KIER DISCUSSION PAPER SERIESKYOTO INSTITUTEOFECONOMIC RESEARCH
Discussion Paper No.1043
Factor Adjustments and Liquidity Management:
Evidence fromJapan's Two Lost Decades and Financial Crises
Hirokazu Mizobata
Hiroshi Teruyama

October 2020

KYOTO UNIVERSITY
KYOTO, JAPANFactor Adjustments and Liquidity Management:
Evidence from Japan’s Two Lost Decades andFinancial Crises ∗Hirokazu Mizobata†

Hiroshi Teruyama‡

October, 2020

Abstract
To reveal the cause of Japan’s recent weak physical investment, this study estimatesand compares the Euler equations for physical investment, R&D investment, andemployment. We construct an unbalanced panel from Japanese firms’ microdata
from 1994 to 2014. The estimation results suggest that firms face weak financial
constraints in the sense that their borrowing amount is not restricted, but their internal funds are insufficient. To address such constraints, firms first allocate their
cash flows and cash reserves to buffer their employment and then incur R&D investment rather than protect physical investment. We suggest the following reason
for this result: employment and R&D investment are more productive and/or impose larger adjustment costs than physical investment, and thus, firms prioritize
the stabilization of employment and R&D over funding physical investment. This
study also shows that young, small-sized, and manufacturing firms are likely to∗ Corresponding author:

Hirokazu Mizobata; Email: mizobata@kansai-u.ac.jp. This study received funding from the Japan Society for the Promotion of Science (JSPS) (KAKENHI Grant Number
26285068). This grant enabled us to obtain questionnaire information pertaining to the Basic Surveyof Japanese Business Structures and Activities. We thank Yasuo Goto, the principal investigator of the
project. We also acknowledge financial support from the JSPS Core-to-Core Program (A. Advanced
Research Networks), KAKENHI Grant Number 20H00076, and the International Joint Research Center of Advanced Economic Theory, Kyoto University. We are grateful to the participants of the INCASAnnual Conference (FU Berlin, 2018) and Monetary Economic Workshop (Ryukoku University, 2016)
for their comments and suggestions.
† Faculty of Economics, Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan.
‡ Institute of Economic Research, Kyoto University, Yoshidahommachi, Sakyo, Kyoto 606-8501,
Japan.

suffer from weak financial constraints. Furthermore, even during financial crises,
firms rely only on internal funding and are not restricted by external funding in thesame way as they are during usual times.
JEL Classification: D25, G31, G32
Keywords: physical investment, research and development investment, employment, adjustment cost, financial constraint, Euler equation

1 Introduction
Japan’s physical investment (or fixed investment) has remained stagnant since the1990s. The economic stagnation period after the collapse of the Japanese asset pricebubble in 1991 to 2010 is referred to as the “two lost decades.” Despite the relatively long economic expansions during this period, physical investment growth
was generally slower than before. This weak investment has continued from 2010
until now. In particular, the recovery of physical investment after the global financial crisis in 2008 was slower than that of corporate profit, which showed resilience.
Weak physical investment during a long expansion is uncommon, as there are usually enough internal and external funds in a good economic environment.
This unusual state of Japan’s physical investment is explained mainly fromthree non-exclusive viewpoints. The first point is the existence of investment adjustment costs. Convex adjustment costs make investment sluggish compared tothe economic recovery. The second point is financial constraints. Even in the current low interest rate era, external financing, such as bank borrowing or corporatebond issuance, may hit the ceiling, reflecting the uncertain business conditions offirms. Similarly, as external finance carries higher interest rate premiums than internal finance, the investment may be constrained by internal funding, such as cash
flows. Alternatively, firms may face the constraint of not being able to increase
enough retained earnings for future borrowing constraints. External and internalfinancial constraints arising from such factors restrain investment. The third pointis the allocation of funds among multiple types of investment. Firms generally decide the amount of physical investment simultaneously with the amount of otherkinds of investment. Research and development (R&D) investment is typical of1such investment. For example, decreases in physical investment may be induced
by a shift of funds to R&D expenditure, and overall investment may expand. Thismay occur because the marginal profitability of physical capital declines more thanthat of R&D capital.
Our main purpose is to obtain insights into the dominant source of the recent
weak capital investment in Japan. To this end, it is useful to consider firms’ dynamic decision-making behavior on multiple stock types. In addition, if employment incurs adjustment costs, which is plausible in Japan, it varies with rigidity
and its determination becomes dynamic, like other stocks.1 A firm’s optimal dynamic stock decision is described by the Euler equations. Thus, for our purpose,
it is suitable to estimate regression equations of physical investment, R&D investment, and employment induced by the Euler equations.
The pioneers of estimating investment by Euler equation considering corporate
financial structure are Whited (1992) and Bond and Meghir (1994). Whited (1992)
considers the investment decision with an external upper limit of borrowing, andestimates a non-linear Euler equation. Bond and Meghir (1994) examine structuresthat generate financial hierarchy, such as tax systems and transaction costs, andinvestigate conditions under which the level of liabilities affects the Euler equation.
Subsequently, the authors test the implications of financial constraints using theEuler equations in a linear form.
Several variants of these two studies follow. Among others, Hall (1995) estimates the Euler equations of investment and R&D employing a model close tothat of Whited (1992) but explicitly considers the financing phase of stock issuance,
which is not included in the abovementioned two models. Gilchrist and Himmelberg (1998) solve Euler equations embedding financial constraints and linearly approximate them, and then construct a vector autoregression model that forecasts
future fundamentals and financial state variables. Brown and Petersen (2015) extend Bond and Meghir’s (1994) estimation equations by adding some finance variables that do not appear in the Euler equations without financial constraints to
examine their significance. In addition, the authors estimate both capital and R&D
investment from the same viewpoint as our third point outlined above to explain
the unusual physical investment contraction. We extend Brown and Petersen’s
1 The change in the labor force as a production factor corresponding to investment (e.g., a change

in capital stock or R&D stock) is the number of employees hired or dismissed. We use the term
“employment” as a concept that corresponds to investment in the sense of flow. Meanwhile, theterm “employment stock” is used when referring to the number of employees.

2

(2015) approach to include the employment Euler equation, since labor adjustment
costs are non-negligible in Japan.
Our study contributes to the literature that investigates the weak physical investment problem. Previous studies have dealt with this problem by employing
the extended Q-type investment function, rather than the Euler equation. Nakamura (2017) addresses Japan’s weak physical investment from three aspects: managerial entrenchment, precautionary saving, and inefficient internal capital market.
He uses Japanese listed company data and estimates the Q-type investment equation to show that weak investment is brought about by managerial entrenchment
and precautionary saving. Weak physical investment is also observed in the US.
Gutiérrez and Philippon (2017) analyze this phenomenon in the US using a wide
range of aggregate-, industry-, and firm-level data. The authors estimate the Qtype investment equation with several core variables that support four hypotheses:
financial constraints, changes in the nature of investment, decreased competition,and tighter corporate governance. The authors show that the rise of intangible
investment explains one-third of the drop in physical investment, while decreased
competition and changes in corporate governance explain the rest. Our study has acommon research interest with these studies but differs in that we comprehensively
and consistently analyze the firm’s dynamic decision-making from the viewpointof fund allocation among different production factors.
The rest of this paper is organized as follows. Section 2 provides an overview
of Japanese physical and R&D investment as well as employment at the aggregate
level. Section 3 presents the investment model with financial constraints and provides empirical implications. Section 4 describes the data characteristics. Section
5 introduces the basic estimation results and compares them by splitting the sample based on firms’ characteristics. In addition, this section examines the impact offinancial crises. Section 6 concludes.

2 Overview of investment and employment behavior
This section presents an overview of aggregate investment and employment behavior in Japan since the middle of the 1990s based on data used in our analysis.
The data are from the Basic Survey of Japanese Business Structures and Activities(BSBSA) conducted by the Ministry of Economy, Trade and Industry (METI).2 We
2 Section 4 describes the data and our sample selection scheme.

3

Figure 1: Aggregate capital and R&D investment
aggregate the amounts of physical and R&D investment and the number of employees defined in Section 4.
Figure 1 compares aggregate amounts of physical and R&D investment (innominal terms). The shaded areas indicate the recession period officially set by
the Japanese government. The figure shows that, prior to the 2010s, physical investment was well above the level of R&D investment. However, while physicalinvestment fluctuated greatly and sensitively according to business cycles, R&D
investment was growing steadily. Subsequently, after the global financial crisisof 2008, physical investment in Japan stagnated, as mentioned in Section 1, but
R&D investment continued to grow shortly after declining during the crisis. Consequently, R&D investment has exceeded physical investment since 2010.
Figure 2 shows the growth rate of physical and R&D investment alongside that4

Figure 2: Dynamics of investment and employmentof the aggregate number of employees.3 Employment is more stable than the othertwo investment variables; that is, the labor force is not a production factor thatcan be adjusted flexibly. Firms cannot always hire or dismiss workers freely, since
they face, for example, recruitment costs and legal or implicit dismissal costs. In
addition, firms may invest in human capital, which is a sunk cost. Thus, employment adjustments also impose costs, making a firm’s decision about employment
dynamics like that of physical and R&D investment.
Between the two series of physical and R&D investment, physical investment
is more volatile. This feature is observed markedly in recessions. In a recession,
the growth rate in physical investment drops sharply to a negative value, whereasthat in R&D investment remains positive or near zero, except for a large decrease
just after the 2008 financial crisis.
3 These employees are regular and non-regular employees, as explained in Section 4.

5

Figure 2 indicates that, overall, firms adjust these three variables contrastively.
Employment is stable against negative economic shocks. R&D investment responds
to negative shocks but recovers quickly. Conversely, physical investment declines
significantly due to negative shocks and remains sluggish. The large volatility ofphysical investment suggests that the adjustment cost of physical investment isrelatively small. However, the long slump in physical investment after the 2008
financial crisis, as opposed to R&D investment, cannot be explained by small adjustment costs, suggesting that some other sources exist.
In the following sections, we investigate the reasons for the different behavior
based on firms’ dynamic decisions. We also compare the patterns of production
factor adjustment among different firms according to their characteristics, such asage, size, payout, debt level, and industry.

3 Background Model and Implications
3.1

The Setting

We consider the firm’s dynamic optimization problem for three production factors
under financial constraints. In this section, we first describe this model, and thenderive the estimable form of the firm’s optimal first-order conditions. Each firm
maximizes the expected present value of future dividends,[
Vt = Et]∞∑ βt,t+ j Dt+ j ,

(1)

j =0

where Vt denotes the firm’s market value at time t, Et [·] denotes the expectation
operator conditional on information available at time t, β t,t+ j denotes the firm’s
discount factor (product of the reciprocal of investors’ gross required rate of return)from time t to t + j, and Dt is the dividend paid to shareholders at time t.
The firm’s dividend at time t is defined as(
Dt ≡ Pt

2

F (Kt+1 , Nt+1 ) − ∑ ψi ( Iit , Ki,t+1 ) − ϕ( Ht , Nt+1 )
i =1

)

2

− ∑ Pit Iit − Wt Nt+1
i =1

+ NBt − rt Bt .
(2)

6

The function F represents a production function, and the functions ψi and ϕ are
the adjustment cost functions. The vector K = (K1 , K2 ) represents a stock vectorof capital. The variable N is the employment stock. The subscript i refers to afunction or variable relating to physical capital when i = 1, and R&D capital wheni = 2. A stock variable with time notion t indicates its beginning-of-the-period
value at time t. This specification of production timing is the same as in Bond andMeghir (1994).4 The variable P is the output price. The variable Pi is the price ofinvestment goods, and the amount of investment is Ii . The variable W denotes thewage rate, and the variable H is changes in the number of employees. The variable
B is outstanding debt with interest rate r, and the associated flow variable is NB.5
The law of motion for K, N, and B is
Ki,t+1 = Kit + Iit ,i ∈ {1, 2},(3)

Nt+1 = Nt + Ht ,(4)

Bt+1 = Bt + NBt .

(5)

Here, we ignore the depreciation of capital or exogenous job separation (due to,e.g., retirement age), as these factors provide no additional insights into the analysis in our framework.
The financial constraints are summarized as
Dt ≥ 0,
Bt∗+1≥ Bt+1 .

(6)(7)

Conditions (6) and (7) introduce financial constraints in a manner commonly usedin the literature, including by Whited (1992) and Whited and Wu (2006). The constraint (6) prevents firms from issuing new shares. In other words, the marginal
cost of issuing shares is prohibitively expensive. As Gilchrist and Himmelberg
4 Here, the notation of time is different to that of Bond and Meghir (1994), who use the notationt for the end-of-period t stock value, while we use t for the beginning-of-period t stock value. Hall
(1995) argues that the timing at which investment becomes capitalized and contributes to production
affects the specifications of the Euler equations. She argues that in an adjustment cost framework, it
is more reasonable to assume that the investment is available for production in the first period thanto assume that it does not become productive until the next period. This is because the existence ofadjustment costs allows for a diminishing marginal product in the first period.
5 Brown and Petersen (2015) assume that firms have positive cash holdings (or retained earnings),
which can be used to relax financial constraints in the future. We make the same assumption by
allowing a negative value of outstanding debt.

7

(1998) note, a binding non-negativity constraint on dividends, or the assumptionthat shareholders prefer dividends to investment, is required for debt issuance asa marginal financing source. The condition (7) restricts the quantity of debt outstanding. Hence, if firms raise an insufficient amount of money, severe financial
conditions might give rise to a situation in which firms must give up some investment or employment in a bad economic condition.6
Inserting Equations (2)–(5) into Equation (1), we obtain the following Euler
equations for the variables Ki , N, and B:()
Pit + Pt ψi,Iit + ψi,Ki,t+1 =)][(1 + λ t +1
( Pt+1 ψi,Ii,t+1 + Pi,t+1 ) , i ∈ {1, 2}, (8)Pt FKi,t+1 + Et β t,t+1
1 + λt[()]()
1 + λ t +1
Wt + Pt ϕHt + ϕNt+1 = Pt FNt+1 + Et β t,t+1
Pt+1 ϕHt+1 ,(9)
1 + λt[]1 + λt = Et β t,t+1 (1 + λt+1 )(1 + rt+1 ) + γt ,(10)where variable λ and γ are the Lagrange multipliers regarding the non-negativity
constraint on dividends (6) and the constraint on new debt issues (7), respectively.
The term Fx is the marginal product of raising variable x, and the terms ψi,x and ϕx
are the marginal costs of adjusting variable x. The left-hand side of Equation (8) is
the marginal purchase and adjustment costs associated with the current investmentin physical or R&D capital. The right-hand side of Equation (8) represents themarginal profits of the current investment, which consist of the marginal productand the expected marginal investment costs that can be reduced in the next period.
The next-period values are discounted by the one-period discount factor and therelative shadow cost of the internal finance. Equation (9) is similar to Equation(8); the only difference is that the cost of purchasing marginal investment goods isincurred once while wages for marginal hiring are paid in each period. Equation(10) is the first-order condition for debt, which links the shadow costs of internalfinance to those of external finance.

6 As an alternative way to incorporate financial frictions implicitly, Gilchrist and Himmelberg(1998) assume an external financing premium that depends on outstanding debt and other factors.
Meanwhile, Bond and Meghir (1994) introduce a borrowing constraint into the model more explicitly by considering taxation and bankruptcy costs. However, these different assumptions on financial
frictions do not make an essential difference in the final form of the estimation equations.

8

3.2

Empirical Implications

To obtain the empirical implications from the model, we categorize the situations
depending on whether the constraints (6) and (7) bind. This process leaves thefollowing three possible cases.7
Regime 1.

Dt > 0, Bt∗+1 > Bt +1(No Financial Constraints)

In this regime, the firm pays positive dividends to shareholders and can afford toissue new debt. The firm finances its investment and employment sufficiently. The
sources of finance can be cash flows, cash holdings (or retained earnings), and/or
borrowing when the expected interest rates on all assets or liabilities are equal,
as we assume in the model in Subsection 3.1. Capital structure does not matter.
Furthermore, the firm does not predict financial constraints in the future.8 In thiscase, any variables concerned with a firm’s financing decisions do not affect theinvestment and employment levels. We call this regime “no financial constraints.”
In this regime, all investment and employment are at the desired level for the firm,
and the low physical investment is a result of its low profitability.
Regime 2.

Dt = 0, Bt∗+1 > Bt +1(Weak Financial Constraints)

In this regime, the firm pays no dividends to shareholders and can afford to issue
new debt. If there are no hierarchies for financing in a perfect capital market, thisregime is trivial. In other words, new debt allows the firm to achieve desirable
levels of investment and employment while maintaining zero dividend conditions.
However, in reality, this regime may have essential implications. Unlike theassumption of the model in Subsection 3.1, corporate debt and savings are not really perfectly substitutable financing tools owing to bankruptcy costs and taxation
system (Bond and Meghir, 1994), as well as monitoring costs and moral hazard
costs (Gilchrist and Himmelberg, 1998). If debt is a more costly financing tool thanretained earnings, this regime may address the situation when the firm prepares
7 Strictly speaking, there are four possible cases depending on whether constraints (6) and/or (7)bind. However, we focus on three cases here, as the situation in which the firm pays positive dividends and faces debt constraint rarely occurs. We can understand this logic intuitively by checking
the optimal condition (10): a firm borrows to the limit because of the lower interest rate relative tothe required rate of return.
8 Solving a stochastic difference equation (10) forward shows that λ is the (weighted) sum oft
expected future values of γ if the solution exists.

9insufficient net revenues and retained earnings to finance all its desired investment
and employment but finds it optimal not to issue new debt up to the borrowing
limit. This outcome may occur simply because the firm uses a combination of prioritized financing tools to achieve desirable investment and employment levels.
Another possibility is that the firm cannot achieve the desired investment and employment with internal funds alone, but does not issue new debt, because the highcost of external funds is not worth the further investment or employment. Alternatively, the firm predicts severe financial distress in the future, and currently saves
as much as possible.9 In any case, firms’ investment or employment would expand
if additional cash flows or cash holdings were available. We call this regime “weakfinancial constraints.”
In this regime, physical investment, R&D investment, and employment are constrained by the availability of the firm’s internal funds. Note that the weights,(1 + λt+1 )/(1 + λt ), that cause the distortion are the same across the three Euler
equations, but the degree of investment or employment deviation from the desired level varies depending on marginal conditions.10 The low physical investment could be caused by the allocation of limited internal funds across investment
and employment.
Regime 3.

Dt = 0, Bt∗+1 = Bt +1(Strict Financial Constraints)

In this regime, the firm again exhausts its net revenues to finance its investment andemployment but has sufficiently attractive investment and employment opportunities remaining, even considering that borrowing is more costly. Borrowing to the
maximum level in this period means that the firm cannot issue new debt in the
future without repayment; however, the firm finds it optimal to borrow as much as
it can. The financial crisis may be categorized into this regime, for example, as theupper bound is significantly lowered. In this case, in addition to the availability
9 In other words, the firm increases the size of negative NB until D = 0 in Equation (2).
tt
10 In this regime, Equation (10) indicates that the relative shadow costs of internal finance in thenext period to those in the current period are equal to the ratio of (gross) debt interest rate to therequired rate of return. If the required rate of return and the debt interest rate are the same (thisassumption is natural in Regime 1, at least, in the long run), the shadow costs λ are adjusted to beequal in two consecutive periods. In this case, Euler equations in this regime are indistinguishable
from the no-financial-constraint regime. For this regime to be meaningful, the interest rate on debt
should be higher than the required rate of return, in which case the shadow costs of internal finance istime dependent. In fact, Whited and Wu (2006) use the estimated value of λ as a financing constraintindex.

10

of internal funds, additional availability of new debt issues affects investment andemployment activity. As Equation (10) indicates, the Euler equations are affectedby debt outstanding through λ. We call this regime “strict financial constraints.”
In this regime, the firm allocates constrained external and internal funds across investments and employment according to marginal conditions, similar to the caseof Regime 2.
Next, we show the relationship between the abovementioned three regimes andour estimation formula. In Regime 1, the investment and employment Euler equations (8)–(9) reduce to the basic form of those without financial constraints (i.e.,λt = λt+1 = 0). On the other hand, in Regimes 2 and 3, investment and employment are affected by the unobserved positive variables, λt and λt+1 , and the basic
Euler equations are misspecified. In these regimes, observable variables, such as
cash flows, cash holdings, and new debt issues, affect the determination of investment and employment levels.
Many researchers derive non-linear investment Euler equations with financial
constraints similar to Equations (8)–(9), and then linearize them with some modeldependent assumptions for estimation (e.g., Hall, 1995; Love, 2003; Brown et al.,2009; Hall and Lerner, 2010; Brown and Petersen, 2015). We follow this standard
formula in the literature. For the determination of each of the production factors,
we consider the following baseline empirical specifications that are often used in
the literature, which is close to Brown and Petersen (2015).

2
CC
CAPj,t = βC1 CAPj,t−1 + βC2 CAPj,t−1 + β 3 Sale j,t + β 4 Sale j,t−1+ βC5 CFj,t + βC6 CFj,t−1 + βC7 ∆CHj,t + βC8 ∆CHj,t−1(11)+ βC9 DebtIssue j,t + βC10 DebtIssue j,t−1 + dCt + f jC + ϵCj,t ,RD j,t = β 1R RD j,t−1 + β 2R RD2j,t−1 + β 3R Sale j,t + β 4R Sale j,t−1+ β 5R CFj,t + β 6R CFj,t−1 + β 7R ∆CHj,t + β 8R ∆CHj,t−1
R+ β 9R DebtIssue j,t + β 10
DebtIssue j,t−1 + dtR + f jR + ϵ Rj,t ,

11

(12)

2
E
E
EMPj,t = β 1E EMPj,t−1 + β 2E EMPj,t−1 + β 3 Sale j,t + β 4 Sale j,t−1 + ζ 1 Wage j,t + ζ 2 Wage j,t−1+ β 5E CFj,t + β 6E CFj,t−1 + β 7E ∆CHj,t + β 8E ∆CHj,t−1
E+ β 9E DebtIssue j,t + β 10
DebtIssue j,t−1 + dtE + f jE + ϵ Ej,t ,(13)
The subscripts t and j of each variable are indexes of year and individual firm,
respectively. The term CAP is the amount of physical investment, and RD is theamount of R&D investment. Investment equations (11) and (12) have symmetricalfunctional forms. A proxy variable for a firm’s output is sales amount, Sale. The
term EMP is the net employment change (net hiring), and employment equation(13) additionally includes wages per capita, Wage. All variables are normalized by
the beginning-of-the-period stock of total assets in both Equations (11) and (12).
In Equation (13), EMP is normalized by the beginning-of-the-period number ofworkers, while other variables except Wage are the same as those in (11) and (12).11
The Euler equations in the no-financial-constraint regime are explained by only
real variables, and the financing variables described later in this subsection do notappear in these equations. In other words, the model structure suggests that current and lagged finance variables do not appear in an estimation equation merely
because they represent expected future profitability. This implication is the essenceof the Euler-equation approach (Bond et al., 2003; Brown et al., 2009).
The Euler equations can be expressed in a linear form under some reasonableassumptions. The assumptions for linearization vary across studies, which may
affect the interpretation of the coefficients in the estimation equations, but do notmake essential differences. Here, we obtain some implications of the parametersfrom the following settings. First, we assume that the production function F has
α1α2δa Cobb–Douglas form, F (Kt+1 , Nt+1 ) = AK1,t+1 K2,t+1 Nt+1 , where δ = 1 − α1 −α2 . Second, the adjustment cost functions ψi and ϕ have symmetric quadratic
11 This normalization deviates from the specification of the derived Euler equation, where the variables are deflated by the stock amounts corresponding to each type of investment (see the appendix).
However, such normalization is widely adopted in the field of estimating investment functions basedon Euler equations, such as Brown and Petersen (2015). One reason, related to R&D, is that it is difficult to measure the R&D stock owing to the absence of a long-term time series of R&D expenditureand the difficulty of estimating the depletion rate of R&D (Brown et al., 2009). Another reason isthat the existence of firms with very small physical capital stock may make the estimation results
unstable, and deflating stock variables by the same deflator may reduce heteroscedasticity (Whited
and Wu, 2006).

12

forms, ψi ( Iit , Ki,t+1 ) = ( ai /2)( Iit /Ki,t+1 − bi )2 Ki,t+1 for i = 1, 2, and ϕ( Ht , Nt+1 ) =(c/2)( Ht /Nt+1 − g)2 Nt+1 . Subsequently, as shown in the appendix, the current
period’s investment is expressed as a linear function of the lagged values of investment, its square, and output. The current period’s hiring is a linear function of
the lagged values of hiring, its square, output, and real wages. In the no-financialconstraint regime, this functional form should hold, and other finance variables
have no effects.
The appendix shows that the Euler equations with no financial constraint suggest the signs or sizes of some coefficients in reduced-form equations (11)–(13). We
can show that βk1 (k = C, R, E) corresponds to the gross real required rate of return,and βk2 is the negative half of that rate. Thus, βk1 > 1 and βk2 < 0 are expected.
The coefficient of lagged output is shown to be the negative value of the productof the gross real required rate of return and some positive structural parameters;
thus, βk4 ≤ 0. In the hiring equation, the coefficient of lagged wages is the productof some positive structural parameters; thus, ζ 2 ≥ 0. In the quadratic adjustment
cost function, the size of adjustment costs is represented by the structural parameter ai or c. Although we do not estimate these parameters directly, the estimatedcoefficient of the reduced-form equation indicates the size of the adjustment cost.
In other words, the coefficient βk4 or ζ 2 reflects a j or c, and the absolute value of thecoefficient is smaller as the size of the adjustment cost increases. The remaining
coefficients should be zero in the no-financial-constraint regime if the model holds.
We also include current output in each equation and current real wages in (13)to allow a more general lag structure of production. The lag structure of the underlying Euler equation is often extended to avoid an estimation equation being
too strongly dependent on the assumptions of a particular model. From the same
perspective, we do not rigorously check the constraints in the coefficients just examined, but rather focus on the sign conditions that might hold if the non-essential
assumptions of the model change.
In the weak and strict financial-constraint regimes, finance variables influence
the Euler equations through a shadow cost of internal finance λ. Since λ is unobservable, several studies (e.g., Whited, 1992) assume that it is a linear function ofobservable firm characteristics, especially financial conditions. Instead of directly
estimating the non-linear equation with λ function assigned, Brown and Petersen(2015) add variables that may affect financial constraints in a linear form to the es-

13

timation equations.12 Based on the model in Subsection 3.1, we add current andlagged values of cash flows (CF), changes in cash holdings (∆CH), and funds fromnew debt issues (DebtIssue) to the Euler equation with no financial constraint.
In the strict financial-constraint regime, it is expected that investment and employment are positively correlated to cash flows and debt issues, since relaxing
those constraints means adding available funding. Conversely, investment andemployment are expected to correlate negatively to the changes in cash holdings.
The negative correlation reflects funding for investment and employment throughthe release of cash holdings. On the other hand, in the weak financial-constraint
regime, as the firm can issue additional debt if it so desires, finance variables related to debt issuance do not influence its investment or employment decisions.
Only cash flows and changes in cash holdings matter. By testing the empiricalsignificance of these variables, we can identify which regime the firms are in.
Finally, the variables dkt and f jk capture a time-specific effect and a firm-specificeffect, respectively, in each equation.

4 Data and Summary Statistics
4.1

Data source and sample selection

We use data from the BSBSA conducted by the METI. The BSBSA is an annual survey that contains data on the diversification, globalization, and informatization ofJapanese firms, and it is generally used by the METI to inform its economic policymaking. The survey’s scope covers firms with 50 or more employees and paid-up
capital or investment of more than 30 million yen. The industries covered include
mining, manufacturing, wholesale and retail trade, food services, and many otherservice industries. The survey collects basic corporate finance data and detailed
information on various business activities, such as R&D and overseas promotions.
All targeted firms receive a questionnaire from the METI and report the data forthe most recent fiscal year. Although the BSBSA does not include data from microenterprises, it includes a large range of firms and has a large sample size. The collection rate is high (e.g., 84.3% in 2016) and the size of the cross-sectional samples
ranges from 25,000 to 30,000 firms every year.
12 This treatment is interpreted as a linear approximation of the non-linear terms around their sta-tionary solutions, as in Love (2003) and Gilchrist and Himmelberg (1998).

14

Table 1: Sample selection
Observations excluded

Remaining observations

6,269
449,5370473
16,596
26,520572,708
566,439
116,902
116,902
116,429
99,83373,313

Original sample
Outliers
CAP, R&D, EMP are missing or zero
CAP, R&D, SALE, ASSET ≤ 0
Sales growth ≥ 100
5 years’ continuous data
Missing lagged variables

Notes. “CAP,” “R&D,” “EMP,” “SALE,” and “ASSET” mean physical investment, R&D investment,
net increase in number of employees, sales amount, and total asset amount, respectively. “Outliers”
excludes the 1% tails in all key variables. The category “CAP, R&D, EMP are missing or zero” drops
the observations that report missing data or zero values for these variables. The category “5 years’continuous data” requires firms to have at least 5 continuous years of observations. Finally, the category “Missing lagged variables” excludes observations that do not have the lagged values necessary
to estimate the dynamic Euler equations.

We construct an unbalanced panel using the BSBSA data from 1994 to 2014. We
focus on manufacturing and non-manufacturing firms except those from primary,regulated, and financial industries. Our measure of physical investment is the purchase of tangible fixed assets, and that of R&D investment is the sum of own andoutsourced R&D expenses. Employment is defined as the net increase in employment stock. Employment stock corresponds to the total number of paid officers.
Here, the paid officers are defined by regular and non-regular employees with employment contracts that exceed 1 month, or those employees who worked for 18
days or more in the last 2 months of the fiscal year.
We select the sample based on the following criteria. (1) We trim outliers inall key variables at the 1% level. (2) We focus on firms engaged in all activities,
including physical investment, R&D investment, and employment. (3) We drop
any firm-year observations if physical investment, R&D investment, sales, or total
assets are negative, or if sales growth is greater than 100%. (4) We require firms
to have at least 5 years of continuous observations. (5) We eliminate any firm-year
observations whose 1-year lagged values of physical investment, R&D investment,or employment are missing. After imposing these restrictions, the sample consistsof 8,821 firms with 73,313 firm-year observations. To the best of our knowledge, thissample size is the largest among studies that investigate the relationships between
firms’ real economic activities and their financial constraints.

15

Table 2: Firm count by industry
Industry
Food and beverages
TextilePulp and paper
ChemicalsPetroleum and petroleum productsPottery and glass
Steel
Non-ferrous metals
Fabricated metals
Machinery
Electrical machineryTransportation equipment
Instruments
Wood and furniture
Printing and publishing
Leather and rubber
Other manufacturing
Wholesale and retail trade
Information and communications
Business services
Non-business services
Other services

Industry code

Firm countObservation

Share (%)

5
6
7
8
9
10
11
12
13
14
15
16
17
19,20
21
22,23
24
28,29
34,35,36
38
39
32,37

876
211
137
895
396
275
156
204
502
1,040
1,170
586
274
115
93
113
186
1,062
66
153
298
13

7,578
1,654
1,182
8,487
3,572
2,459
1,531
1,800
4,142
8,613
9,436
5,687
2,073
951
811
972
1,552
7,538
363
925
1,850
137

10.3
2.3
1.6
11.6
4.9
3.4
2.1
2.5
5.6
11.7
12.9
7.8
2.8
1.3
1.1
1.3
2.1
10.3
0.5
1.3
2.5
0.2

All

8,821

73,313

100.0

Full sample

Notes. As the BSBSA determines the firm’s industry by its major sales product, the firm’s industry
has changed over time. We adopt the industry from the first time that the firm entered the database.
The industry code in this table describes the number of minor industries reported in Japan’s National
Accounts in 2014.

Table 1 depicts the change of the samples in each step. The second criterion
that the firm engages in all activities decreases the sample size sharply with approximately 450,000 observations deleted. Most of these observations report noR&D or zero R&D data. The fraction of the aggregate R&D investment explainedby the deleted observations is relatively small (about 33% of the original sample).
Furthermore, we calculate the average size of capital and R&D investment for thedeleted observations and find that this value is much less than that of our sample:
the expenditure of capital investment amounts to only about 25% of our sample,
while that of R&D is only about 7% of our sample.
Table 2 presents the industrial distribution of 8,821 firms in our sample. The
top five industry groups with the biggest share are electrical machinery, wholesale16

Table 3: Summary statistics for benchmark sample
Industry

PhysicalInvestment

Share

R&D
Investment

Share

Employment Stock

Share

Food & beverages
TextilePulp & paper
ChemicalsPetroleum & petroleum productsPottery & glass
Steel
Non-ferrous metals
Fabricated metals
Machinery
Electrical machineryTransportation equipment
Instruments
Wood & furniture
Printing & publishing
Leather & rubber
Other manufacturing
Wholesale & retail trade
Information & communications
Business services
Non-business services
Other services

1,260
661
3,054
2,249
1,658
1,197
5,419
2,189
886
1,221
3,146
4,616
836
592
3,723
2,699
789
1,057
1,208
1,381
510
889

6.6
0.8
2.5
13.2
4.1
2.0
5.7
2.7
2.5
7.3
20.5
18.2
1.2
0.4
2.1
1.8
0.8
5.5
0.3
0.9
0.7
0.1

425
431
315
2,168
556
389
1,129
1,079
300
1,226
4,396
5,696
1,166
171
1,033
2,258
1,008
819
311
504
704
215

2.5
0.5
0.3
14.1
1.5
0.7
1.3
1.5
1.0
8.1
31.8
24.9
1.9
0.1
0.6
1.7
1.2
4.7
0.1
0.4
1.0
0.0

28.1
3.6
4.3
30.6
10.8
4.3
9.7
7.3
11.7
29.7
47.9
63.0
6.9
2.6
4.2
5.0
3.8
29.9
6.0
9.3
23.7
1.7

8.2
1.0
1.2
8.9
3.1
1.2
2.8
2.1
3.4
8.6
13.9
18.3
2.0
0.8
1.2
1.5
1.1
8.7
1.7
2.7
6.9
0.5

Full sample

1,971

100.0

1,777

100.0

344.1

100.0

Notes. Each value in the columns of physical investment and R&D investment shows the averageof the corresponding variable in each industry. The column headed “Share” refers to the fraction of
the corresponding variable in the full sample (unit is percent). All monetary values are nominal andin units of million yen. The number of employees is measured at the end of 2014 and is in units of
10,000 people.

and retail trade, machinery, chemicals, and food and beverages. Most of the observations fall into main high-tech industries in Japan. This sample characteristic
is highly dependent on our sample selection, which drops non-R&D or zero R&D
data.

4.2

Summary statistics

Table 3 reports summary statistics of our main economic variables, physical investment, R&D investment, and employment stock, from 1996 to 2014. Each entry is
the average or the share of the corresponding variable in our benchmark sample.
Table 3 shows that the sizes of physical investment and R&D investment are17

Table 4: Sample characteristics by year (1996–2014)
Year

CAP

RD

EMP

CF

∆CH

DebtIssue

1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

0.050
0.051
0.044
0.039
0.041
0.038
0.033
0.035
0.039
0.041
0.044
0.042
0.040
0.029
0.032
0.033
0.035
0.036
0.038

0.02060.02060.02000.02010.01990.01850.01880.01950.01980.01950.01990.01910.01880.01830.01780.01780.01820.01840.0182−0.002−0.0002−0.023−0.017−0.011−0.028−0.0160.0001
0.006
0.013
0.017
0.019
0.007
−0.002
0.005
0.002
0.001
0.005
0.007

0.056
0.053
0.045
0.049
0.050
0.040
0.046
0.053
0.058
0.060
0.061
0.060
0.044
0.046
0.058
0.056
0.057
0.061
0.062

0.019
0.005
−0.0220.014
0.017−0.031−0.004
0.018
0.021
0.024
0.030
0.008
−0.030−0.0003
0.022
0.026
0.003
0.022
0.025

0.013
0.003
−0.0220.004
0.018−0.029−0.016
0.003
0.005
0.012
0.021
−0.005−0.026−0.0130.002
0.010−0.0070.010
0.012

Average

0.039

0.0191−0.001

0.053

0.009

−0.0003

Notes. Each entry refers to the average value of key variables in theestimation equation (Equations (11)–(13)).

relatively large in high-tech industries, such as transportation equipment, electrical machinery, chemicals, and machinery. In the previous subsection, we confirm
that the category of wholesale and retail trade has a large number of observations,while Table 3 shows that the sizes of physical investment and R&D investment inthis group are not as large as those of other industries. Meanwhile, employment
stock shows a slightly different aspect. In addition to wholesale and retail trade,
service industries, such as business and non-business services, have a large shareof employees. There are about 650,000 workers employed in these industries or
20% of all observations. Finally, comparing the mean of physical investment and
R&D investment in the full sample shows that both investment types are of similar
size (nearly 2 billion yen).
Table 4 reports the mean of the key variables used in the estimation for eachyear. These regression variables are as follows. First, CAP and RD are the sameas described in Subsection 4. Second, EMP is calculated by the net increase in em-

18ployment stock (i.e., number of employees) during period t scaled by employment
stock at the beginning of period t. Variables CF, ∆CH, and DebtIssue are financing
variables. We construct CF as the sum of after-tax income and depreciation, ∆CH
as the net increase in liquidity assets during period t, and DebtIssue as the increase
in total debt during period t, whose values are all scaled by the beginning of periodt total assets.
Table 4 provides the following three findings. (1) Consistent with the literature,
RD shows significantly stable movements in these 20 years, but CAP shows very
volatile movements. Specifically, the difference between high and low values of the
RD investment rate is approximately 0.003, while that of CAP is 0.022. (2) EMP
declined at the end of the 1990s, the so-called “job seekers’ ice age” in Japan, but
recovered after 2003 and showed stable movements thereafter. (3) The financing
variables CF, ∆CH, and DebtIssue worsened in 1998, 2001, and 2008; these years
coincide with the Japanese financial crises reported by the Economic and Social
Research Institute of the Cabinet Office in Japan.

5 Estimation and results
5.1 Estimation method
We estimate Equations (11)–(13) by using the system generalized method of moments (GMM) approach developed by Arellano and Bover (1995) and Blundell andBond (1998). We estimate each of the equations by both differences and levels, using lagged levels as instruments for differences, and lagged differences as instruments for levels. We calculate two-step GMM estimates using lagged levels datedt − 3 and t − 4 as instruments for differences and lagged differences dated t − 2for levels.13 In the two-step GMM, the standard covariance matrix yields downward bias in a small sample. To correct this bias, we calculate Windmeijer’s (2005)
suggested covariance matrix.14
Our estimation method largely depends on the validity of the assumption thatthe lagged values used as instruments are not correlated with the expectation er13 We follow the recommendations of Roodman (2009), who addresses the problem of too many
instruments. One suggestion is to limit the lag depth, and the other is to collapse the instrument set.
A combination of these two techniques makes the instrument count invariant to T, the end of theestimation period.
14 Our estimation uses the xtabond2 command of Stata.

19

rors. To assess this instrument validity, we report Hansen J-test statistics with
the null that the over-identifying restrictions are valid. Furthermore, we report
difference-in-Hansen test statistics to check the validity of the instruments for the
regression in levels. Finally, to check the validity of the dynamic panel estimation
setting, we report m1 and m2 statistics for first- and second-order autocorrelation
for the first-differenced residuals.

5.2

Main results

Table 5 expresses the estimation results of Equations (11)–(13). We use the fullsample and report the result for each adjustment of production factors: physicalinvestment (CAP), R&D investment (RD), and employment (EMP).
Before examining the coefficients of each parameter, we first assess the test
statistics to check the validity of our estimation method. Since both the Hansen anddifference-in-Hansen test statistics do not reject the null, the lagged instrumental
variables we use can be considered as exogenous. Next, m1 statistics clearly reject the null that the residuals of the first differences are serially uncorrelated withorder 1 while m2 statistics do not reject the null that these residuals are serially uncorrelated with order 2. Thus, we can consider that our dynamic panel estimation
setting is valid.
Next, we discuss the key estimation results. First, we check the estimation results for the coefficients of the lagged dependent variable and its square in thephysical and R&D investment regressions. The coefficients of the lagged dependent variables in both equations are significantly different from 0 at the 5% level.
The point estimates of both coefficients are positive and slightly less than 1. According to the model in Section 3, the coefficient of the lagged dependent variablecorresponds to the gross real required rate of return and should be more than 1.
Although the point estimates do not satisfy this condition, the 95% confidence interval includes values slightly over 1. Meanwhile, the coefficients of the squares ofthe lagged dependent variables in both equations are significant at the 5% level andnegative, as the model suggests. However, the size of their absolute values largely
exceeds those suggested by the model, that is, half the coefficient of the lagged dependent variable. The reason could be that the adjustment cost functions do nothave monotonous curvatures and might not be captured accurately by a quadratic
function. These estimation results do not strictly satisfy the coefficient constraints

20

Table 5: Baseline estimation
Dependent variable

CAP

RD

EMP

Lagged

0.808∗∗

0.931∗∗∗[0.350]Lagged2−2.846∗∗[1.367]
Sale
0.191∗∗[0.083]Sale(−1)−0.167∗∗[0.070]Wage
Wage(−1)CF
0.278[0.426]
CF (−1)0.172[0.409]∆CH−0.581∗[0.299]∆CH (−1)
0.079[0.214]
DebtIssue
0.242[0.250]
DebtIssue(−1)−0.009[0.269]m1−3.99∗∗∗
m2
0.02
Hansen J-test (p-value)
0.575
Diff-in-Hansen test (p-value)
0.360
Obs
73,313
Firms
8,821
Sum CFs
0.451∗
Chi-squared test (p-value)
0.096
Sum ∆CHs−0.502Chi-squared test (p-value)
0.213
Sum DebtIssues
0.234
Chi-squared test (p-value)
0.525[0.093]−2.100∗∗[1.005]0.043∗[0.022]−0.040∗∗[0.019]0.129[0.117]−0.012[0.099]−0.156∗∗[0.072]0.025[0.036]0.044[0.077]−0.018[0.051]−4.61∗∗∗
1.59
0.901
0.807
73,3138,821
0.116∗∗
0.031−0.130∗
0.087
0.026
0.721

0.053
[0.321]−1.103[0.994]0.208[0.233]−0.151[0.203]−0.069[0.055]0.052[0.040]3.495∗∗∗[1.198]−1.674∗[0.872]−1.690∗∗[0.862]0.045[0.353]0.845[0.643]−0.145[0.421]−3.17∗∗∗
1.04
0.931
0.861
73,3138,821
1.821∗∗∗
0.005−1.645∗∗
0.047
0.700
0.255

∗∗∗ p < 0.01; ∗∗ p < 0.05; ∗ p < 0.1

Notes. The table shows the estimates of Equations (11)– (13). Estimation is by system GMM with lagged levels dated t − 3 andt − 4 used as instruments for differences, and lagged differences
dated t − 2 used as instruments for levels. The regression sample
is the full sample in Table 3. Robust standard errors are reported
in brackets. The term (−1) means the lag for one period. Laggedmeans the one-period lagged dependent variable in each equation, that is, CAP(−1), RD (−1) or EMP(−1). The variable Saleis the amount of sales divided by the beginning-of-period total
assets and the variable Wage is wage per capita defined as theratio of total annual salary payment to the end-of-period numberof employees.

21

of the background Euler equations. However, as shown in the process of deriving
estimable equations in Subsection 3.2, our estimation equations deviate from theunderlying Euler equations from some practical points of view. Considering this,we conclude that our assumption regarding the adjustment cost functions of bothtypes of investment are satisfactory overall.
Second, for the employment regression, we do not reject that both coefficientsof the lagged dependent variable and its square are 0 at the usual significance level.
A straightforward interpretation of this result is that employment does not require
any adjustment costs. Since non-regular workers are considered to be hired or dismissed with small costs, marginal employment adjustments can be made with few
costs with these workers in the normal business cycle. This interpretation is consistent with the widely accepted understanding in Japan that firms use non-regularlabor as an adjustment margin. However, it should be noted that employment did
not dramatically decrease even in the financial crisis period, as shown in Figure
2. This, in turn, suggests that when the reduction of the non-regular employment
stock with low adjustment costs reaches the limit, it is difficult to dismiss regular
employees, as dismissal incurs extremely high adjustment costs. If there are two
types of workers with different adjustment costs, the employment adjustment costfunction is discontinuous and cannot be captured by the symmetric convex function, as we assume.
Third, the coefficient of lagged sales is negative, as the model suggests, and significant for physical and R&D investment. The size of the coefficient of lagged sales
in the physical investment equation is larger than that of the corresponding coefficient in the R&D equation. This finding suggests that the size of the adjustment
costs of physical capital is smaller than that of R&D capital. However, current sales
are positively significant for these two investment types, which is not what themodel suggests directly. In reality, the effect of sales on investment is not discrete
but occurs continuously from the previous to the current period. The inverse signof the coefficient of current sales may reflects the complex process in which sales
are reflected in the profit and then influence investment. The coefficients of lagged
sales and lagged wages are insignificant, which again suggests misspecification of
the quadratic form of the employment adjustment cost function.
Fourth, the coefficients on finance variables CF, ∆CH, and DebtIssue impact
investment and employment. For the investment equations, the coefficients on
the variable CF or CF (−1) are insignificant. For the employment equation, CF is22

significant at the 1% level and positive, and CF (−1) is significant at the 10% leveland negative. The results of the chi-square test show that for all equations, thetotal effect of current and lagged cash flow is positive and statistically significant
at least at the 10% level. The coefficients of ∆CH are significantly estimated at
the 5% or 10% levels for all equations and are all negative, and the total effectsof current and lagged changes in cash holdings are significant and negative for
R&D and employment equations. Finally, we find that the estimated coefficientsof DebtIssue and DebtIssue(−1) are insignificant for all equations, and the total
effects of these variables are also not significant.
We interpret the abovementioned results regarding finance variables in the following way. (1) Debt issues have no impact on investment or employment. Additional debt has no direct relationship to both investment and employment decisions, indicating that firms do not face strict financial constraints. (2) Increases
in cash flows have a positive effect on investment and employment over time. A
cash-flow increase means there are additional internal funds available, which easeinvestment and employment constraints. Therefore, we understand that firms arein the regime of weak financial constraints. They prefer internal to external finance,
the former being constrained, to accommodate investment and employment. (3)The negative coefficient of changes in cash holdings suggests that firms use internal savings to increase investment and employment, resulting in a decrease in theirinternal cash reserves. However, it is worth noting that the significance levels ofboth coefficients of cash flows and changes in cash holdings are low in the physicalinvestment equation. In other words, the firm may allocate its internal funds tobuffer its R&D investment and employment from the financial constraints rather
than protecting physical investment. This finding is consistent with that of Brown
and Petersen (2015) on physical and R&D investment. We also find that Japanesefirms may protect their employees as well as their R&D investment at the expenseof physical investment.
When considering intra-firm fund allocation, it should be noted that in the Euler equations under financial constraints (8) and (9), terms in the next period areweighted equally by the relative shadow cost of internal finance. However, theelasticities of investment or hiring to internal funding differ, since marginal productivity and marginal adjustment costs vary among those variables. While theadditional funds are allocated more to factors with greater marginal productivity, factors with high marginal adjustment costs require more additional funds for23smoothing. Brown and Petersen (2015) refer to large adjustment costs of R&D
as the cause of the firm’s buffering of R&D investment. The authors argue thatR&D expenditure consists mainly of wage payments to scientists and engineers,
and there are multiple types of adjustment costs associated with firing and rehiring these skilled workers. In Japan, this explanation is applied to regular workers
generally, although the adjustment cost function of production workers might bediscontinuous and asymmetric, as argued earlier in this subsection.
The estimation results in this subsection lead to the following implications regarding the recent slump in physical investment in Japan. Convex adjustment costs
are marginally greater for R&D investment than for physical investment . In addition, employment incurs discontinuously high marginal adjustment costs. These
factors concentrate stock adjustments on physical investment. This interpretationis consistent with the volatility of physical investment being greater than that of
R&D investment and that of employment, as shown in Table 4 and Figure 2. However, the low adjustment costs alone are insufficient to explain why physical investment recovered more slowly than R&D investment did after the 2008 financial
crisis. In addition, the decline in the average (or long-term) level of physical investment since the 1990s cannot be explained by its low adjustment costs. These facts
have to do with the finding that Japanese firms have weak financial constraints.
Under this circumstance, the firm has to allocate constrained internal funds among
multiple stock types, and must spend additional funds on more marginally profitable production factors. Marginal profitability positively depends on marginal
productivity and marginal savings in adjustment costs. Thus, the firm uses limited
funds to keep high-productivity stock types stable, and to save money with low
productivity and cheap adjustments. In other words, the results suggest that thestagnation of physical investment is a consequence of not only its low productivity but also financial constraints and the relatively high adjustment costs of R&D
and employment stock. If there is no financial constraint, low investment is theresult of the firm’s optimizing behavior in a given market and technology environment. Conversely, the existence of financial constraints in fact means that not only
physical investment but also R&D investment and employment are at sub-optimallevels. This is suggested as the reason for the high cost of external funds.

24

5.3

Sample splits by firm characteristics

In this subsection, we divide our sample into two groups that may have different
characteristics regarding financial constraints. We separate our sample based onage, capital size, payout ratio (dividend paid to total assets), debt ratio (sum ofshort-term and long-term debt to total assets), and industry. Age, capital size, andindustry may offer more attractive splitting criteria to sort firms because these variables are more exogenous than the other criteria. Although the payout ratio andthe debt ratio are more endogenous, we include them in the criteria, since theseratios are considered as proxies for financial distress, which are closely related toour model implications.
To sort firms based on age or capital size, we first find the median value forthese variables; then, we consider firms as young or small if their age or capitalsize, respectively, is lower than the median values. To sort firms based on the payout ratio or the debt ratio, we first calculate the payout ratio and the debt ratio foreach firm. Subsequently, we consider firms as low-payout firms or low-debt firms
if their average values are less than those of the median value. Finally, we categorize firms into two groups, manufacturing and non-manufacturing, based on theindustry to which the firm belongs.
Table 6 describes the means and standard deviations by firm characteristics.
First, young firms conduct more physical investment, while mature firms conduct
more R&D investment. However, the means of both types of investment are closein value. On average, young firms reduce the number of employees and maturefirms increase the number of employees, although the means are close to 0. Second,on average, large firms have more physical investment, R&D investment, and employment than small firms do, even with similar values of finance variables.15 The
difference between firm sizes is relatively large in R&D investment. Third, fromthe classification of finance variables, high-payout firms or low-debt firms tend toinvest more in R&D capital and employment stock, with more cash flows or cash
reserves obtained. However, the difference in mean is unclear for physical investment. Finally, from the industry classification, firms in the manufacturing sector
conduct more capital and R&D investment, while firms in the non-manufacturing
15 This characteristic of Japanese firms is also shown in Arikawa et al.

(2011) and Sasaki (2016).
However, in the US, it is well known that young firms conduct more physical and R&D investment.
For example, Brown et al. (2009) show that fixed investment of young firms becomes one and a halftimes as large, while R&D investment becomes double that of mature firms.

25

Table 6: Descriptive statistics by firm characteristics
Full

Young

Mature

Small

Large

Low
payoutHigh
payout

High
debt

Low
debt

Mfg.

Nonmfg.

0.0191
0.022

0.01900.023

0.01940.022

0.01570.020

0.02250.024

0.01550.019

0.02400.024

0.01640.020

0.02190.024

0.02010.023

0.0131
0.019

0.039
0.045

0.041
0.049

0.037
0.042

0.036
0.046

0.041
0.045

0.038
0.046

0.040
0.043

0.039
0.047

0.038
0.043

0.041
0.046

0.025
0.037

−0.0010.086−0.007
0.081

0.005
0.091

−0.0020.088−0.0002
0.085−0.001
0.085

0.003
0.083

−0.004
0.091

0.001
0.081

−0.002
0.086

0.006
0.089

0.053
0.048

0.060
0.051

0.046
0.043

0.053
0.046

0.053
0.050

0.047
0.045

0.063
0.047

0.046
0.047

0.060
0.047

0.054
0.048

0.046
0.047

RD
mean
sd
CAP
mean
sd
EMP
mean
sd
CF
mean
sd
∆CH
mean
sd
DebtIssue
mean
sd

0.009
0.083

0.014
0.090

0.004
0.074

0.011
0.085

0.007
0.081

0.008
0.079

0.013
0.082

0.006
0.087

0.012
0.079

0.009
0.082

0.011
0.087

−0.0002
0.085

0.001
0.093

−0.0020.076−0.0002
0.088−0.0002
0.082−0.0003
0.084

0.001
0.080

−0.0001
0.094−0.0003
0.075−0.0004
0.085

0.001
0.086

Obs
Firms

73,3138,821

37,4955,156

35,818
3,665

36,0994,896

37,214
3,925

29,766
3,346

29,7942,978

36,668
4,772

36,645
4,049

62,966
7,229

10,347
1,592

Notes. Except for the grouping by payout ratio, the sum of firms becomes 8,821. The sum of firms in highand low payout ratio groups becomes 6,324, which suggests that about 2,500 firms are non-listed.

sector employ more workers, on average.
Table 7 shows the estimation results for the Euler equation (11)-(13) by firm
characteristics. The upper panel of the table shows the result of the physical investment Euler equation, the middle panel shows that of the R&D investment, andthe bottom panel shows that of employment. As suggested in Subsection 5.2, ourmain concern should be the state of financial constraints and their effects on investment and employment. Thus, we focus on the total effects of finance variables, thatis, the sum of the coefficients of current and lagged finance variables, together withthe p-values from the chi-squared test that the sum of the coefficients equals zero.
We first examine the significance of the sum of the debt issuance coefficientsof all equations for all categories to observe whether firms in a category are understrict financial constraints. Except for the employment equation of firms with highdebt ratios, the sum of the coefficients of the debt issues is insignificant in all cases,
even at the 10% level. Therefore, in general, firms are not restricted by externalfunding. This finding is consistent with the result in Subsection 5.2.
Next, we examine the effects of internal finance variables. Table 7 shows thatwhether the coefficients of those variables are significantly estimated depends on

26

Table 7: Estimation results by sample splits
1: Physical investment regression by sample splitsCFp-value∆CH
p-value
DebtIssue
p-valuem1
m2
Hansen J-test (p-value)
Diff-Hansen test (p-value)
Obs
Firms

Age
Young

Mature

Size
Small

Large

Payout
Low

High

Debt
High

Low

Industry
Mfg..

0.406
0.097
−0.479
0.121
0.436
0.150
−5.09
0.62
0.144
0.080
37,495
5,156

0.278
0.465
−0.202
0.730
0.049
0.923
−3.38
0.30
0.444
0.725
35,818
3,665

0.451
0.304
−0.334
0.552
0.073
0.881
−3.11
0.51
0.905
0.771
36,099
4,896

0.195
0.366
−0.187
0.503
0.029
0.904
−6.25
0.17
0.687
0.582
37,214
3,925

0.732
0.116
−0.864
0.111
0.547
0.304
−1.62
0.11
0.553
0.476
29,766
3,346

0.348
0.217
−0.414
0.296
0.236
0.551
−3.60
−0.11
0.908
0.768
29,794
2,978

0.328
0.155
−0.345
0.263
0.310
0.243
−3.04
0.83
0.447
0.333
36,668
4,772

0.217
0.509
0.026
0.957
−0.267
0.619
−3.31
−1.08
0.822
0.634
36,645
4,049

0.453
0.244
−0.411
0.443
0.130
0.786
−4.09
0.16
0.669
0.481
62,966
7,229

Nonmfg.
0.127
0.828−0.147
0.804
0.200
0.756
−1.99
1.11
0.212
0.117
10,347
1,592

2: R&D investment regression by sample splitsCFp-value∆CH
p-value
DebtIssue
p-valuem1
m2
Hansen J-test (p-value)
Diff-Hansen test (p-value)
Obs
Firms

Age
Young

Mature

Size
Small

Large

Payout
Low

High

Debt
High

Low

Industry
Mfg.

0.067
0.334
−0.0480.582−0.021
0.802
−5.14
1.31
0.853
0.707
37,495
5,156

0.071
0.263
−0.063
0.562
0.023
0.830
−3.54
1.46
0.403
0.807
35,818
3,665

0.154
0.030
−0.158
0.094
0.073
0.352
−4.34
1.73
0.296
0.149
36,099
4,896

0.097
0.265
−0.0720.417−0.007
0.900
−2.94
1.90
0.656
0.444
37,214
3,925

0.181
0.114
−0.221
0.063
0.079
0.320
−2.29
0.98
0.845
0.846
29,766
3,346

0.099
0.264
−0.175
0.115
0.146
0.180
−3.35
1.10
0.997
0.989
29,794
2,978

0.041
0.605
−0.060
0.496
0.062
0.407
−2.16
1.55
0.920
0.832
36,6684,772-0.003
0.964
0.018
0.890
−0.064
0.707
−2.79
1.280
0.796
0.624
36,645
4,049

0.091
0.063
−0.108
0.142
0.011
0.871
−5.21
2.17
0.808
0.852
62,966
7,229

Nonmfg.
0.005
0.958
0.090
0.367
−0.108
0.430
−2.39
0.63
0.882
0.753
10,347
1,592

3: Employment regression by sample splitsCFp-value∆CH
p-value
DebtIssue
p-valuem1
m2
Hansen J-test (p-value)
Diff-Hansen test (p-value)
Obs
Firms

Age
Young

Mature

Size
Small

Large

Payout
Low

High

Debt
High

Low

Industry
Mfg.

1.843
0.034
−1.719
0.089
1.311
0.138
−2.80
1.48
0.970
0.911
37,495
5,156

0.612
0.476
0.760
0.529
−1.021
0.283
−1.51
0.02
0.942
0.870
35,818
3,665

1.132
0.091
−0.946
0.264
0.460
0.470
−2.59
0.49
0.196
0.313
36,099
4,896

0.690
0.141
−0.421
0.467
0.393
0.381
−2.10
0.89
0.096
0.043
37,214
3,925

0.640
0.464
−0.1870.840−0.645
0.336
−2.20
1.37
0.981
0.942
29,766
3,346

1.243
0.028
−1.471
0.076
0.647
0.424
−1.46
0.31
0.938
0.935
29,794
2,978

2.180
0.021
−2.174
0.040
1.347
0.039
−2.25
−0.09
0.728
0.585
36,668
4,772

0.436
0.281
−0.093
0.895
0.241
0.734
−2.93
1.44
0.845
0.720
36,645
4,049

1.300
0.028
−1.261
0.089
0.451
0.365
−3.58
1.29
0.457
0.365
62,966
7,229

Nonmfg.
1.530
0.260−0.2720.879−0.376
0.842
−1.36
−0.48
0.994
0.994
10,347
1,592

Notes. The top panel shows the result of the physical investment Euler equation, the middle panel shows that of the
R&D investment Euler equation, and the bottom panel shows that of the employment Euler equation. Except for thegrouping by payout ratio, the sum of firms in each group becomes 8,821. The sum of firms in the high and low payout
ratio groups becomes 6,324, which suggests that about 2,500 firms are non-listed.

27

the firm characteristics. Comparing young and mature firms by Columns 1 and 2
in each panel of Table 7, we find that no coefficients of the internal (or external, as
just mentioned) finance variables are significant for mature firms, indicating thatthey are not financially constrained. However, for young firms, cash flows and/orchanges in cash holdings show significant effects at the 5% to 10% levels in thephysical investment and employment equations. As debt issuance by young firms
is not restricted, they are weakly financially constrained. These findings on young
firms are in line with the baseline results in Subsection 5.2. However, unlike thebaseline results, for young firms, internal finance variables affect physical investment and employment rather than R&D investment. Equipment and labor expansion may be more profitable than R&D during the growth stage of a firm. On the
one hand, for the difference depending on firm size, Column 4 of Table 7 shows that
there are no internal and external financial constraints among the large-scale firms.
On the other hand, for small-scale firms, Column 3 reveals that internal financeaffects both R&D investment and employment, which is similar to the scale-wide
results in Subsection 5.2.
The results classified by financial status are shown in Columns 5 to 8 of Table 7.
Cash holdings are used to sustain R&D investment in the low-payout firms while
the high-payout firms use cash flows and cash holdings for employment (Columns
5 and 6, respectively). This finding is consistent with the interpretation in Subsection 5.2 that low-payout firms, which are considered to have relatively tight cash
flows, preferentially use internal reserves for R&D investment. However, highpayout firms, not low-payout firms, use internal financing for employment. The
reason is not clear, but it may be that a firm with relatively few investment opportunities returns profits to both shareholders and workers (to the latter, by employment protection). Columns 7 and 8 report the results when firms are classified bydebt ratio. Employment in firms with high debt is affected by both internal andexternal finance. It is a convincing argument that firms with a high debt ratio arein the strict financial-constraint regime, and that this regime can be observed only
when the debt-ratio splitting criterion is used. However, it is unclear why only employment stock suffers from financial constraints. It is possible that, in such firms,
there are few opportunities for profitable physical and R&D investment and thus,
only employment is affected by limited funds.
Finally, Column 9 of Table 7 shows that in the manufacturing sector, internalfinance affects R&D investment and employment; these findings are similar to the28industry-wide results in Subsection 5.2. Conversely, as shown in Column 10, in
the non-manufacturing sector, no finance variables affect the changes in any factor
stock types.
The main conclusions of the comparative analysis by firm attributes in this subsection are summarized as follows. (1) The financial-constraint regime varies acrossfirm characteristics. As shown in the literature, for example, Brown and Petersen(2015), firms that are more likely to face financial constraints due to ex-ante criteria (i.e., young, small-sized, and manufacturing) indeed suffer from financial constraints, which are internal. (2) There are more significant coefficients of financevariables, and their significance levels are higher (p-values are smaller) roughly inthe following order: employment, R&D investment, and physical investment. (3)Young, small-sized, and/or manufacturing firms tend to allocate their cash flows
and cash holdings to buffering their R&D investment and employment rather thanto protect their physical investment.

5.4

Financial crises and difference-in-difference estimation

In this subsection, we confirm whether financially constrained firms are more affected by their financial positions during crisis periods (e.g., the Asian financial crisis, the US dot-com bubble recession, and the 2008 global financial crisis) than noncrisis periods. During financial crises, both firms and banks seriously suffer because of their large decrease in operating incomes and damage to the balance sheet,
which influence the firms’ financial decisions through both the supply and demand
channels. We investigate these effects by using the difference-in-difference estimation technique of Guney et al. (2017). Specifically, we construct a crisis dummy
variable that takes the value one during the following periods: 1998–1999, 2001–
2002, and 2008–2009. We then interact this dummy with the finance variables, suchas cash flows, changes of cash holdings, and debt issues. The estimation methods
are basically the same as those in Subsections 5.2 and 5.3.
Table 8 shows these results. We find that the point estimates for coefficientsof regressors except finance variables take close values to those in Table 5 in thephysical and the R&D investment equations. Similar to the result of Table 5, thesecoefficients are insignificant in the employment equation. Furthermore, most results for the sum of the coefficients of cash flows, changes in cash holdings, anddebt issues are similar to those in Table 5.

29

Table 8: Difference-in-difference estimation for the effects of financial crises
Dependent variable

CAP

I NV (−1)
I NV 2 (−1)
Sale
Sale(−1)
Wage
Wage(−1)
CF
CF (−1)
CF ∗ Crisis
CF (−1) ∗ Crisis∆CH∆CH (−1)∆CH ∗ Crisis∆CH (−1) ∗ Crisis
DebtIssue
DebtIssue(−1)
DebtIssue ∗ Crisis
DebtIssue(−1) ∗ Crisis
m1
m2
Hansen J-test
Diff-Hansen
Obs
Firms
Sum CF (p-value)Sum ∆CH (p-value)
Sum DebtIssue (p-value)
Sum CF ∗ Crisis (p-value)Sum ∆CH ∗ Crisis (p-value)
Sum DebtIssue ∗ Crisis (p-value)

0.751∗∗−2.311
0.119−0.0990.315−0.062−0.7890.769−0.2100.114
0.036−0.272−0.012−0.1050.084
0.378−4.74∗∗∗
0.69
0.325
0.324
73313
8821
0.252
−0.096−0.117−0.020−0.235
0.463

EMP

RD

[0.346][1.526][0.095][0.095][0.573][0.708][1.694][1.806][0.323][0.274][0.757][0.749][0.270][0.313][0.748][0.749](0.454)(0.854)(0.761)(0.944)(0.698)(0.408)0.929∗∗∗−1.983∗0.014−0.0150.176−0.062−0.0780.086−0.052−0.011−0.248
0.068
0.011
0.016
0.202
−0.104−2.05∗∗
1.44
0.878
0.788
73313
8821
0.114
−0.0630.027
0.008−0.1800.098[0.098][1.074][0.027][0.023][0.142][0.139][0.341][0.329][0.074][0.054][0.361][0.153][0.061][0.064][0.332][0.207](0.120)(0.533)(0.741)(0.935)(0.555)(0.657)

0.301
0.311
0.079
−0.056−0.014
0.008
1.107
0.350
3.601
−3.372−0.524−0.768−2.812
1.482
0.191
0.846
2.437
−1.857−2.30∗∗
1.47
0.653
0.506
73313
8821
1.456
−1.2921.036
0.229−1.3300.580[0.245][1.096][0.183][0.159][0.044][0.031][1.229][1.192][3.375][3.263][0.622][0.517][2.463][1.416][0.531][0.575][2.438][1.593](0.024)(0.103)(0.125)(0.712)(0.515)(0.757)∗∗∗ p < 0.01; ∗∗ p < 0.05; ∗ p < 0.1
Notes. The table shows the estimates of (11) to (13) with the crisis dummies. A term with “∗Crisis”
indicates the cross-term of the relevant variable and the crisis dummy. Estimation is by system GMM
with lagged levels dated t − 3 and t − 4 used as instruments for differences, and lagged differences
dated t − 2 used as instruments for levels. The regression sample is the full sample in Table 3. Robust
standard errors are reported in brackets.

30Despite these similarities, the significance of the estimates worsens, as shownin Table 8. This is because the coefficients of additional regressors are not significant. All the interaction terms between the crisis dummies and finance variables
are insignificant, and their sums are also insignificant. This phenomenon is known
as an over-specification problem (see Davidson and Mackinnon, 2004), in which
regressors that do not belong in a true model generally increase the variance ofthe estimates on the regressors that do belong. We interpret this result as Japanesefirms relying on their internal funds in the same way, that is, being weakly financially constrained, during both financial crisis and non-crisis periods.16

6 Conclusion
To reveal the major causes of the different behaviors of physical investment, R&D
investment, and employment, we estimate the modified Euler equations for the determination of these production factors by using the microdata of Japanese firms.
Our results have implications for the analysis of long-term slack physical investment from Japan’s two lost decades and financial crises.
The main estimation results are summarized as follows. First, Japanese firms
face weak financial constraints in the sense that borrowing amount is not restricted,
but internal finance is not sufficient. Second, firms first allocate their cash flows
and/or cash holdings to buffer employment, and then incur R&D investment rather
than protecting physical investment. Third, the following reason for this internalfinancing order is suggested. The marginal profit from R&D investment or employment is larger than that from physical investment. Subsequently, this larger
marginal profit of R&D investment or employment may stem from its larger productivity or larger adjustment cost savings. This reason is consistent with thegreater stability and resilience observed for R&D and employment than that forphysical investment in aggregate levels.
We also examine the differences in firms’ decision making regarding physicalinvestment, R&D investment, and employment among different firm characteristics and find several results, which are summarized as follows. First, firms thatare more likely to face financial constraints due to ex-ante criteria (young, small16 Note that the BSBSA data are annual, not quarterly, like those of, for example, Duchin et al.

(2010). If we were able to use quarterly data, we might capture the impact of the financial crisis moreaccurately in a narrow timeframe. Such information losses may lead to our regression results, thatthe financial crisis dummies have less explanatory power.

31

sized, and manufacturing) indeed suffer from weak financial constraints. Second,
a comparative analysis based on several characteristics of firms does not change
the finding that firms subject to weak financial constraints prioritize the allocationof funds in the following order: employment, R&D investment, and physical investment.
Finally, we examine whether financial constraints are tighter during financial
crisis periods (e.g., the Asian financial crisis, the US dot-com bubble recession, andthe 2008 global financial crisis) than non-crisis periods. The results show that firms
rely on internal funds and are not restricted by external funds even during thefinancial crises, in the same way as they are in normal times.
This study reveals several characteristics of the factor adjustment behavior ofJapanese firms since the mid-1990s by the estimation of Euler equations based on
firm microdata. The Euler equation approach provides insights based on structuralparameters. These insights help to understand the weakness of Japanese physicalinvestment in recent years. However, it should be mentioned that this study does
not identify the structural parameters but infers them from the estimated reducedform coefficients. This indirect method is commonly used in this field because
the identifications of structural parameters are joint hypothesis tests with restrictive assumptions of the structural model, such as functional forms. We estimate
reduced-form equations that seem slightly ad hoc but with a structural model as
the background. Therefore, it is not possible to distinguish clearly between the
productivity and the size of adjustment costs, which constitute factor profitability.
In future research, we aim to identify the parameters of interest without imposing
restrictive conditions as far as possible, and to confirm the insights found in thisstudy.
In addition, we show that estimating and comparing multiple Euler equationsof the production-factor stock yields quite different implications for financial constraints than just examining a single Euler equation. This fact may be relevant not
only in Japan, but also in countries where employment adjustments are costly (e.g.,
European countries where trade unions have power).

32

Appendix
The Euler equation for investment i ∈ {1, 2} with no financial constraint is()[]
Pit + Pt ψi,Iit + ψi,Ki,t+1 = Pt FKi,t+1 + Et β t,t+1 ( Pt+1 ψi,Ii,t+1 + Pi,t+1 ) ,(A1)as explained in Subsection 3.1. To obtain the empirical specification, we assume thefollowing forms of production and adjustment cost functions.
α1α2δF (Kt+1 , Nt+1 ) = AK1,t+1 K2,t+1 Nt+1 .

ai
ψi ( Iit , Ki,t+1 ) =
2

(

Iit
Ki,t+1

− bi

(A2)

)2
Ki,t+1 .

(A3)Then, rearranging Equation (A1) and shifting time back by one period yields
[

{

(

Iit

)

1
+ Pit
ai

}]

− bi
Ki,t+1
(
)2
( 2
)
b
I
I
α Y
1
1
= Pt−1 i,t−1 − Pt−1 i,t−1 − Pt−1 i t−1 + Pt−1 i − bi + Pi,t−1 ,
Kit
2
Kit
ai Kit2
ai
Et−1 β t−1,t

Pt

(A4)

where Yt−1 = F (Kt , Nt ).
Linearly introducing the expectational errors realized at time t, ϵt , into (A4)yields()
Ii,t−1α Y
zt−1,t Ii,t−1 2= zt−1,t−− zt−1,t i t−1
Ki,t+1Kit
2
Kit
ai Kit()( 2)biPi,t−11
Pit− zt−1,t β t−1,t−+ zt−1,t− bi + bi + ϵ t ,
ai
Pt−1
Pt−1
2
Iit

where
zt−1,t =Pt−1,
β t−1,t Pt
1

(A5)

(A6)

is the gross real required rate of return. Our basic Euler equations are close to thoseinduced by Bond and Meghir (1994) with slightly different assumptions.
Assuming that the fourth and subsequent terms on the right-hand side of (A5)
are included in the time-specific effect d and the firm-specific effect f , the basic

33

Euler equation for investment i is obtained as(z)
I= zt−1,t i,t−1 − t−1,tKi,t+1
Kit
2
Iit

(

Ii,t−1
Kit

)2

(

−

αi zt−1,t
ai

)

Yt−1
+ dit + f ji + ϵijt . (A7)Kit

In this derivation, we assume that the final goods market is perfectly competitive. However, the assumption of imperfect competition with the constant priceelasticity demand function of final goods, as in Bond and Meghir (1994), affects
Equation (A7) only through the firm-specific effect, and the implications for thecoefficients in our estimation equations do not change.
Similarly, we obtain the Euler equation for employment as
)
(
)
(
Ht
Ht−1 ( zt−1,t ) Ht−1 2
δzt−1,t Yt−1
= zt−1,t
−
−
Nt+1
Nt
2
Nt
c
Nt
(z
)W
t −1
+ t−1,t+ d Ej + f tE + ϵ Ejt .
c
Pt−1

(A8)

where the labor adjustment cost function is ϕ( Ht , Nt+1 ) = (c/2)( Ht /Nt+1 − g)2 Nt+1 .
We estimate the equations individually by system GMM without explicitly considering the coefficient constraints across these Euler equations.

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