Debt, Ownership Structure, and R&D Investment：
Evidence from Japan

Yasuhiro Arikawa
Graduate School of Finance, Accounting and Law, Waseda University/
WCFIA, Harvard University
Takuya Kawanishi
Graduate School of Law, Waseda University
Hideaki Miyajima
Graduate School of Commerce, Waseda University/RIETI/WIAS

September 2010

This paper is written as a part of RIETI’s project on Corporate Governance in Japan. We
thank Takuji Saito for his comment on earlier version of this paper. This research is
supported by JSPS KAKENHI (Grant No. 19203017).

Abstract

Financial factors as well as ownership structure would be part of the determinants ofcorporate R&D investment. Taking listed firms in R&D intensive industries in the
2000s of Japan, this paper is to examine whether the financial factors and ownership
structure explain the R&D investment in Japan. Following the methodology of Brown etal. (2009) which extends the dynamic investment model of Bond and Maghir (1994) to
R&D investment, we find that only young and small firms mainly listed on newly
established emerging markets face the financial constraint. We also find that large firms
finance R&D investment partly from debt following the optimal debt policy. For firms
with small asset, however, higher leverage leads to lower R&D investment. Lastly, we
do not find any evidence that large shareholdings by foreign investors enforce myopic
behavior on firms.

Keywords: R&D, Debt, Cash flow, Ownership
JEL Classification: D22, G32

1

1.

Introduction
During a period of rapid economic growth, Japanese firms invested mainly on

physical assets. However, they gradually have placed more importance on research anddevelopment (R&D, hereafter) investment since the middle of 1980s. Domestic physicalinvestment peaked in 1991 and dropped sharply after the burst of the bubble andremained low even in the recovery period in the 2000s. R&D investment, on the other
hand, has increased continuously and it surpassed physical investment between 2002
and 04 (METI, 2009). Currently, the size of R&D and physical investment is atcomparable level in Japan.
From 1994 to 2004, firms which recently listed on the market accounted a largepart of R&D investment in the US (Brown et al., 2009). Firms listed on the market at
most 15 years ago account for 45.7% of the aggregate R&D in 1998 and 26.1% in 2004
in the US. By contrast, in Japan, mature firms which drove the high growth period arestill the main players of R&D expenditure. While domestic emerging equity markets
have been organized since 1999 and many new firms went public especially inInformation Technology (IT, hereafter) related sectors, firms which went public after
1990 account for only 3% of total R&D spending in R&D intensive industries. On theother hand, large mature firms whose consolidated assets are more than 500 billion yen
account for nearly 80% of total R&D spending in R&D intensive industries. For
example, Toyota Motor, the top R&D spending firm in Japan, spent 890 billion yen in2006, which was about 60% of its physical investment. The second largest R&D
spending firm Matsushita Electric Industrial (now Panasonic) spent 580 billion yen andthat was more than its physical expenditure of 420 billion yen.
The main purpose of this paper is to examine whether the financial factor explains
the R&D investment in Japan. This paper focuses on three issues.
First, we explore whether the financial factor explains the little presence of R&D
investment by small and young firms in Japan. Following Brown et al. (2009) we
employ the dynamic investment model of Bond and Meghir (1994) to R&D investment,
and examine whether the shift of internal fund can explain the change of R&D
2

investment. Then, we find that contemporaneous cash flow have no statisticallysignificant effect on R&D investment of firms with larger asset. For young and small
firms mainly listed on newly established emerging markets, however, the coefficient ofcontemporaneous cash flow is significantly positive, and this is consistent with thefinding of Brown et al. (2009). On the other hand, we do not find any positive effect ofequity finance on firms with small asset size. This result contrasts with the theoreticalprediction as well as the US case in which the changes of equity finance by the young,high-tech firms explain most of the 1994 to 2004 aggregate R&D cycle (Brown et al,2009).
Second, we examine the role of debt. We extend the work by Brown et al. (2009) byfocusing on debt in addition to equity finance since debt is more important source ofincremental funding than outside equity for most firms in Japan. If a debt is themarginal source of external finance, we expect that the credit restrictions affectcorporate decisions seriously. Ogawa (2007) investigate the relationship between R&D
investment and debt in Japan using the sample period from 1988-1991 and 1999-2001.
Then, it shows that the ratio of debt to total assets had a significant, negative effect on
R&D investment in the late 1990s, while the effect of the debt-asset ratio on R&D
investment was insignificant in the late 1980s.
Using the sample period from 2001 to 2008, we find that the coefficient of lagged
debt and lagged square debt is significantly negative as implied by tax-bankruptcy
specification for firms with large asset. This result implies that large firms finance R&D
investment at least partly from debt following the optimal debt policy. Contrast to large
firms, estimation results for firms with small asset show that the coefficient of lagged
debt is significantly negative, while that of lagged square debt is insignificant,suggesting that, only for firms with small size, higher level of debt financing leads tolower R&D investment.
Third, this paper highlights the effect of increasing foreign ownership on R&D
investment. One characteristic of changes in corporate governance structure forJapanese firms was the shift from insider to outsider ownership (Miyajima and Kuroki,2007). It is plausible that if an investor were myopic and his preference were biased
3

toward immediate dividend, then managers may take into account these myopic
investors and pay dividends by sacrificing R&D investment (Narayanan, 1985, andStein, 1989). To study the effect of myopic investors on R&D investment, we add theinteraction term between contemporaneous cash flow and foreign investor’sshareholding ratio. Then, for small firms which face the financial constraint, we do notfind any evidence that large shareholdings by foreign investors enforce myopic behavior
on firms.
The remainder of this paper is organized as follows. Section 2 overviews R&D
expenditure in Japan from the late 1990s. Section 3 summarizes the financial structuresof R&D intensive firms. Section 4 describes the empirical model and Section 5 reports
the estimation results. Section 6 concludes.

2.

An Overview on R&D Investment

2.1 Macro Trend and R&D Intensive Industries
We first observe macro trend in R&D expenditure from 1980 with Figure 1
borrowed from METI (2009). While domestic physical investment reached its peakvalue of 20 trillion yen in 1991, plunged after the burst of bubble in 1991 and stayedaround 13 trillion yen even in the recovery period in the 2000s, R&D expenditure
steadily increased from 9 trillion yen in 1985 to 12 trillion yen in 2007. Now R&D
investment in Japan has the same scale as physical investment.1== Figure 1 about here ==Table 1 shows industry-level R&D expenditure. Industry classification is based on
Securities Identification Code Committee’s 33 sectors (excluding financial sectors)(Syoken Code Kyogikai 33 Gyoshu).2 The sample is all the listed firms contained in1

According to METI (2009), the composition of R&D spending by corporations in 2007 is labor
39.8%, material 18.4%, depreciation 7.3%, lease 0.7%, and other 33.7%.
2 According to Basic Survey of Japanese Business Structure and Activities by Ministry of Economy,
Trade and Industry, the proportion of R&D expenditure by public firms is 87%.
4

Toyo Keizai dataset. 3 We did not take R&D spending data from profit-and-loss
statement since it is argued that it under-evaluates R&D spending by not taking into
account salaries for researchers. Table 1 shows that manufactures account for 94% of
R&D expenditure. Electric appliances sector has the largest 38% R&D expenditure
share, followed by transportation equipment (24%). Chemicals, machinery, precision
instruments, and information & communication also have high R&D expenditure
share.4 Regarding R&D intensity (R&D expenditure/sales), pharmaceutical is by far thehighest and electric appliances and precision instruments have intensity of over 4%. We
define these seven industries as R&D intensive industries. The total R&D expenditureby the seven industries is 12.5 trillion yen which accounts for 94% of the whole sample
in Toyo Keizai dataset.

== Table 1 about here ==2.2 R&D Investment by Firm Type
Figure 2 summarizes the trend of R&D expenditure by the sample firms in theseven R&D intensive industries used in the empirical analysis below. The average of
R&D spending-assets ratio is stable in the sample period, although it slightly goes up in
the economic downturn and drops in the upturn. This is different from the US casewhere there was a R&D boom in the late 1990s and a sharp decline after the end of the2000. While the R&D-assets ratio has been stable at around 4% since the late 1990s, thecoefficient of variation has upward trend.

== Figure 2 about here ==It is plausible that size and age of a firm would affect on its R&D investment
3

The data is based on the Kaisha Shikiho (Japan Company Handbook) data. Branstter (1996)
discusses quality of the data.
4 Among firms in Information & Communication sector classified by Securities Identification CodeCommittee’s 33 sectors, we only pick up firms in 3 major groups; Communications (37),
Information Services (39), and Internet Based Services (40) in Division G: Information andCommunications by Japan Standard Industrial Classification.
5

substantially. The economy of scale could work on R&D investment. Firms with large
asset is likely to invest on R&D more than firms with small asset because large firms
have high reputation in the capital market and the multi-divisional structure of a large
firm could reduce business risks as a whole. In general, a newly emerged firm is likely
to engage in R&D investment, while a matured firm is less likely to engage in R&D
investment. Considering those points, we sprit sample into several groups based on firm
size and age. We classify firms in the 1st or 2nd section of Tokyo Stock Exchange as
Large if their consolidated assets are 300 billion yen or greater in 1999. We classify
firms in the 1st or 2nd section of Tokyo Stock Exchange as Small if their consolidated
assets are less than 100 billion yen in 1999. Firms are defined as Young if they went
public after 1990 on the Tokyo Stock Exchange, Mothers, Hercules, or JASDAQ. We do
not impose any condition on how long a firm is listed to be classified as a Large or
Small firms. Thus, some firms are classified into both Large and Young (or Small andYoung) at the same time.5 Figure 3 shows that the main players of R&D are Large firms
and Young firms account for only 1.4% in 2006, although they increase its composition.
In the US high-tech industries, young firms account for 40% of R&D expenditure in thelate 1990s.6 The trend of US R&D expenditure since the middle of 1990s was largely
influenced by the behavior of young firms (Brown et al., 2009). In contrast to the US
case, most R&D in Japan was conducted by mature firms despite the increase of young
firms in IT related sectors.

== Figure 3 about here ==3. Financial Structure of R&D Intensive Industries

Figure 4 describes financial structure of the firms in R&D intensive industries.
While financial variables fluctuate, R&D expenditure is fairly stable throughout the5

For example, a firm with total assets 50 billion yen which went public on Tokyo Stock Exchange
1st section in 1995 is classified as a Small as well as Young firm.
6
In Brown et al. (2009), a firm is classified as young for the 15 years following the year it first
appears in Compustat as a listed firm, and mature thereafter..
6

sample period. This tendency continues even after “Lehman shock” which accompanied
drop in cash flow and increase in debt, as far as 2008 data is concerned. The dispersion
in R&D-assets ratio increased especially after 2002, while leverage has been declined.
The R&D-assets ratio slightly went down, but the standard deviation kept its level andcoefficient of variation increased. This dispersion between firms got larger after Lehman
shock.

== Figure 4 about here ==Panel 1 of Table 2 provides descriptive statistics for the variables of the sample
firms. All variables except for foreigner are scaled by beginning-of-period total assets.
Total consists of all firms in the sample which is in the seven R&D intensive industries
described above. Large firm has high R&D-assets ratio and smaller dispersion between
firms. Small firm has lower R&D-assets ratio of about 2.9% on average, but has greater
dispersion. Young firms have similar R&D-assets ratio to Small firms and greater
dispersion compared to the former two groups. Firms in R&D intensive industries
rapidly reduced their debts. According to Panel 2 of Table 2, they reduce theirdebt-assets ratio in the sample period. As a consequence, debt-assets ratio went downfrom 26% in 1999 to 13% in 2006. On the other hand, they experienced rapid increasein foreign and institutional investors and decrease in cross-shareholdings or stableshareholdings. Foreign shareholdings increased from 6.1% in 1999 to 13.7% in 2006.

== Table 2 about here ==Observing by firm group, Large firms had high debt-assets ratio and bondaccounted for the large part of debt finance in 1999. Regarding ownership structure,outsiders had high shareholding ratio. Large firms already had market based governancestructure in the beginning of the sample period. Two important points can be seen in the7change of corporate finance and governance structure for Large firms in the 2000s.7
First, Large firms reduced debt rapidly. One of our interests after section 5 is how thisreduction in debt affected their R&D investment. Second, ownership structure alsochanged rapidly. Foreign shareholding ratio and institutional investor ratio reached 30%
and 44%, respectively in 2006. Moreover, variance in ownership structure declined.
Coefficient of variation of foreign ownership dropped from 0.68 in 1999 to 0.39 in 2006.
Ownership structure of Large firms became more homogeneous toward outsider
oriented ownership.
Small firms reduced its debt-assets ratio and then it dropped 14% point from 1999
and became 12% in 2006. Reduction in bond also took place and the bond-assets ratio
became 1% in 2006. Regarding outsider shareholdings, Small firms have higher
dispersion than Large firms. How the differences in shareholding ratio by foreigners
affect R&D investment is one of our interests in this research.
Lastly, we observe finance and ownership structure of Young firms. Considering
many firms in this group went public between 1998 and 2006, we need to be carefulwhen comparing data through time. Debt-assets ratio of Young firms remained
relatively during the sample period. On the other hand, although Young firms finance bystock issue more than mature firms, the size is not large. They show the characteristics
of entrepreneur firms; director’s stock holding ratio is high. The ratio of shareholding byforeigners and institutional investors is low.

4.

The Empirical Model and Data
In this section we explain the empirical specification for the following analysis. As

Brown et al. (2009), we employ the dynamic investment model of Bond and Meghir(1994) to R&D investment. The empirical model is based on Euler equation which isderived from the dynamic optimization of investment of a firm under imperfect7

Arikawa and Miyajima (2007) investigate the change of corporate finance and governance ofJapanese firms in the 1990s.
8competition with convex adjustment cost.8 The advantage of a structural approach isthat it controls for expectation, and the Euler equation estimation approach eliminatesterms in the solution to the optimization problem that depend on unobservable
expectations. The baseline empirical specification including debt finance is as follows.

where

is R&D spending for firm j in time t,

is square of R&D spending

and derived from the existence of nonlinear adjustment cost,cash flow,

is sales,isis the amount of debt at t-1, andis square of debt. All variables

are scaled by the beginning of period total assets.

is time effect on R&D spending;is time invariant firm effect; andis the error term. To the estimation we include

aggregate year dummies for controlling movements in the aggregate costs capital andtax rates. We also estimate with the interaction terms of year and industry dummies tocontrol for industry-specific changes in technological opportunities.
According to Bond and Meghir (1994),
takes a value greater than 1 in absolute value,

takes a positive and greater than 1,
takes zero under perfect competition

and takes positive values otherwise. The coefficient on the lagged cash flow,, isnegative.
The debt terms control for the non-separability between investment and borrowing
decisions. Under the existence of bankruptcy costs and a tax advantage of debt, the firmraises debt until the tax advantages have been fully exploited. If the firm is underfinancial constraint, the optimal level of debt determines the level of investment directly.
The coefficient on the square of debt term is supposed to be negative unless firms are in
the case where Modigliani-Miller theorem does hold. We also add the lagged debt term
to the Bond and Meghir (1994) model.
8

Derivation of the estimation equation is referred to Bond and Meghir (1994) or Bond and Van
Reenen (2007).
9

The specification in this paper has a dynamic panel structure which has a lag ofdependent variable as an independent variable and we estimate it with system GMM of
Blundell and Bond (1998). Here all independent variables are treated as endogenous
variable and use t-3 and t-4 independent variables as instruments.9
We take financial variables from Nikkei NEEDS, and variables on ownership from
Nikkei NEEDS-Cges (Corporate Governance Evaluation System). We use consolidated
data for all variables. We construct an unbalanced panel of publicly traded firms listed
on TSE 1st and 2nd section in these industries during 2001 to 2008.10 We require firms
to have at least five R&D observations. Panel 1 of Table 2 presents descriptive statisticsof the estimation sample.

5. Estimation Results
5.1 Pooled Sample Estimates
The pooled sample estimation results are summarized in Table 3. Hansen’s J test
rejects the validity of the instruments in the regression of column (1), but do not rejectwith the result in column (2), (3) and (4). It seems that the dynamics implied by the
structural model are not rejected partly. The coefficient on the lagged dependent
variable is significantly positive and not different from one.

== Table 3 about here ==We find the discrepancy between this model and the basic theoretical structure forseveral variables. In each regression result, the coefficient for cash flow is positive,
although the theoretical model implies that this coefficient should be negative, under theassumption that the firm can raise as much fund as it want at a given cost. This positivecoefficient is in line with other literature such as Fazzari et al. (1988), and may reflect
9

In there is no autocorrelation, instruments dated t-2 can be used, but when the error is MA(1), theinstruments must be at least dated t-3.
10Since we need two years lag, and the consolidated accounting formally went effective to the listed
firms after 1999 in Japan, we could not extend our estimation much longer period of time.
10liquidity constraint. We also find that the coefficient of debt and the square of debt are
not significantly negative in the regression results.11
We find similar results when we include the lagged values of funds raised by new
stock issues scaled by beginning-of-period total assets following Bond and Meghir(1994) and Brown et al. (2009). The column (3) and (4) of Table 3 shows that thecoefficient for lagged cash flow is positive but insignificant, and the coefficient of debt
and the square of debt are not significantly negative with the exception of the lagged
debt in column (3). The coefficient of the lagged stock is also insignificant in both
regression results.

5.2 Comparison of Large and Small Firms
To investigate the effect of financial variables on R&D spending further, we split
the sample by a firm characteristic that is likely to be associated with financial
constraints. Hadlock and Pierce (2010) argue that firm size and firm age are closely
related to financial constraints while KZ index advocated by Kaplan and Zingales(1997) is unlikely to useful measure of financial constraints. Then, we split our sample
into two groups, Large and Small, based on firm size. Here, we define firms as Large if
total asset of a firm is larger than 300 billion JPY, and we define firms as Small if total
asset of a firm is less than 100 billion JPY. Expecting that the cost of debt financediffers based on the size of firm, we compare the regression results between Large andSmall firms.12
Column (1) and (2) of Table 4 shows the results. We find again that the coefficient
for lagged cash flow is positive and insignificant for both Small and Large firms. We
examine this insignificance in the next sub section further.
For Large firms, in column (3) and (4), we find that the coefficients of lagged debt
and lagged square debt are also significantly negative in column (4) as implied by
tax-bankruptcy specification. This suggests that Large firms finance R&D investment at

11

The coefficient of the lagged square of RD and the lagged Sales is also insignificant.
The median of total asset for sample firm is 71 billion yen. We also set the 200 billion yen as
threshold to define Small, and results are basically same, but weaker.
1211least partly from debt according to the optimal debt policy. For Small firms, thecoefficient of lagged debt is significantly negative, while that of lagged square debt isinsignificant in each regression result; the relationship between debt and R&D
investment is linearly negative. Firms with small size have no optimal leverage ratio,
and rather higher leverage leads to lower R&D investment. Small firms are more likely
to face financial constraint because of the lack of collateralized asset or the higher
uncertainty of future profit. This makes the cost of debt finance outweigh its benefit as awhole for Small firms. Thus, higher leverage raises the capital cost of R&D investment
and decrease R&D investment.

== Table 4 about here ==To explore the relationship between leverage and R&D investment further, we
conduct two additional tests. First, we investigate whether the firm with higher leverageon their balance sheet at the beginning of the investment decision is more likely to
reduce R&D investment at period t when debt finance increase at period t-1. Then, we
divide the sample into firms with high and low leverage by median debt-assets ratio atthe beginning of the sample period. When firms increase the debt finance at one unit,
the marginal increase of the cost of debt is likely to be higher for high levered firms
than that of low levered firms because of the higher default probability. This differenceof marginal cost of debt finance leads to the different results between high levered andlow levered firms. Table 5 shows the results. Column (1) and (2) of Table 5 shows theresult when firms have higher debt-assets ratio at the beginning of the sample period.
We find that the coefficient of the lagged debt is significantly negative in the regression.
On the other hand, for low levered firms at the initial period, the coefficient of thelagged debt term and the lagged square debt is insignificant. Thus, for high leveredfirms, the increase of debt financing leads to the reduction of R&D investment, while
the larger debt financing does not have any effect on R&D investment for low levered12

firms.13== Table 5 about here ==Second, we explore how the negative relationship between the lagged debt and
R&D investment differ across firms with different business risk as measured by the
number of business unit for each firm. We assume here that having larger number ofbusiness unit in one firm contributes to the reduction of business risk. If the debt financeleads to the reduction of R&D investment because of the increase of default risk, wewould expect to see a weaker relationship between the lagged debt and R&D investment
in firms with more business units. When we introduce the interaction term between thelagged debt and the indicator variable that is equal to one if the number of business unit
in a firm is more than four, and zero otherwise (not tabulated), we find that thecoefficient of the interaction term is significantly positive only for small firms. In fact,
the magnitude of coefficient is large enough to offset the negative effect of the lagged
debt on R&D investment. These results suggest that the higher default probability forsmall firms increases the cost of debt financing, and that is the main reason of thenegative relationship between debt finance and R&D investment.

5.3 Cash Flow and Financial Constraint
To explore the role of financing constraints on R&D investment further, we add
contemporaneous cash flow, which is the standard measure of internal equity financing
in the financing constraint literature following Brown et al. (2009).

Table 6 presents the regression results. The column (1) gives the results with whole13

We also estimate the same model for the high- and low levered firms within small firms. The
results on the lagged debt terms are similar to Table 5.
13

samples, and column (2) and (3) present regression results for small and large firms. In
either regression, the contemporaneous cash flow variable has no significant effect, andthe coefficient of lagged cash flow is also not significantly negative. In fact, we find thesimilar result with the specification without contemporaneous cash flow variable. The
coefficient of lagged debt and lagged square debt is significantly negative for large firm,
while only the coefficient of lagged debt is significantly negative for small firms.14== Table 6 about here ==To investigate further the sensitivity of R&D investment to contemporaneous cash
flow, we add firms which are listed on “emerging market”, JASDAQ, MOTHERS and
Hercules (formerly NASDAQ Japan). These firms constitute a subset of Young firms
defined in Section 2. These three markets, especially, MOTHERS and Hercules, areestablished for start-up firms. In terms of industry distribution, JASDAQ is morediverse and the two newer markets are highly oriented toward the IT industry (Arikawa
and Immaddine, 2010). Moyen (2004) shows that the investment-cash flow sensitivities
in the sense of Fazzari et al. (1988) hold only when constrained firms do not haveenough funds to invest as much as they want. This might be the situation especially forfirms listed on JASDAQ, MOTHERS and Hercules. We expect that Small firms
including firms listed on emerging market are more likely to face severe financial
constraint than other firms.
Column (4) of Table 6 shows that the coefficient of contemporaneous cash flow issignificant. 15 This suggests that R&D investments are financially constrained, andmarginal increase of internal fund leads to the increase R&D investment. Column (5)and Column (6) show the results when we divide the sample into Large and Small firms.
We expect that only for Small firms, the contemporaneous cash flow have a significant
effect. Consistent to our prediction, we find that the coefficient on contemporaneous
14

The results are same when we use year dummy in the regression.
The models which strictly follow Brown et al. (2009) model, namely models which havecontemporaneous variables for all financial variables (both with and without debt terms), provide
similar results.
15

14

cash flow is significantly positive for Small firms, while that of Large firms is
insignificant. Thus, we can conclude that “emerging” firms with small size are likely toface financial constraint for R&D investment. Comparing the result between Column(2) and (4), it is clear that firm listed on JASDAQ, MOTHERS and Hercules face thesevere financial constraint for R&D investment. This result corresponds to the result of
US young firms in Brown et al. (2009).
We also find that the coefficient of lagged debt and lagged square of debt are both
negatively significant for Large firms. On the other hand, for Small firms, we find no
significant result in terms of the debt related variables. The result suggests that Smallfirms, especially start-up firms face the financial constraint, consequently they do notuse debt finance for R&D investment because it is highly costly.
Finally, we find no significant result for the coefficient of the lagged external equity
even we add “emerging” firms which listed on JASDAQ, MOTHERS and Hercules. In
the US, Brown et al. (2009) point out that the supply of equity finance for young
publicly traded firms in high-tech industries have driven much of the R&D boom in the
1990s. In Japan, we find no robust evidence that stock market is the important source offunds for technological development.

5.4 Ownership Structure and R&D Investment
One characteristic of changes in corporate governance structure for Japanese firms
in the 2000s was the shift from insider to outsider ownership, as shown in Section 3.
Then, it is natural question whether ownership structure would influence R&D
investment or not. One of the perditions is that myopic investors influence R&D
investment which requires long time before generating revenues (Narayanan, 1985, andStein, 1989). If an investor is myopic and his preference is biased toward immediate
dividend, then managers may take into account these myopic investors and pay
dividends by sacrificing R&D investment. In this case, firms with more myopic
investors are more likely to face underinvestment in R&D.16
16

It is also possible that investors’ preferences are biased toward high R&D investment, and firms
overinvest on R&D (Aghion and Stein, 2008).
15

To study the effect of myopic investors on R&D expenditure, we add foreign
investor’s shareholding ratio and the interaction term between the contemporaneous
cash flow and foreign investor’s shareholding ratio. Then, we estimate the following
equation:where

is foreign shareholding ratio for firm j in period t andis the interaction of foreign shareholding ratio and cash flow. If foreign investors
demand excessive dividend myopically, we expect the interaction term takes positivecoefficients.
Table 7 shows the estimation results. We use firms which are listed on TSE 1st and2nd, JASDAQ, MOTHERS and Hercules. First, the estimation results for Small firms,
Column (3) and (4), show that foreign investor’s shareholdings ratio take significantly
positive coefficients and their interaction terms with cash flow take significantly
negative coefficients. Thus, for these firms, foreign investors does not decrease raises
R&D expenditure and mitigates financial constraint. Second, regarding Large firms,
Column (5) and (6), we find that the coefficient of foreign shareholder’s ratio and its
interaction terms with cash flow is insignificant.
In summary, we do not find any evidence that large shareholdings by foreign
investors enforce myopic behavior on firms.

== Table 7 about here ==6. Conclusion
In the progress of IT revolution, firms which recently listed on the market
accounted a large part of R&D investment in the US. By contrast, in Japan, large and16mature firms which drove the high growth period are still the main players of R&D
expenditure. While domestic emerging equity markets have been organized since 1999
and many new firms went public especially in IT related sectors, firms which went
public after 1990 account for only 3% of total R&D spending in R&D intensive
industries. On the other hand, large firms whose consolidated assets are more than 500
billion yen account for nearly 80% of total R&D spending in R&D intensive industries.
In this paper, we examine whether the financial factor explains the little presence of
R&D investment by small and young firms in Japan. Then, we find that
contemporaneous cash flow have no statistically significant effect on R&D investmentof firms with larger asset. For young and small firms mainly listed on newly establishedemerging market, however, the coefficient of contemporaneous cash flow issignificantly positive. We also find that firms with large asset finance R&D investment
partly from debt taking the optimal debt policy. For firms with small asset, higher levelof debt financing leads to lower R&D investment.
One characteristic of change in governance structure of Japanese firms was the shift
from insider to outsider ownership. If an investor is myopic and his preference is biased
toward immediate dividend, then managers may take into account these myopic
investors and pay dividends by sacrificing R&D investment. To study the effect ofcorporate governance factors on R&D expenditure, we add the interaction term between
contemporaneous cash flow and foreign investor shareholding ratio. Then, we do notfind any evidence that large shareholdings by foreign investors enforce myopic behavior
on firms.
Finally, we do not find any positive effect of equity finance on firms with small
asset size. This result contrasts with the US case in which the changes of equity financeby the young, high-tech firms explain most of the 1994 to 2004 aggregate R&D cycle(Brown et al., 2009).

17

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Bond, S. and J. Van Reenen, 2007, “Microeconometric Models of Investment and
Employment,” in James J. Heckman and Edward E. Leamer, eds.: Handbook of
Econometrics, Volume 6A (Elsevier, Amsterdam).
Brown, R. J., S. M. Fazzari, and B. C. Petersen, 2009, “Financing Innovation and
Growth: Cash Flow, External Equity, and the 1990s R&D Boom,” Journal of
Finance Vol. 64, pp. 151-185.
Fazzari, S., R. G. Hubbard and B. C. Petersen, 1988, “Financing Constraints andCorporate Investment,” Brookings Paper on Economic Activity Vol. 1, pp. 141-195.
Hadlock, C. and J. R. Pierce, 2010, “New Evidence on Measuring Financial Constraints:
Moving Beyond the KZ Index,” Review of Financial Studies, forthcoming.
Kaplan, S. and L. Zingles, 1997, “Do Investment-Cash Flow Sensitive Provide Useful
Measures of financing constraints? Quarterly Journal of Economics, Vol. 112, pp.
169-215.
Moyen,

N.,

2004,“Investment-Cash

Flow
18

Sensitivities:

Constrained

versusUnconstrained Firms,” Journal of Finance Vol. 69, pp. 2061-2092.
Ministry of Economy, Trade and Industry, Industrial Science and Technology Policy
and Environment Bureau, 2009, Trend in Research and Development Activity on
Industrial Technology in Japan – Major Indexes and Survey Data – 9th Ed.,
Ministry of Economy, Trade and Industry.
Miyajima, H. and F. Kuroki, 2007, “The Unwinding of Cross-Shareholding in Japan:
Causes, Effects, and Implications,” in Aoki, M, G. Jackson and H. Miyajima eds.
Corporate Governance in Japan: Institutional Change and Organizational
Diversity, Oxford University Press, pp.79-124.
Narayanan, M. P., 1985, “Managerial Incentives for Short-term Results,” Journal of
Finance, Vol. 40, pp. 1469–1484.
Ogawa, K., 2007, “Debt, R&D Investment and Technological Progress: A Panel Studyof Japanese Manufacturing Firms’ Behavior during the 1990s,” Journal ofJapanese and International Economies, Vol. 21, pp. 403-423.
Stein, J. C., 1989, “Efficient Capital Markets, Inefficient Firms: A Model of Myopic
Corporate Behavior,” Quarterly Journal of Economics, Vol. 104, Dec. 1989, pp.
655-669.

19

Figure 1: R&D and Physical Investments by Manufacturers
Borrowed from Ministry of Economy, Trade and Industry, Basic Survey of Japanese Business
Structure and Activities. Unit: One billion yen.

R&D inv

Domestic physical inv

20

Dom+Oversea physical inv

Figure 2: Trend of R&D Expenditure
The sample consists of firms in the seven R&D intensive industries in the 1st or 2nd section ofTokyo Stock Exchange and young firms in the same industries who went public after 1990 on theTokyo Stock Exchange, Mothers, Hercules, or JASDAQ. The seven industries consist of chemicals,
pharmaceutical, machinery, electric appliances, transportation equipment, precision instruments, andinformation & communication. Industry classification is based on Securities Identification CodeCommittee’s 33 sectors (excluding financial sectors) (Syoken Code Kyogikai 33 Gyoshu). Amongfirms in Information & Communication sector, we only pick up firms in 3 major groups;
Communications (37), Information Services (39), and Internet Based Services (40) in Division G:
Information and Communications by Japan Standard Industrial Classification.

0.04

0.8
0.79

0.035

0.78
0.03

0.77
0.76

0.02

0.75

CV

R&D/Assets

0.025

Mean
SD

0.74

0.015

0.73
0.01

0.72
0.005

0.71

0

0.7

1999

2000

2001

2002

2003

2004

21

2005

2006

2007

2008

CV

Figure 3: R&D Expenditure by Firm Size
The sample is firms in the seven R&D intensive industries. Large is firms in the 1st or 2nd section ofTokyo Stock Exchange and had consolidated assets 300 billion yen or greater in 1999. Small is firms
in the 1st or 2nd section of Tokyo Stock Exchange and had consolidated assets less than 100 billion
yen in 1999. Young is firms who went public after 1990 on the Tokyo Stock Exchange, Mothers,
Hercules, or JASDAQ.

Trill Yen 12

11

10

9

Total
Large

8

Large+Small
Large+Small+Young

7

6

5
1999

2000

2001

2002

2003

2004

22

2005

2006

2007

2008

Figure 4: R&D Expenditure and Finance
The sample is firms in the seven R&D intensive industries. CF is cash flow. Debt is flow value.

Trill Yen

30
25
20
15
R&D
10

CF
Debt

5

New Share Iss

0
-5
-10
1999

2000

2001

2002

2003

2004

23

2005

2006

2007

2008

Table 1: R&D Expenditure by Industry
The sample is all listed firms in the Toyo Keizai dataset in 2006. Industry classification is based on
Securities Identification Code Committee’s 33 sectors (excluding financial sectors) (Syoken CodeKyogikai 33 Gyoshu). RD/S is R&D expenditure-sales ratio. SD and CV stand for standard
deviation and coefficient of variation, respectively. The unit is 100 million yen.
Sector code
Sector nameNum. firm Total R&D R&D share Av. R&D/SSD R&D/S
50 Fish., Ag. & Forest.
11
98
0.07%
1.29%
1.96%
1050 Mining
7
17
0.01%
0.53%
0.75%
2050 Construction
215
1,045
0.78%
0.24%
0.32%
3050 Foods
153
2,399
1.78%
1.11%
1.76%
3100 Textiles & Apparels
79
1,509
1.12%
1.08%
1.23%
3150 Pulp & Paper
27
307
0.23%
0.83%
0.91%
3200 Chemicals
215
10,745
7.97%
2.95%
2.35%
3250 Pharmaceutical
51
10,347
7.67% 29.66% 74.37%3300 Oil & Coal Products
13
443
0.33%
1.23%
2.16%
3350 Rubber Products
21
1,465
1.09%
2.44%
1.22%
3400 Glass & Ceramics
71
1,283
0.95%
1.59%
1.45%
3450 Iron & Steel
56
1,633
1.21%
0.63%
0.74%
3500 Nonferrous Metals
43
1,598
1.19%
1.08%
0.88%
3550 Metal Products
97
911
0.68%
1.20%
1.14%
3600 Machinery
247
6,325
4.69%
2.24%
2.27%
3650 Electric Appliances309
50,933 37.77%
4.65%
4.55%
3700 Transport Equip.
106
32,017 23.74%
2.02%
1.86%
3750 Precision Inst.
53
2,291
1.70%
4.82%
6.46%
3800 Other Products
116
2,105
1.56%
1.24%
1.48%
4050 Electric Power & Gas
25
1,349
1.00%
0.41%
0.35%
5050 Land Transport.
66
412
0.31%
0.05%
0.20%
5100 Marine Transport.
18
13
0.01%
0.01%
0.01%
5150 Air Transport.
6
5
0.00%
0.44%
0.34%
5200 Warehousing & Harbor
44
4
0.00%
0.04%
0.15%
5250 Info & Communication
359
4,791
3.55%
2.28%
5.55%
6050 Wholesale Trade
387
530
0.39%
0.29%
1.45%
6100 Retail Trade
384
37
0.03%
0.03%
0.19%
8050 Real Estate
132
15
0.01%
0.04%
0.23%
9050 Services
377
228
0.17%
1.38% 10.84%
Total
3,688 134,854 100.00%

24

CV
1.52
1.43
1.35
1.58
1.13
1.10
0.80
2.51
1.76
0.50
0.91
1.19
0.81
0.96
1.01
0.98
0.92
1.34
1.19
0.84
3.96
2.86
0.78
3.51
2.43
4.93
6.54
5.42
7.84

Table 2: Descriptive Statistics
The estimation sample is firms in the seven R&D intensive industries. The seven industries consist of chemicals, pharmaceutical, machinery, electric
appliances, transportation equipment, precision instruments, and information & communication. Industry classification is based on Securities
Identification Code Committee’s 33 sectors (excluding financial sectors) (Syoken Code Kyogikai 33 Gyoshu). Among firms in Information &Communication sector, we only pick up firms in 3 major groups; Communications (37), Information Services (39), and Internet Based Services (40) in
Division G: Information and Communications by Japan Standard Industrial Classification. The estimation period is from 2000-2008. We classify firms in
the 1st or 2nd section of Tokyo Stock Exchange as Large if their consolidated assets are 300 billion yen or greater in 1999. We classify firms in the 1st or
2nd section of Tokyo Stock Exchange as Small if their consolidated assets are less than 100 billion yen in 1999. Firms are defined as Young if they went
public after 1990 on the Tokyo Stock Exchange, Mothers, Hercules, or JASDAQ. Outliers in all variables which are three standard deviations away fromtheir mean are eliminated from the sample. Firms which have five observations or less in the sample period are dropped. All variables except forownership variables are scaled by beginning-of-period total assets. The numbers of firms for ownership related variables are in parentheses in Panel 2.

Panel 1: R&D Expenditure and Finance
Total
Mean
SD
Obs
5,660
R&D
0.032
0.025
Sales
0.938
0.318
Debt
-0.004
0.056
CF
0.059
0.052
New Share Iss
0.002
0.019
Foreigner (%)
0.104
0.114

CV

Mean

0.761
0.339
-13.856
0.887
7.794
1.094

0.044
0.942
-0.003
0.071
0.002
0.251

Large
SD
788
0.024
0.286
0.049
0.049
0.011
0.129

CV

Mean

0.552
0.303
-17.608
0.688
6.228
0.512

0.029
0.932
-0.006
0.056
0.002
0.065

25

Small
SD
2,866
0.022
0.305
0.054
0.049
0.013
0.075

CV

Mean

0.768
0.327
-9.335
0.866
7.180
1.162

0.031
0.935
0.001
0.055
0.005
0.063

Young
SD
1,330
0.027
0.374
0.059
0.058
0.031
0.079

CV
0.875
0.400
42.604
1.053
6.338
1.259

Panel 2: Ownership StructureObs
Debt/Assets
Borrowing/Assets
Bond/Assets
Foreigner (%)
Inst investors (%)
Cross-holding (%)
Director share (%)

Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD

1998
584
0.26
0.19
0.21
0.18
0.05
0.07
6.11
8.24
13.18
10.90
13.44
7.88
6.28
22.26

Total
2001
595
0.20
0.15
0.17
0.15
0.03
0.05
7.03
9.98
14.40
14.20
11.42
8.21
5.53
10.31

2006
647
0.13
0.11
0.11
0.10
0.02
0.03
13.74
12.38
22.89
17.30
9.15
8.00
4.98
9.42

1998
86
0.30
0.17
0.20
0.16
0.10
0.07
16.15
10.99
22.23
11.24
12.70
7.11
0.61
1.73

Large
2001
88
0.23
0.14
0.16
0.12
0.07
0.05
20.50
13.27
31.37
14.47
9.52
6.97
0.49
1.60

26

2006
88
0.16
0.11
0.12
0.10
0.04
0.04
30.32
11.84
43.91
13.49
7.48
5.59
0.45
1.73

1998
278
0.26
0.20
0.22
0.19
0.03
0.06
2.82
4.30
6.72
6.35
13.97
8.17
3.42
6.09

Small
2001
297
0.20
0.16
0.18
0.16
0.02
0.04
3.44
5.53
7.49
8.80
12.11
8.58
3.67
6.84

2006
329
0.12
0.11
0.11
0.11
0.01
0.03
9.15
8.25
16.93
12.82
10.70
8.67
3.48
6.20

1998
123
0.22
0.18
0.20
0.18
0.02
0.04
2.83
4.40
8.15
3.81
6.02
3.32
26.93
48.42

Young
2001
120
0.15
0.14
0.13
0.13
0.02
0.05
3.36
5.33
12.79
10.76
7.18
6.83
20.27
14.00

2006
161
0.11
0.12
0.10
0.11
0.02
0.04
8.36
9.20
14.01
12.64
5.08
5.38
15.25
13.25

Table 3: Estimation Results (Baseline Model)
The dependent variable is R&D expen diture. Estimated by system GMM of Blundell
and Bond (1998) using independent variables dated t -3 and t-4 as instruments. Allvariables are scaled by beginning -of-period total assets. Heteroskedasticity andautocorrelation robust z -values are in parenthese s. Hansen is p-value of Hansen’s J
test for over-identification. AC1 and AC2 are p-values of the Arellano-Bond test for
first and second order autocorrelation which have a null hypothesis of no
autocorrelation and is applied to the differenced residuals. * **, **, * significant at
the 1 %, 5 % and 10% level, respectively.

RDt-1
RD2 t-1
Sales t-1
CFt-1
Debt t-1
Debt 2 t-1

(1)
0.996
(13.92)***

(2)
0.938
(12.16)***

(4)
0.938
(12.72)***

-0.24

0.047

-0.3280.01(-0.32)-0.002(-1.39)0.014(1.32)-0.019(-2.07)**(0.06)-0.001(-0.50)0.003(0.33)-0.014(-1.52)(-0.45)-0.002(-1.45)0.013(1.41)-0.018(-2.08)**(0.01)-0.001(-0.66)0.006(0.60)-0.013(1.56)

0.002

0.013

-0.034-0.031(0.03)(0.18)(-0.41)-0.002(-0.08)YES(-0.38)0.01(0.33)

Stkt-1
Year Dummies
Year*Indust Dum.
AC1
AC2
HansenObservations

(3)
1.003
(14.56)***

YES
0
0.278
0.097
4632

YES
0
0.226
0.147
4632

27

0
0.305
0.125
4615

YES
0
0.251
0.238
4615

Table 4: Estimation Results by Firm Size
The dependent variable is R&D expenditure. Estimated by system GMM of Blundell
and Bond (1998) using independent variables dated t -3 and t-4 as instruments. Allvariables are scaled by beginning -of-period total assets. The sample is divided byfirm size in 1999. Large is firms in the 1st or 2nd section of Tokyo Stock Exchange
and had consolidated assets 300 billion yen or greater in 1999. Small is firms in the
1st or 2nd section of Tokyo Stock Exchange and had consolidated assets less than 100
billion yen in 1999. Heteroskedasticity and autocorrelation robust z -values are inparentheses. Hansen is p -value of Hansen’s J test for over-identification. AC1 andAC2 are p-values of the Arellano -Bond test for first and second order autocorrelation
which has a null hypothesis of no autocorrelation and is applied to the differenced
residuals. ***, **, * significant at the 1 %, 5 % and 10% level, respectivel y.

Small
Small
Large
Large
Small
Small
Large
Large
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
RDt-1
1.036
0.984
1.264
1.18
1.032
0.986
1.238
1.151
(11.82)*** (10.09)*** (7.86)*** (8.10)*** (12.34)*** (10.94)*** (8.23)*** (7.88)***-1.246-1.012-2.358-2.067-1.184-1.029-2.11-1.754RD2 t-1(-1.26)(-0.94)(-1.54)(-1.39)(-1.25)(-1.04)(-1.45)(-1.18)
Salest-1-0.0010-0.005-0.00100-0.0040(-0.36)(-0.15)(-1.74)*(-0.31)(-0.10)(-0.06)(-1.64)(-0.26)
CFt-1
0.009
0.003
-0.002
0.012
0.007
0.005
0.001
0.012
(0.83)(0.28)(-0.16)(0.82)(0.75)(0.47)(0.05)(0.86)
Debtt-1-0.016-0.013-0.021-0.031-0.018-0.016-0.028-0.037(-1.74)*(-1.39)(-1.76)* (-2.93)*** (-2.15)** (-1.93)* (-2.32)** (-3.53)***
0.017
0.01
-0.115-0.241-0.028-0.04-0.119-0.241
Debt2 t-1
(0.24)(0.15)(-1.31) (-2.21)** (-0.47)(-0.70)(-1.29) (-2.24)**
Stkt-1-0.008-0.007-0.008-0.006(-0.31)(-0.26)(-0.21)(-0.15)
Year Dummies
YES
YES
YES
YES
Year*Indust Dum.
YES
YES
YES
YES
AC1
0
0
0
0
0
0
0
0
AC2
0.58
0.612
0.433
0.119
0.602
0.637
0.315
0.104
Hansen
0.328
0.516
1
1
0.525
0.775
1
1
Observations
2883
2883
761
761
2866
2866
761
761

28

Table 5: The Effect of Debt
The dependent variable is R&D expenditure. Estimated by system GMM of Blundell
and Bond (1998) using independent variables dated t -3 and t-4 as instruments. Allvariables are scaled by beginning -of-period total assets. The sample is divided at themedian value of debt-assets ratio in 1999. Heteroskedasticity and autocorrelation
robust z-values are in parentheses. Hansen is p -value of Hansen’s J test forover-identification. AC1 and AC2 are p-values of the Arellano-Bond test for first andsecond order autocorrelation which has a null hypothesis of no autocorrelation and isapplied to the differenced residuals. ***, **, * significant at the 1 %, 5 % and 10%
level, respectively.

RDt-1
RD2 t-1
Sales t-1
CFt-1
Debt t-1
Debt 2 t-1
Stkt-1
Year Dummies
Year*Indust Dum.
AC1
AC2
HansenObservations

Above Med(1)
1.056
(12.19)***Above Med(2)
1.033
(9.90)***

Below Med(3)
1.039
(11.96)***

Below Med(4)
0.957
(10.45)***-1.038-0.884-0.784-0.417(-1.02)0.001(0.37)
0.012(1.13)-0.023(-2.47)**(-0.77)0(0.20)
0.012(1.09)-0.018(-2.13)**(-0.94)-0.004(-2.20)**0.01(0.93)-0.018(-1.41)(-0.46)-0.001(-0.77)0.005(0.46)-0.006(-0.56)

0.025

0.018

-0.127-0.15(0.50)-0.051(-1.83)*
YES(0.35)-0.047(-1.72)*(-1.00)0.005(0.12)
YES(-1.22)0.017(0.37)

YES
0
0.464
0.265
2274

0
0.322
0.071
2274

29

0
0.632
0.168
2341

YES
0
0.665
0.661
2341

Table 6: Tests for the Existence of Financ ial Constraint
The dependent variable is R&D expenditure. Estimated by system GMM of Blundell
and Bond (1998) using independent variables dated t -3 and t-4 as instruments. Allvariables are scaled by beginning -of-period total assets. The sample is divided byfirm size in 1999. Large is firms in the 1st or 2nd section of Tokyo Stock Exchange
and had consolidated assets 300 billion yen or greater in 1999. Small is firms in the
1st or 2nd section of Tokyo Stock Exchange and had consolidated assets less than 100
billion yen in 1999. Emerging is firms who went public after 1990 on the emerging
markets; Mothers, Hercules, or JASDAQ. Heteroskedasticity and autocorrelation
robust z-values are in parentheses. Hansen is p -value of Hansen’s J test forover-identification. AC1 and AC2 are p -values of the Arellano -Bond test for first andsecond order autocorrelation which has a null hypothesis of no autocorrelation and isapplied to the differenced residuals. ***, **, * significant at the 1 %, 5 % and 10%
level, respectively.

RDt-1
RD2 t-1
Salest-1
CF
CFt-1
Debtt-1
2

Debt t-1
Stkt-1
Year Dummies
Year*Indust Dum.
AC1
AC2
HansenObservations

Total
(1)
0.933
(12.67)***
0.047

Small
(2)
0.983
(10.73)***-1.036

Large
(3)
1.148
(7.66)***-1.736(0.06)-0.001(-0.73)0.004(0.30)
0.004(0.29)-0.013(-1.46)(-1.02)0(-0.26)0.021(1.32)-0.006(-0.55)-0.016(-1.87)*(-1.15)0(-0.22)0.004(0.26)
0.009(0.60)-0.037(-3.53)***(0.34)-0.001(-0.86)0.036(2.29)**-0.005(-0.40)-0.001(-0.11)(0.09)
0.001(0.76)
0.046(2.95)***-0.014(-0.94)-0.006(-0.47)(-1.25)-0.001(-0.37)0.017(1.08)-0.005(-0.36)-0.032(-3.35)***-0.032(-0.4)
0.007(0.23)-0.037(-0.66)-0.015(-0.54)-0.242(-2.26)**-0.006(-0.15)-0.067(-1.17)-0.019(-0.98)-0.082(-1.57)-0.029(1.79)*-0.208(1.98)**-0.005(-0.17)

YES
0
0.248
0.201
4615

YES
0
0.726
0.768
2866

YES
0
0.114
1
761

YES
0
0.454
0.266
5660

YES
0
0.955
0.627
3848

YES
0
0.069
1
788

30

Total+Emerging Small+Emerging Large+Emerging
(4)
(5)
(6)
0.892
0.888
1.156
(13.53)***(11.06)***(8.15)***
0.156
0.066
-1.814

Table 7: The Effect of Foreign Investors
The dependent variable is R&D expenditure. Estimated by system GMM of Blundell
and Bond (1998) using independent variables dated t -3 and t-4 as instruments. Allvariables are scaled by beginning -of-period total assets. The sample is divided byfirm size in 1999. Large is firms in the 1st or 2nd section of Tokyo Stock Exchange
and had consolidated assets 300 billion yen or greater in 1999. Small is firms in the
1st or 2nd section of Tokyo Stock Exchange and had consolidated assets less than 100
billion yen in 1999. Emerging is firms who went public after 1990 on the emerging
markets; Mothers, Hercules, or JASDAQ. Heteroskedasticity and autocorrelation
robust z-values are in parentheses. Hansen is p -value of Hansen’s J test forover-identification. AC1 and AC2 are p -values of the Arellano -Bond test for first andsecond order autocorrelation which has a null hypothesis of no autocorrelation and isapplied to the differenced residuals. ***, **, * significant at t he 1 %, 5 % and 10%
level, respectively.

RDt-1
2

RD t-1
Salest-1
CF
CFt-1
Debtt-1
Debt2 t-1
Stkt-1
Frgnt-1
Frgn*CFt-1
Year Dummies
Year*Indust Dum.
AC1
AC2
HansenObservations

Total+Emerging
(1)
(2)
0.964
0.918
(19.14)*** (15.86)***

Small+Emerging
(3)
(4)
0.987
0.946
(15.74)***(13.53)***Large+Emerging
(5)
(6)
1.318
1.302
(9.53)***(10.49)***-0.192(-0.53)-0.003(-1.83)*0.046(2.80)***-0.003(-0.29)-0.008(-0.81)-0.095(-1.71)*-0.023(-1.26)0.011(2.63)***-0.153(3.30)***
YES-0.576(-0.93)-0.001(-0.55)0.057(3.19)***-0.008(-0.67)-0.009(-0.8)-0.068(-1.24)-0.032(1.98)**
0.009(1.48)-0.228(2.41)**
YES-2.889(-2.18)**-0.006(-2.19)**0.006(0.30)-0.025(-1.97)**-0.03(-2.88)***-0.143(-1.78)*-0.002(-0.09)-0.005(-0.65)0.036(0.53)
YES

0
0.736
0.089
5656

-0.014(-0.04)-0.001(-0.83)0.046(2.90)***-0.008(-0.69)0(-0.03)-0.095(-1.75)*-0.018(-1.01)
0.013(3.25)***-0.158(3.61)***
YES
0
0.565
0.334
5656

0
0.675
0.173
3845

31

-0.376(-0.59)0.001(0.50)
0.058(3.39)***-0.012(-0.91)-0.005(-0.51)-0.075(-1.42)-0.033(2.25)**
0.008(1.52)-0.215(2.37)**
YES
0
0.753
0.261
3845

0
0.478
1
787

-3.067(-2.45)**-0.002(-0.87)0.03(1.44)-0.016(-1.21)-0.038(-3.97)***-0.178(-1.92)*-0.003(-0.11)0(-0.07)-0.043(-0.82)
YES
0
0.111
1
787