Large Firms and Soft Budget Constraints for Transition
Economies*
Tomoe Moore
Centre for Empirical Finance, Brunel University
Uxbridge, West London, UB8 3PH, United Kingdom
E-mail: tomoe.moore@brunel.ac.uk, Tel: 018952 67531, Fax: 018952 69770

Abstract

Soft budget constraints (SBCs) are broadly defined as the circumstance where
government will rescue loss-making firms to guarantee their survival in the form of
direct or indirect injection of credit. Corporate finance in the former socialist
economies of Central and Eastern Europe was, in general, characterised by the
syndrome of SBCs, thereby resources to be often used for inefficient investment. In
the European Union (EU), the new member states were party to a series of new
institutional arrangements, including the removal of SBCs. This article empiricallyexamines the impact of SBCs on investment for large firms in six new EU member
states using the panel data at firm level over the sample period of 1995 to 2006. We
develop the Tobin’s Q and Euler investment models from the profit maximization
problem. Due to a potential correlation between regressors and error terms, theinvestment model is likely to suffer from endogeneity problems. To address thisissue, the Generalized Method of Moments techniques are applied for estimation. The
key estimation results are as follows. There appears to be a clear indication of theexistence of SBCs in determining investment in the Czech Republic and Poland,
whereas for Hungary and the Baltic countries, the operation of SBCs seems to be
weak. When the investment model is extended by control variables, it is found thatentry to the EU and financial development are likely to reduce the prevalence ofSBCs, and that the current on-going integration with the EU and the promotion offinancial deepening would enhance and improve the efficient allocation of resources.

Keywords: Investment, Soft budget constraints, Transition economies, Generalized
method of moments panel estimation, Large firms
JEL Classification: C1, G3* Thanks are due to the British Academy, who funded this research (SG-45841). Usual disclaimers
apply.

01. Introduction
The transition economies were, in general, known for soft budget constraints (SBCs).
SBCs imply that government or financial institutions are willing to provide additional
funds to firms, or to bail them out (Kornai, 1992 and Lízal and Svejnar 2002). If
firms take advantage of SBCs, there is a risk that the funds might be used inefficiently
or for survival rather than for viable investment. The operation of SBCs tends tofavour former large state-owned firms, and this causes credit crunches for smallerfirms or new firms, which may face expensive bank loans or are denied access to theloans altogether (Berglöf and Roland 1997, Lizal and Svejnar 2002).

Evidence

indicates that soft budget constraints remain into later stages of transition: although
direct government subsidies have been substantially decreased, indirect subsidies
through banks or tax arrears continue to exist on a large scale (Lízal and Svejnar 2002
and Konings et al. 2003). It is, therefore, not surprising that the transition countries
are prone to excessive government deficits, building up high levels of public debt.
Other detrimental impacts on the economy include excess demand for labour andother resources, lack of fiscal consolidation, financial bubbles and lack of R & D(Kornai 2001 and Brücker et al 2005).
This paper empirically investigates the effect of SBCs on investment
behaviour for large firms in the Czech Republic, Hungary, Poland, Estonia, Latvia
and Lithuania. Data used are the firm level panel data over a relatively long time spanfrom 1995 to 2006. The paper is one of the few empirical works on SBCs for theCentral Eastern European Countries. Since the seminal work by Kornai (1979, 1980),
who attributes the practice of SBCs to government paternalism, a certain amount ofliterature has been published, albeit mostly from the aspect of political economy, e.g.
Hillman et al. (1987) and Shleifer and Vishny (1994) argue that governments allow

1

SBCs in order to gain public support or to avoid unemployment, whereas Dewatripont
and Maskin (1995) investigated a problem of governments’ unintended behaviour
from the initial commitment to not bail out firms. Empirical literature which deals
with the direct impact on investment is very limited. Exceptions include that of Lízal
and Svejnar (2002) over the sample period from 1992-1998 for the Czech Republic,
and Konings et al. (2003) with a time horizon of 1994-1999 for Poland, the Czech
Republic, Bulgaria and Romania. Both are based on accelerator theory using panel
data. Mueller and Peev (2007) also examined SBCs for the transition economies
using aggregate macroeconomic data, pooling twenty-six transition countries during
the period from 1989-2002.
In terms of the model, investment literature for developing economies
frequently involves measuring the impact of cash sensitivity on investment with the
cash flow as a proxy to capture the financial constraints.

This is based on anassumption that a firm is financially constrained if an increase in the supply ofinternal funds results in a higher level of investment spending. In the study ofJaramillo et al. (1996), Günçavdi et al. (1998) and Gelos and Werner (2002), the Euler
model is utilised, whereas Konings et al. (2003), Koo and Maeng (2005) and Bhaduri(2005) adopted the Q model. In many cases, the authors tend to find significant cash
flow sensitivity in investment decisions for developing economies, and a structuralshift is often observed as represented by a lower cash sensitivity of investment in thepost financial reform period. We derive an investment model by drawing on both the
Q and Euler models.
The problem is to find an appropriate proxy variable to measure the extent of
SBCs in empirical analysis. In the work of Brücker et al (2005), SBCs are measured
by the degree of labour productivity, arguing that labour hoarding is one of the key

2

features of a SBCs syndrome producing more labour intensity (Kornai 1979 and 1980,
Hillman et al 1987 and Goldfeld and Quandt 1988 and 1990). There is, however, alimitation to this approximation in applying it to Central Eastern European countries(CEEC). When they paved the way towards membership of the EU, among otherthings, the increase of foreign capital from the old EU should have substantiallyaffected the economies of the new EU member states. For example, FDI consists ofmanagement skill, know-how and technology, and that FDI generates technologicaldiffusion to these transition economies raising economic growth. In this respect,regardless of the presence of SBCs, it is conceivable that there is a large shift inproductivity in the post-entry period. In the study of Lízal and Svejnar (2002),
Konings et al. (2003) and Mueller and Peev (2007), all with the same mission of
investigating SBCs, the principle variable to measure the SBCs is the profit or cash
flow with the assumption that if firms enjoy SBCs, the sensitivity of investment tocash flows or internal funds should be insignificant. This, too, has a downside, as
cash flow may be correlated with investment for other reasons than financial
constraints, for example, the current level of cash flow predicts future profitability offirms (Gelos and Werner, 2002 and Bhaduri 2005). Also one of the main theories that
dominate the capital structure debate is the pecking order theory, where firms have ahierarchy of capital preferences. Such that, if firms are sensitive to cash flows, it
could be due to future prospects of profitability or a preference for using internalfunds for investment, not necessarily due to financial constraints. In this study,
although we also use cash flows as a component of financial constraints, a potential
direct measure of SBCs is specified to mitigate the limitation held in the cash-flow
investment model.

3

We use bank loans as a proxy for SBCs assuming that the banking system isconsidered to be a key route by which budget constraints are softened, where there are
directed (state) credits, which often go to politically powerful firms. Evidence on
whether banks provide funds to firms under the provision of SBCs is, however, lessstraightforward, since it is difficult to interpret the increases in loans observed in thetransition economies. Yet, a large and growing volume of debt classified as ‘bad’ bybanks in transition economies may verify the presence of SBCs through this channel.
Moreover, at the start of the transition period, independent commercial banks werecreated from a former monobank system, and the newly established banks had, in
effect, little capability of appraising projects, meanwhile some were under
government control for continuous credit to existing client firms (Lizal and Svejnar
2002). On the basis of this, the conventional investment model is augmented by thevariable of ‘credit’ as a proxy to capture the impact of SBCs on investment.
It is noted that we focus on large firms on the following grounds: Financial
markets in transition economies were underdeveloped so that firms were liquidityconstrained, whilst SBCs were an important component in the formation of capital
investment. It is theoretically and economically supported that the investment ofrelatively large firms or state-owned establishments was not liquidity-constrained
owing to generous financing from state banks, but that the opposite holds for smallerfirms (Schaffer, 1989). See also Lizal and Svejnar (2002) for empirical evidence forthe large enterprises in the Czech Republic. Large firms are, therefore, suitable forthe investigation of SBCs potentially generating more consistent empiricalperformance, and satisfying the purpose of this paper.
The key estimation results are as follows.

There appears to be a clear

indication of the existence of SBCs in determining investment in the Czech Republic

4and Poland, whereas for Hungary and the Baltic countries, the operation of SBCs
seems to be weak. When the investment model is extended by control variables, it isfound that entry to the EU and financial development are likely to reduce theprevalence of SBCs, and that the current on-going integration with EU financial
markets and the promotion of financial deepening would enhance and improve theefficient allocation of resources.
The rest of the paper is organised as follows. Section 2 presents the structuralinvestment model based on a dynamic optimization problem, and derives theempirical model for estimation. Section 3 describes the data. Section 4 provides theempirical results.

Section 5 examines the effect of entry to the EU, firm size,

financial development and business cycles on SBCs. Section 6 concludes.

2. Investment Model
We derive the Q and Euler models from the profit maximization problem. Following
Gilchrist and Himmelberg (1998), Love (2003) and Koo and Maeng (2005), we
assume that firms maximise the present value of the firm:∞
Vt ( K t , At ) = max Dt + Et ∑ β t + s −1 Dt + s  s =1(1)

Subject to: Dt = Π ( K t , At ) − C ( I t , K t ) − I t
where K t = the beginning of the period capital stock ( K t +1 = (1 − δ ) K t + I t ), At = a
productivity shock, Dt = dividend, β = discount factor, δ = depreciation rate, I t =investment, Π = profit and C = adjustment cost of investment.
1) The Q model5

We define marginal Q as the ratio of the increase in a firm’s value to one additional
unit of capital, then the first-order condition for investment is given by (Koo andMaeng 2005):

 ∂V 
 ∂C 

 =−
 − 1 + β t +1 Et Qt = 0 ∂I  t ∂I  t ∂V 
where Qt =  ∂K  t +1

(2)

For the adjustment cost function, linear homogeneity in capital and investment isassumed, and it is given by:

α  It2I C ( I t , K t ) = 
− γ t −1  K t
2  KtK t −1 (3)

The lagged investment to capital implies that it may be easier for the firm to continue
investment at some fraction γ of the previous period ratio, since, for example,
alternative arrangements would be costly.
We assume the expected returns at the beginning of period t by the actual
returns realized over the period plus an expectation error. Then, substituting (3) into(2) generates the form of:

I 
I 
  = β 0 + β1   + β 2 Qit + u it
 K  it
 K  it −1
We specify the disturbance terms
specific fixed effect and(4), equal towherecapture a firm-

is white noise.

The base model implies that Q is the sole determinant of investment.
Following the investment literature, we specify the cash flow (CF) variable, which
captures firms’ internal financial positions, often providing the impact of financial
constraints on investment. Specific to transition economies, credit flows (Credit), i.e.
bank loans are also specified to capture the impact of SBCs:

I 
I 
 CF 
 Credit   = β 0 + β1   + β 2 Qit + β 3 
 + β4  + u it
 K  it
 K  it −1 K  it
 K  it(5)

6

The positive coefficient on CF/K is usually interpreted as an indication that the firms
are credit constrained. It is based on the assumption that if external finance is costly
due to the underdeveloped capital markets of developing countries, firms use internalfinance for investment. A test for the existence of financing constraints amounts tothat for the statistical significance of CF/K. Lízal and Svejnar (2002) and Konings etal. (2003) argue that a zero coefficient on CF/K signals that firms have easy access tobank credit suggesting the presence of SBCs (see also Mueller and Peev 2007). This
interpretation seems to be simplistic and misleading.

For example as financial

markets develop with an increase in alternative external finance, the coefficient on

CF/K may become insignificant regardless of the presence of SBCs. In order directly
to capture the impact of SBCs on investment, Credit/K is specified as an additionalvariable.
Likewise the limitation of cash flow, the sensitivity to Credit/K may notnecessarily arise as the consequence of the SBCs.

The plausible balanced

interpretation is that if both cash and credit flows are statistically significant, thenfirms are more likely to suffer from financial constraints: investment may be sensitive
to credit, yet the inadequate amount of credit released under SBCs may force firms torely on internal funds, i.e. cash flow. It could be the result of a pecking order in place,but again it indicates the weak operation of SBCs. On the other hand, the highsensitivity of firms’ investment decisions to the supply of bank funds without
resorting to cash flow may signal the dominance of SBCs in corporate finance.
The interpretation of Credit is different from that of Bhaduri (2005), whoapplies the investment function of the sales accelerator type, with the sales, cash flow
and long-term debt as regressors, to panel data for Indian firms. Bhaduri argues thathighly leveraged firms may adversely affect the availability of external finance, since7

an increase in leverage pushes up the cost of borrowing and reduces investment. The
coefficients on credit are found to be mostly negative in Bhaduri’s study, clearly
demonstrating the adverse effect of credit for Indian firms. By contrast, in this study,
the coefficients on Credit/K turn out to be all positive, as shown in Section 4. The
message is that the same interpretation may not be applicable to economies withdifferent economic structures. Günçavdi et al. (1998) point out that if the credit
constraint is binding for firms, investment will be (positively) sensitive to the flow ofcredit, and firms’ investments will be less than predicted in the unconstrained model.
The role of credit in our model is conceptually similar to that of Günçavdi et al.
(1998) apart from the fact that credit is interpreted as a corollary of SBCs.

2) The Euler equation model

From the profit maximization model (1), we introduce financial frictions via Dt ≥ 0 ,i.e. nonnegativity constraint on dividends. The multiplier ( λt ) on the non-negativity
constraint on dividends equals to the extra cost being associated with raising new
equity (Love 2003). This implies that external financing incurs the extra cost. The
equation (2) is modified as given by ∂V 
 ∂C  = − (1 + λt )
 − 1 + β t +1 Et Qt = 0 ∂I  t ∂I  t(6)

The envelope theorem is given by: ∂V 
 ∂D 
Qt −1 = 
 = (1 + λt )
 + (1 − δ ) β t +1 Et Qt ∂K  t ∂K  t(7)

The Euler equation is derived by combining the equations (6) and (7):  ∂Π   ∂C   ∂C 
1+ 
 = β t Et Θ t 
 + (1 − δ )1 +  
 ∂I  t  ∂K  t +1
  ∂I  t +1 (8)8

where∂C∂Π= the marginal adjustment cost of investment and= MPK= the∂I∂Kmarginal product of capital. Following Gilchrist and Himmelberg (1999), we assume
that a production function has a Cobb-Douglas form, thenwhere S represents sales. Also we assume that the firm makes the period t investment
decision at the end of the period t-1, thus the timing for the sales to capital ratio is theend of period t. The relative shadow cost of external finance in periods t and t+1 is 1 + λt +1  referred to as a factor of financial constraint. If theexpressed as Θ t = 
 1 + λt 
shadow cost of external funds is higher in period t than in period t+1, then Θ t < 1 .
This may be a factor which induces the firm to postpone or reduce its investment,
where the firm is said to be ‘financially constrained’ (Love 2003), Θ t is the measureof financing constraints. λt is assumed to be associated with time-varying observablefirm characteristics, and the cash flow is commonly used as the variable.1 However,specifically for the former socialist countries, with the presence of SBCs, the firm
may expect to be more constrained at t+1 than it is today, and at time t its investment
will be less constrained. In this case the firm is more likely to invest at time t. This isequivalent to firms being financially unconstrained. So Θ t ≤ 1 or Θ t ≥ 1 dependingon the extent of SBCs in place, andflows as Θ t = ϕ 0 + ϕ1 (

Θ t is expressed as a function of cash and credit

CF
Credit)t +ϕ2 () t The Euler equation (8) states that theKK.

marginal cost of investing today (adjustment cost and the price of investment goods,
normalized to one) is equal to the sum of the foregone marginal benefit of an extra
unit in capital and the discounted marginal cost of investing tomorrow.

9The stochastic discount factor Θ t in a multiplicative form is specified asadditively. Using a first-order Taylor approximation around the means to linearize
the term, we haveCF
Credit
I 
I 
S
) it + ε it  = β 0 + β1   + β 2   + β 3 ( ) it + β 4 (KK K  it
 K  it −1 K  it(9)This is the Euler model and used for estimation.

3. Data

The firm data are for 6 new EU countries of the Czech Republic, Hungary, Poland,
Slovakia, Estonia, Latvia and Lithuania from 1995 to 2006. The study of relatively
advanced countries among the new EU countries and the Baltic countries may provide
a useful comparative study.
The firm data are collected from the Reuters databases, which contain data oflarge listed firms with a longer sample period. To supplement the data from thissource, firms’ data are also collected from the OSIRIS database, which also contains
major firms. Banks and other financial institutions are excluded. Firms are chosen
where data are available at the latest for the year of 2005 in order to see the effect ofentry to the EU, which took place in May 2004, and also where there are a minimumof 7 consecutive years. The turnover for the firms chosen is greater than US$15
million at 2006. The aggregate data are collected from the World Development
Indicators.
The detail of the variables, means and the standard deviations of the key
variables and a number of firms and observations are found in Table 1. The Czech
Republic has a relatively high standard deviation for the investment-to-capital ratio
amongst other countries. In general, the variable of Q has a lower standard deviation

10

relative to the mean being less noisy as compared with the rest of the variables, except
for Latvia. There are relatively limited data available for the Baltic countries, andthis caveat needs to be taken into consideration in the empirical result, at the sametime this may be one of the reasons why there is no empirical work on firms in thesecountries. As to the variable of credit, we consider the Current Liability (bank loans)rather than long-term loans. Long-term debt is, normally, obtained through the capital
market, and this would increase in line with financial market development, and that it
is unlikely to reflect the extent of the SBCs.
[Table 1 around here]
4. Estimation and result

In equations (5) and (9), the error term may capture shocks derived from changes intechnology or productivity, and in turn the shocks may affect sales, cash flows or
profitability. Hence regressors and error terms are potentially correlated, and theinvestment model is likely to suffer from endogeneity problems. To address thisissue, the Generalized Method of Moments (GMM) techniques of Arellano and Bond
(1991) are applied for estimation. The methodology requires instrument variables.
The GMM estimator minimizes some criterion functions.

It does not require

information on the exact distribution of the disturbances, and parameter estimates arechosen in a way that the correlations between the instruments and disturbances arealmost zero. By choosing an appropriate weighting matrix in the criterion function,
GMM can be made robust to heteroskedasticity and to serial correlation.
In the GMM model, over-identification restrictions are used to test for thevalidity of instrument variables, whereas a serial correlation test is used to check thepresence of individual firm effects. The presence of unobserved firm effects implies
that the GMM is modelled by differencing each variable in order to remove the firm-

11specific effects, which include issues such as ownership, legal status. Arellano andBond (1991) demonstrate that, in a first difference model, good instruments are thevalues of the endogenous explanatory variables dated t-2 or at earlier dates, as they
are unlikely to correlate with the contemporaneous first differenced error term. The
instrument variables used in this study are the lagged dependent variables andregressors, since they are orthogonal to the disturbances. The regression results basedon Q and Euler models are reported in Table 2a-2b.
[Table 2 around here]

The first-and second-order serial correlation test statistics mostly indicate the absenceof serial correlation at the 5% or at least at the 1% level (though some exhibit serial
correlation in either the first order or the second order at the 1% level, but not bothorders together). This suggests that little unobserved individual effects remain in the
GMM estimation results.

Where there is heteroskedasticity detected, White

heteroskedasticity-consistent standard errors are applied. The over-identification tests
indicate that the null is not rejected in all cases at the conventional significance level,
suggesting that the one- and two-lagged levels of variables as instruments are valid.
Overall these tests seem to be satisfactory, supporting the model specification. In
view of the difficulties that are frequently encountered in estimating this type ofmodel, our estimates are encouraging.
The data are increasingly supportive of the Euler rather than the Q model,
when one notices that more coefficients on S/K are statistically significant with thecorrect positive sign than those on Q2. It is often argued that the main limitation with
implementing the Q-theory is to find a proxy for the unobservable marginal q. The
market value of assets relative to their book value is commonly used as a proxy.
However, due to market imperfections, the market’s valuation tends to be

12

considerably divergent, leading to measurement error (see e.g. Kaplan and Zingales
1997 and Gomes 2001). We have proxied the q as the ratio of equity and debt to total
assets, which might raise a measurement error and this may in part explain some ofthe poor performance of the Q model.

Nevertheless, we present the Q model

estimates, as they serve to provide the robustness check for the results of the Euler
model.
Start with Table 2a, which shows the model without Credit/K.

The

coefficients on the cash flow take different values for the different countries in themodel. As to the Czech Republic, Hungary and Poland, the coefficients are either
insignificant or the wrong sign of negative. This seems to imply less financial
constraints in these countries.

On the other hand, the coefficients are highly

significant being positive for Estonia and Lithuania (also Latvia, albeit not in the
Euler model). For Lithuanian firms we find a high coefficient of 7.7 and 4.4 in the Q
and Euler models respectively, suggesting the strong cash sensitivity of investment.
There may be a small degree of operation of SBCs in the Baltic countries, which do
not have easy access to bank loans and so the financial constraints may force theBaltic countries to rely on internal finance.
Table 2b, where the model is augmented with the variable of credit flow, ismore informative. In all cases, the coefficient on the Credit/K is positive and highly
significant at the 1% level. Given the insignificant or negative coefficients on CF/K
for the Czech Republic and Poland, the results may suggest that SBCs prevail, andfirms may not be operating under liquidity constraints.

When access to credit isfacilitated through preferential lending, there is less need of internal finance.
Our result is consistent with the study of Lizal and Svejnar (2002) for large
firms in the Czech Republic, who find an insignificant coefficient on CF/K during

13

1992-1998. However, it is different from that of Konings et al (2003) for the Czech
Republic and Poland, who found statistically significant cash flows and suggested thatthe results were due to a lesser operation of SBCs. The different sizes of firms andalso different sample periods used in their study may have resulted in conflicting
results. It is, however, noted that the available data indicate that, during their sample
period of 1994-1999, domestic credit to the private sector (% of GDP) for the Czech
Republic amounted to a relatively high 67.9% (World Development Indicator). Also,
having gone through an unexpected recession during 1996-1999, the banks in the
Czech Republic relaxed budget constraints for state-owned firms, partly due topolitical pressure. The Czech banks accumulated a large portfolio of nonperforming
loans, and by the mid-to-late 1990s many of them had reached the edge ofbankruptcy. On the other hand, the large scale of corporate bankruptcies was avoided
in the 1990s. It is, therefore, hard to imagine that firms in the Czech Republic facecredit constraints, as Koning et al (2003) claim.

In the case of Poland, thecoefficients on CF/K are negative in both Table 2a and 2b. This seems to reflect thecase (recall a component of cash flow is net profit), where the loss-making firms hadaccess to bank loans under SBCs, and on average were able to maintain investment
rates that were comparable to those of profitable firms (Schaeffer 1998 and Lízal and
Svejnar, 2002).
As to Hungary and the Baltic countries, both coefficients on CF/K and

Credit/K are positive significant (though for Lithuania, CF/K is insignificant in the Qmodel). Investment increases in line with the available cash and credit flows.

The

implication is that firms are financially constrained, indicating the weak operation of
SBCs. It is argued that for Hungarian firms hard budget constraints (HBC) were inplace. Bonin and Schaffer (2002) and Shaffer (1998) demonstrated that Hungarian14

banks were not providing net financing to firms that were unprofitable in 1991 and
1992, and as a consequence large numbers of bankruptcies were observed. Banks
were apparently imposing hard budget constraints on firms in the early period oftransition.
The overall result indicates that the operation of SBCs seems to be weak in
Hungary, Estonia, Latvia and Lithuania, whereas it is strong in the Czech Republicand Poland. There is supporting evidence by looking at the banks’ nonperforming
loans. See Table 3, where the proportion of banks’ nonperforming loans to total loans(%) for these transition economies is presented for the period 2000 to 2004. The
Czech Republic and Poland exhibit a relatively high level of an average of 12.5% and18.8% respectively, whilst Hungary and the Baltic countries indicate less than 7%. If
the non-performing assets in the banking sector are closely associated with SBCs, thisconfirms our results.
[Table 3 around here]

Moreover, although the transition economies have gone through a similarphase of structural change, the privatization process, regulations and institutionalarrangements differ amongst them, and they may have given rise to the fact that sometransition economies were prone to the persistent presence of SBCs.
In the privatization process of the Czech Republic, each bank set up
investment funds, which collected the vouchers held by citizens into a mutual fund.
These vouchers were used to bid for shares of companies. Since more than 60% ofsuch vouchers went to such investment funds, banks became major owners of thelarge enterprises in the Czech Republic. Hence, banks were willing to make loanseven to unprofitable firms in which their investment funds hold large stakes. The
profitability of commercial banks has not improved as expected due to their continued

15

lending to insolvent enterprises (Kutan and Brada, 2000). The role of financial
intermediaries was also strong for Poland, where 15 National investment funds werecreated to own majority of state owned enterprises (SOE). Hungary had a different
stance, since some form of privatization was sought as early as 1968, well before thepolitical changes under the New Economic Mechanism.

First, enterprises wereseparated from direct state control by the introduction of intermediary structures, e.g.
pension funds, municipalities.

In order to ‘corporatize’ enterprises,

joint stockcompanies were established, where not only banks, pension funds and other SOEs
could hold stock, so that stake holders were relatively split among these institutions.
Individual entrepreneurship was allowed through the leasing of state assets toindividuals. Greater autonomy for SOE was granted to management and that decision
making was decentralized, therefore the profit maximising incentive was relatively
strong, and this distinguishes Hungarian firms from those in the Czech Republic andPoland.
In terms of external funds availability, the rights of foreign shareholders under
Hungarian law in Hungarian firms created a strong connection between privatization
and FDI, thus there obtaining external funds from abroad became easier. The relianceon the SBCs could be, as empirically evidenced, minimal. On the other hand, theCzech government was unwilling to allow foreign banks to enter the market, which
has worsened the lack of competition by the banks’ ownership of enterprises and by
the domination of the market by a few large banks.
The enforcement of bankruptcy law may also provide a useful insight into our
results. In Hungary, a bankruptcy law was established in 1991. It was deemed as an
essential component of economic reform, hence the provision of the Bankruptcy Act
was extremely strict. It forced the management of firms to initiate bankruptcy

16

proceedings, once they held overdue debts.

The automatic forced bankruptcy

improved the disciplinary stance amongst managements in firms and hardened thebudget constraint of Hungarian firms.

In the Czech Republic, the bankruptcy

regulation was also enacted in 1991, yet a large number of bankruptcies was avoided,
as mentioned above, due to the number of restrictions under the law. There was also alack of accountability amongst managers of firms, as there was no specified criminal
liability for failing to comply with the law until 2000 (see Janda, 2008 for moredetail). It appears that such a loose bankruptcy law played a role as a prerequisite foreasing the practice of the SBCs in the Czech Republic3.

5. EU entry, firm size, financial development and business cycle

In this section, potential factors which may cause a shift in parameters areinvestigated by augmenting the investment model with proxy variables. It is notedthat these factors may serve as conditional variables.
1) Augmented models
EU entry: In May 2004, 10 Central Eastern European countries joined theEU.

The new member states were conditioned to a series of new institutionalarrangements based on the Copenhagen Criteria, including a constraint on statetransfers to firms, restriction of the payment of subsidies to loss-making enterprises,
decentralization of credit allocation, and privatization of state banks (Brücker et al.
2005). Although the extent of the enforcement of these conditions is not certain,
during the phase of EU integration a decline in the SBCs is envisaged. Also, scope
for the acquisition of external finance becomes wider, reducing the role of cash flow
in investment decisions. Hence, entry to the EU is expected to mitigate financial
constraints and SBCs, promoting more market-determined investment decisions. It is,17therefore, hypothesised that the sensitivity to both cash and credit flows weakens in
the post entry period.
To test whether joining the EU has affected determinants of investment, we
specify slope dummy variables of EU for CF/K and Credit/K, 1 for 2004-2006 and 0
otherwise as given by.
I 
I 
 CF 
 Credit  CF   = β 0 + β 1   + β 2 Qit + β 3 
 + β4  + β5  *d K  it
 K  it −1 K  it
 K  it
 K  it
 Credit + β6 
 * d + ε it
 K  it
(10)
CF
Credit
CF
I 
I 
S
) it + β 4) it + β 5 () it * d  = β 0 + β1   + β 2   + β 3 (KKK K  it
 K  it −1 K  it(11)
Credit+ β6 () it * d + ε itK
where d = EU. The re-specified Q and Euler equations (10) and (11) respectively
apply to other factors of Financial development (FD), Size and Business cycles, andthat d = FD, Size and Business cycle (see below).
Financial development (FD):

Investment behaviour is likely to haveundergone changes as the firms proceeded through the transition process, and so weestimate both models by augmenting them with the variable of financial development(FD).

We consider the stock market development and financial intermediaries’

development as a proxy to FD. Stock market development is the sum of the ratio of
market capitalization to GDP (i.e. the size of stock market) and total value traded toGDP.

Financial intermediaries’ development is the sum of the ratio of liquidliabilities (M3) to GDP and the credit for the private sector to GDP. Note that theCentral Eastern European countries have gone through different phases of exchangerate regime arrangements over the sample period. The fixed regime was an initial
step in an anti-inflation strategy. Hungary and Poland moved from a fixed exchange

18

rate regime with varying bands to a managed and full floating rate system in 2001
and 2000 respectively. The currency crises forced the Czech Republic to introduce
managed floating exchange rates in 1997. The Baltic countries have maintained fixedexchange rates over the sample period, and Estonia and Lithuania joined the ERM II
in 2004. Since the capital market is affected by the exchange rate system, the variableof FD is a way of capturing such changes in financial regimes. Moreover, the findingin Table 2b that Polish and Czech firms are less reliant on internal fund may
potentially reflect that Poland and the Czech Republic have better functioning capital
markets where money is easier to come by than in Hungary or Baltics.

The

specification of FD may control such effect. The main hypothesis is, similar to thecase for EU entry, that financing constraints and SBCs should decrease with financial
development, i.e. the interaction of FD with cash and credit flows is expected to be
negative.
Size effect: Firm size has been commonly used to identify firms that are likely

to be financially constrained. Although we have focused on large firms, there is aconsiderable difference in the scale of operation across large firms, making it
meaningful to investigate the size effect amongst large firms. Smaller firms are more
likely to suffer from financing constraints due to a larger information asymmetry andalso due to a lesser degree of SBCs as compared with larger firms. In order toexamine the contention, the interaction of size (measured by the log of total assets)with CF/K and Credit/K is specified in both models. See equation (10) and (11)above. It is hypothesized that β 5 < 0 , i.e. financial constraints are smaller for larger
firms and β 6 > 0, SBCs is larger for larger firms.
Business Cycles: The financing constraints theory states that the information

asymmetry between borrowers and lenders creates agency costs, which manifest
19themselves in the wedge between internal and external financing costs. These costs
may decrease with an increase in the borrower’s net worth. Since the net worth islikely to be procyclical, the agency costs may decline in a boom and rise in arecession. The interaction of the real GDP growth rate as economic conditions is,therefore, specified. Economic boom periods are associated with the lower level offinancing constraints, i.e. the negative coefficient on the interaction of CF/K*d inequation (10) and (11) is predicted. Whether SBCs contract or expand is difficult topredict. When an economy booms, banks may be willing to release credit under
SBCs with a lower risk of default, on the other hand, it is also true that it makes it
easy for firms to obtain funds from the capital market, reducing borrowing frombanks.

2) Results

The empirical results are found in Table 4. In terms of diagnostics, refer to Section 3,
since, more or less, comments similar to those for Table 2a and 2b apply. The
robustness of the results may be found in that the earlier findings of the stronger roleof SBCs in investments in the Czech Republic and Poland, and the weaker operationof SBCs in Hungary and the Baltic countries, remain to be the same in the augmented
models, when one notices the sign and significance of the coefficients on CF/K and
Credit/K4.
[Table 4 around here]
EU dummies (Table 4a): As to the effect of entry to the EU, since the sign on

the coefficient on Credit/K*EU is negative, the operation of SBCs has weakened for
all cases as predicted (except for Estonia in the Euler model.)Our results are

consistent with those of Brücker et al (2005), who examined the SBCs with the20

variables of labour productivity, using pooled aggregate data for twenty-six transition
countries during 1989-2002. Brücker et al. found that EU candidate countries had alower labour-to-output ratio in the long run, and a high speed of adjustment of labour
productivity to its long-run, which were taken as indicators of the softness of budget
constraints, suggesting that EU entry condition has decreased the practice of SBCs forthe candidate countries. Note, however, that for Poland and Latvia, CF/K*EU is
positively significant. Firms in these countries may face financial constraints, as aresult of reduced SBCs in the post-entry period.
FD (Table 4b):

A negative coefficient on Credit/K*FD for the Czech

Republic and Poland provides evidence that an improvement in the functioning offinancial markets appears to allow firms for easier access to external funds, thus
reducing their reliance on the SBCs. Also, banks may be attracted to more profitablefinancial innovations, and the sources of funds under SBCs may be curtailed. In thecase of Hungary, investment decisions became sensitive to the availability of loans asshown with the positive coefficient on Credit/K*FD, whilst financial constraints are
reduced as given by the negative coefficient on CF/K*FD. The results for the Czech
Republic, Hungary and Poland highlight the weakening of financial constraints, being
consistent with the existing literature.

For example, using the Euler equation

methodology, Laeven (2003) finds that progress in financial liberalization reduces
firms’ financing constraints, and Galindo et al. (2001) find that financial reform has
led to an increase in the efficiency with which investment funds are allocated. As to
the Baltic countries, the positive sign on CF/K*FD and negative sign on Credit/K*FD
seems to imply that FD contributes to the reduction of SBCs, though with a persistentfinancial constraint.

21

Firm Size (Table 4c):

The observation of the positive coefficient on

Credit/K*Size indicates that the larger the firm is, the more SBCs are prevalent in allcases. A negative sign on the coefficient of CF/K*Size for the Czech Republic,
Hungary, Poland and Lithuania appears to suggest that smaller firms tend to sufferfrom credit crunches. Overall results support the prediction that larger firms benefit
from SBCs and smaller firms are disadvantaged in obtaining funds.
Business cycle (Table 4d): A sign on the interaction of cash and credit flows

and business cycles (GDP) is mostly negative. Financial constraints are softened withgrowth in the economy, and a booming economy appears to reduce the need for
rescue funds for the survival offirms, as their profitability increases.

As forHungary, with the positive sign on Credit/K*BUSI, banks who are shy about SBCs as
discussed above, may release more loans as the economy improves with aconsequential lower default risk.

6. Conclusion

Restructuring of firms in terms of their financing is viewed as one of the key factors
to a successful transition from a planned to a market economy. We have usedcomparable large firm-level data for transition economies in order to analyze theimpact of soft budget constraints on firms’ investment behaviour for the new EU
member states.
It is found that the extent of SBCs differs across Central Eastern European
countries. The Czech Republic and Poland show some signs of persistent operationof SBCs with less credit constraints, suggesting that SBCs are an important
determinant of investment. The lower credit constraints point to a lower externalfinance premium. However, in an environment where SBCs are prevalent, this may

22well imply an inefficient financial sector and severe asymmetric information arisingfrom the SBCs (Budina et al 2000). On the other hand, there is a weak indication of
the presence of SBCs for the Baltic countries and Hungary, and these countries may
encounter financing constraints, facing a higher external finance premium. Yet, given
the fact that such factors as joining the EU, financial development and a booming
economy are likely to reduce the practice of SBCs and financial constraints, thecurrently on-going policy of integration with EU financial markets and the promotionof financial deepening would enhance the efficient allocation of resources.
An investment model with the specification of implied bank loans seems tobetter approximate the complexity of corporate finance for these transition economies,
and this study perhaps presents important implications for policy makers.

Inparticular, in view of the current global credit crises, there is a need for ever more
efficient allocation of resources. Note that measuring the extent of the bearing on theeconomy by the SBCs is, as yet, empirically incomplete, since there are variations in
the form of SBCs. A further development of an empirical framework for SBCs would
be useful.

23

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26

Table 1
Variable definitions
Notation

Description

It

Investment: = ( K t +1 + DEPt − K t )Capital at the beginning of period t (= tangible fixed assets at the end of theperiod t-1)
Deprecation during period t

Kt
DEP t
Qt
CF t
Creditt
EU dummy
FD
Size
Business
cyclesAverage Q at the beginning of period t (=(book value of long term debtt-1 +equityt-1)/Total asset t-1)
Cash flow during the period t (= net profit after tax + depreciationt-1)
Credit flow = Current liability t – Current liability t-1
Sales during period t
1 for 2004 -2006 and 0 otherwise= market capitalisation to GDP + total value traded to GDP + ratio of M3 to
GDP + ratio of the credit for the private sector to GDP.
Log of total assets
Annual real growth rate of GDP

Data for the equity is retrieved from Shareholders Equity in a company’s balance
sheet.
Descriptive Statistics
I/K

Q

S/K

CF/K

Credit/K

Firms

Obs.

Czech

Mean

0.038

0.652

1.468

0.165

-0.004

0.219
1.111

0.177
0.689

0.981
8.184

0.238
0.221

0.207
0.068

278

2245

Hungary

Std. Dev.
Mean
Std. Dev.

1.607

0.175

11.128

0.433

1.557

122

1037

Poland

Mean

0.573

0.522

11.132

1.053

0.802

Std. Dev.
Mean
Std. Dev.

0.576
0.187
0.395

0.322
0.694
0.183

11.397
3.708
4.86

1.766
0.486
0.584

11.485
0.303
1.001

368

3123

Estonia42

378

Latvia

Mean

0.148

0.75

2.712

0.318

0.141

0.403
0.301

0.171
0.717

2.943
4.885

0.663
0.21

0.626
1.35

78

678

Lithuania

Std. Dev.
Mean

1.475

0.168

24.6510.267

10.72543

386

Std. Dev.
Sample period: 1995-2006

27

Table 2a Dependent variable I/K (without credit)
Q model(I/K)t-1
Qt(CF/K)tCzech Rep.
Coef.
-0.156*
0.119
0.061

LM auto [1]LM auto [2]LM Hetero [3]Over ident [3].
Euler model(I/K)t-1(S/K)t(CF/K)t
LM auto [1]LM auto [2]LM Hetero [3]Over ident [3].

t-ratio(-1.721)(0.541)(0.705)

Hungary
Coef.
-0.003
1.358
0.047

2.624
3.335
7.257
9.148
Coef.
-0.153*0.086*
0.127

t-ratio(-1.794)(1.873)(1.368)
3.216
3.347
2.415
6.968

t-ratio(-0.649)(1.451)(0.379)PolandCoef.
-0.243***13.024***-7.383***

5.881
0.223
0.933
9.460
Coef.
-0.0030.138***
0.215

t-ratio(-0.523)(192.016)(1.485)4.758
10.1050.121
11.217t-ratio(-8.534)(6.882)(-22.258)

Estonia
Coef.
-0.190*1.115**
0.466***

5.030
3.957
3.793
8.028
Coef.
-0.0070.230***-0.133**t-ratio(-1.524)(160.034)(-2.058)
4.888
5.681
4.930
5.667

t-ratio(-1.771)(2.228)(2.771)

LatviaCoef.
-0.505***-1.121
0.338***

0.139
0.031
1.774
2.083
Coef.
-0.1740.009
0.589***

t-ratio(-1.441)(0.207)(3.547)
0.140
0.044
0.976
0.175

t-ratio(-3.492)(-1.532)(3.735)

Lithuania
Coef.
-0.2870.569
7.705***

0.886
0.467
0.172
3.859
Coef.
-0.437***0.089*
0.137

t-ratio(-2.864)(1.806)(0.922)
0.860
0.563
0.191
3.599

t-ratio(-0.942)(0.245)(5.339)
0.039
0.576
3.127
1.815

Coef.
-0.4100.024***
4.498**

t-ratio(-1.244)(3.061)(2.121)
0.024
0.316
2.506
1.477

t-ratio *,** and *** significant at the 10%, 5% and 1%.
[ . ] degree of freedom. Critical value at the 5% df =1 3.84, df=2 5.99, df=3 7.82, at the 1% df=1 6.64, df=2 9.21, df=3 11.34.
Based on GMM. The instrument variables used are (I/K)t-1, (I/K)t-2, (CF/K)t-1 and (CF/K)t-2 . Qt-1 and Qt-2 for the Q model. (S/K)t-1 and (S/K)t-2 for the Euler models.

28

Table 2b Dependent variable I/K (with credit)
Q model(I/K)t-1
Qt(CF/K)t(Credit/K)tCzech Rep.
Coef.
-0.161*-0.1580.040
0.233***LM auto [1]LM auto [2]LM Hetero [4]Over ident. [4]Euler model(I/K)t-1(S/K)t(CF/K)t(Credit/K)t
LM auto [1]LM auto [2]LM Hetero [4]Over ident. [4]t-ratio-1.860-0.6970.477
3.084

Hungary
Coef.
0.001
0.428
0.824***
0.919***

2.188
3.116
6.515
8.993
Coef.
-0.1220.061
0.039
0.202***

t-ratio-1.439
1.214
0.479
2.931
2.695
3.462
3.859
7.046

t-ratio
0.332
0.633
7.535
12.967

PolandCoef.
-0.0070.614*-0.126*
0.868***

5.308
0.004
9.460
6.999
Coef.
0.002
0.105***
0.938***
1.126***

t-ratio
0.601
6.028
8.567
13.9828.524
0.000
12.79411.900t-ratio-1.4301.818-1.637142.512

Estonia
Coef.
-0.0650.132
0.362***
0.857***

9.419
7.522
10.4068.567
Coef.
-0.008*0.134***-0.0880.363***

t-ratio-1.71913.002-1.404
9.321
6.321
5.691
4.134
7.872

t-ratio-0.736
0.305
2.704
5.527

LatviaCoef.
-0.427***-0.6980.356***
1.196***

0.212
0.193
1.069
2.721
Coef.
-0.0120.045
0.325***
0.903***

t-ratio-0.130
1.324
2.449
6.267
0.212
0.193
0.872
-0.688t-ratio-3.182-1.032
4.302
3.262

Lithuania
Coef.
-0.529***
0.138
1.187
2.384***

0.904
0.687
0.588
4.206
Coef.
-0.439***0.014
0.331**
1.208***

t-ratio-3.074
0.261
2.148
2.888
0.904
0.687
0.458
3.553

t-ratio-4.2960.149
1.444
15.250
0.002
0.115
1.420
4.386

Coef.
-0.3880.087**
4.841**
0.167***

t-ratio-1.262
2.108
2.442
2.743
0.495
0.007
3.456
5.414

t-ratio *,** and *** significant at the 10%, 5% and 1%. [ . ] degree of freedom. Critical value at the 5% df =1 3.84, df=2 5.99, df=4 9.49, at the 1% df=1 6.64, df=2 9.21,
df=4 13.28. Based on GMM. The instrument variables used are (I/K)t-1, (I/K)t-2, (CF/K)t-1 , (CF/K)t-2 , (Credit/K)t-1 and (Credit/K)t-2 . Qt-1 and Qt-2 for the Q model. (S/K)t1 and (S/K)t-2 for the Euler models.

Table 3 Bank non-performing loans to total (%)
Czech Rep
Hungary
Poland Estonia
LatviaLithuania
2000
29.3
3
15.5
1
4.6
11.3
2001
13.7
2.7
18.6
1.3
2.8
8.3
2002
10.6
2.9
22
0.8
2
6.5
2003
4.9
2.6
22.2
0.4
1.4
3
2004
4.1
2.7
15.5
0.3
1.1
2.3
Mean
12.52
2.78
18.76
0.76
2.38
6.28
(Source: World Development Indicators) The data prior to 2000 are not available.

29

Table 4a Dependent variable I/K (with EU dummy)
Q model(I/K)t-1
Qt(CF/K)t(Credit/K)t(CF/K)t*EU(Credit/K)t*EU

Czech Rep.
Coef.
-0.155**-0.5900.150
0.768***-0.040-0.263

LM auto [1]LM auto [2]LM Hetero[6]Over ident.[6]Euler model(I/K)t-1(S/K)t(CF/K)t(Credit/K)t(CF/K)t*EU(Credit/K)t*EU
LM auto [1]LM auto [2]LM Hetero[6]Over ident.[6]t-ratio-1.918-1.5090.660
2.590-0.181-0.518Hungary
Coef.
0.003-0.5540.064
1.291***
0.458*-0.860***

2.072
2.893
6.461
2.543
Coef.
-0.1150.121**-0.036
0.541*
0.145-0.730t-ratio-1.3622.332-0.1561.892
0.648-1.400
2.453
3.462
4.007
6.691

t-ratio
1.268-0.9320.333
17.3081.963-7.641PolandCoef.
-0.012**-0.310-0.615***0.802***
2.052***-0.456***

7.087
6.119
15.974
8.641
Coef.
0.004
0.159***
0.334*
1.376***
0.211-0.691***

t-ratio
1.136
8.866
1.820
16.5840.907-5.807
7.811
7.321
2.337
11.969t-ratio-2.256-1.387-4.3885.207
14.116-2.841

Estonia
Coef.
-0.061
0.191
0.431***
0.863***-0.137-0.004

8.238
5.043
9.832
7.954
Coef.
-0.0030.147***-0.775***0.683***
1.584***-0.324***t-ratio-0.8525.358-8.111
7.142
17.316-3.2494.536
8.101
13.58810.868t-ratio-0.671
0.428
2.805
5.143
-1.222-0.020LatviaCoef.
-0.519***-0.3080.752
0.610***
0.932**-0.192

0.384
0.218
1.536
3.261
Coef.
-0.0180.038
0.380**
0.894***-0.0960.042

t-ratio-0.187
0.968
2.324
5.836
-0.816
0.209
0.384
0.218
2.075
-0.832t-ratio-3.378-0.315
1.476
2.446
2.042
-0.884

Lithuania
Coef.
-0.409***-0.8680.277
2.707***
1.283-0.379

0.798
0.618
0.544
5.126
Coef.
-0.454***0.157*
1.141**
0.456*
1.014**-0.228t-ratio-3.070
1.705
2.172
1.811
2.367
-1.082
0.798
0.618
0.502
3.419

t-ratio-2.830-1.023
0.218
2.850
1.021
-0.367
0.000
0.548
1.608
4.520

Coef.
-0.359***0.049***
4.428***
2.772***-3.167***-0.157t-ratio-2.544
8.954
4.626
3.126
-2.944-0.174
0.073
0.056
1.869
2.618

t-ratio *,** and *** significant at the 10%, 5% and 1%.
[ . ] degree of freedom. Critical value at the 5% df =1 3.84, df=2 5.99, df=6 12.59, at the 1% df=1 6.64, df=2 9.21, df=6 16.81.
Based on GMM. The instrument variables used are (I/K)t-1, (I/K)t-2, (CF/K)t-1 , (CF/K)t-2 , (Credit/K)t-1 , (Credit/K)t-2 , (CF/K)t-1*EU, (CF/K)t-2*EU, (Credit/K)t-1*EU and(Credit/K)t-2*EU. Qt-1 and Qt-2 for the Q model. (S/K)t-1 and (S/K)t-2 for the Euler models.

30

Table 4b Dependent variable I/K (with FD)

Qt(CF/K)t(Credit/K)t(CF/K)t*FD(Credit/K)t*FD

Czech Rep.
Coef.
t-ratio-0.162*-1.877-0.176-0.7760.484
0.370
2.127**
1.963-0.003-0.278-0.019*-1.739HungaryCoef.
t-ratio
0.002
0.655-0.289-0.4713.681***
4.115
2.148***
5.332-0.035***-3.5400.031***
7.753

PolandCoef.
t-ratio-0.004-0.865-0.304*-1.730-1.942-0.8698.233***
18.578-0.054***-7.984-0.105***-17.125

EstoniaCoef.
t-ratio-0.254**-2.2201.266**
2.074
0.365
0.515
0.737*
1.686
0.013
1.395
-0.007-1.348

LatviaCoef.
t-ratio-0.547***-3.7340.163
0.183
6.004***
3.037
1.934**
2.194
0.133***
3.196-0.033*-1.796

Lithuania
Coef.
t-ratio-0.513***-4.041
0.426
0.418
1.017
-0.4013.001**
2.395
0.001
0.015
-0.009-0.469LM auto [1]LM auto [2]LM Hetero[6]Over ident.[6]

2.363
3.191
6.675
9.390

2.639
1.038
19.078
8.935

2.987
1.868
20.342
9.038

0.320
0.200
1.349
3.084

0.696
0.537
0.505
4.679

0.007
0.374
2.077
4.467

(I/K)t-1(I/K)t-1(S/K)t(CF/K)t(Credit/K)t(CF/K)t*FD(Credit/K)t*FD

Coef.
-0.1230.059
0.279
2.107*-0.001-0.019*t-ratio-1.452
1.181
0.217
1.955
-0.124-1.763

Coef.
0.003
0.092***
4.783***
1.011***-0.044***0.022***

t-ratio
0.947
5.917
6.895
3.316
-5.7657.291

Coef.
-0.0020.109***-1.0425.549***-0.008-0.069***t-ratio-0.58618.301-1.38913.927-1.278-12.589Coef.
-0.1650.004
0.726
0.939**
0.017*-0.009*t-ratio-1.426
0.085
1.008
2.108
1.913
-1.662

Coef.
-0.507***0.135
6.452***
1.845**
0.136***-0.034**t-ratio-3.604
1.576
3.353
2.166
3.395
-1.964Coef.
-0.667***
1.521***
7.980
7.303***
0.332***-0.071***

t-ratio-3.1998.292-1.570
8.149
3.713-8.002

LM auto [1]
3.006
4.890
5.977
0.320
0.696
0.057
LM auto [2]
3.646
4.243
7.623
0.200
0.537
0.016
LM Hetero[6]
3.942
8.931
27.485
1.659
0.415
1.629
Over ident.[6]7.318
11.77810.808-0.7592.671
3.760
t-ratio *,** and *** significant at the 10%, 5% and 1%.
[ . ] degree of freedom. Critical value at the 5% df =1 3.84, df=2 5.99, df=6 12.59, at the 1% df=1 6.64, df=2 9.21, df=6 16.81.
Based on GMM. The instrument variables used are (I/K)t-1, (I/K)t-2, (CF/K)t-1 , (CF/K)t-2 , (Credit/K)t-1 , (Credit/K)t-2 , (CF/K)t-1*FD, (CF/K)t-2*FD, (Credit/K)t-1*FD and(Credit/K)t-2*FD. Qt-1 and Qt-2 for the Q model. (S/K)t-1 and (S/K)t-2 for the Euler models.

31

Table 4c Dependent variable I/K (with firm size)(I/K)t-1
Qt(CF/K)t(Credit/K)t(CF/K)t*Size(Credit/K)t*Size

Czech Rep.
Coef.
t-ratio-0.200***-2.473-0.635***-2.9171.187
0.702
3.474**
2.078-0.219***-2.651
0.188
0.956

HungaryCoef.
t-ratio0.011***
3.743
0.687
1.172
7.393***
3.228
4.896***
8.662-0.337***-3.0900.702***
9.206

PolandCoef.
t-ratio-0.003-0.812-0.232*-1.793-6.390***-15.880
4.325***
13.157-0.406***-18.315
0.282***
14.362

2.666
3.191
6.828
6.728

6.604
5.987
9.344
10.1875.562
3.062
13.89011.850LM auto [1]LM auto [2]LM Hetero[6]Over ident.[6](I/K)t-1(S/K)t(CF/K)t(Credit/K)t(CF/K)t*Size(Credit/K)t*Size
LM auto [1]LM auto [2]LM Hetero[6]Over ident.[6]Coef.
-0.171**
0.094**
1.749
4.058**-0.200**0.202

t-ratio-1.964
1.823
1.372
2.545
-2.324
0.990
3.394
3.992
3.727
6.773

Coef.
0.005
0.145***
6.802***
3.630***-0.284**0.209***

t-ratio
1.398
7.190
2.904
2.861
-2.553
3.750
9.650
6.494
13.95011.263Coef.
-0.0020.048***-4.341***
3.996***-0.276***
0.264***

t-ratio-0.5227.809-9.263
12.534-10.161
13.8735.590
4.995
13.60010.998Coef.
-0.233**
1.156*
1.506
1.914
0.107
0.084

Coef.
-0.143
0.015
2.290
1.962
0.147*
0.084

Estoniat-ratio-1.966
1.854
0.957
1.294
1.324
1.161

LatviaCoef.
t-ratio-0.421***-3.129-1.114-1.394
1.239
0.701
1.240
0.335
0.100
0.905
0.000
0.001

Lithuania
Coef.
t-ratio-0.468***-3.765-0.617-0.4951.182
0.151
5.735***
3.942-0.030-0.0791.405***
4.316

0.129
0.091
1.010
0.523

0.781
0.641
0.606
4.999

0.018
0.982
1.044
4.673

t-ratio-1.210
0.312
1.454
1.220
1.828
1.086
0.129
0.091
1.414
-1.307

Coef.
-0.398***
0.064
1.516
1.124
0.108
0.001

t-ratio-2.626
0.794
0.623
0.301
0.758
0.002
0.781
0.641
0.554
4.291

Coef.
-0.466***0.039***
5.745***
4.927**-0.753***0.874***

t-ratio-3.427
9.767
3.582
2.463
-3.467
2.917
0.856
0.164
3.608
3.671

t-ratio *,** and *** significant at the 10%, 5% and 1%.
[ . ] degree of freedom. Critical value at the 5% df =1 3.84, df=2 5.99, df=6 12.59, at the 1% df=1 6.64, df=2 9.21, df=6 16.81.
Based on GMM. The instrument variables used are (I/K)t-1, (I/K)t-2, (CF/K)t-1 , (CF/K)t-2 , (Credit/K)t-1 , (Credit/K)t-2 , (CF/K)t-1*Size, (CF/K)t-2*Size, (Credit/K)t-1*Size and(Credit/K)t-2*Size. Qt-1 and Qt-2 for the Q model. (S/K)t-1 and (S/K)t-2 for the Euler models.

32

Table 4d Dependent variable I/K (with business cycle)(I/K)t-1
Qt(CF/K)t(Credit/K)t(CF/K)t*BUSI(Credit/K)t*BUSICzech Rep.
Coef.
t-ratio-0.041-0.4970.283
1.458-0.179-1.4960.420***
4.974-0.044-1.058-0.041**-2.338

LM auto [1]LM auto [2]LM Hetero[6]Over ident.[6](I/K)t-1(S/K)t(CF/K)t(Credit/K)t(CF/K)t*BUSI(Credit/K)t*BUSILM auto [1]LM auto [2]LM Hetero[6]Over ident.[6]

Coef.
0.001
0.293
0.538
0.282
-0.0670.137*

Hungaryt-ratio
0.233
0.433
0.505
0.775
-0.295
1.788

PolandCoef.
t-ratio-0.007-1.372-0.263-1.244-4.284***-19.828
2.253***
11.335-0.549***-10.792-0.342***-8.564

EstoniaCoef.
t-ratio-0.176-1.552
1.262**
2.309
0.279
1.109
0.736***
2.717-0.050-1.589-0.073**-2.544

LatviaCoef.
t-ratio-0.513***-3.4320.714
0.781
3.298**
2.501
1.315**
2.074
0.515***
2.763-0.146*-1.716

Lithuania
Coef.
t-ratio-0.412***-2.877-2.723**-2.0892.080
1.247
2.966***
4.789-0.113-0.605-0.069-0.9025.595
0.091
21.98013.7493.764
3.645
25.623
5.924

0.284
0.128
1.148
1.078

0.653
0.549
0.461
3.738

0.014
0.041
1.327
2.464

2.877
3.788
6.065
6.842
Coef.
-0.086
0.076*-0.006
0.368***-0.002-0.031**

t-ratio-1.0541.634-0.0434.137-0.040-2.112
3.418
4.154
3.324
6.452

Coef.
0.002
0.107***
2.978***
0.445**-0.436***0.151***

t-ratio
0.590
6.381
5.635
1.972
-3.897
3.276
9.072
0.370
28.78016.501

Coef.
-0.0030.135***-1.283***1.307***-0.158***-0.161***

t-ratio-0.82823.743-6.1978.408-3.772-5.156
3.137
10.66021.477
9.246

Coef.
-0.0830.010
0.318
0.877***-0.047-0.079**t-ratio-0.720
0.241
1.201
3.095
-1.424-2.624
0.264
0.128
0.630
-0.867Coef.
-0.496***0.115
3.324**
1.059*
0.480**-0.135*t-ratio-3.389
1.298
2.566
1.704
2.648
-1.642
0.653
0.549
0.391
3.323

Coef.
-0.402***0.035***
2.954
3.179***-0.203-0.078t-ratio-2.596
9.384
1.242
5.390
-0.575-1.033
0.584
0.035
3.595
5.141

t-ratio *,** and *** significant at the 10%, 5% and 1%.
[ . ] degree of freedom. Critical value at the 5% df =1 3.84, df=2 5.99, df=6 12.59, at the 1% df=1 6.64, df=2 9.21, df=6 16.81.
Based on GMM. The instrument variables used are (I/K)t-1, (I/K)t-2, (CF/K)t-1 , (CF/K)t-2 , (Credit/K)t-1 , (Credit/K)t-2 , (CF/K)t-1*BUSI, (CF/K)t-2*BUSI, (Credit/K)t-1*BUSI
and (Credit/K)t-2*BUSI. Qt-1 and Qt-2 for the Q model. (S/K)t-1 and (S/K)t-2 for the Euler models.

33

Endnotes
1

In perfect capital markets, λ is zero.

2

The coefficients on the Q and S/K are not always significant for these economies, and such results are

not unusual in the investment analysis for emerging markets (see e.g. Lízal and Svejnar, 2002 andBhaduri 2005). In underdeveloped financial markets, the imperfection in the capital market is severe,hence the variables of Tobin’s Q or marginal productivity of capital are not likely to exert a strong
influence.

3

Maskin and Xu (2001) treats the SBCs as a financial commitment problem of not imposing

bankruptcy on the defaulting entrepreneur.

4

Recall that the coefficients on both CF/K and Credit/K tend to be positive and significant for Hungary

and the Baltic countries as an indication of weak SBCs, whereas the coefficient on CF/K isinsignificant or negative and that on Credit/K is significant suggesting a strong role for SBCs for the
Czech Republic and Poland.

34