Discussion Paper No. 607

DEBT, R&D INVESTMENT
AND TECHNOLOGICAL PROGRESS:A PANEL STUDY OF
JAPANESE MANUFACTURING FIRMS
IN THE 90s

Kazuo Ogawa

June 2004

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

December 2003
Revised June 2004

Debt, R&D Investment and Technological Progress:
A Panel Study of Japanese Manufacturing Firms in the 90s*

Kazuo Ogawa
Institute of Social and Economic Research,
Osaka University* I am grateful to Ryuzo Miyao, Hiroyuki Odagiri, Toshitaka Sekine, Kazuyuki Suzuki, Yosuke
Takeda, Fukujyu Yamazaki and the participants of the RIEB-IMF workshop and seminar at Sophia
University for extremely helpful comments and suggestions. I would like to thank Junmin Wan for
excellent research assistance. This research was partially supported by Grants-in-Aid for Scientific
Research 12124207 of the Ministry of Education. Any remaining errors are the sole responsibility of
the author.

Abstract
Based on a panel data set of Japanese manufacturing firms in research-intensive
industries, we investigate quantitatively the extent to which debt outstandings in the 90s
affected the firm’s R&D activities. We find that massive debt outstandings had
significantly negative effect on R&D investment in the 90s. We also find thatinvestment on R&D was closely linked to the firm-level total factor productivity growthin the 90s. In fact, ten-percentage-point increase of debt-asset ratio lowered thefirm-level total factor productivity growth rate by 0.72 percentage point for 1999-2001
by way of withering R&D activities, while the firm-level TFP growth rate remains almost
intact for 1988-91.

JEL Classification Number: D21,D24 and O32
Keywords: R&D investment, Debt, Total factor productivity

Correspondence to:
Institute of Social and Economic Research, Osaka University,
6-1 Mihogaoka, Ibaraki, Osaka, 567-0047 JAPAN
Tel: +81- 6- 6879-8570 Fax: +81- 6-6878-2766
E-mail: ogawa@iser.osaka-u.ac.jp

1. Introduction
Consensus is not yet reached on the causes to bring long stagnancy to theJapanese economy in the 90s. The supply-siders argue that stagnancy is mainly due tosupply factors such as inefficiencies of the production sector as well as banking sector
suffering from massive bad loans and warn that the growth potential of the Japanese
economy is withering. For example, Hayashi and Prescott(2002) demonstrate, based on
the standard growth model, that decline of the total factor productivity(abbreviated as
TFP) growth rate and shortened working hour is responsible for the stagnancy of theJapanese economy. Convincing as their argument is, they are silent on why the TFP
growth rate suddenly dropped in the 90s.
We shed light on this aspect empirically. Recently an attempt is made to find themechanism why the aggregate or industry-level TFP growth rate declined in the 90s.
Nishimura et al. (2003) and Fukao and Kwon(2003) argue that efficient firms exit fromthe market, while inefficient firms remain in the market, which leads to the decline of
the aggregate TFP growth rate in the middle of the 90s. We are interested in thefirm-level TFP growth and its association with the firm’s R&D activities in the 90s.
Based on a panel data of Japanese manufacturing firms, we examine the R&D
investment of the firm and how they are linked to the firm-level TFP growth rate. Our
panel data set is composed of listed firms in chemicals, machinery, electrical machinery,equipment and supplies, transport equipment and precision instrument industries all ofwhich are quite research-intensive. Specifically we investigate the extent to which debt
outstandings of the firm and bad loans burdened on banks affected the R&D investmentof the firm and subsequently the TFP growth rate. To do so, comparison is madebetween the R&D activities of the firm in the late 80s and the late 90s. The former
period is the midst of bubble booms, while the latter period is characterized by heavy
debt overhang in the corporate sector and mounting bad loans in the banking sector.
We preview our main findings. The ratio of debt to total assets has significantly
negative effect on R&D investment in the late 90s, while the effect of debt-asset ratio on
R&D investment is insignificant in the late 80s on the whole. Therefore it is massive
debt outstandings of the firm that inactivated R&D activities in the late 90s. We also1

find that the TFP growth rate is positively linked to R&D investment, which implies
that debt overhang in the 90s is responsible for lowering the firm-level TFP growth rate.
Furthermore, it is found that larger dispersion of debt-asset ratio across firms leads tomore dispersed distribution of R&D investment in the 90s.
The paper is organized as follows. The next section shows salient characteristics
of R&D activities of the Japanese manufacturing industry as a whole as well as
individual firm in our panel data set in the 90s. Section 3 estimates the investment
function of R&D and Section 4 examines the association of investment on R&D withthe firm-level TFP growth rate. Section 5 concludes the paper.

2. Characteristics of R&D Activities and the TFP Growth of the Japanese
Manufacturing Firms in the 90s
We uncover some salient characteristics of R&D activities of the Japanesemanufacturing industry as a whole in the 90s as well as individual firms in our panel
data set. First of all, R&D activities of Japanese manufacturing industry had beenstagnant in the 90s. This can be confirmed by the following three figures. Figure 1
shows the rate of change in intramural expenditures on R&D of large firms in the
manufacturing sector from 1981 to 2002. Large firms are defined as those whose equity
capital is over 1 billion yen. In the 80s the rate of change in intramural expenditures on
R&D exceeded 10 % per annum for most of the period, but it fell sharply in 1993 andstayed low thereafter. The average annual growth rate of intramural expenditures on
R&D during 1981-1990 is 13.5 %, but it is only 2.46 % during 1991-2002. The rate ofchange in persons engaged in R&D activities, shown in Figure2, also exhibits the sametendency. The rate of change in persons engaged in R&D activities fell sharply in 1994
and hovered around zero thereafter. The average annual growth rate of persons engaged
in R&D activities during 1981-1990 is 6.27 %, while it is nearly zero (0.2 %) during
1991-2002. Reflecting low rate of change in intramural expenditures on R&D, the ratio
of intramural expenditure on R&D to sales remained stagnant in the 90s, as is shown in
Figure 3.
The second feature is a tilt toward development research from basic research.
2

Figure 4 shows the proportions of expenditures appropriated for basic R&D research. In
the 80s the proportion of expenditures on basic research increased steadily from 5.18 %in 1980 to 7.07 % in 1992. However, it declined gradually in the 90s and the proportionof development research rose steeply. The share of development research increased
from 1992 to 2000 by 3-percentage point. This might reflect myopic R&D behavior of
manufacturing firms under increasing uncertainty over the investment outcome since ittakes more time for the development research to bear fruit.
Next we characterize the R&D activities of individual firms in our panel data set.
All the firm-level data series are taken from Development Bank of Japan Corporate
Database. Our sampled firms belong to the following five industries: chemicals,
machinery, electrical machinery, equipment and supplies, transport equipment andprecision instrument. These industries are quite research-intensive in the sense that theratio of expenditure on R&D to sales is relatively high. The number of firms as well as
total observations for each industry is shown in Table 1. All the firms do not report thefigures of expenditures on R&D for every year, so that our panel data set is unbalanced.
Table 2 compares the averaged ratio of expenditure on R&D to sales for each industry
between the two periods: 1988-91 and 1999-2001. This ratio falls drastically in the 90s
for electrical machinery, equipment and supplies industry that is most research-intensive.
It should be noted that even the R&D activities of research-intensive firms became
stagnant in the 90s.
Now we see how the firm-level TFP growth rate has changed in the 90s. Based on
the panel data set described above, we compare the average TFP growth rates of fiveindustries between the late 80s and the late 90s. The TFP growth rate is calculated in
two different ways. One way is to subtract the contribution of labor input growth andcapital stock growth from the growth rate of real value-added.1 In other words, thefirm-level TFP growth rate is calculated as

TFPit = ∆ log(Yit ) − s L ,it ∆ log(Lit ) − (1 − s L ,it )∆ log(K it )where TFPit : TFP growth rate of the i-th firm in year t
3(1)

Yit : real value-added of the i-th firm in year t
Lit : labor input of the i-th firm in year t
K it : capital stock of the i-th firm at the end of year ts L ,it : labor share of the i-th firm in year t

The firm-level TFP growth rate thus calculated is averaged out for each industry.
Alternative way to obtain the industry-level TFP growth rate is to estimate the
Cobb-Douglas production function of value-added type by industry with labor, capitalstock and time trend as regressors and identify the estimated coefficient of time trend as
the industry-level TFP growth rate. The production function is estimated by either fixed
effects model or variance components model, depending on the Hausman test statistics.
The estimated TFP growth rates for the two periods: 1988-91 and 1999-2001 areshown in Table 3. The firm-level TFP growth rates constructed by the first method are
given in the first column (1988-91) and second column (1999-2001). Unexpectedly the
TFP growth rates are higher in the late 90s except for chemicals and precision
instrument industries. However, as for the statistical significance of the estimates, theaverage TFP growth rate is significantly positive for all the industries in the late 80s,
while the average TFP growth rate is significantly positive only for electrical machinery,equipment and supplies and transport equipment. This reflects higher standard deviationof the TFP growth rate in the late 90s. It implies that the distribution of the firm-level
TFP growth rate is more dispersed in the late 90s than in the late 80s. Statistically theaverage TFP growth rate is higher for electrical machinery, equipment and supplies in
the late 90s than in the late 80s. For the rest of industries, there is no statisticaldifference in the average TFP growth rate between the two periods.
The third and fourth columns show the estimates of TFP growth rate obtained
from regression results of the Cobb-Douglas production function for 1988-91 and1999-2001, respectively.2

Although the TFP growth rate is significantly positive forall the industries in 1988-91, it is only for electrical machinery, equipment and supplies
that it is significantly positive in 1999-2001. To sum up, it is likely that the TFP growthrate slowed down in the late 90s except for electrical machinery, equipment and4

supplies industry.

3. Determinants of Investment on R&D
We investigate the determinants of R&D activities of the Japanese manufacturingfirms by estimating the investment function of R&D. In particular, we are interested in
the extent to which the firm’s debt outstandings and the bank’s bad loans affected the
R&D activities of the firm in the 90s.
Our empirical strategy is to estimate the investment function of the same
specification, using the two panel data sets in different periods: 1988-91 and 1999-2001.
Two periods are quite contrasted in terms of the phase of business cycles. The former
period is the midst of bubble booms, while the Japanese economy was in the middle ofprolonged depression in the latter period. We use the panel data set introduced in theprevious section that consists of listed firms in research-intensive industries.
Now we specify the investment function of R&D. We choose three factors todetermine R&D investment.3 One is the growth opportunity of the firm. The moreabundant the growth opportunities are, the more active the investment on R&D will be.
We measure the growth opportunities of the firm by the average growth rate of real
sales in the current and past three years ( GSALES ). Second, there has been a longdebate on the Schumpeterian hypothesis that large firms are more active in creative
activities such as R&D investment. To account for this hypothesis in our specification,
we add the logarithm of real sales ( RSALES ) to the list of explanatory variables.
Last but not least, we examine the effect of debt outstandings of the firm and badloans of the bank on investment on R&D. Debt outstandings exert negative effect on
investment in a variety of channels. One channel is through external finance premium
the firm may face. When there is asymmetric information between debtors and creditors,
it will drive a wedge between the cost of external finance and internal finance, called
external finance premium. Note that the external finance premium is inversely
associated with the borrower’s collateralizable net worth relative to the debt
outstandings. Therefore debt increase raises the external finance premium and thus
reduces investment. 4 Moreover, spending on R&D produces fewer collateralizable5

assets and R&D activities have knowledge externalities, all of which tends to raise theexternal finance premium.
The second channel through which corporate debt affects investment is by creatingdebt overhang. Debt overhang is defined as deterrence of new investment by debt
outstanding. It occurs when the debt outstanding is greater than the net present value ofinvestment project since the benefits from new investment will go to the existing
creditors rather than to the new investors.5
In the third channel, debt plays a disciplinary role for managers. Increase of debt
raises the probability of bankruptcy. Managers are more concerned with bankruptcy
than shareholders, since it is highly likely in case of bankruptcy that the managers are
fired. Therefore, faced with increasing debt, managers will make every effort to cut
back investment to raise efficiency. The ratio of debt to total assets ( DEBT ) can beused to capture the effect of debt on investment of R&D.
Recently attention has been also paid to the conditions of the bank’s balance sheet
as one of the important determinants to affect firm’s investment. It is noted that a
number of large firms in Japan belong to industry groups known as keiretsu, where
main bank plays a vital role in mitigating the informational asymmetry between lenders
and borrowers. Information of borrowers has been accumulated in main banks throughlong-term, stable relationships of firms with their main banks. Moreover, bank
employees often hold management positions in financially troubled firms for the
purpose of direct monitoring.6 Therefore, once the bank’s balance sheet deteriorates,
say by mounting bad loans, it will have an adverse effect on intermediary role of banks
and thus the associated firm’s investment activities. Furthermore, if deterioration ofbank’s balance sheet affects lending negatively, it will also lead to a decrease ofinvestment of bank-dependent firms.7 We measure the health of the firm’s main bankby the ratio of bad loans to total loans ( BADLOAN ). 8 Main bank of a firm isidentified as the top shareholder among bank shareholders.9 Unfortunately, the badloans ratio is not available for the period of 1988-1991. Therefore we adopt alternative
measure to represent the bank’s health that is available for both 1988-91 and 1999-2001.
That is the diffusion index of ‘banks’ willingness to lend’ ( LEND ) reported in the Bank
6

of Japan Tankan (Short-term Economic Survey of Corporations). The diffusion index
represents the proportion of entrepreneurs feeling the present lending attitude offinancial institutions to be “accommodative” minus those feeling the present lending
attitude of financial institutions to be “severe”. It is tacitly assumed that this index canreflect the bank health closely. The diffusion index is available by industry.10
The investment function to be estimated is written as 11 12

RDit= α 0 + α1GSALESit + α 2 log( RSALES ) it + α 3 DEBTit −1 + α 4 BADLOAN itYitm+ ∑ d jYDUMMY jt + vi + uit

(2)

j =1

where RDit : real expenditure on R&D
Yit : real value-added
YDUMMY jt : year dummies
vi : firm-specific term
uit : disturbance term

In alternative specification the BADLOAN variable is replaced by the LEND
variable. In eq.(2) the dependent variable as well as cash flow variable is divided by thereal sales to allow for heteroscedasticity. 13
Three types of estimation method are used to estimate eq.(2). One is to estimateeq.(2) both by fixed effects model and random effects model and choose the mostappropriate one by the Hausman test. The second is to estimate the first-difference formof eq.(2) by pooled OLS. The third is to estimate the first-difference form of eq.(2) byinstrumental variable method to handle possible correlations between the firm-specificexplanatory variables and the disturbance term. In estimation we discard theobservations whose ratio of R&D expenditures to real value-added, average growth rateof sales and debt-asset ratio are above or below their mean values by four times of theirstandard deviations.
Table 4 and 5 report the estimation results of eq.(2) for 1999-2001 and 1988-91,

7respectively. The estimation results of fixed effects model are quite similar to those bypooled OLS in first-differenced form. The estimation results by the instrumental
variable method in first differenced form are somewhat less precise in the sense that thecoefficient estimate of debt-asset ratio has larger standard errors. This might be due topoor choice of instruments for debt-asset ratio, leading to low correlation of instrumentswith the debt-asset ratio.
It turns out that three variables are significant determinants of investment on
R&D for 1999-2001. First, the average growth rate of sales has positive effect on
investment on R&D. Second, real sales exert negative effect on the ratio of R&D
investment to value-added. This is contrary to the Schumpeterian hypothesis. Third,
debt-asset ratio has negative effect on R&D investment, implying that massive debt
outstandings deter R&D activities. Contrasted with the significantly negative effect ofdebt on R&D investment, the effect of bank health on R&D investment is insignificant
as a whole. It implies that even if the bank’s balance sheet deteriorates, it will notnecessarily aggravate real activities of the firm by reducing lending. Banks’ lending
attitude is insignificantly positive in the fixed effects model, while it exhibits a wrong
sign in the first-difference model.
Contrary to significantly negative effects of debt-asset ratio on R&D investment
for 1999-2001, the effect of debt-asset ratio on R&D investment is insignificant for
1988-91, irrespective of estimation method, as is seen in Table 5. To sum up, it is onlyin the 90s that debt was a heavy burden for the firm in implementing R&D investment.

4. Does R&D Investment Matter in Raising the Firm-level TFP Growth?
Given our findings that debt had negative effects on R&D activities of theJapanese research-intensive firms in the 90s, the question to be posed is “Does R&D
matter in raising the firm-level TFP growth?” If the answer is yes, then we can conclude
that massive debt outstandings of the firm in the 90s led to a decrease in the firm-level
TFP growth by deterring R&D investment. We examine this link between R&D
investment and the firm-level TFP growth in this section.
First we derive the regression equation to relate the firm-level TFP growth rate
8

given by eq.(1) to R&D investment with market structure of individual industry into
consideration.14 15 Let us denote the value-added production function by
Yit = Fi ( K it , Lit , t )(3)where Yit : real value-added of the i-th firm in year t
K it : capital stock of the i-th firm in year t
Lit :labor input of the i-th firm in year tt :time trend

Differentiating eq.(3) with respect to time and expressing in terms of the rate of change,
we obtain the following equation.

1 ∂Fi⎛ L ⎞ ∂Fi
⎛ K ⎞ ∂Fi
∆lit +∆kit + ⎜ ⎟∆yit = ⎜ ⎟Yit ∂t⎝ Y ⎠it ∂Lit
⎝ Y ⎠it ∂K it(4)

where ∆yit , ∆kit , ∆lit : rate of change in value-added, capital stockand labor of the i-th firm in year t, respectively

The last term of the right-hand-side of eq.(4) is Solow’s residual or the TFP growth rate,
denoted by ∆ε it* . Given output, the demand for inputs is determined by static cost
minimization as follows: 16

λit

∂Fi
=r
∂K it k ,it
(5)

λit

∂Fi
= wit
∂L it

where rk ,it : rental price of capital of the i-th firm in year t

wit : wage rate of the i-th firm in year t

λit : marginal cost of the i-th firm in year t

9Substituting eqs.(5) into eq.(4) and arranging terms, we have⎛r K ⎞⎛ wL ⎞*∆yit = ⎜ k ⎟ ∆kit + ⎜⎟ ∆lit + ∆ε it⎝ λY ⎠it⎝ λY ⎠it(6)

Under the homogeneity assumption of the production function, the following equation
is obtained.

⎛ r K ⎞ ⎛ wL ⎞1= ⎜ k ⎟ +⎜⎟⎝ λY ⎠ it ⎝ λY ⎠ it(7)

Substitution of eq.(7) into eq.(6) yields the following expression.
∆yit − ∆k it = α L ,it µ (∆lit − ∆k it ) + ∆ε it*(8)⎛ wL ⎞⎟⎟ : labor share of value-added of the i-th firm
where α L ,it = ⎜⎜⎝ pY ⎠itin year t⎛ p⎞µ = ⎜ ⎟ ：ratio of output price to marginal cost or⎝ λ ⎠itmark-up ratio.
We assume that the value of µ , mark-up ratio, is constant over time for each industry. It
exceeds unity under imperfect competition of the product market, while it is unity under
perfect competition. We specify the TFP growth rate as a function of the ratio ofinvestment on R&D to value-added in the previous year. Linearizing this relationship,
we have the final expression to be estimated as follows:⎛ RDit −1 ⎞⎟⎟ + vi + uit∆yit − ∆k it = β 0 + β1α L ,it (∆lit − ∆k it ) + β 2 ⎜⎜⎝ Yit −1 ⎠where

β1 = µ10(9)vi :firm-specific term
uit : disturbance term

Based on the panel data set used in the previous sections, we estimate eq.(9) thattakes account of the differences in market structure of individual industry. Specifically,
we replace the second term of the right-hand-side of eq.(9) by the dummy variable for⎞each industry multiplied by α L ,it (∆lit − ∆kit ) . The coefficient estimate of ⎛⎜ RDit −1⎟
Y
it −1 ⎠⎝

can measure the extent to which investment on R&D affects the firm-level TFP growthrate or the marginal rate of return on R&D investment. 17
Equation (9) is estimated for the two periods: 1988-91 and 1999-2001 by threeestimation procedures. One is to estimate it both by fixed effects model and random
effects model and choose the most appropriate one by the Hausman test. The second isto estimate the first-difference form by pooled OLS. The third is to estimate thefirst-difference form by instrumental variable method. Table 6 and 7 show theestimation results of eq.(9) for 1999-2001 and 1988-91, respectively. The effect ofdevelopment and research investment on the firm-level TFP growth rate is significantly
positive for 1999-2001 as well as for 1988-91. However, the impact of R&D investment
on the TFP growth rate for 1999-2001 is 2.1 to 23.0 times as large as that for 1988-91.
18

A ten-percentage-point increase of debt-asset ratio lowers the firm-level TFP growthrate by 0.72 percentage point for 1999-2001 by way of withering R&D activities, while
the firm-level TFP growth rate remains almost intact for 1988-91.19
Combining the findings in the previous section that debt-asset ratio affects R&D
investment in significantly negative manner in the period of 1999-2001, but not in the
period of 1988-91 with more important role of R&D activities in the firm-level TFP
growth rate in the period of 1999-2001, we may conclude that the firm-level TFP
growth rate is much more affected by debt for 1999-2001.
Furthermore, we can explain why the distribution of the firm-level TFP growthrate became more dispersed in the late 90s, as was seen in Section 2. First, let us notethat the distribution of debt-asset ratio is also more dispersed for 1999-2001. In fact the11standard deviation of the debt-asset ratio of our panel data set is 0.2057 and 0.1688 for
1999-2001 and 1988-91, respectively.20 Use of the estimation results of investment
functions of R&D and the firm-level TFP growth rate equations enables us to compare
the firm-level TFP growth rate between the two firms with the same attributes but
debt-asset ratio. Consider a firm with the debt-asset ratio on the 1st quartile, and anotherwith the debt-asset ratio on the 3rd quartile. Using the interquartile value of debt-asset
ratio, we can compute the difference between the firm-level TFP growth rates of the two
firms. The interquartile value of debt-asset ratio is 0.3079 and 0.2500 for 1999-2001
and 1988-91, respectively. Therefore the TFP growth rate of the 3rd quartile firm islower than that of the 1st quartile firm by 2.2 percentage points for 1999-2001 and ishigher only by 0.001 percentage points for 1988-91. 21 To sum up, massive debt
outstandings in the 90s give rise to more dispersed distribution of the firm-level TFP
growth rate across firms.

5. Concluding Remarks
Based on the panel data of Japanese research-intensive manufacturing firms, weinvestigated the R&D activities in the 90s. Main findings are follows. First, massive
debt accumulation had a negative effect on R&D investment in the late 90s, althoughdebt outstandings had little effect on R&D investment in the late 80s. Second,
investment on R&D was closely linked to the firm-level TFP growth rate in the late 90s,
while the link was much weaker in the late 80s.
Our findings suggest that lingering debt outstandings are in part responsible forslowdown of the firm-level TFP growth rate in the 90s. Moreover, we have important
implications for the distributional aspect of the firm-level TFP growth rate. The
distribution of the firm-level TFP growth rate across firms is more dispersed in the 90s,
which is due to more dispersed distribution of the debt-asset ratio across firms in the 90s.
It is true that the average debt-asset ratio of Japanese large manufacturing firms is
declining over the 90s, but there still remain a number of firms that have massive debt
outstandings to be cleaned up. Policy actions to prompt the clean-up of the debt
outstandings in the corporate sector will be an urgent agenda for policy makers to12

recover higher growth potentials of the Japanese economy.

13Footnotes1

See Data Appendix for the procedures to construct the variables used in the text.

2

All the parameters estimates of the Cobb-Douglas production function as well as therelated statistics are shown in Table Appendix.

3

See Cohen and Levin (1989) and Cohen (1995) for comprehensive survey of thedeterminants of R&D activities in empirical context.

4

Empirical support for negative effect of firm leverage on fixed investment is seen inLang et al. (1996). Cantor(1990) and Calomiris et al.(1997) show in a slightly different
context that investment of leverage firm is more responsive to sales and cash flow. For
negative effects of leverage on investment on R&D, see Bronwyn Hall(1990, 1992).

5

See Myers(1977) and Hart(1995) for more detailed discussion on debt overhang.

6

Hoshi et al. (1991) is a pioneering work to show that the firms affiliated with mainbank enjoy lower external finance premium than independent firms using the micro dataof firms.

7

Gibson(1995 and 1997) and Kang and Stulz(2000) examine the effect of bank healthon investment behavior of the Japanese firms based on micro data of firms.

8

The bad loan is defined as the sum of bankrupt loans, delayed loans, and loans
delayed more than three months and mitigated loans. The data is taken from Analysis ofFinancial Statements of All Banks (Japanese Bankers Association).

9

We assume that main bank of a firm holds more than 2 % of total shares of the firm,
so that banks with less than 2 % share of the firm is not identified as the firm’s mainbank even if the bank is top shareholder among bank shareholders.

10

For empirical studies to use the Bank of Japan diffusion index of ‘banks’ willingness
to lend,’ see Motonishi and Yoshikawa (1999) and Ogawa (2003a,b). Using theaggregate data, Motonishi and Yoshikawa obtain the evidence that bank lending is asignificant determinant of business investment of small firms, but not large firms. On
the other hand, Ogawa(2003a,b), using the firm’s micro data, found that lending attitudeof financial institutions affected fixed investment and employment adversely.

11

The subscript i and t refer to the i-th firm and fiscal year t, respectively.

12Year dummies are also added as explanatory variables to capture the yearly shocks
common to individual firms.

14

13

An attempt was made to capture the effect of liquidity constraints on R&D
investment by adding cash flow variable to the list of explanatory variables to. It turns
out that the coefficient estimate of cash flow was significantly negative, possibly due tomulticollinearity with real sales. Thus cash flow was dropped from the equation.
Empirically the effect of cash flow on R&D investment is inconclusive in Japan. For
example, it is significantly positive in Goto et al.(2002) and insignificantly positive inHall et al.(1999), while it is insignificantly negative in Bhagat and Welch(1995).
14

The following discussion originates from Solow(1957) and Robert Hall(1988,1990).
For empirical analysis to follow Hall’s approach to estimate the mark-up ratios in Japan,
see Ariga et al.(1992) and Baba(1995).

15

Alternative way to estimate the impact of R&D investment on productivity is tospecify functional form of production function. The empirical analysis along this line
based on Japanese firm data is Odagiri and Iwata(1986), Goto and Suzuki(1989),
Griliches and Mairesse(1990) and Kwon and Inui(2003).
16

Basu and Kimball(1997) extend the analysis along this line to the dynamic cost
minimization case with adjustment cost of quasi-fixed factors taken into consideration.

17

The above model is extended to account for non-unitary homogeneity of production
function. It turns out that the estimates of marginal rate of return on R&D are essentiallyunaltered, while the estimates of mark-up ratios and homogeneity parameters are
estimated less precisely. Therefore we report only the estimation results under linear
homogeneous technology assumption.
18

Branstetter and Nakamura(2003) find that Japanese R&D productivity growthslowed down in the 90s. Their findings are related to average productivity, while what
we are concerned here is an increase of marginal rate of return on R&D investment in
the 90s.

19

The figures are computed on the basis of parameters obtained from fixed effects
model with banks’ willingness to lend as banks’ health variable for R&D investment
function and fixed effects model for TFP growth rate regression for 1999-2001 and1988-91.

20

When we measure the dispersion of the distribution of debt-asset ratio by coefficientof variation, it is 0.3895 and 0.2721 for 1999-2001 and 1988-91, respectively.

21

For the models used for computing the differences of the TFP growth rate, seefootnote 19.

15

Data Appendix

We give brief explanations of the data construction procedures with special
emphasis on capital stock.

Construction of Capital Stock
Our basic strategy to construct a series of the physical depreciable capital stock
is to follow the perpetual inventory method, as discussed in Hayashi and Inoue(1991) .
The capital stock series is constructed each for six types of assets: nonresidentialbuildings, structures, machinery, vessels, transportation equipment except vessels,
instruments and tools to add up the total capital stock. Our benchmark year is the fiscal
year of 1985 and 1995 for 1988-91 and 1999-2001, respectively. Benchmark stock isconverted into the real value by dividing the book-valued stock by the investment goods
deflator in benchmark year minus the average years elapsed since installation. The
information of the average years elapsed since installation is available from 1970
National Wealth Survey for each type of physical depreciable assets. The investment
goods deflator is constructed for each industry and each type of physical asset bymaking use of the Fixed Capital Formation Matrix in the 1995 Input-Output tables.
Nominal investment is constructed for each type of physical asset as follows:
NI it = INCit − (SRit − DDit )= INCit − {(KGit −1 + INCit − KGit ) − ( ADit −1 + DEPit − ADit )}(A-1)= (KGit − KGit −1 ) − ( ADit − ADit −1 ) + DEPit

where NI it : nominal investment of the i-th firm in year t
INCit : increment of physical asset of the i-th firm in year t
SRit : value of the i-th firm’s physical asset scrapped in year t
DDit : value of the i-th firm’s physical asset scrapped in year tout of the accumulated depreciation
KGit : i-th firm’s physical asset at the end of year t at purchase
value
16

ADit : accumulated depreciation of the i-th firm in year t
DEPit : depreciation of the i-th firm in year t

Nominal investment is divided by the corresponding investment goods deflator to obtainreal investment ( I it ). The physical depreciation rates ( δ ) are based on those reported in
Hayashi and Inoue(1991). They show the rates for five categories of assets, which were
derived from Hulten and Wykoff(1979,1981). They are 4.7 % per annum for
nonresidential buildings, 5.64 % for structures, 9.489 % for machinery, 14.7 % fortransportation equipment, and 8.8838 % for instruments and tools. We use thedepreciation rate of transportation equipment for that of vessels.
Given the benchmark value of depreciable stock, real investment series, anddepreciation rate, we obtain the capital stock of each type from the following formula.
For detailed explanations see Hayashi and Inoue(1991).
K it = (1 − δ ) K it −1 + I it

(A-2)

where K it : i-th firm’s capital stock at the end of year t

Finally, the capital stock is multiplied by the capacity utilization rate index
reported by Ministry of Economy, Trade and Industry.

Value-added
Value-added = (Ordinary Income – Corporate Taxes – Enterprise Tax – Residential
Tax + Compensations to Directors + Wages and Salaries to Employees + Welfare
Expenses + Reserve Fund for Bonuses + Retirement Allowance + Reserve Fund for
Retirement Allowance + Company Pension + Taxes + Rental Fees + Royalties +Interest and Discount paid + Depreciation Allowances).

Labor inputs
The number of employees is multiplied by the Hours Worked Indices (Total
Hours Worked) reported in Monthly Labour Survey (Ministry of Health, Labour and17

Welfare).

Labor Share
Labor share = (Compensations to Directors + Wages and Salaries to Employees+ Welfare Expenses + Reserve Fund for Bonuses + Retirement Allowance + Reserve
Fund for Retirement Allowance + Company Pension)/Value-added.

Investment on R&D
Total expenditure on R&D.

Real sales
Nominal sales are divided by the Corporate Goods Price Indexes of eachindustry (The Bank of Japan).

Cash flow
Cash flow = (ordinary income + depreciation allowance – corporate tax –dividends paid – compensations and bonus to directors).

Debt-asset ratio
Liabilities are divided by total assets.

Bad loans ratio
Bad loans ratio of the firm’s main bank is defined as the ratio of bad loans to totalloans. Main bank of the firm is identified as the top shareholder among bankshareholders. The bad loan is defined as the sum of bankrupt loans, delayed loans, andloans delayed more than three months and mitigated loans. The data of bad loans comesfrom Analysis of Financial Statements of All Banks (Japanese Bankers Association).

The diffusion index of ‘banks’ willingness to lend’
The diffusion index is the proportion of entrepreneurs feeling the present lending18

attitude of financial institutions to be “accommodative” minus those feeling the present
lending attitude of financial institutions to be “severe”. It is reported by the Bank ofJapan Tankan (Short-term Economic Survey of Corporations).

19

References[1] Ariga,K., Kaneko,T., Sakamoto,K. and H.Sano(1992).” Business Cycles of the
Post-war Japan: Price, Wage and Mark-up,” Financial Review 22 (Policy ResearchInstiture, Ministry of Finance), pp.130-161. (in Japanese)[2] Baba,N.(1995),” Causes of Price Differentials between Domestic and Foreign
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[4] Bhagat,S. and I. Welch(1995).” Corporate Research & Development Investments
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[5] Branstetter,L. and Y. Nakamura(2003).” Is Japan’s Innovative Capacity in Decline?”
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to Growth in Japan (University of Chicago Press: Chicago), pp.191-223.

[6] Calomiris, C.W., Orphanides, A., and S.A. Sharpe(1997).” Leverage as a State
Variable for Employment, Inventory Accumulation, and Fixed Investment,” In Capie,F.,
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[7] Cantor, R.(1990).” Effects of Leverage on Corporate Investment and Hiring
Decisions,” Federal Reserve Bank of New York Quarterly Review 15, pp.31-41.

[8] Cohen,W.M.(1995).” Empirical Studies of Innovative Activity,” In Stoneman, P.,(ed.) Handbook of The Economics of Innovation and Technological Change (Blackwell:20

Oxford) pp.182-264.

[9] Cohen,W.M. and R.C. Levin (1989).” Empirical Studies of Innovation and Market
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Organization Vol.2 (North Holland: Amsterdam)[10] Fukao, K. and H.U. Kwon(2003).” The Productivity and the Economic Growth ofJapan: Empirical Analysis Based on Industry-Level and Firm-level Data,” paper
presented at Semi-annual Conference of Japanese Economic Association held at Oita.

[11] Gibson,M.S.(1995).” Can Bank Health Affect Investment? Evidence from Japan,”
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[12] Gibson,M.S.(1997).” More Evidence on the Link between Bank Health andInvestment in Japan,” Journal of the Japanese and International Economies 11,
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[13] Goto, A. and K. Suzuki(1989),” R&D Capital, Rate of Return on R&D Investment
and Spillover of R&D in Japanese Manufacturing,” Review of Economics and Statistics
71, pp.555-564.

[14] Goto, A., Koga, T. and K. Suzuki(2002),” Determinants of Industrial R&D inJapanese Manufacturing Industries,” The Economic Review (Institute of Economic
Research, Hitotsubashi University), Vol.53, No.1, pp.18-23. (in Japanese)[15] Griliches,Z. and J. Mairesse(1990).” R&D and Productivity Growth: Comparing
Japanese and US Manufacturing Firms, in Griliches,Z.(ed.) R&D, Patens, andProductivity (University of Chicago Press: Chicago, IL,USA) pp.317-348.

[16] Hall,B.H.(1990).” The Impact of Corporate Restructuring on Industrial Research

21and Development,” Brookings Papers on Economic Activity, Special Issue,pp.85-124.

[17] Hall,B.H., Mairesse, J., Branstetter, L. and B.Crepon(1999).” Does Cash Flow
Cause Investment and R&D: An Exploration Using Panel Data for Frenncgu, Japanese,and United States Scientific Firms,” in Audretsch,D. and A. Thurik(eds.) Innovation,
Industry Evolution and Employment (Cambridge University Press: Cambridge)[18] Hall,R.E.(1988).” The Relation between Price and Marginal Cost in U.S. Industry,”
Journal of Political Economy 96, pp.921-947.

[19] Hall,R.E.(1990).” Invariance Properties of Solow’s Productivity Residual,” inDiamond,P.(ed.) Growth/Productivity/Unemployment: Essays to Celebrate Bob Solow’s
Birthday (MIT Press: Cambridge, MA, USA) .

[20] Hart, O.(1995). Firms, Contracts and Financial Structure, Oxford Clarendon
Press.

[21] Hayashi, F. and T. Inoue(1991)." The Relation between Firm Growth and Q with
Multiple Capital Goods: Theory and Evidence from Panel Data on Japanese Firms,"
Econometrica 59, pp.731-753.
[22] Hayashi,F. and E.C.Prescott(2002).” The 1990s in Japan: A Lost Decade,” Review
of Economic Dynamics Vol.5, No.1, pp.206-235.

[23] Hoshi, T., Kashyap, A., and D.Scharfstein(1991)." Corporate Structure, Liquidity,
and Investment: Evidence from Japanese Industrial Groups," Quarterly Journal of
Economics 106, pp.33-60.

[24] Hulten,C., and F.Wykoff(1979). “ Economic Depreciation of the U.S. Capital
Stock,” Report Submitted to U.S. Department of Treasury, Office of Tax Analysis,

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[25] Hulten,C., and F.Wykoff(1981). " The Measurement of Economics Depreciation ,"C.Hulten (ed.) Depreciation, Inflation and the Taxation of Income from Capital, Urban
Institute.

[26] Kang, J.K. and R.S. Stulz(2000).” Do Banking Shocks Affect Borrowing Firm
Performance? An Analysis of the Japanese Experience,” Journal of Business 73,
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[28] Lang, L., Ofek,E. and R.M.Stulz(1996).” Leverage, Investment, and Firm Growth,”
Journal of Financial Economics 40, pp.3-29.

[29] Motonishi, T. and H. Yoshikawa(1999).” Causes of the Long Stagnation of Japan
during the 1990’s: Financial or Real?” Journal of the Japanese and International
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[30] Myers, S.C.(1977).” Determinants of Corporate Borrowing,” Journal of Financial
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[31] Nishimura,G.K., Nakajima,T. and K. Kiyota(2003).” What Happened to theJapanese Industry during a Lost Decade? : Entry and Exit of Firms and TFP,” RIETI
Discussion Paper Series 03-J-002. (in Japanese)[32] Odagiri,H. and H.Iwata(1986).” The Impact of R&D on Productivity Increase inJapanese Manufacturing Companies,” Research Policy 15, pp.13-19.

23

[33] Ogawa,K. (2003a),” Financial Distress and Employment: The Japanese Case in the90s,” NBER Working paper No. 9646.

[34] Ogawa,K. (2003b),” Financial Distress and Corporate Investment: The JapaneseCase in the 90s,” Osaka University ISER Discussion Paper No. 584.

[35] Solow,R.M.(1957).”Technological Change and the Aggregate Production
Function,” Review of Economics and Statistics 39, pp.312-320.

24

Table 1 Number of Firms and Total Observations in Panel Data Set
in 1988-91 and 1999-2001:
1988-91(1)(2)

1999-2001(1)(2)

Chemicals

75

284

141

419

Machinery

83

307

147

433

Electrical machinery,equipment and supplies

78

290

133

390

Transport equipment

55

208

102

305

Precision instrument

17

67

32

94

308

1156

555

1641

Total

Data Source: Development Bank of Japan Corporate Database(1): number of firms(2): number of total observations

Table 2 Comparison of R&D Activities between 1988-91 and 1999-2001:
Mean Ratio of Expenditures on R&D to Sales

Chemicals
Machinery
Electrical machinery,equipment and supplies
Transport equipment
Precision instrument

1988-91

1999-2001

0.0589(0.05)
0.0253(0.02)
0.0809(0.09)

0.0528(0.04)
0.0275(0.02)
0.0481(0.04)

0.0326(0.03)
0.0487(0.03)

0.0274(0.03)
0.0505(0.03)

Data Source: Development Bank of Japan Corporate Database
The values in parentheses are standard deviations.

Table 3 Comparison of the TFP Growth Rate by Industry
between 1988-91 and 1999-2001:
TFP growth rate
as Solow’s residual
1988-91
Chemicals
Machinery
Electrical machinery,equipment and supplies
Transport equipment
Precision instrument

1999-01

TFP growth rate estimatedby Cobb-Douglas
production function
1988-91
1999-01

0.0162*** 0.0057(0.10)(0.14)
0.0174** 0.0226(0.14)(0.32)
0.0814*** 0.1102***(0.10)(0.21)

0.0553***(8.57)
0.0432***(6.20)
0.0961***(19.33)0.0034(0.43)
0.0201(1.20)
0.1208***(11.32)0.0398*** 0.0613***(0.15)(0.16)
0.0398*** 0.0223(0.11)(0.17)

0.1088***(8.02)
0.0330***(3.93)

0.0128(1.17)
0.0153(0.94)

Data Source: Development Bank of Japan Corporate Database*** ** * : significant at the 1 %, 5 % and 10% level, respectively
The values in parenthesis in the first and second column are standard deviations.
The values in parenthesis in the third and fourth column are t-ratios.
The production function of chemicals, machinery, electrical machinery, equipment
and supplies and transport equipment for the period of 1988-91 and chemicals,
machinery, transport equipment for the period of 1999-2001 is estimated by fixed
effects model.
On the other hand, the production function of precision instrument for the period of
1988-91 and electrical machinery, equipment and supplies and precision
instrument for the period of 1999-2001 is estimated by variance components
model.

Table 4 Estimation Results of R&D Investment Function: 1999-2001
Estimation
method
1. Fixed effects
model

Growth rateof sales
0.1145**(2.30)

Real
Debt-asset
sales
ratio
-0.1003*** -0.0452*(-6.41)(-1.82)

Ratio ofbad loans

2.Fixed effects
model

0.1296***(2.57)-0.1064*** -0.0660**(-6.47)(-2.43)0.0462(1.34)

Banks’ willingness
to lend0.0266(0.77)

S.E.
Number of observations
0.03381618
0.03421494

3.First0.1302*** -0.1073*** -0.0440*-0.0427***
0.0431
difference(2.86)(-7.35)(-1.73)(-2.61)
1068(Pooled OLS)
4.First0.1660*** -0.1237*** -0.0482*
0.0667*0.0440difference(3.62)(-8.65)(-1.74)(1.89)
975(Pooled OLS)
5. First difference
0.2134*** -0.1130*** -0.0397-0.0393*0.0432(Instrumental(2.70)(-3.62)(-0.28)(-1.84)1068
variables)
6. First difference
0.2012** -0.1480***
0.05470.05780.0445(Instrumental(2.21)(-6.06)(0.37)(1.60)
975
variables)
Notes: *** ** * : significant at the 1 %, 5 % and 10% level, respectively
The values in parenthesis are t-ratios.
Instruments for the 5-th and 6-th row are constant, year dummies, first-lagged and second-lagged debt-asset ratios, second-lagged andthird-lagged average growth rates of sales and logarithm of real sales. It is assumed that banks’ willingness to lend is exogenous in the
5-th equation and that the bank’s bad loan ratio is exogenous in the 6-th equations.

Table 5 Estimation Results of R&D Investment Function: 1988-91
Estimation
method
1. Fixed effects
model

Growth rateof sales
0.1361**(2.10)

Real
Debt-asset
sales
ratio
-0.0834** -0.0006(-2.66)(-0.02)Banks’ willingness
to lend0.0334(0.86)

S.E.
Number of observations
0.05411135

2.First difference(Pooled OLS)

0.0939(0.70)-0.0708(-1.23)-0.0243(-0.34)-0.0125(-0.25)0.1202827

3.First difference(Instrumental
variables

0.2366(0.92)-0.4105(-1.61)-0.6151(-1.44)-0.0102(-0.18)0.1400556

Notes: *** ** * : significant at the 1 %, 5 % and 10% level, respectively
The values in parenthesis are t-ratios.
Instruments for the 3-rd row are constant, year dummies, first-lagged and second-lagged debt-asset ratios, second-lagged average
growth rates of sales and second-lagged and third-lagged logarithm of real sales. It is assumed that banks’ willingness to lend isexogenous.

Table 6 Link of the Firm-level TFP Growth Rate to R&D Investment: 1999-2001
Estimation
method
1. Fixed effects
model
2.Firstdifference(Pooled OLS)

Effect ofresearch anddevelopment
1.5871***(7.64)
0.8053***(15.38)1.6026***(9.92)

0.7729***(6.28)

Mark-up ratio
Electricalmachinery
1.0811***(7.85)

1.6105***(9.60)

0.5265***(4.53)

0.7379***(5.75)

Chemicals

MachineryTransport
equipment
0.8465***(4.58)

Precision
instrument
0.8526**(1.99)

S.E.
Number ofobservations
0.15911068

0.9914***(5.26)

0.9304**(2.09)

0.2338523

3.First0.7085***0.81150.14720.9298***2.8803***3.6346*0.2705(10.29)(0.56)(0.80)(5.17)(5.96)(1.86)
523
Difference(Instrumental
Variables)
Notes: *** ** * : significant at the 1 %, 5 % and 10% level, respectively
The values in parenthesis are t-ratios.
Instruments for the 3-rd row are constant, industry dummies, second-lagged ratio of R&D to value-added, second-lagged and third-lagged growth rate ofcapital stock and growth rate of labor-capital ratio multiplied by labor share.

Table 7 Link of the Firm-level TFP Growth Rate to R&D Investment: 1988-91
Estimation
method
1. Fixed effects
model
2.Firstdifference(Pooled OLS)

Effect ofresearch anddevelopment
0.0691*(1.82)
0.0758*(1.86)

1.6960***(16.96)

1.3386***(23.05)Mark-up ratio
Electricalmachinery
1.5325***(14.30)1.8001***(16.67)1.5123***(22.82)

1.7424***(21.11)

Chemicals

MachineryTransport
equipment
1.8415***(9.51)

Precision
instrument
1.5520***(8.87)

S.E.
Number ofobservations
0.0878827

2.0558***(9.10)

1.6807***(9.11)

0.1338535

3.First0.3442**
1.8682***
1.5826***1.8089***2.9260***2.1810***0.1422(2.50)(3.57)(7.34)(5.58)(4.47)(7.29)
535
Difference(Instrumental
Variables)
Notes: *** ** * : significant at the 1 %, 5 % and 10% level, respectively
The values in parenthesis are t-ratios.
Instruments for the 3-rd row are constant, industry dummies, second-lagged ratio of R&D to value-added, second-lagged and third-lagged growth rate ofcapital stock and growth rate of labor-capital ratio multiplied by labor share.

Table Appendix
Estimation Results of the Cobb-Douglas Production Function by Industry

Labor
elasticity
Chemicals
Machinery
Electrical machinery,equipment and supplies
Transport equipment
Precision instrument

1988-91
Capitalelasticity

Time
trend

S.E.

0.5894*** 0.1940*** 0.0553***(6.34)(4.38)(8.57)
0.3520*** 0.1519*** 0.0432***(2.93)(4.07)(6.20)
0.9530*** 0.1535*** 0.0961***(12.25)(5.96)(19.33)0.07431.1336*** -0.1327
0.1088***(7.73)(-1.50)(8.02)
0.7193*** 0.2311*** 0.0330***(9.18)(4.21)(3.93)

0.0973

0.0931
0.06630.1568Labor
elasticity

1999-2001
CapitalTimeelasticity
trend

0.4301***(4.13)
0.2702(1.61)
0.6770***(17.30)0.2326*** 0.0034
0.1181(3.81)(0.43)
0.3654** 0.0201
0.2533(2.52)(1.20)
0.3511*** 0.1208*** 0.2682(11.25)(11.32)0.4609***(7.12)
0.7119***(6.92)

0.3003*** 0.0128(2.94)(1.17)
0.3138*** 0.0153(4.25)(0.94)

S.E.

0.09680.2619*** ** * : significant at the 1 %, 5 % and 10% level, respectively
The values in parenthesis are t-ratios.
The production function of chemicals, machinery, electrical machinery, equipment and supplies and transport equipment for theperiod of 1988-91 and chemicals, machinery, transport equipment for the period of 1999-2001 is estimated by fixed effects model.
On the other hand, the production function of precision instrument for the period of 1988-91 and electrical machinery, equipment
and supplies and precision instrument for the period of 1999-2001 is estimated by variance components model.

Figure 1 Rate of Change in Intramural Expenditures on R&D:
Large Firms in Manufacturing Sector
25.00

20.00

15.00

%

10.00

5.00

0.00
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-5.00

-10.00

Data Source: Survey of Research and Development (Statistics Bureau of Ministry of Public Management, Home Affairs, Posts and Telecommunications)Figure 2 Rate of Change in Persons Engaged in R&D Activities:
Large Firms in Manufacturing Sector
10.00

8.00

6.00

%

4.00

2.00

0.00
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-2.00

-4.00

-6.00

Data Source: Survey of Research and Development (Statistics Bureau of Ministry of Public Management, Home Affairs, Posts and Telecommunications)Figure 3 Ratio of Intramural Exprnditures on R&D to Sales:
Large Firms in Manufacturing Sector
4.50

4.00

%

3.50

3.00

2.50

2.00
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Data Source: Survey of Research and Development (Statistics Bureau of Ministry of Public Management, Home Affairs, Posts and Telecommunications)Figure 4 Intramural Expenditure on R&D by Types:
Large Firms in Manufacturing Sector
8.00

74.50
74.00

7.00
73.50
73.00
72.50

5.00

72.00
4.00
71.50
3.00

71.00
70.50

2.00

70.00
1.00
69.50
0.00

69.00
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Basic Research

Develoment Resaerch

Data Source: Survey of Research and Development (Statistics Bureau of Ministry of Public Management, Home Affairs, Posts and Telecommunications

Development Reseatch (%)

Basic Research (%)

6.00