# The impact of financial constraints on firm survival and growth

**ABSTRACT** We propose a new approach for identifying and measuring the degree of financial constraint faced by firms and use it to investigate

the effect of financial constraints on firm survival and development. Using panel data on French manufacturing firms over

the 1996–2004 period, we find that (1) financial constraints significantly increase the probability of exiting the market,

(2) access to external financial resources has a positive effect on the growth of firms in terms of sales, capital stock and

employment, (3) financial constraints are positively related with productivity growth in the short-run. We interpret this

last result as the sign that constrained firms need to cut costs in order to generate the resources they cannot raise on financial

markets.

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**ABSTRACT:**We examine the differential effects of financial status and exporting activity on the likelihood of survival for firms in the UK and France - two countries with different financial systems. We aim to answer two main questions: What is the direct impact of financial characteristics and different facets of exporting activity on the likelihood of survival? Do the sensitivities of survival incidence to financial variables vary with the exporting status of firms? We find strong evidence that continuous exporters face a higher probability of survival compared to starters, continuous non-exporters and firms exiting the exporting market. Further, important sensitivities of survival prospects to financial indicators are observed for the UK firms which might be explained by the ''market based'' economy. Finally, a within and across countries comparison reveals that the survival of exporting groups varies substantially depending on firms' financial status, the financial system and the prolonged participation in the export market.Department of Economics, Loughborough University, Discussion Paper Series. 01/2009; - SourceAvailable from: Giulio Bottazzi[Show abstract] [Hide abstract]

**ABSTRACT:**The ability of firms to access external financial resources represents a key factor influencing several dimensions of firms’ dynamics. However, while recent qualitative evidence suggests the existence of heterogeneous and asymmetric reactions of firms to financing constraints (FC) problems, the literature on the empirics of size-growth dynamics focuses on the effects of FC on average growth rate and on the long term evolution of the firm size distribution. In this paper we extend the analysis to a wider range of possible FC effects on firm growth dynamics, including its autoregressive and heteroskedastic structure and the degree of asymmetry in growth shocks distribution. We measure FC with an official credit rating index, which directly captures the borrowers’ opinion about firm’s financial soundness deciding, in turn, the availability and cost of its external resources. Our broader investigation reveals that FC significantly affect firm’s performance and operate through several channels. In the short run, they reduce expected firm growth rate, induce anti-correlation in growth shocks and a milder dependence of growth rates volatility on size, and also operate through asymmetric “threshold effects”, either preventing potentially fast growing firms from enjoying attractive growth opportunities, or further deteriorating the growth prospects of already slow growing firms. The subdiffusive nature of the growth process of constrained firms is compatible with the observed differences in their size distribution.Small Business Economics 01/2010; · 1.55 Impact Factor - SourceAvailable from: Subal C. Kumbhakar[Show abstract] [Hide abstract]

**ABSTRACT:**We use the indirect production function approach in the stochastic frontier framework to estimate separately the output losses due to the presence of a budget constraint and technical inefficiency. We develop a methodology for estimating the severity and testing the significance of the expenditure constraint at individual producer level. Our results, based on the farm data from three Russian regions from 1999 to 2003, show that the majority of the farms studied were expenditure-constrained during the study period. Expenditure constraints caused, on average, a potential output loss of 20 per cent. Output loss due to technical inefficiency, on average, is found to be around 13 per cent. Oxford University Press and Foundation for the European Review of Agricultural Economics 2009; all rights reserved. For permissions, please email journals.permissions@oxfordjournals.org, Oxford University Press.European Review of Agricultural Economics 01/2009; 36(3):343-367. · 1.85 Impact Factor

Page 1

The Impact of Financial Constraints

on Firms Survival and Growth

N° 2007-37

December 2007

Patrick MUSSO

UNSA, GREDEG and CERAM

Stefano SCHIAVO

OFCE-DRIC

Page 2

The Impact of Financial Constraints on

Firm Survival and Growth

Patrick Musso∗

Stefano Schiavo†

November 2007

Abstract

We propose a new approach for identifying and measuring the degree of financial

constraint faced by firms and use it to investigate the effect of financial constraints

on firm survival and development. Using panel data on French manufacturing firms

over the 1996 - 2004 period, we find that (i) financial constraints significantly increase

the probability of exiting the market, (ii) access to external financial resources has a

positive effect on the growth of firms in terms of sales, capital stock and employment,

(iii) financial constraints are positively related with productivity growth in the short-

run. We interpret this last result as the sign that constrained firms need to cut costs

in order to generate the resources they cannot raise on financial markets.

Keywords: Financial constraints, Firm growth, Firm survival

1Introduction

The paper develops a new approach for identifying and measuring the degree of financial

constraint faced by firms, and uses it to investigate the effect of financial constraints

on firm survival and growth.We propose a time-varying and continuous measure of

constraint that recognizes the multifaceted feature of this phenomenon and allows one to

capture different degrees of constraint. Firm exit and growth represent interesting fields

of application, since financial constraints can interfere with market selection mechanisms,

and therefore shape market structures in ways not necessarily consistent with efficiency.

In fact, given the presence of important sunk entry costs in most markets, one can expect

that constrained firms find it more difficult to grow and even to survive. Also, while a

large amount of evidence exists on the relation between financial development and growth,

∗University of Nice - Sophia Antipolis, GREDEG (CNRS) and CERAM Business School.

E-mail: Patrick.Musso@gredeg.cnrs.fr

†Observatoire Fran¸ cais des Conjonctures Economiques, D´ epartement de Recherche sur l’Innovation et

la Concurrence (OFCE-DRIC). E-mail: stefano.schiavo@ofce.sciences-po.fr

Page 3

both cross-country and cross-industry, much less is known at the microeconomic level of

the firm.

We can summarize our main results as follows. First of all, we find that financial

constraints significantly increase the probability of exiting the market. In addition, access

to external financial resources has a positive effect on the growth of firms in terms of sales,

capital stock and employment. Last, the presence of financial constraints is positively

related with productivity growth in the short-run: we interpret this latter result as the

sign that constrained firms need to cut costs in order to generate the resources they cannot

raise on financial markets, and this results in improved efficiency.

Our contribution is twofold. First, we propose a new way to measure the degree of

financial constraint, which we believe is superior to existing methodologies. Second, we

shed new light on the role played by access to external financial resources in shaping firm

growth and survival.

The paper is organized as follows. Next Section presents an overview of the empirical

literature on financial constraints. Section 3 presents our data and illustrates the empirical

methodology we propose to measure financial constraints. In Section 4, we present an

application to French firms that specifically investigates the issue of growth and survival.

Section 5 concludes.

2A glance at the existing empirical literature

Since the late 1980s, a large number of empirical studies have addressed the issue of

financial constraints, mainly in order to study the relation between firm investment and

the availability of internal funds. Indeed, a large and convincing evidence exists showing

that, when a standard investment equation is augmented with cash flow availability, the

fit of the equation improves. Now, under perfect capital markets, internal and external

sources of financial funds are perfectly substitutable (Modigliani and Miller, 1958), so that

the availability of internal funds should not affect investment decisions.

While there is a substantial consensus on the notion that liquidity does matter in

investment equations, much less agreement exists on why this is the case. Chirinko and

Schaller (1995) suggest two possible hypotheses: the existence of financial constraints

(due to the existence of either asymmetric information or transaction costs), and mere

mispecification whereby liquidity takes up the effect of other omitted variables.

2.1Firm growth and survival

The existence of financial constraints can obviously have important effects on the firm’s

ability to grow and stay in the market. A number of studies exist on the topic, al-

though many stem from the finance and growth macro literature (Levine, 2005) and focus

2

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on comparing cross-country performances controlling for the degree of financial develop-

ment. Hence, for instance, Demirguc-Kunt and Maksimovic (1998) and Beck, Demirguc-

Kunt and Maksimovic (2005) find that financial development eases the obstacles firms

face to grow faster and therefore improves macroeconomic performance. Similarly, Beck,

Demirguc-Kunt, Laeven and Levine (2005) use cross-country and cross-industry data to

investigate the role of financial factors in shaping the size distribution of firms. They

conclude that financial development exerts a disproportionately positive effect on small

firms. Yet, financial development is constant across firms operating in the same economic

system. A recent work on the issue, which is very close to our analysis in spirit, is an

article by Aghion, Fally and Scarpetta (2007) where the authors address the impact of

financial development on firm entry, the size at entry and the post-entry performance of

new firms. They find that access to external finance matters most for the entry of small

firms, and that it improves market selection by allowing small firms to compete on a more

equal footing. Also, financial development is shown to ameliorate significantly post-entry

growth of firms. Once again, the paper’s results are based on a cross-country comparison

that takes financial variables as given for all firms located in the same country. Another in-

teresting investigation on the effect of credit constraints on the real economy is undertaken

by Jeong and Townsend (2005), who decompose Total Factor Productivity (TFP) growth

to account for a number of its determinants. They find a significant effect of aggregate

financial development on aggregate TFP growth.

For what concerns microeconometric studies, most works use survey data where firms

give a self-assessment of the difficulty faced in accessing external funds. Often the survey

are targeted to specific issues, most notably R&D expenses or investment in innovation.

So, for instance, Winker (1999) uses data collected from 1,900 enterprises in the 1982–

1991 period to show that the perceived credit constraint has a negative effect on innovation

expenditures and overall investment. These results are very similar to those reported by

Becchetti and Trovato (2002) and Savignac (2006) and reviewed in the previous subsection.

In a different fashion, Holtz-Eakin et al. (1994) exploit a unique dataset matching personal

wealth (in the form of received bequests) to survival rates among US entrepreneurs. They

show that inheritances increase both the probability of survival and future sales growth of

the firm whose owner benefited from the windfall, and interpret this as testifying for the

existence of significant credit constraints. Binks and Ennew (1996) analyze the perceived

credit constraint of 6,000 UK firms using survey data. They find that expected future

growth is associated with a higher perceived constraint, so that the latter seems to play a

relevant role in shaping firm development decisions. More recently, Carpenter and Petersen

(2002) analyze growth of 1,600 small US firms (defined as firms whose total assets range

between 5 and 100 million US dollar at time of entry) and find that asset growth is indeed

constrained by the availability of internal finance. Firms able to raise a lot of external

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funds (relative to the group average) in fact display higher growth rates and therefore

manage to grow faster.

Overall, compelling evidence seems to exist about the substantial role played by finan-

cial constraints in shaping and conditioning firm decisions underlying growth and survival.

2.2 Existing research strategies

The usual empirical strategy adopted to detect the existence and the relevance of financial

constraints entails segmenting the sample ex ante into subgroups of firms with different

likelihood of facing financial constraints, and test whether cash-flow plays a different role

in explaining investment decisions by more/less constrained firms. Thus, for instance,

Fazzari et al. (1988) claim that firms with low dividend payout ratios (likely constrained)

display higher investment-cash flow sensitivity. Subsequent studies tend to find supporting

evidence using a number of different variables to identify constrained firms (Bond and

Meghir, 1994; Gilchrist and Himmelberg, 1995; Chirinko and Schaller, 1995).

The first paper finding opposite results is, to the best of our knowledge, Devereux

and Schiantarelli (1990), which reports a higher cash flow coefficient for larger firms, even

after controlling for sector heterogeneity. But it is only with the work by Kaplan and

Zingales (1997) that the usefulness of investment-cash flow sensitivity as a measure of

financial constraint has been definitely questioned. Exploiting qualitative information

from financial statements of firms classified as constrained in Fazzari et al. (1988), the

authors show that less constrained firms display substantially higher investment-cash flow

sensitivity. Hence, they conclude, the latter can no longer be regarded as a useful measure

of financial constraint. Since then, other authors have reported evidence of a negative

relation between investment-cash flow sensitivity and financial constraints (for instance

Kadapakkam et al., 1998; Cleary, 2006).

Although a reference to the above literature is due, in what follows we will try to steer

clear of this debate and only review in greater detail the way in which different authors

have identified financially constrained firms, irrespective of the specific aim of their study.

In other words, we will review the empirical strategies adopted in the literature, keeping in

mind our goal of building a time-varying and continuous measure of financial constraint.

[Table 1 about here.]

Table 1 reports a list of papers in chronological order and the segmenting variables

used to distinguish among constrained and unconstrained firms. Often firms are simply

placed in two different groups on the basis of some arbitrary threshold, such as median

values, or first quartiles (Devereux and Schiantarelli, 1990; Gilchrist and Himmelberg,

1995; Greenaway et al., 2005; Cleary, 2006). Other authors use a finer classification and

classify firms in three or more groups (Fazzari et al., 1988; Kadapakkam et al., 1998;

4

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Kaplan and Zingales, 1997). Almost all the papers rely on a limited list of variables such

as size, age, dividend policy, membership in a group or conglomerate, existence of bond

rating, and concentration of ownership. All these variables are meant to capture sources

of informational asymmetries which can potentially constrain access to capital markets.

So, for instance, Fazzari et al. (1988) claim that dividends are a residual decision in firm

strategy and, under the assumption that external finance is more costly than internal

funds, paying high dividends in the presence of profitable investment opportunities is not

consistent with profit maximization. Hence, high dividend payout ratios signal the absence

of financial constraints. Big and mature firms are likely to find easier access to external

funds, as it should be easier to collect information on them compared to young and small

enterprises. Similarly, membership in a larger conglomerate should facilitate market access

both because of the signaling exercised and because the single firm can likely receive funds

from its headquarters. Also, the mere existence of a bond rating (even irrespective of

the rating itself) can signal a commitment of the firm vis-` a-vis financial markets. In

a similar vein, the existence of a dominant shareholder is seen as a way to reduce the

agency problem with management and therefore to act as a guarantee toward external

investors. Other papers, namely Becchetti and Trovato (2002) and Savignac (2006), use

survey data whereby firms themselves give a self-assessment of their difficulty to access

external financial funds.

There are a few weaknesses related to the above strategies. First, Hubbard (1998)

notes how most of the chosen criteria tend to be time invariant, whereas one can imagine

that firms switch between being constrained or unconstrained depending on overall credit

conditions, investment opportunities and idiosyncratic shocks. Second, all works relying

on dividend payments are restricted to quoted firms which, at least for what concerns

continental Europe, tend to be larger and more mature. As a further potential problem,

we add that all the above studies rely on a unidimensional definition of financial constraint,

i.e. they assume that a single variable can effectively identify the existence of a constraint,

viewing the latter as a clear-cut phenomenon that is either in place or not, without allowing

for different degrees. Notable exceptions are the works by Lamont et al. (2001), Cleary

(1999, 2006), and Whited and Wu (2006). The first paper proposes a multivariate index

that builds on Kaplan and Zingales (1997), whereby five variables are weighted using

regression coefficients and collapsed into a single indicator.1The main difficulty with this

approach is the need to extrapolate results obtained on a small sample of 49 US quoted

firms (those used in Kaplan and Zingales, 1997) and apply them to a larger population (and

in a different period). Furthermore, one of the variables needed to compute the index is

Tobin’s Q, the use of which as a proxy for investment opportunities is rather controversial

1The variables are (i) cash flow to fixed assets, (ii) market to book ratio, (iii) debt to total assets, (iv)

dividends to fixed assets, and (v) cash to fixed assets.

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and lies at the core of the investment-cash flow debate outlined above. Whited and Wu

(2006) take a similar route, but perform their own estimate and base their index on a

structural model, whereby they measure financial constraints by means of the shadow

price of capital.2

Another interesting attempt to develop a time-varying, continuous measure of financial

constraints is due to Cleary (1999), which uses multiple discriminant analysis (in a way

similar to Altman, 1968) to compute a score based on six variables.3The methodology

entails two steps: first, one needs to classify firms as constrained or unconstrained accord-

ing to some characteristic, and second, the statistical analysis is performed which delivers

a coefficient for each of the (six) control variables.4The score is then obtained as the pre-

dicted value of the empirical model, and it can be applied also to firms excluded from the

first step of the analysis. To separate firms, Cleary (1999) makes the hypothesis that firms

reducing dividend payments one year to the next are likely to be financially constrained,

whereas those augmenting them are likely not to be constrained. Firms keeping dividend

payment constant are not used in the multiple discriminant analysis, but later they are

nonetheless attributed a score.5

3 Data and empirical strategy

3.1 Measuring financial constraints

We have two main aims when looking for a new strategy to measure financial constraints:

the first is to derive a time-varying index that allows for firms being more or less con-

strained in different periods; the second is to account for (possible) different degrees of

financial constraints. We claim that one of the main weaknesses of earlier approaches lies

in the choice of a single variable to classify firms ex ante.

We therefore build a synthetic index, collapsing information coming from seven dif-

ferent variables that we esteem convey important information relative to the existence of

financial constraints. They have been selected on the basis of their performance in ex-

isting studies, and their perceived importance in determining ease of access to external

financial funds. They are: size (measured by total assets), profitability (return on total

assets), liquidity (current ratio: current asset over current liabilities), cash flow generat-

2The variables included in the model are (i) the ratio of long-term to total debt, (ii) a dividend dummy,

(iii) sales growth (both for the individual firm and the sector), (iv) (the log of) total assets, (v) the number

of analysts following the firm, (vi) the ratio of liquid to total assets, (vii) the industry debt to assets ratio.

3There are (i) the current ratio, (ii) the debt ratio, (iii) the fixed charge coverage, (iv) the net income

margin, (v) sale growth, and (vi) slack over total assets. See Cleary (1999) for a definition of the variables.

4This is very much similar to what a probit or a logit estimation would do. In fact, multiple discriminant

analysis is nothing more than an ancestor of these methodologies, which, because of current computer

power, are probably preferable as more robust.

5An obvious requirement of this methodology is working with quoted firms. One could then derive a

score for non quoted firms as well, but it is not clear how well the index would behave.

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ing ability6, solvency (own funds over total liabilities, measuring the ability by a firm to

meet its long-term financial obligations), trade credit over total assets and repaying ability

(financial debt over cash flow).

For each of these seven dimensions, and each year, we first compute the value of the

firm relative to the average of all enterprises belonging to the same 2-digit NACE sector,

and then place it in one of the quintiles in which the resulting distribution is divided.7

Hence, for each firm/year observation we end up with 7 scores ranging from 1 to 5, with

1 containing the smallest values. This information is then combined in different ways to

obtain a synthetic index, which is then rescaled to have a common 1–10 range, with smaller

values being associated with more constrained firms. In what follows, we concentrate on

two ways to combine the information: (i) a simple sum of the 7 scores (Score A); (ii)

the number of dimensions for which the firm/year lies in the first quintile (Score B).

Interestingly, the ranking of firm/year observations is very robust to the different ways to

aggregate the information from the 7 variables, with a correlation of over 0.78.8

3.2Data sources: the EAE survey and the DIANE database

We use data from two main sources. Both of them collects information on French firms,

though their coverage is somehow different. The first (EAE) is a survey that gathers

information from the financial statements and balance sheets of all individual manufac-

turing firms with at least 20 employees, from 1990 to 2004.9Each unit is endowed at

birth with an identifying number that allows us to track the firm over time. We rely on

the following standard definition of continuing and exiting firms (Bellone et al., 2008): an

exiting firm is an identifying number that exists in year t, but not in t + 1; a continuing

firm is an identifying number that exists in years t, t + 1 and t − 1. The second source

of information is the DIANE database published by Bureau van Dijk, which collects data

on over 1 million French firms for the period 1996–2005. This database provides us with

many financial stock variables absent from the EAE survey. Merging the two datasets

yields around 104,000 firm/year observations, stemming from an unbalanced panel of over

16,500 manufacturing enterprises followed over the period 1996–2004.10

In what follows, we compute Total Factor Productivity using the so-called Multilateral

6This is the maximum amount of resources that a firm can devote to self-financing, and corresponds to

the French capacit´ e d’autofinancement.

7To account for the presence of outliers we trim the top and bottom 0.5% observations for each variable.

8We have tried other ways to combine the information, with identical results. Additional details are

available upon request.

9The survey (Enquˆ ete Annuelle d’Entreprises) is conducted by the French Ministry of Industry. The

surveyed unit is the legal (not the productive) unit, which means that we are dealing with firm-level data.

To investigate the role of financial constraints on growth and survival, firm, rather than plant level data,

seem appropriate.

10Chirinko and Schaller (1995) note that focusing on manufacturing only —as it is often done in the

literature— may exaggerate the role played by financial constraints because of the specialized nature of

the assets involved in those firms.

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Productivity Index, first introduced by Caves et al. (1982) and extended by Good et al.

(1997). This methodology consists of computing the TFP index for firm i at time t as

follows:

lnTFPit= ln Yit− ln Yt+

where Yitdenotes the real gross output of firm i at time t using the set of N inputs Xnit,

where input X is alternatively capital stocks (K); labor in terms of hours worked (L);

and intermediate inputs (M). Snit is the cost share of input Xnit in the total cost11.

Subscripts τ and n are indices for time and inputs, respectively. Symbols with an upper

bar correspond to measures for the reference point (the hypothetical firm), computed

as the means of the corresponding firm level variables, for all firms, in year t.

methodology is particularly well suited to comparisons of within firm-level panel datasets

across industries, in that it guarantees the transitivity of any comparison between two

firm-year observations by expressing each firm’s input and output as deviations from a

single reference point.

Labor Productivity is defined as the log-ratio of real value added on labor (hours

worked):

t ?

τ=2

?ln Yτ− ln Yτ−1

?

−

N ?

+

n=1

1

2(Snit+ Snt) (ln Xnit− ln Xnt)

t ?

τ=2

N ?

n=1

1

2(Snτ+ Snτ−1) (ln Xnτ− ln Xnτ−1)

(1)

This

lnLPit= ln

?Vit

Lit

?

(2)

where Vitdenotes the value added of the firm deflated by the sectoral price indexes pub-

lished by INSEE (French System of National Accounts).

4Results

4.1 Firm survival

In this section, we present results from an empirical model that estimates the hazard of

exit controlling for unobserved heterogeneity (for more details see Prentice and Gloeckler,

1978; Jenkins, 1995; Bellone et al., 2008). Suppose there are firms i = 1,...,N, which

enter the industry at time t = 0. The hazard rate function is defined as the probability

of failure in interval t and t + 1 divided by the probability of surviving at least until t.

The hazard rate function for firm i at time t > 0 and t = 1,...,T is assumed to take the

proportional hazard form: θit= θ(t)·X

11See Bellone et al. (2008) for more details on the method and a full description of the variables.

?

itβ, where θ(t) is the baseline hazard function and

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Xitis a series of time-varying covariates summarizing observed differences among firms.

The discrete time formulation of the hazard of exit for firm i in time interval t is given by

a complementary log logistic function such as:

ht(Xit) = 1 − exp

?

−exp

?

X

?

itβ + θ(t)

??

(3)

where θ(t) is the baseline hazard function, relating the hazard rate ht(Xit) at the tth

interval to the spell duration. This model can be extended to account for unobserved but

systematic differences among firms. Suppose that unobserved heterogeneity is described by

a random variable µiindependent of Xit. The proportional hazards form with unobserved

heterogeneity can be written as :

ht(Xit) = 1 − exp

?

−exp

?

X

?

itβ + θ(t)

?

+ µi

?

(4)

where µiis an unobserved individual-specific error term with zero mean, uncorrelated with

the Xs. Model (4) can be estimated using standard random effects panel data methods

for a binary dependent variable, under the assumption that some distribution is provided

for the unobserved term. In this paper, we assume that µiis distributed Normal.12

Results are reported in Table 2. In Columns (1) to (3), we use Score A, whereas in

Columns (4) to (6) we display results obtained using Score B. The probability of exiting the

market is assumed dependent on age, size, profitability, productive efficiency (TFP) and

our measure of financial constraint. All variables have the expected sign and are strongly

significant. The way we build our measure of financial constraint (smaller values associated

with a higher degree of constraint) is consistent with the negative sign associated with the

estimated coefficient: an easier access to external funds (hence a higher Score) lowers

the probability of exiting the market. This results is robust to inclusion of a number of

standard controls used in the literature on hazard rates: all regressions control for the age

of the firm and for its size (in terms of employment), which display the expected negative

sign. In Columns (2) and (3), we also add an index of technical efficiency (TFP) and

a measure of profitability (operating income over assets): both play a significant role in

reducing the hazard rate, and their inclusion slightly reduces the (absolute value of the)

coefficient associated with the index of financial constraint, which nevertheless remains

significant and displays the same order of magnitude.

[Table 2 about here.]

Similarly, results are rather robust to the choice of the way we measure financial

constraints. Substituting Score B for Score A in fact does not alter the results; the only

12See Chapters 17 and 18 of Cameron and Trivedi (2005) for a discussion on the appropriate choice of

distribution for the parameter of unobserved heterogeneity.

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minor change is due to the fact that profitability, while retaining the expected negative

sign, is no longer significantly different from zero. Remarkably, the size of the of the

financial constraints coefficients is very stable and does not depend at all on the choice of

the Score included in the regression.

4.2Firm growth

We move now to investigate the impact of financial constraints on firm growth, both in

terms of size and in terms of productivity. To do this, we will focus hereafter on results

obtained using Score A only, as this does not alter the resulting picture.

[Table 3 about here.]

In Table 3, we report results obtained investigating the relation between size growth at

different time horizons, initial financial constraints, and a set of standard control variables.

We measure size growth in terms of (i) output, (ii) employment, and (iii) capital stock.

Growth is computed over three different time spans: 1-, 3- and 5-years. Controls include

the age of the firm, productive efficiency (TFP), and initial size. Once again, results

are very stable across specifications, choice of the dependent variable, and time horizon.

Access to external financial resources does have a positive effect on firm growth, even after

controlling for productivity, initial size and age. The effect is somewhat smaller over the

longer 5-year horizon, and is less significant for employment growth. As our measure of

employment is worked hours, we suppose that financial constraints pose fewer problems

in this domain: it is reasonably easier to finance an increase in worked hours than the

investment needed to enlarge the capital stock. Initial size is negatively related with

future growth, meaning that smaller firms tend to grow faster. Also, initial productivity

is positively related with growth in employment and in the stock of capital, whereas it

displays a negative coefficient in the output regression.

[Table 4 about here.]

Last, in Table 4, we present results based on a regression where the dependent variable

is the growth rate of productive efficiency, both in terms of TFP and in terms of average

labor productivity. We find that initial size is positively related to future productivity

growth, whereas the latter is lower for more productive firms. Interestingly, the presence

(and degree) of financial constraints exert a positive effect on the dynamic of efficiency.

We read this phenomenon as an indication that constrained firms are forced to improve

their efficiency in order to remain on the market. Since by construction we observe future

productivity growth of successful firms only, there is an evident self-selection that drives

our result. Moreover, such a behavior is consistent with works by Nickell et al. (1997)

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and Nickell and Nicolitsas (1999) on the role of financial pressure on firm performance.

In particular, Nickell and Nicolitsas (1999) find that financial pressure is associated with

gains in productivity, and suggest that financial market discipline helps solving agency

problems and therefore improves firm performance. In light of the results displayed in

Table 4, it would be interesting to pursue this line of research and test the effect of

financial constraints on short- versus long-term efficiency. In fact, it is reasonable to

expect that, in the attempt to free financial resources, constrained firms will scale down

long-term investment (for instance R&D), with detrimental effects on longer term growth

prospects.

5 Conclusions

In this paper, we have proposed a new methodology to measure financial constraints

using a synthetic index based on a number of different variables. Our measure has two

main advantages over existing methods: first, it accounts for the multifaceted nature of

the phenomena under investigation, and, second, it delivers a time-varying, continuous

measure. The index is then applied to the study of firm survival and growth. The relation

between financial constraints at the firm level and structural issues such as innovation and

growth is a long standing issue in the economic arena. It has recently enjoyed renewed

interest, as papers by Aghion et al. (2005), Aghion, Askenazy, Berman, Cette and Eymard

(2007), and Aghion, Fally and Scarpetta (2007) testify.

We have shown that financial constraints play a significant role in determining the

probability of firm survival, even after controlling for size, age, profitability and productive

efficiency. Moreover, access to external funds increases firm growth. On the other hand,

and consistent with previous results, our measure of financial constraint is associated with

positive productivity growth in the short-run. Future work will add R&D and innovation

expenditures to the picture, to see whether financial constraints have a different effect on

long-term efficiency.

Acknowledgements

The authors blame each other for any remaining mistakes. They nevertheless agree on the

need to thank Sylvain Barde, Flora Bellone, Jean-Luc Gaffard, Evens Salies, and above

all Lionel Nesta for useful comments and discussions.

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