Silviase/jgov_v1.4_full_ann huggingface dataset:
pp.33-67.

[20] Miller, M.H. and Modigliani, F. (1961). “Dividend Policy, Growth and the Valuation of Shares,” Journal of Business 44, pp.411-433.

[21] Mitsui, K. and S. Kawachi (1993). “Cyushoukigyou no Setsubitoushi to Shikinchoutatsu (Corporate Investment under Financing Constraints and Government Lending),” Institute for Posts and Telecommunications Policy Discussion Paper No. 1993-21.

[22] Miyagawa, T. (1993), “Kashishiburi to Shinsa Kinou - hiseizougyou Chushoukigyou no setsubitoushi wo chyusin toshite (Reluctance of Lending and Examination Mechanism of the Japanese Banking Sector - Investment of Small-sized Firms in the Non-manufacturing Sectors),” JDB Discussion Paper No.9305, Research Institute of Capital Formation, The Japan Development Bank, Tokyo.

[23] Modigliani, F. and Miller, M.H. (1958). “The Cost of Capital, Corporation Finance, and the Theory of Investment,” American Economic Review 48, pp.261-297.

[24] Modigliani, F. and Miller, M.H. (1963). “Corporate Income Taxes and the Cost of Capital: A Correction,” American Economic Review 53, pp.433-443.

[25] Newey, W. K. and K. D. West (1987). "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Vol. 28, No. 3, pp. 777-787.

[26] Okazaki, T. and A. Horiuchi (1992). “Kigyo no Setsubi Toshi to Mein Banku Kankei (Corporate Investment and Main Bank Relationship),” Kinyu Kenkyu (Research on Finance), Vol.11, No.1, pp.37-59.

[27] Ueda, K. and H. Yoshikawa (1986). “Financial Volatility and the q Theory of Investment,” Economica, 53, pp.11-27.

[28] Wildasin, D. (1984), “The q Theory of Investment with Many Capital Goods,” American Economic Review, 74, pp.203-210.

[29] Whited, T.M. (1992), “Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel Data,” Journal of Finance Vol.XLVII, No.4, pp.1425-1460.
Table 1

Estimates of the Euler Equations

with the Lagrangian Multiplier subsumed into the Error Term

industry	constant	adjustment cost parameter (α1)	constant associated with the discount factor	test of overidentifying restrictions (χ2) p-value
all industries	1.3261(11.228)	0.7712(1.228)	-1.3702(-10.973)	36.01330.001
1.4143(11.374)	1.1231(1.798)	-1.4647(-11.105)	31.40160.005
construction	1.4806(6.958)	0.6601(1.302)	-1.5792(-7.273)	65.56600.000
1.0013(4.364)	0.3183(0.637)	-1.0864(-4.659)	83.61800.000
manufacturing	1.7855(13.326)	3.6546(4.385)	-1.8751(-13.100)	21.54070.089
1.6909(12.662)	3.3730(3.846)	-1.7729(-12.451)	27.41940.017
food and beverages	0.3073(1.800)	-0.3662(-0.695)	-0.3569(-2.058)	24.94520.035
0.4668(2.519)	-0.3406(-0.733)	-0.5193(-2.778)	21.75920.084
textiles	1.1479(3.745)	3.4367(3.571)	-1.1850(-3.769)	15.10440.371
1.5612(5.278)	2.9917(3.199)	-1.6068(-5.291)	15.17680.366
pulp, paper and paper products	0.4845(5.768)	-0.1246(-0.098)	-0.4513(-5.307)	13.24620.507
0.4617(5.512)	-0.0989(-0.076)	-0.4282(-5.044)	12.65780.554
chemicals	1.2350(13.786)	-0.3578(-2.653)	-1.3272(-14.313)	127.98220.000
1.2371(12.357)	-0.1652(-0.982)	-1.3260(-12.651)	85.71980.000
Table 1 (continued)

Estimates of the Euler Equations

with the Lagrangian Multiplier subsumed into the Error Term

industry	cconstant	adjustment cost parameter (α1)	constant associated with the discount factor	test of overidentifying restrictions (χ²) p-value
steel	2.1136	0.2604	-2.3192	68.0921
(6.472)	(1.424)	(-6.697)	0.000
2.8428	1.0631	-3.1459	120.5374
(7.974)	(8.147)	(-8.567)	0.000
non-ferrous metal	0.5991	6.7965	-0.5672	21.3527
(6.700)	(3.157)	(-6.095)	0.093
0.5975	9.6960	-0.5734	26.5185
(7.086)	(4.926)	(-6.527)	0.022
fabricated metal products	0.9650	0.6494	-0.9595	18.8159
(7.071)	(0.834)	(-6.828)	0.172
0.9208	0.1686	-0.9101	11.7834
(5.082)	(0.159)	(-4.869)	0.624
machinery	1.2489	0.6888	-1.3391	22.4447
(5.692)	(2.841)	(-5.906)	0.070
1.3918	0.8280	-1.4839	30.9106
(6.319)	(3.111)	(-6.549)	0.006
electrical machinery	1.0984	0.0742	-1.2522	31.1659
(2.788)	(0.187)	(-3.097)	0.005
1.2946	-0.9944	-1.4375	76.5546
(4.915)	(-2.842)	(-5.299)	0.000
wholesale and retail trade	7.2253	1.9388	-7.8925	149.0070
(17.163)	(5.789)	(-18.144)	0.000
6.2707	1.1258	-6.8249	152.6789
(14.479)	(2.483)	(-15.146)	0.000
real estate	1.9530	1.1110	-1.9676	15.5596
(8.162)	(1.572)	(-7.968)	0.341
1.8779	0.5067	-1.8794	15.4285
(7.011)	(0.566)	(-6.878)	0.350

Notes :

The first row uses the unrestricted covariance estimate under the hypothesis that the Lagrangian multiplier is a linear function of the value of land asset while the second row uses that under the hypothesis that the Lagrangian multiplier is a linear function of the cash flow.

The values in parentheses are the ratio of coefficients estimates to the asymptotic standard errors.

The coefficients estimates of seasonal dummies are suppressed.
Table 2

Estimates of the Euler Equations

with the Lagrangian Multiplier as a Function of Land Value ro Cash Flow

industry	constant	adjustment cost parameter ( $ \alpha_{1} $ )	constant associated with the discount factor	parameter associated with the Lagrangian multiplier	test of overidentifying restrictions ( $ \chi^{2} $ )	joint test of exclusion restrictions on the parameters of Lagrangian multiplier ( $ \chi^{2} $ )
constant coefficient of land or cash flow	p-value	p-value
all industries	0.7533	1.2772	-0.5946	0.2493	-0.0069	11.1730	24.8404
(3.554)	(1.856)	(-1.794)	(4.744)	(-2.511)	0.514	0.000
construction	1.2673	1.1616	-1.3697	0.8250	-1.4597	12.8432	52.7228
(70.836)	(3.936)	(-73.084)	(5.459)	(-3.615)	0.381	0.000
manufacturing	1.3477	2.4478	-1.4132	0.2099	-0.0811	11.0883	10.4524
(49.498)	(4.359)	(-40.892)	(2.961)	(-1.158)	0.521	0.005
chemistry	0.3097	0.1166	0.2754	0.3912	-0.0147	12.5963	115.3858
(1.721)	(0.652)	(0.666)	(8.850)	(-4.063)	0.399	0.000
steel	1.2151	1.2230	-1.4108	0.6883	-0.6071	11.3043	56.7878
(30.257)	(3.657)	(-53.839)	(5.051)	(-1.681)	0.503	0.000
wholesale and retail trade	1.2488	1.7169	-1.5089	1.1500	-0.1974	12.0938	136.9131
(23.598)	(4.575)	(-5.925)	(8.023)	(-1.465)	(0.438)	0.000
## Table 2 (continued)

## Estimates of the Euler Equations

with the Lagrangian Multiplier as a Function of Land Value ro Cash Flow

industry	constant	adjustment cost parameter (α1)	constant associated with the discount factor	parameter associated with the Lagrangian multiplier	test of overidentifying restrictions (χ2)	joint test of exclusion restrictions on the parameters of Lagrangian multiplier (χ2)
constant coefficient of land or cash flow	p-value	p-value
all industries	0.8115	1.1625	-0.7000	0.2229	-0.0091	10.9803	20.4213
(3.514)	(1.730)	(-2.057)	(4.271)	(-1.827)	0.531	0.000
construction	1.2960	1.3207	-1.3887	1.0140	-1.3503	13.6969	69.9211
(55.782)	(3.563)	(-57.523)	(5.338)	(-3.661)	0.320	0.000
manufacturing	1.3528	2.4804	-1.4171	0.4518	-0.3161	10.8775	16.5419
(64.746)	(4.521)	(-50.814)	(3.419)	(-2.493)	0.539	0.000
food and beverages	-0.1505	1.2021	0.3779	0.1516	-0.0038	8.4420	13.3172
(-0.586)	(1.722)	(0.975)	(3.177)	(-0.512)	0.750	0.000
chemistry	0.2680	0.0800	0.3367	0.4036	-0.0282	11.7309	73.9889
(1.252)	(0.422)	(0.661)	(7.518)	(-3.197)	0.468	0.000
fabricated metal products	1.0355	0.1122	-1.0411	-0.0912	-0.0244	10.4397	1.3437
(4.198)	(0.116)	(-4.412)	(-1.302)	(-0.306)	0.577	0.511
wholesale and retail trade	1.2142	1.6013	-1.7020	1.7626	-0.9182	10.9568	141.7221
(14.050)	(3.198)	(-19.054)	(5.108)	(-3.343)	0.533	0.000
real estate	1.6402	1.2018	-1.6578	0.1162	-0.0970	10.5941	4.8344
(17.808)	(1.227)	(-16.897)	(2.049)	(-1.231)	0.564	0.089

Notes: see notes in Table 1.
Sample Average of Lagrangian Multipliers

Table 3

industry	driving force of the Lagrangian multiplier
land	cash flow
whole sample	first-half	latter-half	whole sample	first-half	latter-half
all industries	0.242	0.245	0.230	0.213	0.215	0.204
0.241	0.244	0.230	0.213	0.215	0.204
construction	-0.650	-0.084	-2.986	-0.365	-0.034	-1.730
0.045	0.056	0.000	0.066	0.083	0.000
manufacturing	0.128	0.158	0.001	0.128	0.190	-0.127
0.184	0.185	0.180	0.151	0.178	0.041
food and beverages	0.147	0.148	0.145
0.148	0.148	0.146
chemicals	0.376	0.382	0.353	0.374	0.380	0.352
0.374	0.380	0.351	0.375	0.381	0.354
steel	0.074	0.288	-0.808
0.168	0.209	0.000
wholesale and retail trade	0.915	1.018	0.621	0.685	0.951	-0.076
0.768	1.021	0.038	0.750	0.952	0.171
real estate	0.012	0.036	-0.110
0.006	0.007	0.000

Notes: Whole sample covers from the second quarter of 1970 to the third quarter of 1990. The first-half corresponds to the second quarter of 1970 to the third quarter of 1986, while the latter-half corresponds to the fourth quarter of 1986 to the third quarter of 1990.
Table 4

Estimates of the Euler Equations

industry	constant	adjustment cost parameter (α1)	constant associated with the discount factor	parameter associated with the Lagrangian multiplier	test of overidentifying restrictions (χ2)	joint test of exclusion restrictions on the parameters of Lagrangian multiplier (χ2)
constant coefficient of land or cash flow	p-value	p-value
all industries	0.7533	1.2753	-0.5952	-1.3906	-0.0290	11.1673	24.7436
(3.568)	(1.856)	(-1.803)	(-6.603)	(-1.991)	0.515	0.0000
construction	1.0952	0.0143	-1.1936	3.6234	-13.3057	13.8039	73.5080
(4.884)	(0.037)	(-5.177)	(2.700)	(-3.713)	0.313	0.000
manufacturing	1.1613	2.2921	-1.1794	-1.6779	-0.0141	10.8410	14.9020
(3.713)	(2.553)	(-2.860)	(-4.885)	(-0.204)	0.543	0.000
chemistry	0.3176	0.1120	0.2574	-0.9411	-0.0409	12.6106	115.3650
(1.742)	(0.623)	(0.618)	(-8.286)	(-2.700)	0.398	0.000
steel	1.1900	0.7854	-1.3504	1.8604	-5.8717	11.0322	60.2111
(13.194)	(2.621)	(-12.825)	(1.710)	(-2.103)	0.526	0.000
wholesale and retail trade	1.1881	1.4017	-1.6442	1.2595	-1.9711	11.1177	139.2787
(16.977)	(3.409)	(-21.079)	(2.072)	(-2.041)	0.519	0.000
## Table 4 (continued)

Estimates of the Euler Equations

industry	constant	adjustment cost parameter (α1)	constant associated with the discount factor	parameter associated with the Lagrangian multiplier	test of overidentifying restrictions (χ2)	joint test of exclusion restrictions on the parameters of Lagrangian multiplier (χ2)
constant coefficient of land or cash flow	p-value	p-value
all industries	0.8113	1.1583	-0.6999	-1.5015	-0.0427	10.9680	20.4588
(3.508)	(1.727)	(-2.054)	(-6.419)	(-1.517)	0.532	0.000
construction	1.1756	0.2855	-1.2722	4.8492	-11.9692	14.1143	42.8696
(11.378)	(0.639)	(-11.765)	(2.267)	(-2.721)	0.293	0.000
manufacturing	1.3412	2.5292	-1.4038	-0.6121	-1.4128	10.4922	15.7743
(47.625)	(4.738)	(-39.364)	(-0.529)	(-1.015)	0.573	0.000
food and beverages	-0.1544	1.2056	0.3834	-1.8843	-0.0256	8.4492	13.4115
(-0.600)	(1.727)	(0.985)	(-5.999)	(-0.443)	0.749	0.000
chemistry	0.2610	0.0832	0.3509	-0.9020	-0.0756	11.7140	73.9735
(1.210)	(0.398)	(0.681)	(-6.892)	(-2.185)	0.469	0.000
wholesale and retail trade	1.1641	1.2406	-1.5987	1.2797	-1.5868	10.3240	130.2040
(13.353)	(2.326)	(-16.634)	(3.039)	(-3.595)	0.588	0.000
real estate	1.8278	1.1680	-1.8477	-2.0804	-5.1686	11.1614	4.9535
(44.001)	(1.288)	(-34.952)	(-2.215)	(-1.441)	0.515	0.084

Notes: see notes in Table 1.
Table 5

Effect of an Increase of Land Value or Cash Flow on the Effective Discount Rate

industry	driving force of the Lagrangian multiplier
land 1)	cash flow 2)
all industries	-0.00236	-0.00548
construction	-8.65	-12.35
manufacturing	-0.0796	-0.3997
food and beverages	-0.00612
chemicals	-0.0978	-0.2352
fabricated metal products	-0.604
steel	-6.36	—
wholesale and retail trade	-0.2896	-3.49
real estate	—	-1.93

Notes: 1) reduction of the effective discount rate in terms of percentage points by a trillion yen increase of land value

2) reduction of the effective discount rate in terms of percentage points by one hundred billion yen increase of cash flow
Share of Small-sized Firms in Industry (1991)

Table 6

industry	share of employees	share of sales
all industries	79.2	-
construction	95.4	64.2
manufacturing	73.8	51.8
food and beverages	85.4	84.1
textile	89.2	81.0
pulp, paper and paper products	83.0	66.8
chemicals	53.5	46.5
non-ferrous metal	61.8	47.0
fabricated metal products	88.1	81.5
steel	50.8	41.9
machinery	73.9	55.5
electrical machinery	54.6	30.0
wholesale and retail trade	86.4	65.2
real estate	97.0	64.7

Notes: As for the definition of small-sized firms, it is defined here as a firm where the number of employees is less than 300 except wholesale and retail industries. In wholesale industry it is defined as a firm whose employees are less than 100. In retail trade industry, it is defined as a firm whose employees is less than 50.

Source: Establishment Census of Japan, Statistics Bureau, Management and Coordination Agency Census of Manufactures, Research and Statistics Department, Minister's Secretariat, Ministry of International Trade and Industry Census of Commerce, Research and Statistics Department, Minister's Secretariat, Ministry of International Trade and Industry
Table 7

Adjustment Cost Parameter and Equilibrium q

industry	adjustment cost parameter (α1)	Equilibrium value of q
whole sample	first-half sample	latter-half sample
all industries	L	1.2772	1.018	1.018	1.017
CF	1.1625	1.016	1.017	1.016
construction	L	1.1616	1.018	1.017	1.019
CF	1.3207	1.020	1.020	1.022
manufacturing	L	2.4478	1.036	1.036	1.036
CF	2.4804	1.036	1.036	1.036
food and beverages	CF	1.2021	1.017	1.017	1.017
textile	A	3.4367	1.049	1.050	1.047
B	2.9917	1.043	1.044	1.041
pulp, paper and * paper products	A	2.1426	1.031	1.030	1.033
B	2.1699	1.031	1.031	1.034
chemicals	L	0.1166	1.001	1.001	1.001
CF	0.0880	1.001	1.001	1.001
non-ferrous metal	A	6.7965	1.075	1.075	1.074
fabricated metal products	A	0.6494	1.011	1.010	1.011
B	0.1686	1.002	1.002	1.002
steel	L	1.2230	1.017	1.017	1.018
machinery	A	0.6888	1.011	1.011	1.011
electrical * machinery	A	0.3983	1.006	1.006	1.004
wholesale and retail trade	L	1.7169	1.021	1.022	1.020
CF	1.6013	1.020	1.020	1.018
real estate	A	1.1110	1.009	1.010	1.008
B	0.5067	1.004	1.004	1.003

Notes: “A” is the model without borrowing constraints that uses the unrestricted covariance matrix estimate with the Lagrangian matrix as a function of land.

“B” is the model without borrowing constraints that uses the unrestricted covariance matrix estimate with the Lagrangian matrix as a function of cash flow.

“L” is the model with borrowing constraints that depends on the land value.

“CF” is the model with borrowing constraints that depends on the cash flow.

For information on the sample period, see the notes in Table 3.

$ ^{*} $ computed from the estimates without seasonal dummies
## Appendix Table

Estimates of the Euler Equations

with the Lagrangian Multiplier subsumed into the Error Term No seasonal Dummies

industry	constant	adjustment cost parameter ( $ \alpha_{1} $ )	constant associated with the discount factor	test of overidentifying restrictions ( $ \chi^{2} $ ) p-value
pulp, paper, and paper products	0.4136 (4.836)	2.1426 (3.218)	-0.3980 (-4.499)	16.4360 0.287 15.9346 0.317
0.3941 (4.561)	2.1699 (3.254)	-0.3779 (-4.225)
electrical machinery	0.9903 (2.620)	0.3983 (3.613)	-1.1700 (-3.004)	24.1234 0.044 63.0874 0.000
0.9813 (3.533)	0.4686 (5.412)	-1.1881 (-4.141)

Notes: See notes in Table 1.
Paths of the Lagrangian Multipliers as a Linear Function of Land Value; (All Industries)

Figure 1-2

Paths of the Lagrangian Multipliers as a Linear Function of Land Value; (Construction)

Figure 1-3

Paths of the Lagrangian Multipliers as a Linear Function of Land Value; (Manufacturing)
Paths of the Lagrangian Multipliers as a Linear Function of Land Value; (Chemicals)

Figure 1-5

Paths of the Lagrangian Multipliers as a Linear Function of Land Value; (Steel)

Figure 1-6

Paths of the Lagrangian Multipliers as a Linear Function of Land Value; (Wholesale and Retail Trade)
Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (All Industries)

Figure 1-8

Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (Construction)

Figure 1-9

Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (Manufacturing)
Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (Food and Beverages)

Figure 1-11

Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (Chemicals)

Figure 1-12

Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (Fabricated Metal Products)
## Figure 1-13

Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (Wholesale and Retail Trade)

Figure 1-14

Paths of the Lagrangian Multipliers as a Linear Function of Cash Flow; (Real Estate)
Kazuo Ozawa

Shin-ichi Kitasaka

Toshio Watanabe

Tatsuya Maruyama

Hiroshi Yamaoka

Yasuharu Iwata

Data Appendix for an Analysis of Firm's Investment Demand in Japan
As for the deflator of investment goods $ (p_{t}^{I}) $ , following the procedures adopted by Honmma et al.(1984), we construct two series of the investment goods deflators: one for buildings and structures and the other for machinery. $ ^{1)} $ The book values in the category of nonresidential buildings and structures are divided by the former deflator, while those in the category of machinery, vessels, transportation equipment except vessels, and instruments and tools are divided by the latter. Two types of the deflators are combined into one series with the weights reported in Honmma et al.(1984) to deflate nominal investment series. $ ^{2)} $

As a series of nominal investment, we use the increment of other assets of property, plant, and equipment in the table of changes in fixed assets in QRFS.

The physical depreciation rates( $ \delta $ ) 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 % for transportation equipment, and 8.838 % for instruments and tools. We computed our series of depreciation rates by averaging those rates with the proportions of each fixed assets in NWS as weights. The physical depreciation rates are given for each industry in Table A-2.

Based on the benchmark value of the depreciable stock, real investment series, and depreciation rate, we obtain the series of depreciable capital stock from the following formula.

$$ K_{t}=(1-\delta)K_{t-1}+I_{t} $$

where $K_{t}$ : real depreciable capital stock at the end of period t

$ I_{t} $ : real investment in period t

δ : depreciation rate

The replacement value of real capital stock( $ p_{t}^{I}K_{t} $ ) is computed simply by multiplying the series constructed from eq.(1) by the investment goods deflator.

### 3. Construction of Land Stock

We also follow the perpetual inventory method in calculating the series of land stock. $ ^{3)} $ We choose the fourth quarter of 1970 as our benchmark period. The benchmark stock of land at the market price is computed by multiplying the book value of land stock in QRFS by the market-book ratio. The numerator in the market-book ratio is the market value of the private non-financial incorporated enterprises whose capital stock is larger than ten million yen, which is computed on the basis of the information available in the Annual Report on National Accounts and Annual Report of Financial Statements of Incorporated Business. The denominator is the book value of land stock in QRFS for all industries. This ratio is 5.27, and it is adopted as a conversion factor common to every
industry in order to obtain the market-priced benchmark. $ ^{4)} $

The net investment in land at the market price $ (NILAND_{t}) $ is computed as the increment of land, which is evaluated at current price, in the table of changes in fixed assets in QRFS minus the decrement of land at current price. The decrement of land in the table of changes in fixed assets are originally book-valued, so that it is converted into market-value under the LIFO-type assumption that the land sold in period t was purchased in the most recent period, period t-1. $ ^{5)} $ The net increment of land in period t is expressed as:

$$ NILAND_{t}=ILAND_{t}-DLAND_{t}\frac{p_{t}^{L}}{p_{t-1}^{L}} $$

where $ ILAND_{t} $ : increment of land in period t

DLAND_{t} : decrement of land in period t, which is priced when it was

purchased

$ p_{t}^{L} $ : land price in period t

As for the land price $ (p_{t}^{L}) $ , it is constructed by taking account of the distribution of land holdings over different prefectures where different prices of land are prevailing. Specifically the $ p_{t}^{L} $ variable is computed from two price series of land: land price index of all Japan for all use (except six largest cities) and that of six largest cities for commercial use, which is shown in Land Price Index in Cities published by Japan Real Estate Institute. The original series is semi-annual, so we interpolate them linearly to obtain the quarterly series. The land holdings by the corporations are distributed across all the prefectures each of which have different levels of land prices. With the view to incorporating this distributional characteristics in our calculation, we make the following assumption: the land held by the corporations in Saitama, Chiba, Tokyo, Kanagawa, Aichi, Kyoto, Osaka, Hyogo, and Fukuoka prefectures are valued by the land price of six largest cities, while the rest is evaluated by that of all Japan except six largest cities. Then the rate of change in land price of the corporation can be computed by a weighted average of the rate of change in land prices of two types with the share of the market-priced value of land in each region as a weight. In other words the land price index thus constructed is a Divisia index, represented by $ p_{t}^{L} $ variable, which is given in Table A-3. The land stock at the market price is constructed by the following formula.

$$ L A N D Y_{t}=L A N D Y_{t-1}\frac{p_{t}^{L}}{p_{t-1}^{L}}+N I L A N D_{t} $$

where $ LANDY_{t}(=p_{t}^{L}L_{t}) $ : stock of land at the market price
Figure A-1 to A-14 shows the paths of the ratio of the market value of land stock to the book value for fourteen industries and Table A-4 summarizes the averages of the market-book ratios over the whole sample period (the third quarter of 1968 to the first quarter of 1991) and the two subsample periods (the third quarter of 1968 to the third quarter of 1986; the fourth quarter of 1986 to the first quarter of 1991). In the whole sample period the averages of the market-book ratio range from 3.8776 (construction) to 5.5874 (chemicals). In the first-half of the subsamples there is not so much difference among the averages across the industries. The minimum ratio is 3.7373 for construction and the maximum is 4.8682 for chemicals. The averages of the market-book ratios in the latter-half of the subsamples are uniformly larger than those in the first-half across industries. Moreover they distribute more diversely, ranging from 4.0964 (real estate) to 9.6444 (steel).

For the industries of construction and real estate the average market-book ratios are slightly larger in the latter-half subsample than in the first-half subsample. This is due to active trading of land asset at market prices, which prevents the book value from deviating from the market value. On the other hand in the heavy industries such as chemicals and steel there had been already existed a large part of the land stock when the land price started to soar and the trading of land were relatively inactive thereafter. The upshot is a large divergence of the book value of land from the market value. $ ^{6)} $

Table A-5 compares our estimates of the market-book ratios of land assets with those in other studies. Our series lies in between the Case I and the Case IV in Honmma et al.(1984) and are lower than those in Asako et al.(1989) or in Konya and Wakasugi(1987).

Finally the real net purchase of land $ (IL_{t}) $ is calculated as

$$ IL_{t}=\frac{ILAND_{t}}{p_{t}^{L}}-\frac{DLAND_{t}}{p_{t-1}^{L}} $$

### 4. Market Value of Equity

The market value of equity $ (V_{t}) $ is the multiple of shareholder's equity by the price book-value ratio, which is available in Daiwa Investment Data published by Daiwa Institute of Research.

### 5. Discount Rate

The nominal discount rate of the corporations $(r_{t})$ is computed as follows:

(interest and discount paid + bond interest expenses)/(short-term and long-term loans payable + bonds payable + notes receivable discounted)

### 6. Net Increase of Borrowings