Silviase/jgov_v1.4_full_ann huggingface dataset:
### 3.2 The Rate of Return to R&D

An alternative approach to estimating the contribution of R&D to productivity is the estimation of the rate of return to the R&D rather than the elasticity of value added with respect to R&D. The advantage of this approach is that it can avoid the measurement problem of the R&D capital stock.

To establish a direct relation between the growth of labor productivity and the level of R&D intensity, we assume the following equation:

$$ \Delta_{_{1}}\log(\frac{Y}{L})_{_{it}}=\lambda\Delta_{_{1}}t+\alpha\Delta_{_{1}}\log(\frac{K}{L})_{_{it}}+\beta\Delta_{_{1}}\log R_{_{it}}+\gamma\Delta_{_{1}}\log L_{_{it}}+\Delta_{_{1}}\pmb{\varepsilon}_{_{it}} $$

Equation (4) indicates that the growth of labor productivity is a function of the growth of the physical capital ratio and knowledge capital stock.

By definition, the elasticity of R&D capital stock is given by:

$$ \beta=\frac{\partial Y}{\partial R}\frac{R}{Y}=\rho\frac{R}{Y} $$

that is, the marginal product of R&D multiplied by the ratio of knowledge capital to value added. $ ^{9} $

By substituting equation (5) into equation (4), we obtain:

$$ \Delta_{_{1}}\log(\frac{Y}{L})_{_{it}}=\lambda\Delta_{_{1}}t+\alpha\Delta_{_{1}}\log(\frac{K}{L})_{_{it}}+\mathsf{\rho}(\frac{E}{Y})_{_{it}}+\gamma\Delta_{_{1}}\log L_{_{it}}+\Delta_{_{1}}\pmb{\varepsilon}_{_{it}} $$

where E is the R&D expenditures of firm i in period t. If one assumes that the depreciation rate of the R&D investment is close to zero, the net R&D investment corresponds to the gross R&D investment. $ ^{10} $ The exact meaning of the rate of return to R&D depends on how R&D and the output is measured. $ ^{11} $ If one assumes a non-zero depreciation rate of R&D, we obtain a ‘net’ rate of return, while if one makes the extreme assumption of zero depreciation rate, we obtain a
‘gross’ rate of return. The relationship between net and gross rates of return to R&D is a puzzle (see Harhoff, 1998, for detail). The difference between net and gross rates of return to R&D should be the depreciation rates. Thus, the coefficient for the net rate of return should be three or four times larger than the coefficient of the gross of rate of return to R&D. Hall and Mairesse (1995) found a small difference between gross and net rates, whereas Harhoff (1998) found that the net rate is three to four times larger than gross rate. Goto and Suzuki (1989) obtain mixed empirical results. In this paper, we estimate both the net and gross rates of return to R&D to broaden our understanding of the interpretation of the rate of return to R&D.

### 4. Empirical Results

### 4.1 Production Function Estimates

Table 3 presents the estimates of the production function for the full sample using the data corrected for double-counting and the different estimation methods. Table 3 also shows the estimation results with industry dummies and without industry dummies, respectively. Table 4 also displays the same estimates with imposing the constant returns to scale assumption.

The estimated R&D elasticity is a highly significant for most of the specifications, while the R&D elasticity is not statistically significant in the long differences specification. This fact indicates that the calculation of knowledge capital stock has substantial measurement errors. We also found that the R&D elasticity of the within specification and the first-differences specification, controlled for unobserved effects are smaller than the total regressions. This finding is consistent with the previous studies (Harhoff, 1998). This result suggests that the value of the elasticity of R&D could be overestimated by the omission of firm characteristic variables in the total regressions. The differences of the R&D elasticity between with and without industry dummies are very small in most of the specifications, except in the total regression. The previous studies found that the inter-industry differences in the R&D elasticity are important (Odagiri and Iwata, 1986). Our finding implies that industry effects representing exogenous technical progress and technology opportunities did not contribute significantly to
the growth of productivity. We confirmed that the corrections for double counting are important for identifying the true contribution of R&D to productivity growth. As Table 3 shows, the correction for double counting increases the R&D elasticity by 2 percentage points in the total regressions, whereas the elasticity of within regressions increases by a 0.5 percentage point only (not reported in Table 3).
With respect to returns to the scale, the assumption of the constant return to scale is rejected by most of the econometric specification results. The results of the total regressions show little statistical significance for the increasing returns to scale. Whereas those of other estimation methods show a large degree of statistical significance for decreasing returns to scale irrespective of industry dummies.
As pointed out in the previous study (Mairesse and Sassenou, 1991), the values of parameters on physical and R&D capitals become much smaller in within, first-differences, and long-differences specifications if the assumption of the constant returns to scale is not imposed. This can be interpreted this problem are caused by the random measurement errors and the endogeneity between firm's R&D decisions in output and investment.
Imposing the constant returns to scale assumption may mitigate these problems. As Table4 shows that the estimates of parameters on physical and R&D capital do not become very small in within specification when the constant returns to scale assumption is imposed. These results are consistent with a previous study (Griliches, 1986). We also obtained the surprising results in that R&D elasticity of first-differences specification is larger than that in total regressions. The R&D elasticity of long-differences estimate becomes much smaller, but becomes statistically significant. As a result, we confirm that the contribution of R&D to productivity is significant and large even when the constant returns to scale assumption is imposed (Table4).
In addition to the R&D stock, the physical capital stock also contributes significantly to the growth in labor productivity in all specifications. The estimated capital elasticity tended to be larger than the R&D elasticity in most cases.

In order to examine differences by the firm size, production functions are estimated
separately for three sub-samples. The results of the estimations of the production function by the firms divided by the size based on the number of employees are shown in Table 5. We observe that the small and the large firms have larger and more statistically significant R&D coefficients compared to the medium firms. The regression results on the small firms are similar to those of the full samples. The difference in the impact of R&D on labor productivity by firm size groups is trivial in the total regressions. However, when the unobserved firm specific effects are controlled, we observed that the differences between small firms and large firms are large. This is evidence in support of the hypothesis that, other things being equal, R&D contributed more to the faster growth of labor productivity in larger firms. $ ^{12} $

We also estimated separately the groups of high technology, medium technology, and low technology firms to analyze differences by technology. Table 6 shows that the estimated the R&D coefficients are higher in high-tech firms than in lower technology firms. Indeed, the estimated R&D coefficient is not significant in the group of medium technology firms. The R&D elasticity of the group of the high technology firms is larger than that of the full samples. $ ^{13} $

### 4.2 Estimates of rate of return to R&D

Table 7 provides estimates of the rates of return to R&D using first differences specification. As shown, the gross rate of return R&D is positively significant, but the net rate of return is quantitatively small and insignificant in the full samples. We theoretically expect that the net rate of return to R&D should be higher than the gross rate of return to R&D. We obtained opposite empirical results. The estimated gross rates of return to the R&D fall in the low level of the range of the previous estimates of the rates of return to R&D of the Japanese manufacturing sector, which range from about 0.11 (Odagiri and Iwata, 1986) to 0.56 (Griliches and Mairesse, 1990).

Table 7 also summarizes the regression results by firm size groups. An interesting
empirical result is that the gross rates of return are larger for small firms than for large firms, whereas the contributions of physical capital are lower in the small firms. One explanation for these results is that the share of value-added compensated to the R&D investment is larger for small firms. The regression results of the medium firms are statistically insignificant and relatively low.

Table 7 also contains the results of the regressions on firms divided by the technological level. The results described here are similar to those obtained by estimations based on the firm size groups. The gross rates of return are larger for the low technology firms, and the net rates of return are larger for the high technology firms. In medium technology firms, the effect of R&D is statistically insignificant and negative.

These results suggest that medium sized and medium technology firms are affected by business cycles and macroeconomic disturbances. In addition to R&D in the medium sized and medium technology firms, the role of physical capital in productivity growth is also statistically insignificant.

### 5. Conclusion

The relationship between R&D and productivity in Japanese manufacturing firms was examined. Using more accurate and more recent data on individual firm's R&D activity than the data used in the previous research, we estimated the contribution of the R&D stock to productivity growth.

We find that the R&D stock is significant in explaining productivity growth even in a period of macroeconomic disturbances. We also find that an industry effect in explaining productivity differences among firms is not important. This finding is different from the results in the previous studies.

As might be expected, the estimated R&D elasticities are significantly higher for the large and high technology firms than for other types of firms. In short, our findings strongly suggest that R&D investment contributed to the productivity growth of Japanese manufacturing firms in
the latter part of 1990. This conclusion is similar to the findings of previous R&D studies.

Of course, our work is not finished. We shall immediately go to work to include the effect of R&D spillovers as an extension of this paper. We also admit that more elaborate estimation methods and the long-run R&D data are necessary to remedy simultaneity and measurement errors in the R&D stock.
## References

[1] Baltagi, H (2001) Econometric Analysis of Panel Data. New York: Wiley, Second edition

[2] Branstetter (2001) Are Knowledge Spillovers International or Intranational in Scope? Microeconometric Evidence from Japan and United States, Journal of International Economics, Vol. 14, pp. 73-104.

[3] Goto, A. and Suzuki, K. (1989) R&D Capital, Rate of Return on R&D Investment and Spillover of R&D in Japanese Manufacturing, Review of Economics and Statistics, Vol. 71, pp.555-564.

[4] Griliches, Z. (1986) Productivity, R&D and Basic Research at the Firm Level in the 1970s, American Economic Review, Vol. 76, pp. 141-154.

[5] Griliches, Z. and Lichtenberg, F. (1984) R&D and Productivity Growth at the Industry Level: Is There Still a Relationship? in Z. Griliches (ed.), R&D, Patents, and Productivity, Chicago: University of Chicago Press, pp. 465-496.

[6] Griliches, Z. and Mairesse, J. (1990) R&D and Productivity Growth: Comparing Japanese and US Manufacturing Firms, in C. Hulten (ed.), Productivity Growth in Japan and United States, Chicago: University of Chicago Press, pp.317-348.

[7] Griliches, Z. (1998) R&D and Productivity: the Econometric Evidence, Chicago: University of Chicago Press.

[8] Hall, B. and Mairesse, J. (1995) Exploring the Relationship between R&D and Productivity in French Manufacturing Firms, Journal of Econometrics, Vol.65, pp.263-293.
[9] Harhoff, D. (1998) R&D and Productivity in German Manufacturing Firms, Economics of Innovation and New Technology, Vol.6, pp.28-49.

[10] Ito, K. and Fukao, K (2001), jidosha sangyo no seisansei: kogyo tokei chosa kohyo data ni yoru jisho bunseki, RIETI Discussion Paper Series, 01-J-002.

[11] Jaffe, A. B. (1986) Technological Opportunity and Spillovers of R&D: Evidence from Firm’s Patents, Profits, and Market Value, American Economic Review, Vol. 76, pp. 984-1001.

[12] Klette, T. J. and Johansen, F. (2000) Accumulation of R&D Capital and Dynamic Firm Performance: a Not-so-Fixed Effect Model, in D. Encaoua, B. H. Hall, F. Laisney, and J. Mairesse(ed.), The Economics and Econometrics of Innovation, Boston: Kluwer Academic Publishers, pp. 367-397.

[13] Mairesse, J. and Hall, B. (1996) Estimating the Productivity of Research and Development in French and United States Manufacturing Firms: An Exploration of Simultaneity Issues with GMM Methods, NBER Working Paper, no.5501.

[14] Mairesse, J. and Sassenou, M. (1991) R&D and Productivity: a Survey of Econometric Studies at the Firm Level, NBER Working Paper, no.3666.

[15] Motohashi, K. (1998) Innovation Strategy and Business Performance of Japanese Manufacturing Firms, Economics of Innovation and New Technology, Vol.7, pp.27-52.

[16] Nickell, S. J. (1981) Biases in Dynamic Models with Fixed Effects, Econometrica, Vol. 49, pp. 1417-1426.
[17] Odagiri, H. and Iwata, H. (1986) The Impact of R&D on Productivity Increase in Japanese Manufacturing Companies, Research Policy, Vol.15, pp.13-19.

[18] Roper, S (1999) Under-Reporting of R&D in Small Firms: The Impact on International R&D Comparisons, Small Business Economics Vol. 12, pp131-135

[19] Wakelin, K. (2001) Productivity Growth and R&D Expenditure in UK Manufacturing Firms, Research Policy, Vol.30, pp.1079-1090.

[20] Wooldridge, J. M. (2000) Introductory Econometrics: a Modern Approach, South-Western College Publishing.
Table1: Industrial Classification

Industry	No. of observation	No. of firms R&D intensity(%)
High technology
1. Drugs and medicines	578	144.5	15.2
2. Communication and electronic equipment	1504	376.0	12.3
3. Industrial chemicals and chemical fibers	749	187.3	12.9
4. Oils and paints	300	75.0	13.8
5. Other chemical products	628	157.0	15.2
6. Motor vehicles	1068	267.0	12.3
7. Precision instruments manufacturing	457	114.3	14.5
Medium-technology
8. General machinery manufacturing	2072	518.0	9.3
9. Electrical machinery, equipment and supplies	1063	265.8	10.9
10. Rubber products manufacturing	206	51.5	8.6
11. Plastic products manufacturing	660	165.0	6.1
12. Non-ferrous metals and products manufacturing	414	103.5	6.6
13. Petroleum and coal products manufacturing	90	22.5	6.5
14. Other transportation equipment	198	49.5	9.5
15. Other manufacturing	584	146.0	6.1
Low technology
16. Ceramics	600	150.0	5.2
17. Fabricated metal products manufacturing	1067	266.8	4.8
18. Food manufacturing	1646	411.5	4.4
19. Textiles manufacturing	556	139.0	5.0
20. Pulp and paper manufacturing	294	73.5	4.4
21. Printing and publishing	208	52.0	3.0
22. Iron and steel manufacturing	378	94.5	4.2
Total manufacturing	15320	3830	9.0

1. R&D intensity is the unweighted average value in industry over the 1995-98 period.
Table2: Summary statistics

Sample	Variable	Median	Mean	Standard deviation	Min.	Max.
Full sample
Y	2170.8	11496.7	54653.2	3.7	1477029.0
K	4428.4	31491.1	153768.0	1.7	3539963.0
L	530.3	1765.1	6284.9	97.9	153429.7
E	89.6	1833.0	15660.2	0.8	496212.7
By technology
High-tech
Y	2999.0	18660.1	80527.0	115.3	1477029.0
K	6360.1	42139.9	168431.5	1.7	3089535.0
L	613.1	2466.7	8816.1	97.9	144217.3
E	198.7	3845.0	25074.5	1.0	496212.7
Medium-tech
Y	1870.6	7428.7	32211.9	124.5	957955.9
K	3474.1	19538.4	89563.8	1.7	2402475.0
L	498.0	1401.6	5012.0	100.2	153429.7
E	78.1	1006.3	8270.9	0.8	365174.6
Low-tech
Y	1742.1	8055.0	34341.8	3.7	729344.3
K	4020.0	32949.4	188423.4	7.2	3539963.0
L	507.7	1389.1	3486.8	100.2	54050.2
E	42.0	514.9	2916.5	0.8	67155.2
Small
Y	1034.8	1286.7	939.8	3.7	14016.0
K	1973.3	3136.2	4019.1	1.7	65489.3
L	291.0	310.8	134.6	97.9	633.0
E	34.7	81.1	124.6	0.8	1690.9
Medium
Y	4869.0	6432.6	5863.0	245.7	133522.9
K	9828.2	16397.0	24592.9	112.6	436979.1
L	1098.2	1292.0	610.4	576.0	3113.2
E	224.6	519.1	801.8	0.9	10894.9
Large
Y	37523.2	82952.3	152310.1	2257.2	1477029.0
K	86582.2	233469.3	426727.2	2167.1	3539963.0
L	5973.1	11092.0	16853.2	2895.4	153429.7
E	3978.7	15619.8	46606.1	4.8	496212.7

1. Value-added(Y) and capital stock(K) are in millions of 1990 yen, whereas labor(L) is person-years(thousands).

2. Y, K, and L are not corrected for double counting.

3. E is flow of R&D expenditures.
Table3: Estimates of production function 1995-1998. Corrected for double counting (3830 Firms)

log(K/L)	log(R/L)	log(L)	Obs.
With Industry Dummies
1	Total regressions	0.215(40.32)	0.093(36.80)	0.040(15.04)	15320
2	Within (Firm) regressions	0.065(7.13)	0.023(2.65)	-0.372(-18.78)	15320
3	First differences	0.031(2.37)	0.035(2.75)	-0.501(-15.46)	11490
4	3-year differences	0.079(3.86)	0.014(1.01)	-0.298(-6.93)	3830
5	Total regressions*	0.205(39.93)	0.071(28.97)	0.054(20.70)	15320
Without Industry Dummies
1	Total regressions	0.229(45.88)	0.126(50.37)	0.026(9.08)	15320
2	Within(Firm) regressions	0.074(8.19)	0.026(2.89)	-0.37(-18.40)	15320
3	First differences	0.043(3.29)	0.037(2.93)	-0.483(-14.84)	11490
4	3-year differences	0.113(5.47)	0.018(1.25)	-0.257(-5.78)	3830
5	Total regressions*	0.221(46.57)	0.104(43.47)	0.048(17.54)	15320

1. The dependent variable is log(Y/L).

2. The values in the parentheses are the t-values. The t-values for the total, first differences

and 3-year differenced estimates are corrected for heteroscedasticity.

3. Year dummies are included in all equations, except long differences.

4. All equations except within regressions contain 22 industry dummies.

5. Equation 5 display results of estimation using non-corrected double-counting.
Table4: Estimates of production function 1995-1998. Corrected for double counting (3830 Firms) -CRS imposed

log(K/L)	log(R/L)	Obs.
With Industry Dummies
1	Total regressions	0.224	0.101	15320
(42.46)	(41.17)
2	Within (Firm) regressions	0.118	0.084	15320
(13.45)	(10.29)
3	First differences	0.101	0.149	11490
(7.10)	(10.65)
4	3-year differences	0.120	0.053	3830
(6.24)	(3.81)
5	Total regressions*	0.218	0.083	15320
(42.87)	(35.21)
Without Industry Dummies
1	Total regressions	0.234	0.130	15320
(47.33)	(52.29)
2	Within(Firm) regressions	0.126	0.087	15320
(14.30)	(10.40)
3	First differences	0.110	0.147	11490
(7.69)	(10.48)
4	3-year differences	0.147	0.052	3830
(19.41)	(3.65)
5	Total regressions*	0.231	0.112	15320
(49.07)	(47.84)

1. The dependent variable is $ \log(Y/L) $ .

2. The values in the parentheses are the t-values. The t-values for the total, first differences and 3-year differenced estimates are corrected for heteroscedasticity.

3. Year dummies are included in all equations, except long differences.

4. All equations except within regressions contain 22 industry dummies.

5. Equation 5 display results of estimation using non-corrected double-counting.
Table5: Estimates of production function by size 1995-1998. Corrected for double counting by firm size

log(K/L)	log(R/L)	log(L)	Obs.
Small(50<= employees <=299)
1	Total regressions	0.184 (27.81)	0.093 (26.95)	-0.009 (-1.07)	8344
2	Within regressions	0.045 (4.17)	0.020 (1.74)	-0.397 (-14.34)	8344
3	First differences	0.028 (1.89)	0.032 (2.06)	-0.484 (-11.55)	6267
4	3-year differences	0.073 (2.86)	0.010 (0.48)	-0.286 (-5.91)	2115
Medium(300<= employees <=1499)
1	Total regressions	0.252 (31.69)	0.081 (21.62)	0.001 (0.06)	5401
2	Within regressions	0.078 (4.13)	0.018 (1.21)	-0.324 (-8.71)	5401
3	First differences	0.018 (0.64)	0.028 (1.27)	-0.502 (-8.52)	4050
4	3-year differences	0.102 (2.99)	0.015 (0.63)	-0.280 (-3.24)	1329
Large(employees>=1500)
1	Total regressions	0.359 (21.58)	0.094 (9.60)	-0.012 (-1.20)	1575
2	Within regressions	0.281 (6.65)	0.088 (2.24)	-0.325 (-4.82)	1575
3	First differences	0.158 (2.71)	0.110 (2.22)	-0.481 (-4.66)	1173
4	3-year differences	0.158 (1.23)	0.060 (1.29)	-0.382 (-2.50)	386

1. The dependent variable is $ \log(Y/L) $ .

2. The values in the parentheses are the t-values. The t-values for the total, first differences and 3-year differenced estimates are corrected for heteroscedasticity.

3. Year dummies are included in all equations, except 3-year differences.

4. All equations contain 22 industry dummies.
Table6: Estimates of Production function by technology 1995-1998. Corrected for double counting by Technology

log(K/L)	log(R/L)	log(L)	Obs.
High Technology Firms
1	Within regressions	0.110	0.032	-0.413	5284
(7.11)	(2.03)	(-11.86)
2	First differences	0.084	0.062	-0.493	3969
(3.86)	(3.03)	(-8.88)
3	3-year differences	0.162	0.020	-0.349	1329
(4.27)	(0.70)	(-4.81)
Medium Technology Firms
1	Within regressions	-0.011	0.001	-0.456	5287
(-0.59)	(0.07)	(-12.20)
2	First differences	0.012	0.016	-0.532	3952
(0.42)	(0.66)	(-8.41)
3	3-year differences	0.025	-0.010	-0.371	1316
(0.85)	(-0.41)	(-4.65)
Low Technology Firms
1	Within regressions	0.036	0.024	-0.279	4749
(2.50)	(1.69)	(-8.57)
2	First differences	0.006	0.030	-0.467	3569
(0.31)	(1.55)	(-9.22)
3	3-year differences	0.076	0.033	-0.140	1185
(2.55)	(1.83)	(-1.93)

1. The dependent variable is $ \log(\mathrm{Y}/\mathrm{L}) $ .

2. The values in the parentheses are the t-values. The t-values for the first differences and 3-year differenced estimates are corrected for heteroscedasticity.

3. Year dummies are included in all equations, except 3-year differences.
Table7: Estimates of rate of return 1995-1998. Corrected for double counting

dlog(K/L)	(E or R)/Y	dlog(L)
Gross rate of return (E/Y)
1	Full sample	0.032	0.163	-0.536
(2.43)	(3.97)	(-18.03)
2	By size
Small firms	0.027	0.301	-0.521
(1.88)	(6.63)	(-13.57)
3	Medium firms	0.020	0.063	-0.528
(0.69)	(0.74)	(-9.69)
4	Large frims	0.158	0.139	-0.587
(2.75)	(6.56)	(-6.56)
By Technology
5	High technology	0.084	0.171	-0.558
(3.92)	(4.90)	(-10.66)
6	Medium technology	0.013	0.071	-0.550
(0.45)	(0.75)	(-10.02)
7	Low technology	0.007	0.284	-0.491
(0.35)	(2.10)	(-10.21)
Net rate of return (R/Y)
1	Full Sample	0.032	0.063	-0.535
(2.38)	(0.73)	(-18.00)
2	By size
Small firms	0.028	0.098	-0.515
(1.89)	(0.80)	(-13.27)
3	Medium firms	0.020	-0.035	-0.526
(0.70)	(-0.20)	(-9.83)
4	Large frims	0.155	0.114	-0.592
(2.66)	(1.47)	(-6.54)
By Technology
5	High technology	0.082	0.233	-0.562
(3.79)	(3.05)	(-10.70)
6	Medium technology	0.013	-0.242	-0.538
(0.46)	(1.19)	(-9.87)
7	Low technology	0.006	0.132	-0.495
(0.32)	(1.02)	(-10.23)

1. The dependent variable is dlog(Y/L).

2. The values in the parentheses are the t-values which are corrected for heteroscedasticity.

3. All equations contain 22 industry dummies, except equations by technology.

4. Year dummies are included in all equations.

5. All equations are estimated by first differences.
ESRI Discussion Paper Series No.22

# 消費者信用市場の競争と効率性

一個人金融におけるモラルハザードと法制の在り方—

by

中村賢一

内閣府経済社会総合研究所

December 2002

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
# 消費者信用市場の競争と効率性 一個人金融におけるモラルハザードと法制の在り方—

by

中村賢一 $ ^{*} $

内閣府経済社会総合研究所
## 目次

はじめに

第1章 消費者信用の現状
1-1.消費者信用供給の現状
1-2.金融機関の消費者信用
1-3.ノンバンクの消費者信用
1-4.消費者金融会社

第2章 消費者金融の特徴

第3章 多重債務者問題
3-1.多重債務者の特性
3-2.多重債務者の現状

第4章 救济制度
4-1.救济制度
4-2.救济制度の効果と特徴

第5章 消費者金融市場でのモニタリング
5-1.個人向け融資取引と債務不履行
5-2.貸し手
5-3.多重債務者発生のメカニズム
5-4.モニタリングのインセンティブと条件
（複数の貸し手からの借入れが一般的な市場での問題）
（借り手の返済可能性を考慮せず融資する貸し手の問題）
5-5.安易な破産のモラルハザード
（企業と救济制度）
（個人と救济制度）

第6章 個人信用情報利用の現状
6-1.業界毎の個人信用情報機関の現状
6-2.各機関間の相互交流の現状

第7章 消費者信用市場の競争と効率性
7-1.消費者金融市場と個人信用情報
7-2.消費者信用市場の競争と効率性
（借り手の返済可能性を考慮せず融資するノイズレンダー）
（ノイズレンダー比率の決定）
（ノイズレンダー比率と収益率の長期的関係）
（アドバースセレクション）
（ノイズレンダー比率の長期的変動）
（消費者信用市場の効率性と安定性）

むすび

参考文献

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