Silviase/jgov_v1.4_full_ann huggingface dataset:
Net increase of borrowings $ (NB_{t}) $ is computed as the difference between the outstanding of the short-term and the long-term loans payable at the end of period and those at the end of the previous period.

### 7. Corporate Tax Rate

In computing the “effective” corporate tax rate, we have to take account of special treatment of the enterprise tax. That is, any enterprise tax paid in one year is deductible from the tax base of the next year. Hoshi and Kashyap(1990) demonstrates that the “effective corporate tax rate”( $ \tau_{t} $ ) in this situation is given by:

$$ \tau_{_{t}}=\frac{(u_{_{t}}+\nu_{_{t}})(1+r_{_{t}})}{(1+r_{_{t}}+\nu_{_{t}})} $$

where $ u_{t} $ : corporate tax rate corresponding to the corporate income tax,

prefectural inhabitants tax, and municipal inhabitants tax.

$ v_{t} $ : enterprise tax rate

The tax rate of $ u_{t} $ is computed as the ratio of payments of corporation taxes including inhabitants tax to the taxable income, and the tax rate of $ v_{t} $ is computed as the payment of enterprise taxes to the taxable income. The data necessary for computation are taken from Annual Statistics of National Tax Administration and Local Government White Paper.

8. Present Value of Investment Allowances on a Unit of Current Investment Expenditure ( $ z_{t} $ )

The tax savings $ z_{t} $ is defined as

$$ z_{t}=\sum_{s=t}^{\infty}\tau_{s}D(t,s)\beta_{s} $$

$$ where\ \beta_{s}=\prod_{i=1}^{s-t}(1+r_{_{t+i}})^{^{-1}}\quad(s=t+1,t+2,\cdots)\quad\beta_{t}\equiv1 $$

$$ \begin{aligned}D(t,s):depreciation allowance to be claimed in period s on a unit of current investment expenditure\end{aligned} $$

When the firm adopts fixed percentage method as a computation method of depreciation, $ D(t,s) $ is simply written as

$$ D(t,s)=(DEPR_{t})(1-DEPR_{t})^{s-t} $$
where $ DEPR_{t} $ : accounting depreciation rate, calculated as the ratio of depreciation to the book value of physical capital stock in QRFS

Under the static expectations for the future tax rates and discount rates, substitution of eq.(7) into eq.(6) simplifies the expression for $ z_{t} $ as:

$$ z_{t}=\frac{\tau_{t}(DEPR_{t})(1+r_{t})}{(r_{t}+DEPR_{t})} $$

The $ z_{t} $ series is computed on the basis of eq.(8).

9. Present Value of Tax Savings on the Depreciation Allowances on Investment Made Before Current Period $ (A_{t}) $

The tax savings $ A_{t} $ is defined as

$$ A_{t}=\sum_{s=t}^{\infty}\tau_{s}\beta_{s}\left[\sum_{n=-\infty}^{t-1}D(n,s)p_{n}^{I}I_{n}\right] $$

Static expectations for the future tax rates simplifies eq. $ (9) $ as:

$$ A_{t}=\tau_{t}B_{t} $$

$$ where B_{t}=\sum_{s=t}^{\infty}\beta_{s}\left[\sum_{n=-\infty}^{t-1}D(n,s)p_{n}^{I}I_{n}\right] $$

Hoshi and Kashyap(1990) demonstrates that the following recursive relationship is held for $ B_{t} $ :

$$ B_{t+l}=(1+r_{t})\left[B_{t}+\left(\sum_{s=t}^{\infty}D(t,s)\beta_{s}\right)(p_{t}^{I}I_{t})-\sum_{n=-\infty}^{t}D(n,t)p_{n}^{I}I_{n}\right] $$

As is shown in the section of $ z_{t} $ , the following relationship is held:

$$ \sum_{s=t}^{\infty}D(t,s)\beta_{s}=\frac{(DEPR_{t})(1+r_{t})}{(r_{t}+DEPR_{t})} $$
The term $ \sum_{n=-\infty}^{t}D(n,t)p_{n}^{I}I_{n} $ is nothing but depreciation in period t. Therefore once the starting value for B is given, we can compute a complete series of $ B_{t} $ . The starting value for the fourth quarter of 1968 is obtained by multiplying the conversion factor (CONVT) to the replacement cost of the capital stock. The conversion factor is taken from Table 2-15 in Honmma et al.(1984).

### 10. Profit Rate

The before-tax profit rates $ (\pi_{t}) $ is computed as the ratio of nominal operating profit to real capital stock.

### 11. Cash Flow

Cash flow( $ CF_{t} $ ) is the sum of ordinary profit and depreciation.

### 12. Tax-adjusted Average q

The tax-adjusted average q ( $ AVQ_{t} $ ) is computed as:

$$ \frac{V_{t}+LIB_{t}-CUR_{t}-CONSR_{t}-INTAN_{t}-OTHER_{t}-DEF_{t}-p_{t}^{L}L_{t-1}-A_{t}}{(1-\delta)(1-z_{t})p_{t}^{I}K_{t-1}} $$

where $ LIB_{t} $ : liabilities

$ CUR_{t} $ : current assets

$ CONSR_{t} $ : construction in progress

$ INTAN_{t} $ : intangible fixed assets

OTHER $ _{t} $ : investment and other assets

$ DEF_{t} $ : deferred charges
## Footnotes

1) See pp.72-73 in Honmma et al.(1984).

2) See Table 2-13 of page 79 in Honmma et al.(1984).

3) For real estate industry land asset is defined broadly to include inventories

4) This market-book ratio is in between the case ___ and the case ___ in Honmma et al.(1984). See Table 2-7 of pp.66-67 in Honmma et al.(1984).

5) Similar assumption is made in computing the land stock in Hoshi and Kashyap(1990).

6) Konya and Wakasugi(1987) makes a similar statement that the market-book ratios of land asset in old industries such as textiles or non-ferrous metal where a large part of land was purchased in the pre-war period are large, while those in relatively young industries like electrical machinery or wholesale and retail trade are low.
## References

[1] Asako, K., Kuninori, M., Inoue, T., and H. Murase (1989). “Tochi Hyoka to Tobin no q; Multiple q no Keisoku (Land Evaluation and Tobin’s q: A Measurement of Multiple q),” Economics Today, Vol. 10-3, Japan Development Bank, Tokyo.

[2] 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.

[3] Honmma, M., Hayashi, F., Atoda, N., and K. Hata (1984). Setsubi Toshi to Kigali Zeisei (Corporate Investment and Taxes), Monograph 41, Institute of Economic Research, Economic Planning Agency.

[4] Hoshi, T. and A.K. Kashyap (1990). “Evidence on Q and Investment for Japanese Firms,” Journal of the Japanese and International Economies 4, pp.371-400.

[5] 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, Washington, D.C.

[6]———(1981). “The Measurement of Economics Depreciation,” C.Hulten (ed.) Depreciation, Inflation and the Taxation of Income from Capital, Urban Institute.

[7] Institute for Social Engineering(1976). Houjin Kigyou Toukei no Koudo Riyou ni kansuru Chousa Kenkyu (Research on Advanced Use of Report of Financial Statements of Incorporated Business).

[8] Konya, N. and K. Wakasugi (1987). “Tobin no q to Kabuka (Tobin's q and Stock Price),” Shoken Kenkyu 80, pp. 149-162.
Average Years Elapsed since Installation of Physical Depreciable Assets by Industry

Table A-1

all industries	8.4	9.0	6.1	4.7	2.8	5.0
construction	5.0	5.2	3.2	5.4	1.5	12.2
manufacturing	9.0	8.8	5.9	5.0	3.1	4.5
food and beverages	10.0	7.4	6.5	4.9	2.0	3.7
textiles	11.2	7.7	7.2	3.0	2.7	8.4
pulp, paper and paper products	10.3	9.9	7.3	4.1	2.8	10.0
chemicals	8.4	5.9	5.0	2.3	4.2	5.0
steel	8.2	11.4	7.1	6.9	9.2	5.7
non-ferrous metal	8.5	9.3	4.8	6.4	3.9	5.3
fabricated metal products	5.1	5.2	3.8	3.6	1.8	3.1
machinery	10.0	8.1	5.9	12.4	2.4	4.8
electrical machinery	6.7	7.0	4.9	1.9	2.5	3.6
wholesale and retail trade	6.1	5.0	4.1	5.5	1.6	3.8
real estate	9.2	5.3	3.4	0.9	2.4	4.2

Source: Volume 4 Corporation in 1970 National Wealth Survey of Japan, Economic Planning Agency, 1970.
Physical Depreciation Rates by Industry

Table A-2

Industry	Physical depreciation rate
all industries	0.0772
construction	0.0869
manufacturing	0.0774
food and beverages	0.0735
textiles	0.0744
pulp, paper, and paper products	0.0808
chemicals	0.0778
steel	0.0805
non-ferrous metal	0.0778
fabricated metal products	0.0792
machinery	0.0786
electrical machinery	0.0720
wholesale and retail trade	0.0692
real estate	0.0519

(Notes) The depreciation rates are constructed from the rates reported in Hayashi and Inoue(1991).
Land Price Index for Corporations

Table A-3

Period	Period	Period
68:1	0.3656	76:1	0.8268	84:1	1.3263
2	0.3781	2	0.8299	2	1.3567
3	0.3897	3	0.8330	3	1.3875
4	0.4058	4	0.8388	4	1.4201
69:1	0.4223	77:1	0.8446	85:1	1.4522
2	0.4397	2	0.8487	2	1.4996
3	0.4571	3	0.8522	3	1.5469
4	0.4750	4	0.8585	4	1.6478
70:1	0.4924	78:1	0.8643	86:1	1.7469
2	0.5071	2	0.8732	2	1.8505
3	0.5219	3	0.8821	3	1.9545
4	0.5348	4	0.8960	4	2.0737
71:1	0.5482	79:1	0.9098	87:1	2.1938
2	0.5607	2	0.9317	2	2.4388
3	0.5737	3	0.9531	3	2.6839
4	0.5889	4	0.9763	4	2.8138
72:1	0.6036	80:1	1.0000	88:1	2.9446
2	0.6272	2	1.0214	2	3.0960
3	0.6509	3	1.0429	3	3.2460
4	0.7031	4	1.0616	4	3.4116
73:1	0.7554	81:1	1.0804	89:1	3.5768
2	0.8000	2	1.1031	2	3.7763
3	0.8446	3	1.1259	3	3.9755
4	0.8634	4	1.1460	4	4.2201
74:1	0.8826	82:1	1.1661	90:1	4.4643
2	0.8862	2	1.1835	2	4.5777
3	0.8902	3	1.2005	3	4.6915
4	0.8576	4	1.2188	4	4.6844
75:1	0.8250	83:1	1.2366	91:1	4.6777
2	0.8255	2	1.2527	2	4.6009
3	0.8259	3	1.2683	3	4.5237
4	0.8263	4	1.2973	4	4.3121
92:1	4.0991

(Notes) See the text for the construction procedure of the land price index for corporations.
Average of the Market-Book Ratios of the Land Stock by Industry

Table A-4

industry	68:3 - 91:1	68:3 - 86:3	86:4 - 91:1
all industries	4.3572	4.1056	5.3779
construction	3.8776	3.7373	4.4465
manufacturing	4.8874	4.3804	6.9437
food and beverages	4.8092	4.3765	6.5641
textiles	5.2501	4.6474	7.6942
pulp, paper, and paper products	4.8837	4.2952	7.2701
chemicals	5.5874	4.8682	8.5040
steel	5.4967	4.4740	9.6444
non-ferrous metal	4.4892	3.8609	7.0372
fabricated metal products	4.4842	3.9635	6.5960
machinery	4.8291	4.2697	7.0978
electrical machinery	4.7021	4.4140	5.8706
wholesale and retail trade	4.3222	4.0255	5.5258
real estate	4.0347	4.0194	4.0964

(Notes) See the text for the computation procedure of the market-book ratios of the land stock.
Comparison of the Market-Book Ratios of the Land Stock with Other Studies

Table A-5

year	Honmma et al.(1984)	Konya and Wakasugi (1987)	Asako et al. (1989)	This Study
Case I	Case IV	All industries	Manufacturing

1968	2.71	5.82	5.57	5.41
1969	2.28	5.82	5.57	5.43
1970	3.02	6.06	5.37	5.27
1971	3.17	6.21	5.10	5.12
1972	3.03	5.71	4.83	4.94
1973	3.47	6.39	5.00	5.30
1974	3.62	6.52	22.7	4.65	4.94
1975	3.18	5.64	18.9	3.87	4.14
1976	2.91	5.04	17.7	5.84	3.64	3.85
1977	3.07	5.34	17.0	5.80	3.56	3.71
1978	2.99	5.11	16.7	6.16	3.44	3.70
1979	3.01	5.07	17.3	6.80	3.39	3.77
1980	3.17	5.27	18.7	7.37	3.98	3.84
1981	3.26	5.35	20.4	7.62	3.48	3.87
1982	21.4	7.36	3.49	3.96
1983	22.3	6.92	3.44	3.97
1984	21.6	6.76	3.46	3.94
1985	21.7	6.83	3.55	4.04
1986	25.2	7.90	3.93	4.69
1987	8.98	4.62	5.45
1988	5.20	6.72
1989	5.85	7.53
1990	6.04	8.15
1991	5.86	7.74

(Notes) Figures in Honmma et al.(1984) are taken from Case I and Case IV in Table 2-7 in pp.66-67. Figures in Konya and Wakasugi(1987) are taken from Table 2-1 in p 155. Figures in Asako et al.(1989) are weighted averages of Case I (Base Case) in Table 2-5 in p 50.
Figure A-1

Figure A-2

Market-Book Ratio of Land Asset: Construction

Figure A-3

Market-Book Ratio of Land Asset: Manufacturing
Market-Book Ratio of Land Asset: Food and Beverages

Figure A-5

Market-Book Ratio of Land Asset: Textiles

Figure A-6

Market-Book Ratio of Land Asset: Pule, Paper, and Paper Products
Figure A-7

Figure A-8

Market-Book Ratio of Land Asset: Steel

Figure A-9

Market-Book Ratio of Land Asset: Non-ferrous Metal
Market-Book Ratio of Land Asset: Fabricated Metal Products

Figure A-10

Figure A-11

Market-Book Ratio of Land Asset: Machinery

Figure A-12

Market-Book Ratio of Land Asset: Electrical Machinery
Market-Book Ratio of Land Asset: Wholesale and Retail Trade

Figure A-13

Figure A-14

Market-Book Ratio of Land Asset: Real Estate
Chapter. 3

(論文 3)

# An Empirical Re-evaluation of Wealth Effect in Japanese Household Behavior （家計における資産効果の実証的再評価）

Kazuo Ogawa

Shin-ichi Kitasaka

Hiroshi Yamaoka

Yasuharu Iwata
1) total wealth(= net financial wealth + tangible wealth) (TW)

2) net financial wealth (FW) and tangible wealth (RW)

3) liquid financial wealth (LW) and tangible wealth (RW)

4) securities (SEC), other liquid financial assets (OLW), and tangible wealth (RW)

5) liquid financial wealth (LW)

6) securities (SEC) and other liquid financial assets (OLW)

Case 1 is closest to the LCY-PIH among the six cases. In this specification what matters is total wealth and the tangible wealth has the wealth effects with the same magnitude as the financial wealth. In case 2 the wealth effects can be different in magnitude between real and financial wealth in the framework of the LCY-PIH. This can be interpreted as an extended version of the LCY-PIH with illiquid wealth as a bequest variable. If it is the case, then the wealth effect of tangible assets will be less than that of financial assets. Case 3 differs from Case 2 in that tangible wealth and liquid wealth instead of financial wealth is assumed to affect the consumers' spending. Case 4 breaks down the liquid wealth further into securities and other liquid assets. Under this specification we can test whether the capital gains or losses accrued to the households have the same effects on consumption as other liquid assets. In other words we can shed light to the quantitative importance of capital gains or losses in the consumption allocation which attracted attention of many economists in recent years. Case 5 and 6 lay stress upon the liquidity of wealth and each corresponds to Case 3 and 4 without tangible wealth.

In addition to the wealth variables defined above, we add the following variables to the list of explanatory variables in estimating the consumption functions. They are the disposable income (DY), the number of persons in a household (SETAI), the proportion of the aged above 65 years old (AGE), and the proportion of farm households (FARM). The consumption functions corresponding to the six cases above are specified as follows:

Case 1

$$ \left(\frac{FC}{DY}\right)=\alpha_{0}+\alpha_{1}\left(\frac{TW}{DY}\right)+\alpha_{2}\left(\frac{1}{DY}\right)+\alpha_{3}(SETAI) $$

$$ +\alpha_{4}(AGE)+\alpha_{5}(FARM) $$
Case 2

$$ \begin{aligned}&\left(\frac{FC}{DY}\right)=\alpha_{0}+\alpha_{1}\Bigg(\frac{FW}{DY}\Bigg)+\alpha_{2}\Bigg(\frac{RW}{DY}\Bigg)+\alpha_{3}\Bigg(\frac{1}{DY}\Bigg)+\alpha_{4}(SETAI)\\ &\\ &\quad+\alpha_{5}(AGE)+\alpha_{6}(FARM)\\ \end{aligned} $$

Case 3

$$ \begin{aligned}&\left(\frac{FC}{DY}\right)=\alpha_{0}+\alpha_{1}\Bigg(\frac{LW}{DY}\Bigg)+\alpha_{2}\Bigg(\frac{RW}{DY}\Bigg)+\alpha_{3}\Bigg(\frac{1}{DY}\Bigg)+\alpha_{4}(SETAI)\\ &\\ &\quad+\alpha_{5}(AGE)+\alpha_{6}(FARM)\\ \end{aligned} $$

Case 4

$$ \begin{aligned}&\left(\frac{FC}{DY}\right)=\alpha_{0}+\alpha_{1}\Bigg(\frac{SEC}{DY}\Bigg)+\alpha_{2}\Bigg(\frac{OLW}{DY}\Bigg)+\alpha_{3}\Bigg(\frac{RW}{DY}\Bigg)+\alpha_{4}\Bigg(\frac{1}{DY}\Bigg)+\alpha_{5}(SETAI)\\ &\\ &\quad+\alpha_{6}(AGE)+\alpha_{7}(FARM)\\ \end{aligned} $$

Case 5

$$ \left(\frac{FC}{DY}\right)=\alpha_{0}+\alpha_{1}\left(\frac{LW}{DY}\right)+\alpha_{2}\left(\frac{1}{DY}\right)+\alpha_{3}(SETAI) $$

$$ +\alpha_{4}(AGE)+\alpha_{5}(FARM) $$

Case 6

$$ \begin{aligned}&\left(\frac{FC}{DY}\right)=\alpha_{0}+\alpha_{1}\Bigg(\frac{SEC}{DY}\Bigg)+\alpha_{2}\Bigg(\frac{OLW}{DY}\Bigg)+\alpha_{3}\Bigg(\frac{1}{DY}\Bigg)+\alpha_{4}(SETAI)\\ &\\ &\quad+\alpha_{5}(AGE)+\alpha_{6}(FARM)\\ &\\ &\quad where FC:final consumption expenditure of households\\ \end{aligned} $$

In eqs.(1) to (6) the consumption and wealth variables are divided by the disposable income to allow for heteroscedasticity. This consideration is especially important since we estimate the consumption functions based on the cross-section data. The term of $ \left(\frac{1}{DY}\right) $ is also added as an explanatory variable into eqs.(1) to (6) so that we can take account of the non-unitary income elasticity of consumption. The estimated results of these equations will be presented in the next section.
assistance grants are taken from payment for social aid reported in Annual Report on Local Public Finance.

To sum up, the disposable income is computed in the following manner:

$$ \begin{aligned}(disposable income)&=(compensation of employees)+(property income)\\&+(entrepreneurial income)+(social assistance grants)-(direct tax on income)\\&-(current transfers to private non-profit institutions serving households)\end{aligned} $$

All the data series of wealth, consumption, and income are divided by the number of households and converted into real terms by the deflator of final consumption expenditure of households.

The information of other data set used in estimation is given in Data Appendix.

The estimation results of total consumption function expressed by eqs.(1) to (6) are presented in Table 1. $ ^{8)} $ The first row shows the estimation results of consumption function with conventional wealth variable(TW). The magnitude of wealth effects is 0.0105 and it is significant at the standard significance level. Our estimate is quite consistent with the wealth effect estimates obtained in other studies. To mention a few, Ogawa(1992b) reports the wealth effects to be 0.0155, while Horioka(1993) reports them in the range of 0.0083 to 0.0171. Mutoh, Kawai, and Sano(1993) reports the wealth effects in their base case in the range of 0.012 to 0.015.

However, once the total wealth is split into real and financial wealth, the magnitude of wealth effects is no longer equal between two concepts of wealth. The order of wealth effects is 0.0275 for net financial wealth and 0.0099 for tangible wealth. Our estimates are close to those reported in Takayama et al.(1992). Their study is based on the micro data of National Survey of Family Income and Expenditure in 1979 and 1984. The wealth effect of financial wealth is estimated to be 0.0242 in 1979 and 0.0274 in 1984, while that of tangible wealth is 0.0151 in 1979 and 0.0080 in 1984.

Although the coefficient estimate of financial wealth is not significant in the second row, it turns out to be significant when liquid wealth is used instead of net financial wealth (third row). The magnitude of wealth effects rises to 0.0657. The coefficient estimate of tangible wealth is also significant in the third row. Although both the tangible and liquid wealth variables are significant, the size of wealth effects of liquid wealth is about ten times as large as that of real wealth. Further breakdown of liquid wealth into securities and other liquid wealth is not successful in the sense that both of the coefficient estimates are insignificant. Note, however, that there is not much difference between two estimates of wealth effects. To sum up, total consumption spending is affected significantly by both tangible and liquid wealth in quite a different manner.

4. Estimation of Household Consumption Function by Type of Expenditure with Wealth Variables
households. We examine the sensitivity of residential investment to the wealth level of the households by estimating residential investment function with wealth variables. The specifications of residential investment function are similar to those employed in the previous sections. Six specifications with different combinations of wealth variables are chosen with the ratio of real residential investment to real disposable income as a dependent variable. Added to the list of explanatory variables is the cost of capital of residential investment. The cost of capital of residential investment (CCOST) is defined as follows.

$$ C C O S T=\left(\frac{P R E S}{P C}\right)\left(R+\delta-\left(\frac{P R E S-P R E S_{-1}}{P R E S_{-1}}\right)\right)\times100 $$

where PRES : deflator of residential investment

PC : deflator of final consumption expenditure

R : interest rate (average contracted interest rate on loans and discounts ;all banks)

$ \delta $ : depreciation rate of residential stock assumed to be 0.0263 per annum, which is taken from Ogawa(1991).

The estimation results of residential investment function is shown in Table 3. The estimated coefficients satisfy all the theoretical conditions. It is the level of liquid wealth as well as tangible wealth that affects residential investment significantly. The coefficients of liquid wealth and tangible wealth are 0.0248 and 0.0041 in the third row, respectively.

### 6. How Large Are Wealth Effects on Consumption Spending?

Now quantitative evaluation of wealth effects on aggregate consumption is in order on the basis of the estimation results above. We compute how much change in wealth contributed to a high growth of consumption in the recent business upturns and subsequent slowdown of consumption growth. By doing so we can contrast the role played by wealth in the allocation of consumption spending in the contemporary business cycles with that in the past.

The contribution of wealth to final consumption spending of households is calculated as follows:

(coefficient of wealth variable)×(Δwealth stock)/(Δchange of final consumption expenditure)

Table 4 gives the contribution of wealth stock to final consumption on the basis of three different
have different impacts on consumption spending. What is most important is liquid wealth and it appears as a significant explanatory variable in expenditure function of many commodity groups as well as total consumption. In particular it affects to a large extent the spending for housing and miscellaneous items where most of the spending for consumers' durables and services are included. Since the share stock is a component of liquid wealth, fluctuations of share price are conveyed to those of consumption by way of liquid wealth. In fact our evidence indicates that variations of liquid wealth are responsible for half of the change in consumption in the boom period of the middle of 80's. It is to be noted that the traditional specification of consumption function with the total wealth as an explanatory variable underestimates the effects of wealth on consumption.

The wealth effects are conspicuous for the category of housing and miscellaneous items where expenditure for consumers' durables and services is included. This can explain the high growth of spending on durable goods and services observed in the business upturns in the middle of 80's.

To conclude, the main channel through which the asset markets exert effect on consumption expenditure is via liquid wealth.