Investment determinants of fitness SMES in Portugal.
Nunes, Paulo Macas ; Serrasqueiro, Zelia ; De Carvalho, Pedro Guedes 等
JEL Classification: C23, G31, G32, L26.
Introduction
Various theories have attempted to explain firms' investment
decisions. In this domain, Neoclassical, Free Cash Flow and Agency
theories stand out. According to Neoclassical theory (Hall, Jorgenson
1967; Jorgenson 1971; Chirinko 1993), sales are the main explanatory
factor of firm investment. If there is a positive evolution of sales,
firms increase investment, that investment diminishing if sales fall. In
the context of Neoclassical theory, financial structure is of negligible
importance in explaining firm investment. Fazzari et al. (1988), Fazzari
and Petersen (1993) were the precursors of Free Cash Flow theory.
According to Free Cash Flow theory, internal finance, and more
specifically cash flow, is especially relevant in explaining firm
investment, with that importance increasing as firms' access to
credit is restricted, as a consequence of the information asymmetry
implicit in relationships formed between owners/ managers and creditors.
Finally, according to Agency theory, the problems of information
asymmetry implicit in relationships between owners/managers and
creditors can also affect firm investment (Jensen, Meckling 1976).
According to Jensen and Meckling (1976), owners/ managers can be
attracted to investing in high-risk projects which increase the
likelihood of firm bankruptcy. Recognizing this behaviour, creditors
transfer the consequences of high-risk investments to owners/managers,
increasing the cost of credit. Considering the problems of information
asymmetry between owners/managers and creditors, according to Agency
theory, a negative relationship can be expected between debt and firm
investment.
Small and medium-sized enterprises (SMEs) have particular
characteristics (Diamond 1989; Ang 1991): 1) smaller size; 2) greater
likelihood of bankruptcy; 3) greater capacity to change the nature of
their assets; and 4) less transparence in the information provided to
creditors about firms' specific characteristics. When internal
finance is insufficient, these SME characteristics can mean particular
difficulty in accessing external finance, affecting their investment
options.
Small service firms have particular characteristics that can mean
different market and financing strategies (Roofthooft 2010; Cho et al.
2011, 2012; Serrasqueiro et al. 2011). In this context, fitness firms
are service firms that are generally small in size, and may therefore
have particular market and financing strategies.
At present, fitness firms provide important services to the
population. Physical activity is seen today as preventive medicine
(Andersen-Hanley et al. 2010; Garber et al. 2011). Indeed, it is known
that the European social and economic context currently presents great
financial restrictions in public health systems and retirement pensions.
The consequence of those restrictions will have to mean daily routines
that include physical exercise accompanied by specialist professionals.
In this context, fitness firms are appropriate places for this purpose
(Dale et al. 2009; Garber et al. 2011).
The study of firms' investment determinants has neglected
analysis of small and young service firms. This study aims firstly to
fill that gap in the literature, analysing the investment determinants
of Portuguese fitness SMEs. The choice of Portuguese fitness SMEs as the
subject of analysis in this paper is appropriate for two fundamental
reasons: 1) fitness SMEs in Portugal are quite small and young service
firms, filling the gap in the literature concerning study of the
determinants of small, young service SMEs; and 2) fitness SMEs are quite
important service firms at present, with study of their investment
determinants being needed in order to suggest economic policy measures
to support this type of important service.
Methodologically, we use a sample of 182 fitness SMEs in Portugal
for the period 2004-2009. To estimate results, we use fixed effects
models estimating standard deviations according to the cluster option.
This option allows us to obtain results according to any pattern of
heteroskedasticity and autocorrelation.
The results obtained allow us to make an important contribution to
the literature on service SME investment. The results of the paper
indicate that even in small, young service firms, the main explanatory
theories of investment are not mutually exclusive in explaining
investment decisions, since sales and cash flow are positively related
to investment and debt is negatively related to investment. It is
particularly important to find that Neoclassical theory is relevant in
explaining the investment decisions of fitness SMEs, which indicates
that firms' small size and young age does not always contribute to
them not adjusting investment as a function of sales. In addition,
growth opportunities and government subsidies have a positive influence
on investment in Portuguese fitness SMEs and the financial crisis of
2008 has a negative influence on their investment. Additionally, the
financial crisis of 2008 contributes to greater relevance of cash flow
and government subsidies for increased investment in Portuguese fitness
SMEs and greater relevance of debt for reduced investment.
After this introduction, the paper is structured as follows: 1)
section 1 presents the literature review and research hypotheses; 2)
section 2 presents the methodology, namely the database, variables used
and estimation method; 3) section 3 presents the results; 4) section 4
goes on to discuss the results; and 5) the final section presents the
conclusion and implications of the paper.
1. Literature review and research hypotheses 1.1. Sales
According to Neoclassical theory (Hall, Jorgenson 1967; Jorgenson
1971; Chirinko 1993), the coefficient of the sales variable is
predominant, and statistically significant, in explaining firms'
investment decisions. The authors corroborate the arguments of
Modigliani and Miller (1958) that firms' investment decisions are
not dependent on their financial structure, since firms increase
investment if sales increase and reduce it if sales are falling.
Eisner (1963) and Chirinko (1993) conclude that sales are
statistically predominant over any other variables of investment in
explaining firms' investment decisions. However, the conclusions of
Eisner (1963) and Chirinko (1993) refer to the analysis of large firms
which are subject to fewer credit restrictions than small firms. In
SMEs, the importance of sales is expected to be less than what may be
the case in large firms. In smaller firms, sales are more volatile,
which leads these firms to maintain a suitable level of assets with a
greater degree of liquidity, to avoid debt on unfavourable terms
(Devereux, Schiantarelli 1990). Consequently, sales can be assumed to be
more important for SMEs to cope with treasury difficulties rather than a
way to finance investment.
It is also of note that, according to the Neoclassical theory of
investment, sales emerge as the variable with most explanatory power in
large firm investment, as proposed by the concept of the representative
firm. In this context, and considering that fitness SMEs are generally
small scale, we can expect sales not to be particularly important in the
investment-decision process of this type of firm. Based on the above
arguments, we formulate the following research hypothesis:
H1: Sales are not a positive determinant of investment in fitness
SMEs.
1.2. Cash flow
Stiglitz and Weiss (1981) state that firms with different financial
situations obtain different financing conditions in credit markets. In
this connection, Fazzari et al. (1988) conclude on the need to break
with the concept of the representative firm, proposing in its place
another concept named financial hierarchy. According to Fazzari et al.
(1988), this concept is extremely relevant in the context of information
asymmetry, which occurs in the credit and capital markets, unlike the
"world" of Modigliani and Miller (1958) in a context of
markets with perfect competition and symmetric information, and so the
Neoclassical equation of investment, tested by introducing financial
variables, should reveal very different sensitivity between financial
variables and investment in different groups of firms. From this
argument, formulation of Free Cash Flow theory is begun, based on the
hypothesis of asymmetric information, where firms' internal
factors, and especially cash flow, now become relevant in explaining
firm investment.
Fazzari et al. (1988) show that firm investment is dependent on
cash flow, finding a positive relationship between firms' level of
cash flow and investment. The authors also show that the sensitivity of
investment to cash flow variations is greater for firms that are more
prone to credit rationing, as a consequence of the information asymmetry
in relationships between shareholders/owners and creditors.
Petersen and Rajan (1995), Vermeulen (2002), Silva and Carreira
(2009) argue that cash flow is a particularly relevant variable in
explaining SME investment, given the lesser possibility of obtaining
credit as a consequence of less capacity to provide collateral and the
greater likelihood of bankruptcy.
This positive relationship between cash flow and investment tends
to be stronger in SMEs (Lee, Ratti 2008), as they are the ones most
likely to suffer financial restrictions since their lack of experience
and reputation in the external financing market forces them to fund
themselves firstly through recourse to cash flow (Evans, Jovanovic 1989;
Gilchrist, Himmelberg 1995; Xu 1998; Beck et al. 2006; Brown et al.
2009).
In general, fitness SMEs are small in size and so we can expect
them to have particular difficulties in accessing external finance to
fund their investment, with internal finance being especially important
for this purpose. Based on these arguments, we formulate the following
research hypothesis:
H2: Cash flow is a positive determinant of investment in fitness
SMEs.
1.3. Debt
Agency theory (Jensen, Meckling 1976; Jensen 1986; Stulz 1990) is
based on two types of conflicts: 1) conflicts between owners and
managers; and 2) conflicts between owners/managers and creditors. While
the first type of conflict is more likely to occur in large firms, the
second is more applicable in small firms, where there is greater
probability of bankruptcy and ownership and management usually coincide.
Indeed, in the SME context, problems of information asymmetry
associated with relationships between owners/managers and creditors are
particularly relevant, since in the great majority of cases SME
ownership and management is in the same hands, with problems of
information asymmetry in relationships between owners and managers being
of marginal importance.
Jensen and Meckling (1976) claim that when a firm uses outside
capital, conflicts of interest arise between shareholders/owners and
creditors. Increasing the level of debt, whether to lower the agency
costs of equity or for any other reason, can lead the firm to face
another type of cost: the agency cost associated with outside capital.
This cost, created by the conflict of interest between
shareholders/owners and creditors, forms a major limitation in
firms' recourse to debt. Creditors restrict the amount of credit
granted to firms or increase its cost (Jensen, Meckling 1976; Stiglitz,
Weiss 1981), since shareholders/owners can invest in highrisk projects,
aiming to increase equity value rather than the value of the debt.
Therefore, if the project succeeds, owners receive most of the gains,
whereas if the project fails, creditors bear most of the costs (Jensen,
Meckling 1976). Based on agency problems, Myers (1977) and Zwiebel
(1996) conclude that a negative relationship is expected between debt
and the level of firm investment, since creditors hinder access to
credit in situations of greater information asymmetry concerning the
profitability and risk of projects, with finance only being channelled
to more profitable and low-risk projects.
Considering that fitness firms are generally small in size, we can
expect these firms to have particular difficulty in accessing debt, and
paying off the debt and its charges will mean diminished investment.
Based on these arguments, we formulate the following research
hypothesis:
H3: Debt is a restrictive determinant of investment in fitness
SMEs.
1.4. Other determinants of investment
1.4.1. Age
Gilchrist, Himmelberg (1995) and Moyen (2004) argue that younger,
smaller firms tend to invest more based on their cash flow. Beck et al.
(2006) argue that the younger age of a firm is a proxy for the financial
restrictions it suffers in financing its investment. The authors also
state that as firms get over the first years of their life, the
restrictions felt in financing their investment become less.
Fazzari et al. (1988), and Gilchrist and Himmelberg (1995) argue
that although younger, smaller firms invest more, their investment is
greatly subject to their liquidity restrictions. Fagiolo and Luzzi
(2006) corroborate these arguments, concluding that liquidity
restrictions create a negative effect on firm growth, with the smallest
firms growing more after controlling liquidity restrictions.
Devereux, Schiantarelli (1990) and Oliner, Rudebusch (1992)
concluded that young firms register greater sensitivity of investment to
cash flow, suggesting the youngest firms face more obstacles in
obtaining finance. In this context, Diamond (1989) states that age can
serve as a proxy for firm reputation, and so we can expect that as firm
age increases, there will be a greater possibility of obtaining finance
on more advantageous terms.
Considering that fitness firms are generally small and consequently
exposed to greater problems in financing their investment, age may
contribute to increasing firm reputation, allowing them to obtain
finance on more advantageous terms, which may mean increased investment.
Based on the arguments presented, we formulate the following hypothesis:
H41: Age is a positive determinant of investment in fitness SMEs.
1.4.2. Growth opportunities
Authors such as Fazzari et al. (1988), Ascioglu et al. (2008),
Carpenter and Guariglia (2008), Junlu et al. (2009), Sun and Nobuyoshi
(2009) find a positive relationship between growth opportunities and
investment. According to Carpenter and Guariglia (2008), the explanatory
power of this variable seems to be greater in SMEs, as these are firms
suffering most from financial restrictions resulting from assessment by
external creditors. Fagiolo and Luzzi (2006) conclude that younger,
smaller firms should benefit from greater growth opportunities and need
higher levels of investment to satisfy the multiple growth opportunities
that arise.
Considering that fitness firms are generally small, and
consequently with greater growth opportunities, and considering that
creditors can recognize the good growth opportunities in this type of
firm, we can expect growth opportunities to contribute to increased
levels of investment. Based on the arguments presented, we formulate the
following hypothesis:
H42: Growth opportunities are a positive determinant of investment
in fitness SMEs.
1.4.3. Government subsidies
According to Smallbone et al. (2010), government financial support
is fundamental for the growth, and survival, of small firms, so that
this type of firm can cope with possible restrictions in accessing
finance. Peng (2003) and Manolova et al. (2008) state that the
activities of small firms are particularly influenced by external
factors, for example, macroeconomic conditions. Baker et al. (2005) and
Smallbone, Welter (2006) conclude that public financing policies should
consider the special difficulties of small firms, directing specific
financial support to those with good investment projects but with
clearly insufficient internal funds to finance them. Serrasqueiro et al.
(2011) conclude that service SMEs are particularly affected by problems
of information asymmetry in the relationships formed with creditors,
which can be problematic in terms of financing their activities when
internal finance is found to be insufficient. Serrasqueiro et al. (2011)
suggest that when this is the case, service SMEs should receive specific
financial support through public finance.
Considering that fitness firms are generally small and consequently
with particular difficulty in accessing external finance, we can expect
that when internal finance is insufficient, government subsidies will be
especially important for investment. Based on the arguments presented,
we formulate the following research hypothesis:
H43: Government subsidies are a positive determinant of investment
in fitness SMEs.
1.4.4. Financial crisis
According to Vermoesen et al. (2013), the global financial crisis
implies greater difficulties for SMEs in accessing external finance.
According to the authors, the particular characteristics of SMEs, such
as: 1) greater likelihood of bankruptcy; 2) lower quality of information
supplied to creditors; and 3) greater ease in changing the composition
of their assets, compared to the case of large firms, may contribute
decisively to SMEs having particular difficulties in accessing external
finance in the context of a global financial crisis, thereby reducing
their levels of investment.
SMEs can be expected to experience particular difficulties in
accessing external finance in periods of financial crisis, since
creditors make terms of credit especially difficult at such times.
Therefore, SME investment at times of financial crisis will be
particularly dependent on internal funding and possible government
subsidies, debt being of a particularly restrictive nature. Based on the
above, we formulate the following research hypotheses:
H44: At times of financial crisis, the investment of fitness SMEs
is less.
H45: At times of financial crisis, internal finance and government
subsidies are particularly
relevant for fitness SME investment.
H46: At times of financial crisis, debt is a more restrictive
determinant of fitness SME investment.
2. Methodology
2.1. Database
This study uses the SABI (Analysis System of Iberian Balance
Sheets) database from Bureau van Dijks for the period 2004-2009. As our
subject of analysis is fitness SMEs, we use the NACE classification to
select firms belonging to sub-sector 93.13. Fitness, and as we are
analysing SMEs, for the final selection of firms we follow the European
Union recommendation L124/36, (2003/361/CE). Accordingly, a firm is
considered an SME when it satisfies two of the following three criteria:
1) fewer than 250 employees; 2) annual total assets under 43 million
euros; and 3) business turnover under 50 million euros.
So as to avoid bias of the results obtained, and simultaneously
have a more representative sample of the fitness SME situation in
Portugal, we consider: 1) fitness SMEs belonging to the market for the
whole period of analysis (2004-2009); 2) fitness SMEs that enter the
market during the period of analysis (2004-2009); and 3) fitness SMEs
that leave the market during the period of analysis (2004-2009).
The final sample is made up as follows: 1) 67 fitness SMEs present
in the market for the whole period of analysis (2004-2009); 2) 79
fitness SMEs that enter the market during the period of analysis
(2004-2009); and 3) 36 fitness SMEs that leave the market during the
period of analysis (2004-2009).
The following Table 1 presents the structure of the sample used in
this study.
Aiming to compare the empirical evidence of the determinants of
investment in fitness SMEs with other types of SMEs, we turn to the
European Union recommendation L124/36, (2003/361/CE) to select a sample
of manufacturing SMEs for the period 2004-2009 (1). 478 manufacturing
SMEs were selected: 1) 235 manufacturing SMEs present in the market for
the whole period of analysis (2004-2009); 2) 123 manufacturing SMEs that
enter the market during the period of analysis (2004-2009); and 3) 120
manufacturing SMEs that leave the market during the period of analysis
(2004-2009).
2.2. Variables
The dependent variable is the investment of fitness SMEs, given by
the ratio of fixed capital variation in the present period to fixed
assets in the previous period (2). As principal independent variables,
we consider: 1) sales; 2) cash flow; and 3) debt. As control variables,
we also consider the following investment determinants of fitness SMEs:
1) age; 2) growth opportunities; 3) government subsidies; and 4) a dummy
variable representing the financial crisis.
The following Table 2 presents the variables used in this study,
their corresponding measures, and the expected influence of explanatory
variables on dependent variable. Monetary variables are deflated
according to inflation in Portugal. We construct a price index from 2004
to 2009. 2009 is taken as the base year.
2.3. Estimation method
To estimate results, we use various types of regressions.
Initially, we estimate regressions considering determinants representing
the main explanatory theories of investment, namely sales as a variable
representing Neoclassical theory, cash flow as a variable representing
Free Cash Flow theory, and debt as a variable representing Agency
theory. Afterwards, we consider in the regressions other possible
determinants of investment, namely age, growth opportunities and
government subsidies. In addition, we consider a dummy variable
representing the financial crisis of 2008. Finally, we consider in the
regressions interaction variables between the dummy representing the
financial crisis of 2008 and the financial variables considered in the
paper, namely cash flow, debt and government subsidies, aiming to
determine the influence of the financial crisis of 2008 on the impact of
financial variables on investment. The regressions to estimate can be
presented as follows:
[I.sub.i,t] = [[beta].sub.0] + [[beta].sub.k] [X*.sub.k,i,t-1] +
[u.sub.i] + [e.sub.i,t], (1)
where: [X*.sub.k,i,t-1] is the vector of independent variables:
[X*.sub.1,i,t-1] = f([SALES.sub.it-1], [Cf.sub.i,t-1],
[LEV.sub.i,t-1], [d.sub.t]); (2)
[X.sub.*2,i,t-i] = f([SALES.sub.i,t-1], [Cf.sub.i,t-1],
[LEV.sub.i,t-1], [AGE.sub.i,t-1], [GO.sub.i,t-1], [GS.sub.i,t- 1],
[d.sub.t]); (3)
[X*.sub.3,i,t-1] = f([SALES.sub.i,t-l], [CF.sub.i,t-l],
[LEV.sub.it-1], [AGE.sub.i,t-1], [GO.sub.i,t-1], [GS.sub.i,t- 1],
[D.sub.08-09]), (4)
where: [SALES.sub.i,t-1] are sales in the previous period;
[Cf.sub.i,t-1] is cash flow in the previous period; [LEV.sub.i,t-1] is
debt in the previous period; [AGE.sub.i,t-1] is age in the previous
period; [GO.sub.i,t-1] are growth opportunities in the previous period;
[GS.sub.i,t-1] are government subsidies in the previous period;
[D0.sub.8-09] is the dummy variable representing the financial crisis of
2008; [d.sub.t] are annual dummy variables; [u.sub.i] are not directly
observable fixed effects; and [e.sub.i,t] is the error term which is
assumed to have normal distribution.
At a later stage, aiming to test the effect of the 2008 financial
crisis on the relationship between the financial variables (CF, LEV, GS)
and investment in fitness SMEs, we estimate the relationships between
determinants and fitness SME investment in the periods 2008-2009 and in
the period 2004-2007. Initially, we only consider the impact of the
determinants: CF, LEV and GS on investment in fitness SMEs.
Subsequently, we also consider the other principal remaining investment
determinants considered in the study, namely: SALES, AGE and GO. The
regressions to estimate can be presented as follows:
[I.sub.i,t] = [[beta].sub.o] + [[beta].sub.k] [X*.sub.k,i,t-1] +
[u.sub.i] + [e.sub.i,t], (5)
where: [X*.sub.k,i,t-1] is the vector of independent variables:
[X*.sub.4,t-1] = f([CF.sub.i,t-1], [LEV.sub.i,t-1] [GS.sub.i,t-1],
[d.sub.t]); (6)
[X*.sub.5,it-1] = f([CF.sub.i,t-1], [LEV.sub.i,t-1],
[GS.sub.i,t-1], [SALES.sub.i,t-1], [AGE.sub.i,t-1], [GO.sub.i,t- 1],
[d.sub.t]). (7)
Mateev and Anastasov (2011), Antao and Bonfim (2012) control for
the effects of firm size and age on the results obtained, respectively,
between determinants and firm growth and between determinants and firm
debt. The fact that the great majority of fitness SMEs are small and
young prevents us from making a division between micro fitness, small
fitness and medium-sized fitness, and between young fitness and old
fitness, since sub-samples of small fitness and medium-sized fitness,
and of old fitness would contain a very limited number of firms.
Therefore, aiming to test the effect of size and age on relationships
between the determinants: SALES, CF, LEV, AGE, GO, GS, and investment,
initially we consider two dummy variables: 1) a dummy variable with the
value of 1 if the size of fitness SMEs at a given moment is above the
median of size and 0 if it is under the median; and 2) a dummy variable
with the value of 1 if the age of fitness SMEs at a given moment is
above the median of age and 0 if it is under the median. Then we
multiply the dummy variables by the determinants: SALES, CF, LEV, AGE,
GO, GS. In these circumstances the regressions to estimate can be
presented as follows:
[I.sub.i,t] = [[beta].sub.0] + [[beta].sub.K] [X*.sub.k,i,t-1] +
[u.sub.i] +[e.sub.i,t], (8)
where: [X*.sub.k,i,t-1] is the vector of independent variables:
[X*.sub.6,i,t-1] = f([D.sub.S] X [SALES.sub.i,t-1], [D.sub.S] x
[CF.sub.i,t-1], [D.sub.S] x [LEV.sub.i,t-1], [d.sub.t]): (9)
[X*.sub.6,i,t-1] = f([D.sub.S] X [SALES.sub.i,t-1], [D.sub.S] x
[CF.sub.i,t-1], [D.sub.S] x [LEV.sub.i,t-1], [D.sub.S] x
[AGE.sub.i,t-1], [D.sub.S] x [GO.sub.i,t-1], [D.sub.S] x [GS.sub.i,t-1],
[d.sub.t]); (10)
[X*.sub.8,i,t-1] = f([D.sub.A] x [SALES.sub.i,t-1], [D.sub.A] x
[LEV.sub.i,t-1], [d.sub.t]); (11)
[X*.sub.9i,t-1] = f([D.sub.A] x [SALES.sub.i,t-1], [D.sub.A] x
[CF.sub.i,t-1], [D.sub.A] x [LEV.sub.i,t-1], [D.sub.A] x
[AGE.sub.i,t-1], [D.sub.A] x [GO.sub.i,t-1], [D.sub.A] x [GS.sub.i,t-1],
[d.sub.t]). (12)
Finally, we compare the results obtained for fitness SMEs with
manufacturing SMEs concerning the relationships between determinants and
investment. We use the Chow test to test the differences between the
estimated parameters measuring the relationships between investment and
determinants in fitness SMEs and manufacturing SMEs.
In all the regressions we admit the existence of fixed effects (3)
considering the cluster option (4). This option allows us to estimate
standard deviations consistent with any type of heteroskedasticity and
autocorrelation. As we use regressions admitting the existence of fixed
effects, use of lagged investment would lead to correlation between
fixed effects ([u.sub.i]) and the lagged investment ([I.sub.i,t-1])
which would mean bias of the estimated parameter measuring the
relationship between investment in the previous period ([I.sub.i,t-1])
and investment in the current period ([I.sub.i,t). However, since we use
the cluster option, the results estimated through the regressions
admitting the existence of fixed effects are consistent with the
possible existence of autocorrelation, and so possible autocorrelation
does not mean bias in the estimated results measuring relationships
between determinants and investment.
Aiming to test the robustness of the results obtained, we estimate
the regressions related to the determinants of investment using an
alternative measure for defining investment, namely net investment given
by the ratio of variations in fixed capital less depreciation to total
assets, and considering the determinants (SALES, CF, LEV, AGE, GO, GS)
two periods out of step. Furthermore, we turn to OLS regressions,
considering investment one period out of step as an additional
determinant of investment. The results are presented in Appendix B,
Table B1.
In Appendix C, so as to test the robustness of the empirical
evidence obtained, we add two possible determinants of fitness SME
investment not dealt with in the literature on firm investment, namely:
1) effective tax rate (ETR), measured by the ratio of total tax expenses
to earnings before taxes; and 2) risk, i.e. earnings volatility (EVOL),
measured by the absolute value of the ratio of variation of earnings
before taxes and interest to earnings before taxes and interest in the
previous period. The results are presented in Appendix C, Table C1 (5).
3. Results
3.1. Descriptive statistics and correlation matrix
The following Table 3 presents the descriptive statistics of the
variables used in this study.
From analysis of the descriptive statistics, we can see the small
size and young age of fitness SMEs in Portugal. We opt to present the
descriptive statistics of the size and age variables in logarithms, to
agree with the definition used and the way they are introduced in the
regressions. However, aiming for better perception of the size and age
of fitness SMEs in Portugal, we present the averages of size and age
without the variables being in logarithms: 1) average size is 102898.87
euros; and 2) average age is 6.2099 years. We find, therefore, that on
average, besides the small size, the age of fitness SMEs is also low. In
Appendix A, Table A1, we present the descriptive statistics of the
variables referring to manufacturing SMEs. Compared to the size and age
of manufacturing SMEs (6), we find that on average the size and age of
fitness SMEs are considerably lower. This fact reveals the distinct
characteristics of fitness SMEs, which may have a particular effect on
their investment options.
We also find that: 1) investment, cash flow, growth opportunities
and government subsidies are quite volatile determinants, since the
standard deviations of the variables are above the respective means; and
2) sales, debt and age are determinants showing little volatility, since
the standard deviations are under the respective means.
The following Table 4 presents the correlation coefficients between
the variables used in this study.
Gujarati and Porter (2010) conclude that when the correlation
coefficients between independent variables are under 50%, the problem of
collinearity between explanatory variables will not be particularly
relevant. From observation of the correlation coefficients between the
independent variables used, in no circumstances are they over 50%, and
so the problem of collinearity between explanatory variables will not be
relevant in this study (7).
3.2. Investment determinants
The following Table 5 presents the results referring to the
relationships between determinants and investment.
Whatever the regression estimated, the results obtained indicate
that: 1) the relationship between sales and investment is positive and
statistically significant; 2) the relationship between cash flow and
investment is positive and statistically significant; and 3) the
relationship between debt and investment is negative and statistically
significant.
Regarding relationships between the other determinants and
investment, we can conclude that: 1) the relationship between age and
investment is not statistically significant; 2) the relationship between
growth opportunities and investment is positive and statistically
significant;
3) the relationship between government subsidies and investment is
positive and statistically significant; 4) the relationship between the
dummy variable representing the financial crisis of 2008 and investment
is negative and statistically significant.
The results presented in Appendix B, Table B1, refer to the use of
an alternative measure of investment and use of the determinants (sALEs,
CF, LEV, Age, Go, Gs) two periods out of step. Concerning the magnitude
and statistical significance of the estimated parameters, the results
presented in Table B1, corroborate those presented in Table 5,
confirming the empirical evidence obtained in this study.
In appendix C, Table C1 presents the regressions of Table 5,
considering two more possible determinants of fitness SME investment,
namely effective tax rate (ETR) and risk-earnings volatility (EvoL) (8).
The empirical evidence indicates that: 1) the relationship between
effective tax rate and fitness SME investment is negative and
statistically significant; and 2) the relationship between risk and
fitness SME investment is not statistically significant.
Table 6 presents the results referring to the impact of greater
size and greater age on relationships between the determinants: SALEs,
CF, LEv, Age, Go, GS, and investment in fitness SMEs.
Comparing the estimated parameters for the relationships between
determinants and investment presented in Table 6, with those presented
in Table 5, greater size of fitness SMEs means: 1) a greater increase in
investment as a consequence of growth opportunities; 2) a lower increase
in investment as a consequence of cash flow and government subsidies;
and 3) a non-significant impact of debt on investment. Greater age of
fitness SMEs means: 1) a lower increase in investment as a consequence
of government subsidies; and 2) a non-significant impact of growth
opportunities on investment.
Table 7 presents the results of the investment determinants for
manufacturing SMEs.
Irrespective of the regression estimated, we find that: 1) the
relationship between sales and investment is not statistically
significant; 2) the relationship between cash flows and investment is
positive and statistically significant; and 3) the relationship between
debt and investment is positive and statistically significant.
Concerning relationships between the other determinants and
investment, we find that: 1) the relationship between age and investment
is negative and statistically significant; 2) the relationship between
growth opportunities and investment is positive and statistically
significant; 3) the relationship between government subsidies and
investment is not statistically significant; 4) the relationship between
the dummy variable representing the financial crisis and investment is
negative and statistically significant.
Table 8 below presents the results of the Chow test of the
differences in estimated parameters measuring relationships between
determinants and investment in fitness SMEs and manufacturing SMEs.
Irrespective of the regression estimated, for each of the
parameters estimated, we find we reject the null hypothesis of equality
of estimated parameters measuring relationships between determinants and
investment in fitness SMEs and manufacturing SMEs. The results of the
total difference of estimated parameters, in each regression, confirm
those differences. Based on these results, we can conclude there are
significant differences between the investment determinants of fitness
SMEs and manufacturing SMEs.
Aiming to determine the effect of the 2008 financial crisis on
relationships between the financial variables: CF, LEV, and GS and
investment in fitness SMEs, Table 9 presents the results regarding the
determinants of fitness SME investment for the periods 2008-2009 and
2004-2007.
The empirical evidence indicates that: 1) in the periods 2008-2009
and 2004-2007, the relationships between cash flow and investment in
fitness SMEs are positive and statistically significant; 2) in the
periods 2008-2009 and 2004-2007, the relationships between debt and
investment in fitness SMEs are negative and statistically significant;
and 3) in the periods 2008-2009 and 2004-2007, the relationships between
government subsidies and investment in fitness SMEs are positive and
statistically significant. However, the magnitude of estimated
parameters is found to be greater in the period 2008-2009 than in the
period 2004-2007, which reveals that cash flow and government subsidies
are especially relevant for increased investment in fitness SMEs in the
period 2008-2009, with debt being a particularly restrictive determinant
of fitness SME investment in the period 2008-2009. The test results of
difference for each of the parameters estimated confirm the differences.
4. Discussion of the results
We find a positive and statistically significant relationship
between sales and investment. Therefore, if sales increase, investment
increases, with investment diminishing if sales fall. So we reject
hypothesis H1, since sales are a positive determinant of investment in
fitness SMEs. This empirical evidence partially corroborates the
arguments, and results, of Eisner (1963) and Chirinko (1993), since
sales are an explanatory determinant of investment, but they are not
predominant over other possible investment variables, since, for
example, cash flow and debt are also explanatory variables of
investment. Nevertheless, given the positive relationship between sales
and investment, we cannot consider Neoclassical theory irrelevant in
explaining the investment decisions of Portuguese fitness SMEs.
In manufacturing SMEs, we find a statistically insignificant
relationship between sales and investment. Unlike the case of fitness
SMEs, Neoclassical theory is irrelevant in explaining the investment
decisions of manufacturing SMEs. It is interesting to note that while
fitness SMEs are on average smaller than manufacturing SMEs, the former
adjust their investment as a function of sales, contradicting the idea
that in small firms sales will be more important to cope with treasury
difficulties than to finance investment, i.e. contradicting the idea
that Neoclassical theory is more relevant in explaining the investment
decisions of large firms than in explaining those of small ones.
The relationship between cash flow and investment is positive and
statistically significant. Therefore, we cannot reject hypothesis H2,
since cash flow is important for investment in Portuguese fitness SMEs.
Based on this result, we can consider the assumptions of Free Cash Flow
theory as valid in explaining the investment decisions of Portuguese
fitness SMEs. We also find that greater size of fitness SMEs means less
impact of cash flow on investment, which reveals that Free Cash Flow
theory becomes more relevant in explaining the investment decisions of
fitness SMEs the smaller their size.
This result agrees with the conclusions of Fazzari et al. (1988),
since terms of finance are relevant for the investment decisions of
firms in general, and small firms in particular. The positive
relationship between cash flow and investment corroborates the empirical
evidence obtained by Fazzari et al. (1988), Fazzari and Petersen (1993),
Kaplan and Zingales (1997, 2000), Mizen and Vermeulen (2004), Hung and
Kuo (2011).
The fact that cash flow is important in explaining investment in
Portuguese fitness SMEs, that importance being greater the smaller their
size, reveals the importance problems of information asymmetry in
relationships between owners/managers and creditors may represent in the
activities of this type of firm.
Although cash flow is a determinant stimulating investment in
manufacturing SMEs, we find it is relatively less important in
explaining investment than in fitness SMEs. The fact that fitness SMEs
are on average smaller than manufacturing SMEs may contribute to
internal finance being more relevant in explaining investment in the
former, due to this type of firm's greater difficulty in accessing
external funding. Based on these results we can conclude that Free Cash
Flow theory is of greater relative importance in explaining the
investment decisions of fitness SMEs, than in explaining those of
manufacturing SMEs.
We find a negative relationship between debt and investment in
Portuguese fitness SMEs. Based on this result, we cannot reject the
previously formulated hypothesis H3, since debt is a restrictive
determinant of investment in Portuguese fitness SMEs. This result
indicates that the assumptions of Agency theory, namely problems of
information asymmetry in the relationships formed between
owners/managers and creditors, can be considered valid in explaining the
investment decisions of Portuguese fitness SMEs.
In situations of information asymmetry in relationships between
owners/managers and creditors, the latter can limit the amount of credit
or increase its cost, which may explain the negative relationship
identified between debt and investment in Portuguese fitness SMEs.
Indeed the limited size of these firms can make it impossible in many
situations to provide creditors with reliable information. Due to the
great uncertainty associated with small firms' activities together
with the lack of reliable information, creditors could hinder access to
debt through raising its cost, this corresponding to debt's
negative influence on investment.
Considering that Portuguese fitness SMEs are very small firms, the
negative relationship identified between debt and investment in those
firms may be the consequence of their particular difficulty in obtaining
external finance, given their small size and consequently lesser
reputation in the market and greater likelihood of bankruptcy. The
importance of size in explaining the relationship between debt and
investment is revealed by the fact that greater size of fitness SMEs
contributes to the impact of debt on investment being not significant,
Agency theory seeming to be not relevant in explaining the investment
decisions of fitness SMEs the greater their size.
The negative relationship between debt and investment agrees with
the empirical evidence obtained by Lang et al. (1996), Aivazian et al.
(2005), Ahn et al. (2006) and Firth et al. (2008). In manufacturing SMEs
we find debt to be a determinant stimulating investment. This result is
particularly relevant because it indicates that the greater size of
manufacturing SMEs, compared to fitness SMEs, may contribute to
diminished information asymmetry in relationships with creditors, due to
greater size meaning less likelihood of bankruptcy.
We find that Neoclassical, Free Cash Flow and Agency theories are
not necessarily exclusive in explaining the investment decisions of
Portuguese fitness SMEs. Indeed, although the problems of information
asymmetry explicit in relationships between owners/managers and
creditors seem to be particularly relevant, from the fact of cash flow
contributing to increased investment and debt meaning less investment,
the importance of sales as a determinant of investment in Portuguese
fitness SMEs cannot be ignored.
As for relationships between the other determinants considered in
this study and investment in Portuguese fitness SMEs, the one between
age and investment is not statistically significant, and those between
growth opportunities and investment and between government subsidies and
investment are positive and statistically significant.
The statistically insignificant relationship between age and
investment lets us reject the previously formulated hypothesis H41,
since greater age of Portuguese fitness SMEs does not mean increased
levels of investment. The fact that the age of Portuguese fitness SMEs
does not mean increased investment as a consequence of having been able
to get over initial financial restrictions and the reputation effect
conferred by age, may be related to the need for considerable investment
in fixed assets in the early periods of the life cycle, since the
initial cost of fixed assets is particularly important in the activities
of this type of firm.
In manufacturing SMEs we find age to be a restrictive determinant
of investment. As the age of manufacturing SMEs increases, the lower is
the investment. This result reveals the greater relative importance of
investment in fixed assets in this type of SME, compared to the case of
fitness SMEs.
The relationship between growth opportunities and investment is
positive and statistically significant. Therefore, we cannot reject the
previously formulated hypothesis H42, since growth opportunities are a
positive determinant of investment in Portuguese fitness SMEs.
Creditors may recognize good growth opportunities, lessening the
difficulties in granting credit to small firms (Carpenter, Guariglia
2008), and young, small firms may have good growth opportunities,
increasing investment to take advantage of them (Fagiolo, Luzzi 2006).
However, the positive effect is more significant in larger fitness SMEs,
but insignificant when considering older fitness SMEs, revealing that
whereas greater size can be a fundamental characteristic for creditors
recognizing growth opportunities in fitness SMEs, this does not happen
with greater age.
It also stands out that growth opportunities are of greater
relative importance for investment in manufacturing SMEs than for
investment in fitness SMEs. This evidence indicates, firstly, that
creditors can recognize more effectively the growth opportunities of
manufacturing SMEs, and secondly, that there is a greater association
between intangible assets and fixed assets in manufacturing SMEs than in
fitness SMEs.
The relationship between government subsidies and investment is
positive and statistically significant. Based on this result, we cannot
reject the previously formulated hypothesis H43, since government
subsidies are a positive determinant of investment in Portuguese fitness
SMEs.
Government subsidies may be especially relevant for small firms in
overcoming possible financing restrictions. The lesser importance of
government subsidies for investment in larger, older fitness SMEs
emphasizes the importance of this type of support the smaller and
younger they are. This result reveals the importance of government
subsidies for small, young fitness SMEs, firms that could be very
restricted financially due to the limited capacity to generate internal
funding and the difficulty in accessing external finance.
The fact that government subsidies do not stimulate investment in
manufacturing SMEs, while being a positive determinant of investment in
fitness SMEs, indicates that government subsidies can be particularly
relevant for increased investment in small, young service firms such as
fitness SMEs. The smaller size and young age of fitness SMEs, compared
to manufacturing SMEs, could contribute to this type of support being
particularly important for investment in the former, since whereas
manufacturing SMEs seem to finance their investment through debt, this
does not happen in fitness SMEs.
We find a negative relationship between the dummy variable
representing the financial crisis of 2008 and investment in fitness
SMEs. This being the case, we conclude that the financial crisis of 2008
is a restrictive determinant of fitness SME investment, and so we cannot
reject the previously formulated hypothesis H44.
This result indicates that the financial crisis of 2008 meant
diminished investment in fitness SMEs, probably due to worsening terms
of credit available to them in that period and the following one. Also
in the case of manufacturing SMEs, a negative relationship is found
between the dummy variable representing the financial crisis of 2008 and
investment. Nevertheless, in comparative terms, the negative impact of
the financial crisis of 2008 on investment in fitness SMEs is of a
greater magnitude than on investment in manufacturing SMEs. This finding
reinforces the idea that terms of credit for fitness SMEs may have been
harsher than those applied to their manufacturing counterparts.
The financial crisis of 2008 contributes to internal finance and
government subsidies being of greater relative importance for increased
investment in fitness SMEs, and also to debt being of greater relative
importance for diminished investment. Therefore, we cannot reject the
previously formulated hypotheses H45 and H46. This empirical evidence is
particularly relevant as it indicates that the financial crisis of 2008
has a particularly restrictive effect on fitness SMEs' access to
external finance as a way of funding investment, internal finance and
government subsidies being of greater relative importance for increased
investment in this type of firm. The fact of the financial crisis of
2008 contributing to greater relevance of cash flow for increased
investment and greater relevance of debt for reduced investment, reveals
that the financial crisis contributes to greater relevance of Free Cash
Flow and Agency theories in explaining the investment decisions of
fitness SMEs.
We also find that a higher level of tax paid by fitness SMEs means
lower levels of investment. Taxes paid are a restrictive determinant of
investment in fitness SMEs, probably due to less capacity to finance
their investment opportunities. Level of risk appears not to influence
fitness SME investment. Greater operational risk, and consequently
greater likelihood of bankruptcy, does not mean diminished investment in
fitness SMEs, i.e. apparently, fitness SMEs with a greater probability
of bankruptcy does not reduce their levels of investment.
Conclusion and implications
Based on a sample of 182 Portuguese fitness SMEs this paper studies
the investment determinants of Portuguese fitness SMEs. The paper
contributes to the literature by showing that the main explanatory
theories of firm investment are not necessarily mutually exclusive in
explaining the investment decisions of small and young service SMEs,
more specifically, fitness SMEs, because: 1) adjustment of investment is
found to be a function of sales, corroborating the assumptions of
Neoclassical theory; 2) cash flow is relevant for increased investment,
agreeing with the assumptions of Free Cash Flow theory; and 3) debt is a
restrictive determinant of investment, revealing the importance of
problems of information asymmetry in relationships between
owners/managers and creditors, which corroborates the assumptions of
Agency theory. More specifically, the paper contributes to the
literature showing that Neoclassical theory is relevant in explaining
the investment decisions of small, young service SMEs, which reveals
that small size and young age do not always contribute to firms failing
to adjust investment as a function of sales.
The fact that Portuguese fitness SMEs adjust investment as a
function of cash flow and that debt restricts investment reveals the
importance of problems of information asymmetry in relationships between
the owners/managers of these Portuguese fitness SMEs and creditors.
However, the fact that Portuguese fitness SMEs adjust their level of
investment as a function of sales illustrates that this type of firm
does not adjust investment only as a function of financial
characteristics, but also considering the possibilities for expansion in
operating markets in investment decisions.
It stands out, firstly, that greater size is of particular
importance for Free Cash Flow theory being less relevant, and Agency
theory being not relevant, in explaining the investment decisions of
fitness SMEs. Secondly, Neoclassical theory is seen to be relevant in
explaining the investment decisions of fitness SMEs, something which is
not found in manufacturing SMEs, with Free Cash Flow and Agency theories
also being more relevant in explaining the investment decisions of
fitness SMEs than in explaining those of manufacturing SMEs.
As for the other investment determinants used in this study, growth
opportunities and government subsidies are found to be positive
determinants of investment, the financial crisis of 2008 has a negative
influence on investment, and age is neither a positive nor restrictive
determinant of investment in Portuguese fitness SMEs. Firstly, the fact
that Portuguese fitness SMEs adjust investment as a function of growth
opportunities reinforces the idea that this type of firm does not only
consider financial conditions as investment determinants. Secondly,
government subsidies are a positive determinant of investment,
indicating that when internal finance is insufficient, due to the
difficulties in accessing external finance, public funding could be
fundamental for financing investment in Portuguese fitness SMEs.
Thirdly, the fact that the financial crisis of 2008 has a negative
impact on fitness SME investment indicates that when internal funding is
insufficient, in periods of financial crisis, conditions for fitness
SMEs accessing credit could be particularly adverse. We also find that
the financial crisis of 2008 contributes to cash flow and government
subsidies being of greater relative importance for increased investment,
and to debt being of greater relative importance for diminished
investment in fitness SMEs. These results strengthen the notion that the
financial crisis of 2008, besides restricting investment, also
contributes to fitness SME investment being more dependent on internal
finance and government subsidies and less dependent on debt. In
addition, higher levels of taxes paid are found to mean diminished
investment in fitness SMEs, with operational risk not contributing to
either an increase or reduction of investment in fitness SMEs.
Fitness SMEs provide important services of preventive medicine to
improve the population's health and well-being. However, in most
cases, their small size and young age may affect their strategies for
implementation in their operating markets. The empirical evidence
obtained in this study of Portuguese fitness SMEs allows us to suggest
the following measures for economic policy in general, and industrial
policy in particular: 1) considering that debt is a restrictive
determinant of investment, with cash flow and government subsidies being
positive determinants, we suggest reinforcing government subsidies for
Portuguese fitness SMEs with particular difficulties in accessing
external finance and without the internal finance necessary to fund
their good investment opportunities. At times of financial crisis, when
internal finance is insufficient, strengthening government subsidies
could be particularly relevant for increased investment in fitness SMEs;
and 2) considering that sales and growth opportunities are positive
determinants of investment, specific financial support is suggested for
Portuguese fitness SMEs with good investment and growth projects, but
with difficulty in financing those projects.
In future research, we intend to study specifically the capital
structure of Portuguese fitness SMEs, so as to understand how the
specific characteristics of these firms affect, or do not affect, their
financing decisions. Furthermore, we intend to carry out cross-cultural
comparisons within the country, so as to ascertain whether the different
cultural situations in Portuguese regions have an influence on the
relationships found between determinants and investment in fitness SMEs.
APPENDIX A.
Descriptive statistics and correlation matrix--manufacturing SMEs
Table A1. Descriptive statistics
Variable Firms Observations
[I.sub.i,t] 478 1994
[SALES.sub.i,t] 478 1994
[CF.sub.i,t] 478 1994
[LEV.sub.i,t] 478 1994
[AGE.sub.i,t] 478 1994
[GO.sub.i,t] 478 1994
[GS.sub.i,t] 478 1994
[D.sub.08-09] 478 1994
Variable Mean Median S.D.
[I.sub.i,t] 0.1430 0.1309 0.3174
[SALES.sub.i,t] 14.714 14.598 1.9492
[CF.sub.i,t] 0.0749 0.0769 0.1983
[LEV.sub.i,t] 0.6283 0.4981 0.2092
[AGE.sub.i,t] 2.7333 2.6390 0.6838
[GO.sub.i,t] 0.0130 0.0034 0.0498
[GS.sub.i,t] 0.0085 0 0.0348
[D.sub.08-09] 0.3766 0 0.4846
Variable Min Max
[I.sub.i,t] -0.7183 7.9102
[SALES.sub.i,t] 9.8617 17.723
[CF.sub.i,t] -1.6494 1.2083
[LEV.sub.i,t] 0 0.9438
[AGE.sub.i,t] 0 4.6821
[GO.sub.i,t] 0 0.7593
[GS.sub.i,t] 0 0.4594
[D.sub.08-09] 0 1
Table A2. Correlation matrix
[I.sub.i,t] [SALES.sub.i,t-1]
[I.sub.i,t] 1
[SALES.sub.i,t-1] 0.035* 1
[CF.sub.i,t-1] 0.219** 0.256**
[LEV.sub.i,t-1] 0.156** 0.098**
[AGE.sub.i,t-1] -0.109** 0.030*
[GO.sub.i,t-1] 0.129** 0.087**
[GS.sub.i,t-1] -0.010 0.011
[D.sub.08-09] -0.078** -0.089**
[CF.sub.i,t-1] [LEV.sub.i,t-1] [AGE.sub.i,t-1]
[I.sub.i,t]
[SALES.sub.i,t-1]
[CF.sub.i,t-1] 1
[LEV.sub.i,t-1] -0.239** 1
[AGE.sub.i,t-1] 0.140** 0.278** 1
[GO.sub.i,t-1] 0.135** -0.112** -0.013
[GS.sub.i,t-1] -0.106** 0.098** -0.132**
[D.sub.08-09] -0.220** -0.286** 0.154**
[GO.sub.i,t-1] [GS.sub.i,t-1] [D.sub.08-09]
[I.sub.i,t]
[SALES.sub.i,t-1]
[CF.sub.i,t-1]
[LEV.sub.i,t-1]
[AGE.sub.i,t-1]
[GO.sub.i,t-1] 1
[GS.sub.i,t-1] 0.071** 1
[D.sub.08-09] 0.011 0.128** 1
Notes: "Statistical significant at 1% level; * Statistical
Significant at 5% level.
APPENDINX B. Investment determinants of fitness SMEs--alternative
estimations
Table B1. Investment determinants of fitness SMEs--alternative
measure of dependent variable, lagged explanatory variables and OLS
regressions
Dependent Variable: [I.sub.i,t]
I II III
[I.sub.i,t-1] 0.11891 ** 0.10293 ** 0.12955 **
(0.03049) (0.02718) (0.03411)
[SALES.sub.i,t-2] 0.01517 * 0.01819 * 0.01761 *
(0.00742) (0.00876) (0.00815)
[CF.sub.i,t-2] 1.10981 ** 1.15169 ** 1.03728 **
(0.17896) (0.21589) (0.17652)
[LEV.sub.i,t-2] -0.12998* -0.10762 * -0.15412 **
(0.06276) (0.05255) (0.04471)
[AGE.sub.i,t-2] 0.01167 0.01353
(0.15268) (0.15689)
[GO.sub.i,t-2] 0.10828 * 0.11982 **
(0.05294) (0.05881)
[GS.sub.i,t-2] 0.58919 ** 0.61728 **
(0.12939) (0.15446)
[D.sub.08-09] -0.23891 **
(0.06355)
CONS 0.02389 ** 0.02091 ** 0.02256 **
(0.00615) (0.00651) (0.00725)
[R.sup.2] 0.1877 0.2051 0.2198
Firms 156 156 156
Observations 360 360 360
Notes: 1. CONS is the constant of the regressions; 2. Standard
deviations in parenthesis using cluster option; 3. ** statistical
significance at 1 % level, * statistical significance at 5 % level;
4. The estimates include time dummy variables, but are not shown.
APPENDIX C. Investment determinants of fitness SMEs - additional
determinants
Table C1. Investment determinants of fitness SMEs: more explanatory
variables
Dependent Variable: [I.sub.i,t]
I II III
[SALES.sub.i,t-1] 0.03152 ** 0.02953 ** 0.02347 **
(0.00812) (0.00704) (0.00457)
[CF.sub.i,t-1] 1.2098 ** 1.16719 ** 1.03743 **
(0.22691) (0.20752) (0.22450)
[LEV.sub.i,t-1] -0.19162 ** -0.13711 ** -0.16224 **
(0.07004) (0.04019) (0.04656)
[AGE.sub.i,t-1] 0.01813 0.02178
(0.12087) (0.13078)
[GO.sub.i,t-1] 0.23445 ** 0.19466 **
(0.05980) (0.04476)
[GS.sub.i,t-1] 0.73489 ** 0.72889 **
(0.16110) (0.18234)
[D.sub.08-09] -0.26514 **
(0.06889)
[ETR.sub.i,t-1] -0.15849 ** -0.14293 ** -0.16446 **
(0.02819) (0.02615) (0.03019)
[EVOL.sub.i,t-1] 0.10293 0.12939 0.09717
(0.45819) (0.54152) (0.43828)
CONS 0.01891 ** 0.01283 ** 0.01412 **
(0.00417) (0.00389) (0.00415)
[R.sup.2] 0.2698 0.2987 0.3213
Firms 156 156 156
Observations 360 360 360
Notes: 1. CONS is the constant of the regressions; 2. Standard
deviations in parenthesis using cluster option; 3. ** statistical
significance at 1 % level, * statistical significance at 5 % level;
4. The estimates include time dummy variables, but are not shown.
References
Ahn, S.; Denis, D.; Denis, D. 2006. Leverage and investment in
diversified firms, Journal of Financial Economics 79(2): 317-337.
http://dx.doi.org/10.1016/j.jfineco.2005.03.002
Aivazian, V.; Ge, Y.; Qiu J. 2005. The impact of leverage on firm
investment: Canadian evidence, Journal of Corporate Finance 11(1-2):
277-291. http://dx.doi.org/10.1016/S0929-1199(03)00062-2
Andersen-Hanley, C.; Nimon, J.; Western, S. 2010. Cognitive health
benefits of strengthening exercise for community-dwelling older adults,
Journal of Clinical and Experimental Neuropsychology 32(9): 996-1001.
http://dx.doi.org/10.1080/13803391003662702
Ang, J. 1991. Small business uniqueness and the theory of financial
management, Journal of Small Business Finance 1(1): 1-13.
Antao, P.; Bonfim, D. 2012. The dynamics of capital structure
decisions, Working Papers No 201206 [online], [cited 23 April 2013].
Economics and Research Department, Banco de Portugal, Portugal.
Available from Internet:
http://www.bportugal.pt/en-US/BdP%20Publications%20Research/wp201206.pdf
Arellano, M.; Bond, S. 1991. Some tests of specification for panel
data: Monte Carlo evidence and an application to employment equations,
Review of Economic Studies 58(2): 277-297.
http://dx.doi.org/10.2307/2297968
Ascioglu, A.; Hegde, S.; McDermott, J. 2008. Information asymmetry
and investment-cash flow sensitivity, Journal of Banking & Finance
32(6): 1036-1048. http://dx.doi.org/10.1016/j.jbankfin.2007.09.018
Baker, T.; Gedajlovic, E.; Lubatkin, M. 2005. A framework for
comparing entrepreneurship across nations, Journal of International
Business Studies 36(5): 492-504.
http://dx.doi.org/10.1057/palgrave.jibs.8400153
Beck, T.; Demirgu^-Kunt, A.; Laeven, L.; Maksimovic, V. 2006. The
determinants of financing obstacles, Journal of International Money
& Finance 25(6): 932-952.
http://dx.doi.org/10.1016/j.jimonfin.2006.07.005
Brown, J.; Fazzari, S.; Petersen, B. 2009. Financing innovation and
growth: cash flow, external equity and the 1990s R&D boom, The
Journal of Finance 64(1): 151-185.
http://dx.doi.org/10.1111/j.1540-6261.2008.01431.x
Carpenter, R.; Guariglia, A. 2008. Cash-flows, investment and
investment opportunities: new tests using UK panel data, Journal of
Banking and Finance 32(9): 1894-1906.
http://dx.doi.org/10.1016/j.jbankfin.2007.12.014
Chirinko, S. 1993. Business fixed investment spending: modelling
strategies, empirical results and policy implications, Journal of
Economics Literature 31(4): 1875-1911.
Cho, I.; Park, H.; Choi, J. 2011. The impact of diversity of
innovation channels on innovation performance in service firms, Service
Business 5(3): 277-294. http://dx.doi.org/10.1007/s11628-011-0114
Cho, I.; Park, H.; Kim, J. 2012. The moderating effect of
innovation protection mechanisms on the competitiveness of service
firms, Service Business 6(3): 369-386.
http://dx.doi.org/10.1007/s11628-012-0140-3
Dale, S.; Godinet, S.; Kearse, N.; Field, A. 2009. The future of
fitness, a White Paper. Ed. Les Mills International Ltd, 1-21.
Devereux, M.; Schiantarelli, F. 1990. Investment, financial
factors, and cash flow: evidence from U.K. panel data. Asymmetric
Information, Corporate Finance, and Investment, University of Chicago
Press, 279-306.
Diamond, D. 1989. Financial intermediation and delegated
monitoring, Review of Economic Studies 51(3): 393-414.
http://dx.doi.org/10.2307/2297430
Eisner, R. 1963. Investment: fact and fancy, American Economic
Review 53(2): 237-246. http://www.jstor.org/stable/1823867
Evans, D.; Jovanovic, B. 1989. An estimated model of
entrepreneurial choice under liquidity constraints, Journal of Political
Economics 97(4): 808-827. http://dx.doi.org/10.1086/261629
Fagiolo, G., Luzzi, A. 2006. Do liquidity constraints matter in
explaining firm size and growth? Some evidence from the Italian
manufacturing industry, Industrial and Corporate Change 15(1): 1-39.
http://dx.doi.org/10.1093/icc/dtj001
Fazzari, S.; Hubbard, G.; Petersen, B. 1988. Financing constraints
and corporate investment, Brookings Papers on Economics Activity 1(1):
141-195. http://dx.doi.org/10.2307/2534426
Fazzari, S.; Petersen, B. 1993. Working capital and fixed
investment: new evidence on financing constraints, Rand Journal of
Economics 24(3): 328-342. http://dx.doi.org/10.2307/2555961
Firth, M.; Lin, C.; Wong, S. 2008. Leverage and investment under a
state-owned bank lending environment: evidence from China, Journal of
Corporate Finance 14(5): 642-653.
http://dx.doi.org/10.1016/j.jcorpfin.2008.08.002
Garber, C.; Blissmer, B.; Deschenes, M.; Franklin, B.; Lamonte, M.;
Lee, I.-M.; Nieman, D.; Swain, D. 2011. Quantity and quality of exercise
for developing and maintaining cardiorespiratory, musculoskeletal, and
neuromotor fitness in apparently healthy adults: guidance for
prescribing exercise, Medicine & Science in Sports & Exercise
43(7): 1334-1359. http://dx.doi.org/10.1249/MSS.0b013e318213fefb
Gilchrist, S.; Himmelberg, P. 1995. Evidence on the role of
cash-flows for investment, Journal of Monetary Economics 36: 541-572.
http://dx.doi.org/10.1016/0304-3932(95)01223-0
Gujarati, D.; Porter, D. 2010. Essentials of econometrics. 4th ed.
New York: McGraw--Hill International, 1-547.
Hall, R.; Jorgenson, D. 1967. Tax policy and investment behaviour,
American Economic Review 57(3): 391-414.
Hung, J.-H.; Kuo, Y.-P. 2011. The effect of family control on
investment-cash flow sensitivity, Applied Financial Economics 21(12):
897-904. http://dx.doi.org/10.1080/09603107.2010.539533
Jensen, M. 1986. Agency costs of free cash-flow, corporate finance
and takeovers, AEA Papers and Proceedings 76(2): 383-393.
Jensen, M.; Meckling, W. 1976. Theory of the firm: managerial
behaviour, agency costs and ownership structure, Journal of Financial
Economics 3(4): 305-360. http://dx.doi.org/10.1016/0304-405X(76)90026-X
Jorgenson, D. 1971. Econometric studies of investment behaviour: a
survey, Journal of Economics Literature 9(4): 1111-1147.
http://www.jstor.org/stable/2721137
Junlu, M.; Zeguang, L.; Qunyong, W. 2009. Financial constraints,
agency cost and firm's investment behavior: evidence from listed
firms of China, Frontiers of Economics in China 4(3): 384-405.
http://dx.doi.org/10-1007/s11459-009-0021-4
Kaplan, S.; Zingales, L. 1997. Do financing constraints explain why
investment is correlated with cash-flow?, Quarterly Journal of Economics
112(1): 169-215. http://dx.doi.org/10.1162/003355397555163
Kaplan, S.; Zingales, L. 2000. Investment cash-flow sensitivities
are not valid measures offinancing constraints, Quarterly Journal of
Economics 115(2): 707-712. http://dx.doi.org/10.1162/003355300554782
Lang, L.; Ofek, E.; Stulz, R. 1996. Leverage, investment, and firm
growth, Journal of Financial Economics 40(1): 3-29.
http://dx.doi.org/10.1016/0304-405X(95)00842-3
Lee, S.; Ratti, R. 2008. Bank concentration and financial
constraints on firm-level in Europe [online], [cited 23 April 2013].
Department of Economics, University of Missouri, Columbia. Available
from Internet: http://www.businessandeconomics.mq.edu.au/our_departments/Economics/Econ_docs/
research_seminars/2007_research_seminars/ratti_BankConcentration.pdf
Manolova, T.; Eunni, R.; Gyoshev, B. 2008. Institutional
environments for entrepreneurship: evidence from emerging economies in
eastern Europe, Entrepreneurship Theory and Practice 32(1): 203-218.
http://dx.doi.org/10.1111/j.1540-6520.2007.00222.x
Mateev, M.; Anastasov, Y. 2011. On the growth of small and medium
sized enterprises in Central and Eastern Europe: a dynamic panel
analysis, The Banking and Finance Review 3(2): 81-104.
http://www.bankingandfinancereview.com/bfr/index.php/bfr/article/view/193
Mateev, M.; Tsekov, I. 2012. Do Central and Eastern European
countries posses FDI advantages to more developed Western Countries?
[online], [cited 23 April 2013]. Available from Internet: http://ssrn.
com/abstract=2139361
Mizen, P.; Vermeulen, P. 2004. Corporate investment and financial
constraints: what drives the relationship? [online], [cited 23 April
2013]. University of Nottingham and European Centre Bank. Available from
Internet: http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp485.pdf
Modigliani, F.; Miller, M. 1958. The cost of capital, corporation
finance and the theory of investment, American Economics Review 48(3):
261-297. http://www.jstor.org/stable/1809766
Moyen, N. 2004. Investment cash-flow sensitivities: constrained
versus unconstrained firms, Journal of Finance 59(5): 2061-2092.
http://dx.doi.org/10.1111/j.1540-6261.2004.00692.x
Myers, S. 1977. The determinants of corporate borrowing, Journal of
Financial Economics 5(2): 147-175.
http://dx.doi.org/10.1016/0304-405X(77)90015-0
Oliner, S.; Rudebusch, G. 1992. Sources of the financing hierarchy
for business investment, The Review of Economics and Statistics 74(4):
643-654. http://www.jstor.org/stable/2109378
Peng, M. 2003. Institutional transitions and strategic choices,
Academy of Management Review 28(2): 275-296.
http://dx.doi.org/10.2307/30040713
Petersen, M.; Rajan, R. 1995. The effect of credit market
competition on lending relationship, Quarterly Journal of Economics
110(2): 407-443. http://dx.doi.org/10.2307/2118445
Roofthooft, W. 2010. Customer equity: a creative tool for SMEs in
the services industry: how small and medium enterprises can win the
battle for innovation, Service Business 4(1): 37-48.
http://dx.doi.org/10.10077s11628-009-0082-6
Serrasqueiro, Z.; Armada, M.; Ma^as Nunes, P. 2011. Pecking order
theory versus trade-off theory: are service SMEs' capital structure
decisions different?, Service Business 5(4): 381-409.
http://dx.doi.org/10-1007/s11628-011-0119-5
Silva, F.; Carreira, C. 2009. No deep pockets: some stylized
results on firms' financial constraints [online], [cited 23 April
2013], Estudos do GEMF 06/2009. Coimbra: GEMF, Universidade de Coimbra.
Available from Internet:
http://gemf.fe.uc.pt/workingpapers/abstracts/2009/resumo2009_06.htm
Smallbone, D.; Welter, F. 2006. Conceptualising entrepreneurship in
a transition context, International Journal of Entrepreneurship and
Small Business 3(2): 190-206.
http://dx.doi.org/10.1504/IJESB.2006.008928
Smallbone, D.; Welter, F.; Voytovich, A.; Egorov, I. 2010.
Government and entrepreneurship in transition economies: the case of
small firms in business services in Ukraine, The Services Industries
Journal 30(5): 655-670. http://dx.doi.org/10.1080/02642060802253876
Stiglitz, J.; Weiss, A. 1981. Credit rationing in markets with
imperfect information, American Economic Review 71(3): 393-410.
http://www.jstor.org/stable/1802787
Stulz, R. 1990. Managerial discretion and optimal financing
policies, Journal of Financial Economics 26(1): 3-27.
http://dx.doi.org/10.1016/0304-405X(90)90011-N
Sun, J.; Nobuyoshi, Y. 2009. Regional disparities and
investment-cash flow sensitivity: evidence from Chinese listed firms,
Pacific Economic Review 14(5): 657-667.
http://dx.doi.org/10.1111/j.1468-0106.2009.00477.x
Vermeulen, P. 2002. Business fixed investment: evidence of a
financial accelerator in Europe, Oxford Bulletin of Economics and
Statistics 64(3): 213-231. http://dx.doi.org/10.1111/1468-0084.00020
Vermoesen, V.; Deloof, M.; Laveren, E. 2013. Long-term debt
maturity and financial constraints of SMEs during the global crisis,
Small Business Economics 41(2): 433-448.
http://dx.doi.org/10.1007/s1187-012-9435-y
Xu, B. 1998. A reestimation of the Evans-Jovanovic entrepreneurial
choice model, Economics Letters 58(1): 91-95.
http://dx.doi.org/10.1016/S0165-1765(97)00255-3
Zwiebel, J. 1996. Dynamics capital structure under management
entrenchment, American Economics Review 86(5): 1197-1215.
http://www.jstor.org/stable/2118286
Paulo Ma^as NUNES, PhD in Economics, Associate Professor at the
Department of Management and Economics to Beira Interior University and
Researcher in CEFAGE-UE (Center for Advanced Studies in Management and
Economics). He is the author of articles in various journals: Research
Policy, Journal of Evolutionary Economics, etc. The research interests
include applied economics, corporate finance, industrial economics and
microeconomics.
Zelia SERRASQUEIRO, PhD in Management, Associate Professor at the
Department of Management and Economics to Beira Interior University and
Researcher in CEFAGE-UE (Center for Advanced Studies in Management and
Economics). He is the author of articles in various journals: Research
Policy, Journal of Evolutionary Economics, etc. The research interests
include corporate finance and entrepreneurial finance.
Pedro GUEDES DE CARVALHO, PhD in Economics, Assistant Professor at
theDepartment of Sport Sciences to Beira Interior University and
Researcher in CIDESD Research Center, UTAD University. He is the author
of various journals such as International Journal of Sport Policy and
Politics, etc. The research interests include sport economics, sport
management and sport sciences in general.
Paulo Macas NUNES (a), Zelia SERRASQUEIRO (b), Pedro GUEDES DE
CARVALHO (c)
(a,b) Management and Economics Department, Beira Interior
University and CEFAGE Research Center, Evora University, Portugal
(c) Sport Sciences Department, Beira Interior University and CIDESD
Research Center, UTAD University, Portugal
Received 23 October 2012; accepted 02 November 2013
Corresponding author Paulo Macas Nunes
E-mail:
[email protected]
(1) We use NACE classification to select manufacturing SMEs: codes
10 to 33.
(2) Aiming to test the robustness of the results obtained, we use
an alternative measure of investment, namely net investment, given by
the ratio of fixed assets variation (i.e. fixed assets in the present
period less fixed assets in the previous period) less depreciations to
fixed assets in the previous period.
(3) Mateev and Tsekov (2012) state the importance of considering in
the regressions the non-observable individual effects of the units of
analysis. In this study, by considering the existence of firms'
non-observable individual effects, we consider the influence of
firms' specific characteristics, not measured by the explanatory
variables, on fitness SME investment, which allows us to obtain more
robust explanatory models of investment in fitness SMEs.
(4) According to Arellano and Bond (1991), firms must be present in
the database for at least four consecutive years to be considered in the
econometric analysis and in the second-order autocorrelation tests that
are essential to validate the robustness of results. Since the period of
analysis is not very long and some fitness SMEs are not included in the
database for 4 years, for example, fitness SMEs that enter the market
during the period of analysis, use of dynamic panel estimators would
imply elimination of some fitness SMEs, which would lead to not
considering part of the investment dynamics of fitness SMEs that enter
the market during the period of analysis.
(5) Firstly, we can expect a higher effective tax rate, i.e. a
higher level of taxes paid in relation to taxable income, to mean
diminished firm investment, since they have less capacity for
investment. Secondly, we can expect firms with a higher level of
operational risk to have a greater likelihood of bankruptcy, and
consequently less capacity to finance their investment opportunities. We
can therefore expect a higher level of risk to mean diminished
investment.
(6) The average size of manufacturing SMEs is 2517974.34 euros and
the average age is 15.4912 years.
(7) The correlation coefficients of the variables used in the
context of manufacturing SMEs are presented in Appendix A, Table A2. We
also find that in no circumstances are the correlation coefficients
between the independent variables above 50% and so, also when our
subject of analysis is manufacturing SMEs, problems of collinearity will
not be particularly relevant.
(8) The mean value of ETR is 0.23747 and the mean value of EVOL is
1.64564.
Table 1. Sample description
Fitness SMEs
Firms Observations
Incumbent firms in all period 2004-2009 67 335
Firms entering in the period 2004-2009 79 256
Firms exiting in the period 2004-2009 36 133
Total of Firms 182 724
Table 2. Variables and measurement
Variables Measurement
Investment ([I.sub.i,t]) Ratio of fixed assets variation (i.e.
fixed assets in present period less
fixed assets in previous period) to
fixed assets in the previous period
Sales ([SALES.sub.i.t]) Logarithm of sales
Cash Flow ([CF.sub.i,t]) Ratio of earnings after taxes to total
assets
Debt ([LEV.sub.i,t]) Ratio of total liabilities to total
assets
age ([AGE.sub.i,t]) Logarithm of the number of years since
starting activity
Growth Opportunities Ratio of intangible assets to total
([GO.sub.i,t]) assets
Government Subisdies Ratio between government subsidies and
([GS.sub.i,t]) total assets
Dummy Financial Crisis Dummy with value 1 in 2008 and 2009
([D.sub.08-09]) years and 0 in other years
Variables Expected Influence of
Explanatory Variables on
Dependent Variable
Investment ([I.sub.i,t])
Sales ([SALES.sub.i.t]) Not significant
Cash Flow ([CF.sub.i,t]) +
Debt ([LEV.sub.i,t])
age ([AGE.sub.i,t])
Growth Opportunities +
([GO.sub.i,t])
Government Subisdies +
([GS.sub.i,t])
Dummy Financial Crisis ([D.sub.08-09])
Table 3. Descriptive statistics
Variable Firms Observations Mean Median
[I.sub.i,t] 182 724 0.1192 0.1082
[SALES.sub.i,t] 182 724 11.556 10.782
[CF.sub.i,t] 182 724 0.0716 0.0738
[LEV.sub.i,t] 182 724 0.6451 0.5019
[AGE.sub.i,t] 182 724 1.8711 1.6094
[GO.sub.i,t] 182 724 0.0097 0
[GS.sub.i,t] 182 724 0.0071 0
[D.sub.08-09] 182 724 0.4378 0
Variable S.D. Min Max
[I.sub.i,t] 0.2898 -0.6718 4.0203
[SALES.sub.i,t] 1.8814 5.0277 17.458
[CF.sub.i,t] 0.1800 -1.5431 1.1475
[LEV.sub.i,t] 0.2144 0 0.8718
[AGE.sub.i,t] 0.5818 0 3.1354
[GO.sub.i,t] 0.0377 0 0.3120
[GS.sub.i,t] 0.0301 0 0.2589
[D.sub.08-09] 0.4964 0 1
Table 4. Correlation matrix
[I.sub.i,t] [SALES.sub.i,t-1] [CF.sub.i,t-1]
[I.sub.i,t] 1
[SALES.sub.i,t-1] 0.212 ** 1
[CF.sub.i,t-1] 0.358 ** 0.176 ** 1
[LEV.sub.i,t-1] -0.189 ** 0.013 -0.281 **
[AGE.sub.i,t-1] 0.010 0.017 0.079 *
[GO.sub.i,t-1] 0.121 ** -0.026 -0.016
[GS.sub.i,t-1] 0.242 ** -0.034 0.201 **
[D.sub.08-09] -0.269 ** -0.198 ** -0.157**
[LEV.sub.i,t-1] [AGE.sub.i,t-1] [GO.sub.i,t-1]
[I.sub.i,t]
[SALES.sub.i,t-1]
[CF.sub.i,t-1]
[LEV.sub.i,t-1] 1
[AGE.sub.i,t-1] 0.133 ** 1
[GO.sub.i,t-1] -0.015 0.129 ** 1
[GS.sub.i,t-1] 0.017 0.010 -0.066*
[D.sub.08-09] -0.361 ** 0.219 ** -0.145**
[GS.sub.i,t-1] [D.sub.08-09]
[I.sub.i,t]
[SALES.sub.i,t-1]
[CF.sub.i,t-1]
[LEV.sub.i,t-1]
[AGE.sub.i,t-1]
[GO.sub.i,t-1]
[GS.sub.i,t-1] 1
[D.sub.08-09] 0.156 ** 1
Notes: 1. "Statistical significant at 1% level; * Statistical
Significant at 5% level.
Table 5. Investment determinants of fitness SMEs
Dependent Variable: [I.sub.i,t]
I II III
[SALES.sub.i,t-1] 0.02938 ** 0.02612 ** 0.02549 **
(0.00718) (0.00672) (0.00583)
[CF.sub.i,t-1] 1.17380 ** 1.11094 ** 1.07817 **
(0.20182) (0.19087) (0.20198)
[LEV.sub.i,t-1] -0.17811 ** -0.14859 ** -0.16838 **
(0.06718) (0.03901) (0.04541)
[AGE.sub.i,t-1] 0.01738 0.02098
(0.10839) (0.12838)
[GO.sub.i,t-1] 0.21920 ** 0.18726 **
(0.05681) (0.04858)
[GS.sub.i,t-1] 0.68190 ** 0.70187 **
(0.15480) (0.17612)
[D.sub.08-09] -0.24989 **
(0.06517)
CONS 0.02012 ** 0.01617 ** 0.01710 **
(0.00511) (0.00498) (0.00527)
[R.sup.2] 0.2238 0.2564 0.2891
Firms 182 182 182
Observations 542 542 542
Notes: 1. CONS is the constant of the regressions; 2. Standard
deviations in parenthesis using cluster option; 3. ** statistical
significance at 1 % level, * statistical significance at 5 % level;
4. The estimates include time dummy variables, but are not shown.
Table 6. Investment determinants of fitness SMEs
Dependent variable:
[I.sub.i,t]
I II
[D.sub.S] x [SALES.sub.i,t-1] 0.02790 ** 0.03090 **
(0.00645) (0.0074)
[D.sub.S] x [CF.sub.i,t-1] 0.78919 ** 0.74157 **
(0.18723) (0.14526)
[D.sub.S] x [LEV.sub.i,t-1] (0.05818) -0.04346
(0.07343) (0.07192)
[D.sub.S] x [AGE.sub.i,t-1] 0.02019
(0.12918)
[D.sub.S] x [GO.sub.i,t-1] 0.38919 **
(0.0864)
[D.sub.S] x [GS.sub.i,t-1] 0.32399*
(0.16018)
[D.sub.A] x [SALES.sub.i,t-1]
[D.sub.A] x [CF.sub.i,t-1]
[D.sub.A] x [LEV.sub.i,t-1]
[D.sub.A] x [AGE.sub.i,t-1]
[D.sub.A] x [GO.sub.i,t-1]
[D.sub.A] x [GS.sub.i,t-1]
CONS 0.02391** 0.01509**
(0.00559) (0.00513)
[R.sup.2] 0.1781 0.2590
Firms 182 182
Observation 542 542
Dependent variable:
[I.sub.i,t]
III IV
[D.sub.S] x [SALES.sub.i,t-1]
[D.sub.S] x [CF.sub.i,t-1]
[D.sub.S] x [LEV.sub.i,t-1]
[D.sub.S] x [AGE.sub.i,t-1]
[D.sub.S] x [GO.sub.i,t-1]
[D.sub.S] x [GS.sub.i,t-1]
[D.sub.A] x [SALES.sub.i,t-1] 0.02809 ** 0.02696 **
(0.00698) (0.0089)
[D.sub.A] x [CF.sub.i,t-1] 1.06171** 1.08172**
(0.18718) (0.20198)
[D.sub.A] x [LEV.sub.i,t-1] -0.18142** -0.15698**
(0.05612) (0.04198)
[D.sub.A] x [AGE.sub.i,t-1] 0.01461
(0.11761)
[D.sub.A] x [GO.sub.i,t-1] 0.07818
(0.07309)
[D.sub.A] x [GS.sub.i,t-1] 0.25167*
(0.12340)
CONS 0.01837** 0.01668**
(0.00490) (0.00478)
[R.sup.2] 0.2209 0.2709
Firms 182 182
Observation 542 542
Notes: 1. CONS is the constant of the regressions; 2. Standard
deviations in parenthesis using cluster option; 3. ** statistical
significance at 1 % level, * statistical significance at 5 % level;
4. The estimates include time dummy variables, but are not shown.
Table 7. Investment determinants of manufacturing SMEs
Dependent Variable: [I.sub.i,t]
I II III
[SALES.sub.i,t-1] 0.01066 0.01149 0.01374
-0.01289 (0.01390) (0.01488)
[CF.sub.i,t-1] 0.65167 ** 0.60918 ** 0.64631 **
-0.12839 (0.10654) (0.13949)
[LEV.sub.i,t-1] 0.20192 * 0.19445 * 0.24959 **
-0.09871 (0.09507) (0.07612)
[AGE.sub.i,t-1] -0.06182 * -0.05918 *
(0.02918) (0.02854)
[GO.sub.i,t-1] 0.69812 ** 0.71098 **
(0.10928) (0.11433)
[GS.sub.i,t-1] 0.11902 0.13291
(0.40981) (0.47818)
[D.sub.08-09] -0.10982 *
(0.05409)
CONS 0.01543 ** 0.01394 ** 0.01092 **
-0.00378 -0.00308 -0.00287
R2 0.1394 0.2019 0.2394
Firms 478 478 478
Observations 1516 1516 1516
Notes: 1. CONS is the constant of the regressions; 2. Standard
deviations in parenthesis using cluster option; 3. ** statistical
significance at 1 % level, * statistical significance at 5 % level;
4. The estimates include time dummy variables, but are not shown.
Table 8. Chow test of equality of estimated parameters
Dependent Variable: [I.sub.i,t]
I II III
[SALES.sub.i,t-1] 13.30 ** 12.98 ** 12.56 **
[CF.sub.i,t-1] 11.54 ** 11.23 ** 10.12 **
[LEV.sub.i,t-1] 30.18 ** 28.10 ** 29.87 **
[AGE.sub.i,t-1] 11.14 ** 10.87 **
[GO.sub.i,t-1] 11.90 ** 12.23 **
[GS.sub.i,t-1] 15.10 ** 15.66 **
[D.sub.08-09] 10.76 **
Global Difference 22.09 ** 20.16 ** 19.76 **
Notes: 1. Significant at 1% level.
Table 9. Investment determinants of fitness SMEs in periods 20082009
and 2004-2007
Dependent Variable:
[I.sub.i,t]
Period 2008-2009
I II
[CF.sub.i,t-1] 1.65362 ** 1.62831 **
-0.28199 (0.27618)
[LEV.sub.i,t-1] -0.26471 ** -0.28812 **
(0.08828) (0.09532)
[GS.sub.i,t-1] 1.02165 ** 1.04112 **
(0.25647) (0.27623)
[SALES.sub.i,t] 0.02178 **
(0.00561)
[AGE.sub.i,t] 0.01225
(0.08918)
[GO.sub.i,t] 0.06194
(0.03817)
CONS 0.04516 ** 0.01961 **
(0.01118) (0.00612)
[R.sup.2] 0.1891 0.2219
Firms 155 152
Observations 310 310
Dependent Variable:
[I.sub.i,t]
Period 2004-2007
I II
[CF.sub.i,t-1] 0.63901 ** 0.61842 **
(0.12388) (0.11784)
[LEV.sub.i,t-1] -0.06178 * -0.06263 *
(0.02912) (0.03012)
[GS.sub.i,t-1] 0.37182 ** 0.35077 **
(0.09182) (0.08610)
[SALES.sub.i,t] 0.02998 **
(0.00712)
[AGE.sub.i,t] 0.02189
(0.12056)
[GO.sub.i,t] 0.31949 **
(0.07154)
CONS 0.03841 ** 0.01491 **
(0.00981) (0.00381)
[R.sup.2] 0.2022 0.2716
Firms 144 144
Observations 387 387
Dependent Variable:
[I.sub.i,t]
Chow Test
I II
[CF.sub.i,t-1] 17.90 ** 17.66 **
[LEV.sub.i,t-1] 13.40 ** 14.02 **
[GS.sub.i,t-1] 14.47 ** 15.58 **
[SALES.sub.i,t]
[AGE.sub.i,t]
[GO.sub.i,t]
CONS
[R.sup.2]
Firms
Observations
Notes: 1. CONS is the constant of the regressions. 2. Standard
deviations in parenthesis using cluster option. 3. ** statistical
significance at 1 % level; * statistical significance at 5 % level.
4. The estimates include time dummy variables, but are not shown.
