Hi-Stat

Discussion Paper Series
No.21

The effects of land price on the quality of capitaland multi-factor productivity

Hiromi Nosaka
March 2004

Hitotsubashi University Research Unit
for Statistical Analysis in Social Sciences
A 21st-Century COE Program

Institute of Economic Research
Hitotsubashi University
Kunitachi, Tokyo, 186-8603 Japan
http://hi-stat.ier.hit-u.ac.jp/

The effects of land price on the quality of capital and multi-factor
productivity
Hiromi Nosaka∗

Abstract
I study a model of replacement problem with liquidity constraint, where the land is used as acollateral as well as a factor of production. The collateral value of the land restricts the available
funds for the firm, which works as a capacity constraint of firms. Due to this constraint, thereplacement can be enhanced when the positive technology and/or demand shocks arrives. This
stands in contrast with some types of replacement models, where the positive demand shocks
delay the replacement. The rise of the land price enlarges the available funds for the firms which
requires the efficient use of the land, when the firms are under liquidity constraint. It also raises
the user’s cost of land, hence, the replacement of machine is enhanced. The effects of the land
price on the the multi-factor productivity and replacement are examined by the data of Japan
during 1970 and 1998. The estimated results show that the rise of the land price enhance thereplacement and improves the multi-factor productivity in the non-service sectors, but I do not
observe the direct relationship between the replacement and the land price in the service sectors.
These results are consistent with the view that the land price affects the replacement decisions
and productivity in non-service sectors. In service sectors, however, the other factors such as
the quality of investments could be important.

∗

Department of Economics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HongKong. Tel.: +852-2358-7604; fax: +852-2358-2084. Email address: hnosaka@ust.hk

1

Introduction

The replacement decision of the machine is an important factor to determine the quality of capitaland productivity of firms. In the economy where the technology is embodied in capital, the frequent
replacement of capital implies the increase of productivity. In this paper, I propose a model of
replacement and show how the land price affects the replacement decision of the firms with andwithout liquidity constraint. I investigate the predictions of the model by using the Japanese data
after 1970, when the land price has significantly fluctuated.
In Japan, the land price rose rapidly in the late 80s and has dropped after the 90s. As Table 1
shows, the change is significant. In the same periods, the multi-factor productivity has changed its
direction in 1990. But the direction of changes depend on industries. Table 2 shows the multi-factor
productivity of Japan.1 As this table shows, the multi-factor productivity of non-service sectors
has declined after 1990s, but the direction is opposite for the service industries. Many factors affectthe multi-factor productivity, because the calculated multi-factor productivity, basically based on
the Solow’s residual, is affected by returns to scale and labor hoarding. But in this paper, I focus on
the replacement decisions of the firms, and investigate how they affect the quality of capital and the
productivity, when the land price fluctuate. For this purpose, I first develop a model of replacement
decisions of the firms with and without liquidity constraint. In the model, the land is used as afactor of production but also works as a collateral to mitigate the liquidity constraint. Since theliquidity constraint restrict the available funds of firms, the firms consider the most efficient use ofavailable funds.
Such restrictions work as capacity constraints for the firms, and have important implications
on the replacement decisions of firms. In order to understand it, think about the rise of demand in
one industry. In response to the rise of demand, the firms would like to expand their capacity but
the financial constraints prevent it. In some cases, the firms tend to destroy less efficient machineand purchase new machine in order to save the available funds.
This implication is related to the literature of the machine replacement models. In the vintage
capital model of Solow (1956), the labor input works as a fixed costs, and the introduction of
new technology raise the labor demand and the wages, which make the use of old, less efficient
capital unprofitable (Boucekkine, Germain, and Licandro (1997) for more recent theoretical work).
The similar mechanisms work in different models such as machine replacement problems in Rust
(1987), Jovanovic and Rob (1997), and Cooper, Haltiwanger, and Power (1999). In their models,
1

Construction of data is explained later.

1the embodied technological advance enhances the replacement, because the embodied technologicaladvance makes the existing machine relatively more obsolescent and less attractive.2 There, investments are directly related to the replacement, because there is a capacity constraint of capital, inthe sense that one firm owns only one machine to operate. That capacity constraint is critical forthe replacement decisions, because the firm would operate two machines (old and new machines)
without such constraints. The assumption of such capacity constraints can be supported by several
reasons such as the limited ability of the manager to operate multiple machines. My model add
one possible constraint in the sense that the capital capacity is constrained by the available funds.
One advantage of my model is that I can make the capacity constraint endogenous, and analyze
how the land price explicitly affect the replacement by changing the capacity itself.
The optimal replacement decisions are also studied in the creative destruction models (Caballero and Engel (1999) and Aghion and Howitt (1994)). In those models, there are no capacity
constraints, and the replacement is mainly determined by its profitability that is the productivity
minus the fixed costs. For example, when the labor costs are fixed costs, the models predicts more
replacement when fixed costs (wage rates) increase or the productivity declines, because the oldmachine is less profitable. Especially, the positive demand shock delays the replacement. This relationship is apparently correct for job destruction model (Caballero and Hammour (1994)), wheremore job destruction (i.e., more replacement) is observed in recession. For the case of machine
replacement, Cooper and Haltiwanger (1993) observed that the machine replacement is more likely
to occur during downturns where the resource cost of replacement is lower. Campbell (1998) also
finds the negative relation between the replacement and the output by using the exit rates as aproxy for the replacement. Clearly, this prediction is different from the vintage capital model above,
where the investment is equal to the replacement, thus, the replacement is more frequent in theeconomic boom.
In this paper, I propose a model to mitigate these opposing views by introducing the liquidity
constraint. Due to the liquidity constraint, the available funds restrict the capital that the managers
can purchase. In this respect, the model is closer to the replacement model, since, there, the numberof capital is restricted. As a result, the positive demand shocks enhance the replacement. On theother hand, I can show that the firms may delay the replacement in the economic booms, when theliquidity constraint is not so restrictive. That is more in line with the creative destruction model.
My model is closely related to the literature of investment under liquidity constraint (Kiyotaki
2

In Greenwood, Hercowitz, and Krusell (1997), the firms destruct less efficient, old machine by using the oldmachine more intensively when the positive embodied technical shocks arrive.

2

and Moore (1997), Bernanke, Gertler, and Gilchrist (1999), Carlstrom and Fuerst (1997)), although
they do not take the replacement decisions into account. As I show it later, the decisions ofinvestments and replacements are different, although they are related.
The model predicts the effects of the land price on the replacement decisions and multi-factor
productivity. There are two effects for the firms with liquidity constraint when the land pricerises. The first effect is caused by the rise of capital costs. This raises the fixed costs of holding
capital and increases the replacement, which improve the productivity of remaining capital (thus,
raises the multi-factor productivity). The second effect is caused by the weakening of the liquidity
constraint. When the land price rises, the firms can purchase more new capital, thus, increases
the replacement. As a result, I show that the rise of the land price enhances the replacement andimprove the productivity for the firms under the liquidity constraint.
On the other hand, when the firm face no liquidity constraint, only the first effect works without
the second effect. Thus, the replacement is also enhanced, but the degree is smaller. In addition,
when the land price rises very rapidly, then holding the land itself produces the net capital gains,
and the land is considered as an asset rather than a cost of production. In such cases, the situationis reversed, and the rise of land price reduces the total cost of capital (including the land andmachine) and allow the firms to use less efficient capital. This is possible explanation why the land
price reduces the multi-factor productivity when the land price rises rapidly. And this effect isstronger for the firms without liquidity constraint.
In order to understand the validity of the model, I use the Japanese data for estimation. It is
important to use the Japanese data since the land prices have fluctuated significantly over the pastfew decades. I focus on the two types of Japanese data in estimation: the multi-factor productivity
and the replaced value of capital. In the first estimation, I use the multi-factor productivity whose
trends are summarized in Table 2. The data is taken from Fukao, Inui, Kawai, and Miyagawa(2003), who calculate the multi-factor productivity from the Japan Industrial Database (the JIP
Database). They take into accounts the effect of intermediate goods, capital utilization rates, andquality of labor. Since they do not consider the effect of production of scale, this number is affectedby the business cycles.3
In the second estimation, I use the replaced capital as an dependent variable. The data of
the replaced capital is estimated from the Quarterly Review of Non-financial enterprizes in Japan.
This data allow us directly to examine the effect of the land prices on the replacement, although
3

Especially, when there is an increasing return to scale, the calculated multi-factor productivity has an upwardbias (downward bias, respectively) in case of economic booms (recessions).

3

the data may include noises, because it is the book value of replaced capital. By using the single
equation GMM, I have the results that the rise of land prices enhance the replacement and raises
the productivity in the non-service sectors. But the effects of the land price on the replacement is
not clear for service sectors. Thus, the overall result is consistent with the model in the non-service
sector, although it is not clear in the service sectors. The multi-factor productivity in the service
sectors may be more affected by the reasons other than the replacements.
The rest of this paper is organized as follows: Section 2 presents a basic model. In section 3,
I present the data of Japan and conduct the estimation. In section 4, alternative explanation isconsidered and the related result in the US is also discussed.

2

Model

I follow the strand of the literature in which the firms invest under the liquidity constraint (Kiyotaki
and Moore (1997), Bernanke, Gertler, and Gilchrist (1999)). I consider the discrete time model,
and all aggregate variables such as the aggregate shock of At , the product price (Pt ), the priceof land Qt , and the capital price (PtI ) are realized at the beginning of the period. There is ameasure of firms (thus infinitely many) who are risk-neutral and have ability to produce the goods
competitively. Then, the firms, who have the existing capital stock of Kt−1 and the land of Tt−1 ,choose the amount of investments (It ) and land (Tt ). They also borrow the necessary funds (Bt )from the banks. After this transaction is completed, the idiosyncratic shock of jt is realized foreach machine, and the firms make the optimal destruction decisions of machine. The production
is conducted at the end of period, and the repayment of borrowing and the second hand markets
for capital and land opens at the beginning of the next period. Thus, due to this assumption, thecapital gain or loss may happen by holding the asset and capital, although there is no uncertainty
during the production process.
The efficiency of each machine is denoted by jt , which follows an iid process (across machines
and time) whose mean is one and cdf is defined by F (). In this economy, each firm uses thecontinuum of capital; therefore, each firm faces no uncertainty concerning jt . I will show that the
optimal policy is to set the cut-off level of t , below which the machine is destroyed.4 Denote thecapital level before and after the machine destruction by K b and K, respectively. They have the4

I assume that there are no second hand markets during the period. Thus, the firms need to destroy the machinein order to prevent the fixed costs.

4

following relationship:
Ktb = It + Kt−1 ,Kt = F̄ (t )Ktb .

(1)

In the above expression, It is the amount of investment, and F̄ is the fraction of  above the criticalvalue (), i.e., F̄ () = 1 − F ().
The aggregate production function depends on the total number of capital measured in termsof the efficiency unit:
Kte =

Z
j

jt d j = Ktb

Z +∞
t

0 d F (0 ) = Φ(t ) Kt .

(2)

where the integral in the first equation is taken over the existing capital stock in the firm, and Φ
is an average quality of capital after destruction, and defined by Φ(t ) =R  d F /F̄ .

The total flow profit from operating the continuum of capital after machine destruction isderived as follows:
Pt At Kte − cf Kt = Pt At Φ(t ) Kt − cf Kt .

(3)

Here, At is the aggregate productivity in the economy.5 Pt is a product price, and cf is a fixed costin holding one unit of capital. I assume that the constant returns to scale technology.6
Following the literature of investment models under liquidity constraint, I assume the risk
neutral firms with the discount factor of β, who delay their own consumption in the future. Theylose the ability of producing the goods with probability z. After that event, they are pure consumers.
In order to assure these situations in the steady state, I assume the following:I
Assumption 1 Pt At − cf − τ (Rt Qt − Qt+1 ) + Pt+1
> Rt PtI ,βRt < 1.

The first assumption is to ensure that the investment project is profitable so that all of the available
resources are used for investments as long as the managers have ability to produce the goods. By
the second condition, the firms consume immediately after they lose the production ability. The
amount of investments are restricted by the following flow of funds equation:
PtI (It + Kt−1 ) + Qt Tt ≤ Wt + Bt ,(4)

where Bt stands for the amount of the loan, and Qt is the land price. Wt is a net wealth of thefirm after repaying the debt:
Wt = Pt−1 At−1 Φ(t−1 )Kt−1 − cf Kt−1 + Qt Tt−1 + PtI Kt−1 − Rt−1 Bt−1 ,5(5)When the labor input is included in production, this term is considered as the reduced form after optimally

adjusting the labor input. I ignore the fluctuation of wage rates.
6
I study the effects of the increasing returns to scale in the Appendix.

5

where Rt is one plus the interest rate of risk-free assets.
Next, consider how the land is used in the production process. Here, in order to simplify the
analysis, I assume the Leontief production function, in which each machine requires τ amount ofland for production.7 Specifically,
Tt = τ K t ,(6)

where τ is a constant. Now I consider the two types of firms: the first type of firms is constrainedby liquidity, and the other type is not.
Firms without liquidity constraints
When the firms are not constrained by liquidity, they invest as much as possible to the limit ofavailable investment project, K̄t , because the rate of returns is higher than the return on the otherassets (Rt ).
Ktb ≤ K̄t .

(7)Here, note that the limited number of projects is the amount of capital before destruction. Thus,
the firms are constrained by the number of profitable projects rather than the loanable funds. By
denoting the value function of firms by V , we can define the maximization problem for the firms
as follows:
V (St , Wt ) =max Et β(1 − z)V (St+1 , Wt+1 ) + βzWt+1 ,t ,Kt ,Bt

s.t.

 PI
t

F̄ ()+ τ Qt Kt ≤ Wt + Bt ,I
Wt+1 = Pt At Φ(t ) − cf + τ Qt+1 + Pt+1
Kt − Rt Bt ,Kt ≤ K̄t F̄ (t ),(8)

where St is a vector of aggregate variables, St = (Pt , PtI , Qt ). The first constraint is the flow offunds equation (4), where I use the definition of investments, (1). The second constraint is thedefinition of net wealth, (5). The last constraint is from (7). The Bellman’s equation reflect theassumption that the firms lose their abilities to produce the goods with probability z, in whichcase they consume all of their net wealth. Otherwise, they continue to operate, then their value is
V (St+1 , Wt+1 ).
7

In the Appendix, I consider the case in which the fixed amount of land (Tt ) is used for production. This is anotherextreme case. In this alternative assumption, the land price is not directly included in the first order condition. Thus,
without scale of returns, the land price does not affect the replacement decisions.

6It is clear that the value function is an increasing function of W without uncertainty. In thatcase, the optimal cutoff point (t ) is derived by maximizing Wt+1 . By substituting the constraints,
Wt+1 isI
Wt+1 = Pt At Φ(t ) − cf + τ Qt+1 + Pt+1
− τ Rt Qt F̄ (t )K̄t − Rt PtI K̄t + Rt Wt .

I assume the interior value of t by the following assumption.
Assumption 2 cf + τ (Rt Qt − Qt+1 ) − PtI > 0.
Then, the first order condition is,I
Pt At t − cf − τ (Rt Qt − Qt+1 ) + Pt+1
= 0.

(9)

The first term is the marginal revenue in operating the marginal quality of machine. The otherterms are the fixed costs of holding machine. The last term is the return when they sell the machinein the next period. The cost of machine, PtI , is not included in deciding the optimal destruction,
since the cost is already sunk. The intuition is standard and same as Caballero and Hammour(1994): The firms delay the destruction of machine when the demand (the product price) increases
or the fixed costs decline.
The effects of the land price depend on cases. When Rt Qt > Qt+1 , it is costly to hold theland. In that case, the rise of land price raises the fixed cost of holding the machine, because someamount of land is required for production. But, when the land price is expected to rise significantly(Rt Qt < Qt+1 ), the land is considered as an asset to produce the net profit. Then, the rise of land
price increase the rate of return on holding the machine. Then, the less efficient machine (i.e.,
machine with low ) can survive, since its low return is compensated by the higher return on theland.8Proposition 1 Without uncertainty and liquidity constraint, the unexpected rise of Pt and/or At
lower . The proportionate increase of Qt and QIt+1 raises  when Rt Qt > Qt+1 , but lowers  whenRt Qt < Qt+1 .

Firms under liquidity constraints
8

When the returns on the land is greater than Rt , the demand for the safe asset is zero without uncertainty. In

order to justify the situation, I need to assume that only managers can hold the land.

7

Next, consider the optimal decision of firms under liquidity constraint. I assume that thelending relationship ends in one period and lenders have no ways to enforce the loan contracts tofirms except for the use of the land as collateral. Thus, the minimum amount that the banks canensure is the expected market value of collateralized land. Whenever the banks lend more thanthat amount, the managers use the entire resource for their private use. Suppose that the fraction,γ, of land holding can be used as a collateral. Then, the limit of loanable funds is,
Rt Bt ≤ γEt Qt+1 Tt .

(10)

The maximization problem in this case is similar to (8), but now the amount of loan (B) is restrictedby (10). By assuming that the firms borrow all available funds, we have the maximization problemof the firms under the liquidity constraint:

V (St , Wt ) = max Et β(1 − z)V (St+1 , Wt+1 ) + βzWt+1 ,t ,Kts.t.

PtIγKt + τ Qt Kt ≤ Wt + τ Q∗t+1 Kt ,RtF̄ ()

I
Wt+1 = Pt At Φ(t ) − cf + τ Qt+1 + Pt+1
− τ γQ∗t+1 Kt ,(11)

where Q∗t+1 is an expected value of Qt+1 . Again, without any uncertainty, the value function is anincreasing function of Wt+1 . Then, the maximization is:maxt ,KtI
Pt At Φ(t ) − cf + τ Qt+1 + Pt+1
− τ γQt+1 Kt ,

s.t.

 PI
t

F̄ ()+ τ Qt − τγ
Qt+1 Kt ≤ Wt .
Rt

Under the current assumption, the firms invest as much as possible, and the constraint is binding.
Then, by eliminating the capital (Kt ) in the expression, the maximization is to maximize the rateof return on the wealth.
max

I − τ γQ
Pt At Φ(t ) − cf + τ Qt+1 + Pt+1
t+1
PtI
+ τ Qt − τ Rγt Qt+1
F̄ ()

t

Wt .

(12)

Under the assumption 2, the solution is interior. The first order condition is,I − τ γQ
Pt At Φ(t ) − cf + τ Qt+1 + Pt+1
t+1
PtI
+ τ Qt − τ Rγt Qt+1
F̄ ()=Pt At (Φ(t ) − t )F̄ (t )
,
PtI

(13)

In order to understand the proposition, I modify the equation (13): P A (Φ( ) −  )F̄ ( )

γ
t t
t
t  t
Qt+1
− Rt ,
I
Rt
Pt



γ
Wt+1
= τ Qt −Qt+1− Rt .
(14)
Rt
Wt


I
Pt At t − cf − τ Rt Qt − Qt+1 + Pt+1
= τ Qt −8

The second equation comes from the first order condition of (13) and the definition of wealth, (12).
Thus, the second bracket on the right-hand side is the excess return on the investments over themarket interest rate (i.e., Wt+1 /Wt − Rt ), which is always positive under the current assumption.
Without liquidity constraint, the right-hand side is always zero, which is clear from the optimal
condition of the firms without the liquidity constraint, (9). The firms without liquidity constraint
simply makes the marginal product equal to the marginal costs. The firms under the liquidity
constraint, however, have additional constraint. Due to the liquidity constraint, the firms compare
two alternative choices: keep to use the machine of marginal quality or replace it with the newmachine. By keeping the marginal machine, the firms get the benefits expressed in the left-handside. On the other hand, when they replace it with the new one, then τ units of land are available
for replacement, but they do not have liquidity to purchase the new machine. In order to buy
the new machine, they need to sell some amount of land, but it reduces the borrowing limit offirms (say, by ∆Bt ). By selling τ units of land, the total amount of money free for purchasing themachine and land is τ (Qt − ∆Bt ) = τ (Qt − (γ/Rt )Qt+1 ). For each unit of money, the return is
Wt+1 /Wt . As the right-hand side of the first equation shows, this benefit is calculated based on
the expected returns on the new machine, since the quality of new machine is uncertain.
From this equation, several properties are clear. First, the critical value of t is always higher
for the firms with liquidity constraint than those without it, by comparing this expression with (9).
This is because the financial constraint restricts the use of available funds, inducing more efficientuse of land. Second, under the liquidity constraint, the rise of land prices raises the replacement
when the growth rate of the land price is low. In addition, it can be shown that the effect on
the marginal quality of machine, t , is stronger for the firms with the liquidity constraint than for
those without it. Equation (14) shows this point intuitively. As the left-hand side of the equationshows, the land price raises the quality of marginal machine directly since the rental price of landrises. This effect is same for both the firms with and without liquidity constraint. But the firms
under the liquidity constraint have an additional shock that is shown on the right-hand side of (14).
As the financial constraint is relaxed, the firms can spend more on purchasing the new machine
which is potentially more productive. That enhances more replacement. Although the effect on
the right-hand side is more complicated, I can show that this intuition is basically true when theland price does not decline so heavily.
Third, the positive demand shocks (the rise of Pt ) and technology shocks (the rise of At )increase the replacement of machine in some cases. As shown in the equation (14), the rise of Pt
and At raises the returns of both the marginal machine (LHS) and the replaced machine (RHS).
9

When this industry is land intensive (high τ ) and/or this firms are more heavily constrained byliquidity (low γ), the available funds by destroying one machine is larger. As a result, the total
return of replacement is higher than that of keeping the marginal machine. As a result, the firms
are more selective in choosing the quality of machine. This property is similar to the standard
machine replacement problem where the firm can operate only one machine. In those models, thereplacement happens because the number of machine is exogenously given. In my model, however,the number of available machine is determined by the available funds which are endogenously
determined. Without liquidity constraint, the model would become a standard creative destruction
model, where the destruction of machine is solely determined by the productivity and the fixed
costs.
I can summarize the result in the following proposition:
Proposition 2 Without aggregate uncertainty but under the liquidity constraint, the (unexpected)
I
proportionate increase of PtI and Pt+1
reduces t . The (unexpected) rise of Pt and/or At raise t ,I
when τ (1 − γ)Qt+1 + Pt+1
> cf , but they reduce t , otherwise. The proportionate increase of Qt

and Qt+1 raises t , when Qt+1 /Qt < g1∗ for some g1∗ > Rt . Otherwise, it reduces t . The change oft is greater for the firms under the liquidity constraint than those without it, when Qt+1 /Qt > g2∗
for some g2∗ < Rt .
Proof.

3

See the Appendix.

Data and Estimation

I conduct two types of estimation. In the first estimation, I investigate the effect of land prices on
the multi-factor productivity. Next, I directly estimate the effect on the replaced value of plantand equipment investments.
For the first estimation, the data of multi-factor productivity is taken from Fukao, Inui, Kawai,
and Miyagawa (2003). They are yearly data, whose sample periods is from 1970 to 1998. In calculating the multi-factor productivity, the authors adjusted the labor quality, the capacity utilization
rates in calculation, but not the returns to scale. I eliminate this bias by including the output
as an explanatory variables in estimation. Specifically, I directly estimate the production function
with the elasticity of scale of ν. The specific form of function is same as in the basic model which
includes t , but I allow for the returns to scale and the variable capacity utilization rates.


Yt = At Ψ(t )ut Kt
10

ν

,

(15)

where ut is the capacity utilization of the firm. By taking the log and difference, and assumingthat ln At follows the random walk with drift (ln At = ln At−1 + ηt + a0 ),9 we have,d (yt − ut − kt ) = a0 + ν d ln Ψ(t ) + (ν − 1)(d ut + d kt ) + ηt ,(16)

where xt = ln Xt . On the other hand, the multi-factor productivity calculated in JIP database is,dM F Pt = d yt − d ut − d kt .

(17)

By eliminating the d ut + d k from the above expression, we have,ν d M F Pt = a00 + ν d ln Ψ t (At , Qt /Pt , PtI /Pt ) + (ν − 1)d yt + ηt .

(18)

Here, the marginal quality of capital, , is replaced by the policy function, where the state variables
are (At , Qt /Pt , PtI /Pt ).10 Equation (9) implicitly defines this function when the firms are not underliquidity constraint, while this function is (14) when the firms are constrained by the liquidity. Bytaking the log and linealization, we have,d M F Pt = a0 + a2 d (qt − pt ) + a3 d (pIt − pt ) + a4 d yt + ηt .

(19)

The effects on the MFP are same as those on : The rise of pI decreases  and M F P . The rise of
Qt increases  and M F P for the firms, especially for the firms under the liquidity constraint. But it
can reduces the  and M F P in some cases. The effects of Y is positive when there is an increasing
return to scale. Since the current explanatory variables are affected by the current productivity
shock, I use the GMM without serial correlation. I use the lagged variables (both dependent andexplanatory variables) as the instruments of estimation. Since I take the first difference and needto have enough lags, the estimated sample period is 1977 to 1998 (22 years).
Table 3 shows the estimated results for service and non-service industries. The sign conditionsof variables are correct for the output and the capital price. For the land prices, the effects areopposite for service and non-service industries. The land price raises the efficiency of capital forthe non-service, while it reduces MFP for service sectors. One explanation in relation to the modelis that the firms are more liquidity constrained in the non-service industries, which justifies the
positive effect on productivity. On the other hand, the rise of the land prices is so high for the9

The aggregate technology growth may be correlated with the past explanatory variables such as the past factor

inputs (Hall (1988)). I, however, use my current formulation partly because I assume that  can explain these
movements of the Solow residuals, and partly because of the difficulty of obtaining effective instruments.
10
I omitted the interest rates for estimation.

11

service industry that the firms consider the land as assets than the cost of capital. This reduces
the productivity of capital.
I also estimate the effect of the land prices on the replaced value of capital directly. The replaced value of the plant and equipment is constructed based on the Financial Statements Statistics
of Corporations prepared by the Ministry of Finance of Japan. It reports the book value of discarded and sold equipments quarterly.11 Since the reported data are book values after deducting
depreciation, these values are subject to the depreciation methods and price changes over time.
Thus, I constructed the values of replaced capital on the constant price basis. I use the benchmark
year method in calculating the present value of capital stock, investments, and replaced capital(although the benchmark year method is not necessary in calculating the replaced capital, it isimportant to construct the capital stock to see if the produced capital and the replaced capitalare consistent. Refer to the Appendix for the detail). This approach, using the replaced values ofcapital, looks better than the estimation of multi-factor productivity, since we can study the effectof the land price directly, but there is a problem of measurement error as the replaced series are
originally book values.
Figure 1 shows the trends of these series. The level of replaced capital is fairly stable if I
compared them with the investments.12 But, the yearly fluctuation is large and the magnitudes
are almost same as those of investments. It is also important to find that the trends of thegrowth rates of the replaced equipments are much different from those of investments. Figure
2 reports the replacement ratio by industry and firm size. It is noteworthy that the small size
(non-manufacturing) firms increased the replacement during the late 80s, when the land price rose
rapidly.
In order to estimate the effect of the land price, I estimate the relationship between t and Rt
derived from the model. In the model, Ktb = Kt−1 , thus, I
Rt(1 − F̄ (t ))(It + Ktb ))
t
I
=
=
F

(A
,
Q
/p
,
P
/p
,
R
/p
)
(
+ 1).
t
t
t
t
t
t
t
t
Kt−1
Kt−1
Ktb

(20)

By taking the log and linealizing the expression, we have,d rt = b0 + b1 d (qt − pt ) + b2 d (pIt − pt ) + b3 d it + b4 d yt + ηt ,(21)

where rt is a replacement rate (rt = ln Rt /Kt−1 ), and it is an investment rate (it = ln It /Kt−1 ).
Here, I include the effect of output to see the effect of the demand and technology shocks. Since
11

Although it is quarterly, I use the annualized data to avoid the possible serial correlation and to keep theconsistency with the JIP data. In addition, the quarterly land price is not available.
12
It may be due to the book value nature of this series.

12the F is an increasing function, the critical value (t ) and the replacement (rt ) move in the same
direction: the rise of pI decreases  and rt . The rise of Qt increases  and M F P for the firms when
the land prices are not expected to rise so high. Otherwise, the effect may be negative. The effectof investments, it , is positive. The effects of output, yt , is negative, when the positive shocks to the
output delays the replacement as indicated by the creative destruction model. It is positive, when
the technological progress is embodied and the machine replacement model is more relevant. In
order to avoid the simultaneous bias, I use the single equation GMM without serial correlation. The
instruments are the first-differenced lagged variables (both dependent and explanatory variables).
The results are shown in Table 4. The table shows that the effect of capital price is negative
as expected in non-service sectors, but not clear in the service sectors. Investments raises thereplacements in the service industry which is consistent with the model, but not true in non-service
sectors. The effects of output are negative for both sectors, which indicate the positive productivity
shocks delay the replacement, which is more in line with the creative destruction model, and it ismore consistent with the case of the firms without liquidity constraint.
The effects of land price is positive for the non-service sectors. This result is consistent withthe first estimation in Table 3: The rise of land prices enhance the replacement and raises the
productivity. But the effect of the land price is not clear for service sectors. Thus, the effect on
the replacement may not be so important to characterize the overall decline of productivity growthduring the estimated periods when the land prices rose sharply. But overall, the coefficients are not
so robust for the specification of instruments and the choice of explanatory variables. In addition,
the sample size is small. Thus, we need to interpret the estimation results with caution.

4

Discussion

Other interpretations
There is a vast literature for the costly external finance (Jensen and Meckling (1976) and
Myers and Majluf (1984)). In those models, the increase of asset prices leads to the increaseof free cash flow and expands the investments. The managers may choose the inefficient way
of investments when the cash flow is abundant when the firms prefer the growth over the profit(Jensen (1986)).13 . Clearly, this paper is not an alternative to those models but complements themby introducing different perspective, since my model focuses on the replacement problem rather
13

The possibility of the over-investment may not be applied to Japan according to Hoshi, Kashyap, and Scharfstein(1991).

13

than the investment problem. As we have seen in the introduction, the movements of replacements
and investments are different. In addition, my model shows that the rise of the land prices can
reduce the efficiency of capital even for the large firms where the agency problems are less important.
Reallocation effects
In the United States, the significant amount of total productivity of industry is affected by thereallocation effect, in the sense that the high productivity firms expands, while the low productivity
firms lose their shares. This reallocation effect also works when the asset price rises. When the
productivity of liquidity constrained firms are higher than others, then the expansion of those firms
improve the aggregate productivity of the economy (Kiyotaki and Moore (1997), for example).
Jermann and Quadrini (2002) show that the reallocation effect was important in the early 90s in
US, when the stock prices went up.
The situation, however, looks different in Japan. By using the plant level data in Japan, Fukao
and Kwon (2003) shows that the effect of reallocation is not so strong. Second, the share of thesmall firms in investments did not expand in the late 80s when the stock and land prices rose
rapidly. As is clear from the direct observation, there are no major discrepancies of investment
behaviors by firm sizes, especially during the late 80s. This situations stand in contrast with thosein US, and justify to focus on the behaviors of individual firms as in the current paper.

14

Appendix
Alternative assumtion on the use of land in production process.
In this Appendix, I consider the case in which the fixed amount of land is used for production.
T1 = 1.
In addition, the technology exhibits the returns to scale whose elasticity of scale is denoted by ν.
Let’s consider the two cases, the firms without and with liquidity constraint, in turn.
Firms without liquidity constraints
When the firms are not liquidity constrained, they face the condition of available projects, (7).
Then, the maximization problem is similar to (8):V (St , Wt ) =max Et β(1 − z)V (St+1 , Wt+1 ) + βzWt+1 ,t ,Kt ,Bt

s.t.

PtI
Kt + Qt ≤ Wt + Bt ,F̄ ()I
Kt − Rt Bt ,Wt+1 = pt At Φ(t )γ Ktγ − cf Kt + Qt+1 + Pt+1
Kt ≤ K̄t F̄ (t ),(A.1)

Without uncertainty, the optimal cutoff point () is derived by maximizing Wt+1 . By substituting
the constraints, Wt+1 isI
Wt+1 = pt At (Φ(t ))γ (F̄ (t )K̄t )γ − cf F̄ (t )K̄t + Qt+1 + Pt+1
F̄ (t )K̄t − Rt PtI K̄t − Rt Qt + Rt Wt .

The first order condition is,I
γpt At (Φ(t )F̄ (t ) K̄t )γ−1 t − pt cf + Pt+1
= 0.

(A.2)

It is clear that the land price is not included in this first order condition.
Proposition 3 Suppose that there is no uncertainty in the aggregate variables. Then, the unexpected rise of Pt and/or At lower . The land price does not affect this decision.
Firms under liquidity constraints
As in (10), the limit of loanable funds is,
Rt Bt ≤ γEt Qt+1 Tt ,
15

(A.3)

The maximization problem for the firms is similar to (11):

V (St , Wt ) = max Et β(1 − z)V (St+1 , Wt+1 ) + βzWt+1 ,t ,Kt

s.t.

PtI
γ ∗
Kt + Qt ≤ Wt +Q ,Rt t+1F̄ ()I
Wt+1 = pt At Φ(t )γ Ktγ − cf Kt + Qt+1 + Pt+1
Kt − γQ∗t+1 ,(A.4)

Again, without any uncertainty, the value function is an increasing function of Wt+1 . Then, themaximization is:maxt ,KtI
pt At Φ(t )γ Ktγ − cf Kt + Qt+1 + Pt+1
Kt − γQt+1 ,

s.t.

γ
PtI
Qt+1 .
Kt + Qt ≤ Wt +RtF̄ ()The first order conditions are:Iγ pt At Φ(t )γ Ktγ−1 − cf + Pt+1
= λt

γ pt At Φ(t )γ−1 Ktγ

PtI,F̄ (t )

∂Φ
PtI
= λt
f (t ) Kt .
∂ tF̄ (t )2Combining together and eliminating λt , we have the optimal condition for tIγ pt At Φ(t )γ−1 Ktγ−1 t − cf + Pt+1
= 0.

(A.5)

Substituting the budget constraint into the above expression to remove Kt , we can show the following expression:γ pt At (PtI )−1 (Wt − Qt +1 γ−1γ−1 
γI= cf − Pt+1.
Qt+1 )
Φ(t )F̄ (t )tγ−1
Rt

(A.6)

It is clear that the land price affect the replacement decision except for the case of constant returnto scale (γ = 1). Also, note that the definition of the wealth produces the following relation:
Wt − Qt +γγγ
Qt+1 = pt−1 At−1 Φ(t−1 )γ Kt−1
− cf Kt−1 + PtI Kt−1 − γEt−1 Qt +Qt+1 . (A.7)
Rt
Rt

Thus, the unexpected rise of the current and future land prices, Qt+j (j ≥ 0), increases the firstbracket of the left-hand side of (A.6). When γ > 1, we can show that t is lowered (low destruction)
when the second bracket of the left-hand side is an increasing function of t . This condition issatisfied when,(C1) :

Φ(t )F̄ (t ) > (γ − 1)2t f (t ),
16

(A.8)

where f is a density of F (). This condition is satisfied when γ is close to one,  is low, and f
is small. Intuitively, when the land price rises, the investments increase, and so does the output.
Then, when there is an increasing return to scale technology, the productivity of the firm improves.
As in the case of no liquidity constraint, the improvement of productivity reduces the replacement decision. But, the effect on the multi-factor productivity is uncertain, because the reductionof machine replacement lower the multi-factor productivity, but the increased output raises the
productivity due to the increasing returns to scale.
Proposition 4 When there is no uncertainty and the condition (C1) holds, the unexpected rise ofPt and/or At lowers . The increase of Qt+1 also reduces .

Proof of Proposition 2.
By rearranging the expression of (13), we have,






γ
I
PtI τ (1 − γ)Qt+1 + Pt+1
− cf = Pt At τ (Qt −Qt+1 ) Φ(t ) − t F̄ (t ) − t PtI
Rt

(A.9)

First, I consider the effect of the rise of P I . Suppose that both sides of the equation are positive. Then, theright-hand side declines due to the rise of P I , but the left-hand side increases. In order to keep the equality,
t must fall, since the left-hand side is a decreasing function of t . We can do the same thing when bothsides are negative.
Next, consider the effect of the increase of At (the effect of Pt is exactly same). Suppose that both sides
Iof the equation are positive, that is, τ (1 − γ)Qt+1 + Pt+1
> cf . Then, the increase of At raises the right-handside, so t must rise in order to restore the equality. When both sides are negative, then we can get the
opposite conclusion.
Now, I investigate the effect of the proportionate rise of Qt and Qt+1 . By taking the total differentiationof equation (14) with respect to t and Qt keeping gt = Qt+1 /Qt unchanged, we have the following partial
derivative:h ∂  iLiquidity Const.
t

∂ Qt



−
R
)
τ Rt − gt + (1 − Rγt gt )( WWt+1
t
t

 .
=
F̄ ( )γ
Pt At 1 + τ (1 − Rt gt )Qt P It(A.10)t

The denominator is always positive, but the numerator is positive if and only if,
Rt > gt − (1 −

γ
Wt+1
gt )(
− Rt ).
Rt
Wt(A.11)

When we define the critical value of g1∗ that holds the above expression with equality, then t rises if andonly if gt < g1∗ . Note also that g1∗ > Rt .
Lastly, I compare the effect of the land price on t for the firms with and without liquidity constraints.
The effect with the liquidity constraint is shown in (A.10). The effect without the liquidity constraint is17derived from the first order condition of (9).
h ∂  iW/O Liquidity Const.
t

∂ Qt

=

τ (Rt − gt ).
Pt At

(A.12)

By comparing the two expressions, I can show that the effect is greater for the firms with the liquidity
constraint if and only if,(Rt − gt )

τ Qt F̄ (t )Wt+1<− Rt .
Wt
PtI(A.13)

It is always true when gt ≥ Rt . When gt < Rt , we can define the critical value of g2∗ that satisfies the above
expression with equality. Then, the above inequality is true if and only if gt > g2∗ . Clearly, g2∗ < Rt . Q.E.D.

Construction of replaced value of capital.
In constructing the replaced value of capital, I use the quarterly financial statement published by the Ministryof Finance. Although the industry total is only available for this database, it reports the quarterly data for
fifty years (from the second quarter of 1954 to present). The reported data includes the value of capitalstock, investments, and replaced values of capital stocks by industry and firm size. Since all of them are bookvalues, I constructed these variables of the constant price basis. For the calculation, I use the benchmark
year method. First, the investment series are divided by the deflators of investment goods to produce thereal series (taken from SNA accounts). Then, the value of the initial capital stock (in the first year of 1955)
is set six times higher than the book value. This choice is arbitrary, but the choice does not affect the trend
fifteen years later (after 1970), which I use for estimation.
By initially using the yearly depreciation rate of 5%, I construct the tentative capital stock series byusing the following formula:
Kt = It + (1 − 0.05)Kt−1 .

(A.14)

eta = 0.05Kt−1 . Now I use the actual
As a result, we can get the tentative series of replaced capital as R
etb . Since this is the values when they were purchased, I inflated the data by using
replaced value of capital, R
the price of investment goods for the past four years.
etr =
R

etb
R
.
Pt + Pt−1 + ... + Pt−3

(A.15)The lag for inflation (four years) comes from the fact that the cross correlation between the investments
and replacements are largest when the lag of correlation is two years. Since this replaced value is net of
depreciation, I multiply the series by some constant to recover the gross value of capital. The constant is
eta to R
etr (but not exactly same). If I compare the two series, R
eta and R
etr ,determined by taking the ratio of R
then the long-term trends are quite similar, which confirms the validity of data construction.

18

References
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Bernanke, B. S., M. Gertler, and S. Gilchrist (1999): “The Financial Accelerator in a
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and M. Woodford, vol. 1C, pp. 1341–1393. Elsevier Science.
Boucekkine, R., M. Germain, and O. Licandro (1997): “Replacement Echoes in the Vintage
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Manufacturing: A Generalized (S, s) Approach,” Econometrica, 67(4), 783–826.
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Cycle: Lumps and Bumps,” The American Economic Review, 89(4), 921–946.
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Growth in Japan, 1970-98: An Empirical Analysis Based on the JIP Database,” in Productivity
and Growth: East Asia Seminar on Economics, ed. by T. Ito, and A. Rose, vol. 13. The Universityof Chicago Press.
Fukao, K., and H. U. Kwon (2003): “Nippon no Siesansei to Keizai Seicho (The Productivity
and the Economic Growth of Japan: Empirical Analysis based on Industry-Level and Firm-Level
Data,” a paper presented at the Semi-annual Conference of Japan Economic Association at Oita,
Japan.
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19

Kiyotaki, N., and J. Moore (1997): “Credit Cycles,” The Journal of Political Economy, 105(2),211–248.
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Journal of Economics, 70(1), 65–94.

20

Table 1. The trend of the land price of Japan
1980/1970 1990/1980 1998/1990
Total industries
6.38%
9.12%
-6.36%
Source: Ministry of Land, Infrastructure, and Transport of Japan.
Note: Growth rates of the commercial areas in the three major urban
regions in Japan. Unit: Annual rates.

Table 2. Multi-factor productivity of Japan
1980/1970 1990/1980 1998/1990
Total industries
0.10%
0.25%
0.08%
Non-service
0.66%
0.73%
0.05%
Service-0.52%-0.37%
0.12%
Source: JIP Database (2003), Table 6-14.
Note: The numbers are annual rates over the corresponding years.

Figure 1. Trends of investment – capital ratio and replacement capital ratio(1) Level
25%
20%
15%
10%

I/K(-1)
R/K(-1)

5%

19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00

0%

(2) Growth rate
30%
25%
20%
15%
10%
5%
0%

Ihat
Rhat

-5%
-10%
-15%

19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00

-20%

Source: Author’s calculation based on “Quarterly Financial Statement of Incorporation,” by Ministry of
Finance of Japan.
Note: Growth rate is the rate from the 4 quarters before.

Figure 2. Replacement - Capital ratio by industry and firm size
(1) Industry
9%8%

manufact
uring

7%
6%

nonmanufact
uring

5%
4%
3%
2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

(2) By firm size
12%

Big firms
Small firms

11%
10%
9%
8%
7%
6%
5%
4%
3%
2%
2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

Source: Author’s calculation based on “Quarterly Financial Statement of Incorporation,” by Ministry of
Finance of Japan.
Note: Growth rate is the rate from the 4 quarters before.
Big firms: Firms with capital of 1 billion yen or more in all industry.
Small firms: Firms with capital of less than 100 million yen in non-manufacturing industry.

Table 3. Estimation: Dependent variable is MFPCapital priceLand priceOutputConstant

Over-identification
Null = only
constant

Non-service
Industry
Theta
SE-0.0728
0.03360.02170.00730.17460.02320.00100.0010t-value-2.16902.96127.52910.9594Service Industry
Theta
SE-0.4406
0.1428-0.0550
0.01480.12730.0913-0.01310.0034t-value-3.0857-3.7151
1.3947-3.8880

J-stat15.95

Critical J
27.59

p-value
0.6181J-stat19.79

Critical J
27.59

p-value
0.3601254.667.81

0.0000

41.35

7.81

0.0000Notes: estimation period: 1977 to 1998.
Instruments: The first-differenced MFP, land prices, production, and capital prices.
5 Lags are taken for each instrument (i.e., variables of 2 years to 6 years
before).
Note: Estimation period is 1977 to 1998.
Instruments: the first-differenced MFP, land price, production, and capital prices.
5 lags are taken for each instrument (i.e., variables of 2 to 6 years before).

Table 4. Estimation: Dependent variable is replaced value of capitalNon-service
Industry
Theta
SE
Capital priceInvestment
Land priceOutputConstant-0.1661-0.1397
0.0372-0.07470.00070.00910.04750.00380.02280.0006t-value

Service Industry
Theta
SE

t-value

18.1745-2.9438
9.8968-3.2786
1.12090.00210.20230.0011-0.1271
0.00380.0372
3.3897
0.0918-2.9086
2.92650.0576
0.05970.01180.04370.0013J-statCriticalJ p-value
J-statCritical J p-value
Over-identification
18.1474 26.2962
0.40
16.8151 26.2962
0.49
Null = only
constant
379.0942
9.48770.00
33.0418
9.48770.00
Note: Estimation period is 1977 to 1998.
Instruments: the first-differenced R/K, land price, investment, production, and capital prices.
4 lags are taken for each instrument (i.e., variables of 2 to 5 years before).

Table 5 Effects on MFP by industry

11 Livestock products
12 Processed marine products
13 Rice polishing, flour milling
14 Other foods
15 Beverages
16 Tobacco
17 Silk
18 Spinning
19 Fabrics and other textile products
20 Apparel and accessories
21 Lumber and wood products
22 Furniture
23 Pulp, paper, paper products
24 Publishing and printing
25 Leather and leather products
26 Rubber products
27 Basic chemicals
28 Chemical fiber
29 Other chemicals
30 Petroleum products
31 Coal products
32 Stone, clay & glass products
33 Steel manufacturing
34 Other steel
35 Non-ferrous metals
36 Metal products
37 General machinery equipment
38 Electrical machinery
39 Equipment and supplies for household use
40 Other electrical machinery
41 Motor vehicles
42 Ships
43 Other transportation equipment
44 Precision machinery & equipment
45 Other manufacturing
46 Construction
47 Civil engineering
48 Electricity
49 Gas, heat supply
50 Waterworks
51 Water supply for industrial use
52 Waste disposal
53 Wholesale
54 Retail55 Finance
56 Insurance
57 Real estate
58 Housing
59 Railway
60 Road transportation
61 Water transportation
62 Air transportation
63 Other transportation, packing
64 Telegraph, telephone
65 Mail66 Education (private, non-profit)
67 Research
68 Medical, hygiene (private)
69 Other public services
70 Advertising
71 Rental of office equipment and goods
72 Other services for businesses
73 Entertainment
74 Broadcasting
75 Restaurants
76 Inns
77 Laundry, hair-cutting , public bath
78 Other services for individuals
79 Education (public)
80 Medical, hygiene (public)
81 Public administration
82 Medical, hygiene (non-profit)
83 Others(non-profit)

Coefficients
Capital Price
Land Price
OutputConstant
0.369-0.065
0.228
0.006
0.116
0.000
0.131
0.003
0.308
-0.137
0.111
0.001
0.860
-0.0640.772
0.026-0.068-0.091
1.197-0.0240.237-0.0750.417-0.009-2.1791.107
4.108-0.0270.119-0.0680.224
0.002-0.021
0.074
0.372
0.017
0.814
-0.0580.328
0.018-0.039-0.106
0.298
0.010
0.236
-0.098
0.235
0.000
0.312
-0.0200.184
0.009-0.022-0.001
0.194
0.000
0.280
-0.121
0.416-0.007
0.266-0.090
0.491
0.015
0.264
-0.036
0.404
0.003
0.128
-0.010
0.127
0.006
0.087
0.009
0.484
0.010
0.353
0.033
0.172
-0.001-0.1270.120
0.616-0.006
0.448
0.043
0.572
-0.0110.172-0.073
0.292
0.005
0.071
0.060
0.428
0.006
0.206
-0.074
0.510
0.003
0.109
-0.0400.197-0.0040.475-0.122
0.385
0.001
0.329
-0.0380.254-0.001
0.297-0.1240.355-0.004-0.078
0.191
0.125
0.002
-0.019
0.110
0.173
0.006
0.103
-0.0070.059-0.002
0.212
0.064
0.298
-0.0040.161-0.0500.233-0.0040.139-0.0560.274
0.002-0.0410.030
0.197-0.0020.288-0.0220.322-0.003
0.163
0.031
0.351
-0.007-0.0280.039
0.611-0.020
0.192
0.119
0.137
0.010
0.227
-0.0030.329-0.013
0.196
0.046
0.664
-0.0070.105-0.0100.697-0.0340.375-0.2920.733-0.0150.127-0.0900.697-0.018
0.175
0.013
0.540
-0.0120.140-0.2160.770-0.0070.317-0.2530.960-0.055-0.6420.099-0.837
0.021
0.211
0.165
0.370
-0.0080.359-0.0530.593-0.0150.177-0.191
0.810
0.007
0.090
0.005
0.248
-0.014
0.221
0.015
0.823
-0.031-0.1020.055
0.684-0.026-0.0160.067
0.891-0.0220.298-0.0670.431-0.0050.525-0.036
0.410
0.011
0.233
-0.0620.580-0.031
1.477
0.220
0.313
0.015
0.295
-0.0430.625-0.0050.820-0.4880.194-0.018-0.3880.022
0.348-0.0280.477-0.1990.563-0.0200.285-0.2140.731-0.0320.503-0.009
0.313
0.001
0.666
-0.1370.570-0.0040.666-0.002
0.413
0.008
0.076
-0.0100.604-0.016
0.057
0.064
0.599
-0.0170.408-0.1420.640-0.0310.163-0.0020.358-0.0120.190-0.0130.387-0.026

Table 5 Effects on MFP by industry (continued

11 Livestock products
12 Processed marine products
13 Rice polishing, flour milling
14 Other foods
15 Beverages
16 Tobacco
17 Silk
18 Spinning
19 Fabrics and other textile products
20 Apparel and accessories
21 Lumber and wood products
22 Furniture
23 Pulp, paper, paper products
24 Publishing and printing
25 Leather and leather products
26 Rubber products
27 Basic chemicals
28 Chemical fiber
29 Other chemicals
30 Petroleum products
31 Coal products
32 Stone, clay & glass products
33 Steel manufacturing
34 Other steel
35 Non-ferrous metals
36 Metal products
37 General machinery equipment
38 Electrical machinery
39 Equipment and supplies for household use
40 Other electrical machinery
41 Motor vehicles
42 Ships
43 Other transportation equipment
44 Precision machinery & equipment
45 Other manufacturing
46 Construction
47 Civil engineering
48 Electricity
49 Gas, heat supply
50 Waterworks
51 Water supply for industrial use
52 Waste disposal
53 Wholesale
54 Retail55 Finance
56 Insurance
57 Real estate
58 Housing
59 Railway
60 Road transportation
61 Water transportation
62 Air transportation
63 Other transportation, packing
64 Telegraph, telephone
65 Mail66 Education (private, non-profit)
67 Research
68 Medical, hygiene (private)
69 Other public services
70 Advertising
71 Rental of office equipment and goods
72 Other services for businesses
73 Entertainment
74 Broadcasting
75 Restaurants
76 Inns
77 Laundry, hair-cutting , public bath
78 Other services for individuals
79 Education (public)
80 Medical, hygiene (public)
81 Public administration
82 Medical, hygiene (non-profit)
83 Others(non-profit)

Standard Errors
Capital Price
Land Price
Output
0.107
0.026
0.097
0.028
0.056
0.041
0.044
0.043
0.021
0.016
0.177
0.031
0.119
0.110
0.039
0.049
0.025
0.013
0.131
0.048
0.064
0.038
0.026
0.028
0.118
0.021
0.006
0.012
0.112
0.032
0.052
0.012
0.084
0.043
0.016
0.014
0.031
0.022
0.036
0.012
0.065
0.054
0.112
0.059
0.101
0.020
0.047
0.015
0.030
0.009
0.049
0.022
0.124
0.015
0.071
0.010
0.067
0.017
0.082
0.028
0.039
0.013
0.094
0.022
0.097
0.077
0.022
0.004
0.173
0.027
0.057
0.021
0.060
0.024
0.039
0.011
0.083
0.017
0.034
0.046
0.090
0.045
0.081
0.073
0.072
0.054
0.033
0.009
0.021
0.012
0.057
0.033
0.028
0.018
0.102
0.047
0.470
0.077
0.152
0.048
0.025
0.016
0.053
0.045
0.145
0.048
0.044
0.022
0.026
0.021
0.012
0.015
0.009
0.012
0.041
0.031
0.067
0.037
0.234
0.099
0.063
0.025
0.046
0.064
0.240
0.088
0.110
0.064
0.032
0.011
0.047
0.021
0.016
0.017
0.305
0.089
0.130
0.110
0.068
0.023
0.094
0.033
0.094
0.014
0.045
0.026

Constant
0.063
0.098
0.113
0.133
0.084
0.102
0.103
0.062
0.024
0.068
0.056
0.052
0.030
0.024
0.067
0.017
0.059
0.018
0.030
0.027
0.181
0.113
0.030
0.089
0.030
0.071
0.044
0.013
0.022
0.020
0.010
0.044
0.047
0.005
0.027
0.051
0.039
0.030
0.101
0.073
0.077
0.107
0.057
0.058
0.039
0.043
0.021
0.084
0.293
0.162
0.024
0.043
0.032
0.048
0.059
0.028
0.030
0.034
0.067
0.086
0.019
0.088
0.203
0.132
0.038
0.044
0.009
0.100
0.107
0.079
0.075
0.125
0.054

0.003
0.004
0.005
0.002
0.002
0.004
0.003
0.006
0.002
0.004
0.003
0.002
0.002
0.001
0.004
0.001
0.002
0.001
0.002
0.001
0.010
0.005
0.002
0.004
0.001
0.003
0.002
0.001
0.001
0.002
0.002
0.002
0.007
0.001
0.002
0.002
0.002
0.001
0.003
0.006
0.004
0.008
0.005
0.002
0.002
0.002
0.003
0.006
0.012
0.006
0.001
0.003
0.002
0.003
0.006
0.001
0.002
0.004
0.003
0.010
0.002
0.012
0.020
0.005
0.003
0.003
0.001
0.007
0.009
0.002
0.008
0.004
0.003

Table 5 Effects on MFP by industry (continued

11 Livestock products
12 Processed marine products
13 Rice polishing, flour milling
14 Other foods
15 Beverages
16 Tobacco
17 Silk
18 Spinning
19 Fabrics and other textile products
20 Apparel and accessories
21 Lumber and wood products
22 Furniture
23 Pulp, paper, paper products
24 Publishing and printing
25 Leather and leather products
26 Rubber products
27 Basic chemicals
28 Chemical fiber
29 Other chemicals
30 Petroleum products
31 Coal products
32 Stone, clay & glass products
33 Steel manufacturing
34 Other steel
35 Non-ferrous metals
36 Metal products
37 General machinery equipment
38 Electrical machinery
39 Equipment and supplies for household use
40 Other electrical machinery
41 Motor vehicles
42 Ships
43 Other transportation equipment
44 Precision machinery & equipment
45 Other manufacturing
46 Construction
47 Civil engineering
48 Electricity
49 Gas, heat supply
50 Waterworks
51 Water supply for industrial use
52 Waste disposal
53 Wholesale
54 Retail55 Finance
56 Insurance
57 Real estate
58 Housing
59 Railway
60 Road transportation
61 Water transportation
62 Air transportation
63 Other transportation, packing
64 Telegraph, telephone
65 Mail66 Education (private, non-profit)
67 Research
68 Medical, hygiene (private)
69 Other public services
70 Advertising
71 Rental of office equipment and goods
72 Other services for businesses
73 Entertainment
74 Broadcasting
75 Restaurants
76 Inns
77 Laundry, hair-cutting , public bath
78 Other services for individuals
79 Education (public)
80 Medical, hygiene (public)
81 Public administration
82 Medical, hygiene (non-profit)
83 Others(non-profit)

t-values
Land Price
OutputCapital Price
3.454-2.5441.195-0.0055.531-3.33219.601-1.490-3.185-5.5761.340-2.401-18.348
10.0983.018-1.393-0.8495.838
6.235-1.215-0.604-2.8049.109-3.4592.638-0.942-3.629-0.102
2.495-3.8415.074-7.745
3.157-0.8368.042-0.708
2.835
0.401
9.890
2.690
-1.951
2.208
3.986
0.739
1.705
-3.671
1.492
4.085
6.973
-8.3062.221-1.823
3.845-8.3984.651-3.6574.427-7.234-0.9496.925-0.4958.539
1.103-0.336
2.196
0.839
7.276
-11.3770.804-2.087-0.7291.431
4.848-0.9104.142
2.952-0.333
2.278
5.580
2.558
2.520
-0.064
2.420
0.640
1.451
-0.19411.320-33.7815.960-7.503
3.092
0.383
4.952
-11.792
3.101-5.436-1.368
1.286
1.387
3.447
14.222-3.2253.326-4.207
0.622
0.104
5.066
0.688
-3.9032.600-1.2664.538
31.977-5.56312.839-1.162
3.477-1.680
6.311
2.236
4.677
-1.73517.700-7.666-1.6140.243
4.325-3.1279.005-18.951
10.678-0.39941.443-8.311
2.183-0.0250.583-0.086
0.836
2.805
4.349
-4.2891.727-0.1144.208-0.479Constant
3.606
1.344
0.979
5.813
14.211
4.109
40.0733.623
15.248
4.807
5.313
4.554
6.229
8.242
6.190
29.7966.881
6.948
15.933
6.321
3.398
5.061
9.604
4.812
17.2882.791
8.704
20.16615.812
6.406
16.9971.348
6.353
43.067
10.1663.876
8.282
11.559
6.072
1.868
4.257
6.182
12.231
12.59317.88712.70836.322
11.484-2.8562.280
24.55818.971
7.728
17.08011.66531.74814.191
11.9718.618
3.620
33.212
2.214
1.716
4.263
19.0797.095
64.740
4.127
5.647
7.629
8.510
2.856
7.240

1.867
0.595
0.162
16.143-10.861-1.982-9.660
0.406
7.287
4.927
3.875
0.091
3.924
-0.244-1.851
11.823
1.283
7.359
4.082
-1.769-0.616-2.005
3.151
1.522
2.085
-1.3060.702-0.919-2.4990.717
2.934-0.797-0.600-6.1370.936-1.208-1.537-6.859-5.9491.713-3.039-0.904-7.545-7.083-10.949-5.187-2.793-8.6661.749-1.352-13.990
2.399-5.975-10.400-4.546-23.339-3.331
2.884-9.7821.499-2.642-1.513-1.413-4.232-12.9450.353-3.282
1.217-1.942-6.943-3.912-3.007-7.618

Table 5 Effects on MFP by industry (continued

11 Livestock products
12 Processed marine products
13 Rice polishing, flour milling
14 Other foods
15 Beverages
16 Tobacco
17 Silk
18 Spinning
19 Fabrics and other textile products
20 Apparel and accessories
21 Lumber and wood products
22 Furniture
23 Pulp, paper, paper products
24 Publishing and printing
25 Leather and leather products
26 Rubber products
27 Basic chemicals
28 Chemical fiber
29 Other chemicals
30 Petroleum products
31 Coal products
32 Stone, clay & glass products
33 Steel manufacturing
34 Other steel
35 Non-ferrous metals
36 Metal products
37 General machinery equipment
38 Electrical machinery
39 Equipment and supplies for household use
40 Other electrical machinery
41 Motor vehicles
42 Ships
43 Other transportation equipment
44 Precision machinery & equipment
45 Other manufacturing
46 Construction
47 Civil engineering
48 Electricity
49 Gas, heat supply
50 Waterworks
51 Water supply for industrial use
52 Waste disposal
53 Wholesale
54 Retail55 Finance
56 Insurance
57 Real estate
58 Housing
59 Railway
60 Road transportation
61 Water transportation
62 Air transportation
63 Other transportation, packing
64 Telegraph, telephone
65 Mail66 Education (private, non-profit)
67 Research
68 Medical, hygiene (private)
69 Other public services
70 Advertising
71 Rental of office equipment and goods
72 Other services for businesses
73 Entertainment
74 Broadcasting
75 Restaurants
76 Inns
77 Laundry, hair-cutting , public bath
78 Other services for individuals
79 Education (public)
80 Medical, hygiene (public)
81 Public administration
82 Medical, hygiene (non-profit)
83 Others(non-profit)

J-statistics
J
5% critical p value
15.70027.5870.635
18.78127.5870.425
12.67927.5870.818
15.41227.5870.654
19.19227.5870.398
18.17527.5870.465
17.58127.5870.506
13.15527.5870.792
16.50027.5870.580
21.56427.5870.259
14.62027.5870.706
15.88527.5870.622
19.06827.5870.406
15.43927.5870.652
18.73827.5870.427
17.61127.5870.504
14.73627.5870.698
19.13727.5870.401
13.26827.5870.786
18.56227.5870.439
16.13127.5870.606
19.16027.5870.400
18.08627.5870.471
16.24127.5870.598
13.54727.5870.770
15.90827.5870.621
20.12527.5870.340
19.11127.5870.403
18.69927.5870.430
19.88927.5870.354
15.31927.5870.660
17.78927.5870.491
18.38527.5870.451
17.18627.5870.533
17.19727.5870.532
17.84427.5870.488
14.64827.5870.704
16.69127.5870.567
19.86527.5870.355
17.74327.5870.495
19.08527.5870.405
17.09127.5870.539
16.80227.5870.559
13.38327.5870.780
13.95827.5870.746
17.18227.5870.533
16.49727.5870.580
20.92527.5870.293
13.30127.5870.784
20.09027.5870.342
13.31027.5870.784
16.52927.5870.578
13.91827.5870.749
16.03727.5870.612
18.69727.5870.430
19.65127.5870.369
14.97327.5870.683
14.84327.5870.691
20.75827.5870.303
18.51927.5870.442
18.23827.5870.461
17.61727.5870.503
17.87127.5870.486
19.57227.5870.374
14.36127.5870.722
18.32627.5870.455
17.84127.5870.488
15.81827.5870.627
16.04727.5870.611
18.55027.5870.440
18.24727.5870.460
15.90327.5870.621
20.40127.5870.323

Null=all coefficinets are zeros
Statistics
5% critical v p value
29.9877.815
0.000
28.8767.815
0.000
47.0657.815
0.00019804.277
7.815
0.000
1628.682
7.815
0.000
190.0697.815
0.000
5361.939
7.815
0.000
61.1727.815
0.000
373.2337.815
0.000
73.7857.815
0.000
49.4197.815
0.000
106.8147.815
0.000
68.0407.815
0.000
243.0707.815
0.000
106.139
7.815
0.000
1868.877
7.815
0.000
468.5217.815
0.000
132.3437.815
0.000
395.8497.815
0.000
1737.044
7.815
0.000
32.2357.815
0.000
128.9647.815
0.000
112.4117.815
0.000
53.3357.815
0.000
342.3337.815
0.000
25.7047.815
0.000
129.4027.815
0.000
496.2397.815
0.000
353.4637.815
0.000
673.0657.815
0.000
707.332
7.815
0.000
21.6427.815
0.000
197.6147.815
0.000
2353.212
7.815
0.000
1509.153
7.815
0.000
68.0177.815
0.000
261.6907.815
0.000
259.080
7.815
0.000
123.3897.815
0.000
133.3147.815
0.000
82.2717.815
0.000
173.6507.815
0.000
705.1407.815
0.000
2402.970
7.815
0.000
355.8567.815
0.000
345.0987.815
0.000
1410.789
7.815
0.000
275.7417.815
0.000
22.8107.815
0.000
107.7237.815
0.000
1209.009
7.815
0.000
746.255
7.815
0.000
125.5567.815
0.000
615.495
7.815
0.000
169.259
7.815
0.000
2339.541
7.815
0.000
1049.723
7.815
0.000
1895.507
7.815
0.000
153.408
7.815
0.000
206.3817.815
0.000
6295.240
7.815
0.000
1492.949
7.815
0.000
21.0067.815
0.000
48.5337.815
0.000
651.8717.815
0.000
267.4487.815
0.000
23642.518
7.815
0.000
44.7577.815
0.000
139.002
7.815
0.000
183.5457.815
0.000
491.7467.815
0.000
35.3797.815
0.000
117.5117.815
0.000