry) and INVE_Op3 (the execution of R&D projects) are likely
to have the largest and second largest explanatory power of national CARs; specifically, thelatter is related to the precautionary motive. As stressed by G&L (2018), macroeconomic
variables are important. The agency motive, by contrast, has limited explanatory power over
the total variation in national CARs.
[Table II near here]
The second extension is to look at the explanatory power of the agency motive by placing a
focus on the change in the simple average of national CARs from 2007 to 2017. Here, I notate
this change in a variable (X) with ◊17–07 [X]. For CAR on a standardized basis, I have ◊17–
07[CAR] which is equal to 0.51.

I consider the contributions that the statistically significant

regressors make to ◊17–07[CAR].

Specifically, I calculate ◊17-07[X]s for these regressors,

including the three time-trend terms, and multiple these ◊17–07[X]s with the regressors’ estimatedcoefficients (ĥs), as shown below:◊17–07[CAR] = 0.51
= 0.60◊17–07[TT] + (–0.09)◊17–07[TT2] + 0.01◊17–07[TT3]+ 0.22◊17–07[INVE_Op1] + (–0.53)◊17–07[IBL] + (–0.33)◊17–07[CA–1] + (0.05)◊17–07[CF]16

+ (–0.14)◊17–07[CC1] + 0.09◊17–07[Unce2]+ 0.14◊17–07[INVE_Op2] + 0.45◊17–07[INVE_Op3]+ (–0.67)◊17–07[CG1] + (–0.18)◊17–07[CG2] + (–0.25)◊17–07[CG3] + 0.46◊17–07[CG4]+ 0.27◊17–07[CG5] + ◊17–07[MC],(2)where MC is miscellaneous contributions made by the residual (ε) as well as other regressors,whose coefficients are interpreted as zero, in Eq. (1). As underlined for all regressors, a productof ◊17–07[X] and its ĥ is X’s contribution to ◊17–07[CAR]. Meanwhile, ◊17–07[IE] is zero for all
sample countries.
I calculate these products as follows: +0.92 for TTs (the sum of all terms in the second lineof Eq. (2)), –0.06 for INVE_Op1, +0.05 for IBL, +0.10 for CA–1, –0.03 for CF, –0.06 for CC1,
–0.12 for Unce2, –0.06 for INVE_Op2, +0.11 for INVE_Op3, +0.28 for CG1, –0.04 for CG2, –0.03 for CG3, –0.14 for CG4, + 0.02 for CG5, and –0.43 for MC. Apart from the time-trend
terms, an increase in INVE_Op3 (the execution of R&D projects) and a decline of CG1 (themeasure of managers’ ethical behavior) are two major contributors to pushing up ◊17–07[CAR].
A decrease in Unce2 (the uncertainty of future financing needs at the macro level) and a declineof CG4 (the strength of auditing and reporting standards) are two major contributors to pushingdown ◊17–07[CAR]. These suggest that both of the precautionary and agency motives should be
important to the increase in the simple average of national CARs from 2007 to 2017, and thatso should be macroeconomic variables.
The third extension is to consider the dependence of the precautionary motive on the stateof corporate governance as well as the dependence of the transaction-costs motive on the stateof corporate governance. I add to Eq. (1) an interaction term made by interacting one of CG1–
CG5 with one of the other statistically significant regressors, including INVE_Op1, IBL, CA–1,
CF, CC1, Unce2, INVE_Op2, and INVE_Op3. Since I add only one interaction term in orderto preserve the interpretability of its coefficient, I conduct 40 (5×8) panel-data regressions using
the same sample. Table III shows that 11 out of the 40 interaction terms are estimated to havestatistically significant coefficients whose signs are shown in parentheses: CG1×INVE_Op1 (+),
CG1×IBL (+), CG1×CC1 (–), CG1×Unce2 (+), CG2×CC1 (–), CG2×INVE_Op3 (+),
CG3×INVE_Op2 (+), CG3×INVE_Op3 (–), CG4×Unce2 (+), CG5×CA–1 (+), andCG5×INVE_Op2 (–). These interaction terms illuminate the following global trends.

17

[Table III near here]
First of all, CG1 (the measure of managers’ ethical behavior) was interacted with no lessthan four variables: INVE_Op1 (+), IBL (+), CC1 (–), and Unce2 (+). This suggests that ethicalmanagers are likely to have increased the precautionary motive (INVE_Op1, IBL, and Unce2)
and the transaction-costs motive (CC1).
CG2 (the accountability of managers) was interacted with CC1 (–) and INVE_Op3 (+),
suggesting a similar global trend that accountable managers are likely to have increased theprecautionary motive (INVE_Op3) and sensitivity to the cost of carry (CC1).
CG3 (the strength of investor protection) was interacted with INVE_Op2 (+) and INVE_Op3(–). The signs of their estimates differ from each other. I interpret these interactions as implying
a global trend that, to comply faithfully with shareholder interests, managers are likely toenhance the precautionary motive for short-term expansions of investment opportunities due toan improving business climate but to repress it for long-term expansions of opportunities due
to increasing R&D projects.
CG4 (the strength of auditing and reporting standards) was interacted with Unce2 (+),
suggesting that harsher auditing and reporting standards are likely to encourage managers tohold more cash as a precaution in response to high uncertainty.
CG5 (the depth of indirect debt finance) was interacted with CA–1 (+) and INVE_Op2 (–).
These appear to imply that managers tended to borrow more money in response to a greaterrisk of holdup by major lenders, which reduced the attractiveness of perfect liquidity andabsence of transaction costs of cash while relaxing their precautionary motive for short-term
expansion of investment opportunities due to an improving business climate.
The last extension is to consider the determinants of the annual changes in national CARs.
I take the first differences of stock variables, including national CARs, as well as indicators forthe state of corporate governance. I keep the other flow variables, a constant term, the threetrend terms, as well as the IEs unchanged. As detailed in Appendix E, the result of estimatingsuch a first-difference model using the Least Square Dummy Variables (LSDV) method forstandardized variables is summarized below:∆CAR = 1.32*** – 0.60TT + 0.09TT2 –0.01TT3– 0.04Unce1 – 0.03∆INVE_Op1 – 0.29*∆RTA – 0.08CF_Av – 0.02INVE_Av

18

– 0.56***∆IBL + 0.26***∆CA–1 + 0.08∆CL + 0.09**CF – 0.14**INVE– 0.13CC1 – 0.02CC2 – 0.08Unce2 + 0.47***∆Unce3+ 0.06∆INVE_Op2 – 0.02∆INVE_Op3– 0.09∆CG1 – 0.35***∆CG2 – 0.06∆CG3 + 0.05∆CG4 + 0.09∆CG5 + 0.06∆CG6(# of observations = 134, Radj2 = 0.48),(3)

where the superscripts ***, **, and * stand for one per cent, five per cent, and ten per centlevels of statistical significance, respectively, and the p-values used are the averages of twocases in which I adjust for ε’s cross-section heteroskedasticity, period heteroscedasticity,
contemporaneously correlation, and serial correlation separately. 18

The fixed-effects

estimates which are statistically significant are ∆RTA (–), ∆IBL (–), ∆CA–1 (+), CF (+), INVE(–), ∆Unce3 (+), and ∆CG2 (–). The signs in parentheses are those of their corresponding
estimates. The signs for ∆IBL and CF are the same as in the baseline estimation. The sign for
INVE is the same as expected above. RTA is estimated to have a statistically insignificant
coefficient in the baseline estimation while ∆RTA is estimated here to have a statisticallysignificantly negative coefficient, suggesting that economies of scale should matter for annualchanges in national CARs. Notably, CA–1 is estimated to have a statistically significantly
negative coefficient in the baseline estimation while ∆CA–1 is estimated here to have astatistically significantly positive coefficient. This suggests that the attractiveness of cash for
managers, namely perfect liquidity and absence of transaction costs, rather than substitution
between cash and non-cash current assets, is likely to explain the annual changes in national
CARs over the last decade.
As in the baseline estimation of the level of national CARs, the precautionary motive(represented by ∆IBL), the transaction-costs motive (represented by ∆RTA, ∆CA–1, CF and
INVE), and the agency motive (represented by ∆IBL and ∆CG2) jointly explain annual changes
in national CARs over the last decade.19 As in G&L (2018), CF and INVE are significant

Two technical notes are as follows. First, using ∆INVE_Op3 as a regressor makes it impossible to calculatecross-section weights (variance) for the GLS method used in the baseline estimation. Last, ∆INVE_Op2 is not thefirst difference. INVE_Op2 is the expected GDP growth rate one-year ahead in the baseline estimation. Here,INVE_Op2 is the gap between this expectation and expected GDP growth rate for the current year, both of which
are as of October of the current year and taken from the International Monetary Fund’s World Economic Outlook(October). That is, ∆INVE_Op2 here stands for an expected improvement/deterioration of business climate.
19
I do not mention here ∆Unce3 as a factor of the precautionary motive because the regressor is estimated to havea statistically significantly positive coefficient, contrary to my expectation.
18

19

regressors for these changes. Because I standardize all variables, I can compare the marginal
impacts of the effective regressors with the absolute values of their coefficients. Although∆IBL is estimated to have a statistically significantly negative coefficient, the main reason
why managers increased hoarding money after the global financial crisis is still unclear in thesense that the reduction in the interest-bearing debt ratios discouraged them from economizing
cash reserves while encouraging them to keep more cash based upon the precautionary motive.
Now, I can analyze the explanatory power of the seven effective regressors by following
the same procedure as above. Table IV reports increases in Radj2 in response to increasing
regressors one by one as follows: ∆RTA (+1.9pp), ∆IBL (+18.7pp), ∆CA–1 (+5.7pp), CF(+2.6pp), INVE (+3.4pp), ∆Unce3 (+5.0pp), and ∆CG2 (+1.9pp). The main driver of annualchanges in national CARs over the last decade is ∆IBL. In line with the first extension, theagency motive represented by ∆CG2 here has limited explanatory power over the changes.
[Table IV near here]
Finally, based upon Eq. (3), I analyze what regressors contribute to the change in the simple
average of national ∆CARs on a standardized basis from 2008 to 2017, or ◊17–08[∆CAR] whichis equal to 0.21. By going through the procedure used in the second extension, I obtain thefollowing contributions by regressor: –0.03 for ∆RTA, +0.23 for ∆IBL, +0.02 for ∆CA–1, +0.01
for CF, +0.22 for INVE, +0.02 for ∆Unce3, +0.01 for ∆CG2, and –0.26 for MC. A decreasein ∆IBL (annual changes in interest-bearing debt ratios) and a decline of INVE (cash outflows
due to business investment) are two major contributors to ◊17–08[∆CAR]. This suggests thatthe increase in the global average CAR growth of 0.21 is related to the precautionary,transaction-costs, and agency motives. To put it plainly, on a global average basis, managers
expanded the annual growth of national CARs in 2017 more than in 2008 because a slowdown
in the annual change in interest-bearing debt ratios encouraged the managers to increase theprecautionary motive while discouraging them from making prudent cash management, whendecreasing business investment made it less necessary for them to spend increasing cash
reserves.

5. Concluding Remarks
Through a global examination of CARs of publicly-traded non-financial lightly-regulated firms
at an aggregate level in 20 countries over the last decade, I verify theoretical predictions on

20

corporate cash holdings by showing that differences in national CARs among countries andover time are explained by country-specific fixed effects as well as the precautionary,transaction-costs, and agency motives. The standardized impact of the agency motive is greaterthan the other two while having limited explanatory power over the total variation in countyspecific CARs. One caveat is that this finding depends on the relative importance of the
precautionary and agency motives behind the significant negative association between national
CARs and ratios of interest-bearing debt to total assets.
I contribute to the literature mainly by accumulating global evidence for the relevance of theagency motive to the burgeoning of corporate cash holdings since the global financial crisis.
Managers with weaker governance tend to hold more cash and be less sensitive to theprecautionary and transaction-costs motives.

Managers tend to refrain more frommisappropriating and hiding cash in response to harsher auditing and reporting standards.
Managers tend to hold more cash as a result of holdup problems with banks in countries whose
financial systems are more bank-centered. When one dares to abstract from country-level
heterogeneity and takes the simple average of national CARs, an increase in this global average
CAR from 2007 to 2017 is explained mainly by the agency motive related to worsening
business ethics and the precautionary motive related to increasing R&D projects.
I also contribute to the literature by verifying G&L’s (2018) finding for the U.S. at a global
level: macroeconomic variables explain aggregate corporate cash holdings more than aggregate
corporate financial indicators do. I show that relevant macroeconomic variables are those
representing the cost of carry, the execution of R&D projects, the economic climate, and thelevel of uncertainty, the last three of which relate to the precautionary motive. Meanwhile, thecost of carry and the execution of R&D projects had the most explanatory power, apart fromcountry-specific fixed effects.
I close this article with future research suggestions. First, a significant negative association
between the annual change in countries’ CARs and the annual change in countries’ interestbearing debt ratios requires scrutiny.

This association appears to be the result of the

precautionary and agency motives, although these two still merit separate attention. A potential
relationship between cash reserves and net worth deserves to be considered because of its effecton debt ratios. Retained profits are a component of net worth. As profits increase, debt ratios
decrease as total assets increase. At the same time, CARs will increase if cash reserves function21

as a major investment destination of the increased profits. On the other hand, buybacks using
reserves can reduce CARs while increasing debt ratios through a reduction of net worth.
The second is the question of whether it is necessary to push down national CARs or not.
Although the methodology of this study is not ideal for determining country-specific optimallevels of CARs, its finding that the level of and the change in national CARs have been based
upon reasonable responses to market and information imperfections suggests that differencesin these imperfections among countries should be important. From this perspective, one canargue that weak corporate governance runs the risk of inviting an over-accumulation of
corporate cash, and that institutional reforms for improving corporate governance are capableof reducing national CARs significantly.
The last research suggestion is to make clear what inefficient behavior managers engaged inregarding their cash reserves in the case of weak governance. The indicators used are notclosely linked with managers’ specific actions, such as neglecting good investment
opportunities, reducing dividends to shareholders, and amassing wealth for future private
consumption, the last of which may be more acute the laxer are auditing and reporting standards.

Appendix A[Appendix Table A here]

Appendix B
This appendix details definitions and sources of the data used.


CAR stands for cash-to-assets ratio. It is defined as the ratio of aggregate cash and cash
equivalents to total assets for publicly-traded non-financial lightly-regulated companies(PTNFLR companies, hereinafter). Per-share year-end cash and cash equivalents are divided
by per-share year-end total assets for individual industry sub-indices for each country. The
CAR for the country is the market-capitalization (m-cap, hereinafter) weighted average of theseindustry-specific ratios. The data source is Bloomberg (BLM, hereinafter). The unit is percent (%, hereinafter).



Unce1 stands for uncertainty, or the volatility of cash flows. It is defined as the standard
deviation of cash flow ratios for PTNFLR companies for the three years from τ–3 to τ–1. Here,cash flows are per-share net income before extraordinary items minus expected per-share

22

dividends, where dividends at τ are assumed to be the same as those at τ–1. Cash flow ratios
are obtained by dividing the cash flows by per-share year-end total assets. This ratio is
calculated for individual industry sub-indices for each country. The national cash flow ratio isthe m-cap weighted average of these industry-specific ratios. The data source is BLM. The
unit is %.


INVE_Op1 stands for investment opportunities, or average q. The market value of total assets
is divided by the book value of total assets, for PTNFLR companies. The former is the sum of
the year-end m-cap and the book value of year-end total liabilities. All figures are per-sharevalues. The q ratio is calculated for individual industry sub-indices for each country. The
national q ratio is the m-cap weighted average of these industry-specific ratios. The data source
is BLM.



RTA stands for per-company average of real assets. It is defined as the inflation-adjusted andU.S. dollar based value of total assets for PTNFLR companies. Per-share year-end total assetsof an industry sub-index for each country are deflated with national inflation rates, converted
into U.S. dollars with reference to the year-end FX rates in the market, multiplied with theindex’s divisor, and divided by the number of constituent companies. The RTA for the country
is the m-cap weighted average of these industry-specific ratios. The data sources are IMF,
World Economic Outlook (WEO) 2019 April for national inflation rates; and, BLM for otherindicators.



CF_Av stands for average cash flows. It is defined as the average of cash flow ratios for
PTNFLR companies for the three years from τ–3 to τ–1. Here, cash flow is per-share net
income before extraordinary items minus expected per-share dividends, where dividends at τ
are assumed to be the same as those at τ–1. Cash flow ratios are obtained by dividing cash
flow by per-share year-end total assets. This ratio is calculated for individual industry subindices for each country. The national cash flow ratio is the m-cap weighted average of theseindustry-specific ratios. The data source is BLM. The unit is %.



INVE_Av stands for average investment expenditures. It is defined as the average of investment
cash flow ratios for PTNFLR companies for the three years from τ–3 to τ–1. Investment cash
flow ratios are obtained by dividing per-share annual investment cash flow by per-share yearend total assets. This ratio is calculated for individual industry sub-indices for each country.
The national investment cash flow ratio is the m-cap weighted average of these industry-23

specific ratios. The data source is BLM. The unit is %.


IBL stands for interest-bearing liabilities. It is defined as the ratio of debt to total assets for
PTNFLR companies. Per-share year-end debt is divided by per-share year-end total assets forindividual industry sub-indices for each country. The IBL for a country is the m-cap weighted
average of these industry-specific quotients. The data source is BLM. The unit is %.

CA stands for non-cash current assets. It is defined as the ratio of current assets other than cash
and cash equivalents to total assets for PTNFLR companies. Per-share year-end non-cash
current assets are divided by per-share year-end total assets for individual industry sub-indicesfor each country. The CA for a country is the m-cap weighted average of these industry-specificratios. The data source is BLM. The unit is %.

CL stands for current liabilities. It is defined as the ratio of current liabilities to total assets for
PTNFLR companies. Per-share year-end current liabilities are divided by per-share year-end
total assets for individual industry sub-indices for each country. The CL for a country is them-cap weighted average of these industry-specific ratios. The data source is BLM. The unit
is %.



CF stands for contemporaneous cash flow. It is defined as the cash flow ratio for PTNFLR
companies at τ. Here, cash flow is per-share net income before extraordinary items minus
expected per-share dividends, where dividends at τ are assumed to be the same as those at τ–1.
Cash flow ratios are gained by dividing cash flow by per-share year-end total assets. This ratio
is calculated for individual industry sub-indices for each country. The CF for a country is them-cap weighted average of these industry-specific ratios. The data source is BLM. The unit
is %.



INVE stands for contemporaneous investment expenditures. It is defined as the investment
cash flow ratio for PTNFLR companies at τ. Investment cash flow ratios are obtained by
dividing per-share annual investment cash flow by per-share year-end total assets. This ratio
is calculated for individual industry sub-indices for each country. The INVE for a country is
the m-cap weighted average of these industry-specific ratios. The data source is BLM. The
unit is %.

CC1 stands for cost of carry, or expected net cost of keeping money in bank accounts. It isdefined as the difference between the expected rate of return minus the ex-post real one-year24interest rate. The expected rate of return represents the opportunity cost of hoarding cash, orthe cost of hypothetically lost investment, and is measured by the 10-year average of annual
real GDP growth rates. The ex-post real one-year interest rate represents interest income
arising from the bank accounts and is measured by one-year zero-coupon yields on sovereign
bonds at end of year minus annual inflation rates. The data sources are IMF WEO 2019 April
for annual real GDP growth rates and inflation rates; and, Bloomberg for the one-year interest
rates. The unit is percentage pointsCC2 stands for cost of carry, or corporate tax cost. It is defined as the ratios of total tax tocorporate profits. The data source is World Bank Group (WBG), Trade and Competitiveness
Data 360 (TCdata360), and the original source is World Economic Forum (WEF), The Global
Competitive Report. The unit is %.



Unce2 stands for uncertainty, or stock return volatility, defined as the standard deviations ofweekly percentage changes in the national stock market index during τ. See Appendix A for
names of national stock market indices. The data source is BLM. The unit is %.



Unce3 stands for uncertainty, or political stability. It is an index reflecting measured perceptionof the likelihood of political instability and/or politically-motivated violence. The data sourceis the World Bank (WB), Worldwide Governance Indicators. Percentile rank among all
countries ranges from 0 (least stable) to 100 (most stable).



INVE_Op2 stands for investment opportunities, or business climate. It is given by the IMF
projections of national real GDP growth rates one-year ahead in October of each year. For∆INVE_Op2, see the footnote 14 in the text. The data source is IMF WEO October in stheample years. The unit is %.



INVE_Op3 stands for investment opportunities, or the execution of R&D projects. It is definedas the ratio of R&D expenditures to national nominal GDP. The data source is WB, World
Development Indicators (WDI). The unit is %.

CG1 stands for the level of managers’ ethical behavior. It is a measured perception of theinternationally-compared status of domestic companies’ ethical behavior in interactions withpublic officials, politicians, and other companies. The data source is WBG, TCdata360, andits original source is WEF, Executive Opinion Survey (EOS). It ranges from 1 (worst in theworld) to 7 (best in the world).

25

CG2 stands for the accountability of managers to investors and board members. It is ameasured perception of the internationally-compared extent of that accountability. The data
source is WBG, TCdata360, and its original source is WEF, EOS. It ranges from 1 (worst in
the world) to 7 (best in the world).

CG3 stands for the strength of investor protection. It is a combination of the “Extent ofDisclosure Index” (transparency), “Extent of Director Liability Index” (liability for selfdealing), and “Ease of Shareholder Suit Index” (shareholders’ ability to sue officers anddirectors for misconduct). The data source is World Bank Group, TCdata360, and its original
source is WBG, Doing Business: Measuring Regulatory Quality and Efficiency. It ranges from1 (worst in the world) to 10 (best in the world).



CG4 stands for the strength of auditing and reporting standards. It is a measured perception of
the internationally-compared strength of these standards. The data source is WBG, TCdata360,
and its original source is WEF, EOS. It ranges from 1 (loosest in the world) to 7 (harshest in
the world).

CG5 stands for the depth of indirect debt finance (bank loans). It is defined as the ratio ofdomestic credit to the private sector by banks relative to national nominal GDP. The data
source is WB, WDI. The unit is %.



CG6 stands for the depth of direct debt finance (corporate bonds). It is defined as debt
securities issued by domestic companies in domestic and foreign markets relative to nominal
GDP. The data source is the Bank for International Settlements, Debt Securities Statistics. The
unit is %.

Appendix C[Appendix Table C here]

Appendix DThis appendix considers a dynamic adjustment of national CARs by adding as a regressor its
lagged-value (CAR–1) to Eq. (1). The result of estimating this version of Eq. (1) using panel
generalized method of moments (panel GMM) is shown in the right column of Table I andsummarized below:

26

CAR = 0.33**CAR–1 + 0.68***TT – 0.10***TT2 + 0.01***TT3+ 0.10Unce1 + 0.13INVE_Op1 – 0.06RTA + 0.09CF_Av – 0.17*INVE_Av– 0.43***IBL – 0.17CA–1 – 0.07CL + 0.07**CF – 0.09*INVE – 0.13**CC1 + 0.05CC2+ 0.05Unce2 – 0.07Unce3 + 0.16INVE_Op2 + 0.68*INVE_Op3– 0.40**CG1 – 0.16CG2 – 0.16CG3 + 0.39**CG4 + 0.19CG5 – 0.22CG6(# of observations = 133, J-statistic = 108.0 <p-value: 0.15>),
where the superscripts ***, **, and * stand for one per cent, five per cent, and ten per centlevels of statistical significance, respectively, and the Hansen-test p-value for the J-statistic is
not small enough to reject the null hypothesis that the regression equation is valid. Using
Arellano-Bond type instrumental variables, I find that the GMM estimates which arestatistically significant are CAR–1 (+), INVE_Av (–), IBL (–), CF (+), INVE (–), CC1 (–),
INVE_Op3 (+), CG1 (–), and CG4 (+). I conduct 1-step GMM estimation because standard 2step estimation is not computable (e.g., see the last footnote in Table I for details). In general,
the 1-step GMM estimates are consistent but are not necessarily efficient. The signs inparentheses are those of the corresponding ĥs. CAR–1 is estimated to have a statisticallysignificant coefficient, suggesting a global trend that the present values of national CARs
depend on their past values. The signs of the other eight effective regressors are the same as in
the baseline estimation, save INVE_Av and INVE, both of which are estimated to havestatistically insignificant coefficients in the baseline estimation.

The negative estimatedcoefficient on INVE_Av suggests that managers who are keen to make business investments
should tend to hold less cash, and this tendency can in part relate to the transaction-costs motive.
The negative estimated coefficient on INVE suggests the use of cash holdings for investments
within an optimal range on the basis of the transaction-costs motive.
Because all variables are standardized, the marginal impacts of the effective regressors on
CAR are comparable with each other using the absolute values of their ĥs. For the transactioncosts motive to hold cash, the impact is 0.33. This is the sum of three estimates: 0.17 for
INVE_Av, 0.07 for CF, and 0.09 for INVE. For the agency motive to hold cash, the impact is
0.79, or the sum of two estimates: 0.40 for CG1 and 0.39 for CG4. The agency motive impact
is larger than the transaction-costs motive impact as in the baseline estimation.

27

Whether or not the agency motive impact is larger than the precautionary motive impact
depends on how to attribute IBL to these two motives. In the extreme case where I arbitrarily
attribute IBL to the precautionary motive only, the precautionary motive impact is 1.11, or thesum of two estimates: 0.43 for IBL and 0.68 for INVE_Op3. In another extreme case where I
arbitrarily attribute IBL to the agency motive only, the agency motive impact increases by 0.43,
from 0.79 to 1.22, while the precautionary motive impact decreases by the same amount, from
1.11 to 0.68. Consequently, the agency motive will be larger than the other two as long as the
cash-holding motive represented by IBL is based mainly upon the agency motive, or as long as
debt encourages borrowers to conduct efficient management of cash reserves.
Notably, two macroeconomic variables controlling for the precautionary motive, uncertainty(Unce2) and future investment opportunities (INVE_Op2), are statistically significant
regressors in the baseline estimation but not so in this alternative estimation controlling for
CAR–1. An interpretation of this difference is that managers’ determination of the level of CAR
at the end of the previous year (τ–1) reflected their expectation of a change in their precautionary
motive due to macroeconomic conditions during the current year (τ). Part of the simple
autoregressive adjustment of national CARs could be driven by the precautionary motive under
the influence of macroeconomic conditions and, in this case, the impact of the precautionary
motive on national CARs approximated above should be larger with this addition.

Appendix E[Appendix Table E here]

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30

Figure 1. National CARs
25
20

Group of seven countries

(%)

USA

JPN

GBR

FRA

DEU

ITA

CAN

15
10
5
0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 201830(%)

Other advanced countries
AUS

20

ESP

FIN

NOR

PRT

SWE

HKG

KOR

IRL

10
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 201830(%)

Emerging countries

20
10
CHN

0

IDN

THA

ZAF

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 201815(%)

Global average CAR

12962003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

This figure plots national CARs: ratios of aggregate cash and cash equivalents to total assets for publiclylisted non-financial lightly-regulated companies. The global average CAR is the average of national
CARs, excluding those for FIN, ITA, NOR, and SWE.

31

Tables
Table I
Results of Estimating Eq. (1)Dependent variable: CAR. # of observations = 153 in A & B. 133 in C.
Model

A: Pooling

B: Fixed effectSpecification of IE

No

Yes: Constant

Estimation method

OLS

LSDV

-

-

Adjustments on residuals (ε )Estimates

C: GMMn.a.

Weighted GLS

Arellano-Bond instruments

White period

White cross-section

PCSE

CSH, PH, & SC
are adjusted for.

CSH & CCE areadjusted for.

CSH & PH are adjusted for.

ĥs

ĥs

ĥs

ĥs

ĥs

-0.312-1.100-1.123-1.123n. a. (added to the instrument list)[0.431][0.000][0.000][0.000]

n. a.

n. a.

n. a

n. a

0.033

0.626

0.604

0.604

[0.895][0.002][0.001][0.000][0.001]

Quandratic time trendTT2-0.003-0.096-0.087-0.087-0.103[0.954][0.005][0.003][0.000][0.004]

Cubic time trend

TT 3

0.001

0.005

0.004

0.004

0.005

[0.777][0.001][0.005][0.000][0.005]-0.176

0.054

0.106

0.106

0.098

[0.167][0.561][0.301][0.056][0.340]-0.104

0.135

0.221

0.221

0.131

[0.371][0.190][0.071][0.031][0.260]-0.020-0.045-0.045-0.059Regressors

Constant term

C

Lagged value

CAR –1

Linear time trend

TT

0.326
[0.031]

Uncertainty (volatility of cash flows)
Investment opportunies (average q)

Unce1
INVE_Op1

0.681

Per-company average of real assets

RTA

0.146
[0.054][0.836][0.608][0.625][0.697]

Average cash flow in the past

CF_Av

0.116

0.028

0.066

0.066

0.094

[0.471][0.805][0.631][0.525][0.438]-0.097-0.161-0.086-0.086-0.166[0.298][0.047][0.178][0.052][0.068]-0.445-0.531-0.531-0.426

Average investment expenditures in the past

INVE_Av

Interest-bearing liabilities

IBL-0.079[0.407][0.000][0.000][0.000][0.000]Non-cash current assets

CA –1-0.547-0.352-0.333-0.3330.109[0.000][0.001][0.000][0.000][0.560]

0.154

0.083

-0.023-0.0230.074[0.100][0.336][0.641][0.615][0.366]-0.029

0.067

0.050

0.050

[0.620][0.079][0.064][0.078][0.055]-0.050-0.059-0.059-0.089Current liabilities

CL

Contemporaneous cash flows

CF

0.065

Contemporaneous investment expenditures

INVE-0.145[0.061][0.382][0.148][0.201][0.100]

Cost of carry (expected net cost)

CC1-0.012-0.131-0.134-0.134-0.125[0.861][0.024][0.007][0.000][0.055]

Cost of carry (corporate tax cost)

CC2

0.149

0.022

0.057

0.057

0.045

[0.059][0.858][0.645][0.482][0.746]

0.170

0.067

0.091

0.091

[0.028][0.198][0.025][0.004][0.306]0.066-0.066-0.066-0.069Uncertainty (stock return volatility)

Unce2

0.052

Uncertainty (political stability)

Unce3-0.181[0.122][0.662][0.571][0.332][0.709]Investment opportunies (business climate)

INVE_Op2

0.482

0.178

0.136

0.136

0.155

[0.000][0.101][0.123][0.067][0.132]

0.468

0.756

0.448

0.448

0.677

[0.000][0.010][0.053][0.008][0.100]0.165-0.408-0.671-0.671-0.401[0.255][0.032][0.001][0.004][0.042]-0.160-0.186-0.186-0.157

Investment opportunies (R&D executions)

INVE_Op3

The observance of business ethics

CG1

The accountability to investors & board members

CG2-0.431[0.006][0.259][0.044][0.000][0.373]The strength of investor protection

CG3

0.156

-0.240-0.255-0.255-0.159[0.041][0.028][0.029][0.000][0.283]The strength of auditing and reporting standards

CG4

0.050

0.419

0.463

0.463

0.389

[0.731][0.008][0.003][0.000][0.039]-0.079

0.215

0.273

0.273

0.192

[0.328][0.052][0.003][0.045][0.271]-0.212-0.110-0.127-0.127-0.220[0.014][0.423][0.279][0.290][0.185]

0.672

0.891

The depth of indirect debt finace (banks)
The depth of direct debt finace (bonds)
2

Radj

CG5
CG6

0.942
14.344

F-test on H0: Pooling model > Fixed-effect model[0.000]J-statistic [Hansen-test p value]

n. a.

32

n.a.
n.a.
106.24[-0.131]This table shows the results of estimating Eq. (1): CARi,τ = h0 + h1TT + h2TT2 + h3TT3 + h4Unce1i,τ+h5INVE_Op1i,τ + h6RTAi,τ+ h7CF_Avi,τ + h8INVE_Avi,τ + h9IBLi,τ + h10CAi,τ–1 + h11CLi,τ + h12CFi,τ +h13INVEi,τ + h14CC1i,τ + h15CC2i,τ + h16Unce2i,τ + h17Unce3i,τ + h18INVE_Op2i,τ + h19INVE_Op3i,τ +h20CG1i,τ + h21CG2i,τ + h22CG3i,τ + h23CG4i,τ + h24CG5i,τ + h25CG6i,τ + IEi + εi,τ. Notes are as follows.
First, ***, **, and * stand for one per cent, five per cent, and ten per cent levels of statistical significance.
Second, I follow the conventional procedure in specifying the nature of IE. I estimate the pooling modelusing the OLS method and I estimate the fixed-effects model using the Least Square Dummy Variables(LSDV) method. I justify the addition of constant IEs by using the F-test to check how much and how
significantly that addition reduces the residual sum of squares. If the fixed-effects model is selected,
then, to compare it with the random-effects model, it is necessary to use the Hausman test to test the null
hypothesis that IEs are uncorrelated with explanatory variables. This comparison, however, is not
feasible here because the number of countries in the sample is less than the number of regressors for the
between estimator to estimate the random effects innovation variance. Third, shading indicates
regressors with statistically significant estimates and a specification of IE with statistical adequacy. I
select the fixed-effects model. Fourth, CSH stands for cross-section heteroskedasticity, PH for period
heteroskedasticity, SC for serial correlation, and CCE for contemporaneously correlated errors. Using
the statistical software package, EViews 10, I address these potential irregular aspects of the residuals(εi,τ) by using two kinds of adjusted standard errors. EViews 10’s option for a panel-data regression,
White period, is used to obtain standard errors adjusted for the risks of PH and SC, while White crosssection is used to obtain those adjusted for the risks of CSH and CCE. In estimating the fixed-effects
model, I also use its option Cross-section weights, which also makes it possible to control for the risk of
CSH. Reed and Ye (2011) demonstrate that estimators gained by using this weighted-GLS method
together with each of the two options for adjusted standard errors are excellent in terms of the estimators’
asymptotical efficiency and the accuracy of confidence intervals across them. Finally, in the GMM
estimation, CARi,τ–1 is added as a regressor and the Arellano-Bond type instrumental variables include
all the other regressors. In order to remove cross-section fixed effects, I apply the first-differencetransformation. If the innovations are i.i.d., the transformed innovations follow an integrated MA(1)process. The number of irritations is one; that is, fixed weights are used for the first difference of thei.i.d. innovations in a GMM weighting matrix – 1-step GMM estimation. Unfortunately, standard 2step GMM estimation is not computable. PH and SC are adjusted for by using SUR (seemingly
unrelated regressions) weights. That is, the innovations have the same time-series correlation structure
for all cross-sections and I use panel-corrected standard errors, or PCSE.

33

Table II
Changes in Radj2 in Response to the Addition of Regressors
Regression equations
(1)
(2)
(3)

CAR i,τ = a 0 + IE i + ε i,τ2

3TT, TT , and TT are added to (1) above.
Unce1 i,τ , RTAi,τ, CF_Av i,τ , INVE_Av i,τ , CL i,τ , INVE i,τ ,CC2 i,τ , Unce1 i,τ , CG6 i,τ are added to (2) above

Economic implications of the added regressors

Radj2

Individual effects

82.5%

Liner, quadratic, and cubic trend terms

82.8%

Insignificant regressors in the baseline estimation

87.6%

Investment opportunies (average q)

88.9%

(4)

INVE_Op1 i,τ is added to (3) above.

(5)

IBL i,τ is added to (4) above.

Interest-bearing liabilities

89.2%

(6)

CA i,τ-1 is added to (5) above.

Non-cash current assets

89.9%

(7)

CF i,τ is added to (6) above.

Contemporaneous cash flows

89.3%

(8)

CC1 i,τ is added to (7) above.

Cost of carry (expected net cost)

93.5%

(9)

Unce2 i,τ is added to (8) above.

Uncertainty (stock return volatility)

89.4%

(10)

INVE_Op2 i,τ is added to (9) above.

Investment opportunies (business climate)

89.3%

(11)

INVE_Op3 i,τ is added to (10) above.

Investment opportunies (R&D executions)

93.2%

(12)

CG1 i,τ is added to (11) above.

The observance of business ethics

93.4%

(13)

CG2 i,τ is added to (12) above.

The accountability to investors & board members

93.7%

(14)

CG3 i,τ is added to (13) above.

The strength of investor protection

93.3%

(15)

CG4 i,τ is added to (14) above.

The strength of auditing and reporting standards

93.6%

(16)

CG5 i,τ is added to (15) above.

The depth of indirect debt finace (banks)

94.2%

This table shows the changes in Radj2 when national CARs are regressed on different sets of variables
ranging from a coarse set (1) to a full set (16). Notes are as follows. First, I estimate the fixed-effects
models by weighted GLS for all equations so as to maintain the comparability. Last, in all estimations,
to control for the potential of residuals’ cross-section heteroskedasticity, period heteroskedasticity, andserial correlation, I use EViews 10’s two options for panel-data regression: White period and Crosssection weights. Reed and Ye (2011) demonstrate that weighted-GLS estimators obtained by using these
two options for adjusted standard errors are excellent in terms of the estimators’ asymptotical efficiency
and the accuracy of confidence intervals across them.

34

Table III
Estimated Coefficients to Interaction Terms Added Separately to Eq. (1)The observanceof businessethicsInvestment opportunies (average q)

INVE_Op1

Interest-bearing liabilities

IBL

Non-cash current assets

CA –1

Contemporaneous cash flows
Cost of carry (expected net cost)
Uncertainty (stock return volatility)
Investment opportunies (business climate)
Investment opportunies (R&D executions)

CF
CC1
Unce2
INVE_Op2
INVE_Op3The
The strength of
The strength ofaccountability
auditing andinvestor
to investors &reportingprotection
board members
standards

The depth ofindirect debt
finace (banks)

CG1

CG2

CG3

CG4

CG5

0.125

-0.043-0.040-0.114-0.053[0.030][0.535][0.487][0.171][0.514]0.244-0.007

0.052

0.155

-0.041[0.022][0.933][0.299][0.239][0.677]0.013-0.086

0.006

0.041

0.230

[0.894][0.119][0.944][0.589][0.000]

0.024

0.026

0.011

0.052

-0.033[0.705][0.687][0.858][0.438][0.380]-0.106

-0.08

0.010

-0.0600.04[0.000][0.053][0.812][0.243][0.123]

0.063

0.009

0.018

0.052

0.018

[0.097][0.770][0.413][0.079][0.644]-0.129

0.005

0.110

0.048

-0.162[0.165][0.932][0.042][0.527][0.038]-0.0540.180-0.171

0.039

0.138

[0.578][0.058][0.062][0.708][0.500]This table shows the estimated value and statistical significance of coefficients on interaction terms
separately added to Eq. (1). Notes are as follows. First, figures in [ ] are p-values. Second, shading
indicates interaction terms with statistically significant estimates based upon a 10 per cent level ofstatistical significance. Third, I estimate the fixed-effects models by weighted GLS for all equations so
as to maintain comparability. Last, in all estimations, to control for the potential of residuals’ crosssection heteroskedasticity, period heteroskedasticity, and serial correlation, I use EViews 10’s two
options for a panel-data regression: White period and Cross-section weights. Reed and Ye (2011)
demonstrate that weighted-GLS estimators obtained by using these two options for adjusted standard
errors are excellent in terms of the estimators’ asymptotical efficiency and the accuracy of confidence
intervals across them.

35

Table IV
Changes in Radj2 in Response to the Additions of Regressors
Regression equations(1)∆ CAR i,τ = b 0 + b 1 TT + b 2 TT 2 + b 3 TT 3 + IE i + ε i,τ

Economic implications of the added regressors

Radj2

Individual effects-1.1%

Insignificant regressors

8.9%∆Unce1 i,τ , ∆ INVE_Op1 i,τ , CF_Av i,τ , INVE_Av i,τ , ∆ CL i,τ , CC1 i,τ ,(2)CC2 i,τ , Unce2 i,τ , INVE_Op2 i,τ , ∆ INVE_Op3 i,τ , ∆ CG1 i,τ , ∆ CG3 i,τ ,∆ CG4 i,τ , ∆ CG5 i,τ , and ∆ CG6 i,τ are added to (1) above(3)∆RTA i,τ is added to (2) above.

Per-company average of real assets

10.8%

(4)

∆IBL i,τ is added to (3) above.

Interest-bearing liabilities

29.5%

(5)

∆CA i,τ-1 is added to (4) above.

Non-cash current assets

35.2%

(6)

CF i,τ is added to (5) above.

Contemporaneous cash flows

37.8%

(7)

INVE i,τ is added to (6) above.

Contemporaneous investment expenditures

41.2%

(8)

∆Unce3 i,τ is added to (7) above.

Uncertainty (political stability)

46.2%

(9)

∆CG2 i,τ is added to (8) above.

The accountability to investors & board members

48.1%This table shows the changes in Radj2 when the annual changes in national CARs are regressed on
different sets of variables ranging from a coarse set (1) to a full set (9). Notes are as follows. First, I
estimate the fixed-effects models by weighted GLS for all equations so as to maintain comparability.
Last, in all estimations, to control for the potential of residuals’ cross-section heteroskedasticity, period
heteroskedasticity, and serial correlation, I use EViews 10’s two options for a panel-data regression:
White period and Cross-section weights.

36Appendix Table A
The Stock Market Indices Referred to
Stock market indices# of industry sub-indicessynthesized# of resulting
constituent firms

Years when national
CARs are available

AUS

S&P/ASX 300

7

184–239

2004–2018

CAN

S&P/TSX Composite Index7

160–223

2002–2018

CHN

Shanghai Shenzhen CSI 300 Index7

207–240

2007–2018

DEU

Prime All Share

12

249–327

2003–2018

ESP

Madrid Stock Exchange General Index

4

81–94

2005–2018FIN

OMX Helsinki All Share Index7

110–117

2012–2018

FRA

CAC All-Tradable Index7

197–399

2002–2018

GBR

FTSE All Shares20

269–427

2002–2018

HKG

Hang Seng Composite Index

1

19–25

2002–2018

ITA

FTSE Italia All-Share Index7

149–175

2009–2018

IDN

Jakarta Stock Exchange Composite Index6

225–387

2002–2018

IRL

ISEQ

1

39–54

2002–2018

JPN

TOPIX

25

1165–1579

2003–2018

KOR

KOSPI

13

552–657

2002–2018NOR

OSE All Share Index12

141–170

2005–2014PRT

PSI All-Share Index

5

32–43

2002–2018SWE

OMX Stockholm All Share Index7

222–287

2012–2018

THA

Stock Exchange of Thai14

177–254

2004–2018

USA

S&P 50080

320–339

2003–2017

ZAF

FTSE/JSE Africa All Share Index

20–21

95–111

2002–2018

This table shows sample countries’ stock market indices. The industry sub-indices of these indices areused for calculating national CARs and relevant financial indicators. Notes are as follows. First,
financial, real estate, utilities, and telecommunications industries are excluded. Second, the number ofindustries synthesized differs by sample year for ZAF. Last, the number of resulting constituent
companies differs by sample year for all countries.

37

Appendix Table C
The Risk of Multicollinearity among CG1–CG4
CG1 vs. CG2

CG1 vs. CG3

CG1 vs. CG4

CG2 vs. CG3

CG2 vs. CG4

CG3 vs. CG4

AUS

1.1

1.1

1.0

1.6

1.5

1.0

CAN

1.2

1.2

1.3

3.5

3.4

2.1

CHN

1.7

1.0

3.0

1.3

1.5

1.0

DEU

1.2

4.3

1.1

1.8

2.0

1.0

ESP

1.1

2.3

2.4

1.7

1.2

1.1

FIN

4.6

1.1

7.6

1.1

4.7

1.1

FRA

2.8

2.2

1.0

3.4

1.0

1.0

GBR

1.7

2.1

1.2

2.0

3.3

1.1

HKG

1.8

1.7

1.4

1.6

5.3

1.5

ITA

1.2

3.3

1.5

1.0

2.0

1.1

IDN

1.0

1.0

1.0

2.1

1.3

1.0

IRL

2.4

1.2

1.4

2.2

1.3

1.0

JPN

1.1

1.0

1.8

8.9

2.8

1.8

KOR

3.3

2.2

2.4

1.1

10.6

1.0

NOR

3.5

5.3

2.7

2.6

8.3

2.9

PRT

1.0

1.8

4.0

1.4

1.0

4.8

SWE

1.2

1.5

2.9

1.0

1.6

1.1

THA

1.0

1.0

1.0

3.3

1.0

1.0

USA

1.0

1.0

1.0

3.5

3.7

1.4

ZAF

1.4

1.5

1.7

1.0

5.1

1.1

This table shows VIFs (variance-inflation factors) among four kinds of scores on the state of corporate
governance: CG1–CG4. Notes are as follows. First, a VIF is defined as 1/{1 – (correlation
coefficients)2}. Last, all the VIFs are much smaller than 10, the criterion proposed by Snee andMarquardt (1984) as defining negligible risk of multicollinearity caused by the independent variables.
I ignore one exception found between CG2 and CG4 for South Korea.

38

Appendix Table E
Results of Estimating the Determinants of the Annual Change in National CARs
Dependent variable: ∆CAR. # of observations = 134
Model

A: Pooling

B: Fixed effectSpecification of IE

No

Yes: Constant

Estimation method

OLS

Adjustments on residuals (ε )-Estimates

LSDVWhite period

White cross-section

PH, & SC are
adjusted for.

CSH & CCE areadjusted for.

ĥs

ĥs

ĥs

Constant term

C

0.846

1.320

1.320

[0.252][0.083][0.089]Linear time trend

TT-0.385-0.589-0.589[0.298][0.168][0.110]Quandratic time trend

TT 2

0.061

0.086

0.086

[0.300][0.229][0.142]

Cubic time trend

TT 3-0.003-0.004-0.004[0.299][0.271][0.164]

Uncertainty (volatility of cash flows)

Unce1

0.029

-0.037-0.037[0.717][0.723][0.649]Investment opportunies 1 (IO1): average q∆INVE_Op1-0.027-0.028-0.028[0.795][0.710][0.721]Per-company average of real assets∆RTA-0.009-0.287-0.287[0.965][0.053][0.098]Average cash flow in the past

CF_Av

0.053

-0.080-0.080[0.567][0.565][0.434]

Average investment expenditures in the past

INVE_Av

0.091-0.020-0.020[0.174][0.902][0.840]Interest-bearing liabilities∆IBL-0.635-0.561-0.561[0.000][0.000][0.000]Non-cash current assets∆CA –1

0.104

0.257

0.257

[0.292][0.031][0.000]Current liabilities

∆CL

0.015

0.078

0.078

[0.884][0.517][0.447]

Contemporaneous cash flows

CF

0.045

0.092

0.092

[0.235][0.041][0.007]

Contemporaneous investment expenditures

INVE-0.042-0.132-0.132[0.449][0.100][0.027]

Cost of carry (expected net cost)

CC1-0.055-0.125-0.125[0.194][0.209][0.138]

Cost of carry (corporate tax cost)

CC2

0.082

-0.020-0.020[0.052][0.863][0.828]

Uncertainty (stock return volatility)

Unce2-0.053-0.085-0.085[0.409][0.191][0.405]

Uncertainty (political stability)∆Unce3

0.391

0.473

0.473

[0.018][0.008][0.002]IO2: unexpected business climate change∆INVE_Op2

0.050

0.060

0.060

[0.501][0.563][0.246]

IO3: R&D executions∆INVE_Op3

0.385-0.217-0.217[0.4362][0.755][0.736]The observance of business ethics∆CG1-0.188-0.089-0.089[0.427][0.663][0.771]

The accountability to investors & board members∆CG2-0.320-0.347-0.347[0.075][0.008][0.000]The strength of investor protection∆CG3-0.040-0.063-0.063[0.724][0.611][0.361]The strength of auditing and reporting standards

∆CG4

0.165

0.054

0.054

[0.359][0.856][0.793]The depth of indirect debt finace (banks)

∆CG5

0.075

0.094

0.094

[0.730][0.701][0.797]The depth of direct debt finace (bonds)∆CG6-0.005

0.065

0.065

[0.976][0.603]

Regressors

Radj2[0.302]

0.318

0.481
2.896

F-test on H0: Pooling model > Fixed-effect model[0.001]

39

This table shows the results of estimating the following: ∆CARi,τ = b0 + b1TT + b2TT2 + b3TT3 +b4Unce1i,τ + b5∆INVE_Op1i,τ + b6∆RTAi,τ + b7CF_Avi,τ + b8INVE_Avi,τ + b9∆IBLi,τ + b10∆CAi,τ–1 +b11∆CLi,τ + b12CFi,τ + b13INVEi,τ + b14CC1i,τ + b15CC2i,τ + b16Unce2i,τ + b17∆Unce3i,τ + b18∆INVE_Op2i,τ
+ b19∆INVE_Op3i,τ + b20∆CG1i,τ + b21∆CG2i,τ + b22∆CG3i,τ + b23∆CG4i,τ + b24∆CG5i,τ + b25∆CG6i,τ + IEi+ εi,τ. Notes are as follows. First, ***, **, and * stand for one per cent, five per cent, and ten per centlevels of statistical significance. Second, I follow the conventional procedure in specifying the natureof IE. I estimate the pooling model using the OLS method, and I estimate the fixed-effects model with
the Least Square Dummy Variables (LSDV) method. I justify the addition of constant IEs by checking
with the F-test by how much and how significantly that addition reduces the residual sum of squares. If
the fixed-effects model is selected, then, to compare it with the random-effects model, it is necessary to
use the Hausman test to test the null hypothesis that IEs are uncorrelated with explanatory variables.
This comparison, however, is not feasible here because the number of sample countries is less than thenumber of regressors for the between estimator to estimate the random effects innovation variance.
Third, shading indicates regressors with statistically significant estimates and a specification of IE withstatistical adequacy. I select the fixed-effects model. Fourth, CSH stands for cross-section
heteroskedasticity, PH for period heteroskedasticity, SC for serial correlation, and CCE forcontemporaneously correlated errors. Using the statistical software package, EViews 10, I cope withthese potential irregular aspects of residuals (εi,τ) with reference to two kinds of adjusted standard errors.
EViews 10’s option for a panel-data regression, White period, is used to gain standard errors adjusted
for the risks of PH and SC, while White cross-section to gain those adjusted for the risks of CSH and
CCE. Last, using ∆INVE_Op3 as a regressor makes it impossible to calculate cross-section weights(variance) for the GLS method used in the baseline estimation of Eq. (1), whose results are shown in
Table I.

40