bank competition, financial dependence and...

Bank Competition, Financial Dependence and Productivity

Growth in Europe

Preliminary version

Aurelien Leroy∗

March 1, 2015

Abstract

This paper empirically analyses the links between banking competition and man-

ufacturing productivity growth for a sample of 10 European countries over the period

1999-2009. To test this relationship, which is from a theoretical point of view un-

clear, we use a difference-in-difference methodology close to the one proposed by

Rajan and Zingales (1998). We find that the total factor productivity of the most

financially dependent industries grows at a slower rate in economies where bank-

ing competition is fiercer. We explain this result by the fact that market power,

i.e. low competition, would promote relationship-banking, as theoretically argued

by Petersen and Rajan (1995) for instance. Indeed relationship banking would al-

low banks to reduce information asymmetries, which would benefit to the small

and/or young firms. In this way, the allocation of funds would be better. Banks

may select more the best firms which would increase total factor productivity of the

industries more dependent on external finance. Furthermore, by improving firm dy-

namics, bank market power could spur the productivity growth of incumbents firms.

Keywords: bank competition, total factor productivity, economic growth,

industrial growth

JEL Codes: D4, G21, L11

∗Laboratoire d’Economie d’Orleans (LEO), UMR CNRS 7322, Rue de Blois, BP 26739, 45062 OrleansCedex 2, France. E-mail: [email protected]

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1 Introduction

The slowdown of total factor productivity growth (i.e. technical progress) observed these

two last decades, and speeded up since the crisis, has contributed to the emergence of the

idea that the developed economies are slipping into secular stagnation’s state as argued

by Gordon (2012) and more recently by Summers (2014) among others. This pessimism

vision necessarily leads to question about the factors which could make this prospect

less likely. In this paper, we deal with this issue by focusing on the role played by the

financial sector, and especially by banking competition in the development of technical

progress and through this economic growth.

Numerous contributions, including for instance the well-known Schumpeter (1912),

Gurley and Shaw (1955), Goldsmith (1969) or King and Levine (1993), have underlined

the need to take into account the financial sector to understand economic growth. Even

if Lucas (1988) among others, has dismissed this need, there is now a large consensus

about the role of financial sector for the long run growth. In order to test this link be-

tween real and financial sectors, most of empirical studies have tested the contribution

of financial development to economic growth. In general, it has been shown that finan-

cial development significantly contributes to growth (see, King and Levine, 1993; Levine

and Zervos, 1998; Rajan and Zingales, 1998; Guiso et al., 2004; Levine, 2005; Loayza

and Ranciere, 2006). The recent contribution of Arcand et al. (2012) has challenged

this result by highlighting the presence of “too much” finance, but has not denied the

significant (non-monotonic) effect of financial depth on growth.

This extensive literature has almost exclusively focused its attention on the size, and

not on other characteristics of the financial sector, such as the banking competition.

This lack of concerns about the effects of banking competition on growth is harming to

the extend that banking literature has shown that banking competition has numerous

spillover effects (in terms of efficiency and stability) on the other sectors of the economy.

From a theoretical point of view, there are uncertainties about the effects of banking

competition on growth. On the one hand, the conventional microeconomic theory shows

that market power leads to an inefficient equilibrium and social losses. Thus, a lack of

banking competition would induce a weaker efficiency, namely higher bank rates, higher

fees, lesser banking innovations, etc., which reduces the availability of credit for firms

and therefore economic growth. On the other hand, banking literature argues that mar-

ket power can favour growth. Indeed, bank market power gives incentives to banks to

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invest in soft information acquisition, by fostering close relationships with borrowers

over the long run, increasing through this the access of firms to credit and consequently

the growth (Rajan, 1992; Petersen and Rajan, 1995). Thus, in Petersen and Rajan’s

(1995) model, monopolistic banks invest in first period for new firms in soft information

(by charging lower interest rates than the equilibrium interest rates) and make their

investment profitable only over the long run. By contrast, when the market is perfectly

competitive, banks cannot invest in soft information since firms are not “locked in” and

can easily pass to another bank, making investment unprofitable. Consequently in this

case, banks charge high interest rates that distort firm’s incentives (due to asymmetric

information). This leads to reduce the availability of credit for the most information-

ally opaque firms. Therefore, the credit allocation will not necessary go toward the

most efficient firms (Dell’Ariccia and Marquez, 2004). Boot (2000) confirms theoretical

ambiguities since he shows that firms have better and greater accesses to credit when

competition is limited, but also underlines that firms become “locked in”, allowing banks

to extract rents from them (“hold-up” problem).

The theoretical ambiguity about the effect of banking competition on growth, has

led to the need of empirical investigations. In this regard, the study of Cetorelli and

Gambera (2001) investigates the effects of banking competition (proxied by the structure

of the market) on economic growth. This seminal work shows that the availability of

credit is greater for the most dependent firms (within their framework the young firms),

in concentrated markets, confirming Petersen and Rajan (1995) theory. To achieve these

findings, the empirical methodology used is close to the Rajan and Zingales’ (1998) ap-

proach. The latter consists to examine the performance of economies from economic

performances at a disaggregated level, namely at the industry level of each country. The

starting point of this approach consists to postulate that the need of external finance is

heterogeneous between the different industries for technical reasons. Therefore, whether

the dependence on external finance diverges, it is expected that the most dependent

sectors grow faster in the countries where, in the context of the paper of Rajan and Zin-

gales (1998), the financial sector is more developed. To conduct this test, the authors

interact a country-specific measure of financial development and an industry-specific in-

dicator of financial dependence and observe the effects of this interaction variable on the

sectoral growth rates, by controlling for unobservable factors specific to country and to

industry. Finally, the approach of Rajan and Zingales (1998) by observing the influence

of financial development on credit constraint, highlights a specific mechanism by which

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finance influences growth.1 While the study of Rajan and Zingales (1998) focuses on the

link between financial development and growth, Cetorelli and Gambera (2001) extend

their empirical specification by including an indicator of bank market structure, enabling

them to observe the effects of the latter on economic growth.

Since the market structure is certainly not a good proxy of banking competition (see,

Claessens and Laeven, 2004; Carbo et al., 2009), other studies have checked whether the

findings of Cetorelli and Gambera (2001) are consistent when non-structural measures

of banking competition are applied. For instance, Claessens and Laeven (2005) study

the effect of banking competition on economic growth, using the Panzar and Rosse

H-statistic, and they show that the most financially dependent sectors grow faster in

countries where banking markets are highly competitive. Therefore the authors reject

the hypothesis that market power improves access to finance. Furthermore, their study

shows that bank concentration has no significant effect on economic growth. On the

same basis, but using the Lerner Index as a measure of banking competition, de Gue-

vara and Maudos (2011) find evidence that bank market power has an inverted-U shaped

effect on economic growth. Thus, their results support in part the lending relationship

benefits as shown by Petersen and Rajan (1995) for instance, but also underline that too

much market power is detrimental for growth which could be explained by the “hold-

up” problem as argued by Boot (2000). A recent contribution of Liu and Mirzaei (2013)

seems support this last argument, since it states that competition proxied by what the

authors call “efficiency competition” matters for growth by facilitating new entries of

firms.

Our contribution is in line with the 4 previous cited papers since we apply a close

empirical methodology to test the effect of banking competition on growth. However, we

depart from these latter papers by adopting a schumpeterian view of the economy and of

the relationship between banking competition and growth. In practice the novelty of our

approach is that we postulate that banking competition can affect the economic growth,

mainly by leading banks to select the best firms and most promising entrepreneurs. By

selecting the best projects, the productivity of the sector will increase due to a real-

location of funds toward more productive firms. According to Schumpeter (1912) the

selection of most efficient projects is the raison d’etre of financial intermediaries, and this

function passes by acquisition of soft information about borrower firms. Since as shown

by Petersen and Rajan (1995) the acquisition of soft information requires that banks

1One of the main comparative advantage of such a specification is that it complies with omittedvariables bias and reverse causality issue.

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have market power, we do the assumption that market power can improve the growth

of productivity, especially the productivity of the most external financially dependent

sectors. Therefore, contrary to previous contributions on this matter, we do not consider

that banking market power could induce a growing amount of flows, which could explain

the growth of capital accumulation. Instead, we presume that market power improves

the allocation of funds, especially by improving the credit availability for more opaque

and small firms which faced more asymmetric information. In our view, banking com-

petition would have an influence on the efficiency rather than the volume of investment.

According to Goldsmith (1969) the financial development would have comparable effect

in allowing higher growth through efficiency gains.

Furthermore, there is an extensive literature that shows that the reallocation of resources,

i.e. firm dynamics, in our case influenced by the level of banking competition, has also an

influence on innovation and productivity growth of incumbents firms (Bartelsman et al.,

2004; Aghion and Howitt, 2006). To explain firm dynamics, the literature has mainly

underlined the influence of labour and market regulations (see, Nicoletti and Scarpetta,

2003; Bourles et al., 2013). By contrast, with the exception of the theoretical work of

Aghion et al. (2007), the role of banking imperfections in the reallocation of resources

and through this incentives to productivity growth for incumbents firms has received to

our knowledge little attention.

By investigating the link between banking competition and productivity growth, our

paper is in midstream between the two previous cited literatures. To test the effects of

banking competition on total factor productivity, we employ the difference-in-difference

approach of Rajan and Zingales (1998), as used by Cetorelli and Gambera (2001) for

instance. To conduct this test, we interact an industry-specific indicator of financial

dependence and a country-specific measure of banking competition that we proxy by

the Boone Indicator from the data set of Clerides et al. (2014). A distinctive feature of

our approach is that we also relate to the empirical specification used by Bourles et al.

(2013) and explain productivity growth by productivity growth of the country at the

technology frontier.

Our findings suggest for a sample of 10 European countries and 11 manufacturing

sectors over the period 1999-2009, that bank market power improves the productivity

growth. Indeed, we find that our indicator of market power interacted with an external

financial dependence indicator is positively associated with countries industrial produc-

tivity growth, meaning that that the most financially dependent industries grow at a

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slower rate in economies where the banking competition is higher. In our view, this is

the evidence that bank market power would favour relationship-banking, which would

improve the allocation of funds toward more efficient firms, especially by improving the

availability of funds for more opaque firms. Furthermore these findings are robust to a

number of sensitivity tests, as for instance the modification of our indicators of banking

competition or of external financial dependence. Our findings also show that the eco-

nomic effect is sizeable. Indeed, our estimations reveal that the difference in the growth

rate of productivity between highly and lowly financially dependent industries is 1.20%

higher when the competition is at a low level than when the latter is at a high level.

Finally, our study allow us to assess the consequences of the productivity growth effects

of bank competition in terms of industry’s value added growth as well as competitiveness

(measured in terms of Revealed Comparative Advantages).

The remainder of the paper is structured as follows. Section 2 is devoted to the

presentation of our empirical methodology. Section 3 describes data used in this study.

Section 4 discusses in detail our empirical findings, while section 5 concludes.

2 Empirical Specification

The methodology used to test whether banking competition has a significant effect on

the growth rate of productivity is based on the idea that the dependence on external

finance varies across manufacturing sectors. This is linked to differences in production

technology and differences in average firm size. For instance, some sectors, as metal in-

dustry, are highly capitalistic. Others, need to finance large R&D programs as chemical

industry or are characterized by a more atomistic markets (food industry for instance).

By assuming that these differences in dependence of external finance are intrinsic, and

not country-specific, the productivity of sectors more dependent on external finance

should be more influenced by banking competition.

To test this effect, we use a difference-in-difference methodology in line with the one

originally used by Rajan and Zingales (1998). These authors test whether the industries

most dependent on external finance are characterized by a higher growth rate of value

added in countries where the level of financial development is higher. To conduct this

test, the authors interact a country-specific measure of financial development and an

industry-specific indicator of financial dependence. This presents a twofold benefit since

it addresses problems of reverse causality and omitted variable bias. However, even if we

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refer to the innovative specification of Rajan and Zingales (1998), we update the latter

in some points.

First, we do not consider cross-section data and prefer panel data. A same specification

is used by Dell’Ariccia et al. (2008), Borensztein and Panizza (2010) or Arnold et al.

(2011). Our motivations are both statistic and theoretical. Indeed, as we will discuss

later, the number of industry-country is limited and furthermore panel-specification al-

lows relying our work with neo-schumpeterian growth model.

Second, we update the seminal framework of Rajan and Zingales (1998) to our problem-

atic by replacing financial development by banking competition and the growth of value

added by growth of total factor productivity.

This leads to the following specification:

∆lnTFPcs,t = as + bc,t + βFDs ∗BCc,t + εcs,t (1)

where ∆lnTFPcs,t is the growth of total factor productivity for country c in sector s

at time t. As explanatory variables, we consider industry and country-year fixed ef-

fects (respectively as and bc,t) to control for unobserved heterogeneity across industries,

countries and time,2 and our main variable of interest, namely the interaction term of

financial dependence (FDs) and banking competition (BCc,t). Last, εcs,t denotes the

error term.

We extend this basic specification in line with Rajan and Zingales’ contribution, in

order to comply with Aghion and Howitt’s (1998) endogenous growth model. In the

model of Aghion and Howitt, productivity growth depends on both the ability to catch

up and the ability to innovate. An important number of contributions have empirically

implemented of such neo-schumpeterian models (Nicoletti and Scarpetta, 2003; Aghion

et al., 2009; Griffith et al., 2010; Bourles et al., 2013; Arnold et al., 2011, see,).

Formally the implementation consists first to consider an auto-distributed lags (1,1)

(ARDL(1,1)) model in which the level of productivity is explained by its lag, the level

of productivity of the frontier country (TFPfront) and in our case by the interaction of

financial dependence term and banking competition:

2That allows to avoid omitted variable bias. Note that we also consider in our empirical resultpart other structures of fixed effects to take into account all potential dimension of unobserved factorsinfluencing the productivity growth.

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lnTFPcs,t = as + bc,t + β0lnTFPcs,t−1 + β1lnTFPfront,s,t−1

+ β2lnTFPfront,s,t−1 + β3FDs ∗BCc,t + εcs,t (2)

Under the assumptions of long run homogeneity (β0 + β1 + β2 = 1) and cointegration

between the level of TFP of a lambda country and the level of TFP of the frontier

country, the ARDL can be re-written in an error correction form:

∆lnTFPcs,t = as + bc,t + β1∆lnTFPfront,s,t + (1 − β0)ln(TFPcs,t−1

TFPfront,s,t−1)

+ β3FDs ∗BCc,t + εcs,t (3)

Thus, we consider that productivity growth depends on productivity growth of the fron-

tier country, the distance to the frontier, namely the error correction mechanism and on

the scale of spillover effects of banking competition. Equation 3 corresponds to our base-

line specification. We estimate this model by Ordinary Least Square (OLS). Standard

errors are adjusted for heteroskedasticity using Huber-White correction and are clus-

tered at the country∗year level to allow the error term to be correlated across sectors in

the same country. This correction accounts for the presence of a common random effect

at the country-year level and it is importance since banking competition occurs at the

country-year level. Same approach is used for instance by Levchenko et al. (2009) and

it is standard in difference-in-difference framework.3

3 Data and descriptive statistics

Our empirical analysis spreads over 2000-2009 and includes 11 industries in 10 different

countries (1100 observations). The countries included in the study are: Austria, Belgium,

Finland, France, Germany, the Netherlands, Spain, Sweden and the United-Kingdom.

This sample is exclusively based on availability of data of EU-KLEMS database, which

includes only 12 countries. The two countries which are not considered in our empiri-

cal analysis are the U.S and Japan since the former is used as a country frontier (as a

3As argued by Bertrand et al. (2002) serial correlation could be a problem in difference-in-differenceset-up. In response to that, we also report results with clustering standard errors at the country-industrylevel. This accounts for cross-country correlation as well as serial correlation but no correlation betweensectors of a same country. Generally, we find in the latter case, smaller standard errors, and consequentlyprefer report “conservative” standard error by clustering at the country country-year level.

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benchmark) and since for the latter we prefer to focus exclusively on Europe.

As regard industry choice, we have retained in this study exclusively the 11 manufac-

turing sector presented in EU-KLEMS.4 Our focus on manufacturing is consistent with

Rajan and Zingales (1998).

The data require for our empirical analysis are of two different levels. We use country-

data for measurement of banking competition whereas all others variables are measured

at the industry level. In this section, we present the different data needed for our study.

We begin by introduce the main variables used, namely bank competition, financial de-

pendence and productivity. Table ?? in appendix sums up description and source of

data used in this study.

3.1 Bank Competition

Assessing the level of competition of a market is undoubtedly a difficult task. Indeed

bank competition is not observable and is a complex and proteiform notion. It has re-

sulted that many different methods for evaluating and measuring competition have been

proposed in the literature. These different methods are traditionally divided into two

streams: structural and non-structural approaches (see, Bikker and Haaf, 2002). The

first approach is based on the Structure-Conduct-Performance paradigm (SCP) follow-

ing Bain (1951). Loosely speaking, this paradigm supposes that the structure of the

market alleviates the bank behaviour. Especially, more concentrated markets are in this

view more collusive. Consequently according this paradigm, concentration ratios (the k

bank concentration ratio, the Herfindahl-Hirschman Index (HHI)) are natural proxy of

banking competition. However, many contributions have called into question the idea

that concentration would be a good measure of banking competition (Claessens and

Laeven, 2004; Carbo et al., 2009, see e.g.,). In response, empirical banking literature

has suggested the use of non-structural measures of banking competition. The New

Empirical Industrial Organisation (NEIO) paradigm proposes measures of banking com-

petition that directly assess the competitive conduct of bank and which are generally

based on oligopoly theory. Among the measures proposed in the literature, we can cite

4The manufacturing sectors are: “Food products, beverages and tobacco”, “Textiles, wearing apparel,leather and related products”, “Wood and paper products, printing and reproduction of recorded media”,“Coke and refined petroleum products”, “Chemicals and chemical products”, “Rubber and plasticsproducts, and other non-metallic mineral products”, “Basic metals and fabricated metal products, exceptmachinery and equipment”, “Electrical and optical equipment”, “Machinery and equipment n.e.c.”,“Transport equipment”, “Other manufacturing, repair and installation of machinery and equipment”.EU-KLEMS manufacturing sectors are in the majority of cases the agglomeration of different two digitsector, that explains the more limited number of manufacturing sectors than Rajan and Zingales (1998)for instance.

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the Lerner Index, the measure of Bresnahan (1982) or those of Panzar and Rosse (1987).

More recently, Boone (2008) has developed a new method for estimating competition.

The Boone indicator considers that competition improves the performance of efficient

banks and weakens the performance of inefficient ones. Thus, competition would lead

to a speed up of the reallocation of profits toward better and more efficient banks. This

theoretical prediction is linked to the efficiency structure hypothesis of Demsetz (1973)

and the empirical work of Berger and Hannan (1998) which shows that banks are less

efficient when the market is weakly competitive.

Although no general argument prevails about the best non-structural measure to

gauge competition, we prefer opt in this study for the Boone indicator.5 The theoretical

foundation of the Boone indicator can allow justifying this choice. For Boone (2008)

competition can increase for two different reasons: either a decrease of bank entry cost

or because products become closer substitutes. As mentioned by Tabak et al. (2012),

this assumption offers a comparative advantage to Boone indicator over concentration

ratio or other non-structural measures of banking competition. Thus, an increase of

product substitution leads the efficient banks to gain more profit or market share, that

increases the competition such as evaluated by the Boone indicator. By contrast, this

could lead to an increase of banking competition according concentration ratio. Indeed,

if more efficient banks have already the majority of market shares, the increase of prod-

uct substitution means an increase of concentration ratio. Likewise, the Lerner Index

could increase, which indicates less-competition since efficient banks have lower marginal

costs.

The simple way to define the Boone indicator is to consider the following specification.

Πit = α+ βtmcit (4)

where Πit stands for the log of profits measured by the Return On Assets and mcit, the

log of the marginal costs of bank i in period t.6 Last βt is the Boone indicator. Thus

the Boone indicator corresponds to the elasticity of profits to marginal costs. Since

more profits have to be achieved by more efficient banks, the more negative the Boone

indicator, the fiercer competition is.

5Nevertheless, we test in this study the robustness of our results for other competition proxies (seesection 4.2). Indeed, as noted by Liu and Mirzaei (2013) the different measures of banking competitiondo not provide the same conclusion about competition.

6The marginal cost is computed from the first derivative of a bank cost function.

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Furthermore as can be shown in equation 4, another advantage of the Boone indicator

is that it allows capturing the dynamic of competition, in contrast to the H-Statistic of

Panzar and Rosse (1987) for instance, since we can compute dynamic elasticity (βt).

The estimations of the Boone indicator for each year and our 10 European countries used

in this study come from the data set of Clerides et al. (2014). For robustness checks, we

have also used estimations of Boone Indicator from Global Financial Database, created

by the World Bank. However, we are convinced that Clerides et al. (2014) has a twofold

interest. First, the data set does not comprise estimate values that we could consider as

outliers,7 and which could lead to estimation bias.8 Second, the estimation methodology

of marginal cost is based on smooth coefficient model, that is a semi-parametric method

as developed by Delis (2012) that allows for a more flexible cost function structure.9

Table 1: Boone Indicator from Clerides et al. (2014)

Austria Belgium Finland France Germany Italy the Netherlands Spain Sweden United Kingdom

1999 -0.478 -0.43 -0.393 -0.453 -0.434 -0.451 -0.586 -0.431 -0.587 -0.4842000 -0.608 -0.47 -0.387 -0.462 -0.444 -0.436 -0.553 -0.43 -0.612 -0.4062001 -0.503 -0.433 . -0.441 -0.445 -0.437 -0.527 -0.428 -0.521 -0.4582002 -0.456 -0.422 . -0.431 -0.428 -0.401 -0.508 -0.422 -0.413 -0.4892003 -0.428 -0.404 -0.391 -0.412 -0.418 -0.384 -0.471 -0.416 -0.39 -0.3972004 -0.445 -0.401 -0.383 -0.425 -0.412 -0.39 -0.571 -0.458 -0.366 -0.3952005 -0.484 -0.541 -0.378 -0.447 -0.407 -0.408 -0.561 -0.518 -0.456 -0.4392006 -0.514 -0.561 -0.388 -0.455 -0.406 -0.416 -0.51 -0.555 -0.444 -0.4582007 -0.517 -0.633 -0.395 -0.485 -0.412 -0.443 -0.445 -0.629 -0.519 -0.4652008 -0.532 -0.629 -0.432 -0.521 -0.417 -0.483 -0.425 -0.645 -0.537 -0.462009 -0.465 -0.547 -0.397 -0.5 -0.401 -0.419 -0.408 -0.497 -0.41 -0.418

Average -0.493 -0.497 -0.393 -0.457 -0.420 -0.424 -0.505 -0.493 -0.477 -0.442Std. Dev. 0.049 0.087 0.015 0.032 0.012 0.029 0.061 0.083 0.082 0.033

Beyond the Boone indicator, we also consider alternative measures of banking com-

petition since as mentioned above there is no consensus about the best way to measure

competition. First, we use as an alternative indicator of banking competition, the Lerner

Index from Clerides et al. (2014) data set. The Lerner Index is a common measure of

bank market power in the banking literature and it is defined as the difference between

output prices and marginal costs (relative to output prices). Since a higher value of

index indicates less competition, we expect same sign as the Boone Indicator.

Then, we employ two measures based on observed structures of the different banking

7This is for instance the case for Finland in World Bank data set.8Especially, since we use least square estimates whose we know that are highly sensitive to outliers.9Delis et al. (2014) show that semi-parametric methods for estimating marginal cost perform better

than parametric techniques.

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markets: a concentration ratio (here, the market share of the fifth largest banks) and

the share of foreign bank assets in the total of bank assets of an economy, whose data

are made available by the World Bank.

3.2 Financial Dependence

In more of bank competition variables, our empirical strategy requires the assessment

of the intensity of external financial dependence at the sectoral level. To estimate the

spillover effects of bank sector on the manufacturing industry, we opt for two different

indicators (FD1 and FD2).

First, we use the Rajan and Zingales (1998) measure of financial dependence by sec-

tor based on U.S. firm-level data. Thus, we assume that US is the benchmark country to

assess the external financial dependence of firms. That seems relevant since firms have

no problem in their access to financing in US, especially by direct funding10 but simul-

taneously implies that US external financial dependence of each sector is a structural

parameter, which can raise questions (see, Von Furstenberg and Von Kalckreuth, 2006).

Rajan and Zingales (1998) define their external financing dependence as the ratio of

capital expenditures not financed by cash flow over capital expenditures (i.e. the part

of investment not financed by retained earning). First their indicator is calculated at a

firm-level, using all data from Compustat Database of US manufacturing publicly quoted

firms. Then, the ratio is aggregated at the two digit ISIC (International Standard of

Industrial Classification) level sectors.

The external financing dependence indicators used in this study are taken from Rajan

and Zingales (1998) and modify (by considering average between sector) to correspond

to our industry-classification based ISIC rev 4. Appendix Table 8 displays the industry-

level external financial dependence. Note that for robustness checks, we use external

financial figures estimated over another period (1980-99).11 Correlations are highs, and

results not affected by this change (see, Table 5).

As mentioned earlier, we also consider in this study an alternative way to measure

financial dependence. For this purpose, we refer Arnold et al. (2011) or Bourles et al.

(2013) among others. In the latter papers, the spillover effects of sectors X on Y are

measured from the weight in term of intermediate consumption of sector X in the output

of sector Y. Thus, the indicator assesses the relative weight of finance for each sector. To

10That avoid the problem of identification between demand and supply of funds.11Data come from Kroszner et al. (2007).

12

obtain, this intersectoral linkage between the financial sector and manufacturing indus-

try, we use OECD Input-Output tables. To minimize endogeneity issues and following

the same logic as Rajan and Zingales (1998), we have to select a benchmark country.

For that we use the 2000 US Input-Output table.12

Our two baseline indicators of financial dependence (denoted FD1 for the Rajan and

Zingales’ external financial dependence and FD2 for US Input-Ouput table) are crossed

to our proxy of banking competition to create the main variable of interest of this study,

namely an index of sensitivity to bank competition.

Indexcs,t = FDs ∗ Competitionc,t (5)

where Indexcs,t is an indicator of sensitivity to bank competition specific to each country-

industry and time-varying. FDcs is a measure of financial dependence which is constant

over time13 and specific to each sector. Last, Competitionc,t corresponds to a proxy of

banking competition specific to each country and varying over the time.

3.3 Total Factor Productivity

The dependent variable in our empirical specification is the Total Factor Productivity

(TFP) growth. TFP growth corresponds to the Solow residual obtained from a Neo-

classical production function of the following form:

Ycs,t = Acs,tF (Lcs,tKcs,t) (6)

where Ycs,t refers to the value added of sector s in country c in time t. F is a production

function homogeneous of degree one and exhibits decreasing returns to each factor of

production (L and K respectively for labour and capital) and Acs,t is the total factor

productivity.14

Suppose a Cobb-Douglas function, TFP growth is calculated as follow:

∆lnTFPcs,t = δlnYcs,t − αsδlnLcs,t − (1 − αs)δlnKcs,t (7)

where αs is equal to the output elasticity of labour.

The TFP growth measures employed in our regressions follow this methodology and

12As a robustness check, we consider in Table 5 the 2005 US Input-Output table.13That is necessary to disentangle, the changes in industry technology and the effects of banking

competition.14”technical change is a “manna from heaven that, for any given capital/labor ratio, proportionally

increases total output”.

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come from EU-KLEMS.

Our baseline model (equation 3) also requires the computation of a gap between

country c and the country frontier (the U.S). Following our baseline specification, the

formal expression of gap is: ln(TFPcs,t

TFPfront,s,t). That implies that we have to determine

TFP level for the ten countries in our sample and the U.S, the frontier. Since TFP

level is not provided by EU-KLEMS, one possibility would consist to build the TFP

level from data on hours worked for instance and capital services or net capital stock.

Unfortunately, data limitation of capital services or net capital stock for some countries

makes it not feasible.15

To take in consideration gap in productivity and productivity growth through conver-

gence to the productivity of US, we consider the difference between the labour produc-

tivity of each sector for our 10 European countries in our sample and the U.S. corre-

sponding labour productivity, calculated as the ratio of value added in millions of dollars

over number of hours worked, whose data are taken from EU-KLEMS.

4 Results

4.1 Baseline Results

Our basic results are reported in Table 2. Columns (1) and (2) report the results of our

baseline specification (equation 3) for the two measures of financial dependence consid-

ered (respectively, FD1 for the Rajan and Zingales external financial dependence and

FD2 for US Input-Ouput table). Columns (3)-(6) present estimation results of slightly

amended specifications of our baseline model since we consider other forms for the fixed

effects. Instead to consider industry and country-time fixed effects as in our baseline

specification, we include industry ∗ year, country ∗ year and country ∗ industry fixed

effects in a first specification, and industry ∗ year, country ∗ year in another. Last, in

columns (5)-(6), we consider a more parsimonious version of our baseline model omit-

ting growth of productivity of the frontier country as well as gap of productivity to the

frontier. These specifications are more in line with Rajan and Zingales framework and

correspond to equation 1.

All the regression results presented in Table 2 highlight significant and positive coef-

ficients of the interaction terms between our two proxies of external financial dependence

15France for instance.

14

and banking competition. That means that the industrial sectors the most dependent on

external finance tend in average to be significantly characterised by lower productivity

growth in European countries where banking competition is higher.

These findings are evidence in favour of benefits of market power for industries more re-

lied on external finance. Thus, our results support the theoretical argument advanced by

Rajan (1992) or Petersen and Rajan (1995). Low competition may favour relationship-

banking, which enhances the growth of productivity of the most dependent sector. Since

the effects of bank competition are not uniform, but specific to each manufacturing

sector, it is the evidence that banking competition leads to redistribution effects. To

complete the analysis of these results, we can refer to schumpeterian arguments. Thus,

our findings can be explain by the fact that market power increases the ability of bank

to screen borrowers which increases the firm access to external finance and especially

for the young and small firms. In this way, bank market power induces a reallocation

of funds toward the best firms and a growth of productivity of the most financially de-

pendent sectors. This forms a direct channel of banking competition on productivity

growth. A second potential transmission channel passes through incentives for technical

progress. Indeed by improving screening ability, the market power increases the entry

of new industrial firms on the markets, increasing competition which could stimulate

innovation of incumbents firms, and therefore the total factor productivity of the most

financially dependent sectors.

Apart from these main results, our estimations show that the growth of US indus-

try does not have a significant effect on the growth of European manufacturings. This

finding raises the question of the choice of US as a frontier. Due to international spe-

cialization, it is likely that the frontier industry varies from country to country. Another

complementary explanation is that the lag structure arbitrary considered and fixed to

zero is not right. The fact that the variable “gap” is highly significant, gives credit to

this second explanation.

Further to the fact that bank competition has a significant effect on the growth of

productivity of the most financially dependent industries, we must check that the eco-

nomic effects are sizeable. To gauge the scale of economic effects, we compare an industry

at the 75th percentile of the financial dependence with an industry at the 25th percentile

of the financial dependence, for a “high” and “low” level of banking competition.16 To

16here, since the Boone Indicator is an averse proxy of banking competition “high” level correspondto the 25th percentile and a “low” level to the 75th percentile.

15

make our analysis, we consider regression results of the two first columns of Table 2. For

column 1, we find that the difference in growth rate of productivity between an industry

highly and lowly financially dependent is 1.20% higher when the competition is at a

low level than when the latter is at a high level. Estimations of economic effects from

column 2 lead to results of the same order (1.24%). In other words, the rise of banking

competition reduces the growth of productivity of the most financially dependent firms.

Table 2: Bank Competition and TFP Growth: Baseline Results

(1) (2) (3) (4) (5) (6) (7) (8)TFP TFP TFP TFP TFP TFP TFP TFP

US TFP Growth 0.0346 0.0367 0.0515 0.0537 0.00244 0.266***(0.0436) (0.0433) (0.0441) (0.0441) (0.113) (0.0916)

Gap Labour 2.393** 2.207** 4.443** 4.334** 3.817 3.583Productivity (1.023) (1.021) (2.009) (2.016) (2.009) (2.005)

FD1*Boone 38.26*** 33.63** 48.90*** 34.17**(13.81) (15.39) (15.81) (14.25)

FD2*Boone 28.90*** 23.13** 32.35** 29.84***(10.53) (11.05) (13.38) (10.82)

Constant 6.950*** 27.86*** 5.025*** 21.44** 7.994*** 30.62*** 7.474*** 29.52***(1.531) (8.979) (1.562) (9.252) (1.993) (11.20) (1.547) (9.209)

Fixed EffectsTime*Country Yes Yes Yes Yes Yes Yes Yes YesIndustry Yes Yes No No No No Yes YesTime*Industry No No Yes Yes Yes Yes No NoCountry*Industry No No No No Yes Yes No No

N 1053 1053 1053 1053 1053 1053 1053 1053R-sq 0.363 0.363 0.433 0.432 0.546 0.545 0.351 0.352

Robust standard errors in parentheses clustered at the country-year level. * p¡0.1, ** p¡0.05 and ***p¡0.01

4.2 Robustness Checks

In this subsection, we perform several robustness checks to examine whether our findings

are robust to the estimation methodology and the variables used.

Alternative specification and estimation strategies

First, we expand our baseline model by adding an interaction term between our measures

of financial dependence and the level of financial development. The fact that regressions

in column (1) and (2) include this new interaction term allows controlling more fully for

country ∗ year heterogeneity than the country ∗ year fixed effects and allows checking

16

whether our results are not due to correlation between financial sector development and

banking competition. Furthermore this is a way to conform with the extensive literature

on the link between financial growth and financial development, and the more limited

literature on the effects of banking competition on VA growth as Cetorelli and Gambera

(2001), Claessens and Laeven (2005) or de Guevara and Maudos (2011), which system-

atically controls for the level of financial development.

As can be shown, these new results do not alter our main findings. The coefficients be-

hind our main variable of interest remain positive and highly significant. An interesting

observation is that the estimated coefficients of interaction terms between financial de-

velopment (proxied by the ratio of credit to GDP) and financial dependence are negative

and not significant. This result is in line with the idea of “Too much” finance devel-

oped by Arcand et al. (2012). According to this contribution, beyond a threshold lies

between 110% and 120% of the credit to GDP ratio, the correlation between financial

development and growth becomes negatives. Since our sample consists of high financial

developed countries, our results seem coherent.

Another concern with our initial specification is that we consider panel data instead

cross-section data as in Rajan and Zingales (1998). Consequently in order to check our

results, we modify our baseline specification and replace all the variables in equation 3

by their average over the period 1999-2009.17 Columns (3) and (4) in Table 3 display the

results and confirm our previous findings. This is an evidence that the effects of banking

competition on productivity growth rate of industry more dependent on external finance

are not limited to country-within variation of banking competition.

The remaining four columns of Table 3 show that our result are robust to outliers.

Indeed, in columns (5) and (6) we report estimation of robust regression routine,18 which

consists to under-weighted the most extreme values.19 We find for the two indicators of

financial dependence that our previous conclusions are not driven by outliers. However,

we find lower coefficient estimates, which means that the economic effects of competition

are less important in this case. In the same spirit columns (7) and (8) display estimation

of our baseline model by Least Absolute Deviation (LAD), i.e. quantile regression. 20

17For growth of productivity the average considered is the compound average. Note also that weinclude country and industry fixed effects.

18We use Stata’s rreg routine.19Robust regression is an iterative reweighted procedure of least squares by using Huber weights and

biweights.20In this case we minimize the sum of the absolute residuals rather than the sum of the squares of the

17

Finally, we made different clustering assumptions. Thus, the results are robust to

clustering standard errors at the country-industry level, i.e. robust to relax the assump-

tion that the error term is serially independent.

Table 3: Bank Competition and TFP Growth: Specifications and Estimations robustnesschecks

(1) (2) (3) (4) (5) (6) (7) (8)TFP TFP Avg. TFP Avg. TFP TFP TFP TFP TFP

US TFP Growth 0.032 0.036 0.059*** 0.062*** 0.056** 0.059**(0.039) (0.043) (0.018) (0.018) (0.023) (0.025)

Labour Productivity 2.355* 2.191* 1.921*** 1.774*** 3.372*** 3.060***Gap (1.011) (1.047) (0.373) (0.371) (0.445) (0.496)

FD1*Boone 34.29*** 22.76*** 18.28**(12.32) (8.307) (7.768)

FD1*Fin. Dev. -0.0274(0.0224)

FD2*Boone 28.48*** 16.09** 3.329(9.387) (6.593) (2.484)

FD2*Fin. Dev. -0.413(2.459)

Avg. US TFP Growth 1.434** 6.587***(0.458) (1.733)

Labour Productivity 1.503* 1.278Initial Gap (0.816) (0.793)

FD1*Mean Boone 51.86**(20.11)

FD2*Mean Boone 60.21***(16.70)

Constant 6.937*** 28.19** 4.430* 45.96** 5.334** 16.81** -16.49*** -18.24***(2.047) (10.27) (1.702) (12.70) (1.629) (5.795) (2.912) (2.862)

Fixed Effects:Time*Country Yes Yes Only country fixed effects Yes Yes Yes YesIndustry Yes Yes Yes Yes Yes Yes Yes Yes

N 1053 1053 110 110 1053 1053 1053 1053R-sq 0.364 0.363 0.448 0.469 0.568 0.567


Alternative indicators of banking competition and financial dependence

Another way to check the robustness of our result is to run our baseline regression with

alternative indicators of both banking competition and financial dependence.

residuals as in OLS regression.

18

As mentioned in our section Data, we consider a set of other proxies of banking

competition. Thus, we replace the Boone indicator from Clerides et al. (2014) by: (1)

the Boone Indicator from World Bank, (2) the Lerner Index from Clerides et al. (2014),

(3) a concentration ratio from World Bank and (4) the part of foreign assets in total

assets from World Bank.

From Table 4 we observe that the results are consistent with our main findings when

we consider the alternative Boone Indicator and the Lerner Index. By contrast con-

centration ratio does not have a significant effect on productivity growth. Nevertheless,

the estimated coefficients have the expected signs. These results underline that market

structures are not a predictor of industrial sector growth and constitute certainly a poor

proxy of banking competition, especially on our sample of countries which comprises

economies of different size. We also argue that foreign bank penetration could be a fac-

tor contributing to the increase of banking competition. Regression results in columns

(7)-(8) show that more the share of foreign banks in total assets, significantly less the

growth of the most dependent firms is.

As a subsequent robustness check, we perform estimations by considering three other

financial dependence indicators. First, we substitute the original Rajan and Zingales’s

index of external financial dependence by its updated version built over a more recent

period. Data of this index are taken from Kroszner et al. (2007). Second, we update our

index of external financial dependence based on the weight of intermediate consumption

of financial sector, by using US 2005 input-output tables instead US 2000 input-output

tables. Finally, we define a new indicator of financial dependence. This indicator is equal

to the share of R&D expenditures in US industry. This indicator involves assumptions.

As our other indicators, we have to believe that R&D expenditures of each industry

are avstructural factor. Furthermore, we have to considered that needs of large R&D

programs induce a higher financial dependence.

Whether you check correlation between the different indicators, we find that this in-

dicator is positively and significantly correlated with the original Rajan and Zingales’s

indicator at 68% and with our second basic indicator of financial dependence at 51.63%

Table 5 presents the estimations with these three new indicator of financial depen-

dence. Overall, we observe no significant change of our previous findings. Furthermore,

it appears that banking competition tends to reduce the growth rate of total factor

productivity of industries which devote the largest share of their expenditures in R&D

expenditures.

19

4.3 Extensions

The empirical findings presented so far provide evidence that banking competition mat-

ters for productivity growth. The aim of this subsection is to assess whether the beneficial

effect of bank market power influences value added growth and competitiveness of in-

dustries.

First, we assess the effects of banking competition on VA growth. The expected

effect is unclear. Indeed, VA growth is both function of multifactor productivity growth,

for which we have revealed the significant effect of bank competition, and factor accu-

mulation, for which we do not have idea concerning the expected effect. Even if the

productivity growth is the most important factor contributing to VA growth, a positive

contribution of bank competition on factor accumulation may lead to negative effects of

bank market on VA growth.

Table 6 display the estimation results of bank competition on VA growth. The empirical

specification considered are similar to those of our baseline model, apart from the fact

that we consider industries’ VA growth, US industries’ VA growth and the initial share

of the industry value added in overall value added instead of industry’ TFP growth, US

industry TFP growth and the gap in productivity respectively. Estimations show that

market power has a weakly significant positive effect on VA growth. We explain this

result by the insignificant effect of bank competition on factor accumulation and other

effects for which bank competition does not have influence (for instance, the growth in

labour.). This result is in line with the idea that bank competition, as well as financial

development, influence economic growth through productivity growth rather than factor

accumulation.

Second, we analyze the potential repercussions of the positive effect of market power

on TFP growth, in terms of competitiveness. Our empirical analysis of competitiveness is

based on revealed comparative advantage (RCA) and especially the indicator of Balassa

(1965). The latter has been widely used in the literature to approximate countries

industry specialisation and comparative advantages. The underlying idea of Balassa

index is to compare the performance of a country in one industry, revealed by the

amount of exports, to the average performance of a reference group. In this way, the

Balassa index gives a measure of country-industry’s competitiveness and can also be

20

view as a measure of comparative advantages.21 The indicator is specified as follow:

RCA = (Xij/Xit)/(Xnj/Xnt) (8)

where X represent export in millions of dollars. Further, i, j, n and t denote the country,

the industry, the set of industries and the set of countries respectively. Here, the set of

countries is limited to the OECD members and the set of industries to manufacturing

industry. Note also that data on export flows are provided by OECD statistic database.

According Ricardian comparative advantage model a country should export more in

industry in which it is relatively more productive. Since, market power increase the

productivity of more dependent sector, we can expect that bank market power implies

higher Balassa index for the most dependent sectors. In addition to test the effect of

bank competition on Balassa index, we also test the effect on industry export growth.

Results are reported in Table 7. First, we find in all specifications considered that bank

market power leads to an increase of export growth rate of the most dependent industry.

As regards the effects on Balassa index, we find mixed results. On the one hand, the

coefficients of our interaction term between the first proxy of financial dependence and

bank competition (FD1 ∗ Boone) are positive and highly significant. On the other

hand, the other coefficients estimated are not significant. These findings call for further

investigations. Especially, the numerous criticisms of Balassa Index should be taken into

account (see Hinloopen and Van Marrewijk, 2001 ; Costinot et al., 2012).

5 Conclusion

This paper empirically analyses the effects of banking competition, using the Boone

indicator, on manufacturing productivity growth for a sample of 10 European coun-

tries over the period 1999-2009. We find that our indicator of market power interacted

with a measure of external financial dependence is positively associated with countries

industrial productivity growth, suggesting that more competitive banking markets are

detrimental to finance the most external financially dependent firms. In our view, our

results are the evidence that bank market power favours relationship-banking. Indeed

relationship-banking allows bank reducing information asymmetries, which would bene-

fit especially to the small and/or young firms. In this way, the allocation of funds would

be better. Banks would select more the best firms which would increase more the total

21Although several other factors, not linked to comparative advantage can affect the indicator. Tradebarriers are an example. However, since the reference considered is limited to OECD member, we assumethat this effect is limited.

21

factor productivity of the sectors more dependent on external finance. Furthermore, by

improving firm dynamic, bank market power could incite incumbents firms to increase

their productivity. Our findings point out that the economic effect is sizeable. Indeed,

our estimations reveal that the difference in growth of productivity between highly and

lowly financially dependent industries is 1.20% higher when the competition is at a low

level than when the latter is at a high level. These findings are robust to a number of

sensitivity tests. We find that the use of the Lerner Index or the share of foreign banks

in total assets instead of the Boone indicator lead to similar results. However, we find no

evidence that market structure (i.e. concentration ratio) has an impact on manufactur-

ing productivity growth, which highlights the fact that concentration and competition

are different notions.

From a policy perspective, our results underline that competition may be detrimental

to total factor productivity growth, and consequently to economic growth. Our empir-

ical findings support the special role of bank in financial systems. Policy makers have

to take this into account for future regulatory and structural reforms, such as develop-

ment of financial market in Europe and the support of financial disintermediation for

instance, which could lead to an increase of banking competition and to incentive losses

of banking-relationship.

Future research should focus on the consequences of this higher productivity growth

for the most financially dependent firms when banks have more market power. In partic-

ular, we need to check whether this positive effect of market power on productivity is not

entail by a negative effect on capital accumulation, which would make the global effect of

banking competition on growth unclear. Furthermore, empirical works could investigate

more accurately that we have done in this paper, the effects of banking competition on

international comparative advantages.

22

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26

Table 4: Bank Competition and TFP Growth: Alternative measures of banking compe-tition

(1) (2) (3) (4) (5) (6) (7) (8)TFP TFP TFP TFP TFP TFP TFP TFP

US TFP Growth 0.042 0.041 0.035 0.030 0.043 0.045 0.043 0.043(0.044) (0.044) (0.043) (0.043) (0.044) (0.044) (0.045) (0.044)

Labour Productivity 2.172** 2.072** 2.348** 2.276** 2.106** 1.961* 2.261** 2.117**Gap (0.978) (0.988) (1.029) (1.014) (0.993) (1.041) (0.993) (0.974)

FD1*Boone 1.863***(0.545)

FD2*Boone 2.120**(0.808)

FD1*Lerner 38.89**(16.71)

FD2*Lerner 28.05**(12.16)

FD1*Cr5 0.068(0.058)

FD2*Cr5 7.324(5.398)

FD1*Foreign -0.106**(0.048)

FD2*Foreign -0.069*(0.036)

Constant 3.158*** 3.406*** 2.872*** -1.920 2.715*** -3.109 2.861*** 3.513***(0.618) (0.649) (0.575) (2.127) (0.715) (4.564) (0.639) (0.633)

Fixed Effects:Time*Country Yes Yes Yes Yes Yes Yes Yes YesIndustry Yes Yes Yes Yes Yes Yes Yes Yes

N 1074 1074 1053 1053 1074 1074 1074 1074R-sq 0.350 0.352 0.362 0.361 0.350 0.351 0.353 0.352


27

Table 5: Bank Competition and TFP Growth: Alternative measures of financial depen-dence

(1) (2) (3)TFP TFP TFP

US TFP Growth 0.031 0.035 0.034(0.043) (0.043) (0.043)

Labour Productivity 2.234** 2.187** 2.401**Gap (1.025) (1.028) (1.019)

FD3*Boone 8.323**(3.896)

FD4*Boone 25.95**(12.74)

FD5*Boone 3.769***(1.125)

Constant 2.753*** 25.46** 5.547***(0.615) (10.91) (0.908)

Fixed Effects:Year*Country Yes Yes YesIndustry Yes Yes Yes

N 1053 1053 1053R-sq 0.359 0.361 0.368


Table 6: Bank Competition and VA Growth

(1) (2) (3) (4) (5) (6) (7) (8)

VA VA VA VA VA VA VA VA

US VA Growth 0.106** 0.110** 0.0978* 0.0994* 0.103* 0.102*(0.0384) (0.0542) (0.0546) (0.0545) (0.0528) (0.0527)

VA initial 10.05 10.63 -167.2*** -164.5*** -174.2*** -171.8***(7.503) (7.276) (29.25) (29.53) (29.43) (29.57)

FD1*Boone 23.61* 18.57 26.37** 25.50(11.27) (16.20) (10.96) (17.18)

FD2*Boone 26.92** 13.82 6.886** 25.47**(10.45) (10.60) (2.795) (10.52)

Constant 5.787*** 26.30*** 34.22*** 43.70*** 35.57*** 38.29*** 7.602*** 26.79***(1.358) (8.944) (5.090) (9.748) (5.159) (5.783) (1.835) (8.870)

Fixed Effects:Time*Country Yes Yes Yes Yes Yes Yes Yes YesIndustry Yes Yes Yes Yes Yes Yes Yes YesTime*Industry No No Yes Yes Yes Yes No NoCountry*Industry No No No No Yes Yes No No

N 1053 1053 1053 1053 1053 1053 1053 1053R-sq 0.487 0.489 0.561 0.561 0.494 0.494 0.478 0.479


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Table 7: Bank Competition and TFP Growth: Export Growth and Revealed Compara-tive Advantage (RCA)

(1) (2) (3) (4) (5) (6) (7) (8)

Export Export Export Export RCA RCA RCA RCA

FD1*Boone 0.355** 0.448** 2.798*** 0.481**(0.174) (0.199) (0.778) (0.227)

FD2*Boone 0.266* 0.408** 1.406 0.155(0.141) (0.170) (1.072) (0.142)

Constant 0.0662*** 0.223** 0.0692*** 0.317*** 1.401*** 2.318** 1.597*** 1.684***(0.017) (0.097) (0.025) (0.115) (0.074) (0.910) (0.028) (0.128)

Fixed Effects:Time*Country Yes Yes Yes Yes Yes Yes Yes YesIndustry Yes Yes Yes Yes Yes Yes Yes YesTime*Industry No No Yes Yes No No Yes Yes

N 880 880 880 880 1078 1078 1078 1078R-sq 0.730 0.730 0.739 0.739 0.139 0.137 0.988 0.988


Table 8: Financial dependence indicators

FD1 FD2 FD3 FD4 FD5

Value:Basic metals and fabricated metal products, except machinery and equipment 0.113 1.334 -0.037 1.356 0.488Chemicals and chemical products 0.185 1.465 -0.77 1.56 6.193Coke and refined petroleum products 0.22 1.213 -0.3 1.175 0.434Electrical and optical equipment 0.77 2.172 0.24 1.978 10.672Food products, beverages and tobacco -0.077 1.992 -0.42 1.89 0.405Machinery and equipment n.e.c. 0.45 1.685 -0.04 1.613 2.677Other manufacturing; repair and installation of machinery and equipment 0.47 1.582 0.28 1.454 0.609Rubber and plastics products, and other non-metallic mineral products 0.362 1.451 -0.142 1.445 0.944Textiles, wearing apparel, leather and related products 0.053 1.067 -1.89 1.237 0.478Transport equipment 0.635 1.61 0.32 1.279 5.064Wood and paper products; printing and reproduction of recorded media 0.225 1.791 -0.275 1.898 0.397

Correlation:FD1: Rajan and Zingales’s indicator 1FD2: based on 2000 US Input-Outuput table 0.409*** 1FD3: Updated Rajan and Zingales indicator 0.655*** 0.541*** 1FD4: based on 2005 US Input-Output table 0.121*** 0.897*** 0.239*** 1FD5: constructed from R&D expenditures 0.68*** 0.516*** 0.275*** 0.375*** 1

This table reports our 5 different indicators of financial dependence as well as their correlations. *p¡0.1, ** p¡0.05 and *** p¡0.01

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Table 9: Descriptive statistics: Total Factor Productivity growth rate by sector

Mean Std. Dev. Min Max

Austria 1.819 9.055 -24.346 39.825Belgium 0.169 6.196 -23.022 13.905Finland 1.536 12.245 -33.957 39.36France 1.394 6.931 -19.4 30.29Germany 0.617 9.134 -30.397 30.246Italy -1.822 6.887 -31.735 23.241the Netherlands 1.915 7.655 -26.017 27.446Spain -0.382 3.744 -11.973 7.526Sweden 2.013 7.435 -28.553 29.238United kingdom 2.184 5.121 -17.085 14.904

Table 10: Descriptive statistics: Total Factor Productivity growth rate by sector

Mean Std. Dev. Min Max

Basic metals and fabricated metal products, except machinery and equipment -0.265 7.391 -33.957 17.924Chemicals and chemical products 2.306 6.058 -14.243 21.564Coke and refined petroleum products 0.15 15.875 -31.735 39.825Electrical and optical equipment 2.667 9.564 -26.017 29.238Food products, beverages and tobacco 0.796 4.908 -15.273 19.347Machinery and equipment n.e.c. 1.021 8.14 -30.397 25.28Other manufacturing; repair and installation of machinery and equipment 0.642 5.169 -15.393 11.031Rubber and plastics products, and other non-metallic mineral products 0.498 5.44 -22.342 10.926Textiles, wearing apparel, leather and related products 1.679 5.374 -15.407 13.809Transport equipment 0.099 8.5 -29.564 13.36Wood and paper products; printing and reproduction of recorded media 0.563 5.037 -32.551 11.644

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bank competition, financial dependence and...

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