Silviase/jgov_v1.4_full_ann huggingface dataset:
Horioka, C. Y. (1990) “Why is Japan’s Household Saving Rate So High? A Literature Survey,” Journal of the Japanese and International Economies, 4, 49-92.

Horioka, C. Y. (2010) “The (Dis)saving Behavior of the Aged in Japan,” Japan and the World Economy, 22, 151-158.

Kohara, M. and F. Ohtake (2005) “Shouhi-zei ha Hontou-ni Gyakushin-teki ka? (Is the Consumption Tax Really Regressive?),” Ronza, December. Tokyo: Asahi Shimbun, 44-51 (in Japanese).

Néstor, G. (2015) "Do the Rich Save More in Latin America?" Documento de Investigación, No. 102, Universidad ORT Uruguay.

Staiger, D. and J. H. Stock (1997) “Stock J, Staiger D. Instrumental Variables Regression with Weak Instruments,” Econometrica, 65(3), 557-586.

Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. Identification and inference for econometric models: Essays in honor of Thomas Rothenberg.
Table 1. Regression of the subjective lifetime earnings on possible determinants

Dependent variable: Household subjective lifetime income (log)	Coef.	Std. Err.	Coef.	Std. Err.
(1)	(2)
Annual income (log)	0.258	***	(0.016)
Self-perception of economic affluence dummy (Base: Normal)
Very affluent	0.128	(0.135)
Affluent	0.208	***	(0.042)
Slightly affluent	0.139	***	(0.023)
Slightly poor	-0.125	***	(0.024)
Poor	-0.163	***	(0.041)
Very poor	-0.187	*	(0.098)
Type of household head occupation dummy (Base: Full-time, 1,000 or more)
Housewife/Househusband	-0.495	***	(0.063)	-0.685	***	(0.065)
Self-employed	-0.103	***	(0.032)	-0.212	***	(0.034)
Business manager	-0.023	(0.061)	0.047	(0.066)
Full-time, Civil service	-0.052	*	(0.028)	-0.085	***	(0.031)
Part-time worker	-0.367	***	(0.046)	-0.589	***	(0.047)
Full-time, 29 or fewer	-0.150	***	(0.033)	-0.296	***	(0.035)
Full-time, 30-449	-0.114	***	(0.024)	-0.185	***	(0.026)
Full-time, 500-999	-0.095	***	(0.035)	-0.123	***	(0.038)
Other	-0.107	***	(0.041)	-0.250	***	(0.042)
Type of spouse occupation dummy (Base: Housewife/Househusband)
Single (No spouse)	-0.146	***	(0.038)	-0.197	***	(0.037)
Part-time worker	0.008	(0.028)	-0.014	(0.030)
Full-time worker	0.089	***	(0.020)	0.094	***	(0.021)
Household head educational attainment dummy (Base: High school or less)
Junior college	0.062	**	(0.027)	0.108	***	(0.029)
University (undergraduate)	0.120	***	(0.022)	0.206	***	(0.023)
Graduate school	0.227	***	(0.039)	0.346	***	(0.042)
Spouse educational attainment dummy (Base: Single (no spouse))
High school or less	0.210	***	(0.033)	0.257	***	(0.030)
Junior college	0.155	***	(0.035)	0.252	***	(0.033)
University (undergraduate)	0.198	***	(0.038)	0.318	***	(0.037)
Graduate school	0.282	***	(0.086)	0.448	***	(0.094)
Number of obs.	2,672	2,986
Adj. R-squared	0.433	0.297
Root MSE	0.422	0.481

Notes: Coefficients are estimated using OLS. Standard errors are shown in parentheses.

***/** indicates significance at the 1/5/10% level, respectively.

Table 2. Regression of the change in lifetime earnings on the change in annual income

Dep.: $ \Delta $ Household lifetime income	Coef.	Std. Err.
$ \Delta $ Household annual income	0.979	$ ^{*} $	( 0.586 )
$ \Delta $ Self-perception of economic affluence dummy	-147.152	( 231.014 )

Notes: Coefficients are estimated using OLS. Standard errors are shown in parentheses.

***/** indicates significance at the 1/5/10% level, respectively.

In addition to the explanatory variables above, the regression also includes household head age, age squared, age cubed, and a constant.
Table 3. Summary Statistics: Saving, Income, Assets, etc.

Family and Lifestyle Survey, 2011, 2012
All sample households	Aged 60 or below	Aged 61 or above
Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.
Saving ratio	1,870	0.143	0.119	0.462	1,268	0.157	0.159	0.249	602	0.100	0.036	0.723
Disposable income	1,870	476	520	323	1,268	500	560	336	602	380	436	273
Consumption	1,870	360	392	179	1,268	400	413	186	602	350	349	155
Net assets	1,798	1,460	2,493	3,895	1,214	800	1,609	3,346	584	3,233	4,332	4,295
Financial assets	1,852	500	1,237	1,933	1,256	400	871	1,608	596	1,200	2,007	2,303
Real assets	1,820	1,355	1,952	3,008	1,227	1,000	1,645	2,930	593	1,750	2,587	3,071
Liabilities	1,847	0	704	1,549	1,251	175	915	1,734	596	0	260	916
Subjective lifecycle income	1,768	18,000	18,687	7,784	1,188	17,500	18,520	7,563	580	18,583	19,030	8,213
Lifecycle income - male	1,867	22,667	23,931	10,264	1,268	22,500	23,872	10,387	599	23,000	24,056	10,007
Lifecycle income - female	1,707	5,000	6,819	5,593	1,124	5,667	7,356	5,827	583	4,333	5,785	4,955
Household head age	1,870	50.00	51.50	14.03	1,268	43.00	43.62	9.23	602	67.00	68.11	5.07
Number of family members	1,870	2.00	2.46	1.17	1,268	3.00	2.70	1.32	602	2.00	1.95	0.40
Family Income and Expenditure Survey, from 2002-2012
All sample households	Aged 60 or below	Aged 61 or above
Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.
Saving ratio	101,349	0.173	-0.115	20.294	62,392	0.255	0.051	22.675	38,957	-0.004	-0.380	15.743
Disposable income	101,349	470	525	292	62,392	589	621	300	38,957	352	371	196
Consumption	101,349	396	440	210	62,392	425	469	218	38,957	357	393	186
Net assets	101,349	2,133	2,925	3,282	62,392	1,330	2,071	2,697	38,957	3,485	4,293	3,651
Financial assets	101,349	920	1,569	1,986	62,392	672	1,134	1,476	38,957	1,551	2,265	2,446
Real assets	101,349	1,454	1,792	2,052	62,392	1,295	1,587	1,936	38,957	1,635	2,121	2,186
Liabilities	101,349	0	436	945	62,392	20	650	1,086	38,957	0	93	495
Household head age	101,349	55.00	54.64	15.01	62,392	45.00	44.81	9.38	38,957	70.00	70.38	6.57
Number of family members	101,349	3.00	3.09	1.13	62,392	4.00	3.49	1.10	38,957	2.00	2.43	0.81
Table 4 Median Regressions of Saving Rate on Current Income Quintiles

Households with head aged between 20 and 60	Households with head aged 61 or above
Family and Lifestyle Survey, 2011, 2012
Quintile 1	0.0000	(0.0229)	-0.0833	(0.0277)
Quintile 2	0.0808 *** ††	(0.0281)	0.0000 *** †	(0.0311)
Quintile 3	0.1284 ***	(0.0273)	0.0750 *** †	(0.0257)
Quintile 4	0.1731 *** †	(0.0223)	0.1200 ***	(0.0228)
Quintile 5	0.2798 *** ††	(0.0284)	0.2957 *** ††	(0.0378)
Sample size	1,228	642
Pseudo R2	0.1018	0.1143
Coefficient on annual income/1,000,000 (yen)	0.0309 ***	(0.0026)	0.0376 ***	(0.0054)
Family Income and Expenditure Survey, 2002-2012
Quintile 1	0.0705	(0.0050)	-0.7186	(0.0182)
Quintile 2	0.2558 *** ††	(0.0043)	-0.0484 *** ††	(0.0069)
Quintile 3	0.3201 *** ††	(0.0045)	0.0824 *** ††	(0.0067)
Quintile 4	0.3648 *** ††	(0.0046)	0.1492 *** ††	(0.0068)
Quintile 5	0.4307 *** ††	(0.0045)	0.2620 *** ††	(0.0075)
Sample size	60,263	41,086
Pseudo R2	0.0521	0.0651
Coefficient on income/1,000,000 (yen)	0.0427 ***	(0.0005)	0.1217 ***	(0.0020)

Notes: Coefficients are from median regressions. Bootstrapped standard errors are shown in parentheses.

*** ( $ ^{**} $ / $ ^{*} $ ) indicates significance at the 1 (5/10) % level. ††† ( $ ^{††} $ / $ ^{†} $ ) indicates that the coefficient is

significantly greater than that for the previous quintile, on the basis of a two-sided 1 (5/10) % test.

The regressions also include dummy variables for five-year age brackets and, in the case of the regressions based on the FIES data, for the starting month of the survey.

Table 5 Median Regressions of Saving Rate on Permanent Income Quintiles

Family and Lifestyle Survey, 2011, 2012
Households with head aged between 20 and 60	Households with head aged 61 or above
Permanent income
Quintile 1	0.0776	(0.0356)	0.0000
Quintile 2	0.1263 $ {}^{*} $ $ {}^{\dagger} $	(0.0288)	0.0500
Quintile 3	0.1276	(0.0300)	0.0741 $ {}^{**} $
Quintile 4	0.1875 $ {}^{***} $ $ {}^{\dagger\dagger} $	(0.0250)	0.1160 $ {}^{***} $
Quintile 5	0.2589 $ {}^{***} $ $ {}^{\dagger\dagger\dagger} $	(0.0294)	0.1667 $ {}^{***} $ $ {}^{\dagger} $
Sample size	1,228	642
Pseudo R2	0.0383	0.0246
Coefficient on permanent income/10,000,000 (yen)	0.0083 $ {}^{***} $	(0.0014)	0.0133 $ {}^{***} $

Notes: See the footnotes for Table 4.
Table 6(a). Median Instrumental Variable Regressions of Saving Rate on Lifetime Wealth Quintiles

Households with head aged between 20 and 60	Households with head aged 61 or above
FLS: Proxied Permanent Income Quintile
Inst1: Lagged income
Quintile 1	0.0000	(0.0282)	-0.0533	(0.0468)
Quintile 2	0.0566	* †	(0.0345)	0.0089	(0.0449)
Quintile 3	0.1286	*** †††	(0.0338)	0.0500	**	(0.0351)
Quintile 4	0.1540	***	(0.0250)	0.0600	**	(0.0308)
Quintile 5	0.2320	*** †††	(0.0260)	0.1833	*** †††	(0.0315)
Sample size	1,228	642
Pseudo R2	0.0696	0.0454
First stage F-statistic	591.6435	226.3701
Coefficient on permanent income/10,000,000(yen)	0.0205	***	(0.0027)	0.0347	***	(0.0051)
FIES: Proxied Current Income Quintile
Inst1: Lagged income
Quintile 1	0.1718	(0.0048)	0.0136	(0.0090)
Quintile 2	0.2713	*** †††	(0.0050)	0.0242	(0.0091)
Quintile 3	0.3180	*** †††	(0.0043)	0.0352	***	(0.0097)
Quintile 4	0.3447	*** †††	(0.0044)	0.0427	***	(0.0097)
Quintile 5	0.4008	*** †††	(0.0045)	0.1278	*** †††	(0.0107)
Sample size	60,263	41,086
Pseudo R2	0.0227	0.0036
First stage F-statistic	73517.1490	15984.4900
Coefficient on (annual) income/1,000,000(yen)	0.0344	***	(0.0006)	0.0401	***	(0.0026)

Notes: Coefficients are from median regressions. Bootstrapped standard errors are shown in parentheses.

*** ( $ ^{**} $ / $ ^{*} $ ) indicates the coefficient is significantly greater than the first quintile at the 1 (5/10) % level.

††† (††/†) indicates that the coefficient is significantly greater than that for the previous quintile, on the basis of a two-sided 1 (5/10) % test.

The regressions also include dummy variables for five-year age brackets and, in the case of the regressions based on the FIES data, for the starting month of the survey.

The instrument can be weak if the first stage F-statistic is less than 10 (Staiger and Stock, 1997; Stock and Yogo, 2005).

Table 6(b). Median Instrumental Variable Regressions of Saving Rate on Lifetime Wealth Quintiles

Households with head aged between 20 and 60	Households with head aged 61 or above
FLS: Proxied Permanent Income Quintile
Inst2: Education & Type of (longest-held) Job
Quintile 1	0.1250	(0.0286)	0.0400	(0.0290)
Quintile 2	0.1617	(0.0273)	0.0700	(0.0335)
Quintile 3	0.1667	(0.0261)	0.1000	(0.0376)
Quintile 4	0.1950	*	(0.0300)	0.1000	(0.0304)
Quintile 5	0.2564	*** †	(0.0324)	0.1200	**	(0.0328)
Sample size	1,228	642
Pseudo R2	0.0188	0.0068
First stage F-statistic	26.5833	5.9070
Coefficient on permanent income/10,000,000(yen)	0.0106	***	(0.0023)	0.0104	**	(0.0047)
FIES: Proxied Current Income Quintile
Inst2: Type of (current) job (Working households only)
Quintile 1	0.2006	(0.0050)	0.1798	(0.0207)
Quintile 2	0.2947	*** ††	(0.0045)	0.2022	(0.0202)
Quintile 3	0.3037	*** †	(0.0047)	0.2676	*** ††	(0.0169)
Quintile 4	0.3402	*** ††	(0.0045)	0.2234	*** ††	(0.0186)
Quintile 5	0.3761	*** ††	(0.0046)	0.2299	***	(0.0178)

Sample size	58,671	8,990
Pseudo R2	0.0196	0.0069
First stage F-statistic	1587.7619	102.6379
Coefficient on (annual) income/1,000,000(yen)	0.0453	***	(0.0008)	0.0015	(0.0051)

Notes: See the footnotes for Table 6(a).
Table 6(c). Median Instrumental Variable Regressions of Saving Rate on Lifetime Wealth Quintiles

Households with head aged between 20 and 60	Households with head aged 61 or above
FLS: Proxied Permanent Income Quintile
Inst3: Consumption
Quintile 1	0.3270	(0.0298)	0.1467	(0.0586)
Quintile 2	0.2258	***	(0.0331)	0.1000	(0.0338)
Quintile 3	0.1963	***	(0.0257)	0.0409	*	(0.0316)
Quintile 4	0.1491	***	(0.0196)	0.0909	(0.0247)
Quintile 5	0.1143	***	(0.0225)	0.0609	(0.0180)
Sample size	1,228	642
Pseudo R2	0.0513	0.0077
First stage F-statistic	155.9888	86.5647
Coefficient on permanent income/10,000,000(yen)	-0.0239	***	(0.0042)	-0.0124	(0.0104)
FIES: Proxied Current Income Quintile
Inst3: Nondurable consumption
Quintile 1	0.3564	(0.0048)	0.2386	(0.0080)
Quintile 2	0.3508	(0.0048)	0.1344	***	(0.0070)
Quintile 3	0.3201	***	(0.0051)	0.0635	***	(0.0078)
Quintile 4	0.2892	***	(0.0046)	-0.0238	***	(0.0073)
Quintile 5	0.2153	***	(0.0048)	-0.2633	***	(0.0089)
Sample size	60,263	41,086
Pseudo R2	0.0126	0.0259
First stage F-statistic	26512.1150	6222.9545
Coefficient on (annual) income/1,000,000(yen)	-0.0404	***	(0.0014)	-0.2551	***	(0.0090)

Notes: See the footnotes for Table 6(a).

Table 6(d). Median Instrumental Variable Regressions of Saving Rate on Lifetime Wealth Quintiles

Households with head aged between 20 and 60	Households with head aged 61 or above
FLS: Proxied Permanent Income Quintile
Inst4: Lagged consumption
Quintile 1	0.2160	(0.0367)	-0.0139	(0.0538)
Quintile 2	0.2404	(0.0376)	0.0231	(0.0547)
Quintile 3	0.2654	(0.0364)	-0.0686	(0.0379)
Quintile 4	0.2535	(0.0251)	0.0364	(0.0408)
Quintile 5	0.2368	(0.0320)	0.0667	(0.0334)
Sample size	1,228	642
Pseudo R2	0.0071	0.0134
First stage F-statistic	155.9888	86.5647
Coefficient on permanent income/10,000,000(yen)	0.0060	*	(0.0000)	0.0198	***	(0.0078)
FIES: Proxied Current Income Quintile
Inst5: Purchase prices
Quintile 1	0.3000	(0.0048)	0.0972	(0.0082)
Quintile 2	0.3045	(0.0052)	0.0714	***	(0.0077)
Quintile 3	0.3045	(0.0054)	0.0395	***	(0.0075)
Quintile 4	0.3044	(0.0046)	0.0238	***	(0.0084)
Quintile 5	0.3075	**	(0.0052)	-0.0469	***	(0.0081)

Sample size	60,263	41,086
Pseudo R2	0.0037	0.0046
First stage F-statistic	1590.8474	268.0598
Coefficient on (annual) income/1,000,000(yen)	0.0045	*	(0.0027)	-0.2828	***	(0.0253)

Notes: See the footnotes for Table 6(a).
Table 6(e). Median Instrumental Variable Regressions of Saving Rate on Lifetime Wealth Quintiles

Households with head aged between 20 and 60	Households with head aged 61 or above
FLS: Proxied Permanent Income Quintile
Inst6: Assets
Quintile 1	0.1017	(0.0305)	-0.0200	(0.0326)
Quintile 2	0.1143	(0.0328)	0.0102	(0.0299)
Quintile 3	0.1393	(0.0293)	0.0687	**	(0.0305)
Quintile 4	0.1777	**	(0.0322)	0.0979	***	(0.0303)
Quintile 5	0.2282	***	(0.0284)	0.1283	***	(0.0329)
Sample size	1,087	534
Pseudo R2	0.0280	0.0189
First stage F-statistic	699.7361	2225.1076
Coefficient on permanent income/10,000,000(yen)	0.0106	***	(0.0031)	0.0117	***	(0.0017)
FIES: Proxied Current Income Quintile
Inst6: Assets
Quintile 1	0.2826	(0.0043)	0.1015	(0.0083)
Quintile 2	0.2806	(0.0042)	0.0758	***	(0.0078)
Quintile 3	0.3119	***	(0.0044)	0.0439	***	(0.0096)
Quintile 4	0.3215	***	(0.0039)	0.0072	***	(0.0088)
Quintile 5	0.3204	***	(0.0044)	-0.0302	***	(0.0100)

Sample size	60,263	41,086
Pseudo R2	0.0048	0.0044
First stage F-statistic	3979.8282	1759.5546
Coefficient on (annual) income/1,000,000(yen)	0.0089	***	(0.0017)	-0.1081	***	(0.0084)

Notes: See the footnotes for Table 6(a).
### Appendix A. Construction of the purchase price measure

The FIES data provide information on the quantity of and the expenditure on each good purchased for each household. Taking advantage of this rich information, we developed a new proxy of household lifetime wealth that represents the “rank” of the goods purchased by households. We constructed our purchase price measure in the following way. First, we calculated the price of the goods purchased by a household by dividing the expenditure on those goods by the quantity purchased for goods for which information on expenditure and quantity purchased are available. $ ^{15} $ Second, we calculate the quintiles of the price of the goods. Third, we construct a variable $ q_{ij} $ indicating which quintile (from 1, the lowest, to 5, the highest) the price of good j purchased by household i belongs to for each good purchased by each household.

Finally, we define the purchase price proxy as follows:

$$ Q_{i}\equiv\frac{1}{n_{i}}\sum_{j}q_{ij} $$

where $ n_{i} $ is the number of goods that are purchased by household i and for which price information is available. $ Q_{i} $ therefore is the average of $ q_{ij} $ across the goods purchased by household i.

Figure A.1 shows the distribution of the constructed purchase price measure proxy. As can be seen, the distribution has a symmetric bell shape around 3.
Figure A.1 Distribution of the Purchase Price Measure

List of 175 goods/services used to calculate the purchase price measure.

Foods (113): rice, white bread, other bread, non-dried "udon" & "soba", dried "udon" & "soba", instant noodles, other noodles, wheat flour, mochi, (rice-cakes), other cereals, tuna fish, horse mackerel, sardines, bonito, flounder, salmon, mackerel, saury, sea bream, yellowtail, cuttlefish, octopus, shrimps & lobsters, crabs, other raw fish, short-necked clams, fresh water clams, oysters, other shellfish, salted salmon, salted pollack roe, dried young sardines, dried horse mackerel, dried small sardines, other salted & dried fish, bonito fillets & fish flakes, beef, pork, chicken, other raw meat, ham, sausages, bacon, fresh milk, powdered milk, butter, cheese, eggs, cabbage, spinach, Chinese cabbage, Welsh onions, lettuce, broccoli, bean sprouts, sweet potatoes, white potatoes, taros, radishes, carrots, burdocks, onions, lotus roots, bamboo shoots, other root vegetables, string beans, pumpkins, cucumbers, eggplants, tomatoes, green peppers, fresh Japanese mushrooms, other mushrooms, other fresh vegetables, dried Japanese mushrooms, wakame (seaweed), dried tangle, bean curd, umeboshi, pickled plums, pickled radishes, pickled Chinese cabbage, tangle prepared in soy sauce, apples, mandarin oranges, other citrus fruits, pears, grapes, persimmons, peaches, watermelons, melons, strawberries, bananas, other fruits, edible oil, margarine, salt, soy sauce, miso (soybean paste), sugar, vinegar, worcester sauce, tomato ketchup, mayonnaise & mayonnaise flavor seasoning, jam, instant curry mix, green tea, black tea, sake, shochu (distilled spirits), beer, whisky, wine.

Housing (2): private rents for dwelling, "tatami" reupholstering.

Fuel, light & water charges (3): electricity, liquefied propane, kerosene.
Furniture & household utensils (13) : electric cooking appliances, refrigerators, vacuum cleaners, washing machines, sewing machines, air conditioners, stoves & fan heaters, chest of drawers, beds, quilts, sheets, bowls & dishes, pans & kettles.

Clothing & footwear (34): women's "kimono", women's "obi", men's suits, men's jackets, men's slacks, men's coats, boys' school uniforms, ready-made women's dresses, skirts, women's slacks, women's coats, girls' school uniforms, children's dresses, babies' clothing, men's business shirts, other men's shirts, men's sweaters, women's blouses, other women's shirts, women's sweaters, children's shirts, children's sweaters, hats & caps, neckties, gloves, men's socks, women's stockings, women's socks, children's socks & stockings, men's shoes, women's shoes, canvas shoes, children's shoes, sandals.

Transportation & communication (1): gasoline.

Culture & recreation (3): TV sets, portable equipment for recording & play-back of sound & pictures, video recorders & players.

Other consumption expenditures (6): charges for barbers' services, permanent wave charges, umbrellas, handbags, knapsacks & other bags for students, suitcases.
ESRI Discussion Paper Series No.218

設備投資分析の潮流と日本経済

一過剰投資か過少投資か一

宮川 努・田中賢治

June 2009

內閣府経済社会総合研究所

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
# 設備投資分析の潮流と日本経済 $ ^{*} $ —過剰投資か過少投資か—

2009 年 6 月

宫川 努†

田中賢治

## 要旨

日本の設備投資は1990年代以降伸びが低下したが，国際比較の観点からは設備投資の水準は必ずしも低くなく，設備投資動向の判断はどちらの視点でどの時期を考えるかによって異なる.80年代以降の設備投資の実証分析はTobinのQ理論を中心に展開されてきたが，それに流動性制約や不確実性の影響を考慮することによって，設備投資が過小となる可能性が指摘されてきた．しかし，不確実性や不可逆性を踏まえると，企業が設備投資を手控えた後一気に設備投資を実行した場合には断続性が生じ，設備投資に大きな山（investment spike）が生まれる可能性がある．また，ITのような革新的技術による誘発や企業の「横並び」行動によってもinvestment spikeは起こり得る．こうした横並び行動は必ずしも合理的な判断に基づく行動とは言えず，その結果，過剰投資が生じる可能性もある．そこで，investment spikeの要因についてプロビット推計の手法で実証分析したところ，横並び行動の影響が示唆される結果が得られた．設備投資行動の同調性は，特にバブル崩壊前まで強く，そのために変動も大きく，景気全体の変動に大きな影響を与えていたが，近年はこうした同調性が薄れ，むしろキャッシュ・フローの影響力が強くなっている．

Keyword: Tobin の Q 理論，流動性制約，投資の不可逆性，investment spike，probit 推計 JEL classification numbers: E22, E32, G31
# Corporate Investment Behavior in the Japanese Economy

## Abstract

The purpose of the paper is to examine what kind of investment theory can explain the recent movements in corporate investment in Japan. To explain the drastic decline in corporate investment in the wake of the bubble economy, economists applied Tobin's Q theory with liquidity constraints or uncertainty in sales or profits to empirical studies on investment behavior. On the other hand, the investment spike induced by the behavior under uncertainty is likely to generate overinvestment if many firms undertake large investments simultaneously. Using probit estimation, we examine the determinants of the investment spike. In this estimation, we find that not only Tobin's Q and cash flow but also simultaneous investment behavior affects investment spikes.

Keywords: Tobin's Q, liquidity constraint, irreversibility in investment, investment spike, probit estimation

JEL classification numbers: E22, E32, G31

Kenji Tanaka

Economic Research Office, Development Bank of Japan

1-9-1, Otemachi, Chiyoda-ku, Tokyo, Japan

E-mail:kntanak@dbj.jp
### 1. 日本経済における設備投資の重要性

高度成長期以来設備投資は GDP の一需要項目であるにもかかわらず，その他の需要項目よりも脚光を浴びてきた．これは，GDP に占めるシェアは民間家計消費支出を下回るにもかかわらず，その変動が大きいため，景気循環に大きな影響を与えるとみなされてきたからである．吉川（1999）も，GDP の需要項目に関する寄与度を 1980 年から 98 年まで調べ，「成長率の変動を生み出す「主役」は設備投資である」（p. 14）と述べている．

図表1は，1980年代以降の景気循環に合わせて，GDPとその主要項目の伸びを示したものである．これを見ると確かに，設備投資の変動は大きく，設備投資の増減と景気の回復，後退が対応していることがわかる．特に90年代以降の景気後退では，設備投資の伸びが大幅なマイナスとなっており，これが90年代の経済全体の伸びを大きく制約したことは疑いがない．ただし2002年以降の景気回復期だけは様相を異にしている．すなわちこの時期は，米国や中国の経済成長を背景に輸出が急増した結果，外需が大幅に伸び，これが経済全体を牽引する役割を果たした．この時期における設備投資のGDP成長率への寄与度は0.65％と過去の景気回復期の寄与度よりは小さくなっている．

## (図表1挿入)

このように，最近では景気への影響力が低下している日本の設備投資だが，国際的に見た場合その動きはどのように評価できるのだろうか．図表2－1は，EUKLEMS Databaseを利用して，日本，米国，英国，韓国における市場経済部門の設備投資/GDP比率（名目ベース）を比較したものである $ ^{1} $ ．比較を市場部門に限定しているのは，公共セクターだけでなく教育部門や社会福祉などの非営利部門の大きさが各国によって異なり，その影響を除くためである．これを見ると，韓国が一貫して高い設備投資/GDP比率を維持していることがわかる．日本の設備投資/GDP比率は，韓国に次いで20％から25％の間を推移しており，一貫して米国や英国を上回っている．ただし，設備投資の変動は各国によって異なり，韓国は1990年代のアジア通貨危機以前まで非常に高い設備投資/GDP比率を記録したが，アジア通貨危機以降は，30％を下回る水準で推移している．日本は80年代後半のバブル期に設備投資/GDP比率が20％から25％まで上昇したが，90年代に入ってからは20％の前半を推移し，2002年以降の景気回復で再び上昇傾向を見せている．米国と英国は，おおむね15％から20％の水準で推移しているが，90年代の後半に設備投資/GDP比率が上昇した．これは主にIT投資が活発化したからであり，ITバブルが崩壊後は，設備投資/GDP比率は再び下降している．

## (図表2−1挿入)
次に設備投資/資本ストック比率の動向を図表2－2で見ると，日本は1980年代後半のバブル期に大きな盛り上がりを見せた後は，10％を下回る水準で推移している．これとは対照的に，韓国は90年代の投資ブーム期に大きく設備投資/資本ストック比率を上げている．また米国も90年代に入ってから設備投資/資本ストック比率が急速に上昇し，日本や韓国を上回る水準となっている．この設備投資/資本ストック比率の上昇は，ITバブル崩壊で一段落するが，その後復調を示している．一方英国もIT投資を中心に設備投資が増加した以降は，設備投資/資本ストック比率が上昇傾向にある．何故設備投資/GDP比率では日本や韓国よりも低水準にある米国の設備投資/資本ストック比率が日本や韓国を上回っているのだろうか．この背景には，減価償却率において米国が両国を上回っているという点がある．最近期の米国の償却率を計算すると12％にも上っており，これは日本の8％，韓国の6％よりはるかに高い．すなわち米国は，新技術を体化した設備に対する投資を進めているため，平均的な償却率が上昇し結果的に資本ストックが急増しない体質になっているのである．

## (図表 2−2 挿入)

このため図表 2−3 の資本係数の推移をみると，米国は 4 ヶ国中最も低い水準で推移している．逆に日本では，1990 年頃までは資本係数は安定して推移していたが，90 年以降はじりじりと上昇傾向を示している．

## (图表 2−3 插入)

こうしてみると，日本の設備投資は国内だけで見ると1990年代以降かつてのような伸びを示しておらず，また最近の景気回復期ではGDPへの寄与度が低下するなど投資意欲が衰えているように見える．しかしながら国際的に比較してみると，設備投資水準は必ずしも低くなく，むしろ資本ストックは米国に比べて過剰気味ですらある．したがって，日本の設備投資動向を判断する場合，どちらの視点をどの時期に考えるかによって分析方法も異なってくる．また設備投資が過小か過剰かでは，とるべき政策手段にも違いが生じる．もし設備投資が過小であれば，設備投資減税を実施して投資需要を喚起することが適切な政策であると言えよう．しかしながら設備が過剰気味であるならば，とるべき政策は，設備投資減税ではなく，古くなって堆積している設備を破棄し，新しい産業を興しやすい環境を整える構造改善政策であると言える．

本稿では，まずこの並立する日本の設備投資に対する見方に対し，どのような分析が行われてきたかを概観する．特に1990年代以降は，企業のミクロ・データを利用した実証分析が盛んとなり，ミクロ・レベルの投資行動について多くの知見が得られるようになっている．我々は，まずこの1990年代から今日までの設備投資の実証分析を通して，日本の設備投資行動がどのように理解されてきたかについて考察する．