Available via license: CC BY 4.0
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=rbec20
Baltic Journal of Economics
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rbec20
The impact of credit shocks on the European
labour market
Katalin Bodnár , Ludmila Fadejeva , Marco Hoeberichts , Mario Izquierdo
Peinado , Christophe Jadeau & Eliana Viviano
To cite this article: Katalin Bodnár , Ludmila Fadejeva , Marco Hoeberichts , Mario Izquierdo
Peinado , Christophe Jadeau & Eliana Viviano (2021) The impact of credit shocks on the European
labour market, Baltic Journal of Economics, 21:1, 1-25, DOI: 10.1080/1406099X.2020.1871213
To link to this article: https://doi.org/10.1080/1406099X.2020.1871213
© 2021 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 17 Jan 2021.
Submit your article to this journal
View related articles
View Crossmark data
The impact of credit shocks on the European labour market*
Katalin Bodnár
a
, Ludmila Fadejeva
b
, Marco Hoeberichts
c
, Mario Izquierdo
Peinado
d
, Christophe Jadeau
e
and Eliana Viviano
f
a
European Central Bank, Frankfurt, Germany;
b
Monetary Policy Department, Latvijas Banka, Riga, Latvia;
c
Economics and Research Division, De Nederlandsche Bank, Amsterdam, Netherlands;
d
Banco de España,
Madrid, Spain;
e
Engineering and Statistical Methodology Department, Banque de France, Paris, France;
f
Economics and Research Department, Bank of Italy, Rome, Italy
ABSTRACT
The sovereign debt crisis led to financial difficulties for European firms
andadeclineintheuseoflabourinput.Weusequalitativefirm-level
data for 24 European countries, collected within the third wave of the
Wage Dynamics Network (WDN3) of the ESCB, to propose a cross-
country analysis of the relationship between a credit shock and
labour markets. We firstderiveasetofindicesmeasuringdifficulties
in accessing the credit market for the period 2010–2013. Second, we
provide a description of the relationship between credit difficulties
and changes in labour input, both along the extensive and the
intensive margins as well as on wages. We find strong and
significant correlation between credit difficulties and adjustments
along both the extensive and the intensive margin. In the presence
of credit market difficulties, firms also cut wages by reducing the
variable part of wages. This evidence suggests that credit shocks
can affect not only the real economy, but also nominal variables.
ARTICLE HISTORY
Received 15 June 2020
Accepted 18 December 2020
KEYWORDS
Credit difficulties; labour
input adjustment; intensive
margin; wage adjustment;
survey data
JEL
D53; E24; E44; G31; G32
1. Introduction
In many European countries, the early 2010s have been characterized by significant difficul-
ties in accessing credit by firms, as well as households and governments. The global financial
crisis, having originated in 2007 in the US subprime market, and the subsequent sovereign
debt crisis, which hit Europe in the summer of 2011, forced European banks to considerably
tighten their credit conditions for firms in many economies and for several years.
The global financial crisis and sovereign debt crisis renewed the interest for the effect of
credit shocks on the real economy. Before the global financial crisis, the relationship
between credit constraints and employment was investigated in the literature, analysing
the link between financial development and growth (e.g. Beck & Demirguc-Kunt, 2006;
Klapper et al., 2006). Following the global financial crisis, many papers focussed on the
effect of credit shocks on the real economy (Acharya et al., 2018; Berg, 2018; Bottero
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed underthe terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/
by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
CONTACT Ludmila Fadejeva ludmila.fadejeva@bank.lv Monetary Policy Department, Latvijas Banka, Riga, Latvia;
2a Kr.Valdemara street, Riga LV1050, Latvia
*The views expressed in this paper are those of th e authors and do not involve the responsibility of their institutions or
the Eurosystem.
BALTIC JOURNAL OF ECONOMICS
2021, VOL. 21, NO. 1, 1–25
https://doi.org/10.1080/1406099X.2020.1871213
et al., 2020; Cingano et al., 2016; Degryse et al., 2016), and the labour market in particular
(Bentolila et al., 2018, June; Berton et al., 2018; Buera et al., 2015; Chodorow-Reich, 2014;
Duygan-Bump et al., 2015; Hochfellner et al., 2016; Pagano & Pica, 2012; Popov &
Rocholl, 2018). The existing literature builds primarily on linked firm-bank data and exam-
ines the impact of exogenous credit supply shocks, making use of the sticky lender–bor-
rower relationship. Different types of financial shocks have been examined: Popov and
Rocholl (2018) focus on the effect of the funding shock of German savings banks during
the US mortgage crisis, Chodorow-Reich (2014) take advantage of the different exposures
of the lenders on the syndicated market to mortgage-backed securities in the US. Acharya
et al. (2018) look at bank-firm relationships in Europe to analyse several outcomes includ-
ing employment. Bentolila et al. (2018, June) use the differences in Spanish banks’health at
the start of the Great Recession. Other papers derive local-level measures of credit supply
(e.g. Greenstone et al., 2020). These papers typically focus on a single country, and do not
analyse the heterogeneity in firms’adjustments across countries.
This paper looks at the link between credit shocks and adjustment in labour from a
European perspective, which is fundamental for the European policy makers. To do so,
we use a unique, fully harmonized survey conducted in 25 European countries by the
Wage Dynamics Network (WDN), a research network of the European System of Central
Banks.
1
The survey, which was the third one conducted by the network and thus is
labelled as WDN3 in this paper, focusses on the period between 2010 and 2013 and
asks firms to report both their difficulties in accessing credit and the ways of adjusting
labour costs, be it through employment or through wages. We construct an index of
credit difficulties which is cleaned from the impact of demand and other shocks. This
index is fully comparable across firms in different countries, and we use it to analyse
the intensity of credit restrictions in different EU countries. Then we relate our index of
credit difficulties to firms’labour cost adjustments. First, we find that credit difficulties
were extremely heterogeneous both within and across countries. According to our esti-
mates, in countries with low average values of the credit difficulty index, the within-
country variability was also quite low. On the contrary, in the most severely hit countries
(mainly Southern European and some Eastern European countries) the within-country
variability was remarkably high. If we compare countries, we find that the interquartile
range (the difference between the 25th and the 75th percentiles) of the distribution of
our index in Austria, the country registering the lowest level of credit difficulties, was
three times lower than that observed in the most severely hit countries. Second, we
find that European firms hit by a credit shock report more frequently a reduction in
both employment and wages than firms without financing difficulties.
We estimate that a 1 point increase in our index of credit difficulties is associated with an
increase in the probability to adjust employment by close to 2 pp (over a mean probability
of 16%). As the survey collects detailed information on the strategies to adjust labour costs,
we can distinguish also between adjustment along the extensive (i.e. change in head-
count) and the intensive margin (i.e. reduction in hours per employee). Consistently
with Berton et al. (2018), who focus on one Italian region, we find that credit supply
shocks affected both the extensive and the intensive margin. More importantly, we find
that the reduction of the intensity of the use of labour as a response to a credit shock
was not confined to Italy, as found by Berton et al. (2018), where the subsidized reduction
2K. BODNÁR ET AL.
of hours was widely used, but also happened in other European countries, mainly through
non-subsidized reduction of hours (i.e. part-time work arrangements).
Workers are affected heterogeneously by firms’credit difficulties. We find that the prob-
ability of an adjustment in case of an adverse credit shock was higher for temporary
workers. This finding is consistent with Bentolila et al. (2018, June), who focus on Spain,
and Caggese and Cuñat (2008), who examine the case of Italy. Firms also decreased
their hiring, with a particularly significant effect on the employment opportunities of
younger job-seekers. Labour market adjustment as a response to credit constraints is
thus a potential explanation behind the considerable rise of youth unemployment in
most European countries (see Hoynes et al., 2012 for the US and Verick, 2009 for European
countries for a description of how youth unemployment developed following the financial
crisis).
In addition to the adjustment in employment, we find that firms also adjust wages
when they face credit difficulties. The relationship between credit shocks and wage
dynamics has been less investigated so far by the literature, probably because of data
constraints and because it has not been clear whether European firms have margins to
adjust wages. An exception are Hochfellner et al. (2016) and Moser et al. (2020), who
use employer-employee matched data for a sample of German firms and, by the use of
different strategies, examine the impact of credit shocks on earnings, and Adamopoulou
et al. (2020) who focus on Italy. We find that in our sample an increase of 1 point of our
credit difficulty index is associated with an increase in the probability to cut wages, by
around 1pp. over an average of 14%. The impact of credit difficulties is stronger for the
flexible part of workers’compensation, whereas the impact on base wage is small and
not precisely estimated. This is probably related to the institutional rigidities which
prevent cuts of base wages.
We find that the effect of credit market conditions on the extensive margin of adjust-
ment was rather similar along several firm characteristics: the interaction of the extensive
margin of adjustment with firm’s size, sector and autonomy is insignificant. For firms
which are neither parent nor subsidiary/affiliate we find a higher probability to adjust
the extensive margin when the credit difficulty index increases. We also find that the inter-
action term between the credit difficulty index and the extensive margin is higher in case
of domestic firms than foreign owned ones. For a given credit supply shock, the reaction
of firms is show some degree of heterogeneity across European countries (controlling for
differences in the economic structure, i.e. firm size and sector). The most significant differ-
ences can be detected between the Eastern European / Baltic group of countries. This
suggests that the different impact of credit shocks on employment and wages can
occur not only due to heterogeneity in the intensity of the shocks across countries but
also due to country specific factors such as labour policy protection or wage bargaining.
Our estimation strategy is also supported by some additional exercises based on
matching banks in the credit registers of France and Italy with firms in the WDN3
survey. We are aware that estimates using WDN3 are based on qualitative self-reported
information, which do not allow for the identification of the effect of an exogenous
credit supply shock to firms’labour costs. Nevertheless our findings are robust as
confirmed by the use of quantitative information from the credit register data. Based
on these data, we construct a credit supply shock index and we show that it correlates
BALTIC JOURNAL OF ECONOMICS 3
with our survey-based measure of credit difficulties. Within this rather different setting our
main results are fully confirmed.
This paper is organized as follows. Section 2 describes the main features of the WDN3
data. Section 3 explains the methodology for calculating the index of credit difficulties. In
Section 4, we show how the index correlates with measures about employment and wage
adjustments in our sample of firms. In Section 5, we focus on France and Italy and on a
sample of WDN3 firms matched with credit register data. Last, Section 6 briefly concludes.
2. The WDN3 survey
We use firm-level survey data collected by the Wage Dynamics Network (WDN). WDN is a
research network of the European System of Central Banks, dedicated to the study of the
features and sources of wage and labour cost dynamics and their implications for mon-
etary policy in the euro area. The first and the second surveys on firms’price and wage
setting practices have been carried out in 2007 and 2009. The third survey, the results
of which are used in this paper, was conducted in 2014 by national central banks in 25
countries of the European Union. It covers the 2010–2013 period, and was answered
by over 25,000 firms (see Izquierdo et al., 2017 for details).
Following the global financial crisis, several European countries have been confronted
with a severe sovereign debts crisis. The latter, together with more stringent regulation
about capital requirements and the related tightening of credit standards, transmitted
into the second phase of the double-dip recession from the last quarter of 2011 until
the first quarter of 2013 in the European Union as a whole. Firms were hit by adverse
demand and credit shocks, and both types of shocks affected their strategies to adjust
their labour input during 2010–2013. Therefore, the third wave of the WDN survey
(WDN3) was designed specifically to differentiate by types of shock (to product
demand, demand volatility, credit availability, customers’ability to pay and supply avail-
ability) and their intensity, as well as to explore firms’adjustment strategies during this
period. Special attention was given to firms’adjustments of labour input, wage dynamics
and wage settings practices. For a more detailed description of the WDN3 survey, see
Appendix 1.
This paper uses four sets of questions from the survey (see Table A2 in the Appendix for
the exact list of the questions).
First, we use the questions on credit availability and credit conditions. Six questions
aim at capturing the taxonomy in the severity of credit constraints. They consider both
the worsening in the quantity or access to credit and the costs and conditions of credit
supplied by the banks. Firms were also asked to qualify the intensity of the difficulties.
Both the questions on access to credit and credit conditions were asked in relation to
three types of requested credit (financing working capital, financing new investments,
refinance debt).
Second, a group of questions was asked on the changes in economic conditions faced
by the firms during 2010–2013. Firms could choose between five symmetrical responses
describing the change in level of demand, volatility of demand, customers’ability to pay
and availability of supplies (the potential answers were: strong decrease, moderate
decrease, unchanged, moderate increase, strong increase). We use these questions to
4K. BODNÁR ET AL.
control for the effects of other shocks, deriving an uncorrelated measure of credit
difficulties.
Third, several questions were asked on the channels of labour market adjustments
used by the firms during 2010–2013. Firms were asked if they needed to significantly
reduce their labour input or to alter its composition. Firms that needed to adjust their
labour input were asked about the exact way of doing so (e.g. layoffs, reduction of
hours, freeze of new hires, etc.). We use these questions to make a distinction between
the extensive and the intensive margins of labour adjustment. Adjustment along the
extensive margin is defined as individual or collective layoffs, while the intensive
margin is defined as a reduction of working hours per worker (be it unsubsidized or
carried out in the framework of subsidized schemes).
Finally, some questions allow us to measure the propensity of firms to adjust base and
variable wages. (For a description of the answers on labour adjustment please see Table
A3 in the Appendix.) We combine these four sets of data and control for firm-level charac-
teristics to examine the connection between credit shocks and labour adjustment.
3. Measuring credit difficulties using WDN3
Using the questions about credit difficulties, we construct firm-specific indices of credit
constraints, comparable for 24 EU countries included in the WDN3 survey. Data for
Ireland are excluded as answers about the availability of credit are not collected for
this country. We focus on firms in manufacturing, trade and business services sectors
(we call the latter two sectors together private services). Our final sample consists of
around 19,000 firms.
2
See Table A1 for a description of our sample.
A look at the raw data confirms the presence of strong cross-country heterogeneity.
Table 1 reports the share of firms stating that the lack of credit for a given purpose, or
the cost of credit was a relevant or very relevant problem. Over 40% of firms in Greece,
Bulgaria, Poland and Slovenia report that credit difficulties were relevant or very relevant
for their activity, but the values are also high for Italy, Spain, Portugal and Cyprus. While in
Greece, Slovenia, Italy, Spain, Portugal, and Cyprus the high values are likely to reflect the
impact of the sovereign debt crisis on financial intermediation, in Poland the reason may
be the high share of self-financing (Strzelecki & Wyszyński, 2016). In Malta and Austria, on
the other hand, only a minor proportion of firms faced difficulties in getting credit. Within
firms, the responses about the difficulties to obtain credit for different purposes are highly
correlated. This explains why the average share of firms reporting problems to obtain
credit is similar for different credit types within one country.
We derive a unique comparable measure of credit difficulties across European
countries. We take advantage of the high correlation between the six credit availability
measures. We combine both conditions and quantity aspects of credit availability via prin-
cipal component analysis (PCA).
3
Before applying PCA, we remove the part of the corre-
lation which could be due to other shocks hitting firms and affecting also their ability to
access credit. To do so, we first regress our basic measures of credit restrictions on vari-
ables measuring demand and demand volatility shocks, customers’ability to pay, the
availability of supplies and firms’characteristics, such as sector and firm size. We use
the residuals of these six regressions to carry out the PCA. The descriptive statistics of
the obtained components are given in Table 2. The first principal component explains
BALTIC JOURNAL OF ECONOMICS 5
70% of the total variance in credit difficulty measures and has positive loadings of roughly
similar size for all the six questions, therefore representing the overall credit difficulty for a
firm.
Figure 1 reports the average firm scores of the ‘credit difficulty index’by country, as
measured by the first principal component. Countries are ranked according to their
average level of firms’credit difficulties. The values are normalized around the average
level of credit indexes for all countries. Thus, values above zero reflect above-average
levels of credit difficulty. The index has a value above the whole-sample average in
Italy, Spain, Portugal, Poland, Slovenia, Bulgaria and Greece. The distribution of countries
by credit difficulty is quite symmetric, with a roughly similar number of countries experi-
encing above-average and below-average level of credit problems.
Table 1. Share of firms in manufacturing, trade and business services, who viewed that credit
access problem in 2010–2013 (as described in the credit accessibility questions) was relevant or
very relevant, %.
Credit was NOT available to Credit was available to
finance working
capital
finance new
investment
refinance
debt
finance working
capital,
finance new
investment,
refinance
debt,
but conditions were too onerous
AT 5.4 3.6 1.5 4.3 2.0 0.9
BE 16.3 20.8 15.9 17.7 18.0 12.0
BG 52.2 51.5 44.3 53.4 52.6 49.6
CY 36.8 35.0 30.8 35.7 31.0 28.9
CZ 12.3 13.7 10.5 18.2 18.5 15.2
DE 10.0 9.2 8.9 7.7 6.6 5.9
EE 11.0 13.3 7.3 14.1 13.3 8.0
ES 32.5 32.8 29.5 38.4 38.2 34.3
FR 14.0 16.1 11.4 8.2 8.3 6.7
GR 56.3 53.1 46.5 54.2 41.9 46.2
HR 30.9 28.7 22.1 39.3 41.1 35.3
HU 9.1 10.5 9.5 26.5 26.4 24.4
IT 29.3 39.2 27.0 34.9 27.6 33.4
LT 24.1 19.1 12.3 27.9 21.4 14.6
LU 17.1 23.0 13.5 15.9 15.1 10.3
LV 33.0 22.8 17.3 28.8 24.3 18.4
MT 4.6 3.1 1.5 6.1 6.2 2.3
NL 23.4 26.2 16.8 18.4 19.4 13.5
PL 51.3 46.8 23.5 47.7 43.5 26.7
PT 31.4 31.3 25.3 42.8 40.5 33.7
RO 21.2 21.0 16.2 31.7 29.4 24.7
SI 46.4 46.6 36.7 47.3 47.4 40.9
SK 26.4 34.5 19.8 33.6 38.8 26.8
UK 28.7 26.6 21.6 24.4 24.3 24.6
Note: Frequency. Data weighted by employment weight.
Table 2. Principal component analysis of the credit difficulty measures.
Component Eigenvalue Difference Proportion Cumulative Loading 1
1 4.353 3.605 0.726 0.726 0.403
2 0.748 0.392 0.125 0.850 0.403
3 0.356 0.110 0.059 0.910 0.404
4 0.246 0.088 0.041 0.950 0.416
5 0.158 0.018 0.026 0.977 0.411
6 0.140 0.023 1.000 0.412
Note: PCA on answers about credit difficulties, after removing variables measuring demand and volatility shocks, difficul-
ties in customers’ability to pay, availability of supplies, sector and size dummies.
6K. BODNÁR ET AL.
The lower graph of Figure 1 shows the distribution of the obtained credit difficulty
indices by country, with the lower and upper borders representing the 25th and the
75th percentiles, respectively. The line in the box shows the median. In all countries,
the distribution of the credit difficulty index has a long positive tail, suggesting that
even in countries where a majority of the firms had no credit difficulties, quite a large min-
ority faced financing problems. Austria and Malta are extreme cases, where over 75% of
firms had the same low level of credit difficulty.
4
In Poland, Slovenia, Bulgaria and Greece,
the distribution of the credit difficulty index is more even. In these countries the occur-
rence of both the very low and the very high values of the credit difficulty indices was
rare and the majority of firms had similar, relatively severe credit access problems.
Overall, the figure shows the presence of high heterogeneity of credit difficulties across
EU countries: the interquartile range (the difference between the 25th and the 75th per-
centile of the distribution) of the index of credit difficulties in Austria is around three times
lower than that observed in Italy or in Greece.
Figure 2 shows the distribution of the derived credit difficulty index for all countries in
the sample, weighing observations to reflect total employment in the countries. The large
mass in the negative interval reflects the high weight of France, Austria and Germany in
the total sample of firms and rather good credit availability in these countries. The right
tail is much longer and mostly positive, reflecting the overall severity of credit conditions
for many firms.
To cross-check whether our index indeed captures the credit difficulties that we intend
to measure, we compare it with external data sources. For this cross-check, we first look at
the Survey on the access to finance of enterprises (SAFE), conducted by the ECB and the
Figure 1. Country averages of credit difficulty index and box-plot analysis of firm level variation.
Note: Sample is restricted to manufacturing, trade and business service firms.
BALTIC JOURNAL OF ECONOMICS 7
European Commission since 2009. The SAFE Survey is comparable to the WDN3 Survey in
the sense that it measures credit difficulties as perceived by the firms, and not by the
banks as a supplier of credit. The SAFE survey collects data from small and medium-
sized enterprises (SMEs) in Europe asking, among other things, what the most pressing
problem for firms during the reference period is. Figure 3, panel a, is based on the
2013 SAFE survey and refers to the same period as the WDN3. It compares the share of
firms reporting at least one obstacle in obtaining a (bank) loan in the SAFE survey in
2013 (vertical axis) to the country level average of the index of access to finance from
the WDN3 survey (horizontal axis). The figure confirms the high correlation between
the two measures.
The ECB’s Bank Lending Survey (BLS) provides another possibility to validate our
results, by looking at credit conditions from the banks’perspective. The BLS was launched
in 2003 by the ECB to enhance the Eurosystem’s knowledge of the financing conditions in
the euro area. It can be seen as the European equivalent of the Senior Loan Officer
Opinion Survey on Bank Lending Practices in the US. In the BLS, a sample of banks is
asked every quarter about, among others, how they changed their credit standards in
the previous three months for loans to non-financial enterprises. We have extended
the ECB sample of the euro area countries with data from the Czech Republic, Poland
and Hungary, making use of data collected and published by national central banks.
For each country and every quarter, a net percentage of banks tightening (+) and loosen-
ing (−) their credit conditions is reported. Figure 3, panel b reports, on the vertical axis, the
average of the net percentages of tightening banks for each country during the 16 quar-
ters over 2010Q1 to 2013Q4 (2012Q1-2013Q4 for CZ). The horizontal axis shows the first
principal component from the WDN3 data. The positive correlation between the BLS-
measure of credit supply conditions and the WDN3-measure of firms’difficulties in
Figure 2. Histogram of credit difficulty index.
Note: Sample is restricted to manufacturing, trade and business service firms. Data weighted to reflect an overall employ-
ment in the country.
8K. BODNÁR ET AL.
obtaining finance, gives confidence to our interpretation of the first principal component
as a measure of credit supply difficulties.
Finally, Figure 4 plots the correlation between our credit difficulty index and important
labour market macro variables, drawn from national accounts, measuring changes in the
use of labour (measured by total hours worked) and nominal hourly wages during the
period 2010–2013. Figure 4 shows that there is a clear negative correlation between
the change in employment at the macro level and our index of credit difficulties. A
(weaker) negative correlation also arises between credit difficulties and nominal wage
growth. This preliminary look at the aggregate data gives comfort to our interpretation
of the credit difficulty index and provides a strong motivation for our micro analysis.
4. Credit market access and labour adjustments: evidence from microdata
Aggregate data may hide considerable differences across firms. Thus, we look at the micro
data to see whether the correlation between credit difficulties and labour adjustment is
observable on the firm level and if there are any differences in terms of the type of adjust-
ment. Furthermore, the countries analysed in this study have very different labour market
institutions, which can affect the firms’response to shocks. Therefore, it is worthwhile to
look at several different channels of adjustment.
Figure 3. Correlations of the results of SAFE survey on firms and BLS on banks, and index of credit
difficulties.
Note: Panel a: Safe survey, share of firms reporting credit availability as the more pressing problem and index of credit
difficulty (mean values for each country). Panel b: BLS survey, share of banks reporting a tightening in conditions and
index of credit difficulty (mean values for each country).
BALTIC JOURNAL OF ECONOMICS 9
Starting with the total adjustment (question about the need to reduce labour
input), adjustments along the extensive margin (i.e. if the firm undertook individual
or collective layoffs) and adjustments along the intensive margin (subsidized as well
as non-subsidized reductions of hours), we construct a set of dummy variables
equal to 1 if firm iadjusted its labour input using a specific method of adjustment
k,andzerootherwise.
5
We also look at the other instruments to adjust labour input
and in particular at firms that stopped new hiring and did not renew temporary job
contracts. These outcomes are particularly relevant, because they help understanding
theimpactofthesovereigndebtcrisisonspecificdimensionsoftheEuropeanlabour
markets, for example, the rise in youth unemployment, which could have been particu-
larly affected by the reduction in hiring, and the segmentation between temporary and
permanent job contracts.
We extend our analysis to adjustments in wages as a response to credit difficulties.
Data limitations have prevented analysing this relationship until now. Based on the
WDN3 survey, however, we can check whether firms adjusted base and variable wage
components in response to credit shocks.
To check for a correlation between labour cost adjustments and the measures of
credit market difficulties described in Section 3, we consider the following baseline spe-
cification:
pr(adj ki=1) =
a
+
b
∗credit difficultyi+
g
∗Xi+ui(1)
Figure 4. Correlations between adjustments in employment (total hours worked, panel (a) and
nominal hourly wages (panel b) and index of credit difficulties).
Note: National accounts (Private sector only) and index of credit difficulty (mean values for each country).
10 K. BODNÁR ET AL.
where
- adj_k
i
is a dummy variable on k-th type of labour market adjustment for firm i(equal to 1
in case of strong or moderate decrease in the use of the method of adjustment),
- credit_difficulty
i
is the measure of credit constraint experienced by firm i,
-X
i
is a vector of firm-level control variables, which in all models correspond to country,
sector and size dummies.
The results on labour input adjustments are reported in Tables 3 and 4and those on
wage adjustments in Table 5.
As shown by Table 3, the index of credit market difficulty correlates positively with the
probability to adjust labour input, a result which is in line with the current literature on the
employment effect of credit shocks. Our findings, however, suggest that adjustments took
place along both the extensive and the intensive margins, although with a somewhat
higher intensity in the case of the extensive margin. In particular, we estimate an increase
in the probability to adjust employment by close to 2 pp (over a mean probability of 16%)
after a 1 point increase in our index of credit difficulties. This result is also robust to the
inclusion of additional controls such as the share of labour costs in total costs, the
share of flexible labour costs, and dummies on the degree of firm´s autonomy
(parent company, subsidiary/affiliate or other), structure (single establishment or multi-
establishment firm) and ownership (mainly domestic or foreign) (see Table 4).
This result is confirmed when the methods to adjust labour input are analysed sep-
arately (Table 5). For all the methods, credit market difficulties are always positively cor-
related to the probability to adjust firm workforce. Results show that firms more
strongly affected by credit difficulties as measured by our index tend to use individual
layoffs to adjust their labour force more than collective or temporary layoffs, probably
reflecting higher institutional rigidities to use these alternative methods of adjustment.
Also, credit difficulties are positively associated with the freeze or reduction of new
hires and the non-renewal of temporary contracts, while the impact on early retirement
or temporary agency workers is more limited. On the intensive margin, firms hit by
credit shocks tend to use non-subsidized reduction of hours with a higher probability.
At the same time, higher credit difficulties are not significantly associated with a higher
incidence of subsidized reductions of working hours, probably because the latter lead
to a decline of labour costs which relaxed the financing difficulties. Subsidized
reduction of working hours was available on a large scale only in a few countries
during the sovereign debt crisis.
Table 6 reports the marginal effect of worsening credit conditions on the probability to
adjust wages. Our estimates confirm the positive correlation between credit market
difficulties and the adjustment of wages, reflecting their impact on the adjustment of
Table 3. Labour input adjustments and credit availability. Probit marginal effects.
(1) Adjust labour input (2) Adjust the extensive margin (3) Adjust the intensive margin
Index of credit difficulties 0.018*** 0.017*** 0.010***
[0.000] [0.000] [0.001]
Observations 18,139 18,282 18,282
Mean probability 0.303 0.153 0.105
Note: Robust p-values in brackets ***p< 0.01, **p< 0.05, *p< 0.1. The models include country, sector and size dummies.
BALTIC JOURNAL OF ECONOMICS 11
flexible wages (increasing this probability by almost 1 pp). By contrast, the impact on base
wages is not significant, possibly showing the larger institutional rigidities to adjust base
wages in European countries (on average, just 5% of European firms adjusted base wages
over this period).
We check whether firm-specific factors, such as size, sector, ownership, structure and
autonomy, affect the response of labour market variables to credit shocks. For this
purpose, we include an interaction term between our index of credit difficulties and
firms’characteristics (see Table 7). The interaction of the extensive margin of adjustment
with firm’s size, sector and autonomy is insignificant, suggesting that the effect of credit
market conditions on the extensive margin of adjustment was rather similar across firms
of different size, sector and autonomy level. Interestingly, for firms which are neither
parent nor subsidiary/affiliate we find a higher probability to adjust the extensive
margin when the credit difficulty index increases. We also find that the interaction term
between the credit difficulty index and the extensive margin is higher in case of domestic
firms than foreign-owned ones. The results for the interaction term with the intensive
margin of adjustment are not statistically significant.
We then look more closely at country heterogeneity. We define three geographical
areas corresponding to (i) Continental EuropeandUK,(ii)EasternEuropeanandBaltic
countries and (iii) Southern European countries. The grouping is based on differences
in the financial sector. Firms in Continental Europe and in the UK are typically less
dependent on banks for their financial needs (this is true especially for UK, see Brown
et al., 2009) and are characterized by lower leverage (see, e.g. Bach Outlook no.2,
2014). Eastern European and Baltic countries are grouped together because their
banking sectors are characterized by a large market share of foreign banks, and a con-
siderabledegreeofdependenceonbankingfinance. Finally, in the Southern European
countries the banking sector suffered the most during the period 2010–2013 because of
their exposure to sovereign debt risk. We interact the index of credit difficulties with
area dummies to check the differences in the elasticity of employment to credit
Table 4. Labour input adjustments and credit availability (robustness check). Probit marginal effects.
(1) Adjust labour input
Index of credit difficulties 0.019*** 0.020*** 0.018***
[0.001] [0.000] [0.001]
Extra control dummies: autonomy ownership structure
Observations 15,458 16,659 16,075
Mean probability 0.326 0.311 0.328
(2) Adjust the extensive margin
Index of credit difficulties 0.019*** 0.019*** 0.018***
[0.000] [0.000] [0.000]
Extra control dummies: autonomy ownership structure
Observations 15,532 16,752 16,145
Mean probability 0.172 0.162 0.174
(3) Adjust the intensive margin
Index of credit difficulties 0.012*** 0.012*** 0.011***
[0.001] [0.000] [0.002]
Extra control dummies: autonomy ownership structure
Observations 15,532 16,752 16,145
Mean probability 0.107 0.106 0.110
Note: The models also include country, sector and size dummies, as well as the share of labour costs in total costs, the
share of flexible labour costs.
12 K. BODNÁR ET AL.
Table 5. Labour input adjustments and credit availability, by detailed method of adjustment. Probit marginal effects.
(1) Collective layoffs (2) Individual layoffs (3) Temporary layoffs
Index of credit difficulties 0.002 0.017*** 0.001
[0.270] [0.000] [0.502]
Observations 18,133 18,134 13,403
Mean probability 0.061 0.121 0.038
(4) Subsidized reduction of hours (5) Not subsidized reduction of hours (6) No renewal of temporary job contracts
Index of credit difficulties 0.004 0.010*** 0.010***
[0.143] [0.000] [0.003]
Observations 15,853 18,128 18,131
Mean probability 0.071 0.071 0.125
(7) Early retirement (8) Freeze/reduction new hire (9) Reduction temporary work agency
Index of credit difficulties 0.003* 0.013*** 0.007**
[0.090] [0.001] [0.019]
Observations 17,391 18,133 18,129
Mean probability 0.044 0.170 0.099
Note: Robust p-values in brackets ***p< 0.01, **p< 0.05, *p< 0.1. The models include country, sector and size dummies.
BALTIC JOURNAL OF ECONOMICS 13
difficulties in the different areas. The results of this exercise are reported in Table 8. Inter-
estingly, we do not find much evidence that the elasticity of employment to credit
shocks was different across these areas. In particular, no significant differences are
found between group (i) and (iii) in any method of labour cost adjustment, although
the impact of credit difficulties on the employment adjustment in the intensive
margin and flexible wage adjustment seems to be lower in the Eastern European and
Baltic countries.
For completeness, we also provide estimates in which the credit difficulty index is inter-
acted with country dummies (see Table 9). We take France as baseline. The coefficients
within Southern European countries are quite homogenous, the only difference being
smaller effect on extensive margin in Italy. In general, some cross-country heterogeneity
emerges, with no clear pattern. Some significant differences can be detected only within
the Eastern European/Baltic group. Firms in Slovenia, Slovakia Poland, Lithuania and
Romania tend to rely less on labour or flexible wage margins of adjustment in response
to worsening credit supply conditions. The use of labour and wage adjustment margins in
two Baltic countries, i.e. Latvia and Estonia, as well as Hungary, Croatia, Bulgaria does not
differ significantly from the baseline. This result suggests that the heterogeneous reaction
of the EU labour markets in response to the sovereign debt crisis is explained not only by
the differences in the intensity of the crisis across countries, but also by country-specific
factors. This conclusion is in line with the results by Mathä et al. (2019) showing that strict
employment protection and high centralization or coordination of wage bargaining make
it less likely that firms reduce wages when facing negative shocks (irrespective of the type
of negative shock).
5. Robustness checks: evidence from France and Italy
Our index of credit difficulties has been calculated on qualitative data from the
survey. In this section, based on French and Italian data on loans to firms we
construct a quantitative index of credit supply and we test whether our results are
still confirmed.
We combine the WDN3 sample with data from the credit register in the two
countries, administered by the Banque de France and the Bank of Italy, respectively.
The data includes all credit commitments by all banks operating in each country
exceeding 25,000 euros in France and 30,000
6
euros in Italy. The thresholds, which
are quite similar, therefore exclude only very small companies with low credit facilities
granted. Both databases include firm identifiers (tax codes) that make it possible to link
firms with WDN3 data and identify their lending banks. The data have a monthly
Table 6. Wage adjustments and credit availability, base wage and variable wage components. Probit
marginal effects.
(1) (2) (3)
Wages (total) Base wages Flexible wages
Index of credit difficulties 0.009** 0.002 0.009***
[0.016] [0.188] [0.004]
Observations 18,282 18,282 18,282
Mean probability 0.142 0.050 0.123
Note: Robust p-values in brackets ***p< 0.01, **p< 0.05, *p< 0.1. The models include country, sector and size dummies.
14 K. BODNÁR ET AL.
Table 7. Credit availability and labour market adjustments by type of firm. Probit marginal effects.
(1) Adjust labour input
Index of credit difficulties 0.005 0.028*** 0.024***
[0.565] [0.000] [0.000]
Interaction dummy with credit difficulty index Autonomy (subsidiary) Autonomy (other) Ownership (foreign) Structure (multi-establishment)
0.013 0.038*** −0.026** −0.009
[0.322] [0.002] [0.049] [0.413]
Observations 15,646 15,646 15,734
Mean probability 0.339 0.339 0.338
(2) Adjust the extensive margin
Index of credit difficulties 0.013* 0.026*** 0.018***
[0.064] [0.000] [0.001]
Interaction dummy with credit difficulty index Autonomy (subsidiary) Autonomy (other) Ownership (foreign) Structure (multi-establishment)
−0.006 0.025** −0.025** 0.001
[0.571] [0.020] [0.022] [0.907]
Observations 15,728 15,728 15,817
Mean probability 0.181 0.181 0.181
(3) Adjust the intensive margin
Index of credit difficulties 0.015*** 0.015*** 0.014***
[0.009] [0.000] [0.001]
Interaction dummy with credit difficulty index Autonomy (subsidiary) Autonomy (other) Ownership (foreign) Structure (multi-establishment)
−0.008 −0.002 −0.008 −0.008
[0.371] [0.755] [0.325] [0.258]
Observations 15,728 15,728 15,817
Mean probability 0.113 0.113 0.112
Note: Robust p-values in brackets ***p< 0.01, **p< 0.05, *p< 0.1. The models include area, country, sector and size dummies. The baseline for autonomy –parent firm, baseline for ownership –
domestic firm, baseline for structure –single establishment.
BALTIC JOURNAL OF ECONOMICS 15
frequency. For each company-bank pair, the total credit granted at the end of the year
is recovered.
7
Credit register data are used to construct a quantitative index of credit supply, which
can be assigned to the firms in the WDN3 sample. We consider the universe of banks in
Italy and France from 2007 to 2013, i.e. before the burst of the global financial crisis and
after the sovereign debt crisis. Aggregating loan data by bank, we calculate the three-year
percentage change in total loans for each bank. This way we remove bank fixed effects.
We then carry out the simple regression [2] to remove a time trend t, aimed at capturing
aggregate common factors (e.g. credit demand).
D3Lbt =
a
+
b
t+ut(2)
Last, we take the residuals of [2], and in particular residuals in year 2013, ˆ
ub,t=2013. The
use of residuals instead of D3Lbt allows us to get a quite conservative measure of credit
supply, since, for each bank, we consider how much bank’sbcredit grows compared
to the aggregate trend. Finally, we assign the residuals for the period 2010–2013 to
each WDN3 firm. Firms have multiple bank relationships, thus, we weight each residual
with the share of loans Lfb,t=2009 of firm f(in the WDN3 sample) in the total amount of
loans of the firm with any bank bin the register in 2009, i.e. the year preceding the
survey reference period. This strategy allows us to limit the impact of possible selection
bias in the firm-bank relationship in response to the global financial and/or the sovereign
debt crisis. In particular we calculate the index of credit supply to firm f,CSf, as:
CSf=Lfb,t=2009
Lf,t=2009
∗ˆ
ub,t=2013 (3)
This procedure leads toan imperfect match between the two datasets and we get around
750 observations per country.
8
We normalize the two indices and pool the two datasets.
Figure 5 compares the index of credit difficulties drawn from the WDN3 survey with this
measure of credit supply change. As expected, the two indices are negatively correlated.
Our estimation results are reported in Table 10 (the regression models also include
sector and size dummies and a country dummy). The first column refers to the correlation
of CSfand our credit difficulty index, entirely based on WDN3 data: firms matched with
banks with lower credit growth are more likely to report difficulties in accessing to
Table 8. Credit availability and labour market adjustments by geographical area. Probit marginal
effects.
(1) (2) (3) (4)
Extensive margin Intensive margin Base wage Flexible wage
Credit difficulties index 0.019*** 0.013*** 0.001 0.009**
[0.001] [0.006] [0.700] [0.036]
Southern Europe * Credit diff. Index 0.003 −0.003 0.001 0.010
[0.751] [0.715] [0.835] [0.211]
Eastern/Baltic * Credit diff. Index −0.014* −0.007 0.002 −0.014**
[0.061] [0.196] [0.423] [0.017]
Observations 18,282 18,282 18,282 18,282
Mean probability 0.153 0.105 0.050 0.123
Note: Robust p-values in brackets ***p< 0.01, **p< 0.05, *p< 0.1. Southern Europe includes: Spain, Italy; Greece, Portu-
gal, Cyprus. Eastern Europe/Baltic countries includes: Czech Republic, Estonia, Croatia, Hungary, Romania, Bulgaria,
Latvia, Lithuania, Poland, Slovenia, and Slovakia. The models include area, country, sector and size dummies.
16 K. BODNÁR ET AL.
credit and the correlation between the two indices is statistically significant.. This is an
indirect validation of our approach based on qualitative self-reported information.
Columns 2–5 show the estimated probabilities to reduce the extensive margin, the inten-
sive margin, the base wage and the variable wage, respectively. The results are substan-
tially confirmed. The correlation between credit supply and the probability to reduce
Table 9. Credit availability and labour market adjustments by country. Probit marginal effects.
(1) Extensive margin (2) Intensive margin (3) Base wage (4) Flexible wage
Credit difficulties index 0.035*** 0.024*** 0.003 0.010
[0.001] [0.008] [0.649] [0.166]
Austria * Credit diff. Index 0.023 −0.072** −0.019 −0.190***
[0.469] [0.049] [0.211] [0.000]
Belgium * Credit diff. Index 0.012 0.002 0.011 −0.001
[0.453] [0.888] [0.217] [0.951]
Germany * Credit diff. Index −0.007 −0.016 0.003 0.014
[0.585] [0.137] [0.681] [0.139]
Luxemburg * Credit diff. Index −0.042* 0.002 0.000 0.005
[0.050] [0.865] [0.983] [0.807]
Malta * Credit diff. Index 0.030 0.007 −0.055*** −0.297***
[0.432] [0.789] [0.000] [0.001]
The Netherlands * Credit diff. Index −0.018 −0.019 −0.006 0.015
[0.343] [0.206] [0.559] [0.278]
The United Kingdom * Credit diff. Index −0.042** −0.015 −0.013 −0.032***
[0.016] [0.321] [0.108] [0.009]
Cyprus * Credit diff. Index −0.034* −0.025* −0.007 −0.017
[0.090] [0.072] [0.484] [0.264]
Spain * Credit diff. Index 0.021 −0.015 0.003 0.018
[0.169] [0.268] [0.745] [0.160]
Greece * Credit diff. Index −0.018 −0.022 0.002 0.004
[0.261] [0.141] [0.841] [0.746]
Italy * Credit diff. Index −0.039** −0.014 −0.009 0.004
[0.027] [0.285] [0.345] [0.780]
Portugal * Credit diff. Index −0.020 −0.004 −0.001 −0.005
[0.138] [0.747] [0.919] [0.597]
Bulgaria * Credit diff. Index −0.002 −0.006 0.005 −0.000
[0.920] [0.645] [0.519] [0.980]
Czechia * Credit diff. Index −0.025* −0.016 0.004 −0.014
[0.052] [0.144] [0.616] [0.149]
Estonia * Credit diff. Index −0.015 −0.018 −0.004 −0.017
[0.414] [0.223] [0.707] [0.330]
Croatia * Credit diff. Index −0.027 −0.012 0.003 0.000
[0.110] [0.430] [0.719] [0.998]
Hungary * Credit diff. Index −0.004 0.001 0.006 −0.007
[0.745] [0.935] [0.456] [0.451]
Lithuania * Credit diff. Index −0.011 −0.024** −0.016 −0.035**
[0.598] [0.039] [0.117] [0.027]
Latvia * Credit diff. Index −0.021 −0.021 0.000 0.007
[0.214] [0.141] [0.966] [0.614]
Poland * Credit diff. Index −0.035** −0.017 −0.004 −0.029**
[0.011] [0.100] [0.633] [0.021]
Romania * Credit diff. Index −0.030*** −0.016 −0.001 −0.000
[0.010] [0.107] [0.913] [0.996]
Slovenia * Credit diff. Index −0.030** −0.028** 0.003 −0.004
[0.017] [0.021] [0.650] [0.626]
Slovakia * Credit diff. Index −0.045* −0.056*** −0.003 −0.034**
[0.054] [0.001] [0.712] [0.029]
Observations 18,282 18,282 18,282 18,282
Mean probability 0.154 0.105 0.050 0.123
Note: Robust p-values in brackets *** p< 0.01, ** p< 0.05, * p< 0.1. Base country –France. The models include area,
country, sector and size dummies.
BALTIC JOURNAL OF ECONOMICS 17
labour is negative. The same holds for flexible wages, but not for base wages, as found in
the examination of all countries.
6. Conclusions
In this paper, we provide empirical evidence about a strong correlation between credit
shocks and labour market adjustments in Europe during the sovereign debt crisis. We
rely on WDN3 survey data, which has the advantage to offer a unique European perspec-
tive, providing comparable, harmonized results for 24 countries.
We are aware of the limits of our approach. First, the data allow us to calculate only
the probability of an adjustment and not how much of the observed employment drop
Figure 5. Correlations between the change in credit supply and the index of credit difficulties.
Note: Credit supply (measured on credit registers and normalized between zero and one in both countries) and index of
credit difficulty.
Table 10. Italy and France. Alternative index of credit supply and labour market adjustments. Pooled
data.
(1) (2) (3) (4) (5)
Index of credit
difficulties
Extensive
margin
Intensive
margin
Base
wage
Flexible
wage
Alternative index of credit supply −1.212** −0.009*** −0.008*** −0.001 −0.008***
[0.000] [0.001] [0.004] [0.277] [0.003]
Observations 1558 1558 1558 1603 1603
Note: Index of credit supply, based on credit registers. Robust p-values in brackets ***p< 0.01, **p< 0.05, *p< 0.1. The
models include country, sector and size dummies.
18 K. BODNÁR ET AL.
can be imputed to credit difficulties. Second, since we use survey data on self-reported
credit difficulties and other shocks, our index of credit difficulty does not allow for a
proper identification of the credit supply shock hitting the various countries, net of
any demand effect. Thus, our main estimates are simple correlations between credit
difficulties and firms’labour cost adjustment strategies. Even with this limitation, our
results confirm that credit shocks are important determinants of labour market fluctu-
ations in Europe.
Second, credit market difficulties are associated not only with a decrease in employ-
ment, but also with a decline in the intensity of the use of labour in terms of hours
worked. One consequence of this finding is that after large-scale shocks like the sovereign
debt crisis, special attention may be needed on measures of labour market slack, other
than the unemployment rate (which is a simple headcount ratio of how many people
are without a job over active population).
Finally, our results suggest also that, in response to credit difficulties, base wages are
quite rigid and do not adjust on average. Nevertheless, European firms reduce nominal
wages by cutting the variable part of employee compensation (bonuses, performance-
related premia, etc.). Thus, credit difficulties may have consequences not only on real
variables but also on nominal ones (e.g. Adamopoulou et al., 2020; Moser et al.,
2020; for an analysis on the impact of credit difficulties on prices, see also Duca
et al., 2017).
Notes
1. Previous WDN surveys do not allow to make a similar analysis on the impact of credit con-
strains on labour cost adjustments. WDN1 survey did not include any question regarding
difficulties in access to finance while WDN2, which was an update of WDN1 with small
sample sizes and conducted only in 10 European countries, included merely one question
regarding the extent of difficulties in access to finance for firms. Using this dataset, Fabiani
et al. (2015), although they focus on demand shocks, find that negative finance shocks
increase the likelihood to adjust margins and costs at the firm level and once the impact
of demand shocks is taken into account, financially constrained firms are more likely to
adjust non-labour costs.
2. Firms’non-response to the credit availability questions is not homogenous across countries.
In the UK, almost 30% of firms in manufacturing, trade and business services sectors haven’t
provided answers to this block of questions. In Greece this share is 12%, followed by Hungary
(9%), Latvia (9%) and Italy (8%). In the remaining of the countries the non-response rate was
smaller.
3. As robustness check we derive credit difficulty index using factor analysis. The obtained
results lead to the same findings. The difference in the size of the marginal effects using
both measures (with standardized variance) is negligible.
4. The countries with low credit difficulties on average are characterized by a good cyclical pos-
ition at the start of the sovereign debt crisis, as shown for example by their low unemploy-
ment rates. At the same time, in Austria and Malta, the low share of state-owned banks and in
the latter, the high share of foreign owned banks have also likely played a role. In Austria, only
a low percentage of the firms applied for a credit in the period examined in the WDN3 survey.
See Stiglbauer (2017) and Micallef and Caruana (2017).
5. The dummy for the adjustment on the extensive margin is equal to 1 if the firm answered that
individual and/or collective layoffs were used moderately or strongly, and 0 otherwise. The
dummy for the intensive margin is equal to 1 if the firm answered that the decrease of
BALTIC JOURNAL OF ECONOMICS 19
hours worked per worker, either subsidized or non-subsidized, was used moderately or
strongly, and 0 otherwise.
6. Prior to 2009, the Italian credit register only included loans in excess of €75,000. Companies
not present in the credit register before 2009 are therefore excluded from the sample.
However, this selection has an extremely limited impact since the Italian WDN3 sample
mostly includes companies with at least five employees (see Izquierdo et al., 2017 for
details), which meet the requirement for inclusion in the register.
7. When dealing with bank data one of the main problems concerns bank mergers and how to
reattribute loans to firme made by the banks involved in the M&A. The data used in this
paper presents the same problem and in case of merge the information related to
the bank-firm match is dropped (and the firm is excluded from the sample). However, in
the period under scrutiny, during which two deep financial crises occurred, merges in both
countries were rather limited. Even if this potentially generate some selection bias, the
type of selection induced by bank M&A is not clear and likely not correlated to labour
market outcomes.
8. Most firms drop out because of errors in tax codes.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
Katalin Bodnár is an economist in the Supply Side, Surveillance and Labour Markets Division at the
European Central Bank. Her interests include labour economics and potential output.
Ludmila Fadejeva is a research economist at Latvijas Banka. Her research interests include monetary
and labour economics, particularly the study of monetary policy transmission and income/wealth
inequality.
Marco Hoeberichts is an economist in the Economics and Research Division at De Nederlandsche
Bank. His research interests I include labour economics and inflation dynamics.
Mario Izquierdo Peinado is Head of the Supply and Labor Market Analysis Unit at the Directorate
General of Economics and Statistics of the Bank of Spain. His research interests include various
labor market issues, such as wage dynamics, wage inequality and the impact of labor market
institutions.
Christophe Jadeau is an economist at Banque de France, Engineering and Statistical Methodology
Department.
Eliana Viviano is Head of the Labour Market Division at the Economics and Research Department of
the Bank of Italy. She has published both micro and macro papers on various labor market issues,
such as labor market reforms, determinants of wage dynamics, methodological aspects related to
the estimation of unemployment and employment rates.
ORCID
Ludmila Fadejeva http://orcid.org/0000-0002-6822-1577
References
Acharya, V. V., Eisert, T., Eufinger, C., & Hirsch, C. (2018). Real effects of the sovereign debt crisis in
Europe: Evidence from syndicated loans. The Review of Financial Studies,31(8), 2855–2896.
https://doi.org/10.1093/rfs/hhy045
20 K. BODNÁR ET AL.
Adamopoulou, E., De Philippis, M., Sette, E., & Viviano, E. (2020). The long run earnings effects of a
Credit Market Disruption, IZA DP No. 13185.
Beck, T., & Demirguc-Kunt, A. (2006). Small and medium-size enterprises: Access to finance as a
growth constraint. Journal of Banking & Finance,30(11), 2931–2943. https://doi.org/10.1016/j.
jbankfin.2006.05.009
Bentolila, S., Jansen, M., & Jimenez, G. (2018, June). When credit dries up: Job losses in the Great
Recession. Journal of the European Economic Association,16(3), 650–695. https://doi.org/10.
1093/jeea/jvx033
Berg, T. (2018). Got rejected? Real effects of not getting a loan. Review of Financial Studies,31(12),
4912–4957. https://doi.org/10.1093/rfs/hhy038
Berton, F., Mocetti, S., Presbitero, A., & Richiardi, M. (2018). Banks, firms and jobs. Review of Financial
Studies,31(6), 2113–2156. https://doi.org/10.1093/rfs/hhy003
Bottero, M., Lenzu, S., & Mezzanotti, F. (2020). Sovereign debt exposure and the bank lending
channel: Impact on credit supply and the real economy. Journal of International Economics,
103328
Brown, J. R., Fazzari, S. M., & Petersen, B. C. (2009). Financing innovation and growth: Cash flow,
external equity, and the 1990s R&D boom. Journal of Finance,64(1), 151–185. https://doi.org/
10.1111/j.1540-6261.2008.01431.x
Buera, F., Fattal-Jaef, R., & Shin, Y. (2015). Anatomy of a credit crunch: From capital to
labor markets. Review of Economic Dynamics,18(1), 101–117. https://doi.org/10.1016/j.red.2014.
11.001
Caggese, A., & Cuñat, V. (2008, Nov.). Financing constraints and fixed-term employment
contracts. The Economic Journal,118(533), 2013–2046. https://doi.org/10.1111/j.1468-0297.
2008.02200.x
Chodorow-Reich, G. (2014). The employment effects of credit market disruptions: Firm-level evi-
dence from the 2008–9financial crisis. Quarterly Journal of Economics,129(1), 1–59. https://doi.
org/10.1093/qje/qjt031
Cingano, F., Manaresi, F., & Sette, E. (2016). Does credit crunch investments down? New evidence on
the real effects of the bank-lending channel. Review of Financial Studies,29(10), 2737–2773.
https://doi.org/10.1093/rfs/hhw040
Degryse, H., De Jonghe, O., Jakovljević, S., Mulier, K., & Schepens, G. (2016). ‘The impact of bank
shocks on firm-level outcomes and bank risk-taking’, Paris December 2016 Finance Meeting
EUROFIDAI - AFFI. Available at SSRN: https://ssrn.com/abstract=2788512
Duca, I., Montero, J. M., Riggi, M., & Zizza, R. (2017). I will survive. Pricing strategies of financially dis-
tressed firms. Temi di discussione (Economic working papers) 1106, Bank of Italy, Economic
Research and International Relations Area.
Duygan-Bump, B., Levkov, A., & Montoriol-Garriga, J. (2015). Financing constraints and unemploy-
ment: Evidence from the Great Recession. Journal of Monetary Economics,75,89–105. https://
doi.org/10.1016/j.jmoneco.2014.12.011
Fabiani, S., Lamo, A., Messina, J., & Rõõm, T. (2015). European firm adjustment during times of econ-
omic crisis, ECB Working Paper no 1778.
Greenstone, M., Mas, A., & Nguyen, H. L. (2020). Do credit market shocks affect the real economy?
Quasi experimental evidence from the great recession and ‘normal’economic times. American
Economic Journal: Economic Policy 12,200(225). doi:10.1257/pol.20160005
Hochfellner, D., Montes, J., Schmalz, M., & Sosyura, D. (2016). Winners and losers of financial crises:
Evidence from individuals and firms, mimeo, University of Michigan.
Hoynes, H., Miller, D., & Schaller, J. (2012). Who suffers during recessions? Journal of Economic
Perspectives,26(3), 27–48. https://doi.org/10.1257/jep.26.3.27
Izquierdo, M., J. F. Jimeno, T. Kosma, A. Lamo, S. Millard, T. Rõõm, & E. Viviano (2017)Labour market
adjustment in Europe during the crisis: microeconomic evidence from the Wage Dynamics Network
survey, ECB Occasional Paper n 192.
Klapper, L., Laeven, L., & Rajan, R. (2006). Entry regulation as a barrier to entrepreneurship. Journal of
Financial Economics,82(3), 591–629. https://doi.org/10.1016/j.jfineco.2005.09.006
BALTIC JOURNAL OF ECONOMICS 21
Mathä, T. Y., Millard, S., Rõõm, T., Wintr, L., & Wyszyński, R. (2019). Shocks and labour cost adjustment:
evidence from a survey of European firms, ECB Working Paper Series no 2269.
Micallef, B., & Caruana, K. (2017). Results of the 2014 Wage Dynamics Network for Malta, ECB WDN3
Country Report.
Moser, C., Saidi, F., Wirth, B., & Wolter, S. (2020). Credit supply, firms, and earnings inequality, MPRA
Paper 100371.
Pagano, M., & Pica, G. (2012). Finance and employment. Economic Policy,27(69), 5–55. https://doi.
org/10.1111/j.1468-0327.2011.00276.x
Popov, A., & Rocholl, J. (2018). Do credit shocks affect labor demand? Evidence for employment and
wages during the financial crisis. J. Financial Intermediation,36,16–27. https://doi.org/10.1016/j.
jfi.2016.10.002
Stiglbauer, A. (2017). The third wage dynamics network firm survey: Country Report on Austria, ECB
WDN3 Country Report.
Strzelecki, P., & Wyszyński, R. (2016). Poland’s labour market adjustment in times of economic slow-
down –WDN3 survey results, NBP Working Papers 233, Narodowy Bank Polski, Economic
Research Department.
Verick, S. (2009). Who is hit hardest during a financial crisis? The vulnerability of young men and
women to unemployment in an economic downturn, IZA DP No. 4359, August 2009.
Appendix 1. The WDN3 survey
In this paper, we use so-called WDN3 survey, conducted in 2014 by national central banks in 24
countries of the European Union. This survey constitutes the main data source we use to deal
with these issues. This survey is the third wave of enquiries led by the Wage Dynamics Network
(WDN) of the European System of Central Banks, a research network dedicated to the study of
the features and sources of wage and labour cost dynamics and their implications for monetary
policy in the euro area. The first survey on firms’price and wage setting practices has been
carried out by 17 national central banks in 2007–2008. Additional questions –mainly to respondents
of the first wave –have then been issued in a short second wave in 2009, in order to assess the firms’
reaction to the global financial crisis of 2007–2008.
Since late 2009, the European countries have been confronted to the sovereign debts crisis, and
labour market reforms have occurred: the third wave was designed to measure the nature of shocks
and the firms’reaction during the period 2010–2013, and especially the adjustments they made in
their price and wage settings practices. The harmonized questionnaire contains three main parts:
the nature of the shocks (changes in demand, in accessibility of funding, costs and mainly elements
of the labour costs), the adjustments on employment and wages, and the main obstacles to hiring.
Each participating national central bank was responsible for the translation of the questionnaire
and for the conduct of the survey in the country. Each central bank chose both the sample compu-
tation and the data collection method for its national data, leading to a large variety of sample com-
putation and data characteristics. More than 24,000 firms were surveyed during the year 2014: if the
perimeter of sectors can differ from a country to another, the manufacturing, trade, business services
and, to a lesser extent, construction are well represented across the participating countries. To
improve firm comparability between countries we restrict our analysis to the three main sectors –
manufacturing, trade and business services (see Table A1 for detailed information on sample).
In most countries, firms with less than 5 employees were excluded from the survey: they only
represent 2% of the data. 29% of firms have 5–19 employees, 24% 20–49, 25% 50–199 and 20%
more than 200 employees. For all countries we only include firms with at least 5 employees.
22 K. BODNÁR ET AL.
Table A1. Survey sample by country, sector and size (firms that provided answers about credit availability).
Country
Number of firms that
provided answers about
credit availability (all
sectors)
Share of firms in
manufacturing, trade or
business services (%)
Firms in manufacturing, trade or business services
Average item non-response (% of
total number of firms)
Distribution by sector
(%) Distribution by employee number (%) for credit
availability
questions
for labour input
adjustment
questionsManufacturing Trade
Business
services <5 5–19 20–49 50–199 200 and >
AT 744 83.9 34.8 25.8 39.4 0.6 18.1 22 31.6 27.7 1.9 3.2
BE 958 77.5 54.3 14.8 30.9 –22.8 23.9 42.6 10.8 2.1 0.6
BG 507 75.9 14 59.5 26.5 –72.5 18.2 7 2.3 1.4 0
CY 167 85 22.5 32.4 45.1 26.1 41.5 15.5 9.9 7 5.4 2.6
CZ 944 91.5 54.5 16.1 29.4 –15.4 18.9 25.8 39.9 4.7 1.8
DE 2297 80.8 34 28.1 37.9 9.2 24 28 27.2 11.7 3.8 2.9
EE 500 76.6 35 24 41 –36 34.7 23.2 6 0 0
ES 1975 99.1 25.9 30.7 43.5 –73.1 18 6.6 2.3 0 0
FR 1120 85.4 51.4 25.2 23.4 –18.4 22.2 27.7 31.7 2.6 0.8
GR 348 100 39.4 35.3 25.3 –11.2 36.2 34.8 17.8 11.5 2.0
HR 301 90.4 42.6 21 36.4 –30.1 25.7 33.1 11 0 0
HU 1782 90.1 43.7 23.6 32.7 –10.5 29.5 40.1 19.9 9.2 0
IT 919 97.6 51.7 21.1 27.2 –6.7 51.4 29 12.5 8.3 0.5
LT 515 77.3 19.1 42.5 38.4 –57.5 19.3 18.3 4.8 0 0
LU 661 64.9 17.2 35.9 46.9 23.5 35.7 21.9 14.9 4 1.3 0
LV 463 85.3 20.8 36.7 42.5 –47.6 25.8 20.8 5.8 8.6 0
MT 178 73 24.6 20 55.4 –13.8 24.6 37.7 23.8 0 0
NL 727 58.2 22.9 34.8 42.3 –45.6 25.8 24.3 4.3 0 0
PL 1414 84.4 33.9 34 32.1 20.8 27.9 15.3 22.4 13.7 3.5 4.8
PT 1261 70.9 47.5 20.4 32.1 –13.4 23.6 36.4 26.6 3.7 0
RO 2030 89.4 60.4 16.1 23.5 –– 8.2 14.7 77.1 0.4 0.1
SI 1269 80.9 40.8 20.1 39.1 –48.3 20 20.5 11.2 0 0
SK 601 84.7 37.3 24.4 38.3 –25.9 27.3 32.6 14.1 2.1 0
UK 395 72.4 23.1 19.2 57.7 5.6 6.6 24.1 28.7 35 29.6 0.8
BALTIC JOURNAL OF ECONOMICS 23
Table A2. List of the WDN3 questions used in this paper.
Credit availability With regard to finance, please indicate for 2010–2013 how relevant were for your firm each
one of the following happenings? Please choose ONE option for each line (not relevant, of
little relevance, relevant, very relevant). Note: Credit here refers to any kind of credit, not
only bank credit.
Credit was not available to finance working capital
Credit was not available to finance new investment
Credit was not available to refinance debt
Credit was available to finance working capital, but conditions (interest rate and other
contractual terms) were too onerous
Credit was available to finance new investment, but conditions (interest rate and other
contractual terms) were too onerous
Credit was available to refinance debt, but conditions (interest rate and other contractual
terms) were too onerous
Labour force adjustment During 2010–2013 did you need to significantly reduce your labour input or to alter its
composition? (Yes/No)
If YES, which of the following measures did you use to reduce your labour input or alter its
composition when it was most urgent? Please choose ONE option for each line (not at all,
marginally, moderately, strongly)
Collective layoffs
Individual layoffs
Temporary layoffs (NOT asked in: CZ, DE, EE, IT, LT, LV and MT)
Subsidized reduction of working hours (NOT asked in EE, LT, LV, UK and PT)
Non-subsidized reduction of working hours (including reduction of overtime)
Non-renewal of temporary contracts at expiration
Early retirement schemes (NOT asked in EE)
Freeze or reduction of new hires
Reduction of agency workers and others
Change in economic
conditions
How did the following factors affect your firm’s activity during 2010–2013? Please choose
ONE option for each line (Strong decrease, Moderate decrease, Unchanged, Moderate
increase, Strong increase)
The level of demand for your products/services
Volatility/uncertainty of demand for your products/services
Customers’ability to pay and meet contractual terms
Availability of supplies from your usual suppliers
24 K. BODNÁR ET AL.
Table A3. Share of firms in manufacturing, trade and business services, who reported need to reduce labour input or alter its composition; and use of labour
adjustment measures by corresponding firms, %.
Country
Need to significantly
reduce labour input or to
alter its composition?
If YES, which of the following measures did you use to reduce your labour input or alter its composition when it was most
urgent? *
Negative wage
adjustment *
No Yes
Extensive margin
(collective or
individual layoffs)
Intensive margin (subsidized
or non-subsidized reduction
of working hours)
Non-renewal of
temporary contracts
at expiration
Freeze of
new hires
Temporary
layoff
Early
retirement
Base
wage
Flexible
wage
AT 76.2 23.8 35.5 40.8 4.1 55.2 9.1 1.7 1.8 14.2
BE 59.8 40.2 44.8 11.9 33.3 62.7 37.7 15.0 5.4 3.8
BG 76.2 23.8 58.6 11.5 27.6 62.5 31.3 15.4 25.9 38.6
CY 48.6 51.4 40.7 25.9 19.3 49.3 13.5 7.7 83.1 83.9
CZ 63.6 36.4 55.9 21.9 38.4 58.0 –13.0 9.7 31.1
DE 78.4 21.6 41.1 45.4 28.1 42.9 –13.3 9.4 8.6
EE 86.7 13.3 43.4 29.2 13.0 42.0 ––22.7 23.4
ES 54.1 45.9 53.6 29.1 49.2 32.6 24.7 18.4 12.4 44.8
FR 74.3 25.7 51.2 30.3 47.4 74.4 4.9 7.4 4.4 22.3
GR 44.7 55.3 36.5 32.7 19.1 59.9 2.8 6.4 78.4 66.8
HR 60.5 39.5 50.6 11.7 45.7 33.5 8.2 29.5 29.6 38.2
HU 84.2 15.8 42.4 19.8 24.7 29.9 12.0 16.8 8.4 32.0
IE 65.8 34.2 42.1 43.2 23.9 52.2 12.1 3.4 25.2 34.9
IT 53.4 46.6 53.5 69.8 53.1 71.1 –21.8 5.9 30.0
LT 80.9 19.1 23.5 13.8 25.0 36.0 –3.1 16.6 18.9
LU 74.4 25.6 34.6 16.2 36.5 50.2 4.3 19.6 7.7 37.4
LV 77.3 22.7 26.7 21.6 10.5 22.8 –1.5 19.7 12.2
MT 76.0 24.0 18.8 23.5 17.8 41.0 –13.2 5.1 2.1
NL 48.4 51.6 48.2 8.0 53.5 51.8 1.4 9.4 21.8 37.7
PL 61.7 38.3 65.2 30.7 53.0 72.7 18.8 24.8 8.7 17.0
PT 74.9 25.1 52.3 28.7 61.8 74.3 6.2 12.9 23.0 39.9
RO 73.8 26.2 61.0 35.1 31.5 57.5 13.5 11.5 10.2 23.8
SI 74.0 26.0 47.0 19.6 42.8 49.3 10.8 20.1 32.0 48.6
SK 66.4 33.6 80.6 23.4 38.4 75.3 8.4 23.4 7.9 36.3
UK 78.3 21.7 55.3 18.9 13.5 47.3 2.1 2.3 6.1 23.8
Note: * - share of firms, of those, who reported necessity to reduce labour input or its composition, and used a particular type of labour adjustment moderate or strongly. Sample is restricted to
manufacturing, trade and business service firms. Data weighted to reflect an overall employment in the country.
BALTIC JOURNAL OF ECONOMICS 25
