Banks credit and productivity growth. No 2008 / February 2017.
Date: 2017-02-24
Institutions: European Central Bank
Subjects: bank, credit, financial market, investment policy, productivity

Working Paper Series 

Fadi Hassan, Filippo di Mauro, 
Gianmarco I.P. Ottaviano 

Banks credit and productivity growth 

CompNet

The Competitiveness Research Network

No 2008 / February 2017 

Disclaimer: This Working Paper should not be reported as representing the views of the European Central 
Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Competitiveness Research Network 

This paper presents research conducted within the Competitiveness Research Network (CompNet). The network is composed of 
economists from the European System of Central Banks (ESCB) - i.e. the 29 national central banks of the European Union (EU) and the 
European Central Bank – a number of international organisations (World Bank, OECD, EU Commission) universities and think-tanks, as 
well as a number of non-European Central Banks (Argentina and Peru) and organisations (US International Trade Commission). The 
objective of CompNet is to develop a more consistent analytical framework for assessing competitiveness, one which allows for a better 
correspondence between determinants and outcomes.  

The research is carried out in three workstreams:  

1) Aggregate Measures of Competitiveness;  

2) Firm Level;  

3) Global Value Chains  

CompNet is chaired by Filippo di Mauro (ECB). Workstream 1 is headed by Pavlos Karadeloglou (ECB) and Konstantins Benkovskis 
(Bank of Latvia); workstream 2 by Antoine Berthou (Banque de France) and Paloma Lopez-Garcia (ECB); workstream 3 by João 
Amador (Banco de Portugal) and Frauke Skudelny (ECB). Monika Herb (ECB) is responsible for the CompNet Secretariat.  

The refereeing process of CompNet papers is coordinated by a team composed of Filippo di Mauro (ECB), Konstantins Benkovskis 
(Bank of Latvia), João Amador (Banco de Portugal), Vincent Vicard (Banque de France) and Martina Lawless (Central Bank of Ireland). 

 The paper is released in order to make the research of CompNet generally available, in preliminary form, to encourage comments and 
suggestions prior to final publication. The views expressed in the paper are the ones of the author(s) and do not necessarily reflect 
those of the ECB, the ESCB, and of other organisations associated with the Network. 

ECB Working Paper 2008, February 20171 
 
 
Abstract:

Financial institutions are key to allocate capital to its most productive uses.
In order to examine the relationship between productivity and bank credit
in the context of diﬀerent ﬁnancial market set-ups, we introduce a model
of overlapping generations of entrepreneurs under complete and incomplete
credit markets. Then, we exploit ﬁrm-level data for France, Germany and
Italy to explore the relation between bank credit and productivity following
the main derivations of the model. We estimate an extended set of elastici-
ties of bank credit with respect to a series of productivity measures of ﬁrms.
We focus not only on the elasticity between bank credit and productivity
during the same year, but also on the elasticity between credit and future
realised productivity. Our estimates show a clear Eurozone core-periphery
divide, the elasticities between credit and productivity estimated in France
and Germany are consistent with complete markets, whereas in Italy they
are consistent with incomplete markets. The implication is that in Italy
ﬁrms turn to be constrained in their long-term investments and bank credit
is allocated less eﬃciently than in France and Germany. Hence capital misal-
location by banks can be a key driver of the long-standing slow productivity
growth that characterises Italy and other periphery countries.

Keywords: Bank Credit, Capital Allocation, Productivity, Credit Constraints. JEL Clas-

siﬁcations: G10, G21, G31, D92, O16.

ECB Working Paper 2008, February 201721

Introduction

A fundamental role of the ﬁnancial sector is to allocate capital eﬃciently.

This implies that banks should invest capital in the sectors and ﬁrms that

are expected to have higher returns and withdraw it from those with poor

prospects. In this paper we analyze the allocative eﬃciency of bank credit

across a group of European countries exploiting ﬁrm-level data on loans and

productivity.

Our point of departure is a theoretical model, which aims at examining

the relationship between productivity and bank credit in the context of

diﬀerent ﬁnancial market set-ups. The model is a two-periods OLG model of

entrepreneurs and credit under both complete and incomplete market close

in spirit to the one of Aghion et al. (2010). With complete credit markets,

the model predicts a negative correlation between credit and productivity

at time t and a positive one at time t+1 ; whereas with incomplete credit

markets the correlation at time t turns positive.

In our empirical analysis we estimate the elasticity of banks credit with re-

spect to productivity, by looking at the correlation of credit growth with

productivity growth at the ﬁrm-level across the countries in our sample.

These estimates can be interpreted as a proxy for how quickly credit is re-

allocated to the ﬁrms with faster productivity growth and follow the main

derivations of the model. Data come from the ECB-CompNet database,

which provides data on bank credit, from ﬁrms’ balance sheet, matched with

various ﬁrm-level measures of productivity. We use these data by country

in order to document the joint distribution of credit and productivity and

to estimate the elasticities of bank credit with respect to ﬁrm-level TFP, the

marginal product of capital, labor productivity and value added. Moreover,

we analyse the heterogeneity of such estimates by ﬁrm-size. Finally, we esti-

mate the correlations between bank credit growth and productivity growth

not only at time t, but also at time t+1 and t+2, so we can look the relation

between credit and the realised future productivity.

The results show a divide between the Eurozone core and periphery. France

and Germany have a large negative elasticity between credit and productiv-

ECB Working Paper 2008, February 20173ity at time t, whereas Italy has a positive one. Moreover, even if all countries

have a positive elasticity between credit and productivity at time t+1 and

t+2 in Italy it is signiﬁcantly smaller than in the other countries, which

implies that credit is allocated slower to ﬁrms that then experience faster

productivity growth. Finally in all countries we ﬁnd that the elasticities

tend to be signiﬁcantly bigger for small ﬁrms with respect to large ﬁrms.

The contribution of our paper is to show that an OLG model of credit

allocation with complete and incomplete markets provides clear guidance

about the correlation we should expect between credit and productivity at

diﬀerent points in time. In particular, under complete markets we should

expect a negative correlation between credit and productivity growth at

time t and a positive one at time t+1, whereas with incomplete markets

the correlation turns positive also at time t. As the model makes clear, this

implies that the ﬁrms in a country that face this type of correlation are

constrained in their long-term investments plan. The second contribution of

the paper is to measure and interpret the relation between bank credit and

productivity at the ﬁrm level for a sample of Eurozone countries. To our

knowledge there are not studies that document these type of patterns for a

group of Eurozone countries. This is a critical analysis since TFP accounts

for most of the income diﬀerences across countries (Caselli (2005), resource

allocation is a key determinant of TFP (Hsieh and Klenow, 2009), and - in

turn - bank credit is a key determinant in the allocation of capital.

Our paper relates to the literature that analyses the eﬀects of ﬁnance on

economic growth (King and Levine, 1993; Levine, 1997; Rajan and Zin-

gales, 1998; Guiso et al., 2004; Ciccone and Papaioannou, 2006; Beck et

al., 2008); to the literature on the real eﬀects of bank credit as Jimenez et

al. (2014), Schnabl (2012), Amiti and Weistein (2011) and Khawaja and

Mian (2008); and to the literature on resource misallocation in Europe like

Gopinath et al. (2015). However, the paper closest in spirit to ours is Wu-

gler (2000) which represents one of the few attempts to speciﬁcally assess

the role of the ﬁnancial sector in allocating capital eﬃciently. This study

takes the sector-level elasticity of investment on value added as a measure

of the allocative eﬃciency of capital and then rely on cross-country reduced

form regressions to evaluate the impact of diﬀerent ﬁnancial factors on such

ECB Working Paper 2008, February 20174measure.1 Hartmann et al. (2007) extend the work of Wurgler by looking
at the impact that diﬀerent characteristics of the ﬁnancial sector have on
the volume of investments.2

Our paper takes a step forward in this literature by i) elaborating a theoret-

ical model that helps connecting the empirically found elasticities between

bank credit and productivity with the relative eﬀectiveness of the underly-

ing ﬁnancial markets, and ii) by estimating such elasticities using a novel

ﬁrm-level dataset across a set of eurozone countries. In Section 2 we intro-

duce the theoretical model that serves us as a guide to interpret and test

the interaction between bank credit and productivity; Section 3 presents

the empirical speciﬁcations. Section 4 discusses the main results and policy

implications; and Section 5 concludes.

2 A model of Credit, Productivity and Market

Failure

Our point of departure is a simple model of investment allocation in the

spirit of Aghion, Angeletos, Banerjee and Manova (2010), in which an en-

trepreneur chooses between short- and long-term capital goods.

In this

framework diﬀerent patterns of correlation emerge between bank credit and

productivity depending on the completeness of the credit market. This is a

critical input for our successive empirical analysis since the model will help

us interpret the sign and the magnitute of such correlations - which we com-

pute with our ﬁrm level data set. In particular, via the model we wil be able

to draw inference on the allocative conditions prevailing in the underlying

ﬁnancial markets of the diﬀerent countries included in our dataset.

1Wugler (2000) regresses this elasticity on the level of ﬁnancial development measured
as the sum of stock market capitalization and private and non-ﬁnancial public domestic
credit.

2They develop 17 indicators of diﬀerent aspects of the ﬁnancial system that can be
mainly grouped into size, innovation and completeness, transparency, corporate gover-
nance, regulation, and competition.

ECB Working Paper 2008, February 201752.1 Production, Investment and Capital Goods

Consider an entrepreneur who can be active for at most two periods, t

(‘short run’) and t + 1 (‘long run’), and maximizes the present expected
value of ﬂow proﬁts over the two periods.3 The entrepreneur is endowed
with Lt = Lt+1 = L units of labor in both periods, and Ht units of human
capital. Human capital can be thought of as skills and other know-how that

the entrepreneur decides to invest in the ﬁrst period for the creation of both

short-term and long-term capital.

The technologies for transforming human capital in capital goods are as-
sumed to be linear and to share the same productivity θt with the supplies
of the short- and long-term capital goods given by Kt = θHk,t and Zt = θHz,t
respectively, where Hk,t and Hz,t are the amounts of human capital used as
inputs for the two goods (Hk,t + Hz,t = Ht, or equivalently Kt + Zt = θHt).

Turning to the production of the ﬁnal good, in period t the entrepreneur sup-
plies this good combining her ﬁrst-period labor endowment Lt with the in-
stalled amount of the short-term capital good Kt through the Cobb-Douglas
technology

Yt = AtKα

t L1−α,

where: Yt is the ﬁnal good output; At ∈ [Amin, Amax], with 0 < Amin <
Amax < ∞, is TFP; and α ∈ (0, 1) is the income share of the short-term
capital good. Analogously, in period t + 1 the entrepreneur supplies the ﬁnal

good combining her second-period labor endowment with the installed (and
tooled) long-term capital good Zt through the Cobb-Douglas technology

Yt+1 = At+1Zα

t L1−α,

where: Yt+1 is the ﬁnal good output; At+1 ∈ [Amin, Amax] is TFP; and
α ∈ (0, 1) is the income share of the long-run capital good.

3While, for realism, we could have more than two periods, two will make the logic of

the argument more transparent.

ECB Working Paper 2008, February 201762.2 Borrowing and Credit Constraint

We assume that the entrepreneur can only borrow from and lend to banks
at an exogenously (risk-free) rate Rt. We distinguish two scenarios: one in
which the credit market is complete so that there are no borrowing con-

straints; the other in which the credit market is incomplete and the en-

trepreneur faces an ad-hoc borrowing constraint such that her net borrowing

in period t cannot exceed a multiple µ ≥ 0 of her contemporaneous income.

In the latter case we also assume that the entrepreneur is not able to meet

the maximum liquidity shock when credit markets are suﬃciently tight (i.e.
µ is suﬃciently small).4

Under these assumptions, the budget and borrowing constraints (when rel-

evant) of the entrepreneur in period t can thus be stated as

Πt + qt(Kt + Zt) + Stet = Yt + Bt,

Bt ≤ µYt,

(1)

where: Πt is proﬁt in period t; qt is the unit (shadow) price of capital goods;
qt(Kt + Zt) is expenditures on capital goods; St is the liquidity shock; et is
an indicator function valued 1 if the entrepreneur covers the liquidity shock
and 0 otherwise; Bt is borrowing (or lending, if negative); and Yt is revenue
(as the price of the ﬁnal good is normalized to one). Diﬀerently, as in the

second period the entrepreneur cannot borrow (being this her last period of

activity), her budget constraint in period t + 1 is given by

Πt+1 + (1 + Rt)Bt = [Yt+1 + (1 + Rt)St] et,

(2)

where (1 + Rt)Bt is borrowing and associated interest repayment (or lending
and associated interest repayment if Bt is negative) and (1 + Rt)St is the
recovery of the tooling cost with interest.

4The formal condition for this to happen is smax > Amaxθα (Lt/Ht)1−α. This condition
states that, even after devoting (in the limit) all human capital to the production of the
short-term capital good and achieving maximum productivity in the ﬁrst period, the
entrepreneur would not generate enough income in that period to meet the maximum
possible realization of the liquidity shock.

ECB Working Paper 2008, February 201772.3 Productivity Shocks and Borrowing Response

To understand how borrowing reacts to productivity shocks, we characterize

the composition of investment that maximizes the present expected value of

the entrepreneur’s ﬂow proﬁts

Πt + (1 + Rt)−1Et[Πt+1]

(3)

in our two scenarios: when the credit market is complete so that credit con-

straints are not binding; and when it is incomplete so that credit constraints

are binding.

2.3.1 Complete Credit Market

When the credit market is complete, the entrepreneur can borrow as much

as she wishes in the ﬁrst period of her life. She can thus meet the liquidity

shock after it happens if she wants to. This is always the case as the net

present value of meeting the liquidity shock is positive:

(1 + Rt)−1 [Yt+1 + (1 + Rt)St] − (1 + Rt)−1St = (1 + Rt)−1Yt+1 > 0.

The fact that it is always optimal for the entrepreneur to meet the liquidity
shock implies that she always sets et = 1. With this result at hand, we can
use the budget constraints (1) and (2) to substitute for Πt and Πt+1 in (3)
so as to write the entrepreneur’s maximization problem

Atkα

t l1−α

t + (1 + Rt)−1Et

(cid:2)At+1zα

t l1−α
t

(cid:3) − qtkt − qtzt

max
kt,zt

subject to

kt − zt = θ,

where lt ≡ Lt/Ht, kt ≡ Kt/Ht and zt ≡ Zt/Ht denote the ‘normalized’ levels
of Lt, Kt and Zt while kt − zt = θ comes from the technology and resource
constraints for capital goods production requiring Kt + Zt = θHt.5

The ﬁrst order conditions of this problem with respect to kt and zt then
imply that the marginal products of short- and long-term capital goods are

5By assumption, the normalized level of labor lt ≡ Lt/Ht is exogeneously given.

ECB Working Paper 2008, February 20178equalized in present expected value

αAt (θ − zt)α−1 l1−α

t = (1 + Rt)−1Et

(cid:2)αAt+1zα−1

t

l1−α
t

(cid:3) .

Rewriting this condition as

(cid:18) zt

(cid:19)1−α

θ − zt

= (1 + Rt)−1 Et [At+1]

At

(4)

reveals that, as Rt is exogenously given, larger At leads to smaller zt whereas
larger Et [At+1] leads to larger zt. In other words, when the credit market is
complete the correlation between borrowing and contemporaneous productiv-

ity growth is negative whereas the correlation between borrowing and future

productivity growth is positive. This is a standard ‘opportunity cost eﬀect’:

if productivity increases in period t (period t + 1) relative to period t + 1

(period t), the entrepreneur has an incentive to increase the supply of the

short-term (long-term) capital good relative to the long-term (short-term)

capital good, thus decreasing (increasing) her level of borrowing.

2.3.2

Incomplete Credit Market

Consider now the case in which the credit market is incomplete. As before,

if the entrepreneur has or can borrow enough funds to meet the liquidity

shock the ﬁrst period, she will ﬁnd it optimal to do so. However, diﬀerently

from before, the entrepreneur now faces a credit constraint as in the ﬁrst

period she can borrow at most a fraction µ of her contemporaneous income,

and thus there is uncertainty about whether she will be able to meet the

liquidity shock. This implies that the maximum liquidity available to the
entrepreneur in period t equals (1 + µ)Yt and she meets the liquidity shock
if and only if St ≤ (1 + µ)Yt. Accordingly, given our distributional as-
sumption on st, the entrepreneur meets the liquidity shock with probability
Φt ≡ Φ((1 + µ) (Yt/Ht)) = (cid:2)(1 + µ)Atkα
, and faces ‘failure’ or
‘liquidation’ of her long-term investment with probability 1 − Φt (‘liquidity
risk’).

t l1−α
t

/smax

(cid:3)φ

Using the budget constraints and borrowing (1) and (2) to substitute for Πt
and Πt+1 in (3), we can state the entrepreneur’s problem with incomplete

ECB Working Paper 2008, February 20179credit market as:6

Atkα

t l1−α

t + (1 + Rt)−1Et

(cid:2)ΦtAt+1zα

t l1−α
t

(cid:3) − qtkt − qtzt

max
kt,zt

subject to

kt + zt = θ.

The ﬁrst order conditions of this problem with respect to kt and zt now
require the equalization of the marginal product of the short-term capital

good with liquidity-risk-adjusted marginal product of the long-term capital

goods in present expected value

αAt (θ − zt)α−1 l1−α

t + (1 + Rt)−1Et

(cid:20) ∂Φt
∂kt

(cid:21)

At+1zα

t l1−α
t

= (1 + Rt)−1Et

t

(cid:2)αΦtAt+1zα−1
(cid:20) ∂Φt
∂zt

At+1zα

(cid:3)

l1−α
t

(cid:21)

t l1−α
t

+(1 + Rt)−1Et

or equivalently

(cid:18) zt

(cid:19)1−α

θ − zt

= (1 − τt) (1 + Rt)−1 Et [At+1]

At

(5)

with

τt ≡ 1 − Φt +

(cid:18) ∂Φt
∂kt

−

∂Φt
∂zt

(cid:19) zt
α

.

If the credit constraint were not binding, the entrepreneur would meet
the liquidity shock with certainty, which implies Φt = 1 and ∂Φt/∂kt =
∂Φt/∂zt = 0. In this case τt would equal one and (5) would coincide with
(4): the choice between short- and long-term capital goods would only de-

pend on the opportunity costs of production. When, instead, the credit

constraint binds, whether the entrepreneur can meet the liquidity shock is

uncertain and depends on the realisation of the shock. In this case, we have
Φt < 1, ∂Φt/∂kt > 0 and ∂Φt/∂zt < 0 so that, given the deﬁnition of Φt, τt

6With et = 1 and binding credit constraint Bt = µYt, (1) and (2) become Πt + qt(Kt +

Zt) + ΦtSt = Yt (1 + µ) and Πt+1 + (1 + Rt)µYt = Φt [Yt+1 + (1 + Rt)St] respectively.

ECB Working Paper 2008, February 201710evaluates to

τt = 1 −

(cid:20) (1 + µ)At (θ − zt)α l1−α
smax

t

(cid:21)φ (cid:18)

1 − 2φ

(cid:19)

.

zt
θ − zt

(6)

This shows that the incompleteness of the credit market works as a ‘tax’ τt
on the return of investment in the long-term capital good due to the fact

that this investment has a positive probability of failure. This probability

increases with the supply of the long-term capital good as larger supply of

that good drains the income from short-term production that can be used

to meet the liquidity shock, both directly and indirectly as collateral for
(constrained) borrowing. For given zt, the ‘tax’ τt is higher when the credit
constraint is more severe (smaller µ), when the probability of a sizeable
liquidity shock is higher (larger smax or larger φ), and when the productivity
of capital goods production is lower (smaller θ).

For our purposes, however, the crucial aspect of the ‘tax’ in (6) is that it

depends on the (expected) productivity of ﬁnal good production in the two
periods, both directly through At as well as Et [At+1] and indirectly through
zt. Accordingly, the entrepreneur’s choice between short- and long-term cap-
ital good supply depends not only on the opportunity costs of production
(i.e. (1 + Rt)−1 (Et [At+1] /At)) but also on a ‘wedge’ (i.e. (1 − τt)) intro-
duced by the ‘tax’ between the marginal products of short- and long-term

capital goods. To see what this implies, we can substitute (6) in (5) to ob-
tain the proﬁt-maximising implicit relation of investment, zt, with current
and expected future productivity levels, At and Et [At+1]:

(cid:18) zt

(cid:19)1−α

θ − zt

(cid:40)(cid:20) (1 + µ)At (θ − zt)α l1−α
smax

t

=

(cid:21)φ (cid:18)

1 − 2φ

(cid:19)(cid:41)

zt
θ − zt

(1+Rt)−1 Et [At+1]

At

(7)

This relation is analyzed graphically in Figure 1. In the ﬁgure the left and
right hand sides of (7) are plotted against investment zt measured along the
horizontal axis. In particular, the left hand side (LHS) is represented by

the upward sloping curve starting from the origin while the right hand side

(RHS) is represented by the downward sloping curve meeting the horizontal
axis at zt = θ/ (2φ + 1). The optimal level of investment z∗
t corresponds to

ECB Working Paper 2008, February 201711the crossing of the two curves. Given the slopes and the intercepts of the

two curve, this level is unique.

What is the impact of higher expected productivity in period t + 1 on bor-
rowing in period t? Larger Et [At+1] does not aﬀect the left hand side,
whereas it rotates the right hand side clockwise to RHS’. It follows that
the optimal level of investment z∗
t and thus borrowing in period t increase.
As in the case of complete credit market, higher expected productivity in

the second period always entails more production of the long-term capital

good in the ﬁrst period. Hence, the correlation between borrowing and future

productivity growth is positive also when the credit market is incomplete.

What about the impact of higher productivity in period t on borrowing
in that period? In principle, the answer depends on how larger At aﬀects
two opposite eﬀects. The ﬁrst is the ‘opportunity cost eﬀect’ that is also

present with complete credit market. It works through the fall in relative
(expected) future productivity Et [At+1] /At and makes the entrepreneur
decrease the supply of the long-term capital good. The second eﬀect is

t

(cid:3)φ

/smax

the ‘liquidity risk eﬀect’ that works through the increase in the probability
of covering the liquidity shock Φt = (cid:2)(1 + µ)At (θ − zt)α l1−α
: all
the rest equal, larger At allows to meet larger shocks. Which of the two
eﬀects dominates hinges on the comparison between φ and 1 given that the
change in Φt is proportionate to Aφ
t while the change in productivity ratio
is proportionate to A−1
. Under our assumption φ > 1, the ‘liquidity risk
eﬀect’ dominates. The reason why for φ > 1 the increase in Φt associated
with larger At is strong is that the density of the liquidity shock distribution
is disproportionately concentrated close the upper bound of its support. As
a result, for φ > 1 the right hand side of (7) rotates clockwise to RHS’ as At
rises, leading to more investment on long-term capital goods and thus more

t

borrowing. Accordingly, higher productivity in the ﬁrst period entails more

production of the long-term capital good in that period. Hence, diﬀerently

from when the credit market is complete, the correlation between borrowing

and contemporaneous productivity growth is positive when the credit market
is incomplete.7

7Beyond our assumption, for φ = 1 the ‘opportunity cost eﬀect’ and the ‘liquidity risk
eﬀect’ exactly oﬀset each other; while for φ < 1 the ‘opportunity cost eﬀect’ dominates.

ECB Working Paper 2008, February 2017123 Empirical Analysis

The model presented in Section 2 provides theoretical guidance for our em-

pirical analysis, where we focus on the elasticity of bank credit with respect

to productivity at diﬀerent points in time. We use a novel ﬁrm-level data
set, which underlies the CompNet database of the ECB.8 One of the main
advantage of this source is that it provides comparable estimates of ﬁrm-level

characteristics across a set of European countries, since variable deﬁnitions

and data treatment are carefully homogenised across the participant country

teams.

3.1 The dataset

Unlike in the original CompNet database, the ﬁrm-level data we are using

are not pooled at the sector level, but they were separately managed by the

individual national teams participating to this speciﬁc exercise. As usual in

CompNet, key ﬁrm-level variables were harmonised across countries. In Ta-

ble 1 we provide a summary of the speciﬁc data source and sample extension

of the countries we use in this paper.

For each ﬁrm we have data on bank credit,

leverage, return on assets.

marginal product of capital, TFP, labor productivity, real value added. For

ﬁnancial data, loans corresponds to the entry ’liabilities to ﬁnancial institu-

tions’ in the ﬁrms’ balance sheet. Returns on assets are deﬁned as operating

proﬁt/loss over the average of total assets at t and t-1. Finally, leverage is

the ratio of total debt on total assets.

As for data on productivity, the CompNet Database computes the ﬁrm-level

TFP using the approach of Wooldridge (2009), which follows the approach

of Olley and Pakes (1996) and Levinshon and Petrin (2003) to deal with

The reason why for φ < 1 the increase in Φt associated with larger At is weak is that
the density of the liquidity shock distribution is disproportionately located close the lower
bound of its support. As a result, for φ < 1 the right hand side of (7) would rotate
counterclockwise as At rises, leading to less investment in the long-term capital good and
thus less borrowing. Accordingly, even when the credit market is incomplete, the impact
of higher productivity in the ﬁrst period on the production of the long-term capital good
in that period would still be negative if the entrepreneur were more likely to be exceptional
than standard at solving tooling problems.

8See Lopez-Garcia and di Mauro (2015) for details about the CompNet dataset.

ECB Working Paper 2008, February 201713the problem of endogeneity between TFP and inputs (see the Appendix

for details about TFP estimation). Real value added is computed using

country-sector speciﬁc deﬂators. Labor productivity is deﬁned as real value

added per employee. Finally the marginal product of capital is deﬁned as

the ratio of real value added over the capital stock accounting for the ﬁrm

level elasticity of capital in the production function.

3.2 Econometric speciﬁcations

We run a series of ﬁrm level regressions in reduced form, but that follow

the main intuitions of the model derived in Section 2. The regressions are

implemented separately by country. The main purpose of this empirical

exercise is to uncover some relevant pattern in the data about the relation

between bank credit and productivity.

Wurgler (2000) shows that according to the q-theory of investments, ﬁrms

with better growth prospects should experience faster investment growth.

This implies that in a country with a high elasticity of bank credit to produc-

tivity, capital can get allocated to ﬁrms with better growth prospects more

quickly. Similarly, in this paper we focus on the elasticity of productivity

and credit itself documenting its pattern across diﬀerent countries.

We compute the elasticity of bank loans at time t with respect to various

measures of productivity at time t + 1 and t + 2. We put bank credit on

the left-hand side, because the purpose of our research is to understand the

allocation of credit and analyse how this relates to ﬁrms short-term and

long-term productivity. There is an extensive literature that investigate the

impact that credit constraints have on ﬁrm’s productivity. However we look

at the problem from a diﬀerent angle, as we are interested in analysing how

a given allocation of credit relates to ﬁrms’ productivity at diﬀerent point in

times in order to study the allocative eﬃciency of bank credit in the diﬀerent

countries in pur sample.

Equation 8 represents out main speciﬁcation. We control for a proxy of

external ﬁnance demand, ﬁnancial health of a ﬁrm, year, sector, and ﬁrm

ECB Working Paper 2008, February 201714ﬁxed eﬀects.

Credit Growthist = β0 + β1P roductivity Growthist+k+

β2Growth with internal f undsist + β3Leverageist−1 + δt + γs + ψi + (cid:15)ist
(8)

where the dependent variable is the growth rate of credit (loans and bonds)

of ﬁrm i in sector s at time t; the explanatory variable of interest is pro-

ductivity growth at time t + k, k = 0, 1, 2; we use diﬀerent productivity
measures alternatively at various points in time t + k; δt is a year dummy;
γs is a sector dummy; ψi is a ﬁrm dummy, and (cid:15)ist is the error term.

The measures of productivity enter in the regression at time t, t+1 and t+2.

These are realised productivities, which are equal to expected productivities

under the assumption that banks and markets have rational expectations

with perfect foresight. If these assumptions are violated realised productiv-

ities are not equal to the expected ones, so we have a measurement error in

the independent variable of interest. Nevertheless, this measurement error

would generate an attenuation bias in our estimates, so our results would

provide a lower bound of the true elasticities for each country and a lower

bound of the elasticity diﬀerences across countries. It is important to stress

that we do not give to our estimates causal interpretation, as they might

subject to endogeneity issues. Nevertheless, they are still valid correlations,

which by deﬁnition capture the elasticity of bank credit respect to produc-

tivity.

Notice, that in our dataset bank lending information des not come from the

banks’ balance sheets, but rather from bank borrowing activities of the ﬁrms

which are included in our dataset. However, the sample of ﬁrms is large and

representative, so our results can be indicative of the overall banking sector.

In order to control for ﬁrm’s ﬁnancial health, we use leverage. This measure

enters at time t − 1 and not t to avoid endogeneity, given that loans and

bonds enter the numerator of leverage. We expect a negative coeﬃcient on

this variable as banks and markets will be less willing to provide capital to

ﬁrms in worse ﬁnancial conditions.

ECB Working Paper 2008, February 201715If we want to interpret the results in (8) as an elasticity of credit allocation,

we need to isolate the supply eﬀect from the demand eﬀect. We cannot ob-

serve directly the ﬁrm’s demand for credit, but we account for the external

ﬁnancial need of a ﬁrm. To do we rely on the maximum rate of internally

planning as in Guiso et al.

ﬁnanced growth following the ’percentage of sales’ approach to ﬁnancial
(2004), and Higgins (1977).9. This captures
the fact that credit would be demanded for the growth in excess to the one
that could be internally ﬁnanced. We expect the coeﬃcient β2 to be nega-
tive and signiﬁcant as ﬁrms with higher growth through internal resources

will demand less credit and hence they will be negatively correlated with
credit allocation.10 Moreover, we also have ﬁrm ﬁxed eﬀects that control for
time-invariant ﬁrm characteristics and, as Khwaja and Mian (2008) show,

these ﬁrm ﬁxed-eﬀects capture overall ﬁrm-level credit demand due to time

invariant characteristics.

In addition to the baseline speciﬁcation we run (8) for ﬁrms below and above

50 employees separately, so we can compare the diﬀerences in elasticity be-

tween large and small ﬁrms. Also, we compute the elasticity both before and

after the 2008 crisis introducing an interaction term between productivity

growth and a temporal post-2008 dummy.

4 Empirical Results

The results of our baseline regression on loans for the countries of our sample

are in Tables 2. The analysis of the empirical results oﬀers a number of

information, which are critical for policy, also because they are based on

granular ﬁrm level information, normally not available, particularly for the

9This will depended on return to assets. Speciﬁcally Financial demandist = 1 −

Maximum rate of internally ﬁnanced growth = 1 − ROA

1−ROA .

10We do not control for alternative sources of external ﬁnance. Data on issued shares
are unavailable for most countries and involves a low number of ﬁrms. Data on bonds
are used as a dependent variable in a separate regression (results available upon request)
rather than as an additional control in the speciﬁcation with loans. This should not bias
our results given the small number of ﬁrms that issue bonds in the countries of our sample.
They do not exceed the 1.5% of ﬁrms and observations in all countries, with the exception
of Germany where about 25% of ﬁrms issue bonds; this might generate an omitted variable
bias for the coeﬃcients on Germany.

ECB Working Paper 2008, February 201716cross-country comparison. The main pattern in the data is that there is

a signiﬁcant and negative correlations between credit and productivity at

time t and a positive one at time t+1 and t+2. This is fully in line with

our model. Italy is a notable exception to this pattern as it has a positive

correlation between credit and both TFP and labor productivity at time t,

and a positive, but small one, in subsequent periods.

A key point is to understand whether the negative correlation between TFP

and credit at time t is just a mechanical consequence that stems from the

TFP estimation or if it has an economic interpretation. Our TFP measure

control for the simultaneous determination of TFP and production inputs,

which can be favoured by a rise in credit, so this aspect should not be of
concern.11 Finally, this negative correlation does not involve only the TFP
but also alternative measures of productivity such as labor productivity and

the marginal product of capital.

One important empirical result is that the size of the coeﬃcients varies

considerably across countries (Figure 1). This suggests in turn that the

eﬃciency of capital allocation is highly heterogenous across countries. In

Italy bank loans responds very little to changes in productivity, while in the

other countries this is not the case, especially in France. In all countries

the coeﬃcients for t + 2 tend to be smaller than the ones for t + 1, but

this is because the growth rate of our regressors at time t + 2 is taken with

respect to t + 1. So, for example, if a new project ﬁnanced by loans at time t

increases productivity at time t + 1, we will not see a signiﬁcant correlation

between loans at time t and the productivity growth at t + 2, as it is the

case for Germany.

Turning to an interpretation of our empirical results with the model predic-

tions, it would seem that in Italy, credit markets would be ”incomplete”. In

particular, the positive correlation between bank credit and productivity at

11If a ﬁrm has a positive productivity shock, then the ﬁrm is likely to invest, possibly
through accessing credit, and will increase capital and labor. This would bias the estimate
of TFP coming from a Cobb-Douglas production function, but i) the TFP estimates we
rely on control for this simultaneity issue and ii) capital should respond to the positive
productivity shock by an amount so large to push a downward bias of the estimate of TFP
growth into negative territory, which is implausible given the magnitude of the increase
that would be required and the fact that capital needs time to be put in place.

ECB Working Paper 2008, February 201717time t would suggest that banks would be aﬀected by some sort of ’short-

termism’, whereby funds are preferably allocated to projects to immediate

short term returns, rather than initiatives, possibly more risky, but that

would imply - if chosen correctly - higher future returns and thus higher

ﬁrm productivity in the following period.

In other words, the pattern of

correlations we observe in the data suggests that access to credit for long-

term investment is more of an issue in Italy than in the other countries in

our sample. This result implies that during the last ﬁfteen years bank credit

in Italy may have constrained the long-term investments of ﬁrms, as banks

focused mostly on short-term investments associated with low ﬁrm produc-

tivity going forward. This would result in a misallocation of resources and

is consistent with the ﬁndings on misallocation of Calligaris et al. (2016).

Our results relates also to the extent in which ﬁrm size matters as regards

the credit-productivity elasticity. Tables 3 show that it does. In most cases,
the elasticity of loans to productivity is inversely correlated with ﬁrm size.12
Several interpretation are possible. First, there might be a selection issue

due to relational banking; given that large ﬁrms are cross-selling clients

for whom loans represent only one of the ﬁnancial services they may ask,

banks can choose to ﬁnance also less promising projects for such ﬁrms as

the overarching business relation is still proﬁtable. Even if this can still

be optimal from a bank perspective, it has macroeconomic implication in

terms of capital allocation towards its most productive uses. Second, it

could well be that larger ﬁrms are less dependent from bank loans (and

related conditions applied by the banks), given their larger access to capital

markets, typically unavailable for smaller ﬁrms. Third, it could also be that

the average commitment and complexity of loans to larger ﬁrms is bigger;

hence, it might be more complicated to reallocate credit across large ﬁrms

than small ﬁrms.

Large ﬁrms represent a big share of employment and value added in an

economy. This implies that the allocation of credit towards the more pro-

ductive ﬁrms among large ﬁrms is particularly important for the long-term

prosperity and productivity growth. Therefore, policy makers should pay

particular attention to the degree of credit reallocation among large ﬁrms, as

12The threshold between small and large ﬁrms is 50 employees.

ECB Working Paper 2008, February 201718our empirical ﬁndings suggest that the elasticities are lower than what they

could be in comparison with small ﬁrms. Possible policy recommendations

include exploring the possibility to reduce the concentration limits of banks

for loans on speciﬁc ﬁrms. This might provide incentives for ﬁrms to increase

the number of lenders for large projects, hence reducing the commitments

that a single bank face and easing a relocation of credit towards other ﬁrms.

Moreover, the regulator could consider demanding a higher weight of spe-

ciﬁc productivity measures in the risk assessment models that banks use for

lending. This would provide an incentive for banks to lend to more pro-

ductive ﬁrms reducing the level of credit misallocation. Such requirement

could also be conﬁned to large ﬁrms only, for which the information needed

to compute productivity measures such TFP could be retrieved more easily.

5 Conclusions

In this paper we analyze the relationship between bank credit and produc-

tivity at the ﬁrm level for a group of Eurozone countries. To study this

issue we propose a model of overlapping generations of entrepreneurs, which

invest in capital building in the context of two opposite ﬁnancial markets

set-ups, one complete and the other incomplete. The model suggests that

the sign of the correlation between bank loans and productivity varies in

accordance with the relevant market set-up which prevails. In the empirical

analysis we put this hypothesis at a test, using a novel ﬁrm level data set

for a number of EU countries. To do so, we estimate the elasticity of bank

credit with respect to various measures of productivity at diﬀerent points

in time, as we look at the contemporaneous elasticity between credit and

productivity as well as between credit and realised future productivity.

The general pattern of the data is such that there is a strong negative elas-

ticity between contemporaneous bank credit and productivity and a posi-

tive one between bank credit and realised productivity. Italy is a notably

exception to this pattern as it shows a positive elasticity also between con-

temporaneous credit and productivity. Reading these results with the eye of

the theoretical model would suggest that overall - for core European coun-

tries considered - ﬁnancial markets appear to be approaching the ”complete”

ECB Working Paper 2008, February 201719state as deﬁned by the model, although with diﬀerent degrees of ”complete-

ness” across countries. On the other hand, for Italy, the empirical results

would suggest that incomplete markets are more likely to be prevalent. This

implies that during the last ﬁfteen years bank credit in Italy may have con-

strained the long-term investments of ﬁrms, with bank focusing merely on

short-term investments, unlikely to have substantial future returns and as-

sociated with low ﬁrm productivity going forward.

Second, our results show that in most countries credit is more elastic to

productivity for small rather than large ﬁrms. This means that for the same

amount of credit provided, smaller ﬁrms would have a larger productivity

outcome than the one experienced by larger ones. This is a relevant result

because large ﬁrms represent a big share of employment and value added in

an economy. Therefore, making sure that capital gets allocated to its most

productive uses is particularly important across large ﬁrms.

In order to improve the overall allocation of credit and its impact on long-

term growth, policy makers could explore the possibility to reconsider the

role of productivity in the models of risk assessment that banks use to de-

termine lending. Putting a higher weight on productivity and asking for

productivity measures such as TFP would provide an incentive for banks to

lend to more productive ﬁrms. This should be feasible especially for large

ﬁrms that can provide all the information needed at a lower cost than small

ﬁrms. This would improve the allocation of bank credit from a macroeco-

nomic perspective and ensure a higher productivity growth for the economy.

References

[1] Amiti, M., and D.E. Weinstein, 2011. ”Exports and Financial Shocks”,

Quarterly Journal of Economics, 126, 1841-1877.

[2] Aghion, P., Angeletos,, G.-M., Banerjee, A., Manova, K. 2010. ”Volatil-

ity and growth: Credit constraints and the composition of investment,”

Journal of Monetary Economics, 57, 246–265.

ECB Working Paper 2008, February 201720[3] Beck, T., Demiguruc-Kint, A., Laeven, L., Levine, R., 2008. ”Finance,

ﬁrm size, and growth,” Journal of Money, Credit, and Banking, 40,

1379-1405.

[4] Calligaris, S, Del Gatto, M., Hassan, F., Ottaviano, G.I.P., and F.

Schivardi. 2016. ”Italys Productivity Conundrum: a Study on Resource

Misallocation in Italy”, European Commission Discussion Paper, n.

030, May 2016.

[5] Caselli, F. 2005. ”Accounting for Cross-Country Income Diﬀerences”,

in Handbook of Economic Growth, ed. by P. Aghion, and S. Durlauf, 1,

679-741.

[6] Ciccone, A., Papaioannou E., 2006. ”Adjustment to target capital, ﬁ-

nance, and growth,” CEPR Discussion Paper n. 5969.

[7] Gopinath, G., Kalemli-Ozcan S., L. Karabarbounis L., and C. Villegas-

Sanchez (2015): ?Capital Allocation and Productivity in South Eu-

rope,? NBER Working Paper No. 21453.

[8] Guiso, L., Sapienza P., Zingales, L., 2004. ”Does local ﬁnancial devel-

opment matter?,” Quarterly Journal of Economics, 119, 929–969.

[9] Hartmann, P., Heider, F., Papaioannou, E., Lo Duca, M., 2007. ”The

Role of Financial Markets and Innovation in Productivity and Growth

in Europe,” ECB Occasional Paper n. 72.

[10] Hsieh, Chang-Tai, and Peter J. Klenow, 2009. Misallocation and man-

ufacturing TFP in China and India. Quarterly Journal of Economics,

74, 1403-1448.

[11] Higgins, R.C., 1977. ”How much ﬁnance can a ﬁrm aﬀord?,” Financial

Management, 6, 3–16.

[12] Jensen, M., 1986. ”Agency costs of free cash ﬂow, corporate ﬁnance and

takeovers,” American Economic Review, 76, 323–329.

[13] Jimenez, G., Ongena, S., Peydro J.L., and J. Saurina, 2014. ”Hazardous

Times for Monetary Policy: What do 23 Million Loans Say About the

Impact of Monetary Policy on Credit Risk-Taking?”, Econometrica, 82

(2), 463-505.

ECB Working Paper 2008, February 201721[14] Khwaja, A. I., and A. Mian,2008. ”Tracing the impact of bank liquid-

ity shocks: Evidence from an emerging market”, American Economic

Review, 98, 1413?1442.

[15] King, R.G., Levine, R., 1993. ”Finance and growth: Schumpeter might

be right,” Quarterly Journal of Economics, 108, 717–738.

[16] Levine, R., 1997. ”Financial development and economic growth: views

and agenda,” Journal of Economic Literature, 35, 688–726.

[17] Levine, R., 2005. ”Finance and growth: theory and evidence,” in Hand-

book of Economic Growth, P. Aghion and S.N. Durlauf (eds.), Elsevier

B.V.

[18] Levinsohn, J. and A. Petrin, 2003. ”Estimating Production Functions

Using Inputs to Control for Unobservables,” Review of Economic Stud-

ies, 70, 317-342.

[19] Olley, G.S. and A. Pakes, 1996. ?The dynamics of productivity in

the telecommunications equipment industry,? Econometrica, 64, 1263-

1297.

[20] Rajan, R.G., Zingales, L., 1998. ”Financial dependance and growth,”

American Economic Review, 88, 559–586.

[21] Wooldridge, J.M., 2009. ”On estimating ﬁrm-level production functions

using proxy variables to control for unobservables,” Economics Letters,

104,112-114.

[22] Wurgler, J., 2000. ”Financial markets and the allocation of capital,”

Journal of Financial Economics, 58, 187–214.

ECB Working Paper 2008, February 201722Appendix: estimation of ﬁrm-level TFP

The starting point of the estimation of ﬁrm-level TFP is a standard Cobb-

Douglas production function:

Yit = AitKα

itL1−α
it

where Yit is real value added of ﬁrm i at time t, K is the real book value of
net capital, L is total employment, and A is the object of interest TFP.

As it is well renown, estimating TFP using a standard Cobb-Douglas set-

ting is subject to endogeneity problems between the input levels and the

unobserved ﬁrm-speciﬁc productivity. Therefore, following the approach of

Olley and Pakes (1996) and Levinshon and Petrin (2003) the unobserved

ﬁrm-speciﬁc productivity is controlled for by a proxy of the unobserved pro-

ductivity derived from a structural model. This proxy is a function of capital

and material inputs that is approximated by a third-order polynomial, as in

Petrin et al. (2004). Therefore, the following regression is then estimated on

a 2-digit industry level using GMM, with the moments restrictions speciﬁed

as in Woolridge (2009):

yit = β0 + β1kit + β2ki(t−1) + β3mi(t−1) + β4k2
β7m3
i(t−1) + β8ki(t−1)mi(t−1) + β9ki(t−1)m2
ωlit

i(t−1) + β5m2

i(t−1) + β6k3
i(t−1) +
i(t−1)mi(t−1) + γY eart +

i(t−1) + β10k2

All variables are in logs, yit is the real value added of ﬁrm i at time t, k is the
real book value of net capital, m is material inputs, and l is total employ-

ment. While capital takes time to build, labor and TFP are simultaneously

determined, so labor is instrumented by its ﬁrst lag.

TFP is then retrieved as T F Pit = rvait − ( ˆβ0 + ˆβ1kit + ˆγY eart + ˆωlit).
Two key assumptions of this methodology are that i) productivity follows

a ﬁrst-order Markov process and ii) capital is assumed to be a function of

past investments and not current ones. These imply that productivity shocks

at time t do not depend from capital at time t, but on past productivity

realizations and that an increase in bank credit at time t, even if used for

ECB Working Paper 2008, February 201723investment, does not aﬀect capital at time t as capital needs time to build

up.

ECB Working Paper 2008, February 201724Tables

Table 1: Sample summary

Country

France

Germany

Italy

Data Source

Banque de France Bundesbank

ISTAT

Years

Firms

Observations

1995-2012

1997-2012

2001-2012

93,569

589,609

42,726

393,489

184,807

1,721,881

ECB Working Paper 2008, February 201725s
n
a
o
l

n
o

s
t
l
u
s
e
r

e
n
i
l
e
s
a
B

:
2

e
l
b
a
T

*
*
*
%
5
0
0
.
0
-

*
*
*
%
1
.
0

*
*
*
%
3
.
0
-

*
*
*
%
8
.
3

*
*
*
%
1
.
5

*
*
*
%
4
2
-

*
*
*
%
2

*
*
*
%
6
.
7

*
*
*
%
1
5
-

%
0

%
0

*
*
*
%
4
.
3

*
*
*
%
4
.
4

%
2
.
1

*
*
*
%
2
1

%
1

%
3
.
1

*
*
*
%
7
.
5

*
*
*
%
7
-

*
*
*
%
5
.
3

*
*
*
%
3
.
0
1

*
*
*
%
7
1
-

*
*
*
%
8
.
8

%
1
.
0
-

*
*
*
%
4
.
6

*
*
*
%
5
.
2
2

*
*
*
%
7
1

2
+
t

%
1
.
0

y
l
a
t
I

1
+
t

t

*
*
*
%
4
.
2

*
*
*
%
8
.
0

2
+
t

%
1

1
+
t

t

2
+
t

1
+
t

t

*
*
*
%
1
.
6

*
*
*
%
8
-

*
*
*
%
4
.
4

*
*
*
%
4
.
4
1

*
*
*
%
7
2
-

y
n
a
m
r
e
G

e
c
n
a
r
F

:
o
t

s
n
a
o
l

k
n
a
b

f
o

y
t
i
c
i
t
s
a
l
E

K
P
R
M

d
o
r
P
L

P
F
T

A
V
R

e
r
a

y
e
h
t

d
n
a

y
l
t
n
e
d
n
e
p
e
d
n
i

n
o
i
t
a
c
ﬁ
i
c
e
p
s

h
c
a
e

r
e
t
n
e

s
r
o
s
s
e
r
g
e
r

e
h
T

.
y
l
e
t
a
r
a
p
e
s

d
e
t
u
p
m
o
c

e
r
a

2
+
t

d
n
a

1
+
t

e
m

i
t

t
a

s
e
i
t
i
c
i
t
s
a
l
e

e
h
T

.
l
e
v
e
l

%
0
1

d
n
a
%
5

,

%
1

e
h
t

t
a

t
n
a
c
ﬁ
i
n
g
i
S

*

,
*
*

,
*
*
*

.
s
e
i
m
m
u
d

r
o
t
c
e
s

d
n
a

e
m

i
t

e
d
u
l
c
n
i

s
n
o
i
t
a
c
ﬁ
i
c
e
p
s

l
l

A

.
d
e
d
d
a

e
u
l
a
v

l
a
e
r

d
n
a

,
y
t
i
v
i
t
c
u
d
o
r
p

r
o
b
a
l

,
y
t
i
v
i
t
c
u
d
o
r
p

r
o
t
c
a
f

l
a
t
o
t

,
l
a
t
i
p
a
c

f
o

t
c
u
d
o
r
p

l
a
n
i
g
r
a
m
e
h
t

ECB Working Paper 2008, February 2017262
+
t

%
1
.
0

%
1
-

1
+
t

y
l
a
t
I

t

*
*
*
%
5
.
2

*
*
*
%
1

%
6
.
0

*
*
*
%
6
.
1
-

2
+
t

%
1
.
0

%
1
.
1

1
+
t

t

2
+
t

1
+
t

t

*
*
*
%
3
.
7

*
*
*
%
9
-

*
*
*
%
7
.
3

*
*
*
%
8
1

*
*
*
%
9
2
-

*
*
*
%
1
.
5

*
*
*
%
8
-

*
*
*
%
6
.
5

*
*
*
%
9

*
*
*
%
2
2
-

*
*
%
2
.
0
-

%
3
0
.
0

-

%
1
.
0

%
4
.
0

*
*
*
%
0
-

*
*
*
%
1
.
0

*
*
*
%
3
.
0
-

*
*
*
%
9
.
3

*
*
*
%
6
.
4

*
*
*
%
4
2
-

*
*
*
%
7
.
1

*
*
*
%
0
1

*
*
*
%
9
5
-

*
*
*
%
9
.
0
-

*
*
*
%
8
.
3

*
*
*
%
2
.
5

*
*
*
%
4
2
-

*
*
%
3
.
3

*
*
*
%
5

*
*
*
%
6
3
-

*
%
2
.
1
-

*
*
%
6
.
4

%
1
.
0

*
*
*
%
6
.
4

%
7
.
0

%
2
.
1

*
*
*
%
7

*
*
*
%
5

*
*
*
%
5
-

*
*
%
2
3
.
2

*
*
*
%
4
1

*
*
*
%
6
1
-

*
*
*
%
7
-

*
*
*
%
2
.
5

*
*
*
%
5

*
*
*
%
8
1
-

%
0

%
1
.
0
-

%
3
.
1

%
3
.
0

*
*
*
%
3
1

*
*
%
7
.
2

*
*
*
%
3
.
0
1

%
3
.
0
-

*
*
*
%
7
.
5

*
*
*
%
6
2

*
*
*
%
5
1

*
*
*
%
5

%
1
.
0

*
*
*
%
8

%
0

*
*
*
%
4
.
7

*
*
*
%
8
1

*
*
*
%
2
2

l
l
a
m
S

e
g
r
a
L

l
l
a
m
S

e
g
r
a
L

l
l
a
m
S

e
g
r
a
L

l
l
a
m
S

e
g
r
a
L

K
P
R
M

P
F
T

d
o
r
p
L

A
V
R

e
z
i
s

m
r
ﬁ

y
b

s
t
l
u
s
e
r

e
n
i
l
e
s
a
B

:
3

e
l
b
a
T

y
n
a
m
r
e
G

e
c
n
a
r
F

:
o
t

s
n
a
o
l

k
n
a
b

f
o

y
t
i
c
i
t
s
a
l
E

e
r
a

y
e
h
t

d
n
a

y
l
t
n
e
d
n
e
p
e
d
n
i

n
o
i
t
a
c
ﬁ
i
c
e
p
s

h
c
a
e

r
e
t
n
e

s
r
o
s
s
e
r
g
e
r

e
h
T

.
y
l
e
t
a
r
a
p
e
s

d
e
t
u
p
m
o
c

e
r
a

2
+
t

d
n
a

1
+
t

e
m

i
t

t
a

s
e
i
t
i
c
i
t
s
a
l
e

e
h
T

.
l
e
v
e
l

%
0
1

d
n
a
%
5

,

%
1

e
h
t

t
a

t
n
a
c
ﬁ
i
n
g
i
S

*

,
*
*

,
*
*
*

.
s
e
i
m
m
u
d

r
o
t
c
e
s

d
n
a

e
m

i
t

e
d
u
l
c
n
i

s
n
o
i
t
a
c
ﬁ
i
c
e
p
s

l
l

A

.
d
e
d
d
a

e
u
l
a
v

l
a
e
r

d
n
a

,
y
t
i
v
i
t
c
u
d
o
r
p

r
o
b
a
l

,
y
t
i
v
i
t
c
u
d
o
r
p

r
o
t
c
a
f

l
a
t
o
t

,
l
a
t
i
p
a
c

f
o

t
c
u
d
o
r
p

l
a
n
i
g
r
a
m
e
h
t

ECB Working Paper 2008, February 201727Figures

Figure 1: Productivity shock and borrowing under incomplete markets

ECB Working Paper 2008, February 201728Acknowledgements 

We are deeply indebted for access to the data to: Joao Amador, Antoine Berthou, Elena Biewen, Jan Hagemejer, Carmen Martinez, 
Filippo Oropallo, Charlotte Sandoz-dit-Bragard, Nurmi Satu, and Ana Cristina Soares. 

Fadi Hassan 
Trinity College Dublin and CEP. 

FIippo di Mauro 
National University of Singapore; email: bizfdm@nus.edu.sg 

Gianmarco I.P. Ottaviano 
London School of Economics, CEP, CEPR and University of Bologna. 

© European Central Bank, 2017 

Postal address 
Telephone 
Website 

60640 Frankfurt am Main, Germany 
+49 69 1344 0 
www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

ISSN  
ISBN  

1725-2806 (pdf) 
978-92-899-2730-7 (pdf) 

DOI 
EU catalogue No 

10.2866/584220 (pdf) 
QB-AR-17-020-EN-N (pdf)