GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 323

CORPORATE CASH HOLDING IN ASIA

Charles Yuji Horioka, Akiko Terada-Hagiwara

October 2013

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

CORPORATE CASH HOLDING IN ASIA*

Charles Yuji Horioka**
School of Economics, University of the Philippines; Institute of Social and Economic Research,
Osaka University; and National Bureau of Economic Research, Inc.

Akiko Terada-Hagiwara
Economic and Research Department, Asian Development Bank

This version October 23, 2013

ABSTRACT: In this paper, we analyze the determinants of corporate saving in the form of changes in thestock of cash for 11 Asian economies using firm-level data from the Oriana Database for the 2002–2011
period. We find some evidence that cash flow has a positive impact on the change in the stock of cash,
which suggests that Asian firms are borrowing constrained and that they save more when their cash flow
increases so that they will be able to finance future investments. Moreover, we find in the developed
economy sample that, as expected, cash flow has a positive impact on the change in the stock of cash onlyin the case of the smallest firms, which are more likely to be borrowing constrained, and find in thedeveloping economy sample that, as expected, the positive impact of cash flow on the change in the stockof cash declines with firm size. In addition, we find that the cash flow sensitivity of cash declined after the
global financial crisis. Finally, we find some evidence that Tobin’s q has a positive impact on the change inthe stock of cash.
Keywords: corporate saving, corporate investment, borrowing constraints, liquidity constraints, cash flow,
cash holdings, cash flow sensitivity of cash, Tobin’s q, firm size, productivity shocks, Asia, Oriana Database,financial sector development, global financial crisis
JEL Classification: D92, E21, E22, G11, O53*This paper was prepared for the Asian Development Bank TA 7428-REG on “Sustaining Asia’s Growthand Investment in a Changing World.” The authors are indebted to Maria Socorro Bautista, Kazuo Ogawa,and Noli Sotocinal and to Xiang Lin and the other participants of the Ninth Annual Conference of the
Asia-Pacific Economic Association (APEA) 2013, held in Osaka, Japan, on July 27-28, 2013, for theirvaluable comments and to Romana Domingo for her superb assistance.

Any remaining errors are theauthors’.
**Corresponding author: Charles Yuji Horioka, Professor of Economics, Encarnacion Hall, Room 223,
School of Economics, University of the Philippines, Diliman, Quezon City 1101, Philippines.

E-mail:horioka@iser.osaka-u.ac.jp. Telephone: +63-2-927-9686 to 89, ext. 222. Facsimile: +63-2-920-5461

I.

INTRODUCTION

Corporate saving has always played an important role, but it has shown a substantial
upward trend in many, if not most, of the developed and Asian economies for the past 2
decades, as pointed out by Karabarbounis and Neiman (2012), and thus is of evenmore importance now. Yet most previous analyses of saving have focused onhousehold (or personal) saving or national (or domestic) saving, and there have been
relatively few analyses of corporate (or firm) saving (refer to the papers in thereferences section for a representative sampling of previous theoretical and empiricalanalyses of corporate saving).

Why do firms accumulate liquid assets? In other words, why do firms channel
their cash flow into liquid assets rather than into physical capital (capital formation) or
into shareholder distributions (dividends)? The high saving rates in Asia have beencalled a “saving glut” and have been blamed for the pre-global financial crisis “global
imbalances,” and both academics and policy makers have expended much effort intrying to understand the saving behavior of this region generally (see, for example,
Horioka and Terada-Hagiwara (2011), but the factors driving the surge in corporatesaving in Asia have yet to be disentangled. The objective of this paper is to fill this voidby doing an empirical analysis of the saving behavior of Asian firms.

The recent literature on corporate saving has proposed two main explanations
for the excess corporate saving in emerging markets. First, emerging markets have alimited supply of financial assets and are financially constrained (see, for example,

1

Dooley, Folkerts-Landau, and Garber 2005; Matsuyama 2007; Ju and Wei 2006, 2010;
and Caballero, Farhi, and Gourinchas 2008). In this explanation, the underdeveloped
financial sector serves as the main driver of corporate saving behavior.

The second strand of the literature on corporate saving focuses on the role ofthe precautionary motive. In this explanation, excess saving and thence net capitaloutflows result from precautionary saving arising from idiosyncratic risk (see, forexample, Mendoza, Quadrini, and Rios-Rull 2009, Sandri, 2010, and Benhima, 2010).
In these precautionary saving models, rising uncertainties cause a decline in corporate
investment, as has been particularly noticeable in some of the Southeast Asian
economies.

The goal of this paper is not to directly address the macroeconomic
phenomenon of the saving glut, but rather to understand the process whereby firms
accumulate liquid assets or saving by analyzing firm-level income statement data. In
particular, we are interested in why firms channel their cash flow into liquid assets(defined to include cash as well as other financial assets) rather than into physicalcapital. We also seek to determine whether and when corporate saving behavior canserve as a useful indicator of the extent to which firms face external borrowing
constraints. Thus, we focus on two specific determinants of corporate saving: income
uncertainty and the cost of external finance.

In this paper, we analyze the determinants of corporate saving in the form ofchanges in the stock of cash for 11 Asian economies using firm-level data from the2

Oriana Database for the 2002–2011 period. Figure 1 shows trends in our measure ofcorporate saving during the sample period. The change in the stock of cash (as a shareof total assets) was positive throughout the sample period, which indicates that cash
holdings have been increasing throughout this period. This supports the view that there
was a “saving glut” in Asia. However, some variations can be observed across time andamong groupings of economies. Firms in our sample continued to save more cash until
2010 despite the slight dip in 2008, and the cash saving rate peaked in 2010 before
dropping quite sharply in 2011. This general trend can be observed in both developed
and developing economies, but firms’ cash holdings increased significantly more indeveloping economies than in developed economies after 2007, until they dropped
sharply in 2011.

To preview our main findings, we find some evidence that cash flow has apositive impact on the change in the stock of cash, which suggests that Asian firms areborrowing constrained and that they save more when their cash flow increases so thatthey will be able to finance future investments. Moreover, we find in the developed
economy sample that, as expected, cash flow has a positive impact on the change inthe stock of cash only in the case of the smallest firms, which are more likely to beborrowing constrained, and find in the developing economy sample that, as expected,
the positive impact of cash flow on the change in the stock of cash declines with firmsize. In addition, we find that the cash flow sensitivity of cash declined after the global
financial crisis. Finally, we find some evidence that Tobin’s q has a positive impact on
the change in the stock of cash.

3

The remainder of the paper is organized as follows. We discuss theoreticalconsiderations in Section II, the estimation model and the estimation method in Section
III, the data source in Section IV, the estimation results in Section V, and the summaryand conclusions in Section VI.

II.

THEORETICAL CONSIDERATIONSIn this section, we consider the determinants of corporate saving from a theoreticalperspective. Our discussion draws heavily on the analyses on Almeida, Campello, andWeisbach (2004), Khurana, Martin, and Pereira (2006), Riddick and Whited (2009), andAlmeida, et al. (2013) (see also Karabarbounis and Neiman (2012) and Huang (2011),
who also discuss the importance of borrowing constraints as a motive for corporatesaving).

Households and firms are very different economic entities with very different
objective functions, with households consuming in order to maximize their utility andfirms investing in plant and equipment and using that plant and equipment in order toproduce goods and services and make profits. However, there are many similarities
between the two types of economic entities nonetheless. For example, in the same waythat households save in order to finance their future consumption, firms save in order tofinance their future investment. Moreover, in the same way that households have a
choice between financing their consumption by borrowing or by drawing down theirpreviously accumulated saving, firms have a choice between financing investment byborrowing or by drawing down their previously accumulated saving. Furthermore, in the4same way that borrowing constraints and borrowing costs will influence households’
decisions about how to finance their consumption, borrowing constraints and borrowing
costs will also influence firms’ decisions about how to finance their investment.

As the Fisher Separation Theorem states, the firm's decision regarding
physical investment is, of course, paramount, and once this decision is made, the firm
will then decide how to finance the investment, whether it be from internal funds (cash)
and/or external funds (loans and bond and equity issues). It is in this context that firms
will decide whether and how much financial assets (cash) to hold, and it is this decision
upon which we will focus in this paper.

If firms did not face borrowing constraints, they could finance their investment
in plant and equipment by borrowing as much as they needed from external sources atmarket rates and therefore would not need to hold financial assets in preparation for thesudden and unexpected appearance of profitable investment projects (projects with apositive net present value). However, if firms faced borrowing constraints, as a result of
which they had to pay more than the market rate of interest when borrowing fromexternal sources, they might choose to hold at least some financial assets at all times toensure that they were able to take advantage of any profitable investment projects thatmight suddenly and unexpectedly arise without having to borrow from external sources,
thereby saving on expected future financing costs.

Note, however, that holding financial assets confers costs as well as benefits.
One cost of holding financial assets is that doing so reduces the amount of current
5investments that can be financed from internal sources, and in the case of borrowing
constrained firms, this in turn may reduce the amount of current investments the firm
can do. Another cost of holding financial assets is that the firm must pay taxes on theinterest income earned on holdings of financial assets. Thus, firms must balance thecosts of holding financial assets (the cost of foregone current investment projects andthe tax liability on the interest income accruing to holdings of financial assets) against
the benefits of doing so (the reduction in expected future financing costs) when deciding
how much of their assets to hold in the form of financial assets.

We turn next to what these theoretical considerations imply concerning the“cash flow sensitivity of cash,” the impact of cash flow on how much firms save in theform of cash, where cash is construed broadly to include all liquid financial assets.
According to the analysis of Almeida, Campello, and Weisbach (2004), an increase in
cash flow will cause firms to want to increase current investment as well as future
investment. A firm that is not borrowing constrained will not necessarily increase its
cash holdings in response to an increase in its cash flow and may use the entire
increase in its cash flow to finance current investment because it knows that it will beable to finance future investment using external funds without any difficulty. Thus, anincrease in cash flow will not have a systematic impact on how much unconstrained
firms save in the form of cash (that is, the so-called “cash flow sensitivity of cash” ofunconstrained firms would be expected to be zero).

However, a firm that is borrowing constrained will use at least part of theincrease in its cash flow to increase its cash holdings so that it will be able to increase its
6

future investment without relying on external funds. Thus, an increase in cash flow willcause borrowing constrained firms to save more in the form of cash (that is, the “cashflow sensitivity of cash” of borrowing constrained firms will be positive).

By contrast, Riddick and Whited (2009) extend the theoretical analysis of
Almeida, Campello, and Weisbach (2004) and show that the cash flow sensitivity of
cash may be negative in the case of borrowing constrained firms. They obtain a very
different prediction regarding the sign of the cash flow sensitivity of cash because they
assume that the firm faces positively serially correlated productivity shocks. As a result,a positive productivity shock will cause the firm’s cash flow to increase and its capital to
become more productive, and its productivity will revert to its mean only slowly. This will
induce the firm to shift its assets from cash to physical capital, and if this substitution
effect is strong enough to offset the income effect identified by Almeida, Campello, andWeisbach (2004) and discussed above, the firm will invest and draw down its cash
holdings in response to an increase in cash flow caused by a positive productivity shock.
Thus, an increase in cash flow will cause firms to save less in the form of cash (that is,
the “cash flow sensitivity of cash” will be negative).

Thus, the sign and magnitude of the “cash flow sensitivity of cash” is theoreticallyambiguous and will depend on whether or not firms are borrowing constrained and onwhether or not increases in cash flow are accompanied by increases in productivity.
Empirical analysis is needed to determine the sign and magnitude of the “cash flow
sensitivity of cash.”

7

III.

ESTIMATION MODEL AND ESTIMATION METHODSIn this section, we describe our econometric model, which is based on the theoreticalconsiderations discussed in the previous section, as well as the estimation methods
used to estimate our model.

Following Almeida, Campello, and Weisbach (2004), Khurana, Martin, andPereira (2006), and Riddick and Whited (2009), we estimate the following baseline
equation:

CHCASHA(i,t) =a0 + a1 * CFA(i,t) + a2 * q(i,t) + a3 * SIZE(i,t) + e(i,t)(1)

where CHCASHA(i,t) is the ratio of the change in the stock of cash to total assets,
CFA(i,t) is the ratio of cash flow to total assets, q(i,t) is Tobin’s q, and SIZE(i,t) is firm
size measured by total assets of firm i at time t. e(i,t) is an error term of firm i at time t.

As explained in the previous section, Almeida, Campello, and Weisbach (2004)
predict that the coefficient of cash flow, a1, will be positive in the case of borrowing
constrained firms but that it will be indeterminate in the case of unconstrained firms.
This prior implies that firms should increase their stocks of liquid assets in response topositive cash flow innovations if they are borrowing constrained. By contrast,
unconstrained firms should not display such systematic behavior when managing theirliquidity; i.e., their cash flow sensitivity of cash should not be statistically different from8

zero.

This prediction of a positive propensity to save out of cash flow for borrowing
constrained firms is due primarily to Almeida, Campello, and Weisbach’s (2004)
assumption that an increase in cash flow is not accompanied by higher capital
productivity. Therefore, the firm has no incentive to transform liquid assets into physicalassets, and an increase in cash flow produces a pure positive income effect on saving.
Riddick and Whited (2009), on the other hand, stress the importance of the substitution
effect. The substitution effect implies that the firm saves less in the form of liquid assets
because it wants to shift some of its liquid assets into physical assets that have become
relatively more productive.

We use two definitions of Tobin’s q (q1, the ratio of market price to book value,and q2, the sum of market capitalization and debt as a ratio of total assets). However,as the results were not very sensitive to the definition of Tobin’s q, we report only the
results for q1.

We do the estimations with and without one-digit industry dummies, andalthough the results were not found to be very sensitive to the inclusion of industry
dummies, we report the results with and without industry dummies.

Finally, in addition to estimating the baseline regression [equation (1)], we also
tried augmenting the regression with three additional explanatory variables (capital
expenditures, working capital, and short-term debt).
9

We estimate this equation using three estimation methods: ordinary leastsquares (OLS), the procedure proposed by Fama and MacBeth (1973), and thegeneralized method of moments (GMM). The Fama and MacBeth (1973) procedure
consists of estimating the equation for each year using OLS, then pooling the yearly
estimates. Since it is likely that there is measurement error in Tobin’s q, we follow
Riddick and Whited (2009) in using GMM to control for measurement error in Tobin’s q.

We do the estimates for the full sample, for the subsamples of developed
economies and developing economies, and for individual economies.

In addition, in order to gauge the impact of the global financial crisis on thesaving behavior of firms, we try dividing the time period of our analysis into pre-crisis(2002–2007) and post-crisis (2008–2011).

Finally, we try dividing the sample into various subsamples by firm size on the
grounds that firm size will be a good proxy for borrowing constraints. In particular, we try
dividing the sample into firms above and below the mean (median) and into firm size
quintiles on the grounds that firms that are relatively small will be more likely to beborrowing constrained whereas firms that are relatively large will be less likely to beborrowing constrained.

IV.

DATA SOURCE

10

In this section, we discuss the source of the data used in our analysis. The data we usein

ouranalysis

are

taken

from

the

BureauVan

Dijk

OrianaDatabase(https://oriana.bvdinfo.com/), a comprehensive database that contains financial
information on public and private companies. We use data from 11 Asian economies:
Australia; the People’s Republic of China (PRC); Hong Kong, China; Indonesia; Japan;
the Republic of Korea; Malaysia; the Philippines; Singapore; Thailand; and Viet Nam.
We use data for the 10-year period from 2002–2011.

When we divide the sample into developed economies and developing
economies, we classify Australia; Hong Kong, China; Japan; New Zealand; andSingapore as developed economies and, the PRC, Indonesia, the Republic of Korea,
Malaysia, the Philippines, Thailand, and Viet Nam as developing economies.

Comparing our sample to those of previous firm-level analyses of thedeterminants of corporate saving, Almeida, Campello, and Weisbach (2004) conduct
their analysis only for firms in the United States (US), whereas Riddick and Whited(2009) conduct their analysis only for firms in Canada, Japan, France, Germany, theUnited Kingdom, and the US. Thus, this paper is the first analysis to include Asian
economies other than Japan in the sample and also the first analysis to include
developing economies in the sample.

Turning to sample selection, following Riddick and Whited (2009), we deleted
firm-year observations with missing data and for which total assets, the gross capitalstock, or sales are either zero or negative, selected the longest consecutive time series
11

of data for each firm, and deleted firms with only one observation. Following Riddick andWhited (2009), we also omitted all firms whose primary Standard Industrial
Classification (SIC) code is between 4900 and 4999, between 6000 and 6999, or
greater than 9000 because our model is not appropriate for regulated, financial, or
quasi-public firms. Finally, we also excluded outliers (defined as the top and bottom 1%
of firms) and firms from economies with relatively few observations.

Appendix Table 1 shows the variable definitions and data sources for thevariables used in the empirical analysis whereas Appendix Table 2 shows the summary
statistics for these variables.

V.

ESTIMATION RESULTS

In this section, we discuss our estimation results. Our estimation results are shown in
Tables 1–9. The first three tables show estimation results by economy. Table 1 shows
the OLS estimates, Table 2 shows the Fama and MacBeth (1973) estimates, and Table
3 shows the GMM estimates. Two measures of Tobin’s q were used in the estimations,with the first being the ratio of market price to book value and the second being the sumof market capitalization and debt divided by total assets. The left-hand panel of Tables 1
and 2 shows the estimates using the first definition of Tobin’s q, while the right-handpanel shows the estimates using the second definition of Tobin’s q. Only the firstmeasure of Tobin’s q based on the ratio of market price to book value is used in Table 3.

As can be seen from Table 1, in the OLS estimates, the coefficient of greatest
12

interest (the coefficient of cash flow) is positive and statistically significant in all of the 11
economies in our sample regardless of whether or not industry dummies are included.

As can be seen from Table 2, the Fama and MacBeth (1973) estimates are
generally consistent with the OLS estimates with a positive coefficient on cash flow, but
the coefficient of cash flow is statistically significant in a fewer number of economies.
The coefficient of cash flow is positive and statistically significant in six out of the 11
economies in the sample when industry dummies are included and in nine out of the 11
economies in the sample when industry dummies are not included.

As can be seen from Table 3 in the GMM estimates, the coefficient of cash flow
is statistically significant in nine out of the 11 economies in the sample and is positive inall cases in which it is statistically significant.

Turning to the coefficient of Tobin’s q, in the OLS estimates, it is statisticallysignificant in seven (six) out of the 11 economies in the sample (positive and significant
in five (five) economies, and negative and significant in two (one) economies whenindustry dummies are included (omitted). In the Fama and MacBeth (1973) estimates, it
is not statistically significant in any of the 11 economies in the sample regardless ofwhether or not industry dummies are included. In the GMM estimates, the coefficient of
Tobin’s q is statistically significant in only four out of the 11 economies in the sample
and is positive in three out of the four economies in which it is statistically significant.

We also obtained the GMM results for the full sample of economies, the13developed economy sample, and the developing economy sample, and the results areshown in Tables 4, 5, and 6, respectively. As can be seen from these tables, thecoefficient of cash flow is positive and statistically significant in every case in thebaseline regressions and positive and statistically significant in every case in theaugmented regressions except in the developed economy sample.

The coefficient of Tobin’s q is positive and statistically significant in the fullsample of economies and the developing economy sample in the case of the baseline
regressions and in the full sample of economies only in the case of the augmented
regressions.

The fact that the coefficient of cash flow is generally positive suggests that theincome effect analyzed by Almeida, Campello, and Weisbach (2004) is more important
than the substitution effect analyzed by Riddick and Whited (2009), as a result of which
the net impact of cash flow on the change in the stock of cash is positive (i.e., firms savemore in the form of cash when their cash inflows are higher).

As for the impact of Tobin’s q, there is some evidence that its impact on thechange in the stock of cash is positive but the results are not very clear-cut.

Turning to the results for the pre- and post-global financial crisis periods, thecoefficient of cash flow is almost always higher during the pre-crisis period than during
the post-crisis period, which is reasonable since the greater pessimism about future
prospects caused by the global financial crisis presumably reduced the demand for14

cash holdings in preparation for the sudden and unexpected appearance of profitable
investment opportunities. The only exception to this pattern is in the case of the baseline
regression results for the developing economy sample, but the aforementioned patternholds in the case of the augmented regression results for this sample, which arepresumably more reliable than the baseline regression results due to the inclusion of
more explanatory variables.

We now divide the sample into subsamples according to firm size (asset size)in order to test for the possibility of the differential sensitivity of the change in the stockof cash to the cash flow variable by asset size. In this exercise, we use the asset size offirms to divide the sample of firms in each economy into five groups (quintiles). The
grouping is based on the average or median asset size of firms in each economy, andthus, for firms in an economy in which average (median) asset size is relatively large
such as the PRC, Japan, or the Republic of Korea, some of the firms in the bottom
quintile might be larger than those in the highest quintile in other economies such as
Viet Nam where average asset size is relatively small.

The results are shown in Tables 7, 8, and 9, for the full sample of economies,
the sample of developed economies, and the sample of developing economies,
respectively (only the results for the variants with industry dummies are shown due tospace limitations). The estimates for both the developed economy sample and thedeveloping economy sample show that the coefficient of cash flow is almost always
positive and is often statistically significant when it is positive whereas it is nevernegative and statistically significant, contrary to what Riddick and Whited (2009)15

predicted. This is true even when we augmented the specification by adding capital
expenditures, working capital, and short-term debt and estimated the equation using
GMM. This finding constitutes strong support for the income effect posited by Almeida,
Campello, and Weisbach (2004), and there is no evidence of a shift from cash flow tophysical assets.

Turning to patterns by firm size, as explained earlier, Almeida, Campello, andWeisbach (2004) predict that the coefficient of cash flow, a1, will be positive in the caseof borrowing constrained firms but that it will be indeterminate in the case of
unconstrained firms. Since smaller firms are more likely to be borrowing constrained, wewould expect the coefficient of cash flow, a1, to be larger and more highly significant in
the case of smaller firms, and our results are consistent with this expectation.

As the results for developed economies in Table 5 show, the coefficient of cash
flow is positive and significant in the case of the lowest firm size quintile but is notstatistically significant in any other quintile. Moreover, as the results for developing
economies in Table 6 show, the coefficient of cash flow is positive and statisticallysignificant in the case of all firm sizes, but its significant level and absolute magnitudedecline with firm size in the case of the baseline regressions (although the coefficient ofcash flow is not significant for any firm size in the case of the augmented regressions).
Thus, the results for both developed and developing economies are consistent with ourprior that smaller firms are more likely to be borrowing constrained and that they would
be expected to have a stronger tendency to save when cash flow is high.

16

VI.

SUMMARY AND CONCLUSIONSIn this paper, we analyzed the determinants of corporate saving in the form of changes
in the stock of cash for 11 Asian economies using firm-level data from the Oriana
Database for the 2002–2011 period. We found some evidence that cash flow has apositive impact on the change in the stock of cash, which suggests that Asian firms areborrowing constrained and that they save more when their cash flow increases so thatthey will be able to finance future investments. Moreover, we found in the developed
economy sample that, as expected, cash flow has a positive impact on the change inthe stock of cash only in the case of the smallest firms, which are more likely to beborrowing constrained, and find in the developing economy sample that, as expected,
the positive impact of cash flow on the change in the stock of cash declines with firmsize. In addition, we find that the cash flow sensitivity of cash declined after the global
financial crisis. Finally, we found some evidence that Tobin’s q has a positive impact on
the change in the stock of cash.

Turning finally to the policy implications of our findings, our findings suggest thatthe behavior of Asian firms is heavily influenced by borrowing constraints and that
financial sector development would induce Asian firms to invest more in physical assets
and do less saving in the form of cash holdings. In order to better understand the
behavior of Asian firms, particularly in developing countries, future research might
include measures of uncertainty (such as the serial correlation and variance of income),
following Riddick and Whited (2009), to test the hypothesis that uncertainty is at least as
17

important a determinant of the change in the stock of cash as borrowing constraints.

18

REFERENCES

Acharya, Viral, Heitor Almeida, and Murillo Campello. 2007. Is Cash Negative Debt? A
Hedging

Perspectiveon

Corporate

Financial

Policies.

JournalofFinancial

Intermediation. Vol. 16. pp. 515–554.

Almeida, Heitor, Murillo Campello, and Michael S. Weisbach. 2004. The Cash Flow
Sensitivity of Cash. Journal of Finance. Vol. 59, No. 4 (August). pp. 1777–1804.

Almeida, Heitor, Murillo Campello, Igor Kunha, and Michael S. Weisbach. 2013.
Corporate Liquidity Management: A Conceptual Framework and Survey. NBER
Working Paper No. 19502, National Bureau of Economic Research (October).

Bacchetta, Philippe, and Kenza Benhima. 2010. The Demand for Liquid Assets,
Corporate Saving, and Global Imbalances. Mimeo. Lausanne, France: University of
Lausanne.

Bates, Thomas W., Kathleen M. Kahle, and Rene M. Stulz. 2009. Why Do US Firms
Hold So Much More Cash than They Used To? Journal of Finance. Vol. 64, No. 5(October).

Bayoumi, Tamim, Hui Tong, and Shang-Jin Wei. 2010. The Chinese Corporate Savings
Puzzle: A Firm-level Cross-country Perspective. NBER Working Paper No. 16432.
October.
19

Benhima, Kenza. 2010. Financial Development, Technological Change in Emerging
Countries and Global Imbalances. EconomiX EconomiX Working Papers 2008-26.
Cahiers de Recherches Economiques du Departement d'Econometrie et d'Economie
Politique (DEEP), Université de Lausanne.

Caballero, R. J., E. Farhi, and P.-O. Gourinchas. 2008. An Equilibrium Model of ‘Global
imbalances’ and Low Interest Rates. American Economic Review, 98(1). pp. 358–393.

Dooley, M. P., D. Folkerts-Landau, and P. Garber. 2005. Direct Investment, Rising Real
Wages and the Absorption of Excess Labor in the Periphery. In Proceedings, Federal
Reserve Bank of San Francisco. (Feb).

Eisfeldt, Andrea L., and Adriano A. Rampini. 2006. Financing Shortfalls and the Value ofAggregate Liquidity. Working Paper. Evanston, IL: . Northwestern University.

Fama, Eugene, and James MacBeth. 1973. Risk, Return, and Equilibrium. Journal of
Political Economy. Vol. 81. pp. 607–636.

Feldstein, M. S., and J. S. Flemming1971. Tax Policy, Corporate Saving and Investment
Behaviour in Britain. Review of Economic Studies. Vol. 38, No. 4 (October). pp. 415–
434.

20

Gamba, Andrea, and Alexander Triantis. 2008. The Value of Financial Flexibility.
Journal of Finance. Vol. 63. pp. 2263–2296.

Horioka, Charles Yuji, and Akiko Terada-Hagiwara. 2012. The Determinants andLong-Term Projections of Saving Rates in Developing Asia. Japan and the World
Economy, Vol. 24, No. 2. (March), pp. 128-–137.

Huang, Yi. 2011. Can the Precautionary Motive Explain the Chinese Corporate Savings
Puzzle? Evidence from the Liquid Assets Perspective. Mimeo. International Monetary
Fund, Washington, DC. (November).

Ju, J., and S.-J. Wei. 2006. A Solution to Two Paradoxes of International Capital Flows.
NBER Working Paper No. 12668 (November).

———.2010. Domestic Institutions and the Bypass Effect of Financial Globalization.
American Economic Journal: Economic Policy, 2(4). pp. 173–204.

Karabarbounis. Loukas, and Brent Neiman. 2012. Declining Labor Shares and the
Global Rise of Corporate Savings. NBER Working Paper nNo. 18154. (June).

Khurana, Inder K., Xiumin Martin, and Raynolde Pereira. 2006. Financial Development
and the Cash Flow Sensitivity of Cash. Journal of Financial and Quantitative Analysis.
Vol. 41. pp. 787–807.

21

Lintner, John. 1956. Distribution of Incomes of Corporations among Dividends,
Retained Earnings, and Taxes. American Economic Review (Papers and Proceedingsof the Sixty-eighth Annual Meeting of the American Economic Association). Vol. 46, No.
2 (May). pp. 97–113.

Matsuyama, Kiminori. 2007. Credit Traps and Credit Cycles. American Economic
Review 97(1). pp. 503–516.

Mendoza, E. G., V. Quadrini, and J.-V. Ríos-Rull2009. Financial Integration, Financial
Development, and Global Imbalances. Journal of Political Economy 117(3). pp. 371–
416.

Ogawa, Kazuo. 2013. Firm Investment, Liquidity, and Bank Health: A Panel Study ofAsian Firms in the 2000s.

Asian Development Bank Economics Working Paper Series

No. 338 (February).

Riddick, Leigh A., and Toni M. Whited. 2009. The Corporate Propensity to Save.
Journal of Finance. Vol. 64, No. 4 (August). pp. 1729–1766.

Samphantharak, Krislert, and Robert M. Townsend. 2010. Households As Corporate
Firms: An Analysis of Household Financing Using Integrated Household Surveys andCorporate Financial Accounting. Econometric Society Monograph Series. Cambridge,
UK: Cambridge University Press.

22

Sandri, Damiano. 2010. Growth and Capital Flows with Risky Entrepreneurship.
International Monetary Fund Working Paper No. 10/37 (February).

Sufi, Amir. 2009. The Real Effects of Debt Certification: Evidence from the Introductionof Bank Loan Ratings. Review of Financial Studies. Vol. 22. pp. 1057–1088.

Whited, Toni M. 2006. External Finance Constraints and the Intertemporal Pattern ofIntermittent Investment. Journal of Financial Economics. Vol. 81. pp. 467–502.

23

Appendix
Appendix Table 1: Variable Definitions and Data Sources
Variable List

SourceDefinition (Oriana) / Derivation

Cash and cash equivalents (CCE)

raw data Amount of cash in the bank and on handof the company
Change in cash and cash equivalents
derived Change in cash and cash equivalents
between time (t) and time (t-1)
Cash savings: Ratio of change in cash derived Change in cash and cash equivalents
divided by total assets
and cash equivalents to total assets
Ratio of cash and cash equivalents toderived Cash and cash equivalents divided bytotal assets
total assets
Cash flow
Ratio of cash flow to total assets

raw data Profit for period + Depreciation
derived Cash flow divided by total assets

Firm size
Total assets

derived ln(total assets )raw data Fixed assets + Current assets

Market price to book value ratio

derivedTobin's Q

derivedMarket capitalization

Total debt

Market price year-end divided by bookvalue per share

Ratio of the sum of market capitalizationand total debt to total assets
raw data Market value of a listed company iscalculated by multiplying its share price by
the number of shares outstanding
derived Sum of current and non-current liabilities

Source: Bureau Van Dijk Oriana Database (https://oriana.bvdinfo.com/).

24Appendix Table 2: Summary Statistics
Ratio of cash
Cash & cash
Cash flow& cash equiv.
equivalents Cash savings(thous USD)
to total(thous USD)
assetsAustralia
MeanMedian
SD
Min
Max
China
Mean
MedianSD
Min
Max
Hong Kong
MeanMedianSD
Min
Max
Indonesia
MeanMedian
SD
Min
Max
Japan
Mean
Median
SD
Min
Max
Korea
Mean
MedianSD
Min
Max
Malaysia
MeanMedianSD
Min
Max
Philippines
MeanMedianSD
Min
MaxSingapore
MeanMedianSD
Min
Max
ThailandMeanMedianSD
Min
Max
Viet Nam
MeanMedianSD
Min
Max

Ratio of cash
flow to total Total assets
assetsTobin's q
MarketTotal debt
(Market Price
Capitalization(thous USD)
to Book(million USD)
Ratio)

10,663
3,686
24,472
3
333,2770.04010.01530.1472-0.70690.68480.18980.12220.19150.00010.98759,463
3,012
28,345
16404,430

0.12700.09340.11800.0033
1.021285,80432,852247,991
1,796
3,290,548

1.478033
1.127082
1.086585
0.4073761
8.819387

80
24
253
1
5,847

37,583
10,527
144,802127
2,425,978123,838
52,634
252,95026
4,267,429

0.02920.01630.0911-0.2304
0.47160.18230.15030.12970.00050.928355,651
20,873112,392147
1,480,427

0.07280.0626
0.04390.00810.2760728,842
332,192
1,172,018
17,286
9,698,238

1.707129
1.407937
0.9964765
0.4903343
7.377475

595
301
1,135
4
31,849433,903
164,333
798,796959
8,374,440

183,554
50,578417,15868
7,315,390

0.02510.01430.0925-0.3451
0.54300.18330.14290.14600.00010.8717

133,949
29,678410,626
149
7,964,317

0.10080.0886
0.06110.00470.4064

1,428,113
339,5443,497,721
5,115
37,509,104

1.214296
0.9479343
0.832922
0.304493
7.012769

1,034
174
3,329
2
75,388704,121
111,400
1,872,649
510
24,100,000

53,0008,471
122,3845
1,211,231

0.01460.00590.0669-0.22890.28670.12130.08470.11670.00030.661847,667
9,207
115,72936
1,003,891

0.10160.08070.07300.00390.4348399,197
112,208
769,3142,164
5,752,184

1.397483
1.076081
0.9046853
0.3800052
7.326864

458
59
1,279
1
13,896222,228
57,760468,06768
3,743,013

188,815
46,593
575,574
81
16,833,098

0.01440.00980.0498-0.1973
0.23780.16720.13600.12420.00020.8707

100,953
19,909276,97099
5,763,806

0.0646
0.05940.03610.00410.20431,450,882
364,332
3,411,980
11,65430,768,990

1.065206
0.9704849
0.4045207
0.4544477
3.770925

838
148
2,398
3
58,428829,441
168,632
2,230,589
1,096
26,200,000

56,802
8,552
178,748
1
2,158,257

0.00930.00550.0620-0.2675
0.28840.08920.06470.08360.00000.732542,975
6,477
165,77596
3,380,055

0.06440.05220.04880.0026
0.3272

722,769
123,370
2,080,305
7,744
18,927,784

1.132627
0.9741384
0.5468624
0.4364686
4.204416

454
61
1,552
4
24,405422,999
54,135
1,364,849
263
17,700,000

29,691
6,608
96,961
2
1,608,228

0.01420.00790.0606-0.26630.26020.13280.09270.12530.00010.925818,381
4,921
47,83635
636,646

0.08510.07580.05590.00330.3629213,869
69,309482,002
3,815
5,363,550

1.012785
0.8749134
0.5203002
0.4107196
4.924468

147
29
484
1
7,142

103,367
25,677
268,006108
3,589,091

69,438
11,992
175,276
29
1,389,315

0.02010.00710.0759-0.27350.44010.14210.10010.13720.00110.784865,3258,238
190,0226
1,646,965

0.11140.09190.08410.00190.5729480,806
120,753
981,950
1,086
6,330,523

1.337886
1.077721
0.8583651
0.3818043
6.121884

467
69
1,320
1
14,475

266,32253,703634,766145
4,200,584

60,832
15,408
184,79746
2,707,9780.02590.01710.0872-0.35420.45260.19020.15500.13890.00170.921834,4398,805
86,95661
1,050,150

0.09990.08870.06070.00710.4465363,540
98,321
919,174
4,558
10,234,409

1.169542
1.005973
0.5635185
0.4465593
5.150048

279
55
792
2
8,663

199,856
41,909619,628452
8,407,740

29,274
4,965
81,863
1
862,8240.01050.00380.0572-0.20200.29370.09970.0594
0.10840.00010.797239,700
7,979
118,17190
1,585,838

0.11300.10270.07030.00550.4636322,233
83,364
710,762
2,919
5,045,682

1.190743
1.018209
0.5927649
0.4034556
4.20016

270
46
828
1
8,797

160,90835,801
398,097269
4,125,462

6,029
1,763
16,797
6
203,160

0.00360.00090.0919-0.28120.35270.12540.08160.13170.0002
0.9437

4,766
1,685
12,104
18
206,1850.10450.08650.07240.00770.391447,47922,05775,044
1,166
579,4221.060225
0.978878
0.3640701
0.4633949
3.280699

27
8
89
1
1,604

28,226
11,92048,22396419,968

Source: Bureau Van Dijk Oriana Database (https://oriana.bvdinfo.com/).

25

No. of
obs.
1,025

6,418

2,773

1,082

13,289

4,134

3,103

471

2,193

1,604

1,084

Table 1: OLS Estimates by Economy
OLS Estimation (excluding top and bottom 1% outliers, small country firms, and industries related to finance, real estate,
management & government)

Countryq1

CF

Firm Size

Constant

No. of obs.

q1

Without industry dummies
Australia
China, People's Rep. ofHong Kong,China
Indonesia

0.006587*

0.2875***0.004983-0.0599018(0.0028)(0.0393)(0.0039)(0.04189)

0.000617

0.1412***0.003445**-0.026289(0.0005)(0.0255)(0.0011)(0.0138)

0.006485***0.1619***0.0002541-0.0041642(0.0013)(0.0300)(0.0011)(0.0149)0.0005568

0.1696***0.001069-0.016289(0.0012)(0.0290)(0.0013)(0.0147)Japan-0.002921***(0.0005)(0.0127)(0.0003)(0.0037)

Korea, Rep. of

0.004381***0.09530***0.0001762-0.004607(0.0009)(0.0205)(0.0007)(0.0081)

Malaysia-0.001007

0.1802***0.001215-0.0137298(0.0014)(0.0217)(0.0008)(0.0097)

Philippines

0.005674*

0.1509**-0.0002967-0.0037754(0.0026)(0.0462)(0.0023)(0.0269)0.001985

0.3187***-0.001823

0.0130733(0.0016)(0.0314)(0.0014)(0.0165)-0.002903*

0.1329***-0.0003985

0.0048742(0.0014)(0.0219)(0.0010)(0.0118)

0.01691***

0.09799*

0.003492-0.0605037(0.0036)(0.0405)(0.0023)(0.0236)

Singapore
Thailand
Viet Nam

0.2169*** -0.001390*** 0.0223395***

CF

Firm Size

Constant

No. of obs.

With industry dummies
1067
6630
2951
1088

0.007387**

0.2727***0.004529-0.0762584(0.0028)(0.0399)(0.0041)(0.0610)0.0005105

0.1530*** 0.003908***-0.0137352(0.0005)(0.0261)(0.0011)(0.0176)

0.006422***0.1718***0.0007893-0.0062904(0.0013)(0.0305)(0.0012)(0.0210)0.000603

0.1784***0.001351-0.0177784(0.0012)(0.0292)(0.0013)(0.0219)13448-0.003404***(0.0131)(0.0003)(0.0046)4210

0.004316*** 0.09274***0.0004233-0.0150734(0.0005)0.2207*** -0.0006122* 0.0205063***(0.0010)(0.0208)(0.0007)(0.0130)3172-0.001755

0.1904***0.001140.0025149(0.0014)(0.0224)(0.0009)(0.0131)487

0.006522*

0.1773***0.0007429

0.0474772(0.0027)(0.0472)(0.0024)(0.0432)0.001785

0.3264***-0.001651

0.0024863(0.0016)(0.0319)(0.0014)(0.0226)-0.001997

0.1382***-0.0002268-0.0047171(0.0014)(0.0225)(0.0010)(0.0170)0.01688***

0.1041*0.002998-0.106211***(0.0037)(0.0427)(0.0024)(0.0321)

2285
1686
1112

26

1067
6630
2951
1088
13423
4210
3172
487
2285
1686
1112

CF = the ratio of cash flow to total assets, OLS = ordinary least squares
Note: q1 ≡ Market price to book standard errors are reported below estimates in parentheses, * p<0.05, ** p<0.01, ***p<0.001

27

Table 2: Fama–Macbeth Estimates by Economy
Fama-Macbeth Estimation (excluding top and bottom 1% outliers, small country firms, and industries related to finance,
real estate, management & government)

Countryq1

CF

Firm Size

Constant

Ave no. of obs.
(No. of years)

q1

CF

Without industry dummies
Australia

0.006731

0.3211*0.003181-0.0454576(0.0046)(0.1005)(0.0044)(0.0575)

0.004071

0.26770.005726-0.0693159(0.0032)(0.1854)(0.0044)(0.0564)0.002808

0.1585*-0.00243

0.0360237(0.0023)(0.0520)(0.0031)(0.0468)0.001154

0.1574**0.0005429-0.00199(0.0025)(0.0327)(0.0016)(0.0179)Japan

0.001272

0.1790***-0.001982

0.0269692(0.0012)(0.0284)(0.0008)(0.0145)

Korea, Rep. of

0.003344

0.1017***0.0003711 -0.0063699(0.0018)(0.0155)(0.0012)(0.0160)

Malaysia-0.001753

0.12390.001974-0.0205728(0.0022)(0.0629)(0.0015)(0.0167)

Philippines-0.001156

0.16170.0008731 -0.0110197(0.0052)(0.0755)(0.0018)(0.0221)-0.02095

0.2655***-0.091291.040566(0.0216)(0.0464)(0.0897)(1.0287)

0.0495-7.0126

0.0553-0.3825077(0.0477)(7.1468)(0.0555)(0.3780)

0.01192

0.01330.006061-0.0715611(0.0065)(0.0953)(0.0047)(0.0406)

China, People's Rep. ofHong Kong,China
Indonesia

Singapore
Thailand
Viet Nam

Firm Size

Constant

Ave no. of obs.
(No. of years)

With industry dummies
1067

0.008999

0.3161*0.003338-0.0620332(0.0053)(0.0999)(0.0036)(0.0654)0.004872

0.23560.003152-0.225584(0.0052)(0.1365)(0.0029)(0.0622)-0.00247

0.2572**0.001725-0.0128939(0.0053)(0.0693)(0.0012)(0.0246)0.001463

0.1660**0.001176-0.0105716(0.0023)(0.0361)(0.0015)(0.0189)13448

0.000943

0.1882***-0.001248

0.0186758(0.0011)(0.0261)(0.0006)(0.0140)4210

0.003364

0.09840***0.0005865-0.0185968(0.0018)(0.0153)(0.0013)(0.0194)3172-0.006746

0.1649**0.005964-0.059852(0.0043)(0.0355)(0.0051)(0.0579)487-0.00149

0.1915*0.001909-0.0179934(0.0052)(0.0737)(0.0018)(0.0302)2285-0.04604

0.2710***-0.001306

0.0751535(0.0461)(0.0498)(0.0024)(0.0798)1686

0.0031030.1196*0.0001142-0.0005547(0.0068)(0.0399)(0.0012)(0.0158)

0.01261

0.003742

0.005661-0.0659916(0.0076)(0.0957)(0.0050)(0.0563)

6630
2951
1088

1112

28

1067
6630
2951
1088
13423
4210
3172
487
2285
1686
1112

Note: q1 ≡ Market price to Book, Fama-Macbeth standard errors are reported below estimates inparentheses, * p<0.05, ** p<0.01, *** p<0.001

29

Table 3: GMM Estimates by Economy
Without industry dummies, excluding outliers, countries with small no. of firms, and industries related tofinance, real estate, management & governmentq1

CFFirm Size

Australia

Constant

0.005834
0.052230.001593-0.008168(0.0065)(0.1074)(0.0044)(0.0504)
China
0.0007004
0.1239***0.004780***-0.04473**(0.0007)(0.0274)(0.0011)(0.0151)
Hong Kong
0.01075***
0.09956**-0.0001322
0.00142(0.0022)(0.0383)(0.0011)(0.0149)
Indonesia
0.001420.1560***0.002531-0.03506*(0.0025)(0.0392)(0.0015)(0.0168)Japan-0.007000***
0.2204***-0.0003701
0.01238**(0.0008)(0.0156)(0.0003)(0.0040)
Korea
0.003975**
0.08211**0.000245-0.003612(0.0013)(0.0273)(0.0006)(0.0079)
Malaysia
0.003636
0.1709***0.001449-0.02009(0.0030)(0.0298)(0.0010)(0.0113)
Philippines
0.003541
0.1597*0.001958-0.03836(0.0037)(0.0691)(0.0037)(0.0421)
Singapore
0.0002139
0.2828***-0.000837
0.004674(0.0033)(0.0496)(0.0016)(0.0203)
Thailand-0.0009633
0.1003**-0.000432
0.00405(0.0024)(0.0326)(0.0011)(0.0130)
Viet Nam
0.01778*-0.01343
0.002742-0.04496(0.0075)(0.0642)(0.0031)(0.0332)
Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

30

No. of Obs.
506
5837
2139
790
10751
3132
2293
219
1063
1282
423

Table 3: GMM Estimates by Economy (cont’d)With industry dummies, excluding outliers, countries with small no. of firms, and industries related tofinance, real estate, management & governmentq1

CFFirm Size

Australia

Constant

0.002591
0.13350.0008672-0.01304(0.0055)(0.0943)(0.0036)(0.0409)
China
0.0007998
0.1242***0.004727***-0.04488**(0.0007)(0.0272)(0.0011)(0.0149)
Hong Kong
0.01101***
0.09472*-0.0001486
0.001305(0.0022)(0.0379)(0.0011)(0.0146)
Indonesia
0.001956
0.1617***0.001227-0.02134(0.0024)(0.0382)(0.0014)(0.0151)
Japan-0.007211***0.2150***-0.0001852
0.009503*(0.0008)(0.0154)(0.0003)(0.0040)
Korea
0.003778**
0.07744**0.000335-0.004584(0.0012)(0.0270)(0.0006)(0.0077)
Malaysia
0.004382
0.1631***0.00167-0.02312*(0.0028)(0.0290)(0.0010)(0.0110)
Philippines
0.005612
0.1163*0.001099-0.02692(0.0031)(0.0588)(0.0029)(0.0332)
Singapore0.001944
0.2702***-0.001099
0.006726(0.0031)(0.0488)(0.0016)(0.0198)
Thailand-0.003503
0.1072***-0.00005619
0.001397(0.0023)(0.0316)(0.0011)(0.0122)
Viet Nam
0.01797*0.001126
0.001828-0.03583(0.0072)(0.0593)(0.0029)(0.0313)
Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

31

No. of Obs.
506
5837
2139
790
10751
3132
2293
219
1063
1282
423

Table 4: GMM Estimates: Full Sample
Baseline Regressions
FULL SAMPLE : excluding outliers, countries with small no. of firms, and industries related tofinance, real estate, management & government

Full period (2002 - 2011)
Without industry dummies
With industry dummies
Pre-GFC period (2002 - 2007)
Without industry dummies
With industry dummies
GFC period (2008 - 2011)
Without industry dummies
With industry dummies

q1

CF

Firm Size

Constant

No. of Obs.

0.002770***(0.0004)
0.002796***(0.0004)

0.1111***(0.0122)0.1092***(0.0119)0.0009037***(0.0002)0.0009457***(0.0002)-0.009476**(0.0031)-0.01021***(0.0030)28435

0.003318***(0.0007)0.003392***(0.0007)0.1494***(0.0159)
0.1471***(0.0156)-0.0006043(0.0004)-0.0004437(0.0003)0.0003078(0.0044)-0.002051(0.0043)11309

0.002042***(0.0005)
0.002024***(0.0005)

0.07024***(0.0163)
0.06894***(0.0158)

0.002071***(0.0003)
0.002066***(0.0003)-0.01623***(0.0042)-0.01630***(0.0041)

17126

Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

32

28435

11309

17126

Table 4: GMM Estimates: Full Sample (cont..)
Augmented Regressions
FULL SAMPLE : excluding outliers, countries with small no. of firms, and industries related to finance, real estate, management & government

Full period (2002 - 2011)
Without industry dummies
With industry dummies
Pre-GFC period (2002 - 2007)
Without industry dummies
With industry dummies
GFC period (2008 - 2011)
Without industry dummies
With industry dummies

q1

CF

Firm Size

CapitalExpenditures

Working Capital

Short-term Debt

Constant

No. of Obs.

0.003426**(0.0010)0.003062**(0.0010)0.09622***(0.0182)
0.1016***(0.0180)-0.0009364***(0.0003)-0.0006604*(0.0003)-0.08290*(0.0392)-0.1076**(0.0357)

0.01992(0.0602)0.1193*(0.0500)

0.5601***(0.0996)0.5095***(0.0943)0.01352***(0.0037)
0.009024*(0.0036)20287

0.004911**(0.0019)0.005275**(0.0017)0.1082***(0.0313)0.1008***(0.0297)-0.001201**(0.0005)-0.001182**(0.0004)

0.04361(0.0722)0.04061(0.0623)

0.2963**(0.0905)
0.3096***(0.0728)0.3445**(0.1208)0.3052**(0.1127)0.0006364(0.0055)-0.00002382(0.0051)7588

0.003851**(0.0013)0.002724*(0.0013)0.02789(0.0225)
0.05257*(0.0214)-0.000202(0.0004)
0.00005659(0.0004)-0.1529***(0.0461)-0.1831***(0.0424)-0.1344(0.0778)-0.002699(0.0651)0.4505***(0.1246)
0.4275***(0.1133)

0.01665**(0.0052)
0.01150*(0.0050)

Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

33

20287

7588

12699
12699

Table 5: GMM Estimates: Developed Economy Sample
Baseline Regressions
POOLED DEVELOPED COUNTRIES : excluding outliers, countries with small no. of firms, andindustries related to finance, real estate, management & government

Full period (2002 - 2011)
Without industry dummies
With industry dummies
Pre-GFC period (2002 - 2007)
Without industry dummies
With industry dummies
GFC period (2008 - 2011)
Without industry dummies
With industry dummies

q1

CF

Firm Size

Constant

No. of Obs.

-0.0005918(0.0012)-0.0007207(0.0011)0.1963***(0.0235)
0.1844***(0.0216)-0.0007222*(0.0003)-0.0005972(0.0003)0.01053*(0.0043)0.009401*(0.0043)14459-0.001994(0.0018)-0.001794(0.0016)0.2569***(0.0261)
0.2428***(0.0248)-0.0002982(0.0005)-0.000125(0.0005)-0.00486(0.0058)-0.00685(0.0057)6499

0.002911(0.0017)0.002015(0.0016)0.07187*(0.0318)
0.06617*(0.0292)-0.001155*(0.0005)-0.001089*(0.0004)

0.02742***(0.0061)
0.02742***(0.0060)

Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

34

14459

6499

7960
7960

Table 5: GMM Estimates: Developed Economy Sample (cont..)
Augmented Regressions
POOLED DEVELOPED COUNTRIES : excluding outliers, countries with small no. of firms, and industries related to finance, real estate, management &government

Full period (2002 - 2011)
Without industry dummies
With industry dummies
Pre-GFC period (2002 - 2007)
Without industry dummies
With industry dummies
GFC period (2008 - 2011)
Without industry dummies
With industry dummies

q1

CF

Firm Size

CapitalExpenditures

Working Capital

Short-term Debt

Constant

No. of Obs.

0.001523(0.0089)
0.007413*(0.0037)

0.1069(0.0812)0.04742(0.0441)-0.0015(0.0010)-0.001836***(0.0005)-0.02218(0.0534)-0.06856(0.0485)0.1748(0.1301)
0.2991***(0.0868)0.2005(0.1445)0.2569*(0.1187)

0.02194***(0.0063)
0.02157***(0.0049)10268-0.0128(0.0083)0.001469(0.0040)0.2842***(0.0788)
0.1475**(0.0495)

0.0002795(0.0010)-0.001195(0.0006)

0.09941(0.0891)0.0848(0.0787)0.2316(0.1199)0.2330**(0.0880)-0.07001(0.1802)0.1056(0.1331)-0.002424(0.0075)
0.00334(0.0059)-0.03737*(0.0161)0.00155(0.0050)0.4507**(0.1543)0.06785(0.0552)0.00162(0.0014)-0.0006621(0.0006)-0.2437*(0.1130)-0.1870**(0.0645)-0.8606(0.4584)0.2899*(0.1447)

0.06685(0.3530)-0.3902*(0.1989)

0.02123(0.0115)0.02457***(0.0074)

Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

35

10268

4241
4241

6027
6027

Table 6: GMM Estimates: Developing Economy Sample
Baseline Regressions
POOLED DEVELOPING COUNTRIES : excluding outliers, countries with small no. of firms, andindustries related to finance, real estate, management & government

Full period (2002 - 2011)
Without industry dummies
With industry dummies
Pre-GFC period (2002 - 2007)
Without industry dummies
With industry dummies
GFC period (2008 - 2011)
Without industry dummies
With industry dummies

q1

CF

Firm Size

Constant

No. of Obs.

0.003107***(0.0004)
0.003089***(0.0004)

0.09630***(0.0119)0.09510***(0.0118)0.003165***(0.0004)
0.003199***(0.0004)-0.03635***(0.0047)-0.03689***(0.0047)13976

0.003572***(0.0007)0.003550***(0.0007)0.08204***(0.0190)0.07592***(0.0189)

0.002300***(0.0006)
0.002624***(0.0006)-0.02607***(0.0076)-0.03011***(0.0074)

0.002888***(0.0005)
0.002880***(0.0005)

0.1058***(0.0151)
0.1042***(0.0149)

0.003597***(0.0005)
0.003551***(0.0005)-0.04175***(0.0060)-0.04118***(0.0060)

Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

36

13976

4810
4810

9166
9166

Table 6: GMM Estimates: Developing Economy Sample (cont…)
Augmented Regressions
POOLED DEVELOPING COUNTRIES : excluding outliers, countries with small no. of firms, and industries related to finance, real estate, management &government

Full period (2002 - 2011)
Without industry dummies
With industry dummies
Pre-GFC period (2002 - 2007)
Without industry dummies
With industry dummies
GFC period (2008 - 2011)
Without industry dummies
With industry dummies

q1

CF

Firm Size

CapitalExpenditures

Working Capital

Short-term Debt

Constant

No. of Obs.

0.003139*(0.0014)0.003120*(0.0013)0.1280***(0.0255)
0.1264***(0.0240)-0.0008322(0.0006)-0.0006897(0.0006)-0.03306(0.0612)-0.03292(0.0550)

0.3080***(0.0727)0.3095***(0.0618)

0.5162***(0.1568)
0.4934***(0.1392)-0.0001075(0.0072)-0.001672(0.0068)10019

0.001252(0.0023)0.001622(0.0020)0.1788***(0.0399)0.1758***(0.0352)0.0008062(0.0009)
0.001048(0.0008)-0.1699*(0.0844)-0.1656*(0.0705)

0.1195(0.1097)
0.1238(0.0864)0.6403***(0.1777)
0.5386***(0.1439)-0.01141(0.0100)-0.01487(0.0090)0.002366(0.0017)0.002396(0.0015)0.1396***(0.0295)
0.1331***(0.0266)-0.001830*(0.0008)-0.001725*(0.0007)0.0006248(0.0774)
0.03337(0.0664)0.3067***(0.0861)
0.2768***(0.0751)0.6175***(0.1805)
0.5740***(0.1505)

0.0109(0.0096)0.01014(0.0089)

Note: q1 ≡ Market price to Book
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

37

10019

3347
3347

6672
6672

Table 7: GMM Estimates: Full Economy, Breakdown by Firm Size
Baseline Regressions
GMM Estimation on Base Model (FULL SAMPLE with industry dummies)
Grouing based on Firm Size

q1

CF

Size

Constant

No. of obs.

3505

All firms grouped by quintiles based on median size of each firm
Bottom quintile
2nd quintile

FULL SAMPLE

3rd quintile
4th quintile
Upper quintile

0.0002721

0.1845***0.009070***-0.09566***(0.0010)(0.0299)(0.0015)(0.0170)0.002113*

0.1576***0.006712***-0.07959***(0.0009)(0.0244)(0.0012)(0.0142)0.002458**

0.1430***0.005672***-0.07182***(0.0009)(0.0241)(0.0010)(0.0127)0.001915*

0.1055***0.002270*-0.02737*(0.0008)(0.0225)(0.0009)(0.0125)

0.003005**

0.08452***-0.0007737

0.01342(0.0010)(0.0196)(0.0007)(0.0102)

4173
4381
4611
4367

All firms grouped by quintiles based on mean size of each firm
Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile-0.00008039

0.1743***0.008159***-0.08502***(0.0010)(0.0303)(0.0015)(0.0171)

0.002493**

0.1613***0.006677***-0.07958***(0.0009)(0.0238)(0.0012)(0.0142)0.001987*

0.1384***0.005349***-0.06679***(0.0009)(0.0253)(0.0010)(0.0131)0.002361**

0.09121***0.002077*-0.02453(0.0009)(0.0209)(0.0009)(0.0125)

0.002925**

0.09593***-0.0008623

0.01414(0.0009)(0.0196)(0.0006)(0.0093)

Note: q1 ≡ Market Price to Book Ratio
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

38

3489
4183
4364
4609
4392

Table 7: GMM Estimates: Full Economy, Breakdown by Firm Size (cont..)
Augmented Regressions
GMM Estimation on Augmented (1) Model (FULL SAMPLE with industry dummies)
Grouing based on Firm Size

q1

CF

Size

CapitalExpenditure

Working Capital Short-term Debt

Constant

No. of obs.

2347

All firms grouped by quintiles based on median size of each firm
Bottom quintile
2nd quintile

FULL SAMPLE

3rd quintile
4th quintile
Upper quintile

0.0007668

0.07152

0.01137***-0.05363

0.5590***-0.2606-0.1206***(0.0018)(0.0575)(0.0027)(0.1562)(0.1274)(0.2979)(0.0295)-0.0003107

0.1337**0.005933**-0.11170.6390***0.2244-0.07491**(0.0020)(0.0511)(0.0022)(0.1090)(0.1219)(0.2770)(0.0261)

0.003476

0.1113**0.006660***

0.08205

0.4558***-0.137-0.09182***(0.0018)(0.0380)(0.0014)(0.0821)(0.0826)(0.1831)(0.0180)0.0009535

0.08361*0.004559***

0.038510.4554***0.102-0.06429***(0.0019)(0.0388)(0.0012)(0.0743)(0.0853)(0.1300)(0.0159)0.0004138

0.08466*

0.0000666-0.05565

0.3894***0.3409**-0.001305(0.0029)(0.0330)(0.0008)(0.0515)(0.0511)(0.1081)(0.0115)

3001
3208
3465
3369

All firms grouped by quintiles based on mean size of each firm
Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile-0.002124

0.065240.01129***0.000602

0.5151***-0.4514*-0.1158***(0.0016)(0.0554)(0.0026)(0.1641)(0.1287)(0.2241)(0.0287)

0.004076*

0.082960.005272**

0.001542

0.5866***-0.1266-0.06952**(0.0021)(0.0452)(0.0019)(0.0939)(0.1047)(0.2411)(0.0232)

0.003368

0.05772

0.006937***0.2060*0.5932***-0.3938-0.09554***(0.0018)(0.0449)(0.0016)(0.1003)(0.1163)(0.2363)(0.0208)

0.003274

0.08188*

0.003910***-0.02245

0.3733***0.1133-0.05555***(0.0020)(0.0381)(0.0011)(0.0745)(0.0829)(0.1459)(0.0157)-0.0002324

0.1021**-0.0001569-0.05641

0.3467***0.3860***0.002234(0.0031)(0.0367)(0.0008)(0.0528)(0.0723)(0.1170)(0.0112)

Note: q1 ≡ Market Price to Book Ratio
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001
Instruments: Two lags of tangible fixed assets, lagged working capital, lagged short-term debt , twice-lagged sales growth, and country dummies.

39

2333
3034
3201
3416
3406

Table 8: GMM Estimates: Developed Economy Sample, Breakdown by Firm Size
(Australia, Hong Kong, Japan, New Zealand, and Singapore)
Baseline Regressions
GMM Estimation on Base Model (POOLED DEVELOPED COUNTRIES with industry dummies)

Grouing based on Firm Size

q1

CF

Size

Constant

No. of obs.

1801

All firms grouped by quintiles based on median size of each firm

POOLED DEVELOPED COUNTRIES

Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile-0.007304

0.2541***0.002576-0.01951(0.0044)(0.0424)(0.0031)(0.0359)-0.002171

0.1928***0.007598**-0.08909**(0.0024)(0.0386)(0.0027)(0.0329)-0.002923

0.2433***0.004896*-0.06344*(0.0020)(0.0319)(0.0020)(0.0261)-0.004134*

0.1713***0.001675-0.01732(0.0019)(0.0331)(0.0019)(0.0261)-0.005367**

0.1704***0.001482-0.01545(0.0019)(0.0333)(0.0009)(0.0128)

2175
2280
2401
2322

All firms grouped by quintiles based on mean size of each firm
Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile-0.004538

0.2405***0.003686-0.03426(0.0044)(0.0426)(0.0031)(0.0358)-0.001983

0.1752***0.006542*-0.07557*(0.0022)(0.0379)(0.0027)(0.0327)-0.005083*

0.2679***0.005023**-0.06385**(0.0021)(0.0325)(0.0019)(0.0246)-0.002738

0.1578***0.001953-0.02221(0.0019)(0.0325)(0.0019)(0.0263)-0.005739**

0.1842***0.001625-0.01803(0.0018)(0.0307)(0.0008)(0.0122)

Note: q1 ≡ Market Price to Book Ratio
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

40

1789
2186
2269
2409
2326

Table 8 (continued)
Augmented Regressions
GMM Estimation on Augmented (1) Model (POOLED DEVELOPED COUNTRIES with industry dummies)
Grouing based on Firm Size

q1

CF

CapitalExpenditure

Size

Working Capital Short-term Debt

Constant

No. of obs.

1229

All firms grouped by quintiles based on median size of each firm

POOLED DEVELOPED COUNTRIES

Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile-0.0002843

0.1524-0.003823-0.1749

0.5084**0.5332*0.04617(0.0094)(0.0831)(0.0042)(0.1440)(0.1825)(0.2570)(0.0511)0.01335**0.1851***0.004878-0.05947

0.19090.6089*-0.07458(0.0051)(0.0515)(0.0041)(0.1166)(0.1073)(0.2571)(0.0500)

0.007347

0.1402**0.006205*-0.06466

0.5299***0.21-0.09089*(0.0050)(0.0515)(0.0028)(0.0755)(0.1041)(0.1110)(0.0355)

0.008216

0.011380.004018

0.017640.5306***

0.3727***-0.06216(0.0056)(0.0647)(0.0026)(0.0696)(0.1327)(0.0959)(0.0365)

0.01322*-0.0655-0.002365-0.08174

0.5722***0.5198***0.02533(0.0054)(0.0562)(0.0014)(0.0592)(0.1681)(0.1193)(0.0185)

1519
1634
1779
1852

All firms grouped by quintiles based on mean size of each firm
Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile-0.003622

0.1451-0.003811-0.1589

0.6435**0.45190.04906(0.0100)(0.0815)(0.0044)(0.1644)(0.2090)(0.2993)(0.0526)

0.01105*0.1735***0.006536-0.1435

0.2590*0.4988*-0.09099*(0.0055)(0.0496)(0.0038)(0.1117)(0.1196)(0.2517)(0.0462)

0.002748

0.1572**0.004974-0.09206

0.5216***

0.2740**-0.07088(0.0043)(0.0481)(0.0031)(0.0788)(0.1040)(0.1006)(0.0394)

0.009513

0.036310.003056-0.04915

0.4272***0.3692***-0.04918(0.0063)(0.0588)(0.0022)(0.0576)(0.1171)(0.0974)(0.0295)

0.01151*-0.0397-0.002026-0.05061

0.4633**0.4987***0.02131(0.0051)(0.0554)(0.0013)(0.0564)(0.1479)(0.1149)(0.0173)

Note: q1 ≡ Market Price to Book Ratio
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001
Instruments: Two lags of tangible fixed assets, lagged working capital, lagged short-term debt , twice-lagged sales growth, and country dummies.

41

1211
1540
1641
1766
1855

Table 9: GMM Estimates: Developing Economy Sample, Breakdown by Firm Size
(People’s Republic of China, Indonesia, Republic of Korea, Malaysia, Philippines, Thailand, and Viet Nam)Baseline Regressions
GMM Estimation on Base Model (POOLED DEVELOPING COUNTRIES with industry dummies)

Grouing based on Firm Size

q1

CF

Size

Constant

No. of obs.

1704

POOLED DEVELOPING COUNTRIESAll firms grouped by quintiles based on median size of each firm
Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile

0.00004152

0.1599***0.01191***-0.1209***(0.0011)(0.0405)(0.0024)(0.0250)-0.0006656

0.1326***0.01502***-0.1628***(0.0012)(0.0312)(0.0024)(0.0255)-0.001056

0.1273***0.01776***-0.2037***(0.0012)(0.0324)(0.0023)(0.0267)0.0005884

0.08414**0.01290***-0.1542***(0.0011)(0.0291)(0.0022)(0.0274)

0.003701***

0.05135*

0.006222***-0.07790***(0.0011)(0.0251)(0.0017)(0.0230)

1998
2101
2210
2045

All firms grouped by quintiles based on mean size of each firm
Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile

0.000005213

0.1273**0.008906***-0.08897***(0.0011)(0.0408)(0.0025)(0.0261)-0.0003153

0.1544***0.01556***-0.1702***(0.0012)(0.0307)(0.0023)(0.0249)0.0002169

0.09367**

0.01475***-0.1680***(0.0012)(0.0330)(0.0023)(0.0263)0.0003572

0.06870*0.01283***-0.1514***(0.0012)(0.0269)(0.0023)(0.0284)

0.003899***0.06009*

0.006068***-0.07585***(0.0011)(0.0250)(0.0016)(0.0220)

Note: q1 ≡ Market Price to Book Ratio
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001

42

1700
1997
2095
2200
2066

Table 9 (continued)
Augmented Regressions
GMM Estimation on Augmented (1) Model (POOLED DEVELOPING COUNTRIES with industry dummies)
Grouing based on Firm Size

q1

CF

Size

CapitalExpenditure

Working Capital Short-term Debt

Constant

No. of obs.

1118

All firms grouped by quintiles based on median size of each firm

POOLED DEVELOPING COUNTRIES

Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile

0.001414

0.083180.01069*-0.1060.4940**-0.04551-0.1154*(0.0023)(0.0662)(0.0053)(0.1377)(0.1606)(0.2941)(0.0526)-0.0002005

0.11940.005291-0.15590.6543***0.2384-0.06591(0.0031)(0.0619)(0.0059)(0.1251)(0.1738)(0.2773)(0.0616)0.005696

0.06487

0.003424

0.2547*0.3256**-0.1072-0.05621(0.0036)(0.0520)(0.0061)(0.1017)(0.1224)(0.2765)(0.0674)

0.004151-0.04439

0.005359

0.3196*0.3338**-0.3507-0.07135(0.0030)(0.0632)(0.0050)(0.1254)(0.1113)(0.2254)(0.0571)0.004541

0.030220.001210.066220.3819***0.004591-0.02207(0.0036)(0.0588)(0.0023)(0.1002)(0.0635)(0.2406)(0.0315)

1482
1574
1686
1517

All firms grouped by quintiles based on mean size of each firm
Bottom quintile
2nd quintile
3rd quintile
4th quintile
Upper quintile-0.002123

0.06491

0.01498**-0.09513

0.4721**-0.4241*-0.1538**(0.0025)(0.0680)(0.0057)(0.1160)(0.1555)(0.2079)(0.0561)

0.006543*

0.02687-0.0009268

0.077780.6806***-0.1-0.004955(0.0033)(0.0615)(0.0055)(0.1260)(0.1832)(0.2814)(0.0573)

0.008755*

0.01616-0.002367

0.4352***0.3313*-0.14250.00594(0.0035)(0.0605)(0.0058)(0.1311)(0.1478)(0.2534)(0.0626)

0.003521-0.07097

0.008245

0.2745*0.3248**-0.499-0.1(0.0033)(0.0668)(0.0052)(0.1388)(0.1159)(0.2576)(0.0603)

0.005318

0.020960.0004496

0.088470.3747***-0.02353-0.01232(0.0038)(0.0636)(0.0024)(0.1019)(0.0683)(0.2420)(0.0327)

Note: q1 ≡ Market Price to Book Ratio
Robust standard errors are reported below estimates in parentheses* p<0.05, ** p<0.01, *** p<0.001
Instruments: Two lags of tangible fixed assets, lagged working capital, lagged short-term debt , twice-lagged sales growth, and country dummies.

43

1122
1494
1560
1650
1551

Figure 1: Change in the Cash Holdings to Total Assets Ratio
0.025
0.02
0.015
Full sample

0.01

Developing
0.005

Developed0
2003 2004 2005 2006 2007 2008 2009 2010 2011
-0.005-0.01

44