# SMEs' financing: Divergence across Euro area countries?

Sébastien Roux, Frédérique Savignac

► To cite this version:

Sébastien Roux, Frédérique Savignac. SMEs' financing: Divergence across Euro area countries?. 2018. [hal-05411917](https://hal HAL Id: hal-05411917)

https://insee.hal.science/hal-05411917v1

Preprint submitted on 11 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Direction des Études et Synthèses Économiques

G 2018/01

SMEs' financing:
Divergence across Euro area countries?

Sébastien ROUX et Frédérique SAVIGNAC

Document de travail

<figure><img src="image_2.png" /></figure>

Insee
Mesurer pour comprendre

Institut National de la Statistique et des Études Économiques
INSTITUT NATIONAL DE LA STATISTIQUE ET DES ÉTUDES ÉCONOMIQUES

Série des documents de travail
de la Direction des Études et Synthèses Économiques

G2018/01

SMEs' financing:
Divergence across Euro area countries?

Sébastien ROUX* et Frédérique SAVIGNAC**

MAI 2018

We are grateful to Sébastien Perez-Duarte for very helpful discussion on the SAFE data. We also thank Alexandre Cazenave-Lacroutz, Federico Cingano, Bertrand Garbinti, Werner Hölzl, Guillaume Horny, Adrian Pop, Enrico Sette, and participants in the first Banca d'Italia - Banque de France workshop on corporate finance, the 2017 European Economic Association meeting, the 2017 Panel data conference, the 2017 CIRET conference and the 34th Symposium on Money, Banking and Finance for helpful suggestions. This paper presents the authors' views and should not be interpreted as reflecting the views of INSEE or the Banque de France.

<footnote>* Insee - Département des Études Économiques - Timbre G201 - 88, avenue Verdier - CS 70058 - 92541 MONTROUGE
CEDEX
Crest - Ined</footnote>

<footnote>** Banque de France</footnote>
# Le financement des PME :
## divergence entre les pays de la zone Euro ?

### Résumé

Cet article examine dans quelle mesure les conditions de financement des PME ont convergé ou divergé entre pays de la zone Euro au cours de la période 2010-2014. S'appuyant sur une enquête représentative auprès des entreprises conduite dans plusieurs pays de la zone Euro (enquête SAFE), nous construisons pour chaque pays et chaque semestre des indicateurs reflétant la capacité de leurs entreprises à utiliser trois sources de financement externe : les crédits bancaires de long terme (prêts bancaires), les crédits bancaires de court terme (lignes de crédit) et le crédit commercial. On montre ainsi que les différences d'accès des PME aux prêts bancaires ont significativement augmenté au cours de la période. Cette divergence n'est pas liée à une volatilité globale des conditions de financement qui aurait crû après la crise mais plutôt au fait que les pays où les conditions de financement étaient initialement les meilleures (resp. les plus mauvaises) sont également ceux où elles se sont le plus améliorées (resp. empirées). Nous confrontons ces évolutions propres à chaque pays à certaines variables macroéconomiques ou reflétant leur structure bancaire. Les résultats suggèrent que les indicateurs relatifs à la concentration bancaire pourraient être à l'origine de la divergence des taux de recours aux crédits bancaires de long terme.

**Mots-clés** : contraintes de crédit, financements bancaires, crédit commercial, facteurs institutionnels

# SMEs' financing :
## Divergence across Euro area countries?

### Abstract

This paper studies the divergence/convergence process of European countries as regard the financing behavior of small and medium sized enterprises. Using a firm level and country representative survey, we construct country-time indicators of SMEs' use of three external financing sources: bank loans, credit line/overdraft and trade credit. These indicators account for composition effects and demand effects. We find substantial differences between countries in the SMEs' use of the three financing sources. In particular, the cross-country differences related to SMEs' use of bank loans have significantly increased over the period 2010-2014. This divergence is not related to a global increase in the volatility of this use between countries. Instead, it has been driven by a sharper increase (resp. decrease) in the countries where SMEs' use was initially higher (resp. lower). Finally, we investigate whether SMEs' uses of financing sources are correlated at the country level with various macroeconomic and banking structure indicators. The results suggest that indicators about banking concentration are good candidates to explain the cross-country divergence of SMEs' use of bank loans.

**Keywords:** credit constraints, bank financing, trade credit, institutional factors

**Classification JEL** : G31, G01, D22, C35
# 1. Introduction

After the financial and sovereign debt crises, one concern in the Euro area was about the ability of firms and, especially small and medium sized enterprises (SMEs), to rely on the necessary external financing sources for their growth and development. These firms play an important role in the functioning of the economy<fnref n="1" /> and are more dependent on intermediated finance than larger ones. Due to asymmetric information and agency problem, these firms may face however a “premium” that increases the cost of their external finances and may induce financing constraints hampering their ability to invest and grow<fnref n="2" />. At the same time, the Eurosystem has been worrying about the divergence of financial conditions within the Euro area, especially as regards loan interest rates across countries. This heterogeneity in the bank lending supply may induce cross-country heterogeneity in firms financing behaviors through firms’ financing structure (Basset et al., 2014, Becker and Ivashina, 2014) and through their use of alternative source of finances like trade credit (Love et al., 2007). Moreover, there are still many institutional differences across Euro area countries related to the functioning of credit market, the firms-banks relationships or the use of trade credit despite the European financial and banking integration. More generally, the literature finds cross-country heterogeneity in firms’ financing behavior that is explained by institutional differences. In particular, cross-country differences in terms of capital structure and use of bank loan or trade credit are related to financial intermediary developments, bankruptcy law or fiscal policy (e.g. Rajan and Zingales, 1995; Demirgüç-kunt and Maksimovic, 1998; Fisman and Love, 2003, Beck et al. 2003) and persist over time (Lemmon et al., 2008). Most of the previous cross-country studies rely however on a set of countries covering very heterogeneous economic or policy environments. Little is known about the heterogeneity of firms financing behaviors within the Euro area, especially as regards SMEs. Such a financial fragmentation in terms of firms’ financing behaviors could have important implications for designing relevant public policies.

<footnote n="1">¹ In the European Union, SMEs account for about 58% of the value added generated by the non-financial sector and for 67% of total employment in 2014. Source: European Commission.</footnote>

<footnote n="2">² The empirical literature emphasizes the role of firms’ size in explaining the existence of financing constraints and show that small firms are more likely to face such financing constraints when seeking for external financing sources (e.g. Beck et al., 2003; Leary, 2009; Beck, 2013). The growth of small firms may then be hampered by financial constraints (Carpenter and Petersen, 2002a).</footnote>
In this paper, we address the following questions: How does the use of external financing sources, banking ones (bank loans, credit lines/overdraft) and trade credit, by SMEs vary across Euro area countries? Are there country specific factors that explain cross-country differences in addition to the demand for external finances and to firms’ characteristics? How do these country specific factors vary over time? We propose an original empirical approach, taking advantage from the firm level and country representative Survey on Access to Finance by Enterprises (SAFE, see European Central Bank, 2016). This survey is, to our knowledge, the only micro level source focusing on SMEs and providing harmonized and country-representative information about SMEs external finances. We construct comparable country-time indicators of SMEs’ propensities to use external financing sources and analyze the evolution of the variance of these indicators over time. We focus on the main external financing sources for Euro area SMEs: bank financing and trade credit<fnref n="3" />. Among bank sources, we distinguish between loans and credit lines/overdraft. We use 8 waves of the (biannual) SAFE survey covering the period 2010-2014⁴.

Our analysis proceeds then into three steps.

First we build country-time specific propensities to use each source of external finances, which control for firm-level characteristics (activity, internal resources, size, age, etc.) and also for firms’ financing needs. Since the seminal paper by Fazzari et al. (1988), a large body of the empirical literature aims at identifying the existence of financing constraints for firms. We overcome the difficulty in identifying supply and demand effects thanks to the qualitative and self-assessed information provided by SMEs about their need for external finances. The estimated propensities could then be viewed as a country-time indicator of credit supply faced by SMEs controlling for their demand for external finance. Based on

<footnote n="3">³ According to our data, equity financing is used only by a limited proportion of SMEs (less than 7%, compared to more than 35% of them using bank sources and 30% trade credit). Such a fact is in line with previous findings in the literature (cf. Beck et al., 2008). Equity financing is a more common financing source for innovative and high tech firms (see Carpenter and Petersen 2002b; Hall and Lerner, 2010).</footnote>

<footnote n="4">⁴ The survey started in 2009 and is still regularly conducted by the ECB and the European Commission. Some changes in the sampling and in the questionnaire design occur however over time. In order to avoid any bias due to such changes, we restrict our analysis to a period where the questions about our main variables are stable over time. We also restrict our analysis to 11 countries that continuously participate in the survey over this period: Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands and Portugal.</footnote>
these propensities, we can compare the probabilities for SMEs with the same characteristics
to use external financing sources in different countries and at different time periods.

Second, we turn on the assessment of the divergence of SME's financing conditions
across the Euro area countries. To do that, we decompose the estimated country-time
propensity into a country fixed effect, a time cross-country effect and idiosyncratic shocks
and examine the evolution over time of the variance of the country-time propensities.<fnref n="5" /> The
conditional variance of the country-time propensity at time t is then the sum of three
components: i) the variance of the differences between countries that are constant over time,
assessed by the variance of the country fixed effects ii) a measure of the uncertainty faced by
countries, assessed by the conditional variance of the idiosyncratic shocks at time t iii) the
country-specific dynamics of the idiosyncratic shocks, measured by twice the covariance,
conditional on time, between the country fixed effects and the idiosyncratic shocks. We are
thus able to distinguish between two sources of divergence/convergence across countries: the
evolution of the variance of the shocks on the one hand (ii), the evolution of the correlation
between the shocks and the country specific effect on the other hand (iii). We recover
estimates of the parameters through regressions and compute the variance decomposition for
the three financing sources.

Regarding the convergence/divergence across Euro area countries, we find that the
variances of the propensities related to the use of the three financing sources have increased
over the period 2010-2014. We formerly test the cross-country divergence: we regress the
variances of the propensities on a time trend and obtain a significant coefficient for bank
loans which remains robust when excluding from the sample the countries that were severely
affected by the crisis. A key result is provided by our variance decomposition analysis which
shows that the increase in the variance over time is due to the increase in the covariance
between idiosyncratic shocks and the time-invariant country specific effect. These results
show that country specific factors play a crucial role in explaining the evolution of the Euro
area cross-country heterogeneity in firms' financing behaviors. They are related to the

<footnote n="5">⁵ Country-time specific propensities are estimated from individual-firm data. To compute the true variance of these propensities (or other non-linear statistics), we need to correct for the sampling biases induced by this estimation procedure. We propose a way to correct for this in this paper, presented in Appendix C.</footnote>
literature pointing out the persistence of firms’ capital structure over time (Lemmon et al., 2008) and showing the influence of the institutional environment (legal and financial system) on firms’ financing patterns (e.g. La Porta et al., 2000; Beck et al. 2008). Compared to this literature, we adopt another approach which allows us to analyze the convergence/divergence of countries as regard firms’ financing behaviors.

Third, we investigate what could be the underlying economic factors explaining the country-specific propensities of using bank loans. We obtain a negative correlation with banking concentration (as measured by the share of the 5 largest credit institutions), a positive correlation with the number of local branches of the credit institutions and with the deposits to asset ratio. Accounting for these factors, the divergence between countries induced by the country-specific dynamics of the idiosyncratic shocks vanishes. These results may then be related to the discussions in the literature about the effect of market structure, competition and regulation in the banking sector on SMEs’ access to finance (Beck et al., 2004, Carbo-Valverde et al., 2009, Beck, 2013). They suggest that indicators about banking concentration and debt to asset ratio are good candidates to explain the cross-country divergence of SMEs’ use of bank loans.

This paper is organized as follows. Section 2 presents the data we use and Section 3 details our empirical strategy. The regression results from the firm level determinants of the SMEs’ propensities to use the external financing sources are commented in Section 4. The empirical analysis of the cross-country divergence is conducted in Section 5. In Section 6, we investigate the correlation between SMEs country specific propensities to use external financing sources and various macroeconomic and banking structure indicators. Section 7 concludes.

## 2. Data

We use the Survey on the Access to finance of enterprises (SAFE) run every six months by the European Central Bank and the European Commission. The questionnaire covers the firms’ financing needs, their use of financing sources and refers to the last six
months' period. The survey provides then very useful information about firms' financing demand and financial constraints that could not be identified using balance-sheet data.<fnref n="6" />

The questions are asked to country-representative samples of small and median sized enterprises (with less than 250 employees) <fnref n="7" /> in most Euro area countries. Our analysis covers 11 countries which continuously participate in the 8 waves (2010H1 to 2013H2, see Table1) over the period 2010-2014: Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands and Portugal. In order to avoid any bias due to changes in the questionnaire, we restrict our analysis to a period where the main questions we use for our analysis and their place in the questionnaire, are stable over time.

The sampling of the survey is based on firm size and sector and takes into account total employment according to these breakdowns. Four sectors are surveyed: industry, construction, trade and other services. The sampling also includes a panel component (rotating panel). We need however a country representative sample at each point of time as we want to assess the evolution of the cross-country heterogeneity over time. We then rely on the repeated cross-sections and use weighted statistics and regressions for our empirical analysis. Table 1 summarizes the composition of the sample. It includes more than 6,900 SMEs in each survey round (excepted for 2010H1 wave where the sample size was about 5,000 SMEs) and is made up of a total of 53,626 observations for 11 countries over the 8 survey rounds covering the period 2010-2014.

[INSERT TABLE 1]

Firms participating in the survey are asked about their financing behavior. In particular, the survey provides qualitative information on the financing sources that were used over the past 6 months. A detailed list of financing sources is provided, and the firm has to answer whether it has used each of them over the past six months. Table 2 provides the

<footnote n="6">⁶ The available survey data are anonymized and they cannot be merged with extra information about balance sheets.</footnote>

<footnote n="7">⁷ The sample also includes some large firms (with more than 250 employees), but without being representative of this subpopulation in most of the countries. We restrict our empirical analysis to SMEs. Detailed methodological information on the SAFE survey is available on the ECB website: https://www.ecb.europa.eu/stats/pdf/surveys/sme/methodological_informationsurvey_and_user_guide.pdf?578aa5047cb67b72a50c6c5019e3283e</footnote>
percentage of SMEs answering they have used the financing sources by country over our
sample period. Banking sources are the most widely used financing sources by SMEs.
Considering the eleven countries, more than 40% of SMEs have used short term bank sources
(bank overdraft, credit line) and about 35% have relied on bank loans. Trade credit is also one
of the most often used sources of external finances: more than 30% of SMEs have used it.
There is however a wide cross-country heterogeneity in the proportion of SMEs that have
used these financing sources over the past six months. For instance, Ireland is the country
where the use of short term financing sources reaches its maximum (60.7% for bank
overdraft/credit lines, 71.6% for trade credit); for bank loans this maximum is reached in
Belgium (39.4%). At the opposite, Greece is characterized by very low proportions of SMEs
using bank financing sources (12.1% for bank overdraft, 25.4% for bank loans) and Germany
by a limited proportion of SMEs relying on trade credit (16.8%).

The other sources of external finance are less used by SMEs<fnref n="8" />, especially equity (6.62% of SMEs). About 25% of SMEs claim that they have used internal finances (retained earnings or sale of assets) and about 13% have relied on loans from a related company or from shareholders which could also be viewed as an internal financing source.

<caption>[INSERT TABLE 2]</caption>

Most of the information of the survey is qualitative and is based on self-assessed
perceptions by firms. One may be concerned by the reliability of such information to assess
actual financing behaviors. There are no other micro level sources focusing on SMEs and
providing harmonized information about the financing sources we are interested in (bank
financing and trade credit) across different countries. We can however rely on statistics
about credit distributions to see how it correlates with the survey information. We examine
the business volume of loans up to one million provided to non-financial corporations by
credit institutions<fnref n="9" />,<fnref n="10" /> in each country and for time periods corresponding to the reference
periods of the SAFE survey and compare it with the share of SMEs answering in the SAFE

<footnote n="8">⁸ Excepted leasing or hire-purchase or factoring which are also widely used by the SMEs (one third of them).</footnote>

<footnote n="9">⁹ Source MFI statistics. Available on http://sdw.ecb.europa.eu/browse.do?node=2018773.</footnote>

<footnote n="10">¹⁰ Loans up to one million are usually viewed as loans mostly granted to SMEs.</footnote>
survey that they have used bank loans. We obtain a correlation of 0.45 (significant at the 1%
level), which indicates that the qualitative information about the extensive margin provided
by the survey is in line with the available quantitative information on loans distribution to
SMEs. Moreover, a significant negative correlation (-0.49) is also obtained with aggregate
data on bank interest rate.<fnref n="11" />

The comparability of answers across country is another concern with such a survey.
While we cannot exclude country specific effects that could be due to various factors
(culture, translation of questions in the national language, etc.), the setting of the survey,
which is fully coordinated at the European level with a unique questionnaire aims at
mitigating these issues.

3. Empirical strategy

In order to assess the cross-country heterogeneity in SMEs financing behavior, our empirical strategy is in three steps. The first step consists in explaining firms’ financing by their observable characteristics, their demand for external finances and country and time specific effects. We estimate country and time specific propensities to use external financing sources which capture the financing use of observably similar SMEs across countries. The second step uses these country and time specific propensities to examine how their variance has evolved over time. We propose a decomposition of this variance for each period that allows us to assess the potential sources of divergence across countries with respect to the financing behavior of their SMEs. Finally, we examine the correlation between these country and time specific propensities and several macro-statistics first to ensure their interpretation, second to assess their potential determinants.

To ensure the interpretation of our results, we sometimes need to specify more
precisely the stochastic structure of the country-time specific effects, adding structural
constraints and thus decreasing the degrees of freedom of the model. These specifications are
added along the steps of our empirical strategy. Another way would have been to use the

<footnote n="11">¹¹ We use bank interest rate of loans to corporations of up to 1 million with an initial rate fixation period over one and up to five years (Source MFI statistics). See Appendix A for more detailed information.</footnote>
most constrained specification from the 1st step, directly estimating the few structural parameters that define it. We prefer our step-by-step strategy that prevents the results from relying mainly on the structural constraints. It also allows us to present the raw estimates of the cross-country variance. The purpose of adding supplementary structure in the stochastic models is only here to help the interpretation.

## 3.1. Propensity to use a financing source

We estimate at the firm level the propensity to have used a given financing source over the past six months, controlling for observed characteristics of firms and for time-varying country specific effects. The aim of this section is to recover estimates of these country-time specific effects.

The empirical specification for the propensity to use an external financing source is as follows<fnref n="12" />:

$$U_{it}^{S*} = \beta^{S} FirmCharacteristics_{it} + \delta^{S} X_{it} + \sum_{S'} \theta^{S,S'} N_{it}^{S'} + \gamma_{jt}^{S} + \varepsilon_{it}^{S} \quad (1)$$

S is the subscript for the source of external finances (bank loans, bank overdraft or trade credit). The dependent variable $U_{it}^{S*}$ corresponds to the latent variable or the propensity that a firm i used the financing source S over the past 6 months at time t, which is described by the variable $U_{it}^{S}$ in the survey:

$$U_{it}^{S} = \begin{cases} 1 & \text{if i answers it used the type of financing S over the past 6 months} \\ 0 & \text{otherwise} \end{cases}$$

$U_{it}^{S}$ is related to its latent variable through the following relation $U_{it}^{S} = (U_{it}^{S*} > 0)$ as it is usual in limited dependent variable models.

We have thus three equations describing firms’ external financing use: one for bank loans, one for credit line and overdraft and one for trade credit. $FirmCharacteristics_{it}$ are

<footnote n="12">¹² Similar regressions on another question of the SAFE survey asking firms about their financing needs ($N_{it}^{S}$) have also been done and are available upon request.</footnote>
the observed firm characteristics (age, size, annual turnover, main activity, belongs to a group or not), $X_{it}$ the variables reflecting why the firm needs increased external financing (fixed investment, inventories and working capital, lack of internal funds). We also control for the increased needs (or not) of firm i in the three financing sources over the past 6 months ($N_{it}^S$). These variables, together with the variables reflecting the reason why a firm faces increased needs for external finance ($X_{it}$), account for the demand side in explaining the use of an external financing source. The coefficients $\gamma_{it}^S$ correspond to the country-time (semester) specific propensities to use financing source S. $\theta^{S,S'}$ corresponds to the effect of having expressed the need for the financing type S' on the propensity to use the financing type S.

We estimate equations (1) as probit regressions. The regressions are weighted by the number of jobs that each firm represents in its country (e.g. firm sampling weight x firm size, variable WgtOldCommon in SAFE surveys, see European Central Bank, 2016).

The country-time-specific propensities ($\gamma_{it}^S$) incorporate all remaining country-specific factors that are not taken into account by the other explanatory variables. As far as we account for the firm-specific needs in external financing source as well as for differences in firms' characteristics, the estimated coefficients $\hat{\gamma}_{it}^S$ could be viewed as reflecting country-time-specific factors facilitating the use of external financing source S by SMEs.

The coefficients $\hat{\gamma}_{it}^S$ are estimated from a probit regression that describes a qualitative behavior of firms (i.e. whether they have used or not a given financing source). Thanks to this method, we turn individual qualitative information into aggregate quantitative indicators. Standard statistical analysis can be used on these continuous indicators, such as variance decomposition, tests or linear regression. Note that $\hat{\gamma}_{it}^S$ are only estimates of the true values of coefficients $\gamma_{it}^S$, that are not directly observed. This might induce a bias when considering non-linear indicators based on these estimates and not their true values. Appendix C shows how this issue can be solved, relying on the variance of the difference between the estimated coefficients $\hat{\gamma}_{it}^S$ and their true values $\gamma_{it}^S$ that can be derived from the estimation procedure.
## 3.2. Decomposition of the variance of the country-specific propensities

To assess whether the country-time propensities $\gamma_{jt}$ have diverged<fnref n="13" />, we are going to examine the evolution of its variance overtime, i.e. conditional on t, $V(\gamma_{jt}|t)$. To help interpreting its evolution, we decompose the country-time propensity $\gamma_{jt}$ into a country specific fixed effect $\mu_j$, a time cross-country effect $\alpha_t$ and idiosyncratic shocks $v_{jt}$ affecting country j at time t, such that:

$$ \gamma_{jt} = \mu_j + \alpha_t + v_{jt} \quad (2) $$

The estimation of such decomposition is based on the following assumptions

$$ \forall j, E(v_{jt}|j) = 0 $$

$$ \forall t, E(v_{jt}|t) = 0 $$

From this, we derive the standard decomposition of variance: $V(\gamma_{jt}) = V(\mu_j + \alpha_t) + V(v_{jt})$.

Now, we are more interested in $V(\gamma_{jt}|t)$. From the decomposition (2), we derive this conditional variance:

$$ V(\gamma_{jt}|t) = V(\mu_j + \alpha_t + v_{jt}|t) = V(\mu_j) + V(v_{jt}|t) + 2E(\mu_j v_{jt}|t) \quad (3) $$

Since $\alpha_t$ is constant for t given, its conditional variance and covariances with $\mu_j$ and $v_{jt}$ are nil. The first term $V(\mu_j)$, in expression (3), measures the differences between countries that are constant over time. By construction, it does not depend on t and will not affect the convergence or divergence process. The second term $V(v_{jt}|t)$ is the conditional variance of the idiosyncratic shocks $v_{jt}$ at time t. An increase of this component would reflect that shocks are more and more dispersed across countries. Such a rise could be interpreted as an increase of uncertainty faced by countries. The last component, $E(\mu_j v_{jt}|t)$ corresponds to

<footnote n="13">¹³ For the simplicity of exposition, subscripts are removed. They are made explicit in the empirical section.</footnote>
the covariance conditional on time between $\mu_j$ and $v_{jt}$. It reflects the fact that $v_{jt}$ can have a country-specific dynamic that can be related to $\mu_j$. This is the reason why $E(\mu_j v_{jt} | t)$ can be not equal to zero even though $E(v_{jt} | t)$ is by assumption. These components are estimated accounting for the estimation step of $\gamma_{jt}^S$ as described in Appendix C<fnref n="14" />.

To better understand the evolution of $E(\mu_j v_{jt} | t)$, it might be useful to consider the following specification:

$$v_{jt} = \eta_j(t - \bar{t}) + \xi_{jt}, \quad (4)$$

Where $\eta_j$'s are country-specific trends such that $E(\eta_j) = 0$, $\bar{t}$ the average time period and $\xi_{jt}$ an idiosyncratic shock independent of $t$, $\mu_j$ and $\eta_j$.<fnref n="15" /> With this specification we get:

$$E(\mu_j v_{jt} | t) = E(\mu_j \eta_j) (t - \bar{t}). \quad (5)$$

Hence, a positive (resp. negative) correlation between the country-specific trends $\eta_j$ and the country-specific effects $\mu_j$ will result in an increase (resp. decrease) of the variance of $\gamma_{jt}$ conditional on $t$, see (3).

This specification provides also some testable predictions on the evolution of the variance of the residuals:

$$V(v_{jt} | t) = V(\eta_j)(t - \bar{t})^2 + V(\xi_{jt} | t) \quad (6)$$

Hence, if the variance of $\xi_{jt}$ is constant over time, the shape of the variance of $v_{jt}$ should be convex, first decreasing, then increasing. Testing whether $V(v_{jt} | t)$ exhibits a linear trend consists in testing whether $V(\xi_{jt} | t)$ has it since the linear trend of $(t - \bar{t})^2$ is 0. Such a linear trend on this component would mean that the volatility of shocks would have increased over the period.

<footnote n="14">¹⁴ Not accounting for this step overestimates the variances of $\gamma_{jt}^S$ and its components.</footnote>

<footnote n="15">¹⁵ See for instance the Heterogeneous Income Process examined by Baker (1997) or Guvenen (2009) in the literature on earnings.</footnote>
In practice, we get an estimate of $E(\mu_j\eta_j)$ by regressing this covariance on a time trend as suggested by Equation (4). We provide also a test for the linear trend of $V(v_{jt}|t)$. We are not able to verify whether it has the convex profile as suggested by Equation (6) because of the very low number of time periods.

With this setting, we are able to distinguish between two different sources of divergence or convergence. The first one is related to the evolution of the variance of the shocks, the second one to the correlation between the country-specific trends and country-specific fixed effects. A positive correlation (resp. negative) means that the countries with the average highest $\gamma_{jt}$ are also the ones where they increase (resp. decrease) at most.

### 3.3 Correlations of estimated country specific propensities with macroeconomic indicators

In order to investigate the underlying country specific factors explaining the cross-country heterogeneity of SMEs financing behaviors, we examine the correlations between the estimated country-time propensities $\gamma_{jt}$ and various country macroeconomic indicators reflecting the cost of bank loans, the macroeconomic environment and the banking industry structure. This analysis is then conducted at the country level. Due to our limited number of observations (11 countries observed during 8 semesters), we examine the correlations with one indicator $Z_{jt}$, controlling for time and country fixed effects ($\alpha_t^Z$ and $\mu_j^Z$):

$$ \gamma_{jt} = \pi_0 + \pi_1 Z_{jt} + \mu_j^Z + \alpha_t^Z + \nu_{jt}^Z \quad (7) $$

The correlations between the country-time fixed effects ($\gamma_{jt}$) and the economic indicator ($Z_{jt}$) are then identified thanks to their evolutions over time and across countries. The estimation is done on sub-samples of countries to check for the robustness of the results.

We apply the same kind of decomposition as in the paragraph 3.2 above to analyze the evolution of the conditional variance of $\gamma_{jt}$ accounting for other explanatory factors. We also use the correction method in appendix C to account for the estimation step of $\gamma_{jt}$.
## 4. Firm level determinants of financing choices

This section presents the main results obtained from the firm level estimates of the propensity to use bank loans, credit line/bank overdraft and trade credit (Equation 1). The control variables *FirmCharacteristics*<sub>it</sub> are traditional firm level characteristics. The survey provides information on size (number of employees), age, annual turnover, and the main activity of the company. We also know whether the firm is part of another enterprise (subsidiary or branch) or whether it takes autonomous financial decisions. All these variables are available as categorical variables. They could be viewed as proxies for the ability to generate profits and to rely on internal finances. The main activity also accounts for investment opportunities and other features that are specific to an industry. Size and age are usual variables to account for informational problems with external fund providers. In addition, we know for which reasons (which are not mutually exclusive), a firm faces increased needs for external finances: because of fixed investment, inventories and working capital or/and because of the lack of internal funds (*X*<sub>it</sub>). It is crucial information to control for investment opportunities and for the availability of internal funds at the firm level. Finally, the survey also provides qualitative information on the financing needs of firms. They are asked whether their needs for each financing source have increased over the past 6 months (*N*<sub>it</sub><sup>S'</sup>). The regression results are reported in Table 3.<sup>16</sup>

[INSERT TABLE 3]

We obtain significant estimated coefficients for the traditional firms’ financing determinants that are in line with the empirical literature.<fnref n="17" /> The financing behavior of SMEs differs across sectors, firm size and age. In particular, larger SMEs (in terms of number of employees) are more likely to use external financing source, everything else being equal. This size effect on the propensity to use bank loans could partly reflect the existence of

<footnote n="16">¹⁶We consider three specifications: first the standard firms’ characteristics only, second, introducing the control for the nature of the financing needs and third, adding control for having expressed the need for the financing type S’. The various specifications provide coherent messages and we consider the more complete ones as our preferred ones. Detailed results available upon request.</footnote>

<footnote n="17">¹⁷ It is an additional reason to acknowledge the information contents of the qualitative survey.</footnote>
informational problems as larger firms are deemed to be easier to be evaluated by external funds providers and to be more well-known than smaller firms.

The estimated coefficients associated with the age variable are also in line with such an interpretation. The propensity to use banking financing sources is lower for younger firms, even when controlling for their financing needs. As it is more difficult to evaluate these firms than well-established ones, they are more likely to face financing constraints. Another interesting result is the effect of the annual turnover (proxy for firms' profitability and ability to generate resources). It increases the probability that SMEs use bank loans (even when controlling for the financing needs), probably because it is good signal about the quality and the risk of the firm. The correlation with the propensity to use short term bank financing goes however the other way around. We also find a lower probability to use external financing sources over the past six months for SMEs which are subsidiary or branch of another firm, even when controlling for the needs in external finances.

One finds significant differences in the propensity of SMEs to use external financing sources depending on the underlying purpose (investment, inventories and working capital, or lack of internal resource). The propensity to have used bank loans is highly correlated with needs to finance investment. For credit line/overdraft and trade credit, the highest correlations are obtained with the needs to finance inventories and working capital.

As expected, the propensity of having expressed increased needs in a financing source and the propensity to have used this financing source are highly correlated. Moreover, firms with high propensity to have used bank loans are also firms that have expressed increased needs in terms of bank short term finance (bank overdraft, credit line) or firms that do not have increased needs in terms of trade credit. Concerning the use of overdraft/credit line, it is also positively correlated with increased needs in trade credit, while the propensity to have used trade credit is only significantly correlated with increased needs in trade credit. More generally, we find differentiated effects of several explanatory variables (see also age and annual turnover) on the propensity to use trade credit compared to the propensity to use short term bank finances. Such a result is in line with the literature which underlines the specific features of trade credit. As financing source, trade credit could be a substitute to credit lines
when firms face financing constraints or tightening monetary policy (Ng et al., 1999, Biais and Gollier, 1997, Yang, 2011, Carbo-Valverde et al., 2016). Trade credit is however not only used for financing purposes, it is also a crucial element of buyer-supplier relationships that could be used to price discriminate or to reduce transaction costs (Petersen and Rajan, 1997).

# 5. Country-level analyses

## 5.1 Cross-country comparisons

From the probit specification (Equation (1)), it is possible to compute for each country and time period the probability for a firm with a given set of characteristics to use a financing source. For the sake of simplicity, we consider an averaged firm over all countries and periods such that the resulting probability will only differ with respect to the period or country. For such a firm in a country j at period t, its propensity to use a financing source S would be:

$$V_{jt}^{S} = \beta^{S} FirmCharacteristics_{j} + \delta^{S} X_{j} + \sum_{S'} \theta^{S,S'} N_{jt}^{S'} + \gamma_{jt}^{S} \quad (8)$$

Where the notation $A_{it}$ corresponds to the mean of the variable $A_{it}$ over all firms and time periods. Based on this propensity, the net probability for an average firm to use the financing source S is simply $\Phi(V_{jt}^S)$ where $\Phi$ is the cdf of the normal distribution. The differences in the probabilities between countries and time periods will simply reflect the differences in the $\gamma_{jt}^S$.

Table 4 represents the raw and net probabilities of using different financing sources by country averaged over time. In most cases and for each financing source considered, raw and net probabilities are rather close in level and very much correlated. There are however some discrepancies between the two measures: for instance the raw probability for Greek firms to use bank loans is higher than the net probability for an average firm that would be located in Greece (0.29 vs 0.23). Moreover, the same average firm will have a higher probability in Austria or Belgium to use bank loans than the observed firms in these countries. This means that the observable characteristics of firms in Austria and Belgium
make them, on average, less likely to use bank loans than firms in Greece. In particular, in Greece, firms express more often than in other countries increased needs for bank loans (see Table A.1). However, since the environment in Austria and Belgium is much more favorable ($\gamma_{jt}^S$) for this financing source, the net probability for an average firm to use a bank loan is much higher.

[INSERT TABLE 4]

Countries differ not only with respect to the level of their probabilities to use bank loans, but also in their evolutions over time (Figure 1.a). For example, the net probability to use bank loans for an averaged firm have sharply decreased in Greece (from 0.39 to 0.14 between 2010H1 and 2013H2) and Finland (from 0.35 to 0.20) but increased in Belgium (from 0.33 to 0.45).

[INSERT FIGURE 1]

For the other short-term external financing sources, credit lines and trade credit, probabilities to use them are much more stable (Figure 1.b and 1.c).

## 5.2 Cross-country divergence assessment

Using the firm level regression results, we compute the conditional variance $V(\gamma_{jt}^S|t)$ of the country specific propensities in order to test for the existence of a divergence process across country as regards SMEs financing behavior. We then compute the variance decomposition proposed in Section 3 to analyze what are the sources of this divergence process.

The conditional variances $V(\gamma_{jt}^S|t)$ have increased over the sample period<fnref n="18" /> (Figure 2). This divergence in SMEs financing behaviors across country is far much pronounced for banking sources. The variance of country-time specific propensity has been multiplied by 3.0 for bank loans, by 2.4 for overdraft/credit lines, and only by 1.2 for trade credit over the

<footnote n="18">¹⁸ As mentioned in section 3.2, these conditional variances are estimated applying the correction method presented in Appendix C.</footnote>
period 2010-2014. When excluding the countries that suffered severely from the financial and
sovereign debt crisis (Greece, Ireland and Portugal), the average variances of the country
specific propensities are lowered, pointing out that part of the divergence across country is
driven by some countries. (Cf. Figure A.1 in the appendix).

[INSERT FIGURE 2]

We propose a formal test of the divergence process by regressing the variance of the
estimated country-time specific propensities $V(\gamma_{jt}^S|t)$ on a time trend (Table 5). We obtain a
significant estimated coefficient of the time trend on the overall conditional variance $V(\gamma_{jt}^S|t)$
for bank finances, confirming the cross-country divergence diagnosis. This result continues to
hold for bank loans when excluding Greece, Ireland and Portugal.

The variance decomposition provides new insights on the sources of these cross-
country divergences. According to the Figure 1, the increase in the variance of the country
specific propensities over time ($V(\gamma_{jt}^S|t)$) is mostly due to the increase in the covariance
$E(\mu_j^S v_{jt}^S|t)$ over time while the variance of the idiosyncratic shocks ($V(v_{jt}|t)$) exhibits a
quite convex profile, as suggested in Section 3.2.

[INSERT TABLE 5]

When regressing each component of the variance on a time trend (Table 5), the
estimated coefficient of the time trend is positive and significant for the covariance term
$E(\mu_j^S v_{jt}^S|t)$. It is non-significant for the variance term $V(v_{jt}|t)$. These empirical results are
then in line with the specification of idiosyncratic shocks suggested by expression (4): a
positive correlation between country specific trends and country specific effects increases the
variance of the country specific propensities. This result continues to hold for bank loans
when excluding Greece, Ireland and Portugal. The divergence across countries is then found
to reflect the effect of country specific dynamics of the idiosyncratic shocks that are linked to
the country specific effect ($\mu_j^S$), and not to be due to an increase in the volatility of shocks
between countries.
These results suggest that country specific factors play a crucial role in explaining the evolution of the Euro area cross-country heterogeneity in firms' financing behaviors over the period 2010-2014. They are related to the literature pointing out the persistence of firms' capital structure over time (Lemmon et al., 2008) and showing the influence of the institutional environment (legal and financial system) on firms' financing patterns (e.g. La Porta et al., 2000; Beck et al. 2008). Unlike this literature, we estimate country specific propensities as regard SMEs financing behaviour and analyze their convergence/divergence without, at this stage, considering the institutional features that may govern these evolutions. We find that cross-country heterogeneity as regard the use of bank loans by SMEs has increased. We show that this divergence is mainly related to the country specific dynamics of idiosyncratic shocks (related to their country specific effects) and not to an increase in the volatility of these shocks between countries. While identifying the sources of the development of the cross-country heterogeneity is beyond the scope of this paper, we take a step toward this direction and investigate whether various macroeconomic and banking structure indicators are correlated with the country specific propensities and with the components of their variance.

**6. Correlations between SME's financing and macroeconomic and banking structure indicators**

In order to investigate what could be the underlying factors explaining the heterogeneity in the estimated propensities to use the three financing sources and the sources of divergence across country, we consider a large set of country-level indicators. They are related to the cost of bank loans (interest rates of loans distributed by credit institutions to non-financial corporations), the banking industry structure (Herfindahl index and number of branches of the credit institutions within the country, share of the 5 largest credit institutions, ratios of capital and deposits to total assets), the macroeconomic environment (spread of government bonds, spreads of CDS, GDP growth rate, unemployment rate, synthetic indicator of systemic risk) and the corporate specific environment (investment rate and leverage ratio of non-financial corporations). All indicators vary substantially across the considered countries and over time (see Table 6).
[INSERT TABLE 6]

**6.1. Correlations with country specific propensities**

Given the limited number of observations (eight time periods and eleven countries), we regress the country specific propensity on one macro-indicator at a time while we control for time and country fixed effects (Table 7).

[INSERT TABLE 7]

Interesting results are obtained concerning bank financing sources while there is no significant correlation with the trade credit propensities. As expected, one finds a significant positive correlation between the propensities to have used bank finances (both short and long term ones) with the value of distributed loans up to 1 million in a country and a negative one with the interest rate (of loans up to one million with an initial rate fixation period up to one year), even when controlling for country and time effects. One also finds a negative correlation with the unemployment rate and with the spreads of government bonds or CDS which does not however remain significant when excluding the peripheral countries that have been severely affected by the crisis (Greece, Portugal and Ireland, see Table B1 in the appendix). The most striking results are the significant correlations obtained with banking structure indicators, which are robust when excluding Greece, Ireland, and Portugal. Countries with increasing banking concentration (as measured by the share of the 5 largest credit institutions<fnref n="19" />) are also countries where SMEs have a decreasing propensity to use bank financing sources. Given that the estimated propensities account for demand effects, such a result may be interpreted as reflecting a decrease in SMEs’ access to bank finances in countries facing increasing banking concentration. Along the same lines, a robust negative correlation is also obtained between the Herfindahl index of the credit institutions and the propensity to use credit lines/overdraft. Moreover, this propensity is also positively correlated with the number of local branches of the credit institutions. Such results may then be related

<footnote n="19">¹⁹ See Table A.1 in Appendix A for the sources and definitions of the macro variables.</footnote>
to the discussion in the literature about the effect of market structure and competition in the banking sector on access to finance (Beck, 2013). They are both in line with the views pointing out the importance of local bank-firms’ relationships in explaining firms’ access to finance (“Banks that are physically closer to the firm have lower costs of monitoring and transacting with the firm”, Petersen and Rajan, 1995), and with the traditional market efficiency argument that more competition improves credit availability for firms.

Concerning bank balance sheet indicators, some interesting results could be related to the discussions about the link between SMEs lending and regulatory policies (such as capital requirement) or banking activities (Beck. 2013). One obtains a positive correlation between the propensity related to the use of bank loans and the deposits to asset ratio which seems to indicate that countries where the banking industry tends to rely more on traditional activity are countries where SMEs have a higher propensity to rely on bank loans. Moreover, there is a negative correlation between the country specific propensity of SMEs to use credit line/overdraft and the measure of the capitalization of the banking industry: countries where the capitalization of the credits institutions was reinforced are also countries where the SMEs' propensity to rely on short term bank finances decreases.

## 6.2. Correlations with the divergence across country

In Section 5, we find that the cross country divergence related to SMEs’ use of bank loans have increased and is driven by a positive correlation between country specific trends and country specific effects. We now examine if this divergence process can be accounted for by a macro indicator relying on the specifications of Equation (7) and on the results presented in Table 7. For each specification that corresponds to the introduction of a variable Z in Equation (7) we can test if there remains a divergence related to the country-specific dynamics by computing the time trend associated with the covariance $E(\mu_j^{S,Z} \nu_{jt}^{S,Z} | t)$, S being the financing source examined. The results are presented in Table 8.<fnref n="20" />

[INSERT TABLE 8]

<footnote n="20">²⁰ Table 8 focuses on bank loans as we found the divergence diagnosis robust for bank loans, even when excluding Greece, Ireland and Portugal. Results about the other financing sources are in Appendix B (Table B3).</footnote>
The positive correlation between the time trend and the covariance between country specific trends and country specific effects found in Table 5 becomes non-significant when some of the macro-variables are accounted for in Equation (7). It is the case for three banking industry structure indicators: the number of branches, the share of the 5 largest credit institutions, and the deposit to asset ratio.<fnref n="21" />

In other words, the banking industry indicators provide crucial information: they are not only correlated with country-specific propensity to use bank loans (Table 7), they are also able to capture the divergence across country. It suggests that these variables are good candidates to explain the divergence across country as regards SMEs' use of bank loans. More broadly, this suggests that the evolution of banking structure has lead SME's financing structure to evolve differently across countries.

## 7. Conclusion

In this paper, we use a survey to SMEs covering Euro area countries to build some country-specific indicator of the financing environment that similar SMEs would face in each country. We show that the abilities of firms to use bank loans have diverged from 2010 to 2014 across the main countries of the Euro zone. Considering other external financing sources (abilities to use short-term credit or trade credit), the evidence of such a divergence process is weaker (for credit lines/overdraft) or null (for trade credit).

To do so, we have proposed a methodology to recover quantitative country-specific indicators of firms' financing abilities from qualitative indicators based on firm-level representative surveys. This allows us to establish a diagnosis of convergence or divergence of firms' financing conditions that controls for differences in their composition between countries.

<footnote n="21">²¹ Note that the unemployment rate that also appeared as significantly correlated with this indicator (Table 7), but was not robust to exclusion of some countries from the dataset (see Table B.2 in Appendix B), does not capture this divergence.</footnote>
We consider two potential mechanisms of the divergence. In the first one, the divergence would be related to an increase in the variance of shocks that all countries face. In the second one, the divergence would be related to countries having heterogeneous deterministic paths, shocks having a limited effect. We find that the second mechanism is prominent. Moreover, we find that this prominence disappears when we control for the evolution of country-specific banking structures. Even if such evidence remains weak because of the limited time span that we have in our data, the relationship that we exhibit here between the banking structure and the SMEs propensity to use bank loans is worth to be further investigated since up to now only evidence within countries of this relationship has been examined (Beck, 2013).

Our results may have policy implications. In a context of a monetary and banking union in the Eurozone, if the divergence of financing of SMEs is closely related to the one of the banking institutional features, this gives some room for the regulation authorities to limit it.
References

ABOWD J. M., KRAMARZ F. and ROUX S. (2006), Wages, Mobility and Firm Performance: Advantages and Insights from Using Matched Worker-Firm Data, The Economic Journal, 116 (June), 245-285.

BAKER M. (1997), Growth-rate Heterogeneity and the Covariance Structure of Life-Cycle Earnings, Journal of Labor Economics, 15, 338-375.

BASSETT W.F., CHOSAK M.B., DRISCOLL J.C., and ZAKRAJŠEK E. (2014), Changes in bank lending standards and the macroeconomy, Journal of Monetary Economics, 62, 23-40.

BECK T., DEMIRGÜÇ-KUNT A., LAEVEN L. and MAKSIMOVIC V. (2004), Bank Competition and Access to Finance: International Evidence, Journal of Money, Credit and Banking, 36 (3), 627-648.

BECK T., DEMIRGÜÇ-KUNT A., LAEVEN L. and MAKSIMOVIC V. (2006), The determinants of financing obstacles, Journal of International Money and Finance, 25(6), 932-952.

BECK T., DEMIRGÜÇ-KUNT A. and MAKSIMOVIC V. (2008), Financing patterns around the world: Are small firms different?, Journal of Financial Economics, 89 (3), 467-487

BECK T. (2013), Bank financing for SMES-Lessons from the literature, National Institute Economic Review, 225, 23-38.

BECKER B. and IVASHINA V. (2014), Cyclicality of credit supply: Firm level evidence, Journal of Monetary Economics, 62, 76-93.

BIAIS B. and GOLLIER C. (1997), Trade Credit and Credit Rationing. The Review of Financial Studies, 10(4), 903-937.

CARBÓ-VALVERDE S., RODRÍGUEZ-FERNÁNDEZ F., and UDELL G. (2009), Bank Market Power and SME Financing Constraints, Review of Finance, 13(2), 309-340.
CARBÓ-VALVERDE S., RODRÍGUEZ-FERNÁNDEZ F., and UDELL G. (2016), Trade Credit, the Financial Crisis, and SME Access to Finance, Journal of Money, Credit and Banking, 48 (1), 113-143.

CARPENTER R. and PETERSEN B. (2002a), Is The Growth Of Small Firms Constrained By Internal Finance?, The Review of Economics and Statistics, 84(2), 298-309.

CARPENTER R. and PETERSEN B. (2002b), Capital Market Imperfections, High-Tech Investment, and New Equity Financing, Economic Journal, 112(477), F54-F72.

DEMIRGÜÇ-KUNT, A. and MAKSIMOVIC, V. (1998), Law, Finance, and Firm Growth. The Journal of Finance, 53 (6), 2107-2137.

EUROPEAN CENTRAL BANK (2006), Survey on the access to finance of enterprises: Methodological information on the survey and user guide for the anonymised micro dataset. https://www.ecb.europa.eu/stats/pdf/surveys/sme/methodological_informationsurvey_and_user_guide.pdf?578aa5047cb67b72a50c6c5019e3283e

FAZZARI, S. M., HUBBARD R.G., and PETERSEN B.C. (1988), Financing Constraints and Corporate Investment, Brookings Papers on Economic Activity, (1988:1), 141-195.

FISMAN, R., and LOVE, I. (2003), Trade Credit, Financial Intermediary Development, and Industry Growth. The Journal of Finance, 58(1), 353-374.

GUVENEN F. (2009), An empirical investigation of labor income processes, Review of Economic Dynamics, 12, 58-79.

HALL B. AND LERNER J. (2010), The Financing of R&D and Innovation, Handbook of the Economics of Innovation, Elsevier.

LA PORTA R., LOPEZ-DE-SILANES F., SHLEIFER A., and VISHNY R. (2000), Investor protection and corporate governance, Journal of Financial Economics, 58 (1-2), 3-27.

LEARY, M. T. (2009), Bank Loan Supply, Lender Choice, and Corporate Capital Structure. The Journal of Finance, 64 (3), 1143-1185.
LEMMON, M. L., ROBERTS, M. R. and ZENDER, J. F. (2008), Back to the Beginning: Persistence and the Cross-Section of Corporate Capital Structure. The Journal of Finance, 63(4), 1575–1608.

LOVE I., PREVE L.A. and SARRIA-ALLENDE V. (2007), Trade credit and bank credit: Evidence from recent financial crises. Journal of Financial Economics, 83 (2), 453-469.

NG C. K., SMITH J. K., and SMITH R. L. (1999), Evidence on the Determinants of Credit Terms Used in Interfirm Trade. The Journal of Finance, 54(3), 1109–1129.

PETERSEN M.A. and RAJAN R.G. (1995), The Effect of Credit Market Competition on Lending Relationships, The Quarterly Journal of Economics, 110 (2), 407-443.

PETERSEN M.A. and RAJAN R.G. (1997), Trade Credit: Theories and Evidence, The Review of Financial Studies, 10(3), 661-691.

RAJAN, R. G. and ZINGALES, L.. (1995). What Do We Know about Capital Structure? Some Evidence from International Data. The Journal of Finance, 50(5), 1421–1460.

SUFI, A. (2009), Bank Lines of Credit in Corporate Finance: An Empirical Analysis, The Review of Financial Studies, 22 (3), 1057-1088.

YANG X. (2011). Trade credit versus bank credit: Evidence from corporate inventory financing, The Quarterly Review of Economics and Finance, 51(4), 419-434.
<table><caption>Table 1. Sample: Number of observations by country and survey rounds</caption><thead><tr><th rowspan="2">survey round</th><th rowspan="2">Reference period<br>(last six months)</th><th colspan="11">Number of observations by country</th><th rowspan="2">All</th></tr><tr><th>Austria</th><th>Belgium</th><th>Germany</th><th>Spain</th><th>Finland</th><th>France</th><th>Greece</th><th>Ireland</th><th>Italy</th><th>Netherlands</th><th>Portugal</th></tr></thead><tbody><tr><td>2010H1</td><td>March-September 2010</td><td>180</td><td>180</td><td>928</td><td>933</td><td>90</td><td>914</td><td>180</td><td>90</td><td>957</td><td>230</td><td>224</td><td>4,906</td></tr><tr><td>2010H2</td><td>September 2010-February 2011</td><td>461</td><td>494</td><td>890</td><td>890</td><td>485</td><td>898</td><td>485</td><td>485</td><td>909</td><td>466</td><td>478</td><td>6,941</td></tr><tr><td>2011H1</td><td>April-September 2011</td><td>465</td><td>485</td><td>905</td><td>918</td><td>485</td><td>902</td><td>485</td><td>484</td><td>897</td><td>470</td><td>472</td><td>6,968</td></tr><tr><td>2011H2</td><td>October 2011-March 2012</td><td>470</td><td>488</td><td>900</td><td>912</td><td>485</td><td>901</td><td>485</td><td>485</td><td>900</td><td>470</td><td>473</td><td>6,969</td></tr><tr><td>2012H1</td><td>April-September 2012</td><td>476</td><td>485</td><td>905</td><td>901</td><td>485</td><td>900</td><td>485</td><td>485</td><td>900</td><td>466</td><td>471</td><td>6,959</td></tr><tr><td>2012H2</td><td>October 2012-March 2013</td><td>470</td><td>485</td><td>902</td><td>903</td><td>485</td><td>902</td><td>485</td><td>485</td><td>903</td><td>470</td><td>470</td><td>6,960</td></tr><tr><td>2013H1</td><td>April-September 2013</td><td>471</td><td>485</td><td>900</td><td>901</td><td>486</td><td>901</td><td>485</td><td>485</td><td>900</td><td>470</td><td>470</td><td>6,954</td></tr><tr><td>2013H2</td><td>October 2013-March 2014</td><td>470</td><td>491</td><td>900</td><td>900</td><td>487</td><td>905</td><td>485</td><td>485</td><td>901</td><td>474</td><td>471</td><td>6,969</td></tr></tbody><tfoot><tr><td colspan="2">Total</td><td>3,463</td><td>3,593</td><td>7,230</td><td>7,258</td><td>3,488</td><td>7,223</td><td>3,575</td><td>3,484</td><td>7,267</td><td>3,516</td><td>3,529</td><td>53,626</td></tr></tfoot></table>

Note: The source of the data is the SAFE survey run by the European Central Bank and the European Commission every six months. Our sample is restricted to firms with 1 to 249 employees. It covers a time period where the main questions used for the analysis were stable and it includes the Euro area countries that continuously participated in the survey over this time period.
<table><caption>Table 2. Percentage of SMEs using the sources of financing over the past 6 months (2010-2014)</caption><thead><tr><th></th><th>Austria</th><th>Belgium</th><th>Germany</th><th>Spain</th><th>Finland</th><th>France</th><th>Greece</th><th>Ireland</th><th>Italy</th><th>Netherlands</th><th>Portugal</th><th>All</th></tr></thead><tbody><tr><td>Bank overdraft, credit line or credit card overdrafts</td><td>38.83</td><td>39.16</td><td>37.62</td><td>37.78</td><td>22.89</td><td>41.24</td><td>12.11</td><td>60.66</td><td>53.41</td><td>49.35</td><td>38.42</td><td><strong>41.36</strong></td></tr><tr><td>Bank loan (new or renewal)</td><td>36.55</td><td>39.42</td><td>36.36</td><td>34.88</td><td>27.23</td><td>35.99</td><td>25.43</td><td>33.15</td><td>37.04</td><td>32.2</td><td>25.92</td><td><strong>35.01</strong></td></tr><tr><td>Trade credit</td><td>22.25</td><td>24.3</td><td>16.83</td><td>41.28</td><td>51.05</td><td>18.97</td><td>48.17</td><td>71.59</td><td>44.1</td><td>33.22</td><td>31.66</td><td><strong>31.15</strong></td></tr><tr><td>Grants or subsidised loans</td><td>15.53</td><td>16.04</td><td>14.1</td><td>20.25</td><td>11.57</td><td>10.46</td><td>12.23</td><td>13.35</td><td>17.82</td><td>6.2</td><td>18.13</td><td><strong>14.97</strong></td></tr><tr><td>Other loan (from a related company, shareholders, etc.)</td><td>12.19</td><td>17.68</td><td>17.59</td><td>16.44</td><td>15.78</td><td>8.33</td><td>7.43</td><td>18.29</td><td>7.9</td><td>21.92</td><td>9.55</td><td><strong>13.32</strong></td></tr><tr><td>Leasing or hire-purchase or factoring</td><td>43.23</td><td>22.96</td><td>49.48</td><td>24.9</td><td>42.52</td><td>36.27</td><td>14.14</td><td>32.18</td><td>22.69</td><td>36.67</td><td>22.38</td><td><strong>33.40</strong></td></tr><tr><td>Debt security issued</td><td>0.28</td><td>0.83</td><td>0.29</td><td>2.18</td><td>1.18</td><td>0.81</td><td>18.07</td><td>3.55</td><td>1.76</td><td>0.53</td><td>0.89</td><td><strong>1.79</strong></td></tr><tr><td>Subordinated loans, participating loans, etc.</td><td>2.07</td><td>4.58</td><td>2.37</td><td>2.67</td><td>3.05</td><td>0.68</td><td>1.21</td><td>1.89</td><td>1.00</td><td>10.88</td><td>1.11</td><td><strong>2.33</strong></td></tr><tr><td>Equity (quoted or unquoted shares)</td><td>10.05</td><td>6.91</td><td>12.27</td><td>2.89</td><td>7.19</td><td>6.97</td><td>7.87</td><td>8.38</td><td>3.6</td><td>2.71</td><td>1.55</td><td><strong>6.62</strong></td></tr><tr><td>Retained earnings or sale of assets</td><td>34.28</td><td>19.45</td><td>36.55</td><td>23.73</td><td>45.79</td><td>14.15</td><td>21.61</td><td>38.79</td><td>23.71</td><td>24.55</td><td>5.27</td><td><strong>25.35</strong></td></tr></tbody></table>

Note: Weighted percentages obtained from the SAFE survey (ECB) for firms with 1 to 249 employees. Number of observations: 53,626.
<table><caption>Table 3. Propensity to have used bank loans, credit line/bank overdraft and trade credit over the past 6 months</caption><thead><tr><th rowspan="2">Main activity</th><th colspan="3">S=bank loans</th><th colspan="3">S=credit line or bank overdraft</th><th colspan="3">S=trade credit</th></tr><tr><th>Coeff.</th><th>Std.</th><th>Marginal Eff.</th><th>Coeff.</th><th>Std.</th><th>Marginal Eff.</th><th>Coeff.</th><th>Std.</th><th>Marginal Eff.</th></tr></thead><tbody><tr><td>Manufacturing</td><td>0.074 ***</td><td>0.019</td><td>0.024</td><td>0.067 ***</td><td>0.019</td><td>0.022</td><td>0.290 ***</td><td>0.020</td><td>0.091</td></tr><tr><td>Construction</td><td>0.146 ***</td><td>0.023</td><td>0.048</td><td>0.136 ***</td><td>0.023</td><td>0.044</td><td>0.205 ***</td><td>0.023</td><td>0.064</td></tr><tr><td>Wholesale or retail trade</td><td>0.118 ***</td><td>0.017</td><td>0.038</td><td>0.132 ***</td><td>0.017</td><td>0.043</td><td>0.319 ***</td><td>0.018</td><td>0.099</td></tr><tr><td>Services</td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td></tr><tr><td><strong>Number of employees</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1 to 9 employees</td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td></tr><tr><td>10 to 49 employees</td><td>0.221 ***</td><td>0.018</td><td>0.072</td><td>0.156 ***</td><td>0.018</td><td>0.050</td><td>0.114 ***</td><td>0.019</td><td>0.035</td></tr><tr><td>50 to 249 employees</td><td>0.268 ***</td><td>0.026</td><td>0.089</td><td>0.139 ***</td><td>0.026</td><td>0.045</td><td>0.183 ***</td><td>0.028</td><td>0.057</td></tr><tr><td><strong>Financial independence</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Independent financial decisions</td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td></tr><tr><td>Subsidiary or branch of another firm</td><td>-0.313 ***</td><td>0.024</td><td>-0.097</td><td>-0.217 ***</td><td>0.024</td><td>-0.068</td><td>-0.148 ***</td><td>0.025</td><td>-0.044</td></tr><tr><td><strong>Annual turnover</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Up to € 2 million</td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td></tr><tr><td>More than € 2 million and up to € 10 million</td><td>0.128 ***</td><td>0.020</td><td>0.042</td><td>-0.049 **</td><td>0.020</td><td>-0.016</td><td>0.177 ***</td><td>0.021</td><td>0.055</td></tr><tr><td>More than € 10 million</td><td>0.196 ***</td><td>0.028</td><td>0.065</td><td>-0.069 **</td><td>0.028</td><td>-0.022</td><td>0.286 ***</td><td>0.029</td><td>0.090</td></tr><tr><td><strong>Age</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>10 years and more</td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td><td>ref.</td><td></td><td></td></tr><tr><td>5 years to 9 years</td><td>-0.066 ***</td><td>0.020</td><td>-0.021</td><td>-0.008</td><td>0.020</td><td>-0.003</td><td>-0.005</td><td>0.021</td><td>-0.002</td></tr><tr><td>2 years to 4 years</td><td>-0.123 ***</td><td>0.028</td><td>-0.039</td><td>-0.098 ***</td><td>0.028</td><td>-0.031</td><td>0.020</td><td>0.029</td><td>0.006</td></tr><tr><td>less than 2 years</td><td>-0.113 *</td><td>0.058</td><td>-0.036</td><td>-0.291 ***</td><td>0.058</td><td>-0.089</td><td>-0.090</td><td>0.062</td><td>-0.027</td></tr><tr><td><strong>Reasons for extra external financing needs</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Investment</td><td>0.367 ***</td><td>0.019</td><td>0.126</td><td>0.140 ***</td><td>0.020</td><td>0.046</td><td>0.054 ***</td><td>0.021</td><td>0.017</td></tr><tr><td>Inventories and working capital</td><td>0.154 ***</td><td>0.019</td><td>0.051</td><td>0.197 ***</td><td>0.019</td><td>0.065</td><td>0.190 ***</td><td>0.019</td><td>0.059</td></tr><tr><td>Lack of internal funds</td><td>-0.013</td><td>0.020</td><td>-0.004</td><td>0.039 *</td><td>0.021</td><td>0.013</td><td>0.010</td><td>0.021</td><td>0.003</td></tr><tr><td><strong>Increased needs in the following financing source</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bank loans</td><td>0.921 ***</td><td>0.020</td><td>0.335</td><td>0.024</td><td>0.021</td><td>0.008</td><td>0.003</td><td>0.021</td><td>0.001</td></tr><tr><td>Trade credit</td><td>-0.099 ***</td><td>0.024</td><td>-0.032</td><td>0.078 ***</td><td>0.025</td><td>0.025</td><td>1.103 ***</td><td>0.025</td><td>0.373</td></tr><tr><td>Bank overdraft, credit line</td><td>0.119 ***</td><td>0.019</td><td>0.039</td><td>1.175 ***</td><td>0.019</td><td>0.412</td><td>-0.002</td><td>0.019</td><td>-0.001</td></tr><tr><td>country fixed effects, time fixed effects, country-time fixed effects</td><td>yes</td><td></td><td></td><td>yes</td><td></td><td></td><td>yes</td><td></td><td></td></tr></tbody></table>

Note: Probit estimates (weighted regressions). The dependent variable is a qualitative variable equals to one when a firm answer that it has used the financing source over the past 6 months. The marginal effects are computed at the sample mean. Number of observations: 53,626. Full set of 11 countries: Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands, Portugal. Significant at ***1%. **5% and *10%.
<table><caption>Table 4. Average Probabilities to use a financing source</caption><thead><tr><th rowspan="2">Country</th><th colspan="2">S = Bank Loans</th><th colspan="2">S = Credit Lines or Overdraft</th><th colspan="2">S = Trade Credit</th></tr><tr><th>Raw</th><th>Net</th><th>Raw</th><th>Net</th><th>Raw</th><th>Net</th></tr></thead><tbody><tr><td>AT</td><td>0.37</td><td>0.40</td><td>0.37</td><td>0.43</td><td>0.20</td><td>0.25</td></tr><tr><td>BE</td><td>0.38</td><td>0.41</td><td>0.35</td><td>0.42</td><td>0.23</td><td>0.25</td></tr><tr><td>DE</td><td>0.36</td><td>0.38</td><td>0.37</td><td>0.42</td><td>0.16</td><td>0.19</td></tr><tr><td>ES</td><td>0.35</td><td>0.35</td><td>0.35</td><td>0.36</td><td>0.38</td><td>0.43</td></tr><tr><td>FI</td><td>0.26</td><td>0.27</td><td>0.23</td><td>0.26</td><td>0.45</td><td>0.55</td></tr><tr><td>FR</td><td>0.36</td><td>0.34</td><td>0.40</td><td>0.39</td><td>0.18</td><td>0.19</td></tr><tr><td>GR</td><td>0.29</td><td>0.23</td><td>0.12</td><td>0.08</td><td>0.46</td><td>0.44</td></tr><tr><td>IE</td><td>0.33</td><td>0.34</td><td>0.61</td><td>0.61</td><td>0.71</td><td>0.72</td></tr><tr><td>IT</td><td>0.37</td><td>0.37</td><td>0.51</td><td>0.51</td><td>0.40</td><td>0.47</td></tr><tr><td>NL</td><td>0.31</td><td>0.33</td><td>0.46</td><td>0.52</td><td>0.30</td><td>0.35</td></tr><tr><td>PT</td><td>0.26</td><td>0.26</td><td>0.38</td><td>0.38</td><td>0.29</td><td>0.34</td></tr></tbody></table>

The raw probabilities are the weighted average over the whole period for each country of the dummies indicating the use of each financing source. Estimated probabilities to use the financing sources are computed for each period and country for a firm that would have the average characteristics of all firms in the sample based on the probit specification. The column (net) in the table present these estimated probabilities averaged over the period.
<table><caption>Table 5. Tests of divergence between countries of $V(\gamma_{jt}^S|t)$ and of its components</caption><thead><tr><th rowspan="3">Dependent variable</th><th colspan="2">S=bank loans</th><th colspan="2">S=credit line or bank overdraft</th><th colspan="2">S=trade credit</th></tr><tr><th colspan="2">Time Trend</th><th colspan="2">Time Trend</th><th colspan="2">Time Trend</th></tr><tr><th>Linear<br>Coefficient</th><th>Test of nullity<br>P-value</th><th>Linear<br>Coefficient</th><th>Test of nullity<br>P-value</th><th>Linear<br>Coefficient</th><th>Test of nullity<br>P-value</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-weight: bold;">Full sample of 11 countries</td></tr><tr><td>V(γ<sub>jt</sub><sup>S</sup>|t)</td><td>0.008</td><td>0.040</td><td>0.025</td><td>0.003</td><td>0.005</td><td>0.280</td></tr><tr><td>V(v<sub>jt</sub><sup>S</sup>|t)</td><td>-0.003</td><td>0.110</td><td>-0.002</td><td>0.204</td><td>-0.002</td><td>0.197</td></tr><tr><td>E(μ<sub>j</sub><sup>S</sup>v<sub>jt</sub><sup>S</sup>|t)</td><td>0.006</td><td>0.003</td><td>0.013</td><td>0.003</td><td>0.003</td><td>0.084</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Excluding Greece, Ireland and Portugal</td></tr><tr><td>V(γ<sub>jt</sub><sup>S</sup>|t)</td><td>0.005</td><td>0.032</td><td>-0.001</td><td>0.592</td><td>-0.002</td><td>0.564</td></tr><tr><td>V(v<sub>jt</sub><sup>S</sup>|t)</td><td>0.000</td><td>0.724</td><td>0.000</td><td>0.705</td><td>0.000</td><td>0.501</td></tr><tr><td>E(μ<sub>j</sub><sup>S</sup>v<sub>jt</sub><sup>S</sup>|t)</td><td>0.002</td><td>0.015</td><td>-0.001</td><td>0.590</td><td>-0.001</td><td>0.583</td></tr></tbody></table>

<footnote>$\mu_j^S$ is the country fixed effect and $v_{jt}^S$ is the residual in the decomposition $\gamma_{jt}^S = \alpha_t^S + \mu_j^S + v_{jt}^S$ (see section 3.2). The variances and their components are corrected for the estimation step of $\gamma_{jt}^S$. The full sample of 11 countries includes: Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands and Portugal.</footnote>
<table><caption>Table 6 (continued). Macroeconomic and structural indicators of Euro area countries- 2010-2014</caption><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th rowspan="3">Quantitative information on new loans<br>(Millions of euro)</th><th colspan="2">Cost of bank loans</th><th colspan="4">Banking industry structure</th><th colspan="5">Macroeconomic environment</th><th colspan="2">Non financial corporations specific environnement</th></tr><tr><th colspan="2">Interest rate up to 1 to 5 Years</th><th rowspan="2">Herfindahl index</th><th rowspan="2">Number of branches</th><th rowspan="2">Share of the 5 largest Credit Institutions</th><th rowspan="2">Capital to assets ratio</th><th rowspan="2">Deposits to assets ratio</th><th rowspan="2">Spread Government bonds</th><th rowspan="2">Spread CDS</th><th rowspan="2">Systemic risk</th><th rowspan="2">GDP growth rate</th><th rowspan="2">Unemployment rate</th><th rowspan="2">Investment rate</th><th rowspan="2">Leverage ratio</th></tr><tr><th>Loans up to 1 M.</th><th>Interest rate up to 1 year</th></tr><tr><th></th><th></th><th></th><th>(%)</th><th>(%)</th><th></th><th></th><th>(%)</th><th>(%)</th><th>(%)</th><th></th><th></th><th>(%)</th><th>(%)</th><th>(%)</th><th>(%)</th></tr></thead><tbody><tr><td rowspan="3">GR</td><td rowspan="3">Greece</td><td>mean</td><td>449</td><td>7.14</td><td>6.49</td><td>0.159</td><td>3567</td><td>80.5</td><td>10.5</td><td>39.8</td><td>1235</td><td>6574</td><td>0.809</td><td>-6.7</td><td>21.5</td><td>17.0</td><td>57.6</td></tr><tr><td>min</td><td>320</td><td>5.94</td><td>5.53</td><td>0.121</td><td>2899</td><td>70.6</td><td>7.7</td><td>36.9</td><td>640</td><td>568</td><td>0.541</td><td>-9.7</td><td>12.7</td><td>8.3</td><td>43.5</td></tr><tr><td>max</td><td>658</td><td>7.86</td><td>7.16</td><td>0.217</td><td>4005</td><td>94.0</td><td>14.8</td><td>42.9</td><td>2316</td><td>536</td><td>0.959</td><td>-5.1</td><td>27.5</td><td>20.8</td><td>65.0</td></tr><tr><td rowspan="3">IE</td><td rowspan="3">Ireland</td><td>mean</td><td>369</td><td>n.a.</td><td>4.35</td><td>0.066</td><td>1078</td><td>47.6</td><td>9.6</td><td>16.0</td><td>415</td><td>285</td><td>0.573</td><td>2.0</td><td>13.9</td><td>9.6</td><td>37.4</td></tr><tr><td>min</td><td>270</td><td>n.a.</td><td>3.80</td><td>0.063</td><td>1009</td><td>46.4</td><td>5.3</td><td>13.1</td><td>171</td><td>97</td><td>0.135</td><td>-0.7</td><td>12.1</td><td>8.5</td><td>18.1</td></tr><tr><td>max</td><td>626</td><td>n.a.</td><td>4.89</td><td>0.070</td><td>1162</td><td>49.9</td><td>12.4</td><td>19.8</td><td>772</td><td>10731</td><td>0.935</td><td>4.8</td><td>14.7</td><td>11.2</td><td>44.5</td></tr><tr><td rowspan="3">IT</td><td rowspan="3">Italy</td><td>mean</td><td>13907</td><td>5.34</td><td>4.05</td><td>0.041</td><td>32775</td><td>39.7</td><td>9.2</td><td>35.6</td><td>278</td><td>190</td><td>0.577</td><td>0.6</td><td>10.2</td><td>20.7</td><td>40.3</td></tr><tr><td>min</td><td>12978</td><td>4.38</td><td>2.99</td><td>0.041</td><td>31241</td><td>39.5</td><td>8.6</td><td>33.8</td><td>129</td><td>104</td><td>0.221</td><td>-1.9</td><td>8.4</td><td>19.5</td><td>38.3</td></tr><tr><td>max</td><td>14379</td><td>6.05</td><td>4.76</td><td>0.042</td><td>33631</td><td>40.1</td><td>9.8</td><td>37.3</td><td>435</td><td>308</td><td>0.868</td><td>2.7</td><td>12.5</td><td>21.6</td><td>40.9</td></tr><tr><td rowspan="3">NL</td><td rowspan="3">Netherlands</td><td>mean</td><td>1548</td><td>4.15</td><td>3.32</td><td>0.207</td><td>2477</td><td>83.5</td><td>4.6</td><td>36.3</td><td>37</td><td>12</td><td>0.154</td><td>1.4</td><td>5.4</td><td>14.6</td><td>33.9</td></tr><tr><td>min</td><td>1277</td><td>3.77</td><td>2.96</td><td>0.203</td><td>2010</td><td>82.1</td><td>4.1</td><td>34.8</td><td>23</td><td>0</td><td>0.037</td><td>0.1</td><td>4.4</td><td>13.8</td><td>17.7</td></tr><tr><td>max</td><td>1726</td><td>4.72</td><td>3.80</td><td>0.212</td><td>2864</td><td>84.4</td><td>5.0</td><td>38.9</td><td>53</td><td>28</td><td>0.333</td><td>3.5</td><td>6.8</td><td>15.1</td><td>36.8</td></tr><tr><td rowspan="3">PT</td><td rowspan="3">Portugal</td><td>mean</td><td>1699</td><td>6.96</td><td>6.49</td><td>0.120</td><td>6294</td><td>70.4</td><td>8.4</td><td>39.5</td><td>610</td><td>443</td><td>0.732</td><td>-0.9</td><td>14.4</td><td>21.1</td><td>40.0</td></tr><tr><td>min</td><td>1532</td><td>6.32</td><td>5.30</td><td>0.118</td><td>5963</td><td>69.7</td><td>7.3</td><td>37.9</td><td>255</td><td>179</td><td>0.465</td><td>-5.0</td><td>12.0</td><td>18.8</td><td>37.8</td></tr><tr><td>max</td><td>1902</td><td>7.66</td><td>7.52</td><td>0.121</td><td>6587</td><td>70.9</td><td>10.2</td><td>41.1</td><td>1084</td><td>817</td><td>0.950</td><td>2.3</td><td>16.4</td><td>24.7</td><td>41.8</td></tr></tbody></table>

Note: All figures are related to six months periods corresponding to the survey rounds. For data originally available as monthly statistics, the figures have been aggregated over the reference period (loans up to one million) or averaged (for the other variables). For annual frequency indicator, the figures are weighted average of n and n+1 indicators (by the number of covered months) when the survey round run over two calendar years. The detailed information on the source of each indicator is provided in Appendix A.
<table><caption>Table 7. Regressions of the country specific propensities ($\gamma_{jt}^S$) on macro indicators (one by one) - Full sample</caption><thead><tr><th rowspan="2">Macro and banking structure indicators</th><th colspan="3">S=bank loans</th><th colspan="3">S=credit line or bank overdraft</th><th colspan="3">S=trade credit</th></tr><tr><th>Parameter</th><th>P-value</th><th>R-square</th><th>Parameter</th><th>P-value</th><th>R-square</th><th>Parameter</th><th>P-value</th><th>R-square</th></tr></thead><tbody><tr><td>Loans up to 1 M.</td><td>0.470</td><td>0.001</td><td>0.690</td><td>0.329</td><td>0.008</td><td>0.934</td><td>-0.027</td><td>0.872</td><td>0.901</td></tr><tr><td>Interest rate 1 to 5 Years</td><td>-0.003</td><td>0.940</td><td>0.671</td><td>-0.061</td><td>0.122</td><td>0.934</td><td>0.094</td><td>0.069</td><td>0.868</td></tr><tr><td>Interest rate up to 1 year</td><td>-0.103</td><td>0.029</td><td>0.674</td><td>-0.124</td><td>0.004</td><td>0.942</td><td>-0.037</td><td>0.514</td><td>0.898</td></tr><tr><td>Herfindahl index</td><td>-3.501</td><td>0.000</td><td>0.708</td><td>-4.199</td><td>0.000</td><td>0.953</td><td>1.649</td><td>0.172</td><td>0.900</td></tr><tr><td>Number of branches</td><td>0.905</td><td>0.015</td><td>0.679</td><td>1.148</td><td>0.001</td><td>0.944</td><td>-0.128</td><td>0.774</td><td>0.898</td></tr><tr><td>Share of the 5 largest Credit Institutions</td><td>-0.284</td><td>0.000</td><td>0.746</td><td>-0.274</td><td>0.000</td><td>0.954</td><td>0.068</td><td>0.374</td><td>0.899</td></tr><tr><td>Capital to assets ratio</td><td>-3.066</td><td>0.052</td><td>0.669</td><td>-3.235</td><td>0.025</td><td>0.939</td><td>0.876</td><td>0.640</td><td>0.898</td></tr><tr><td>Deposits to assets ratio</td><td>3.650</td><td>0.000</td><td>0.717</td><td>2.344</td><td>0.010</td><td>0.940</td><td>-1.656</td><td>0.160</td><td>0.900</td></tr><tr><td>Spread government bonds</td><td>-0.021</td><td>0.014</td><td>0.680</td><td>-0.029</td><td>0.000</td><td>0.947</td><td>-0.030</td><td>0.002</td><td>0.911</td></tr><tr><td>Spread CDS</td><td>-0.006</td><td>0.000</td><td>0.795</td><td>-0.006</td><td>0.000</td><td>0.963</td><td>0.000</td><td>0.957</td><td>0.897</td></tr><tr><td>Systemic risk</td><td>-0.025</td><td>0.880</td><td>0.650</td><td>-0.146</td><td>0.337</td><td>0.935</td><td>-0.248</td><td>0.202</td><td>0.900</td></tr><tr><td>GDP growth rate</td><td>-0.017</td><td>0.135</td><td>0.661</td><td>-0.008</td><td>0.450</td><td>0.935</td><td>0.020</td><td>0.134</td><td>0.901</td></tr><tr><td>Unemployment rate</td><td>-0.045</td><td>0.000</td><td>0.774</td><td>-0.049</td><td>0.000</td><td>0.967</td><td>0.004</td><td>0.704</td><td>0.898</td></tr><tr><td>Investment rate</td><td>0.008</td><td>0.345</td><td>0.613</td><td>0.020</td><td>0.007</td><td>0.938</td><td>0.018</td><td>0.062</td><td>0.909</td></tr><tr><td>Leverage ratio</td><td>0.004</td><td>0.455</td><td>0.653</td><td>0.005</td><td>0.274</td><td>0.935</td><td>-0.008</td><td>0.141</td><td>0.901</td></tr></tbody></table>

OLS estimates. The dependent variable is the country time specific propensity of using the financing source S ($\gamma_{jt}^{S,U}$). The regressions are done considering one by one macro indicator as explanatory variable, and controlling for country and time effects. See Table A2 in Appendix A for the definitions of the macro and banking structure indicators. Loans up to one million and the number of branches are in log, and we apply a logistic transformation on the share of the 5 largest credit institutions (log(x)/(100-x)). 88 observations. The full sample of 11 countries includes: Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands and Portugal.
<table><caption>Table 8. Tests of divergence between countries of $V(\gamma_{jt}^S)$ and of its components when accounting for macro or banking indicators – Full Sample – S=bank loans</caption><thead><tr><th rowspan="2">Dependent variable</th><th rowspan="2">Macro variable included in Equation 7</th><th colspan="2">Time Trend</th></tr><tr><th>Linear Coefficient</th><th>P-value</th></tr></thead><tbody><tr><td>E(μ<sub>j</sub><sup>S</sup>ν<sub>jt</sub><sup>S</sup>|t)</td><td>-</td><td>0.0058</td><td>0.003</td></tr><tr><td rowspan="15">E(μ<sub>j</sub><sup>S,Z</sup>ν<sub>jt</sub><sup>S,Z</sup>|t)</td><td>Loans up to 1 M.</td><td>-0.0042</td><td>0.172</td></tr><tr><td>Interest rate up 1 to 5 Years</td><td>0.0052</td><td>0.004</td></tr><tr><td>Interest rate up to 1 year</td><td>0.0029</td><td>0.006</td></tr><tr><td>Herfindahl index</td><td>-0.0028</td><td>0.004</td></tr><tr><td>Number of branches</td><td>-0.0017</td><td>0.731</td></tr><tr><td>Share of the 5 largest Credit Institutions</td><td>0.0002</td><td>0.664</td></tr><tr><td>Capital to assets ratio</td><td>0.0044</td><td>0.007</td></tr><tr><td>Deposits to assets ratio</td><td>0.0012</td><td>0.623</td></tr><tr><td>Spread government bonds</td><td>0.0035</td><td>0.002</td></tr><tr><td>Spread CDS</td><td>0.0017</td><td>0.046</td></tr><tr><td>Systemic risk</td><td>0.0057</td><td>0.003</td></tr><tr><td>GDP growth rate</td><td>0.0068</td><td>0.006</td></tr><tr><td>Unemployment rate</td><td>0.0019</td><td>0.019</td></tr><tr><td>Investment rate</td><td>0.0055</td><td>0.005</td></tr><tr><td>Leverage ratio</td><td>0.0065</td><td>0.005</td></tr></tbody></table>

$\mu_j^S$ is the country fixed effect and $\nu_{jt}^S$ is the residual in the decomposition $\gamma_{jt}^S = \alpha_t^S + \mu_j^S + \nu_{jt}^S$ (see section 3.2). The same decomposition applies for $\gamma_{jt}^{S,Z}$. The variance and its components are corrected for the estimation step of $\gamma_{jt}^S$.

The full sample of 11 countries includes: Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands and Portugal.
<caption>Figure 1: Evolution of net probabilities of using financing sources between 2010-1 and 2013-2</caption>

<figure><img src="image_3.png" /><figcaption>Figure 1.b: Credit Lines</figcaption></figure>

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>Figure 1.c: Trade Credit</figcaption></figure>

These figures display the net probability to use bank loans, credit line or bank overdraft and trade credit for two survey periods 2010-1 and 2013-2. These probabilities are computed for an averaged firm. The net probabilities are computed from firm level regressions (probit models) using the SMEs country representative survey SAFE. They account for firm level characteristics (age, size, annual turnover, main activity, belongs to a group or not) and for firm financing demand (increased needs, reasons for increased needs).
<figure><img src="image_6.png" /><figcaption>Figure 2. Variance decomposition of the country-specific propensities $V(\gamma_{jt}^S | t)$</figcaption></figure>

S=Bank loans

S=Credit lines/overdraft

<figure><img src="image_7.png" /></figure>
<caption>Figure 2 (continued) Variance decomposition of the country-specific propensities $V(\gamma_{jt}^S | t)$</caption>

$S=\text{Trade credit}$

<figure><img src="image_8.png" /></figure>

This figure displays the evolution of the variance of the country time specific propensities ($V(\text{gamma}_{jt}/t)$) to use a financing source (bank loans, credit lines and trade credit) and of its three components: the variance of the country fixed effects ($V(\text{mu}_j)$), the conditional variance of the idiosyncratic shocks at time $t$ ($V(\text{nu}_{jt}/t)$), the correlation between the shocks and the country specific effect ($2*\text{cov}(\text{nu}_{jt}, \text{mu}_j/t)$). The country specific propensities are estimated from firm level regressions (probit models) using the SMEs country representative survey SAFE. They account for firm level characteristics (age, size, annual turnover, main activity, belongs to a group or not) and for firm financing demand (increased needs, reasons for increased needs). The variances and their components are corrected for the estimation step of $\text{gamma}_{jt}$. This is the reason why some estimates of $V(\text{nu}_{jt}|t)$ are slightly below zero. This means that the true value of this variance is not significantly different from zero. Uncorrected measures of these variances and their components are available upon request.
# Appendix A: Data

## Dependent variables

The question in the SAFE survey about the use of external financing sources is as follows:

**Q4.** Turning to the financing structure of your firm, to finance normal day-to-day business operations or more specific projects or investments, you can use internal funds and external financing.

For each of the following sources of financing, could you please say whether you used them during the past 6 months, did not use them but have experience with them, or did not use them because this source of financing has never been relevant to your firm?

[READ OUT – ONE ANSWER PER LINE]

- Used in the past 6 months.................................................................................. 1

- Did not use in the past 6 months, but have experience with this source of financing .................................................................................................................. 2

- Did not use as this source of financing has never been relevant to my firm [INSTRUMENT IS NOT APPLICABLE TO MY FIRM] .................................................. 7

- [DK] .................................................................................................................. 9

In order to define our dependent variables $U_{it}^S$, we focus on the answers for the three following financing sources and define our dependent variables as equal to one when the variable takes the value 1 and equal to zero otherwise:

c) Bank overdraft, credit line or credit cards overdraft [BANK OVERDRAFT = NEGATIVE BALANCE ON A BANK ACCOUNT WITH OR WITHOUT SPECIFIC PENALTIES; CREDIT LINE = PRE ARRANGED LOAN THAT CAN BE USED, IN FULL OR IN PART, AT DISCRETION AND WITH LIMITED ADVANCE WARNING; CREDIT CARD OVERDRAFT = NEGATIVE BALANCE ON THE CREDIT CARD] .................................................................................. 1 2 7 9

d) Bank loan (new or renewal; excluding overdraft and credit lines) .................................... 1 2 7 9

e) Trade credit [= PURCHASE OF GOODS OR SERVICES FROM ANOTHER BUSINESS WITHOUT MAKING IMMEDIATE CASH PAYMENT] ........................ 1 2 7 9

## Explanatory variables

In addition to the traditional variables about firm size, age, annual turnover, etc (define as FirmCharacteristics$_{it}$ in equation (1)) that are available as categorical variables, we control for increases in external financing needs thanks to the following question ($N_{it}^{S'}$):

**Q5.** For each of the following types of external financing, please tell me if your needs increased, remained unchanged or decreased over the past 6 months?

[READ OUT – ONE ANSWER PER LINE]

- Increased.................................................................................................................. 1

- Remained unchanged .................................................................................. 2
- Decreased .................................................................................. 3

- [INSTRUMENT NOT APPLICABLE TO MY FIRM] .................... 7

- [DK] .................................................................................. 9

f) Bank overdraft, credit line or credit cards overdraft.................................................. 1 2 3 7 9

a) Bank loans (new or renewal; excluding overdraft and credit lines).......................... 1 2 3 7 9

b) Trade credit .................................................................................. 1 2 3 7 9

We also control for the reason why a firm faces increased needs for external financing ($X_{it}$ in equation (1)) thanks to the following question:

**Q6. For each of the following items, would you say that they have increased, decreased, or had no impact on your firm's needs for external financing over the past 6 months?**

[READ OUT – ONE ANSWER PER LINE]

- Increased needs for external financing.................................................. 1

- No impact on needs for external financing.................................................. 2

- Decreased needs for external financing.................................................. 3

- [NOT RELEVANT, DID NOT OCCUR] .................................................... 7

- [DK] .................................................................................. 9

a) Fixed Investment.................................................................................. 1 2 3 7 9

b) Inventories and working capital.................................................................. 1 2 3 7 9

c) Availability of internal funds .................................................................. 1 2 3 7 9

The sample statistics for the firm level variables are in Table A.1.
<table><caption>Table A.1 Sample statistics: means of firm level variables</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Number of observations</th><th colspan="3">Use of the external financing source</th><th colspan="4">Main activity</th><th colspan="3">Number of employees</th></tr><tr><th>Bank Loans</th><th>Trade Credit</th><th>Bank Overdraft</th><th>Manufacturing</th><th>Construction</th><th>Wholesale or retail trade</th><th>Services</th><th>1 to 9</th><th>10 to 49 employees</th><th>50 to 250 employees</th></tr></thead><tbody><tr><td>AT</td><td>3463</td><td>0.365</td><td>0.395</td><td>0.224</td><td>0.216</td><td>0.088</td><td>0.258</td><td>0.438</td><td>0.373</td><td>0.379</td><td>0.248</td></tr><tr><td>BE</td><td>3593</td><td>0.413</td><td>0.395</td><td>0.252</td><td>0.191</td><td>0.126</td><td>0.306</td><td>0.377</td><td>0.404</td><td>0.412</td><td>0.184</td></tr><tr><td>DE</td><td>7230</td><td>0.363</td><td>0.375</td><td>0.167</td><td>0.283</td><td>0.102</td><td>0.190</td><td>0.425</td><td>0.334</td><td>0.342</td><td>0.324</td></tr><tr><td>ES</td><td>7258</td><td>0.375</td><td>0.382</td><td>0.460</td><td>0.228</td><td>0.128</td><td>0.244</td><td>0.400</td><td>0.335</td><td>0.341</td><td>0.325</td></tr><tr><td>FI</td><td>3488</td><td>0.259</td><td>0.227</td><td>0.519</td><td>0.212</td><td>0.154</td><td>0.160</td><td>0.474</td><td>0.411</td><td>0.410</td><td>0.179</td></tr><tr><td>FR</td><td>7223</td><td>0.371</td><td>0.420</td><td>0.200</td><td>0.251</td><td>0.094</td><td>0.325</td><td>0.330</td><td>0.331</td><td>0.338</td><td>0.330</td></tr><tr><td>GR</td><td>3575</td><td>0.293</td><td>0.112</td><td>0.519</td><td>0.168</td><td>0.092</td><td>0.579</td><td>0.161</td><td>0.408</td><td>0.408</td><td>0.184</td></tr><tr><td>IE</td><td>3484</td><td>0.313</td><td>0.603</td><td>0.727</td><td>0.208</td><td>0.097</td><td>0.375</td><td>0.321</td><td>0.410</td><td>0.410</td><td>0.179</td></tr><tr><td>IT</td><td>7267</td><td>0.404</td><td>0.551</td><td>0.504</td><td>0.466</td><td>0.070</td><td>0.217</td><td>0.247</td><td>0.329</td><td>0.335</td><td>0.336</td></tr><tr><td>NL</td><td>3516</td><td>0.325</td><td>0.497</td><td>0.338</td><td>0.151</td><td>0.104</td><td>0.256</td><td>0.489</td><td>0.381</td><td>0.377</td><td>0.242</td></tr><tr><td>PT</td><td>3529</td><td>0.284</td><td>0.408</td><td>0.326</td><td>0.243</td><td>0.128</td><td>0.292</td><td>0.338</td><td>0.379</td><td>0.380</td><td>0.241</td></tr><tr><td>All</td><td>53626</td><td>0.352</td><td>0.406</td><td>0.371</td><td>0.257</td><td>0.105</td><td>0.278</td><td>0.360</td><td>0.361</td><td>0.366</td><td>0.273</td></tr></tbody></table>
Table A.1 (continued): Sample statistics

<table><thead><tr><th rowspan="2">Country</th><th rowspan="2">Financial independence</th><th colspan="4">Annual turnover</th><th colspan="5">Age</th></tr><tr><th>Subsidiary or Branch of another firm</th><th>Up to € 2 million</th><th>More than € 2 million and up to € 10 million</th><th>More than € 10 million</th><th>Don't know</th><th>10 years and more</th><th>5 years to 9 years</th><th>2 years to 4 years</th><th>less than 2 years</th><th>Don't know</th></tr></thead><tbody><tr><td>AT</td><td>0.158</td><td>0.263</td><td>0.200</td><td>0.511</td><td>0.025</td><td>0.801</td><td>0.125</td><td>0.058</td><td>0.015</td><td>0.072</td></tr><tr><td>BE</td><td>0.179</td><td>0.289</td><td>0.195</td><td>0.480</td><td>0.036</td><td>0.773</td><td>0.134</td><td>0.078</td><td>0.014</td><td>0.020</td></tr><tr><td>DE</td><td>0.119</td><td>0.273</td><td>0.194</td><td>0.512</td><td>0.021</td><td>0.814</td><td>0.112</td><td>0.057</td><td>0.017</td><td>0.088</td></tr><tr><td>ES</td><td>0.093</td><td>0.297</td><td>0.181</td><td>0.488</td><td>0.034</td><td>0.817</td><td>0.131</td><td>0.044</td><td>0.008</td><td>0.037</td></tr><tr><td>FI</td><td>0.118</td><td>0.283</td><td>0.146</td><td>0.563</td><td>0.008</td><td>0.830</td><td>0.116</td><td>0.040</td><td>0.013</td><td>0.015</td></tr><tr><td>FR</td><td>0.114</td><td>0.285</td><td>0.240</td><td>0.467</td><td>0.008</td><td>0.733</td><td>0.152</td><td>0.097</td><td>0.018</td><td>0.020</td></tr><tr><td>GR</td><td>0.051</td><td>0.299</td><td>0.119</td><td>0.549</td><td>0.034</td><td>0.787</td><td>0.142</td><td>0.061</td><td>0.010</td><td>0.021</td></tr><tr><td>IE</td><td>0.137</td><td>0.273</td><td>0.150</td><td>0.560</td><td>0.018</td><td>0.856</td><td>0.100</td><td>0.035</td><td>0.008</td><td>0.029</td></tr><tr><td>IT</td><td>0.069</td><td>0.281</td><td>0.259</td><td>0.447</td><td>0.013</td><td>0.811</td><td>0.121</td><td>0.059</td><td>0.009</td><td>0.036</td></tr><tr><td>NL</td><td>0.170</td><td>0.256</td><td>0.213</td><td>0.504</td><td>0.027</td><td>0.740</td><td>0.154</td><td>0.080</td><td>0.027</td><td>0.018</td></tr><tr><td>PT</td><td>0.105</td><td>0.244</td><td>0.074</td><td>0.629</td><td>0.053</td><td>0.801</td><td>0.129</td><td>0.061</td><td>0.010</td><td>0.018</td></tr><tr><td>All</td><td>0.114</td><td>0.279</td><td>0.190</td><td>0.508</td><td>0.023</td><td>0.796</td><td>0.129</td><td>0.062</td><td>0.013</td><td>0.037</td></tr></tbody></table>
<table><caption>Table A.1 (continued): Sample statistics</caption><thead><tr><th rowspan="2">Country</th><th colspan="3">Reasons for extra external financing needs</th><th colspan="3">Increased needs in the following financing source</th></tr><tr><th>Investment</th><th>Inventories and Working Capital</th><th>Lack of Internal Funds</th><th>Bank Loans</th><th>Trade Credit</th><th>Bank Overdraft</th></tr></thead><tbody><tr><td>AT</td><td>0.133</td><td>0.153</td><td>0.073</td><td>0.114</td><td>0.047</td><td>0.143</td></tr><tr><td>BE</td><td>0.183</td><td>0.196</td><td>0.168</td><td>0.162</td><td>0.091</td><td>0.158</td></tr><tr><td>DE</td><td>0.143</td><td>0.137</td><td>0.070</td><td>0.122</td><td>0.035</td><td>0.131</td></tr><tr><td>ES</td><td>0.169</td><td>0.260</td><td>0.234</td><td>0.198</td><td>0.164</td><td>0.212</td></tr><tr><td>FI</td><td>0.151</td><td>0.157</td><td>0.066</td><td>0.144</td><td>0.069</td><td>0.104</td></tr><tr><td>FR</td><td>0.214</td><td>0.269</td><td>0.197</td><td>0.214</td><td>0.088</td><td>0.246</td></tr><tr><td>GR</td><td>0.187</td><td>0.305</td><td>0.247</td><td>0.311</td><td>0.278</td><td>0.211</td></tr><tr><td>IE</td><td>0.077</td><td>0.236</td><td>0.233</td><td>0.160</td><td>0.222</td><td>0.251</td></tr><tr><td>IT</td><td>0.156</td><td>0.205</td><td>0.177</td><td>0.217</td><td>0.164</td><td>0.286</td></tr><tr><td>NL</td><td>0.166</td><td>0.230</td><td>0.073</td><td>0.147</td><td>0.119</td><td>0.201</td></tr><tr><td>PT</td><td>0.167</td><td>0.184</td><td>0.190</td><td>0.173</td><td>0.122</td><td>0.209</td></tr><tr><td>All</td><td>0.162</td><td>0.214</td><td>0.161</td><td>0.181</td><td>0.124</td><td>0.202</td></tr></tbody></table>
<table><caption>Table A.2. Definitions and sources of the macroeconomic and structural indicators</caption><thead><tr><td>Macroeconomic indicator</td><td>Description</td><td>Original source</td><td>Original frequency</td></tr></thead><tbody><tr><td>Loans up to 1 million</td><td>Bank business volumes - loans to corporations of up to EUR 1M (new business)</td><td>MFI Interest Rate Statistics - ECB</td><td>monthly</td></tr><tr><td>Interest rate up to 1 to 5 Yea</td><td>Bank interest rates - loans to corporations of up to EUR 1M with an initial rate fixation period of over one &amp; up to five years (new business)</td><td>MFI Interest Rate Statistics - ECB</td><td>monthly</td></tr><tr><td>Interest rate up to 1 year</td><td>Bank interest rates - loans to corporations of up to EUR 1M with a floating rate and an initial rate fixation period of up to one year (new business)</td><td>MFI Interest Rate Statistics - ECB</td><td>monthly</td></tr><tr><td>Spread of government bonds</td><td>Long-term interest rate for convergence purposes - Debt security issued (maturity=10 years)</td><td>Interest rate statistics- ECB</td><td>monthly</td></tr><tr><td>Spread CDS</td><td>Sovereign credit default swaps (10 years)</td><td>Thomson Reuters</td><td>monthly</td></tr><tr><td>Systemic risk</td><td>Sovereign Systemic Stress Composite Indicator</td><td>ECB</td><td>monthly</td></tr><tr><td>GDP growth rate</td><td>Gross domestic product at market prices</td><td>Eurostat</td><td>quarterly</td></tr><tr><td>Unemployment rate</td><td>Unemployment rate (Eurostat definition)</td><td>Eurostat</td><td>annual</td></tr><tr><td>Investment rate</td><td>Ratio of gross fixed capital formation to gross value added</td><td>Euro Area Accounts - ECB</td><td>annual</td></tr><tr><td>Leverage ratio</td><td>Ratio of debt to total assets/liabilities</td><td>Euro Area Accounts - ECB</td><td>annual</td></tr><tr><td>Herfindahl index</td><td>Herfindahl index for credit institutions based on total assets</td><td>Banking structural financial indicators-ECB</td><td>annual</td></tr><tr><td>Number of branches</td><td>Number of branches of credit institutions</td><td>Banking structural financial indicators-ECB</td><td>annual</td></tr><tr><td>Share of the 5 largest Credit Institutions</td><td>Shares of the 5 largest credit institutions in total assets</td><td>Banking structural financial indicators-ECB</td><td>annual</td></tr><tr><td>Capital to assets ratio</td><td>Ratio of capital (outstanding amounts) to total assets (outstanding amounts)</td><td>Monetary Statistics - ECB</td><td>monthly</td></tr><tr><td>Deposits to assets ratio</td><td>Ratio of deposits (outstanding amounts) to total assets (outstanding amounts)</td><td>Monetary Statistics - ECB</td><td>monthly</td></tr></tbody></table>

Most of the macroeconomic and structural indicators are available from the ECB statistical data warehouse: https://sdw.ecb.europa.eu/. Other sources are from Eurostat or Thomson Reuters. For data originally available as monthly statistics, the figures have been aggregated over the reference period (loans up to one million) or averaged (for the other variables). For annual frequency indicator, the figures are weighted average of n and n+1 indicators (by the number of covered months) when the survey round run over two calendar years.

When a variable is missing in a given country j for the full period (Interest rate 1 up to 5 years in Ireland), we use the average value across all other countries to impute this variable. When a variable is missing for some periods only, we impute the missing values as the average value of non-missing observations over the sample period for the country.
Appendix B: additional results
<table><caption>Table B1. Regressions of the country specific propensities ($\gamma_{jt}^{S,U}$) on macro indicators (one by one)- Subsample of country excluding Greece, Ireland and Portugal</caption><thead><tr><th rowspan="2">Macro and banking structure indicators</th><th colspan="3">S=bank loans</th><th colspan="3">S=credit line or bank overdraft</th><th colspan="3">S=trade credit</th></tr><tr><th>Parameter</th><th>P-value</th><th>R-square</th><th>Parameter</th><th>P-value</th><th>R-square</th><th>Parameter</th><th>P-value</th><th>R-square</th></tr></thead><tbody><tr><td>Loans up to 1 M.</td><td>0.445</td><td>0.011</td><td>0.690</td><td>0.199</td><td>0.084</td><td>0.938</td><td>0.035</td><td>0.894</td><td>0.896</td></tr><tr><td>Interest rate 1 to 5 Years</td><td>0.039</td><td>0.332</td><td>0.651</td><td>-0.021</td><td>0.426</td><td>0.935</td><td>0.069</td><td>0.228</td><td>0.899</td></tr><tr><td>Interest rate up to 1 year</td><td>-0.070</td><td>0.075</td><td>0.667</td><td>-0.034</td><td>0.179</td><td>0.937</td><td>0.089</td><td>0.120</td><td>0.901</td></tr><tr><td>Herfindahl index</td><td>0.129</td><td>0.915</td><td>0.644</td><td>-2.074</td><td>0.006</td><td>0.944</td><td>1.858</td><td>0.287</td><td>0.898</td></tr><tr><td>Number of branches</td><td>0.205</td><td>0.510</td><td>0.648</td><td>0.503</td><td>0.010</td><td>0.943</td><td>0.095</td><td>0.833</td><td>0.896</td></tr><tr><td>Share of the 5 largest Credit Institutions</td><td>-0.291</td><td>0.024</td><td>0.681</td><td>-0.200</td><td>0.016</td><td>0.942</td><td>-0.143</td><td>0.456</td><td>0.897</td></tr><tr><td>Capital to assets ratio</td><td>-0.676</td><td>0.744</td><td>0.645</td><td>-3.814</td><td>0.003</td><td>0.946</td><td>-3.405</td><td>0.254</td><td>0.898</td></tr><tr><td>Deposits to assets ratio</td><td>2.064</td><td>0.015</td><td>0.686</td><td>-0.086</td><td>0.879</td><td>0.935</td><td>-2.155</td><td>0.083</td><td>0.902</td></tr><tr><td>Spread government bonds</td><td>-0.034</td><td>0.239</td><td>0.655</td><td>-0.032</td><td>0.088</td><td>0.938</td><td>0.022</td><td>0.607</td><td>0.896</td></tr><tr><td>Spread CDS</td><td>-0.057</td><td>0.195</td><td>0.657</td><td>-0.042</td><td>0.139</td><td>0.937</td><td>0.048</td><td>0.456</td><td>0.897</td></tr><tr><td>Systemic risk</td><td>-0.251</td><td>0.113</td><td>0.663</td><td>-0.172</td><td>0.093</td><td>0.938</td><td>-0.037</td><td>0.872</td><td>0.896</td></tr><tr><td>GDP growth rate</td><td>0.009</td><td>0.634</td><td>0.646</td><td>-0.021</td><td>0.090</td><td>0.938</td><td>0.042</td><td>0.140</td><td>0.900</td></tr><tr><td>Unemployment rate</td><td>-0.015</td><td>0.286</td><td>0.653</td><td>-0.016</td><td>0.086</td><td>0.938</td><td>0.041</td><td>0.042</td><td>0.904</td></tr><tr><td>Investment rate</td><td>-0.008</td><td>0.309</td><td>0.642</td><td>0.008</td><td>0.113</td><td>0.940</td><td>0.007</td><td>0.496</td><td>0.901</td></tr><tr><td>Leverage ratio</td><td>-0.010</td><td>0.138</td><td>0.660</td><td>0.008</td><td>0.057</td><td>0.939</td><td>-0.005</td><td>0.621</td><td>0.896</td></tr></tbody></table>

OLS estimates. The dependent variable is the country time specific propensity of using the financing source $S(\gamma_{jt}^{S,U})$. The regressions are done considering one by one macro indicator as explanatory variable, and controlling for country and time effects. See Table A2 in the appendix for the definitions of the macro and banking structure indicators. Loans up to one million and the number of branches are in log, and we apply a logistic transformation on the share of the 5 largest credit institutions (log (x)/(100-x)). 64 observations. Subsample of 8 countries excluding Greece, Ireland, and Portugal.
<table><caption>Table B2. Robustness: Sub-sample of country excluding Greece, Ireland, and Portugal- Regressions of the variance of the country-time specific propensities and its components on a time trend and on macro variables (one by one)</caption><thead><tr><th rowspan="3">Dependent variable</th><th colspan="6">Explanatory variables</th></tr><tr><th rowspan="2">macro variable</th><th colspan="2">Trend</th><th colspan="2">Trend</th><th colspan="2">Trend</th></tr><tr><th>Parameter</th><th>P-value</th><th>Parameter</th><th>P-value</th><th>Parameter</th><th>P-value</th></tr></thead><tbody><tr><td>V(y<sub>jt</sub>|t)</td><td>-</td><td>0.0045</td><td>0.032</td><td>-0.0015</td><td>0.592</td><td>0.0080</td><td>0.377</td></tr><tr><td>V(v<sub>jt</sub>|t)</td><td>-</td><td>-0.0004</td><td>0.724</td><td>-0.0001</td><td>0.705</td><td>0.0008</td><td>0.595</td></tr><tr><td>E(μ<sub>j</sub>v<sub>jt</sub>|t)</td><td>-</td><td>0.0025</td><td>0.015</td><td>-0.0007</td><td>0.590</td><td>0.0036</td><td>0.383</td></tr><tr><td>E(μ<sup>S,Z</sup><sub>j</sub>v<sup>S,Z</sup><sub>jt</sub>|t)</td><td>Loans up to 1 M.</td><td>-0.0055</td><td>0.030</td><td>-0.0014</td><td>0.175</td><td>0.0030</td><td>0.486</td></tr><tr><td></td><td>Interest rate 1 to 5 Years</td><td>0.0027</td><td>0.013</td><td>-0.0007</td><td>0.600</td><td>0.0012</td><td>0.765</td></tr><tr><td></td><td>Interest rate up to 1 year</td><td>0.0024</td><td>0.025</td><td>-0.0007</td><td>0.574</td><td>0.0003</td><td>0.931</td></tr><tr><td></td><td>Herfindahl index</td><td>0.0024</td><td>0.017</td><td>-0.0011</td><td>0.039</td><td>0.0043</td><td>0.170</td></tr><tr><td></td><td>Number of branches</td><td>0.0014</td><td>0.070</td><td>0.0015</td><td>0.297</td><td>0.0013</td><td>0.787</td></tr><tr><td></td><td>Share of the 5 largest Credit Institutions</td><td>0.0001</td><td>0.834</td><td>-0.0015</td><td>0.163</td><td>0.0038</td><td>0.381</td></tr><tr><td></td><td>Capital to assets ratio</td><td>0.0025</td><td>0.013</td><td>-0.0005</td><td>0.689</td><td>0.0046</td><td>0.295</td></tr><tr><td></td><td>Deposits to assets ratio</td><td>-0.0003</td><td>0.627</td><td>-0.0005</td><td>0.660</td><td>0.0002</td><td>0.964</td></tr><tr><td></td><td>Spread government bonds</td><td>0.0025</td><td>0.020</td><td>-0.0006</td><td>0.599</td><td>0.0028</td><td>0.489</td></tr><tr><td></td><td>Spread CDS</td><td>0.0025</td><td>0.024</td><td>-0.0006</td><td>0.639</td><td>0.0023</td><td>0.578</td></tr><tr><td></td><td>Systemic risk</td><td>0.0023</td><td>0.029</td><td>-0.0007</td><td>0.543</td><td>0.0037</td><td>0.366</td></tr><tr><td></td><td>GDP growth rate</td><td>0.0024</td><td>0.017</td><td>-0.0003</td><td>0.810</td><td>0.0048</td><td>0.206</td></tr><tr><td></td><td>Unemployment rate</td><td>0.0020</td><td>0.026</td><td>-0.0005</td><td>0.693</td><td>0.0015</td><td>0.740</td></tr><tr><td></td><td>Investment rate</td><td>0.0022</td><td>0.020</td><td>-0.0005</td><td>0.668</td><td>0.0035</td><td>0.419</td></tr><tr><td></td><td>Leverage ratio</td><td>0.0020</td><td>0.028</td><td>0.0001</td><td>0.935</td><td>0.0033</td><td>0.440</td></tr></tbody></table>

Estimated parameters of the time trend in regressions of the dependent variable $E(μ_j^S v_{jt}^S | t)$ or $E(μ_j^S Z v_{jt}^S | t)$ on a time trend. The variances and their components are corrected for the estimation step of $γ_{jt}^S$. The sample of 8 countries excluding Greece, Ireland and Portugal: Austria, Belgium, Germany, Spain, Finland, France, Italy, Netherlands.
<table><caption>Table B3. Tests of divergence between countries of $V(\gamma_{jt}^S)$ and of its components: Detailed results (full sample)</caption><thead><tr><th rowspan="3">Dependent variable</th><th colspan="2">S=bank loans</th><th colspan="2">S=credit line or bank overdraft</th><th colspan="2">S=trade credit</th></tr><tr><th colspan="4">Explanatory variables</th><th colspan="2"></th></tr><tr><th>Linear coefficient</th><th>Time Trend P-value</th><th>Linear coefficient</th><th>Time Trend P-value</th><th>Linear coefficient</th><th>Time Trend P-value</th></tr></thead><tbody><tr><td>E(μ<sub>j</sub>ν<sub>jt</sub>|t)</td><td>0.0058</td><td>0.003</td><td>0.0135</td><td>0.003</td><td>0.0034</td><td>0.084</td></tr><tr><td>Loans up to 1 M.</td><td>-0.0042</td><td>0.172</td><td>0.0095</td><td>0.008</td><td>0.0037</td><td>0.069</td></tr><tr><td>Interest rate 1 to 5 Years</td><td>0.0052</td><td>0.004</td><td>0.0112</td><td>0.006</td><td>0.0006</td><td>0.884</td></tr><tr><td>Interest rate up to 1 year</td><td>0.0029</td><td>0.006</td><td>0.0097</td><td>0.004</td><td>0.0048</td><td>0.017</td></tr><tr><td>Herfindahl index</td><td>-0.0028</td><td>0.004</td><td>0.0023</td><td>0.494</td><td>0.0033</td><td>0.030</td></tr><tr><td>Number of branches</td><td>-0.0017</td><td>0.731</td><td>0.0133</td><td>0.037</td><td>0.0051</td><td>0.007</td></tr><tr><td>Share of the 5 largest Credit Institutions</td><td>0.0002</td><td>0.664</td><td>0.0015</td><td>0.557</td><td>0.0031</td><td>0.133</td></tr><tr><td>E(μ<sub>j</sub><sup>S,Z</sup>ν<sub>jt</sub><sup>S,Z</sup>|t)</td><td>0.0044</td><td>0.007</td><td>0.0117</td><td>0.009</td><td>0.0026</td><td>0.187</td></tr><tr><td>Capital to assets ratio</td><td>0.0012</td><td>0.623</td><td>0.0124</td><td>0.005</td><td>0.0016</td><td>0.432</td></tr><tr><td>Deposits to assets ratio</td><td>0.0035</td><td>0.002</td><td>0.0096</td><td>0.002</td><td>0.0031</td><td>0.219</td></tr><tr><td>Spread government bonds</td><td>0.0017</td><td>0.046</td><td>0.0010</td><td>0.598</td><td>0.0035</td><td>0.081</td></tr><tr><td>Spread CDS</td><td>0.0057</td><td>0.003</td><td>0.0125</td><td>0.004</td><td>0.0023</td><td>0.177</td></tr><tr><td>Systemic risk</td><td>0.0068</td><td>0.006</td><td>0.0141</td><td>0.003</td><td>0.0022</td><td>0.116</td></tr><tr><td>GDP growth rate</td><td>0.0019</td><td>0.019</td><td>0.0025</td><td>0.275</td><td>0.0031</td><td>0.131</td></tr><tr><td>Unemployment rate</td><td>0.0055</td><td>0.005</td><td>0.0119</td><td>0.010</td><td>0.0029</td><td>0.264</td></tr><tr><td>Leverage ratio</td><td>0.0065</td><td>0.005</td><td>0.0142</td><td>0.005</td><td>0.0008</td><td>0.637</td></tr></tbody></table>

Estimated parameters of the time trend in regressions of the dependent variable $E(μ_j^S v_{jt}^S | t)$ or $E(μ_j^{S,Z} v_{jt}^{S,Z} | t)$ on a time trend. The variances and their components are corrected for the estimation step of $γ_{jt}^S$. The full sample of 11 countries includes: Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands, and Portugal.
<figure><img src="image_9.png" /><figcaption>Figure A.1. Decomposition of the variance of the country-specific propensities $V(\gamma_{jt}^S|t)$-Subsample of country excluding Greece, Ireland and Portugal</figcaption></figure>
<caption>Figure A.1 (continued). Decomposition of the variance of the country-specific propensities $V(\gamma_{jt}^S|t)$- Subsample of country excluding Greece, Ireland and Portugal</caption>

$S=\text{Trade credit}$

<figure><img src="image_10.png" /></figure>

These figure displays the evolution of the variance of the country time specific propensities ($V(\text{gamma}_{jt}/t)$) to use bank loans and of its three components: the variance of the country fixed effects ($V(\text{mu}_j)$), the conditional variance of the idiosyncratic shocks at time t ($V(\text{nu}_{jt}/t)$), the correlation between the shocks and the country specific effect ($2*\text{cov}(\text{nu}_{jt},\text{mu}_j/t)$). The country specific propensities are estimated from firm level regressions (probit models) using the SMEs country representative survey SAFE. They account for firm level characteristics (age, size, annual turnover, main activity, belongs to a group or not) and for firm financing demand (increased needs, reasons for increased needs). Results on the subsample excluding, Greece, Ireland and Portugal.

The variances and their components are corrected for the estimation step of $\text{gamma}_{jt}$. This is the reason why some estimates of $V(\text{nu}_{jt}|t)$ are slightly below zero. This means that the true value of this variance is not significantly different from zero. Uncorrected measures of these variances and their components are available upon request.
Appendix C: Sample bias correction

The coefficients $\gamma_{jt}$ are not directly observed, estimates $\hat{\gamma}_{jt}$ are provided by the estimation method presented in section 3.1. Hence we have

$$ \hat{\gamma}_{jt} = \gamma_{jt} + \varepsilon_{jt} $$

Where the covariance matrix $\Sigma$ of the vector composed of $\varepsilon_{jt}$ (with $J*T$ elements) is derived from the estimation method (in our case the maximum likelihood). $J$ is the number of countries and $T$ is the number of periods. Such a setting can be problematic when you are interested in statistics derived from the parameters that include non-linear terms because the variability of the error term is a source of bias (see for instance Abowd et al, 2006). Such bias can be corrected since we know the covariance matrix of the error terms $\varepsilon_{jt}$ and under the assumption that they are not correlated with the true parameter values²

$$ V(\gamma_{jt}|t) = \frac{1}{J-1} \sum_{j=1}^{J} (\gamma_{jt} - \gamma_{.t})^2 \text{ where } \gamma_{.t} = \frac{1}{J} \sum_{j=1}^{J} \gamma_{jt} $$

Define $e_t$ a column vector of size $T$ such that $e_t[t] = 1$ and 0 for the other coordinated. Using the kronecker product $\otimes$, $I_J \otimes e_t'$ is a matrix such that $(I_J \otimes e_t')\gamma = [\gamma_{1t}, \dots, \gamma_{Jt}]'$, where $I_K$ is the identity matrix of size $K$. Denote $J_K$ the square matrix of size $K$ filled with ones only, such that $J_K^2 = KJ_K$, we can write that

$$ V(\gamma_{jt}|t) = \frac{1}{J-1} \sum_{j=1}^{J} (\gamma_{jt} - \gamma_{.t})^2 = \frac{1}{J-1} \gamma'(I_J \otimes e_t) (I_J - \frac{J_J}{J}) (I_J \otimes e_t')\gamma $$

Hence, in this case, $H = \frac{1}{J-1} (I_J \otimes e_t) (I_J - \frac{J_J}{J}) (I_J \otimes e_t')$.

We can now turn to general case presented in equation (7),

$$ \gamma_{jt} = \pi_0 + \pi_1 Z_{jt} + \mu_j^Z + \alpha_t^Z + \nu_{jt}^Z $$

This corresponds to the regression of $\gamma_{jt}$ on a set of explanatory variables $X_{jt} = [1, Z_{jt}, \mathbf{1}_{j'=j}, \mathbf{1}_{t'=t}]$ where $\mathbf{1}_{j'=j}$ is a row vector of $J-1$ dummies (the individual $J$ being the reference) indicating to which individual the observation corresponds and $\mathbf{1}_{t'=t}$ a row vector of $T-1$ dummies (the period $T$ being the reference). $X_{jt}$ is a row vector of size $1+K+J-$
1+T-1, is the number of explanatory variables introduced in Z. Equation (7) can be transformed into $\gamma_{jt} = X_{jt}\pi + v_{jt}^Z$ where $\pi' = [\pi_0, \pi'_1, \mu_1, ..., \mu_{j-1}, \alpha_1, ..., \alpha_{T-1}]$.

With this setting, we consider two statistics of interest: $V(v_{jt}^Z|t)$ and $E(\mu_j^Z v_{jt}^Z|t)$. With no error term, an unfeasible estimator of $v_{jt}^Z$ is $\tilde{v}^Z = (\mathbf{I}_{JT} - X(X'X)^{-1}X')\gamma$. Defining the matrix $S_\mu$ as the matrix extracting the coefficients $\mu_j$ from the vector of coefficients estimated from equation (7), we have $\mu = S_\mu\pi$. An unfeasible estimator of this vector is $\tilde{\mu} = S_\mu(X'X)^{-1}X'\gamma$.

An unfeasible estimator of $V(v_{jt}^Z|t)$, i-e assuming that there is no error term $\varepsilon$ would be:

$$ \begin{aligned} \hat{V}(\tilde{v}_{jt}^Z|t) &= \frac{1}{J-1} \sum_{j=1}^{J} (\tilde{v}_{jt}^Z - \tilde{v}_{t}^Z)^2 = \frac{1}{J-1} \tilde{v}^{Z'} (\mathbf{I}_J \otimes e_t) (\mathbf{I}_J - \frac{\mathbf{J}_J}{J}) (\mathbf{I}_J \otimes e_{t'}) \tilde{v}^Z \\ &= \frac{1}{J-1} \gamma' (\mathbf{I}_{JT} - X(X'X)^{-1}X')(\mathbf{I}_J \otimes e_t) (\mathbf{I}_J - \frac{\mathbf{J}_J}{J}) (\mathbf{I}_J \otimes e_{t'})(\mathbf{I}_{JT} - X(X'X)^{-1}X')\gamma \end{aligned} $$

Hence the bias induced by the sampling error can then be recovered using the following expression for $H = \frac{1}{J-1}(\mathbf{I}_{JT} - X(X'X)^{-1}X')(\mathbf{I}_J \otimes e_t)(\mathbf{I}_J - \frac{\mathbf{J}_J}{J})(\mathbf{I}_J \otimes e_{t'})(\mathbf{I}_{JT} - X(X'X)^{-1}X')$.

An unfeasible estimator of $E(\mu_j^Z v_{jt}^Z|t)$ would be:

$$ \begin{aligned} \hat{E}(\tilde{\mu}_j \tilde{v}_{jt}^Z|t) &= \frac{1}{J-1} \sum_{j=1}^{J} \tilde{\mu}_j (\tilde{v}_{jt}^Z - \tilde{v}_{t}^Z) = \frac{1}{J-1} \tilde{\mu}' (\mathbf{I}_J - \frac{\mathbf{J}_J}{J}) (\mathbf{I}_J \otimes e_{t'}) \tilde{v}^Z \\ &= \frac{1}{J-1} \gamma' X(X'X)^{-1} S_\mu' (\mathbf{I}_J - \frac{\mathbf{J}_J}{J}) (\mathbf{I}_J \otimes e_{t'})(\mathbf{I}_{JT} - X(X'X)^{-1}X')\gamma \end{aligned} $$

Hence the bias induced by the sampling error can then be recovered using the following expression for the matrix $H = \frac{1}{J-1}X(X'X)^{-1}S_\mu'(\mathbf{I}_J \otimes e_t)(\mathbf{I}_J - \frac{\mathbf{J}_J}{J})(\mathbf{I}_J \otimes e_{t'})(\mathbf{I}_{JT} - X(X'X)^{-1}X')$.
## Liste des documents de travail de la Direction des Études et Synthèses Économiques

ii

<table><tr><td>G 9001</td><td>J. FAYOLLE et M. FLEURBAEY<br>Accumulation, profitabilité et endettement des entreprises</td><td>Macro-economic import functions with imperfect competition - An application to the E.C. Trade</td><td>G 9311</td></tr><tr><td>G 9002</td><td>H. ROUSSE<br>Détection et effets de la multicolinearité dans les modèles linéaires ordinaires - Un prolongement de la référence de BELSLEY, KUH et WELSCH</td><td>I. STAPIC<br>Les échements internationaux de services de la France dans le cadre des négociations multilatérales du GATT<br>Juin 1992 (1ère version)</td><td>G 9303</td></tr><tr><td>G 9003</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Indexation des salaires - la rupture de 1983</td><td>Novembre 1992 (version finale)</td><td>G 9204</td></tr><tr><td>G 9004</td><td>D. GUELEC et P. RALLE<br>Compétitivité, croissance et innovation de produit</td><td>P. SEVESTRE<br>L'économie sur données individuelles- temporelles. Une note introductive</td><td>G 9313</td></tr><tr><td>G 9005</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Les consequences de la désindexation. Analyse dans une maquette prix-salaires</td><td>H. ERKEL-ROUSSE<br>Le commerce extérieur et l'environnement international dans le modèle AMADEUS (révision 1992)</td><td>G 9314</td></tr><tr><td>G 9101</td><td>Équipe AMADEUS - Première partie -<br>Présentation générale</td><td>N. GREENAN et D. GUELLEC<br>Coordination within the firm and endogenous growth</td><td>G 9315</td></tr><tr><td>G 9102</td><td>J.L. BRILLET<br>Le modèle AMADEUS - Deuxième partie -<br>Propriétés variétales</td><td>A. MAGNIER et J. TOUJAS-BERNATE<br>Technology and trade: empirical evidences for the major five industrialized countries</td><td>G 9316</td></tr><tr><td>G 9103</td><td>D. GUELLEC et P. RALLE<br>Endogenous growth and product innovation</td><td>B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHNN<br>Cooperative, non cooperative R & D and optimal patent life</td><td>G 9317</td></tr><tr><td>G 9104</td><td>H. ROUSSE<br>Le modèle AMADEUS - Troisième partie - Le commerce extérieur et l'environnement international</td><td>B. CREPON, E. DUGUET<br>Research and development, competition and innovation: an application of pseudo maximum likelihood methods to Poisson models with heterogeneity</td><td>G 9318</td></tr><tr><td>G 9105</td><td>H. ROUSSE<br>Effets de demande et d'offre dans les résultats du commerce extérieur manufacturé de la France au cours des deux dernières décennies</td><td>J. TOUJAS-BERNATE<br>Commerce international et concurrence impartie - Développements récents et implications pour la politique commerciale</td><td>G 9319</td></tr><tr><td>G 9106</td><td>B. CREPON<br>Innovation, taille et concentration - causalités et dynamiques</td><td>J. GEAUTIE<br>Le chômage des jeunes en France, problème de formation ou phénomène de file d'attente ?<br>Quelques éléments du débat</td><td>G 9401</td></tr><tr><td>G 9107</td><td>B. AMABLE et D. GUELLEC<br>Un panorama des théories de la croissance endogène</td><td>Ch. CASES<br>Durées de chômage et comportements d'offre de travail - une revue de la littérature</td><td>G 9402</td></tr><tr><td>G 9108</td><td>M. GLAUDE et M. MOUTARDIER<br>Une évaluation du coût direct de l'enfant de 1979 à 1989</td><td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique</td><td>G 9403</td></tr><tr><td>G 9109</td><td>P. RALLE et alii<br>France - Allemagne : performances économiques comparées</td><td>N. GREENAN - D. GUELLEC / G. BROUSSAUDIER - L. MIOTTI<br>Innovation organisationnelle, dynamisme technologique et performances des entreprises</td><td>G 9404</td></tr><tr><td>G 9110</td><td>J.L. BRILLET<br>Micro-DMS<br>NON PARU</td><td>P. JAILLARD<br>Le traité de Maastricht : présentation juridique et historique</td><td>G 9405</td></tr><tr><td>G 9111</td><td>A. MAGNIER<br>Effets accélérateur et multiplicateur en France depuis 1970 : quelques résultats empiriques</td><td>J.L. BRILLET<br>Micro-DMS - présentation et propriétés</td><td>G 9406</td></tr><tr><td>G 9112</td><td>B. CREPON et G. DUREAU<br>Investissement en recherche-développement :<br>analyse de causalités dans un modèle d'accélérateur généralisé</td><td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td><td>G 9407</td></tr><tr><td>G 9113</td><td>J.L. BRILLET, H. ERKEL-ROUSSE, J. TOUJAS-BERNATE<br>"France - Allemagne Couplées" - Deux économies vues par une maquette macro-économétique</td><td>S. JACOBZONE<br>Les grands réseaux publics français dans une perspective européenne</td><td>G 9408</td></tr><tr><td>G 9201</td><td>W.J. ADAMS, B. CREPON, D. ENCAOUA<br>Choix technologiques et stratégies de dissuasion d'entrée</td><td>L. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9409</td></tr><tr><td>G 9202</td><td>J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE</td><td>J. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9410</td></tr><tr><td></td><td></td><td>S. JACOBZONE<br>Les grands réseaux publics français dans une perspective européenne</td><td>G 9310</td></tr><tr><td></td><td></td><td>L. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9309</td></tr><tr><td></td><td></td><td>J. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9109</td></tr><tr><td></td><td></td><td>J. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9110</td></tr><tr><td></td><td></td><td>J. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9310</td></tr><tr><td></td><td></td><td>J. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9311</td></tr></table>

<table><tr><td>G 9311</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les décisions de financement des entreprises françaises - une évaluation empirique des théories de la structure optimale du capital</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les échements internationaux de services de la France dans le cadre des négociations multilatérales du GATT<br>Juin 1992 (1ère version)</td><td>G 9312</td></tr><tr><td>G 9313</td><td>Équipes Amadeus (INSEE), Banque de France, Métric (DP)<br>Presentation des propriétés des principaux modèles macroéconomiques du Service Public</td><td>L. BLOCH - B. CŒURE<br>Q de Tobin marginal et transmission des chocs financiers</td><td>G 9312</td></tr><tr><td>G 9314</td><td>B. CREPON - E. DUGUET<br>Research & Development, competition and innovation</td><td>Équipes Amadeus (INSEE), Banque de France, Métric (DP)<br>Presentation des propriétés des principaux modèles macroéconomiques du Service Public</td><td>G 9314</td></tr><tr><td>G 9315</td><td>B. DORMONT<br>Quelle est l'influence du coût du travail sur l'emploi ?</td><td>B. DORMONT<br>Quelle est l'influence du coût du travail sur l'emploi ?</td><td>G 9315</td></tr><tr><td>G 9316</td><td>D. BLANCHET - C. BROUSSE<br>Deux études sur l'âge de la retraite</td><td>B. BLANCHET - C. BROUSSE<br>Deux études sur l'âge de la retraite</td><td>G 9316</td></tr><tr><td>G 9317</td><td>D. BLANCHET<br>Répartition du travail dans une population hétérogène - deux notes</td><td>D. BLANCHET<br>Répartition du travail dans une population hétérogène - deux notes</td><td>G 9317</td></tr><tr><td>G 9318</td><td>D. EYSSARTIER - N. PONTY<br>AMADEUS - an annual macro-economic model for the medium and long term</td><td>D. EYSSARTIER - N. PONTY<br>AMADEUS - an annual macro-economic model for the medium and long term</td><td>G 9318</td></tr><tr><td>G 9319</td><td>G. CETTE - Ph. CUNÉO - D. EYSSARTIER - J. GAUTIE<br>Les effets sur l'emploi d'un abaissement du coût du travail des jeunes</td><td>G. CETTE - Ph. CUNÉO - D. EYSSARTIER - J. GAUTIE<br>Les effets sur l'emploi d'un abaissement du coût du travail des jeunes</td><td>G 9319</td></tr><tr><td>G 9401</td><td>D. BLANCHET<br>Les structures par âge importent-elles ?</td><td>D. BLANCHET<br>Les structures par âge importent-elles ?</td><td>G 9401</td></tr><tr><td>G 9402</td><td>J. GAUTIE<br>Le chômage des jeunes en France, problème de formation ou phénomène de file d'attente ?<br>Quelques éléments du débat</td><td>J. GAUTIE<br>Le chômage des jeunes en France, problème de formation ou phénomène de file d'attente ?<br>Quelques éléments du débat</td><td>G 9402</td></tr><tr><td>G 9403</td><td>P. QUIRION<br>Les déchets en France : éléments statistiques et économiques</td><td>P. QUIRION<br>Les déchets en France : éléments statistiques et économiques</td><td>G 9403</td></tr><tr><td>G 9404</td><td>D. LADIRAY - M. GRUN-REHOMME<br>Lissage par moyennes mobiles - Le problème des extrémités de série</td><td>D. LADIRAY - M. GRUN-REHOMME<br>Lissage par moyennes mobiles - Le problème des extrémités de série</td><td>G 9404</td></tr><tr><td>G 9405</td><td>V. MAILLARD<br>Theorie et pratique de la correction des effets de jours ouvrables</td><td>V. MAILLARD<br>Theorie et pratique de la correction des effets de jours ouvrables</td><td>G 9405</td></tr><tr><td>G 9406</td><td>F. ROSENVALD<br>La décision d'investir</td><td>F. ROSENVALD<br>La décision d'investir</td><td>G 9406</td></tr><tr><td>G 9407</td><td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique de l'hôpital public</td><td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique de l'hôpital public</td><td>G 9407</td></tr><tr><td>G 9408</td><td>L. BLOCH, J. BOURDIEU<br>B. COLIN-SEDILLOT, G. LONGUEVILLE<br>Du défaut de paiement au début de bilan : les banquiers face aux PME en difficulté</td><td>L. BLOCH, J. BOURDIEU<br>B. COLIN-SEDILLOT, G. LONGUEVILLE<br>Du défaut de paiement au début de bilan : les banquiers face aux PME en difficulté</td><td>G 9408</td></tr><tr><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'aidé au logement - quelques éléments d'évaluation</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'aidé au logement - quelques éléments d'évaluation</td><td>G 9409</td></tr><tr><td>G 9410</td><td>F. ROSENVALD<br>Suivi conjoncturel de l'investissement</td><td>F. ROSENVALD<br>Suivi conjoncturel de l'investissement</td><td>G 9410</td></tr><tr><td>G 9411</td><td>C. DEFEUILLEY - Ph. QUIRION<br>Les déchets d'emballages ménagers : une</td><td>C. DEFEUILLEY - Ph. QUIRION<br>Les déchets d'emballages ménagers : une</td><td>G 9411</td></tr></table>

<table><tr><td>G 9412</td><td>J. BOURDIEU - B. CŒURE - B. COLIN-SEDILLOT<br>Investissement, incertitude et irréversibilité<br>Quelques développements récents de la théorie de l'investissement</td><td>J. BOURDIEU - B. CŒURE - B. COLIN-SEDILLOT<br>Investissement, incertitude et irréversibilité<br>Quelques développements récents de la théorie de l'investissement</td><td>G 9412</td></tr><tr><td>G 9413</td><td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'elasticité emploi-salaire dépend-elle des structures de qualification ?</td><td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'elasticité emploi-salaire dépend-elle des structures de qualification ?</td><td>G 9413</td></tr><tr><td>G 9414</td><td>I. KABLA<br>Le Choix de breveter une invention</td><td>I. KABLA<br>Le Choix de breveter une invention</td><td>G 9414</td></tr><tr><td>G 9501</td><td>J. BOURDIEU - B. CŒURE - B. SEDILLOT<br>Irreversible investment and Uncertainty:<br>When is there a Value of Waiting?</td><td>J. BOURDIEU - B. CŒURE - B. SEDILLOT<br>Irreversible investment and Uncertainty:<br>When is there a Value of Waiting?</td><td>G 9501</td></tr><tr><td>G 9502</td><td>L. BLOCH - B. CŒURE<br>Imperfections du marché du crédit, investissement des entreprises et cycle économique</td><td>L. BLOCH - B. CŒURE<br>Imperfections du marché du crédit, investissement des entreprises et cycle économique</td><td>G 9502</td></tr><tr><td>G 9503</td><td>D. GOUX - E. MAURIN<br>Les transformations de la demande de travail par qualification en France</td><td>D. GOUX - E. MAURIN<br>Les transformations de la demande de travail par qualification en France</td><td>G 9503</td></tr><tr><td>G 9504</td><td>N. GREENAN<br>Technologie, changement organisationnel, qualifications et emploi : une étude empirique sur l'industrie manufacturière</td><td>N. GREENAN<br>Technologie, changement organisationnel, qualifications et emploi : une étude empirique sur l'industrie manufacturière</td><td>G 9504</td></tr><tr><td>G 9505</td><td>D. GOUX - E. MAURIN<br>Persistance des hiérarchies sectorielles de salaires : un réxamen sur données françaises</td><td>D. GOUX - E. MAURIN<br>Persistance des hiérarchies sectorielles de salaires : un réxamen sur données françaises</td><td>G 9505</td></tr><tr><td>G 9506</td><td>D. GOUX - E. MAURIN<br>Persistance de inter-industry wages differentials; a reexamination on matched worker-firm panel data</td><td>D. GOUX - E. MAURIN<br>Persistance de inter-industry wages differentials; a reexamination on matched worker-firm panel data</td><td>G 9506</td></tr><tr><td>G 9507</td><td>S. JACOBZONE<br>Les liens entre RMI et chômage, une mise en perspective<br>NON PARU - article sorti dans Économie et Prévision n° 122 (1996) - pages 95 à 113</td><td>S. JACOBZONE<br>Les liens entre RMI et chômage, une mise en perspective<br>NON PARU - article sorti dans Économie et Prévision n° 122 (1996) - pages 95 à 113</td><td>G 9507</td></tr><tr><td>G 9601</td><td>G. CETTE - S. MAHFOUZ<br>Le partage primaire du revenu<br>Constat descriptif sur longue période</td><td>G. CETTE - S. MAHFOUZ<br>Le partage primaire du revenu<br>Constat descriptif sur longue période</td><td>G 9601</td></tr><tr><td>G 9602</td><td>Rapport d'activité de la DESE de l'année 1995</td><td>Rapport d'activité de la DESE de l'année 1995</td><td>G 9602</td></tr><tr><td>G 9603</td><td>J. BOURDIEU - A. DRAZNIEKS<br>L'octroi de crédit aux PME : une analyse à partir d'informations bancaires</td><td>J. BOURDIEU - A. DRAZNIEKS<br>L'octroi de crédit aux PME : une analyse à partir d'informations bancaires</td><td>G 9603</td></tr><tr><td>G 9604</td><td>A. TOPIAL-BENSAID<br>Les implantations japonaises en France</td><td>A. TOPIAL-BENSAID<br>Les implantations japonaises en France</td><td>G 9604</td></tr><tr><td>G 9605</td><td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie, patrón on pafter d'une gestion globale du capital santé ?<br>Une modelisation microéconométrique empirique</td><td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie, patrón on pafter d'une gestion globale du capital santé ?<br>Une modelisation microéconométrique empirique</td><td>G 9605</td></tr><tr><td>G 9606</td><td>C. DOZ - F. L'ENGLART<br>Factor analysis and unobserved component models: an application to the study of French business surveys</td><td>C. DOZ - F. L'ENGLART<br>Factor analysis and unobserved component models: an application to the study of French business surveys</td><td>G 9606</td></tr><tr><td>G 9607</td><td>N. GREENAN - D. GUELLEC<br>La théorie coopérative de la firme</td><td>N. GREENAN - D. GUELLEC<br>La théorie coopérative de la firme</td><td>G 9607</td></tr></table>
<table><tr><td>G 9608</td><td>N. GREENAN - D. GUELEC<br>Technological innovation and employment reallocation</td></tr><tr><td>G 9609</td><td>Ph. COUR - F. RUPPRECHT<br>L'intégration asymptérique au sein du continent américain : un essai de modélisation</td></tr><tr><td>G 9610</td><td>S. DUCHENE - G. FORGEOT - A. JACQUOT<br>S analyses of evolutions récents de la productivité apparente du travail</td></tr><tr><td>G 9611</td><td>X. BONNET - S. MAHFOUZ<br>The influence of different specifications of wages-prices spirals on the measure of the NAIRO : the case of France</td></tr><tr><td>G 9612</td><td>PH. COUR - E. DUBOIS, S. MAHFOUZ, J. PISANI-FERRY<br>The cost of fiscal retrenchment revisited : how strong is the evidence?</td></tr><tr><td>G 9613</td><td>A. JACQUOT<br>Les flexions des taux d'activité sont-elles seule-ment conjonctuelles ?</td></tr><tr><td>G 9614</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique français-chinois</td></tr><tr><td>G 9701</td><td>J.L. SCHNEIDER<br>La taxe professionnelle : éléments de cadrage économique</td></tr><tr><td>G 9702</td><td>J.L. SCHNEIDER<br>Transition et stabilité politique d'un système redistributif</td></tr><tr><td>G 9703</td><td>D. GOUX - E. MAURIN<br>Train or Pay : Does it Reduce Inequalities to En-courage Firms to Train their Workers?</td></tr><tr><td>G 9704</td><td>P. GENIER<br>Deux contributions sur dépendance et équité</td></tr><tr><td>G 9705</td><td>E. DUGUET - N. IUNG<br>R & D Investment, Patent Life and Patent Value<br>An Econometric Analysis at the Firm Level</td></tr><tr><td>G 9706</td><td>M. HOUDEBINE - A. TOPIOL-BENSAID<br>Les entreprises internationales en France : une analyse à partir de données individuelles</td></tr><tr><td>G 9707</td><td>M. HOUDEBINE<br>Polarisation des activités et spécialisation des départements en France</td></tr><tr><td>G 9708</td><td>E. DUGUET - N. GREENAN<br>Le blais technologique : une analyse sur données individuelles</td></tr><tr><td>G 9709</td><td>J.L. BRILLET<br>Analyzing a small French ECM Model</td></tr><tr><td>G 9710</td><td>J.L. BRILLET<br>Formalizing the transition process : scenarios for capital accumulation</td></tr><tr><td>G 9711</td><td>G. FORGEOT - J. GAUTIÉ<br>Insertion professionnelle des jeunes et processus de déclatement</td></tr><tr><td>G 9712</td><td>E. DUBOIS<br>High Real Interest Rates : the Consequence of a Saving Investment Disequilibrium or of an insufficient Credibility of Monetary Authorities?</td></tr><tr><td>G 9713</td><td>Bilan des activités de la Direction des Études et Synthèses Economiques - 1996</td></tr></table>

<table><tr><td>G 9714</td><td>F. LEQUILLER<br>Does the French Consumer Price Index Over-state Inflation?</td></tr><tr><td>G 9715</td><td>X. BONNET<br>Peut-on mettre en évidence les rigidités à la baisse des salaires nominaux ?<br>Une étude sur quelques grands pays de l'OCDE</td></tr><tr><td>G 9716</td><td>N. IUNG - F. RUPPRECHT<br>Productivité de la recherche et rendements d'écelle dans le secteur pharmaceutique français</td></tr><tr><td>G 9717</td><td>E. DUGUET - I. KABLA<br>Appropriation strategy and the motivations to use the patent system in France - An econometric analysis at the firm level</td></tr><tr><td>G 9718</td><td>L.P. PELÉ - P. RALLE<br>Âge de la retraite : les aspects incitatifs du régime général</td></tr><tr><td>G 9719</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique français-chinois, chinois-français</td></tr><tr><td>G 9720</td><td>M. HOUDEBINE - J.L. SCHNEIDER<br>Mesurer l'influence de la fiscalité sur la localisation des entreprises</td></tr><tr><td>G 9721</td><td>A. MOUROUGANE<br>Crédibilité, indépendance et politique monétaire<br>Une revue de la littérature</td></tr><tr><td>G 9722</td><td>P. AUGERAUD - L. BRIOT<br>Les données comptables d'entreprises<br>Le système intermédiaire d'entreprises<br>Passage des données individuelles aux données sectorielles</td></tr><tr><td>G 9723</td><td>P. AUGERAUD - J.E. CHAPRON<br>Using Business Accounts for Compiling National Accounts : the French Experience</td></tr><tr><td>G 9724</td><td>P. AUGERAUD<br>Les comptes d'entreprise par activités - Le passage aux comptes - De la comptabilité d'entreprise à la comptabilité nationale - A partraire</td></tr><tr><td>G 9801</td><td>H. MICHAUDON - C. PRIGENT<br>Presentation du modèle AMADEUS</td></tr><tr><td>G 9802</td><td>J. ACCARDO<br>Une étude de comptabilité générationnelle pour la France en 1996</td></tr><tr><td>G 9803</td><td>X. BONNET - S. DUCHENE<br>Apports et limites de la modélisation « Real Business Cycles »</td></tr><tr><td>G 9804</td><td>C. BARLET - C. DUGUET - D. ENCAOUA - J. PRADEL<br>The Commercial Success of Innovations<br>An econometric analysis at the firm level in French manufacturing</td></tr><tr><td>G 9805</td><td>P. CAHUC - Ch. GIANELLA - D. GOUX - A. ZILBERBERG<br>Equalizing Wage Differences and Bargaining Power - Evidence form a Panel of French Firms</td></tr><tr><td>G 9806</td><td>J. ACCARDO - M. JILSSI<br>La productivité globale des facteurs entre 1975 et 1996</td></tr></table>

<table><tr><td>G 9807</td><td>Bilan des activités de la Direction des Études et Synthèses Economiques - 1997</td></tr><tr><td>G 9808</td><td>A. MOUROUGANE<br>Can a Conservative Governor Conduct an Ac-comodative Monetary Policy?</td></tr><tr><td>G 9809</td><td>X. BONNET - E. DUBOIS - L. FAUVET<br>Asymétrie des inflations relatives et menus costs : tests sur l'initiation française</td></tr><tr><td>G 9810</td><td>E. DUGUET - N. IUNG<br>Sales and Advertising with Spillovers at the firm level : Estimation of a Dynamic Structural Model on Panel Data</td></tr><tr><td>G 9811</td><td>J.P. BERTHIER<br>Congestion urbaine : un modèle de trafic de pointe à courbe débit-vitesse et demande élastique</td></tr><tr><td>G 9812</td><td>C. PRIGENT<br>La part des salaires dans la valeur ajoutée : une approche macroéconomique</td></tr><tr><td>G 9813</td><td>A.Th. AERTS<br>L'évolution de la part des salaires dans la valeur ajoutée en France reflète-elle les evolutions individuelles sur la période 1979-1994 ?</td></tr><tr><td>G 9814</td><td>B. SALANIÉ<br>Guide pratique des séries non-stationnaires</td></tr><tr><td>G 9901</td><td>S. DUCHENE - A. JACQUOT<br>Une croissance plus riche en emplois depuis le début de la décennie ? Une analyse en comparaison internationale</td></tr><tr><td>G 9902</td><td>Ch. COLIN<br>Modélisation des carrières dans Destimie<br>Ch. COLIN<br>Évolution de la dispersion des salaires : un essai de prospective par microsimulation</td></tr><tr><td>G 9903</td><td>Ch. COLIN<br>Modélisation des carrières dans Destimie<br>Ch. COLIN<br>Évolution de la dispersion des salaires : un essai de prospective par microsimulation</td></tr><tr><td>G 9904</td><td>B. CREPON - N. IUNG<br>Innovation, emploi et performances</td></tr><tr><td>G 9905</td><td>B. CREPON - Ch. GIANELLA<br>Wages inequalities in France 1969-1992<br>An application of quantile regression techniques</td></tr><tr><td>G 9906</td><td>C. BONNET - R. MAHIEU<br>Microsimulation techniques applied to inter-generational transfers - Pensions in a dynamic framework : the case of France</td></tr><tr><td>G 9907</td><td>F. ROSENFALD<br>L'impact des contraintes financières dans la décision d'investissement</td></tr><tr><td>G 9908</td><td>Bilan des activités de la DESE - 1998<br>J.P. ZOYEM<br>Contrat d'insertion et sortie du RMI</td></tr><tr><td>G 9909</td><td>Évaluation des effets d'une politique sociale<br>Ch. COLIN - FI. LEGROS - R. MAHIEU<br>Bilans contributifs comparés des régimes de retraite du secteur privé et de la fonction publique</td></tr><tr><td>G 9910</td><td>G. LAROQUE - B. SALANIÉ<br>Une décomposition du non-emploi en France</td></tr><tr><td>G 9911</td><td>B. SALANIÉ<br>Une maquette analytique de long terme du marché du travail</td></tr><tr><td>G 9912</td><td>Ch. GIANELLA<br>Ch. GIANELLA</td></tr></table>

<table><tr><td>Bis</td><td>Une estimation de l'élasticité de l'emploi peu qualité à son coût</td></tr><tr><td>G 9913</td><td>Division « Redistribution et Politiques Sociales »<br>Le modèle de microsimulation dynamique DESTINE</td></tr><tr><td>G 9914</td><td>E. DUGUET<br>Macro-commandes SAS pour l'économie des panels et des variables qualitatives</td></tr><tr><td>G 9915</td><td>R. DUHAUTOIS<br>Évolution des flux d'emplois en France entre 1980 et 1996 : une étude empirique à partir du fichier des bénéfices réels normaux (BRN)</td></tr><tr><td>G 9916</td><td>J.Y. FOURNIER<br>Extraction du cycle des affaires - la méthode de Baxter et King</td></tr><tr><td>G 9917</td><td>B. CREPON - R. DESPLATZ - J. MAIRESSE<br>Estimating price cost margins, scale economies and workers' bargaining power at the firm level</td></tr><tr><td>G 9918</td><td>Ch. GIANELLA - Ph. LAGARDE<br>Productivity of hours in the aggregate production function : an evaluation on a panel of French firms from the manufacturing sector</td></tr><tr><td>G 9919</td><td>S. AUDRIC - P. GIVORD - C. PROST<br>Évolution de l'emploi et des coûts par qualification entre 1982 et 1996</td></tr><tr><td>G 2000/01</td><td>R. MAHIEU<br>Les déterminants des dépenses de santé : une approche macroéconomique</td></tr><tr><td>G 2000/02</td><td>C. ALLARD-PRIGENT - H. GUILMEAU - A. QUINET<br>The real exchange rate as the relative price of nontrables in terms of tradables: theoretical investigation and empirical study on French data</td></tr><tr><td>G 2000/03</td><td>J.-Y. FOURNIER<br>L'approximation du filtre passe-bande proposée par Christiano and Fitzgerald</td></tr><tr><td>G 2000/04</td><td>Bilan des activités de la DESE - 1999<br>B. CREPON - F. ROSENFALD</td></tr><tr><td>G 2000/05</td><td>Investissement et contraintes de financement : le poids du cycle<br>Une estimation sur données françaises</td></tr><tr><td>G 2000/06</td><td>A. FUPO<br>Les comportements matrimoniaux de fait</td></tr><tr><td>G 2000/07</td><td>R. MAHIEU - B. SÉDILLOT<br>Microsimulations of the retirement decision : a supply side approach</td></tr><tr><td>G 2000/08</td><td>C. AUDENS - C. PROST<br>Déficit conjoncturel : une prise en compte des conjonctures passées</td></tr><tr><td>G 2000/09</td><td>R. MAHIEU - B. SÉDILLOT<br>Équivalent patrimonial de la rente et souscription de retraite complémentaire</td></tr><tr><td>G 2000/10</td><td>R. DUHAUTOIS<br>Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertiaire ?</td></tr><tr><td>G 2000/11</td><td>G. LAROQUE - B. SALANIÉ<br>Temps partiel féminin et incitations financières à l'emploi</td></tr><tr><td>G 2000/12</td><td>Ch. GIANELLA<br>Local unemployment and wages</td></tr></table>
G2005/09

N. FERRARI
Prévoir l'investissement des entreprises
Un indicateur des révisions dans l'enquête de
conjoncture sur les investissements dans
l'industrie.

G2005/10

P.-O. BEFFY - C. L'ANGEVIN
Chômage et boucle prix-salaires :
apport d'un modèle « qualifiés/per qualifiés »

G2005/11

B. HEITZ
A two-states Markov-switching model of inflation
in France and the USA: credible target VS
inflation spiral

G2005/12

O. BIAU - H. ERKEL-ROUSSE - N. FERRARI
Réponses individuelles aux enquêtes de
conjoncture et prévision macroéconomiques :
Exemple de la prévision de la production
manufacture

G2005/13

P. AUBERT - D. BLANCHET - D. BLAU
The labour market after age 50: some elements
of a Franco-American comparison

G2005/14

D. BLANCHET - T. DEBRAND -
P. DOUGRONN - P. POLLET
L'enquête SHARE : présentation et premiers
résultats de l'édition française

G2005/15

M. DUÉ
La modélisation des comportements démographiques dans le modèle de microsimulation
DESTINE

G2005/16

H. RAQUI - S. ROUX
Étude de simulation sur la participation versée
aux salarés par les entreprises

G2006/01

C. BONNET - S. BUFFETEAU - P. GODEFROY
Disparités de retraite de droit direct entre
hommes et femmes : quelles évolutions ?

G2006/02

C. PICART
Les gazelles en France

G2006/03

P. AUBERT - B. CRÉPON - P. ZAMORA
Le rendement apparent de la formation continue
dans les entreprises : effets sur la productivité et
les salaires

G2006/04

J.-F. OUVRARD - R. RATHELOT
Demographic change and unemployment:
what do macroeconomic models predict?

G2006/05

D. BLANCHET - J.-F. OUVRARD
Indicateurs d'engagements impliques des
systèmes de retraite : chiffrages, propriétés
analytiques et réactions à des chocs
démographiques types

G2006/06

G. BIAU - O. BIAU - L. ROUVIERE
Non parametric Forecasting of the Manufacturing
Output Growth with Firm-level Survey Data

G2006/07

C. AFSA - P. GIVORD
Le rôle des conditions de travail dans les
absences pour maladie

G2006/08

P. SILLARD - C. L'ANGEVIN - S. SERRAVALLE
Performances comparées à l'exportation de la
France et de ses principaux partenaires
Une analyse structurelle sur 12 ans

G2006/09

X. BOUTIN - S. QUANTIN
Une méthodologie d'évaluation comptable du
cout du capital des entreprises françaises : 1984-2002

G2006/10

C. AFSA
L'estimation d'un coût implicite de la pénibilité du
travail chez les travailleurs âgés

G2006/11

C. LELARGE
Les entreprises (industrielles) françaises sont-elles à la frontière technologique ?

G2006/12

O. BIAU - N. FERRARI
Théorie de l'opinion
Faut-il pondérer les réponses individuelles ?

G2006/13

A. KOUBI - S. ROUX
Une réinterprétation de la relation entre
productivité et inégalités salariales dans les
entreprises

G2006/14

R. RATHELOT - P. SILLARD
The impact of local taxes on plants location
decision

G2006/15

L. GONZALEZ - C. PICART
Diversification, récentrage et poids des activités
de support dans les groupes (1993-2000)

G2007/01

D. SRAER
Alégerements de cotisations patronales et
dynamique salariale

G2007/02

V. ALBOUY - L. LEQUIEN
Les rendements non monetaires de l'éducation :

G2007/03

D. BLANCHET - T. DEBRAND
Aspiration à la retraite, santé et satisfaction au
travail : une comparaison européenne

G2007/04

M. BARLET - L. CRUSSON
Quel impact des variations du prix du pétrole sur
la croissance française ?

G2007/05

C. PICART
Flux d'emploi et de main-d'œuvre en France : un
réexamen

G2007/06

V. ALBOUY - C. TAVAN
Massification et démocratisation de
l'enseignement supérieur en France

G2007/07

T. LE BARBANCHON
The Changing response to oil price shocks in
France: a DSGE type approach

G2007/08

T. CHANEY - D. SRAER - D. THESMAR
Collateral Value and Corporate Investment
Evidence from the French Real Estate Market

G2007/09

J. BOISSINOT
Consumption over the Life Cycle: Facts for
France

G2007/10

C. AFSA
Interpréter les variables de satisfaction :
l'exemple de la durée du travail

G2007/11

R. RATHELOT - P. SILLARD
Zones Franches Urbaines : quels effets sur
l'emploi salarié et les créations
d'établissements ?

G2007/12

V. ALBOUY - B. CRÉPON
Alba moral en santé : une évaluation dans le
cadre du modèle causal de Rubin

G2008/01

C. PICART
Les PME françaises : rentables mais peu
dynamiques

G2008/02

C. AFSA
L'estimation d'un coût implicite de la pénibilité du
travail chez les travailleurs âgés

G2008/03

Y. BARBESOL - A. BRIANT
Economies d'agglomération et productivité des
entreprises : estimation sur données individuelles
françaises

G2008/04

D. BLANCHET - F. LE GALLO
Les projections démographiques : principaux
mécanismes et retour sur l'expérience française

D. BLANCHET - F. TOUTLEMONDE
Évolutions démographiques et déformation du
cycle de vie active : quelles relations ?

G2008/05

D. BLANCHET - F. TOUTLEMONDE
Évolutions démographiques et déformation du
cycle de vie active : quelles relations ?

G2008/06

M. BARLET - D. BLANCHET - L. CRUSSON
Internationalisation et flux d'emplois : que dit une
approche comptable ?

G2008/07

C. LELARGE - D. SRAER - D. THESMAR
Entrepreneurship and Credit Constraints -
Evidence from a French Loan Guarantee
Program

G2008/08

X. BOUTIN - L. JANIN
Are Prices Really Affected by Mergers?

G2008/09

M. BARLET - A. BRIANT - L. CRUSSON
Concentration géographique dans l'industrie
manufacturière et dans les services en France :
une approche par un indicateur en continu

G2008/10

M. BEFFY - É. COUDIN - R. RATHELOT
Who is confronted to insecure labor market
histories? Some evidence based on the French
labor market transition

G2008/11

M. ROGER - É. WALRAET
Social Security and Well-Being of the Elderly: the
Case of France

G2008/12

C. AFSA
Analyser les composants du bien-être et de son
 évolution

Une approche empirique sur données
individuelles

G2008/13

M. BARLET - D. BLANCHET -
T. LE BARBANCHON
Microsimuler le marché du travail : un prototype

G2009/01

P.-A. PIONNIER
Le partage de la valeur ajoutée en France,
1949-2007

G2009/02

Laurent CLAVEL - Christelle MINODIER
A Monthly Indicator of the French Business
Climate

G2009/03

H. ERKEL-ROUSSE - C. MINODIER
Do Business Tendency Surveys in Industry and
Services Help in Forecasting GDP Growth?

G2009/04

P. GIVORD - L. WILNER
Les contrats temporaires : trappe ou marchéied
vers l'emploi stable ?

G2009/05

G. LALANNE - P.-A. PIONNIER - O. SIMON
Le partage des fruits de la croissance de 1950 à
2008 : une approche par les comptes de surplus

G2009/06

L. DAVEZIES - X. DHAULTFOELIILLE
Faut-il pondérer ?... Ou l'eternelle question de
l'économètre contredit à des données enquête

G2009/07

P. BISCOURP - X. BOUTIN - T. VERGÉ
The Effects of Retail Regulations on Prices
Evidence form the Loi Galand

G2009/08

M. CLERC - V. MARCUS
Élastiques-prices des consommations énergétiques
des ménages

G2009/09

G. LALANNE - E. POULIQUEN - O. SIMON
Prix du pétrole et croissance potentielle à long
termes

G2009/10

D. BLANCHET - J. LE CACHEU - V. MARCUS
Adjusted net savings and other approaches to
sustainability: some theoretical background

G2009/11

V. BELLAMY - G. CONSALES - M. FESSEAU -
S. LE LAIDIER - E. RAYNAUD
Une décomposition du compte des ménages de
la comptabilité nationale par catégorie de
ménage en 2003

G2009/12

J. BARDAJI - F. TALLET
Detecting Economic Regimes in France: a
Qualitative Markov-Switching Indicator Using
Mixed Frequency Data

G2009/13

R. AEBERHARDT - D. FOUGÈRE -
R. RATHELOT - D. FOUGÈRE

Discrimination à l'entraîbe : comment exploiter
les procédures de testing ?

G2009/14

Y. BARBESOL - P. GIVORD - S. QUANTIN
Partage de la valeur ajoutée, approche par
données microéconomiques

G2009/15

I. BUONO - G. LALANNE
The Effect of the Uruguay round on the Intensive
and Extensive Margins of Trade

G2010/01

C. MINODIER
Avantages comparés des séries des premières
valeurs publiées et des séries des valeurs
revues : Un exercice de prévision en temps réel
de la croissance trimestrielle du PIB en France

G2010/02

V. ALBOUY - L. DAVEZIES - T. DEBRAND
Health Expenditure Models: a Comparison of Five
Specifications using Panel Data

G2010/03

C. KLEIN - O. SIMON
Le modèle MÉSANGE réestimé en base 2000
Tome 1 - Version avec volumes à prix constants

G2010/04

M.-É. CLERC - É. COUDIN
L'IPC, miror de l'évolution du coût de la vie en
France ? Ce qu'apporte l'analyse des courbes
d'Engel

G2010/05

N. CECI-RENAUD - P.-A. CHEVALIER
Les seuls de 10, 20 et 50 salaires : impact sur la
taille des entreprises françaises

G2010/06

R. AEBERHARDT - P. JOUGET
National Origin Differences in Wages and
Hierarchical Positions - Evidence on French Full-
Time Male Workers from a matched Employer-
Employee Dataset

G2010/07

S. BLASCO - P. GIVORD
Les trajetours professionnelles en début de vie
active : quel impact des contrats temporaires ?

G2010/08

P. GIVORD
Méthodes économétriques pour l'évaluation de
politiques publiques
G2010/09

P.-Y. CABANNES - V. LAPÉGUE -
E. POULIQUEN - M. BEFFY - M. GAINI
Quelle croissance de moyen terme après la
crise ?

G2010/10

I. BUONO - G. LALANNE
La réaction des entreprises françaises
à la baisse des tarifs douaniers étrangers

G2010/11

R. RATHELOT - P. SILLARD
L'apport des méthodes à noyaux pour mesurer la
concentration géographique - Application à la
concentration des immigrés en France de 1968 à
1999

G2010/12

M. BARATON - M. BEFFY - D. FOUGÈRE
Une évaluation de l'effet de la réforme de 2003
sur les départs en retraite - Le cas des
enseignants du second degree public

G2010/13

D. BLANCHET - S. BUFFETEAU - E. CRENNER
S. LE MINEZ
Le modèle de microsimulation Destine 2 :
principales caractéristiques et premiers résultats

G2010/14

D. BLANCHET - E. CRENNER
Le bloc retraites du modèle Destine 2 :
guide de l'utilisateur

G2010/15

M. BARLET - L. CRUSSON - S. DUPUCH -
F. PUECH
Des services échangés aux services échan-
geables : une application sur données françaises

G2010/16

M. BEFFY - T. KAMIONKA
Public-private wage gaps: is civil-servant human
capital sector-specific?

G2010/17

G. LALANNE - O. MONSO - E. POULIQUEN
Le modèle Mésange réestimé en base 2000
Tome 2 - Version avec volumes à prix chaînes

G2010/18

R. AEBERHARDT - L. DAVEZIES
Conditional Logit with one Binary Covariate: Link
between the Static and Dynamic Cases

G2011/01

T. LE BARBANCHON - B. OURLIAC - O. SIMON
Les marchés du travail français et américain face
aux chocs conjonctuels des années 1986 à
2007 : une modélisation DSGE

G2011/02

C. MARBOT
Une évaluation de la réduction d'impôt pour
l'emploi de salariés à domicile

G2011/03

L. DAVEZIES
Modèles à effets fixes, à effets aléatoires,
modèles mixtes ou multi-niveaux : propresés et
mises en œuvre des modélisations de
l'heterogénéité dans le cas de données groupées

G2011/04

M. ROGER - M. WASMER
Heterogeneity matters: labour productivity
differentiated by age and skills

G2011/05

J.-C. BRICONGNE - J.-M. FOURNIER
V. LAPEGUE - O. MONSO

De la crise financière à la crise économique
L'impact des perturbations financières de 2007 et
2008 sur la croissance de sept pays industrialisés

G2011/06

P. CHARNOZ - É. COUDIN - M. GAINI
Wage inequalities in France 1976-2004:

G2011/07

M. CLERC - M. GAINI - D. BLANCHET
Recommendations of the Stiglitz-Sen-Fitoussi
Report: A few illustrations

G2011/08

M. BACHELET - M. BEFFY - D. BLANCHET
Projet l'impact des réformes des retraites sur
l'activité des 55 ans et plus : une comparaison de
trois modèles

G2011/09

C. LOUVOT-RUNAOVT
L'évaluation de l'activité dissimulée des entre-
prises sur la base des contrôles fiscaux et son
insertion dans les comples nationaux

G2011/10

A. SCHREIBER - A. VICARD
La télantisation de l'économie française et le
rationalisme de la productivité entre 1978 et
2008

G2011/11

M.-É. CLERC - O. MONSO - E. POULIQUEN
Les inégalités entre générations depuis le baby-
boom

G2011/12

C. MARBOT - D. ROY
Évaluation de la transformation de la réduction
d'impôt en crédit d'impôt pour l'emploi de salariés
à domicile en 2007

G2011/13

P. GIVORD - R. RATHELOT - P. SILLARD
Place-based tax exemptions and displacement
effects: An evaluation of the Zones Franches
Urbaines program

G2011/14

X. D'HALLTFOEUILLE - P. GIVORD -
X. BOUTIN
The Environmental Effect of Green Taxation: the
Case of the French "Bonus/Malus"

G2011/15

M. BARLET - M. CLERC - M. GARNEO -
V. LAPEGUE - V. MARCUS
La nouvelle version du modèle MZE, modèle
macroéconomique pour la zone euro

G2011/16

R. AEBERHARDT - I. BUONO - H. FADINGER
Learning, Incomplete Contracts and Export
Dynamics: Theory and Evidence from French
Firms

G2011/17

C. KERDRAIN - V. LAPEGUE
Restrictive Fiscal Policies in Europe:
What are the Likely Effects?

G2012/01

P. GIVORD - S. QUANTIN - C. TREVIEN
A Long-Term Evaluation of the First Generation
of the French Urban Enterprise Zones

G2012/02

N. CECI-RENAUD - V. COTTET
Politique salariale et performance des entreprises

G2012/03

P. FEVRIER - L. WILNER
Do Consumers Correctly Expect
Reductions? Testing Dynamic Behavior

M. GAINI - A. LEDUC - A. VICARD
School as a shelter? School leaving-age and the
business cycle in France

G2012/04

M. GAINI - A. LEDUC - A. VICARD
School evidence on young
people entering into a tough labour market

G2012/05

P. AUBERT - M. BACHELET
Disparités de montant de pension et
redistribution dans le système de retraite français

G2012/07

R. AEBERHARDT - P. GIVORD - C. MARBOT

Spillover Effect of the Minimum Wage in France:

G2012/08

A. EIDELMAN - F. LANGUMIER - A. VICARD
Prélevements obligatoires reposant sur les
ménages : des canaux redistributifs différents en
1980 et 2010

G2012/09

O. BARGAIN - A. VICARD
Le RMI et son successeur le RSA débouragent-
ils certains jeunes de travailleur ? Une analyse sur
les jeunes autour de 25 ans

G2012/10

C. MARBOT - D. ROY
Projections du coût de l'APA et des
caractéristiques de ses bénéficiaires à l'horizon
2040 à l'aide du modèle Destine

G2012/11

A. MAUROUX
Le crédit d'impôt dédié au développement
durable : une évaluation économique

G2012/12

V. COTTET - S. QUANTIN - V. RÉGNIER
Coût du travail et allégements de charges : une
estimation au niveau établissement de 1996 à
2008

G2012/13

X. D'HALLTFOEUILLE - P. FÉVRIER -
L. WILNER
Demand Estimation in the Presence of Revenue
Management

G2012/14

D. BLANCHET - S. LE MINEZ
Joint macro/micro evaluations of accrued-to-date
pension liabilities: an application to French
reforms

G2013/01

T. DEROYON - A. MONTAUT - P.-A. PIONNIER
Utilisation rétrospective de l'enquête Emploi à
une fréquence mensuelle : apport d'une
modélisation espace-état

G2013/02

C. TREVIEN
Haberit en HLM : quel avantage monétaire et
quel impact sur les conditions de logement ?

G2013/03

A. POISSONNIER
Temporal disaggregation of stock variables - The
Chow-Lin method extended to dynamic models

G2013/04

P. GIVORD - C. MARBOT
Does the cost of child care affect female labor
market participation? An evaluation of a French
reform of childcare subsidies

G2013/05

G. LAME - M. LEQUIEN - P.-A. PIONNIER
Interpretation and limits of sustainability tests in
public finance

G2013/06

C. BELLEGO - V. DORTET-BERNADET
La participation aux pôles de compétitivité : quelle
incidence sur les dépenses de R&D et l'activité
des PME et ETI?

G2013/07

P.-Y. CABANNES - A. MONTAUT -
P.-A. PIONNIER
Évaluer la productivité globale des facteurs en
France : l'apport d'une mesure de la qualité du
capital et du travail

G2013/08

R. AEBERHARDT - C. MARBOT
Evolution of instability to the French Labour
Market During the Last Thirty Years

G2013/09

J.-B. BERNARD - G. CLEAUD
Oil price: the nature of the shocks and the impact
on the French economy

G2013/10

G. LAME
Was there a « Greenspan Conundrum » in the
Euro area?

G2013/11

P. CHONÉ - F. EVAIN - L. WILNER - E. YILMAZ
Introducing activity-based payment in the hospital
industry : Evidence from French data

G2013/12

C. GRISLAIN-LETRÉMY
Natural Disasters: Exposure and Underinsurance
P.-Y. CABANNES - V. COTTET - Y. DUBOIS
C. LELARGE - M. SICSI
French Firms in the Face of the 2008/2009 Crisis

G2013/13

G2013/14

A. POISSONNIER - D. ROY
Households Satellite Account for France in 2010,
Methodological issues on the assessment of
domestic production

G2013/15

G. CLÉAUD - M. LEMOINE - P.-A. PIONNIER
Which size and evolution of the government
expenditure multiplier in France (1980-2010)?

G2014/01

M. BACHELET - A. LEDUC - A. MARINO
Les biographies du modèle Destine II : rebasage
et héritages ?

G2014/02

B. GARBINTI
L'achat de la résidence principale et la création
d'entreprises sont-ils favorisés par les donations
et héritages ?

G2014/03

N. CECI-RENAUD - P. CHARNOZ - M. GAINI
Évolution de la volatilité des revenus salariaux du
sector privé en France depuis 1968

G2014/04

P. AUBERT
Modalités d'application des reformes des retraites
et prévisibilité du montant de pension

G2014/05

C. GRISLAIN-LETRÉMY - A. KATOSSKY
The Impact of Hazardous Industrial Facilities on
Housing Prices: A Comparison of Parametric and
Semiparametric Hedonic Price Models

G2014/06

J.-M. DAUSSIN-BENICHOU - A. MAUROUX
Turning the heat up: How sensitive are
households to fiscal incentives on energy
efficiency investments?

G2014/07

C. LABONNE - G. LAMÉ
Credit Growth and Capital Requirements: Binding
or Not?

G2014/08

C. GRISLAIN-LETRÉMY et C. TREVIEN
The Impact of Housing Subsidies on the Rental
Sector: the French Example

G2014/09

M. LEQUIEN et A. MONTAUT
Croissance potentielle en France et en zone
euro: un tour d'horizon des méthodes
d'estimation

G2014/10

B. GARBINTI - P. LAMARCHE
Les hauts revenus épargnent-ils davantage ?

G2014/11

D. AUDENAERT - J. BARDAJI - R. LARDEUX -
M. ORAND - M. SICSI
Wage Resilience in France since the Great
Recession

G2014/12

F. ARNAUD - J. BOUSSARD - A. POISSONNIER -
H. SOULAC
Computing additive contributions to growth and
other issues for chain-linked quarterly aggregates

G2014/13

H. FRAISSE - F. KRAMARZ - C. PROST
Labor Disputes and Job Flows
G2014/14 P. GIVORD - C. GRISLAIN-LETRÉMY - H. NAEGELE How does fuel taxation impact new car purchases? An evaluation using French consumer-level dataset

G2014/15 P. AUBERT - S. RABATÉ Durée passée en carrière et durée de vie en retraite : quel partage des gains d'espérance de vie ?

G2015/01 A. POISSONNIER The walking dead Euier equation Addressing a challenge to monetary policy models

G2015/02 Y. DUBOIS - A. MARINO Indicateurs de rendement du système de retraite français

G2015/03 T. MAYER - C. TREVIEN The impacts of Urban Public Transportation: Evidence from the Paris Region

G2015/04 S.T. LY - A. RIEGERT Measuring Social Environment Mobility

G2015/05 M. A. BEN HALIMA - V. HYAFIL-SOLELHAC M. KOUBI - C. REGAERT Quel est l'impact du système d'indemnisation maladie sur la durée des arrêts de travail pour maladie ?

G2015/06 Y. DUBOIS - A. MARINO Disparités de rendement du système de retraite dans le secteur privé : approches intergénérationnelle et intragénérationnelle

G2015/07 B. CAMPAGNE - V. ALHENC-GELAS - J.-B. BERNARD No evidence of financial accelerator in France

G2015/08 O. LAFFÉTER - M. PAK Elasticités des receites fiscales au cycle économique : étude de trois impôts sur la période 1979-2013 en France

G2015/09 J.-M. DAUSSIN-BENICHOU, S. IDMACHICHE, A. LEDUC et E. POULIQUEN Les déterminants de l'attractivité de la fonction publique de l'Etat

G2015/10 P. AUBERT La modulation du montant de pension selon la durée de carrière et l'âge de la retraite : quelles disparités entre assurés ?

G2015/11 V. DORTEFT-BERNADET - M. SICSIC Effet des aides publiques sur l'emploi en R&D dans les petites entreprises

G2015/12 S. GEORGES-KOT Annual and lifetime incidence of the value-added tax in France

G2015/13 M. POULHÉS Are Enterprise Zones Benefits Capitalized into Commercial Property Values? The French Case

G2015/14 J.-B. BERNARD - Q. LAFFÉTER Effet de l'activité et des prix sur le revenu salarial des différentes catégories socioprofessionnelles

G2015/15 C. GEAY - M. KOUBI - G de LAGASNERIE Projections des dépenses de soins de ville, construction d'un module pour Destinie

G2015/16

G2015/17

G2015/18

G2015/19

G2016/01

G2016/02

G2016/03

G2016/04

G2016/05

G2016/06

G2016/07

G2016/08

G2016/09

G2016/10

G2016/11

G2017/01

G2017/02

G2017/03 A. CAZENAVE-LACROUTZ - F. GODET L'espérance de vie en retraite sans incapacité sèvère des générations nées entre 1960 et 1990 : une projection à partir du modèle Destinie

G2017/04 J. BARDAJI - B. CAMPAGNE - M.-B. KHDER - Q. LAFFETER - O. SIMON (Insee) A.-S. DUFERNEZ - C. ELEZAAR - P. LEBLANC - E. MASSON - H. PARTOUCHE (DG-Trésor) Le modèle macroéconomique Mésange : réestimation et nouveauités

G2017/05 J. BOUSSARD - B. CAMPAGNE Fiscal Policy Coordination in a Monetary Union at the Zero-Lower-Bound

G2017/06 A. CAZENAVE-LACROUTZ - A. GODZINSKI Effects of the one-day waiting period for sick leave on health-related absences in the French central civil service

G2017/07 P. CHARNOZ - M. ORAND Qualification, progrès technique et marchés du travail locaux en France, 1990-2011

G2017/08 K. MILIN Modélisation de l'inflation en France par une approche macrosectorielle

G2017/09 C.-M. CHEVALIER - R. LARDEUX Homeownership and labor market outcomes: disentangling externality and composition effects

G2017/10 P. BEAUMONT Time is Money: Cash-Flow Risk and Export Market Behavior

G2018/01 S. ROUX - F. SAVIGNAC SMEs' financing: Divergence across Euro area countries?