# Time is Money: Cash-Flow Risk and Export Market Behavior

Paul Beaumont

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Direction des Études et Synthèses Économiques

G 2017/10

Time is Money: Cash-Flow Risk and Export Market Behavior

Paul BEAUMONT

Document de travail

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

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

G 2017/10

Time is Money: Cash-Flow Risk
and Export Market Behavior

Paul BEAUMONT *

DÉCEMBRE 2017

L'auteur tient à remercier Isabelle MÉJEAN (discutante), Johan HOMBERT, Dominique GOUX, Sébastien ROUX, Claire LELARGE, Corinne PROST, Daniel FERREIRA, Clemens OTTO, Jean-Noël BARROT et Nicolas SERRANO-VELARDE pour leur aide dans l'élaboration et la rédaction de ce travail.

<footnote>* Département des Études Économiques - Division « Marchés et entreprises » Timbre G230 - 15, bd Gabriel Péri - BP 100 - 92244 MALAKOFF CEDEX</footnote>
# Croissance de l'entreprise et risque de trésorerie :
etude sur données d'exportations

## Résumé

Les contraintes de liquidités représentent-elles pour les entreprises un obstacle à l'entrée dans de nouveaux marchés ? Cette hypothèse est testée sur un échantillon de PME exportatrices françaises en utilisant une réforme inscrite dans la Loi de Modernisation de l'Economie (LME) de 2009 limitant les délais de paiement interentreprises à 60 jours. L'analyse empirique utilise la granularité des données de commerce international pour introduire des effets fixes marché à un niveau fin afin de contrôler pour des chocs simultanés à la réforme. L'effet de la réforme sur la variation des délais de paiement est isolé en utilisant comme instrument une mesure d'exposition à la limite des 60 jours. Les résultats suggèrent un effet causal des conditions de financement des besoins de fonds de roulement sur l'expansion à l'international: une baisse des délais de paiement de 10 jours supplémentaires conduit à une augmentation de la trésorerie de 1,4 point de pourcentage et une hausse de la probabilité d'entrer dans un nouveau marché de 0,4 point de pourcentage. Par contraste, aucun effet sur l'évolution du volume des exportations ou sur la probabilité de sortie d'un marché ne peut être mis en évidence.

**Mots-clés** : contraintes de liquidité, délais de paiement, variable instrumentale, exportation

# Time is Money: Cash-Flow Risk and Export Market Behavior

## Abstract

Do liquidity constraints hinder firms' entry in new markets? Exploiting an exogenous variation in payment delays triggered by a 2009 French reform, we use a unique combination of administrative data sets to test whether changes in working capital financing affect the propensity to enter new export markets. The effect of the reform on payment delays is isolated using a threshold rule introduced by the law. The estimations strongly support the idea that access to working capital financing plays a key role in the expansion in international markets: a decrease in payment delays by ten days is found to raise cash holdings by 1.4 percentage points and to increase the probability to reach a new foreign market by 0.4 percentage points. By contrast, the evolution of the volume of exports or the probability of exiting an export market do not seem to be affected by the variation of payment delays.

**Keywords:** liquidity constraints, trade credit, IV estimation, exports

**Classification JEL** : F14, G31
The financing of global trade largely relies on firm-to-firm lending: it is estimated that two thirds of international trade are supported by interfirm trade credit, the remainder being financed by bank-intermediated trade credit (Bank for International Settlements, 2014). It is therefore striking to observe that while many studies have investigated the role of bank financing in export behavior (Amiti and Weinstein, 2011; Paravisini et al., 2014; Schmidt-Eisenlohr and Niepmann, 2016), very little is known on how the inter-firm provision of trade credit shapes international trade patterns.

The formation and the continuation of a customer-supplier relationship commonly requires the supplier to provide short-term financing to the buyer in the form of delayed payments<fnref n="1" />. In presence of competition between suppliers and market power of buyers, the provision of trade credit even appears to be a necessary condition to trade (Breza and Liberman, 2016). In face of financing frictions, the share of working capital that the firm can dedicate to new customers will therefore be determined in part by the amount of liquidity already allocated to existing customers (Clementi and Hopenhayn, 2006). In particular, financially constrained firms might renounce to reach new customers in remote international markets since this entails higher working capital needs<fnref n="2" />. As a consequence, firms with better working capital financing should be able to outperform their competitors by being able to expand further internationally (Frésard, 2010; Boutin et al., 2013).

Using an exogenous and large variation in payment delays triggered by a 2009 French reform, I show that in line with this hypothesis, firms that got paid earlier by their existing customers experienced higher growth in cash holdings and achieved greater consumer capital expansion in international markets. Specifically, a decrease in *net* payment delays (a term that I will define later) by ten days is found to increase cash holdings by 1.4 percentage points and to raise the probability to reach a new foreign market by 0.4 percentage points (a 17% rise compared to the unconditional probability). By contrast, the restriction in trade credit provision appears to have no robust effect neither on the exit probability nor on the evolution of the volume of sales.

The 2009 reform ("Loi de modernisation de l'économie", or LME) limits contractual payment delays to sixty days in any transaction involving French firms. In particular, it affects international operations as long as the transaction is contracted under the French law. Under this legislation, excessive contractual payment delays or non compliance to the payment terms are made subject to civil and penal procedures with fees amounting to 2 millions euros; audits carried out by public authorities ensures that the law is properly enforced. As a

<footnote n="1">¹A large literature has explored the empirical and theoretical determinants of trade credit provision: see for instance Petersen and Rajan (1997); Biais and Gollier (1997); Ng, Smith and Smith (1999); Burkart and Ellingsen (2004); Fisman and Raturi (2004); Giannetti, Burkart and Ellingsen (2011); Klapper, Laeven and Rajan (2012); Fabbri and Klapper (2016)</footnote>

<footnote n="2">²On the basis of survey evidence, Schmidt-Eisenlohr (2013) finds that in 78% of export transactions, the importer pays after reception of the product. This means that shipping time comes in addition to the payment delays that prevail in the foreign market. Amiti and Weinstein (2011) estimate that the median transportation time can be estimated to two months.</footnote>
consequence, customer and supplier payment delays decreased respectively by 6 and 7 days
in the manufacturing and wholesale sectors between the announcement of the reform and
the year of its application.

A precise examination of the effects of this law is particularly challenging since (a) there
is no natural control group to which the econometrician might refer and (b) the reform took
effect at the beginning in 2009, i.e. precisely during the peak of the global financial crisis.
I tackle these identification issues by building an econometric framework based on several
steps. First, I exploit rich fiscal and survey data sets on French firms provided by the French
statistical institute (Insee) to estimate customer payment delays at the sector-level as the
mean of the ratio of accounts receivable over sales. I then recover a firm-level measure us-
ing information on the breakdown of sales of firms between their different business lines.
I proceed similarly to measure suppliers payment delays, which allows me to estimate net
payment delays as the difference between the time needed for a firm to be paid by its cus-
tomers and the time spent to pay its suppliers.

Second, I use the sixty-day rule defined by the law to estimate the firm's *exposure to the reform prior to its enactment* as the changes in net payment delays that would be needed for the rule to be perfectly enforced. Broadly speaking, this variable measures the distance in days to the situation in which the sixty-day rule is perfectly enforced in all the sectors in which the firm operates. This distance is then included as an instrument for the actual variation in net payment delays. This instrument strategy has two main advantages: first, it allows me to overcome the absence of control group by exploiting the *ex ante* variation in treatment intensity. Moreover, it enables me to capture only the part of the variation in net payment delays that can be explained by the enactment of the reform, thereby leaving aside the potential effects of confounding aggregate shocks (e.g., the financial crisis).

Our instrument proves to be a good predictor of the change in payment delays (R-square
of 38%). As expected, a placebo regression shows that the strong correlation between our
measure of exposure to the reform and the variation in payment delays disappears after the
implementation of the reform. Two-stage least squares estimations indicate that a decrease
of 10 days in net payment delays caused a 1.4 percentage points increase in cash holdings,
broadly in line with the estimates of Barrot (2016) of the effects of the 2006 French payment
delays reform in the trucking sector. One might fear that in spite of the instrumentation
strategy, the negative association between the evolution of payment delays and cash hold-
ings might be driven by the coincidence of the financial crisis. These concerns are how-
ever alleviated by the finding that the change in payment delays seems to be uncorrelated
with the evolution of employment and even positively correlated (though imprecisely) with
changes in investment.

Building on Paravisini et al. (2014), I rely on disaggregated export data to develop the
analysis at the market level (an export market being defined as the product of an industry
and a country). With the use of market fixed effects, the estimates of the effect of the varia-
tion in net payment delays on the propensity to enter new export markets are based on the comparison between firms that are differently affected by the reform in a given market. This procedure first allows to control for the presence of market-level shocks that could affect the results. Second, and more importantly, it removes any specialization pattern of firms in certain markets that could create a correlation between the exposure to the reform and export outcomes.

I then take advantage of a unique data set recording the identity of the foreign customers of French exporters in the European Union in order to investigate the effects of the reform on customer capital. The data gives the breakdown of the quantity and the value of exports by product and foreign importer for all French exporters to all countries within the EU<fnref n="3" />. The results indicate that a ten days decrease in net payment delays increases the probability to acquire customers in markets in which the firm is already present by 1.0 percentage points (unconditional probability: 7.0%).

I focus here on the firm-level product market behavior in foreign markets: since export transactions are highly dependent on working capital, international customer-supplier relationships are more likely to be affected by a change in the provision of interfirm lending. Antràs and Foley (2015) show that the negotiation of financing terms is key in building and maintaining trade relationships, and that institutional features of the customer greatly influence this negotiation.<fnref n="4" /> Consistently with their findings, I find that the effects of the change in payment delays are strikingly different whether the export market belongs or not to the European Union.

On a broader level, Manova (2013) and Chaney (2016) argue that the optimal portfolio of foreign markets are determined not only by profit considerations as in Mélitz (2003) but also by whether firms are able to sustain the liquidity needs required by international transactions, a prediction that the empirical results strongly support<fnref n="5" />: consistently with the financing channel, the export behavior of firms that benefit from the internal capital markets of large groups or that face low cash-flow idiosyncratic risk (Bates, Kahle and Stulz, 2009) appears not to be affected by the change of payment delays.

This article also falls within the wide array of articles initiated by Chevalier (1995) and Kovenock and Phillips (1997) that study the interactions between structural characteristics

<footnote n="3">³The identity of foreign customers is recorded by French customs for the collection of value-added tax and checked by the administration, so that they can be considered as being very reliable.</footnote>

<footnote n="4">⁴Regarding the role of insurance on trade credit claims, Schmidt-Eisenlohr and Niepmann (2016) estimate that a one standard deviation negative shock to a country's trade insurance supply lowers export by U.S. firms to that country by 1.5% (see also Aubouin and Engemann (2014)).</footnote>

<footnote n="5">⁵Feenstra, Li and Yu (2014) endogeneizes firms' financial constraints in an agency setting and concludes that all other things equal, exporters are more likely to be financially constrained than domestic producers, which they confirm using financial data on Chinese firms. See also Caggese and Cuñat (2013), Schmidt-Eisenlohr (2013) or Bonfiglioli, Crinò and Gancia (2016) for theoretical contributions and Paravisini et al. (2014), Amiti and Weinstein (2011), Berman and Héricourt (2011), Minetti and Zhu (2011) for empirical ones. I comment extensively on the differences between the results of Paravisini et al. (2014) and mine in the discussion section of the paper.</footnote>
of product markets and capital structure policies, and here more specifically on the role of
cash holdings in entry patterns. Boutin et al. (2013) find evidence that entry is negatively
correlated with cash holdings hoarded by incumbent affiliated groups and positively asso-
ciated with the level of entrant groups’ cash. Frésard (2010) then shows that large cash re-
serves act as a comparative advantage in product markets as they lead to systematic future
market share gains at the expense of industry incumbents. My results suggest that firms op-
erating in countries or in sectors where payment delays are low benefit from a comparative
advantage relative to their international rivals.

The paper closest to this article is Barrot (2016): studying an early implementation of the 2009 reform on the trucking sector, Barrot finds that following the reform, corporate defaults fell and the decrease in payment delays triggered the entry of small firms in the trucking industry. This work can be seen as completing Barrot’s in the sense that I document the effects of payment delays at the intensive margin (expansion in international markets) while his focus is on the extensive one (exit and entry). I also extend his methodology in two directions: first, since the setting of the LME reform does not lend itself well to a differences in differences estimation, I deviate from Barrot by relying on the heterogeneity of the exposure to the reform to instrument the variation in payment delays. Second, I am able to take into account both the effects of the change in customer and in supplier payment delays by computing payment delays in net terms, thereby acknowledging the fact that the reform affected both sides of trade credit.

Barrot and Nanda (2016) further investigate the role of payment delays in firm decisions by exploiting a 2011 US reform that restricted payment delays of government contractors to small businesses and find a significant effect of payment delays on employment. In contrast, I find no effect of payment delays on employment, a difference possibly due to the deteriorated economic situation at the time of the implementation of the LME.

Using information on trade credit in buyer-supplier relationships, Murfin and Njoroge (2015) show that long payment delays are associated with reduction in capital expenditures for the firms who bear the working capital costs. Using product-supplier data from a large Chilean retailer, Breza and Liberman (2016) show that an exogenous restriction on payment delays reduces the probability of continuing trade with small external suppliers. In line with these papers, I show that the effects of payment delays on the acquisition of customers is concentrated on firms with low market power (as proxied by the average market share in its different business lines).

The remainder of the paper is organized as follows. The details of the LME reform are de-
scribed in section 1. The data sets are presented in section 2. I discuss the different measures
of payment delays in section 3. In section 4, I present in detail the identification strategy.
The effects of the reform on payment delays and on export outcomes are displayed in sec-
tion 5 and 6; in section 7 I investigate how the results are affected by firm- and market-level
heterogeneity. Results are discussed in section 9. Section 10 concludes.
# 1 Description of the payment delays reform

The French 2009 reform provides an ideal setting to study the role of trade credit on the formation of customer capital as the enforcement of the law triggered a economically large, economy-wide exogenous shock on payment delays. As an illustration, figure 1 shows the evolution of payment delays between 1999 and 2013 in the manufacturing and wholesale sector (the data sets and the construction of the measures are described below). Between 2007 and 2009, customer and supplier payment delays decreased respectively by 6 and 7 days on average.

The fact that the law was implemented during the crisis might lead to suspect that the fall of payment delays is an incident consequence of the concomitant macroeconomic shock. Two considerations allow lifting this doubt: if anything, the exacerbation of financial constraints should lead firms to further delays their payments, not shorten them. Moreover, if the payment delays were somehow caused by the financial crisis, we would have expected payment delays to come back to their initial level with macroeconomic conditions returning to normal; by contrast, the level of payment delays stays stable after the enforcement of the reform.

[Insert figure 1 here]

Remarkably, payment delays directly started to decline before the implementation of the law in January 2009. A first element of understanding can be found in the fact that the law was first presented to the French parliament in April and subsequently voted in 2008, leaving time for French firms to anticipate the reform. Still, it is counterintuitive that firms chose to bear the working capital costs to comply in advance with the law.

A probable explanation of this anticipation is that the 2009 law was in fact merely introducing ways to enforce preexisting regulations: implementing the directive 2000/35/EC from the European Commission, a 2004 law had already introduced a cap of contractual payment delays of 60 days after reception of the invoice (or 45 days following the end of the month) in the trade code. Firms whose customers were imposing longer contractual payment delays or who did not respect their contractual engagements were allowed to demand interest payments as compensation. A civil procedure could also be initiated by the French government against bad payers; in practice, though, only one case has been brought to court under this legislation. The rapid reaction to the announcement of the reform might therefore be explained by an increase in the perceived probability of sanctions against bad payers.

Acknowledging that the 2004 law was scarcely applied in practice, the French government tried another approach by fostering negotiations between professional organisations representing customers and suppliers of a given sector. This resulted in 2006 in a reform limiting contractual payment delays to thirty days in the trucking sector, and in 2007 in an
agreement to limit contractual payment delays to ninety days in the automobile sector. How-
ever, the difficulties faced by the government to enforce the (non-binding) agreements in the
automobile sector prompted public authorities to move from interprofessional negotiations
toward a law encompassing all sectors. The 2009 law therefore extended the preceding legis-
lations by generalizing as of January 1st 2009 the limit of contractual payment delays to sixty
days in any transaction involving French firms, regardless of the sectors they are operating
in.

The enforcement of the law was based on three pillars. First, excess contractual payment
delays are to be reported to public authorities by firms' accounting auditors: penal proce-
dures can be initiated in case of a violation of the contractual limits and may result in a
75,000 euros fine. Second, firms that do not respect their contractual obligations are subject
to civil sanctions amounting up to 2 millions euros: in 2015, for instance, a major telecom
group had to settle a fine of 750 000 euros following several complaints from suppliers<fnref n="6" />. Im-
portantly, asking suppliers to delay their invoices is considered as an abusive practice and is
subject to the same sanctions. Third, the French competition authority conducts audits to
check that the law is properly applied: in 2010 alone, more than 1700 firms have been au-
dited, with a follow-up rate (in terms of sanctions and/or further audits) of 30%. Moreover,
as of 2015, the government decided to "name and shame" bad payers by publishing the list
of firms subject to sanctions.

The limit of payment delays to 60 days hinges on the idea that beyond a certain thresh-
old, payment delays can be considered as an abuse of power from certain firms toward their
suppliers: in particular, French authorities were concerned about market leaders imposing
late payments to small and medium enterprises (SMEs). However, the legislators were aware
that the positive effects of this restriction might be offset if the payment terms imposed by
the reform were too restrictive: short contractual payment delays might in some cases jeop-
ardize firms' cash-flow management or hurt existing supplier-customer relationships.

The different professional branches have therefore been consulted on the question whether
60-days could be considered as an acceptable boundary between normal and abusive pay-
ment delays. When the 60 days limit was considered as being too restrictive in some sectors,
the law allowed firms to deviate from the rule. In the toy industry where most of the sales
are realized in the holiday season, firms had for instance up to 170 days to repay their cus-
tomers. Conversely, payment delays were restricted to be shorter in sectors involving per-
ishable products. The complete list of derogations is displayed in appendix A. I discuss how
I deal with the issue of derogations in the empirical setting.

Importantly, the reform affected international customer-supplier relationships as well
as long as the involved parties decide that the transaction contract abides to the French law.
If they choose a foreign or the international trade code instead, the French rule does not
apply and the parties are free to determine their payment terms. The choice between the

<footnote n="6">⁶See TelecomPaper.com (2015).</footnote>
different rules of law will then depend on the comparison between the propensity to be free to determine longer payment terms versus the potential costs of operating under a foreign trade code.

# 2 Description of the data sets

## 2.1 International trade data

### 2.1.1 Market data set

International trade data sets come from the French customs (DGDDI). The *product-level* data set gives for each firm identified by its SIREN number the (free on board) value of exports and imports by product for all countries; products are identified by a 6-digit number easily comparable to the French activity nomenclature. A firm operating in the French metropolitan territory must report detailed information to French customs if it exports more than 1,000 euros outside the European Union. To facilitate intra-EU trade, however, firms are not required to provide information at the product-level if their total exports to the European Union for a given year are inferior to 150,000 euros and therefore do not appear in the data.

Exports are clustered at the firm-country-industry level by summing all export flows from a firm *f* to the country *c* in the same 2-digits product classification *i* ("industry"). A pair (*c*, *i*) is called a market *m*. To have a sharp distinction between the intensive and extensive margins, a firm is considered to be *active* in a market if *Exports*<sub>*fm09*</sub> is at least equal to 5,000 euros. The export behavior at the *intensive margin* is defined as the change in the logarithm of exports if firm *f* is active in market *m* without interruption between 2007 and 2009 (Δ*Market exports*<sub>*fm09*</sub>).

The export behavior at the *extensive margin* is treated differently depending if the firm enters or exits a given market. If firm *f* is active in market *m* in 2007 or 2008, I set the variable *Market exit*<sub>*fm09*</sub> equal to one if *f* does not export at all in *m* in 2009 and zero otherwise. In the absence of any restrictions, the set of potential export markets in which a firm can enter is composed of approximately 25 industries *×* 200 countries = 5,000 markets, which generates very low entry probabilities. To facilitate the estimation of probability models, I focus here on the "most reasonable" markets in which firms might enter. To that end, I first compute the top 25 export destinations for French firms in 2007<sup>7</sup>; the set of potential export markets in 2009 of firm *f* is then defined as the product of the industries in which *f* is exporting in 2007 (or selling in the domestic market if *f* does not export in 2007) and the countries included in the top 25. Keeping only the markets in which firm *f* does not export in 2007, I

<footnote n="7"><sup>7</sup>These countries are (in alphabetical order): Algeria, Austria, Belgium, China, Czech Republic, Finland, Germany, Greece, Italy, Japan, Mexico, Morocco, Netherlands, Portugal, Romania, Russia, Singapore, Spain, Sweden, Turkey, United Kingdom, United States.</footnote>
set *Market entry*<sub>fm09</sub> if *f* is active in 2009 in *m* and 0 otherwise.

2.1.2 Customer-product level data set

The customer-level data set is a specific extraction from the customs forms registered by
the DGDDI. The data set has been made available from 2007 to 2012. As part of the European
Single Market, all exports within the European Union are subject to value added tax by the
importing country; exports outside the European Union are exempted from value-added
tax. A unique 13-digit number is therefore attributed by national tax offices to every firm
within the EU to facilitate the collection of the tax. The accuracy of the importer identifier
is then cross-checked by the French customs. The importer number allows in particular to
identify import-export transactions, shipments for assembly by a foreign firm or for trans-
portation via a foreign hub, which I all remove from the data set. I find that on average, 85%
of French exports in value are realized every year by importing firms that were also present
the year before, a sign of good quality of the customer identifier.

The acquisition of a customer is detected by a new exporter-importer link. In the great
majority of cases (79.9%), however, I find that the creation of a new link is associated with the
destruction of another relationship in the same market, indicating that firms tend to switch
between customers. In markets *m* in which firm *f* is active in 2007, I define therefore the
dummy *Customer base increase*<sub>fm09</sub> as equal to one if firm *f* acquires at least one customer
between 2007 and 2009 without dropping any customer during the same period.

## 2.2 Firm data

Balance sheet data between 2006 and 2011 comes from BRN-RSI tax returns collected by the French fiscal administration<fnref n="8" />. This data set gives accounting information for the whole universe of French firms in the private economy (excluding the financial and agricultural sectors). In addition to balance sheet information, a common identifier among all French firm data (Siren number) and a 5-digits sector classification are provided. Since the focus is on the effects of payment delays on the exporting behaviour, only firms belonging to the manufacturing and wholesale sectors (the two main exporting industries) are retained.

To identify precisely the different business lines of firms, I rely on an extensive yearly sur-
vey conducted by the Ministry of Industry (Enquête Annuelle des Entreprises, "EAE"). The
survey is exhaustive for French firms with more than 20 employees or whose sales exceed
5 millions euros (smaller firms are surveyed according to a stratified sample design) and
contains the amounts of sales realized by each surveyed firms in each 5-digits sector. The
firm-level sector code available in the tax returns corresponds to the business line in which
the firm realizes the majority of its activity.

<footnote n="8">⁸See Bertrand, Schoar and Thesmar (2007), Garicano, Lelarge and Van Reenen (2016) or Boutin et al. (2013) for other uses of this data set.</footnote>
Information on group ownership is eventually added using the LIFI ("Liaisons financières") survey; exhaustive on the set of firms that employ more than 500 employees, that generate more than 60 millions euros in revenues or that hold more than 1.2 million euros of traded shares, the LIFI survey is completed by data coming from Bureau Van Dijk (Diane-Amadeus data set) so as to cover the whole universe of French corporate groups. This data set allows us to identify the set of firms that belong to the same corporate group and to determine whether a given group can be classified as a small or medium enterprise (SME), an intermediate or a large enterprise according to the French legislation<fnref n="9" />.

Since my focus is on the effects of payment delays on international transactions, I only retain exporting firms that exported at least 50 000 euros in 2007 (16,671 firms). Moreover, in order to avoid a potential contamination of the results by the presence of active internal capital markets in business groups (see Boutin et al. (2013) for the role of internal capital markets in entry in product markets), I focus on SMEs in the following (9,912 firms) and come back to intermediate or large firms as a robustness check.

## 3 Measuring payment delays

Traditional firm-level data sets such as tax returns databases do not feature direct information on payment delays<fnref n="10" />. However, a measure of the actual customer payment delays can be derived from standard balance sheet data by defining for a firm $f$

$$ \textit{Customer payment delays}_f = \frac{\textit{Account receivables}_f}{\textit{Sales}_f} * 365 $$

The ratio gives the amount of sales that is owed to firm $f$ by its customers; it is multiplied by 365 so as to be readily interpretable in terms of days. $\textit{Customer payment delays}_f$ can be thought as the average payment delays between firm $f$ and its customers for a given fiscal year.

Since the reform affected supplier as well as customer payment delays, it is necessary to find an estimation of the variation in payment delays that takes both sides of trade credit

<footnote n="9">⁹According to the French classification, a group (which can be composed of several firms) is considered as a SME if (1) it employs less than 250 employees and (2) it generates less than 60 millions euros in revenues or possesses less than 43 millions euros in total assets.</footnote>

<footnote n="10">¹⁰See Antràs and Foley (2015) for a recent example of a data set including such information.</footnote>
into account. Supplier payment delays are symmetrically computed as<fnref n="11" />

$$ \textit{Supplier payment delays}_f = \frac{\textit{Account payables}_f}{\textit{Sales}_f} * 365 $$

"Net" payment delays are eventually defined as the difference of the two:

$$ \textit{Net payment delays}_f = \textit{Customer payment delays}_f - \textit{Supplier payment delays}_f $$

Higher net payment delays indicates that on average, firm *f* is paid later by its customers than it pays its suppliers.

[Insert **table 1** here]

There are however several reasons to believe that these measures of payment delays might not be appropriate for the analysis. First, these estimations are subject to measurement error as they compare the amount of sales generated in the whole fiscal year to the amount of trade credit recorded at the time of the tax report. If firm *f*'s sales fall at the time of the tax report, customer trade credit will decrease more than total sales, thereby leading to underestimate *Customer payment delays*<sub>*f*</sub>. Second, these measures may be determined simultaneously with exporting decisions. Firms contemplating exporting in a new sector might for instance sell their customer debts to a factoring firm to finance their cash-flow needs: in this case, customer payment delays would be endogenously negatively correlated to export entry.

This measurement problem can be overcome by noting that if contractual payment delays widely vary across industries, they are quite homogeneous within a given sector (Ng, Smith and Smith (1999) and Costello (2013)). This finding is consistent with the fact that most of the trade credit determinants emphasized in existing trade credit theories<fnref n="12" /> are homogeneous at the sector-level.

These observations suggest that taking sectoral means of payment delays instead of firm-level observations might (1) alleviate the measurement error problem due to accounting issues (2) yield estimates that are presumably uncorrelated with firm decisions as they are determined at the level of the sector<fnref n="13" />. The sectoral averages are denoted by *Customer PD*<sub>*s*</sub>

<footnote n="11">¹¹A more standard computation of supplier payment delays would use the amount of purchases instead of sales in the denominator. My definition has however two advantages: first, it adjusts for the sourcing strategy of the firm. With my definition, a firm that has a higher ratio of purchases over sales will have lower higher supplier payment delays all other things equal. Second, it makes the measures of customer and supplier payment more comparable since they are in the same unit: since I am ultimately interested in the difference of the two, this measure proves more useful.</footnote>

<footnote n="12">¹²Among them one can mention the degree of product market competition (Brennan, Maksimovic and Zechner, 1988), the degree of uncertainty on the quality of the product (Long, Malitz and Ravid (1993) and Lee and Stowe (1993)) and the information advantage of suppliers over banks to observe product quality or to enforce high effort (Smith (1987), Biais and Gollier (1997), Burkart and Ellingsen (2004) or Cunat (2007)).</footnote>

<footnote n="13">¹³I test how export outcomes are impacted by changes in the provision of trade credit using the firm-level measure of payment delays instead of sectoral averages in section 8.</footnote>
and $\overline{Supplier PD}_s$ in a given sector s.<fnref n="14" />

Table 1 displays the sectors with the highest and lowest values of $\overline{Customer payment delays}_s$ and $\overline{Supplier payment delays}_s$. Several patterns emerge from the table. First, highest customers and suppliers payment delays appear mostly in heavy industry, while lowest ones occur mostly to be seen in the food processing industry. This is consistent with the theoretical prediction of Long, Malitz and Ravid (1993) that the durability of the product should be positively correlated to payment delays. However, there is no direct mapping between the sectoral rank of $\overline{Customer payment delays}_s$ and of $\overline{Supplier payment delays}_s$: in 2007, the correlation between the two is only of 46%.

[Insert figure 2 here]

It is informative to see using these measures how the effects of the reform differ between sector. Figure 2 shows the evolution of payment delays in two sectors belonging to the manufacturing industry and wholesale trade sector. In the mechanical engineering industry, supplier payment delays were already low in 2007 and stayed therefore broadly unchanged between 2007 and 2009. By comparison, customer payment delays decreased sharply, leading to a fall of net payment delays from 51 days to 40. In the wholesale trade of textiles, conversely, supplier payment delays decreased more than customer payment delays, making firms pay relatively slower by their customers (NPD from -10 to -3 days). This variability in the evolution of net payment delays across sectors is crucial for the identification of the effect of the reform.

Once $\overline{Customer}_s$ and $\overline{Supplier}_s$ are estimated, I recover a firm-level measure of payment delays using the information of the repartition of sales by business lines given in the EAE dataset; denoting by $\omega_{fs07} = Sales_{fs07}/Sales_{f07}$ the ratio of firm f's sales in sector s to total sales in 2007, customer payment delays are computed as

$$ \overline{Customer payment delays}_{ft} = \sum_{s} \omega_{fs07} \overline{Customer}_{st} $$

and similarly for supplier payment delays.

The definition of the measures of payment delays warrants several comments. First, compared to customer payment delays, the measure of supplier payment delays makes the additional assumption that supplier payment delays are homogeneous in the downstream sector. This condition holds provided that the proportion of inputs does not vary too much between firms of the same sector. Ideally, supplier payment delays could be computed using an input-output matrix as a weighted average of the customer payment delays of upstream sectors; however, the input-output matrices available for the French economy are defined at

<footnote n="14">¹⁴Using the BRN-RSI data set on the whole universe of French firms, I remove for the computation of the means the observations that are superior (resp. inferior) to the median plus (resp. minus) three times the gap between the 5th and the 95th percentile in each sector so as to limit the effects of outliers. Moreover, I discard sectors (defined at the NAF 5-digits level) with less than 10 firms.</footnote>
a rather aggregate level (2-digits French nomenclature), which greatly limits the variability of the measure of supplier payment delays.

Since the measure of payment delays is computed as a linear combination of sectoral indexes, the cross-firm change in the main variable hinges on the heterogeneity of product market portfolios across firms: in this setup, firms are exposed to different payment conditions only through their activity in different business lines. This condition will not hold if firms have enough market power to influence payment terms: this concern is however mitigated by the fact that the analysis focuses mostly on SMEs whose market power is presumably limited.

A last potential problem with this method is that the variation in market portfolio might capture other factors unrelated to the evolution of payment delays. This issue is partially dealt with with the introduction of control variables (section 4.3). Moreover, I directly relate export outcomes to Net payment delays<sub>f</sub> (which does not appeal to information on firm f's business lines) in section 8.

Net payment delays are eventually computed as

$$ \overline{\text{Net payment delays}}_{ft} = \overline{\text{Customer payment delays}}_{ft} - \overline{\text{Supplier payment delays}}_{ft} $$

The main variable of interest is eventually defined as the change in net payment delays between 2007 and 2009 ($\Delta \overline{\text{Net payment delays}}_{f07-09}$).

# 4 Empirical specification

Overall, several specificities of the reform described in section 1 makes it challenging to use for causal inference:

(a): Absence of a natural control group No natural control group emerges as this reform affects all sectors. The trucking sector for which a cap on payment delays has already been implemented is a natural candidate on paper, but trucking firms barely participate to international trade.

(b): 2008-09 financial crisis The 2009 reform took place at the heart of the 2008 financial crisis. French export transactions were greatly affected by the ensuing global financial crisis: Bricongne et al. (2012) find that exports dropped by 16.2% between September 2008 and April 2009, the contribution of the intensive margin being four times higher in magnitude than the extensive margin one. Should this effect not be properly accounted for, any inference in this period would be subject to the risk of being contaminated by the confounding presence of the 2008 financial crisis.

(c): Presence of derogations The 2009 law allowed some sectors to deviate from the sixty-day rule because of particular difficulties (seasonal activity, particular payment usages...)
to implement it as of 2009. If those difficulties are correlated to firms' export market behavior, the measured variation in payment delays might be endogenous.

This section describes the strategy designed to address these three main points.

## 4.1 Payment delays variation and export market behavior

For $Z_{fm09}$ one of our main variables of interest, the baseline regression is specified as

$$Z_{fm09} = \alpha_m + \beta \Delta \overline{\text{Net payment delays}}_{f,07-09} + \gamma X_{f09} + \epsilon_{fm09} \quad (1)$$

where $\alpha_m$ is the industry-country fixed effects and $X_{f09}$ the set of firm-level control variables.

In order to disentangle the effects of the 2009 reform from the presence of the financial crisis (point (a)), I first rely on the disaggregated nature of export data by bringing the analysis to the market level and introducing industry-country fixed effects (Paravisini et al., 2014). By comparing export outcomes within an industry-country pair (a market), I am able to remove any market-level shock that hit demand (e.g., household over-indebtness) or supply (e.g., variation in input prices) between 2007 and 2009 in a given market. Instead of comparing total export variations, the estimations will therefore be based on the comparison of export outcomes in a industry-country pair between firms that were differently affected by the reform.

Market fixed effects additionally allow to take into account a possible correlation between the exposure to the reform and the presence in certain markets. Suppose for instance that because of their position in the input-output network, firms that were exporting plastics and rubber products to the US experienced a strong fall in net payment delays following the reform. A "naive" estimation might erroneously conclude to a significant positive effect of the variation in payment delays on the drop in exports to that market even in the absence of actual causation. Removing average trends at the market level ensures that the estimations are not prone to such potential bias.

Since the left-hand variable is observed at the firm-country-industry-level and right-hand sides variables only at the firm-level, error terms $\epsilon_{fm09}$ will be correlated for a given firm f. Bertrand, Duflo and Mullainathan (2004) and Petersen (2009) show with Monte-Carlo simulations that this will lead to underestimate standard errors and thus to under-reject the null hypothesis of non significance. I follow the econometric literature on that subject and cluster standard errors by firm to allow for arbitrary patterns of cross-correlation.

## 4.2 Description of the IV strategy

There are several reasons to believe that a direct estimation of equation 1 would be biased due to endogeneity issues. As previously noted in point (c), derogations to the 2009 might first cause the variation in payment delays to be driven by export-related factors and
thus create a form of simultaneity in equation 1. More importantly, if there are omitted variables (such as aggregate factors due to the financial crisis) that drive the change in payment delays even in the presence of control variables, the estimated coefficient $\hat{\beta}$ will not reflect the sole effect of the reform. Since there are no natural control group (point (a)), a difference-in-difference approach is excluded.

A natural way to isolate the effect of the reform is then to rely on the sixty-day-rule: since the reform gave a clear ceiling to payment delays, it is possible to determine to which extent sectors were likely to be affected by the reform. Using the information on the different business lines of firms, I can recover an ex-ante measure of the treatment intensity at the firm-level that I use as an instrument for the actual variation in payment delays.

More precisely, for a given sector $s$, I define excessive customers payment delays at the sectoral level as the mean for all firms $f$ in sector $s$ of

$$Excess \ customer \ payment \ delays_{f} = \max(0, Customer \ payment \ delays_{f} - 60)$$

and I proceed similarly for supplier payment delays. According to this formula, if all the firms present in sector $s$ have customer payment delays inferior to sixty days, then the customer payment delays at the sector-level should be unaffected by the reform ($Excess \ customer \ PD_{s} = 0$). The firm-level variable $Excess \ net \ PD_{f06}$ is then computed as

$$Excess \ net \ payment \ delays_{f06} = \sum_{s} \omega_{fs08} (Excess \ customer \ PD_{s06} - Excess \ supplier \ PD_{s06})$$

$Excess \ net \ PD_{f06}$ can be interpreted as a measure of the net change in payment delays needed in 2006 to reach the setting where the reform is perfectly enforced: if $Customer_{f}$ and $Supplier_{f}$ were always inferior to sixty days in sector $s$, then $Excess \ net \ PD_{f07}$ would equal zero. Similarly, if suppliers excessive payment delays were about as high as customer excessive payment delays, the needed change in payment delays would be zero in net terms. In general, a high value of $Excess \ net \ PD_{f07}$ means that customer payment delays are on average higher than sixty days and that suppliers payment delays are relatively low.

This variable is used as in instrument for $\Delta Excess \ payment \ delays_{f09}$. A negative impact on the change in payment delays is expected: the evolution of the provision of trade credit subsequent to the reform should correct for previous excessive payment delays.

The identification assumption behind the IV strategy is that factors other than payment delays that affect export outcomes of firms present in a given market are not correlated to the exposure to the reform. This assumption might be violated if firms with more market power were more likely to be affected by the reform than others. Since one of the main goal of the reform was to put an end to abusive practices resulting (in particular) from dominant positions in supplier-customer relationships, this might create some bias in the estimations since export decisions are presumably correlated with market power. This potential concern
is however alleviated by (i) the fact that I focus on small firms whose market power is limited and (ii) the presence of variables controlling for the size and the market share of firms in their different business lines.

This instrumentation strategy has several advantages in this context: first, in the absence of a control group (point (a)), it allows to turn a *qualitative* assignment (treatment versus control group) into a *quantitative* one (treatment intensity). Second, the instrument is designed so as not to take into account derogations (point (c)). The first-stage estimation thus only captures the change in net payment delays that can be explained by the sixty-day rule and should leave aside the effects of derogations; in the end, derogations should only bring down the coefficient $\hat{\xi}$ but should leave $\hat{\beta}$ unaffected. In other terms, the IV estimator captures the *local average treatment effect* (LATE) by relying on the effects of the reform only on the firms that were affected by and that applied the sixty-days rule (*compliers*).

## 4.3 Control variables

The instrumentation strategy will however not yield unbiased estimates if the impact of the reform is heterogeneous among firms exporting in the same market. I first rely on accounting data to control for firm-level characteristics (a description of the construction of these variables is given in Table 2, panel A); I use in particular the firm-level change in the operational margin ($\Delta EBIT/Turnover_{f,07-09}$) to control for changes in profitability and the variation in the cash-to-assets ratio ($\Delta Cash holdings_{f07-09}$) to control for the evolution of the level of liquidity of the firm. The lag of leverage is also added in the set of explanatory variables to account for differences in debt capacity.

Since Bricongne et al. (2012) find that French exporters’ reaction to the crisis varied a lot with firm’s size<fnref n="15" />, I include the lag of the logarithm of total assets $Size_{f07}$ in the estimations; similarly, a dummy $Group_{f07}$ is added to control for heterogeneity in financing conditions due to the affiliation to a business group.

Another potential concern with this approach is that the methodology designed to compute $\Delta Net payment delays_{f07-09}$ might inappropriately capture sectoral variations of factors correlated to payment delays. In order to control for such correlations, I use the same methodology to build $Sales growth rate_{f09}$: this variable allows to take explicitly into account the effects of the crisis as experienced by firm *f* through the average variation in sectoral sales weighted by the sales of the firm in its different business lines. Using trade credit data, Klapper, Laeven and Rajan (2012) show that large, creditworthy firms receive more favorable payment terms, suggesting that firm *f* with more market power might have been more affected by the reform. To control for this source of heterogeneity, I use the av-

<footnote n="15">¹⁵Bricongne et al. (2012) find that while all firms have been evenly affected by the crisis, large firms did mainly adjust through the intensive margin and by reducing the portfolio of products offered in each destination served while smaller exporters have been instead forced to reduce the range of destinations served or to stop exporting altogether.</footnote>
verage market share in the different business lines of the firm as a proxy for market power
(Market power<sub>f09</sub>).

[Insert **table 2** here]

The change in payment delays might also simply correlate to firm *f*'s exposure to fi-
nancial constraints through its presence on sectors with varying dependence on external
finance (Rajan and Zingales, 1998): I take this eventuality into account by introducing the
sales-weighted average of sectoral financial dependence, <u>RZ index</u><sub>f,07</sub>, where financial de-
pendence is defined as the average share of capital expenditures that is not financed by
operating cash-flows<sup>16</sup>. This measure has been in particular used in previous work on the
links between finance and international trade such as Manova (2013).

## 4.4 Descriptive statistics

To obtain the final data set, I eventually remove outliers by dropping for all variables
the observations that are superior (resp. inferior) to the median plus (resp. minus) five
times the gap between the 95th and 5th percentile and only keep firms with non missing
observations for all variables. The final data set therefore retains 9,286 firms. The sample
used for the analysis of the decisions to enter, exit and the volume of exports to realize in a
market are composed of respectively 350,000, 120,000 and 59,000 firm-market observations;
the acquisition of customers and the evolution of the volume of sales are estimated using
data sets of 67,000 and 34,000 firm-market observations. A majority of firms belong to the
manufacturing sector (71 %) and they are on average relatively mature (the median age is
24 years). Panel B of table 2 shows moreover that average total assets is around 8.9 millions
euros, and that 61 % of the firms in the data set belong to a business group, which is in line
with the importance of business groups in the French economy (Boutin et al., 2013).

[Insert **table 3** here]

The average firm in our data set exports about 3.1 millions euros (2.3 within the EU), is present in 13.5 markets (7.4 within the EU) and has 2.9 customers on average per market within the European Union. Table 3 shows that the average transaction volume for a new customer-supplier relationship is about 50,000 euros, and that it increases to 150,000 euros after five years of uninterrupted trade. The probability that a procurement relationship lasts

<footnote n="16"><sup>16</sup>Rajan and Zingales (1998) recommend to use the average values taken for the US economy, as they advocate that the US financial system is the most developed one and that the values of the RZ index computed for the US economy would therefore capture variation in financial dependence due only to industrial factors (degree of uncertainty, of redeployability of the assets...) and not to variation in development of the financial system. I use here the values taken for the French economy since (1) the identification does not depend on cross-country variations (2) the French financial system is arguably developed enough for the RZ index to mostly capture variation in demand-originated variation in financial dependence.</footnote>
3 years is 24%, with a probability of staying in a market three years only slightly higher (30%). The number of customers increases with the number of years spent in a market, with about 6.9 customers on average after five years compared to 2.6 in the year of entry.

# 5 First stage

## 5.1 Payment delays variation

Figure 3 shows how the variation in customer, supplier and net payment delays between 2007 and 2009 are related to the measure of excessive payment delays in 2006. A strong negative correlation is observed for all three variables, suggesting that the measure of treatment intensity is a good predictor of the variation in payment delays. Figure 3d shows that the correlation between the instrument and the evolution of payment delays breaks down after 2009, which suggests that this association is indeed a consequence of the reform.

[Insert figure 3 here]

Going to multivariate analysis, table 4 displays the results of the estimation of

$$Y_f = \gamma_s + \xi Excess \ net \ payment \ delays_{f07} + \rho X_{f09} + v_{f09}$$

where $Y_f$ is a measure of payment delays and $\gamma_s$ is a sector (2-digits nomenclature) fixed effect; standard errors are clustered by sector. The effect of Excess Net payment delays$_{f07}$ on the evolution of net payment delays is negative and significant; on average, estimations indicate that an increase of 10 days of Excess NPD$_{f07}$ was followed by a decrease of 3 days in the change in net payment delays. The significant relationship is robust to the addition of control variables (column 2). In particular, the non-significant relationship between the change in payment delays and the average sectoral growth rates of the different business lines (Sales growth rate$_{f07-09}$) alleviates the concern of a correlation between the change in payment delays and the incidence of the crisis. In columns 3 and 4, I show in line with figure 3 that customer and supplier payment delays evolved in line with the 60-days threshold: the elasticities to excess payment delays are of -0.29 and -0.17 respectively.

[Insert table 4 here]

The correlation between the variation in payment delays and the instrument might arise mechanically if payment delays exhibit autocorrelation. I test this eventuality by regressing $\Delta Net \ payment \ delays_{f09-10}$ on Excess Net payment delays$_{f08}$ (column 5) and find no effect of a correction of excessive net payment delays after the implementation of the reform.
## 5.2 Firm-level outcomes

In table 5, I investigate how firm-level outcomes have been affected by the reform. This exercise gives indications on how firms have coped with the change in payment delays. I first check that the measure of payment delays based on sectoral means had an impact on the firm's working capital. To that end, I directly compute the change in net payment delays between 2007 and 2009 at the firm-level; the results of column 1 show that this variable is positively and strongly correlated to $\Delta\overline{Net\ payment\ delays}_{f,07-09}$.

[Insert table 5 here]

Column 2 shows that higher payment delays have resulted in lower cash holdings (-1.4 percentage points for an increase of the evolution of payment delays of 10 days). This result is difficult to reconcile with the idea of receivables being liquid assets easily convertible to cash: in fact, the management of trade credit appears to have a first order effect on the ability of firms to gather internal funds.

In the last two columns, I investigate how investment and employment reacted to a variation in payment delays. Unlike Murfin and Njoroge (2015) and Barrot and Nanda (2016), I do not find any effect of payment delays on these two margins. If anything, investment even seems to be positively (but imprecisely) associated with the change of payment delays. This discrepancy might be due to the deteriorated financial conditions during the implementation of the reform: in times of crisis, firms may have preferred to benefit from better working capital financing by accumulating internal funding rather than investing in factors of production. The absence of a strong link between the change in payment delays and the evolution of employment and investment alleviates however the concern that our results might be driven by a spurious correlation between the variable of interest and the exposure to the financial crisis.

# 6 Main results

## 6.1 Market margin: entry

Table 6 displays the results of the regressions on the propensity to enter a new market. Consistently with the effects on the acquisition of a customer base, the results show that the change in payment delays had a positive and highly significant effect on $Market\ entry_{fm09}$. On average, an increase in $\Delta\overline{Net\ payment\ delays}_{f,07-09}$ by 10 days leads to a 0.4 percentage points decrease of the probability of entry. When compared to the unconditional probability (2.3%), such a decrease in $\Delta\overline{Net\ payment\ delays}_{f,07-09}$ would cause a 17% rise of the propensity to enter a new market.

[Insert table 6 here]
Consistently with the results from the first-stage estimations, the Kleibergen-Paap statistic presented in columns 2 to 4 goes in favour of a rejection of the hypothesis of a weak instrumentation. Column 1 shows that simple OLS do not capture a positive effect of payment delays: this suggests the presence of confounding aggregate factors leading direct estimations to yield biased estimates. By capturing the part of the variation in net payment delays that can be explained by the exposure to the reform, I am able to isolate the causal effect of the variation in net payment delays on firms' exporting decisions. Furthermore, it appears that size, market power and leverage are positively associated with the entry rate. By contrast, age is negatively correlated to entry, which can be interpreted as reflecting the fact that firms more mature might have already realized their international expansion.

## 6.2 Customer margin: acquisition of a customer base

Panel A of table 8 displays the result of the estimation of the effects of a change in payment delays in net terms on the probability to acquire a customer in a market. The IV estimations point to a significant impact of an improvement in working capital financing on the propensity to extend the consumer base: a fall in the change in payment delays of 10 days is found to increase the propensity by 0.96 to 1.05 percentage points according to the specification. This result is consistent with the idea that the acquisition of new customers is constrained by the amount of working capital allocated to existing customers.

[Insert table 8 here]

Being affiliated to a group or experiencing an increase in cash holdings seems also to be associated to an increase in the customer base. This finding is reminiscent of Frésard (2010), who finds using an exogeneous change in the competition intensity that high reserves of cash ("deep pockets") is associated to systematic future market share gains.

## 6.3 Market margin: exit and intensive margin

I find no effect of the change in payment delays neither on exits from export markets nor on the volume of exports served in a market (a positive correlation appears for the intensive margin in the first IV estimation but disappears as soon as I introduce control variables). Exit is negatively associated with size, age, and affiliation with a group; group-affiliated firms, firms that experienced higher sectoral growth on average in their different business lines and that had higher growth in profitability and cash holdings grew more in the different export markets they were already serving.

[Insert table 7 here]
The fact that the volume of exports were not affected by the variation in payment delays is in apparent contradiction with the fact that the probability of acquiring consumers decreased with $\Delta Net\ payment\ delays_{f,07-09}$. It is however explained by the fact that exports with new customers are much lower than with existing consumers (see table 3) and that the extension of the customer base is a phenomenon relatively infrequent (7.0% probability), leading firm growth in the different markets relatively unaffected by the reform.

## 6.4 Alternative specifications

To assess the robustness of my results, I reestimate the previous equations using several alternative specifications. I first take the presence of derogations to the sixty-days rules into account in the construction of the instrument (see appendix A for a list of the derogations). The results are displayed in the *Derogations* column and show that the coefficients are nearly unchanged compared to the baseline estimations (though the effect on the probability of entry is less precisely estimated).

[Insert **table 9 here**]

Taking the effects of the customer and supplier payment delays separately, I find that the effects of payment delays on the entry in new market and on the acquisition of customers are only present when looking at the evolution of customer payment delays. While it is difficult to completely disentangle the supply and demand of trade credit in the evaluation of the reform, this finding is consistent with the idea that firms acquired new customers by getting paid quicker by their existing consumers.

In the 2008-2009 reform, I take only the evolution of payment delays and exports between 2008 and 2009 in the computation of the instrumented variable. This exercise allows to show that even if the reform was partially anticipated by firms (through a decrease of payment delays starting in 2008, see figure 2), the change in payment delays realized at the time of the implementation of the reform had an impact on firms' export behavior.

# 7 Heterogeneity of the effects of the reform

In this section, I analyse the role of various sources of firm-level heterogeneity in the sensitivity to variation in payment delays. This exercise serves two main purposes: first, it allows to check that the magnitudes of the effects vary in line with the predictions of the theory, which incidentally highlights the source of identification of the different estimations. Second, systematic unobserved variations between groups differently affected by the reform might lead us to erroneously conclude to a causal effects of $\Delta Net\ payment\ delays_{f09}$ on export outcomes (Bakke and Whited, 2012). By performing the regressions on groups of firms that are supposedly more homogeneous, this exercise directly addresses this concern.
[Insert **table 10** here]

I first check whether the effects of payment delays on the acquisition of customers and on the entry in new markets still hold for non-SMEs. The effects of payment delays should presumably be less important for firms belonging to large groups since the presence of internal capital markets could in principle allow them to circumvent working capital constraints. Accordingly, I find that the effects are absent for firms belonging to big groups.

Surprisingly, I find that effects are actually larger for firms belonging to intermediate groups than for firm belonging to SMEs, suggesting that internal capital markets in intermediate groups are imperfect. A possible explanation might be that if, as heterogenous firms models suggest, the size of the firm increases with productivity, then all others things equal intermediate firms should be more affected by the presence of working capital financial constraints than SMEs since they value more additional funding.

I then rerun the estimations on the subsets of manufacturing and wholesale firms: the suspicion of a confounding role of unobserved heterogeneity between these groups is high since (1) they were unequally affected by the reform (see section 4.4) (2) they gather firms with presumably very different unobserved characteristics which might have affected their export decisions. Precisely, in order to get balanced and homogeneous subgroups, I divide firms between those that belong to the wholesale trade sector, to the heavy industry (metalurgy, machines, information and telecommunications) and to the complement of the two other groups (agroalimentary, chemistry, textiles). Table 10 shows that the results hold for the last subgroup but not for the two others. Predictably, the coefficients are less precisely estimated, since the restriction to subgroups removes part of the inter-sectoral heterogeneity of the exposure to the reform upon which the identification strategy hinges.

[Insert **table 11** here]

I turn in table 11 to other sources of heterogeneity of the effects of the reform, namely financial constraints, idiosyncratic risk and market power. First, I expect the effects to be larger for firms experiencing higher financing constraints. I proxy financial constraints by the measure of dependence to external finance ($RZ\ Index_{f,07}$). In columns 1 and 2 of table 11, I split the sample between firms independent from external finance (first half of $RZ\ Index_{f,07}$) and firms dependent to external finance (second half): I find the effects to be larger for firms independent of external finance, which is in line with columns 4 to 6 of table 10, firms in the heavy industry sector being the most dependent to external finance in our sample. This result casts however some doubt on the validity of our proxy for financial constraints.

In columns 3-4, I measure idiosyncratic risk following Bates, Kahle and Stulz (2009) and split the sub-samples between firms exposed to low (below the median) and high (above the median) idiosyncratic risk. Firms experiencing higher risk are expected to value more internal financing, the trade-off between additional customers and additional funding being
therefore more stringent. On the contrary, the acquisition of customers by firms with low id-
iosyncratic risk should be less dependent to working capital financing since they rely less on
internal liquidity. The results strongly support this interpretation, the effects of the change
in payment delays being significant only for firms experiencing high idiosyncratic risk.

In the last two columns, I perform the same regressions for sub-samples of firms with high and low market power. On the one hand, since firms with low market power were more likely to be hurt by disadvantageous payment terms (Klapper, Laeven and Rajan (2012)), they should have benefitted more from a regulation restricting long payment delays. On the other hand, Breza and Liberman (2016) find that customer-supplier relationships are less often ended by the customer in front of a restriction of trade credit provision when the supplier has high market power. The authors explain this higher resilience by a greater exclusivity of the relationship (the outside option of the customer is low). Accordingly, the estimations give mixed results: if the effects of the variation in payment delays on the acquisition of new customers are concentrated on firms with low market power, the *Market entry*<sub>fm09</sub> coefficient is higher for firms with high market power.

The sample is eventually divided in different geographic zones to investigate the role of
unobserved heterogeneity between export markets. I come back to that end to the analy-
sis on the different market margins, the customer margin being observable only within the
EU. Interestingly, the effects of the variation in payment delays on the probability of entry
becomes not significant when the sample is restricted to markets within the EU. This find-
ing is compatible with the idea that acquiring customers is less sensitive to the financing
of working capital since the markets are geographically closer (requiring therefore shorter
transportation time) and exhibits presumably lower customer risk. The effects of payment
delays on entry persist outside the European Union (Europe outside the EU, Africa, Amer-
ica) except for Asia.

[Insert **table 12** here]

Interestingly, I find that the effect on the exit rate also heavily depends on whether mar-
kets belong to the European Union. The increase in payment delays seems to have caused
firms to exit markets outside the EU but to be associated with a decrease of the exit rate in
EU markets. An interpretation could be that when faced with higher payment delays, firms
have chosen to reallocate their international activity toward the European Union.

**8 Alternative measure of payment delays**

Since my measure of payment delays is based on sectoral averages, the identification
of the impact of the evolution of payment delays rests on the presence of firms in multi-
ple product markets. The corporate finance literature has however documented the fact
that diversified firms may differ from focused firms (Campa and Kedia, 2002): the effects
of the evolution of payment delays could in this case be identified for a potentially non-
representative subset of firms. Moreover, within diversified firms, the choice between dif-
ferent product market portfolio might be the result of unobservable factors related to export
outcomes.

[Insert **table 13** here]

In this section, I address these concerns by turning to measures of payment delays that do not rely on the information on the firms’ business lines. As described in section 3, the computation of the variable *Net payment delays*<sub>*f*</sub> is obtained using only balance sheet items by taking the difference between the accounts receivables and payables scaled by the total turnover of firm *f*. The distribution of the customer, supplier and net payment delays as well as the corresponding 2006 distances to the 60-days threshold are given in table 13.

[Insert **table 14** here]

I reproduce the first stage estimations performed in section 5 to investigate how pay-
ment delays evolved with the exposition to the reform and to assess their impacts on vari-
ous firm-level outcomes. The coefficients in column 1 of table 14 imply that similarly to the
results presented above, firms that were ten more days away from the 60-days rule experi-
enced a subsequent decrease of net payment delays of 1.7 days. The magnitude is similar
for customer payment delays (1.2) ; however, according to this measure, excessive supplier
payment delays seem to be unrelated to the distance to the 60-days threshold<sup>17</sup>. By contrast,
they seem to be negative correlated to the distance in table 4. One possible interpretation
of this discrepancy could be that as intended, using sectoral averages effectively limits the
role of the measurement error in the estimations. The analysis of customer payment delays
further shows that firms with less market power experienced stronger decrease in payment
delays.

Consistently with the results of table 5, we find that a decrease in net payment delays is associated with an increase in cash holdings. With this measure, however, the evolution of payment delays appears to be strongly negatively correlated to the evolution of employment: this result is more in line with the analysis of Barrot and Nanda (2016) who find important aggregate effects of payment conditions on job creation.

[Insert **table 15** and **table 16** here]

Table 15 presents the results of the estimations of equation 1 for the propensity to en-
ter and to exit export markets, the change in exports served in a market and the evolution
of the customer base. In contrast with the results presented in section 6, the regressions

<footnote n="17"><sup>17</sup>This finding is robust to using purchases instead of sales in the denominator of the ratio.</footnote>
performed using the "direct" measure of payment delays do not reveal any association be-
tween export outcomes and the evolution of payment delays. It is however possible that the
specification with detailed export market fixed effects captures most of the cross change in
payment delays. Indeed, table 16 confirms that payment delays seem to affect firm-level
export outcomes<fnref n="18" />: according to the results, a 10 days decrease in net payment delays cause
a decline of total exports by 1.2% and a raise of total number of new export markets reached
by 1.3 units. In line with the main results, the number of exits do not however seem to be
impacted by the evolution of payment delays. Overall, even if this alternative measure of
payment delays leads to some differences with the baseline estimations, the idea that trade
credit plays an important role in shaping firms’ export behavior seems to be broadly con-
firmed by this additional exercise.

9 Discussion of the results

The estimations support overall the thesis that working capital plays a key role in the in-
ternational expansion of firms: when firms get paid quicker, they have more working capital
to allocate to new customers and to reach remote international markets. An alternative in-
terpretation might be that firms must finance internally a sunk cost to enter markets or to
acquire customers (Chaney (2016)), possibly through spending in advertising to reach new
consumers (e.g., Arkolakis (2010), Drozd and Nosal (2012), Gourio and Rudanko (2014)).
This hypothesis gives rise to several testable predictions that could be taken to data in order
to have a clearer view on the mechanisms behind the expansion in new markets.

Though internally coherent, the results are in stark contrast with Paravisini et al. (2014) who find that the credit supply only affects the intensive margin of exports through the variable cost of exporting. Yet, this apparent contradiction can easily be overcome by noting that the two studies look at the export effects of financing shocks of a very different nature: while Paravisini et al. (2014) look at the impacts of a temporary fall in debt supply, I investigate the role of a permanent shift in liquidity risk<fnref n="19" />. In face of a short-lived credit crisis, it may be optimal to temporarily adjust through shifting towards more expensive sources of financing such as factoring; though raising the variable cost of exporting, this solution allows to keep exporting in the same markets (albeit at a lower rate through the impact on prices) which avoids a costly exit for the firm.

Eventually, it is important to recall that these effects might be influenced by the coinci-
dent presence of the financial crisis. Theory in this respect does not provide clear guidance
on whether the financial crisis tends to go for or against the effects of the reform. On the

<footnote n="18"><sup>18</sup>The firm-level analysis suffers however from the caveats mentioned in section 4.1.</footnote>

<footnote n="19">¹⁹The reform might have had an effect on firms’ decisions both through (1) permanent changes in liquidity risk and (2) a temporary impact on cash holdings as firms adjust to their new payment contracts. Since I control for ΔCash holdings<sub>f09</sub>, the regressions should mostly capture the effects of (1) and leave aside (2).</footnote>
one hand, since financial constraints were presumably very high during this period, the real effects of payment delays should have been magnified. On the other hand, precautionary motives might have made firms hoarding cash instead of readjusting their portfolio of export markets. Further research on other regulations on payment delays such as the Federal Quickpay Initiative in 2011 in the US or the Directive 2011/7/EU that generalized the French reform to the whole European Union might show whether the results still hold with more standard financing conditions.

# 10 Conclusion

The results point to a significant effect of the variation in payment delays on the propensity to acquire consumers and to reach international markets. This finding is of particular interest to the study of export barriers in developing countries: while trade credit is a very important source of financing for firms operating in developing countries (Fisman, 2001), excessive payment delays remain a pervasive source of concern while performing day-to-day operations in those markets (ACCA, 2015). In this way, policies aiming at reducing payment delays (such as simplifying customs procedure, see Hummels (2007)) or fostering the development of factoring firms might allow small firms in developing countries to develop customer capital and to access new export markets.

# References

**ACCA.** 2015. "Ending late payments." Association of Chartered Certified Accountants.

**Amiti, Mary, and David E. Weinstein.** 2011. "Exports and financial shocks." *Quarterly Journal of Economics*, 126(4): 1841-1877.

**Antràs, Pol, and C Fritz Foley.** 2015. "Poultry in Motion: A Study of International Trade Finance Practices." *Journal of Political Economy*, 123(4): 853-901.

**Arkolakis, Costas.** 2010. "Market Penetration Costs and the New Consumers Margin in International Trade." *Journal of Political Economy*, 118(6): 1151-1199.

**Aubouin, Marc, and Martina Engemann.** 2014. "Testing the Trade Credit and Trade Link: Evidence from Data on Export Credit Insurance." *WTO Working Paper*.

**Bakke, Tor-Erik, and Toni M Whited.** 2012. "Threshold events and identification: A study of shortfalls." *The Journal of Finance*, 67(3): 1083-1111.

**Bank for International Settlements.** 2014. "Trade finance: developments and issues."

**Barrot, Jean-Noël.** 2016. "Trade Credit and Industry Dynamics: Evidence from Trucking Firms." *The Journal of Finance*, 71(5): 1975-2016.

**Barrot, Jean-Noel, and Ramana Nanda.** 2016. "Can Paying Firms Quicker Affect Aggregate Employment?" National Bureau of Economic Research.

**Bates, Thomas W, Kathleen M Kahle, and René M Stulz.** 2009. "Why do US firms hold so much more cash than they used to?" *The Journal of Finance*, 64(5): 1985-2021.
**Berman, Nicolas, and Jérôme Héricourt.** 2011. "Financial Factors and the Margins of Trade." *Journal of Development Economics*, 93: 206-217.

**Bertrand, Marianne, Antoinette Schoar, and David Thesmar.** 2007. "Banking deregulation and industry structure: Evidence from the French banking reforms of 1985." *The Journal of Finance*, 62(2): 597-628.

**Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan.** 2004. "How Much Should We Trust Differences-In-Differences Estimates?" *The Quarterly Journal of Economics*, 119(1): 249-275.

**Biais, Bruno, and Christian Gollier.** 1997. "Trade credit and credit rationing." *Review of Financial Studies*, 10(4): 903-937.

**Bonfiglioli, Alessandra, Rosario Crinò, and Gino A Gancia.** 2016. "Trade, finance and endogenous firm heterogeneity."

**Boutin, Xavier, Giacinta Cestone, Chiara Fumagalli, Giovanni Pica, and Nicolas Serrano-Velarde.** 2013. "The deep-pocket effect of internal capital markets." *Journal of Financial Economics*, 109: 122-145.

**Brennan, Michael J, Vojislav Maksimovic, and Josef Zechner.** 1988. "Vendor financing." *The Journal of Finance*, 43(5): 1127-1141.

**Breza, Emily, and Andres Liberman.** 2016. "Financial contracting and organizational form: Evidence from the regulation of trade credit." *Journal of Finance*.

**Bricongne, JC., L. Fontagné, G. Gaulier, D. Taglioni, and V. Vicard.** 2012. "Firms and the global crisis: French exports in the turmoil." *Journal of International Economics*, 87(1): 134-146.

**Burkart, Mike, and Tore Ellingsen.** 2004. "In-kind finance: A theory of trade credit." *American Economic Review*, 569-590.

**Caggese, Andrea, and Vicente Cuñat.** 2013. "Financing constraints, firm dynamics, export decisions, and aggregate productivity." *Review of Economic Dynamics*, 16(1): 177-193.

**Campa, Jose Manuel, and Simi Kedia.** 2002. "Explaining the diversification discount." *The Journal of Finance*, 57(4): 1731-1762.

**Chaney, Thomas.** 2016. "Liquidity constrained exporters." *Journal of Economic Dynamics and Control*, 72: 141-154.

**Chevalier, Judith A.** 1995. "Do LBO supermarkets charge more? An empirical analysis of the effects of LBOs on supermarket pricing." *The Journal of Finance*, 50(4): 1095-1112.

**Clementi, Gian Luca, and Hugo A Hopenhayn.** 2006. "A theory of financing constraints and firm dynamics." *The Quarterly Journal of Economics*, 121(1): 229-265.

**Costello, Anna.** 2013. "The determinants and economic consequences of trade credit policy." Working Paper.

**Cunat, Vicente.** 2007. "Trade credit: suppliers as debt collectors and insurance providers." *Review of Financial Studies*, 20(2): 491-527.

**Drozd, Lukasz A., and Jaromir B. Nosal.** 2012. "Understanding International Prices: Customers as Capital." *American Economic Review*, 102(1): 364-395.

**Fabbri, Daniela, and Leora F Klapper.** 2016. "Bargaining power and trade credit." *Journal of Corporate Finance*, 41: 66-80.
**Feenstra, Robert C., Zhiyuan Li, and Miaojie Yu.** 2014. "Exports and Credit Constraints under Incomplete Information: Theory and Evidence from China." *The Review of Economics and Statistics*, 96(4): 729-744.

**Fisman, Raymond.** 2001. "Trade credit and productive efficiency in developing countries." *World Development*, 29(2): 311-321.

**Fisman, Raymond, and Mayank Raturi.** 2004. "Does competition encourage credit provision? Evidence from African trade credit relationships." *Review of Economics and Statistics*, 86(1): 345-352.

**Frésard, Laurent.** 2010. "Financial Strength and Product Market Behavior: The Real Effects of Corporate Cash Holdings." *Journal of Finance*, 65: 1097-1122.

**Garicano, Luis, Claire Lelarge, and John Van Reenen.** 2016. "Firm Size Distortions and the Productivity Distribution: Evidence from France." *American Economic Review*, 106(11): 3439-79.

**Giannetti, Mariassunta, Mike Burkart, and Tore Ellingsen.** 2011. "What you sell is what you lend? Explaining trade credit contracts." *The Review of Financial Studies*, 24(4): 1261-1298.

**Gourio, François, and Leena Rudanko.** 2014. "Customer Capital." *Review of Economic Studies*, 81: 1102-1136.

**Hummels, David.** 2007. "Transportation costs and international trade in the second era of globalization." *The Journal of Economic Perspectives*, 21(3): 131-154.

**Klapper, Leora, Luc Laeven, and Raghuram Rajan.** 2012. "Trade credit contracts." *Review of Financial Studies*, 25(3): 838-867.

**Kovenock, Dan, and Gordon M Phillips.** 1997. "Capital structure and product market behavior: An examination of plant exit and investment decisions." *The Review of Financial Studies*, 10(3): 767-803.

**Lee, Yul W, and John D Stowe.** 1993. "Product risk, asymmetric information, and trade credit." *Journal of Financial and Quantitative Analysis*, 28(02): 285-300.

**Long, Michael S, Ileen B Malitz, and S Abraham Ravid.** 1993. "Trade credit, quality guarantees, and product marketability." *Financial Management*, 117-127.

**Manova, Kalina.** 2013. "Credit Constraints, Heterogenous Firms and International Trade." *The Review of Economic Studies*, 80: 711-744.

**Mélitz, Marc.** 2003. "The impact of trade on intra-industry reallocations and aggregate industry productivity." *Econometrica*, 71(6): 1695-1725.

**Minetti, Raoul, and Susan Chun Zhu.** 2011. "Credit constraints and firm export: Microeconomic evidence from Italy." *Journal of International Economics*, 83: 109-125.

**Murfin, Justin, and Ken Njoroge.** 2015. "The implicit costs of trade credit borrowing by large firms." *Review of Financial Studies*, 28(1): 112-145.

**Ng, Chee, Janet Smith, and Richard Smith.** 1999. "Evidence on the Determinants of Credit Terms Used in Interfirm Trade." *The Journal of Finance*, 54(3): 1109-1129.

**Paravisini, Daniel, Veronica Rappoport, Philipp Schnabl, and Daniel Wolfenzon.** 2014. "Dissecting the Effect of Credit Supply on Trade: Evidence from Matched Credit-Export Data." *Review of Economic Studies*, 1-26.

**Petersen, Mitchell A.** 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches." *Review of Financial Studies*, 22(1): 435-480.
**Petersen, Mitchell A, and Raghuram G Rajan.** 1997. "Trade credit: theories and evidence." *Review of Financial Studies*, 10(3): 661–691.

**Rajan, Raghuram G., and Luigi Zingales.** 1998. "Financial Dependence and Growth." *The American Economic Review*, 88(3): 559–586.

**Schmidt-Eisenlohr, Tim.** 2013. "Towards a Theory of Trade Finance." *Journal of International Economics*, 91(1): 96–112.

**Schmidt-Eisenlohr, Tim, and Friederike Niepmann.** 2016. "No Guarantees, No Trade: How Banks Affect Export Patterns." *Working Paper*.

**Smith, Janet Kiholm.** 1987. "Trade credit and informational asymmetry." *The Journal of Finance*, 42(4): 863–872.

TelecomPaper.com. 2015. *Link to the article*.
# Appendix A Derogations

This appendix gives the maximum contractual payment delays after the date of the in-voice authorized by the LME reform. When the limit varies in 2009 (e.g. 120 days between January 01 and May 31 2009 and 80 days between June 01 and December 31 2009), I report the average number of days (100 days). When the supplier and the customer face different thresholds, the minimum payment limit prevails for the transaction.

* Purchases of living cattle: 20 days

* Purchases of perishable products, purchases of alcoholic beverages: 30 days

* Manufacture and sale of metal food packaging; record industry; recreational fishing; manual, creative and recreational activities: 75 days

* Construction industry; bathroom and heating equipment; sailing stores; industrial tool-ing; industrial hardware; steel products for the construction industry; automotive tools wholesaling: 85 days

* DIY stores; stationery and office supplies; tire industry; drugs with optional medical prescriptions; pet trade; garden stores; coatings, paints, glues, adhesives and inks; sports stores; leather industry; clothing sector: 90 days

* Jewellery, gold- and silversmiths' trade; round wooden elements; food supplements; optical-eyewear industry; cooperage: 105 days

* Firearms and ammunition for hunting: 115 days

* Quads, two- or three-wheeled vehicles, recreational vehicles: 125 days

* Agricultural supplies: 150 days

* Toy stores: 170 days

* Book edition, agricultural machines: 195 days
# Appendix B Tables and figures

<figure><img src="image_4.png" /><figcaption>Figure 1: Effect of the reform on payment delays</figcaption></figure>

*Source:* BRN-RSI tax returns in 2007. *Field:* manufacturing and wholesale sector.

*Lecture:* This table displays the evolution of customer and supplier payment delays between 1999 and 2013 in the manufacturing and wholesale trade sectors. Customer payment delays are computed as the average ratio of customer receivables over sales. Supplier payment delays are computed as the average ratio of supplier debt over sales.

<figure><img src="image_5.png" /><figcaption>Figure 2: Illustration of the sectoral heterogeneity of the effects of the reform</figcaption></figure>

*Source:* BRN-RSI tax returns in 2007. *Field:* manufacturing and wholesale sector.

*Lecture:* This table displays the evolution of customer and supplier payment delays between 2004 and 2011 in the "industrial mechanical engineering" and "wholesale trade of textiles" sectors. Customer payment delays are computed as the average ratio of customer receivables over sales. Supplier payment delays are computed as the average ratio of supplier debt over sales. Net payment delays (NPD) are defined as customer payment delays minus supplier payment delays. Lower net payment delays means that customer payment delays decreased more than supplier payment delays.
<table><caption>Table 1: Top and bottom 5 sectors for customer and supplier payment delays (2007)</caption><thead><tr><th><u>Customer<sub>s</sub></u></th><th><u>Supplier<sub>s</sub></u></th></tr></thead><tbody><tr><td>Manufacture of non-metallic mineral products</td><td>145.1</td><td>Manufacture of ceramic sanitary</td><td>99.7</td></tr><tr><td>Manufacture of industrial gases</td><td>120.1</td><td>Manufacture of batteries</td><td>98.1</td></tr><tr><td>Manufacture of locomotives</td><td>119.7</td><td>Manufacture of fibre cement</td><td>82.8</td></tr><tr><td>Manufacture of steam generators</td><td>118.1</td><td>Manufacture of other mineral products</td><td>80.6</td></tr><tr><td>Manufacture of cement</td><td>112.6</td><td>Wholesale of beverages</td><td>80.2</td></tr><tr><td>Processing and preserving of potatoes</td><td>8.2</td><td>Bakery confectionery</td><td>30.5</td></tr><tr><td>Confectionery shop</td><td>6.7</td><td>Bakery products</td><td>30.4</td></tr><tr><td>Delicatessen</td><td>6.4</td><td>Processing of potatoes</td><td>28.7</td></tr><tr><td>Bakery</td><td>6.1</td><td>Cooked meats production and trade</td><td>28.1</td></tr><tr><td>Industrial bakery</td><td>5.0</td><td>Manufacture of medical equipment</td><td>32.3</td></tr></tbody></table>

*Source:* BRN-RSI tax returns in 2007. *Field:* manufacturing and wholesale sector.

*Lecture:* This table displays the NAF 5-digits sectors in the manufacturing or wholesale sector with the highest and lowest values of average customer payment delays (Customer<sub>s</sub>) and supplier payment delays (Supplier<sub>s</sub>). The values are given in days. A value of 100 for Customer<sub>s</sub> means that in sector s, the average gap between customer payments is of 100 days.
<figure><img src="image_6.png" /><figcaption>Figure 3: Graphical representation of the first stage</figcaption></figure>

Source: BRN-RSI tax returns in 2007. Field: manufacturing and wholesale sector.

Lecture: The y-axis of the graphs give the evolution of customer payment delays (panel (b)), of supplier payment delays (panel (c)) and of the difference of the two (net payment delays) between 2007 and 2009 (panel (a) for firms in the manufacturing and wholesale sectors. Panel (b) gives the evolution of net payment delays between 2008 and 2009 (placebo). The x-axis displays the measure of the distance to the 60-days limit for payment delays imposed by the reform: a higher value indicates that payment delays had to be more reduced for the firm to comply with the law (the exact definition of the variables is given in table 2). The graphs are generated by grouping the x-axis variables into equal-sized bins and then computing the mean of the x-axis and y-axis within each bin.
<table><caption>Table 2: Description of the data set.</caption><thead><tr><th colspan="2">Panel A: Data Definitions</th></tr></thead><tbody><tr><td colspan="2"><strong>Dependent variables</strong></td></tr><tr><td>Customer base increase<sub>fm09</sub></td><td>The customer base of firm <i>f</i> is said to increase in market <i>m</i> between 2007 and 2009 if firm <i>f</i> acquire at least one customer in market <i>m</i> without losing any existing customers. Source: Customs.</td></tr><tr><td>ΔCustomer exports<sub>fm09</sub></td><td>Variation between 2007 and 2009 of the amount of exports (in logarithm) to customers in market <i>m</i> with whom firm <i>f</i> has an active sales relationship in 2007, 2008 and 2009. Source: Customs.</td></tr><tr><td>Market entry<sub>fm09</sub></td><td>Export entry<sub>fm09</sub> = 1 if firm <i>f</i> does not export in market <i>m</i> in 2007 and exports in 2009, and zero otherwise. Source: Customs.</td></tr><tr><td>Market exit<sub>fm09</sub></td><td>Export exit<sub>fm09</sub> = 1 if firm <i>f</i> exports in market <i>m</i> in 2007 or 2008 and does not export in 2009, and zero otherwise. Source: Customs.</td></tr><tr><td>ΔMarket exports<sub>fm09</sub></td><td>Variation of the amount of exports (in logarithm) of firm <i>f</i> in market <i>m</i> between 2007 and 2009 if firm <i>f</i> exports in market <i>m</i> in 2007, 2008 and 2009. Source: Customs.</td></tr><tr><td colspan="2"><strong>Independent variables</strong></td></tr><tr><td>ΔCash holdings<sub>f,07-09</sub></td><td>Variation of cash holdings (scaled by the amount of total assets in 2007). Source: BRN-RSI.</td></tr><tr><td>ΔCustomer payment delays<sub>f,07-09</sub></td><td>Variation of the sales-weighted average of sectoral customer payment delays (see section 3). Source: EAE, BRN-RSI.</td></tr><tr><td>ΔEBIT/Turnover<sub>f,07-09</sub></td><td>Variation in the EBIT over sales ratio. Source: BRN-RSI.</td></tr><tr><td>ΔEmployment<sub>f,07-09</sub></td><td>Variation of employment (scaled by the amount of total assets in 2007 expressed in millions euros). Source: BRN-RSI.</td></tr><tr><td>ΔNet payment delays<sub>f,07-09</sub></td><td>Variation of the sales-weighted average of sectoral net payment delays (see section 3). Source: EAE, BRN-RSI.</td></tr><tr><td>ΔSupplier payment delays<sub>f,07-09</sub></td><td>Variation of the sales-weighted average of sectoral supplier payment delays (see section 3). Source: EAE, BRN-RSI.</td></tr><tr><td>Group<sub>f,09</sub></td><td>Dummy indicating the affiliation to a business group. Source: LIFI.</td></tr><tr><td>ΔInvestment<sub>f,07-09</sub></td><td>Variation of investment (scaled by the amount of total assets in 2007). Source: BRN-RSI.</td></tr><tr><td>log(Age)<sub>f,09</sub></td><td>Age of the firm (in logarithm). Source: BRN-RSI.</td></tr><tr><td>log(Total Assets)<sub>f,07</sub></td><td>Lag of the logarithm of total assets. Source: BRN-RSI.</td></tr><tr><td>Long-term debt/TA<sub>f,07</sub></td><td>Long-term debt to total assets ratio. Source: BRN-RSI.</td></tr><tr><td>Market power<sub>f,07</sub></td><td>Sales-weighted average of firm <i>f</i>'s market share in its different business lines Source: EAE.</td></tr><tr><td>RZ index<sub>f,07</sub></td><td>Sales-weighted average of the sectoral mean of the share of capital expenditures that is not financed by operating cash-flows (computed in 2007). Source: EAE, BRN-RSI.</td></tr><tr><td>Sales growth rate<sub>f,07-09</sub></td><td>Sales-weighted average of sectoral sales growth rates between 2007 and 2009. Source: EAE, BRN-RSI.</td></tr><tr><td colspan="2"><strong>Instruments</strong></td></tr><tr><td>Excess customer payment delays<sub>f,06</sub></td><td>Sales-weighted average of excess sectoral customer payment delays (see section 4.2). Source: EAE, BRN-RSI.</td></tr><tr><td>Excess net payment delays<sub>f,06</sub></td><td>Sales-weighted average of excess sectoral net payment delays (see section 4.2). Source: EAE, BRN-RSI.</td></tr><tr><td>Excess supplier payment delays<sub>f,06</sub></td><td>Sales-weighted average of excess sectoral supplier payment delays (see section 4.2). Source: EAE, BRN-RSI.</td></tr></tbody></table>
Panel B: Summary Statistics

<table><thead><tr><th rowspan="2">Name</th><th rowspan="2"># Obs.</th><th rowspan="2">Mean</th><th rowspan="2">Std. Dev.</th><th colspan="5">Percentiles</th></tr><tr><th>5<sup>th</sup></th><th>25<sup>th</sup></th><th>50<sup>th</sup></th><th>75<sup>th</sup></th><th>95<sup>th</sup></th></tr></thead><tbody><tr><td colspan="9"><strong>Dependent variables</strong></td></tr><tr><td>Customer base increase<sub>fm09</sub></td><td>66724</td><td>0.07</td><td>0.26</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>ΔCustomer exports<sub>fm09</sub></td><td>34384</td><td>-0.02</td><td>0.85</td><td>-1.08</td><td>-0.02</td><td>0.00</td><td>0.38</td><td>1.01</td></tr><tr><td>Market entry<sub>fm09</sub></td><td>350459</td><td>0.02</td><td>0.15</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Market exit<sub>fm09</sub></td><td>119940</td><td>0.22</td><td>0.41</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>ΔMarket exports<sub>fm09</sub></td><td>58888</td><td>-0.36</td><td>1.09</td><td>-2.29</td><td>-0.86</td><td>-0.26</td><td>0.23</td><td>1.26</td></tr><tr><td colspan="9"><strong>Independent variables</strong></td></tr><tr><td>ΔCash holdings<sub>f,07-09</sub></td><td>9286</td><td>0.02</td><td>0.10</td><td>-0.11</td><td>-0.02</td><td>0.00</td><td>0.05</td><td>0.18</td></tr><tr><td>ΔCustomer PD<sub>f,07-09</sub></td><td>9286</td><td>-6.45</td><td>5.83</td><td>-16.45</td><td>-10.25</td><td>-5.71</td><td>-2.06</td><td>1.77</td></tr><tr><td>ΔEBIT/Turnover<sub>f,07-09</sub></td><td>9286</td><td>-0.03</td><td>0.11</td><td>-0.21</td><td>-0.06</td><td>-0.01</td><td>0.02</td><td>0.09</td></tr><tr><td>ΔEmployment<sub>f,07-09</sub></td><td>9286</td><td>-0.38</td><td>1.73</td><td>-2.99</td><td>-0.80</td><td>-0.16</td><td>0.23</td><td>1.53</td></tr><tr><td>ΔNet PD<sub>f,07-09</sub></td><td>9286</td><td>0.22</td><td>5.16</td><td>-8.25</td><td>-2.99</td><td>0.66</td><td>3.84</td><td>8.17</td></tr><tr><td>ΔSupplier PD<sub>f,07-09</sub></td><td>9286</td><td>-6.67</td><td>4.50</td><td>-13.07</td><td>-9.19</td><td>-6.64</td><td>-4.15</td><td>-0.07</td></tr><tr><td>Group<sub>f,09</sub></td><td>9286</td><td>0.62</td><td>0.49</td><td>0.00</td><td>0.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>ΔInvestment<sub>f,07-09</sub></td><td>9286</td><td>-0.00</td><td>0.05</td><td>-0.07</td><td>-0.01</td><td>-0.00</td><td>0.01</td><td>0.06</td></tr><tr><td>log(Age)<sub>f,09</sub></td><td>9286</td><td>3.17</td><td>0.65</td><td>1.95</td><td>2.77</td><td>3.18</td><td>3.64</td><td>3.99</td></tr><tr><td>log(Total Assets)<sub>f,07</sub></td><td>9286</td><td>8.93</td><td>0.88</td><td>7.58</td><td>8.30</td><td>8.88</td><td>9.51</td><td>10.46</td></tr><tr><td>LT debt/TA<sub>f,07</sub></td><td>9286</td><td>0.04</td><td>0.06</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.06</td><td>0.16</td></tr><tr><td>RZ index<sub>f,07</sub></td><td>9285</td><td>-7.64</td><td>2.99</td><td>-12.75</td><td>-9.28</td><td>-7.53</td><td>-5.78</td><td>-3.25</td></tr><tr><td>Sales growth rate<sub>f,07-09</sub></td><td>9286</td><td>-0.16</td><td>0.17</td><td>-0.41</td><td>-0.23</td><td>-0.15</td><td>-0.05</td><td>0.06</td></tr><tr><td colspan="9"><strong>Instruments</strong></td></tr><tr><td>Excess customer PD<sub>f,06</sub></td><td>9286</td><td>30.08</td><td>12.04</td><td>8.05</td><td>22.40</td><td>32.36</td><td>38.54</td><td>46.37</td></tr><tr><td>Excess net PD<sub>f,06</sub></td><td>9286</td><td>9.75</td><td>12.39</td><td>-8.45</td><td>-0.30</td><td>9.63</td><td>18.57</td><td>31.45</td></tr><tr><td>Excess supplier PD<sub>f,06</sub></td><td>9286</td><td>20.33</td><td>9.15</td><td>8.65</td><td>13.35</td><td>18.46</td><td>25.56</td><td>38.04</td></tr></tbody></table>

Source: BRN-RSI, EAE, Customs data. Field: SMEs of the manufacturing and wholesale sector.

<table><caption>Table 3: Description of the export dynamics at the customer- and market-level.</caption><thead><tr><th>Level</th><th>#Years after entry:</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td rowspan="2">Customer</td><td>Export value (mean)</td><td>50409</td><td>79556</td><td>112659</td><td>142777</td><td>151454</td></tr><tr><td>Survival rate (%)</td><td>-</td><td>39.87</td><td>24.05</td><td>16.10</td><td>12.06</td></tr><tr><td rowspan="3">Market</td><td>Export value (mean)</td><td>143684</td><td>237899</td><td>338312</td><td>517903</td><td>602528</td></tr><tr><td>Survival rate (%)</td><td>-</td><td>44.12</td><td>30.49</td><td>22.37</td><td>18.56</td></tr><tr><td># customers (mean, UE)</td><td>2.69</td><td>4.06</td><td>5.05</td><td>6.01</td><td>6.94</td></tr></tbody></table>

Source: BRN-RSI, EAE, Customs data. Field: SMEs of the manufacturing and wholesale sector.

Lecture: The table displays the average export value and survival rate at the customer- and market-level for the five years consecutive to the entry in the market or the formation of a new customer-supplier relationship. The last line indicate the evolution of the number of customers per market in the five years consecutive to the time of entry.
<table><caption>Table 4: Results of the first stage estimation.</caption><thead><tr><th rowspan="2"></th><th colspan="4">OLS</th></tr><tr><th>ΔNet PD<sub>f,07-09</sub></th><th>ΔCustomer PD<sub>f,07-09</sub></th><th>ΔSupplier PD<sub>f,07-09</sub></th><th>ΔNet PD<sub>f,09-10</sub></th></tr></thead><tbody><tr><td>Excess net payment delays<sub>f,t-3</sub></td><td>-0.305***<br>(0.030)</td><td>-0.296***<br>(0.030)</td><td></td><td>0.008<br>(0.030)</td></tr><tr><td>log(Total Assets)<sub>f,t-2</sub></td><td></td><td>0.166*<br>(0.095)</td><td>0.326***<br>(0.115)</td><td>-0.045<br>(0.075)</td></tr><tr><td>Age<sub>f,t</sub></td><td></td><td>0.040<br>(0.080)</td><td>-0.093<br>(0.103)</td><td>-0.133<br>(0.093)</td></tr><tr><td>Group<sub>f,t</sub></td><td></td><td>0.086<br>(0.083)</td><td>-0.144<br>(0.114)</td><td>-0.208**<br>(0.101)</td></tr><tr><td>Sales growth rate<sub>f,t</sub></td><td></td><td>1.725<br>(1.725)</td><td>3.710**<br>(1.650)</td><td>1.753<br>(1.449)</td></tr><tr><td>Long-term debt/Total assets<sub>f,t-2</sub></td><td></td><td>-0.732<br>(0.703)</td><td>-0.516<br>(0.948)</td><td>0.526<br>(0.860)</td></tr><tr><td>Market power<sub>f,t</sub></td><td></td><td>-4.038<br>(12.085)</td><td>-9.426<br>(10.635)</td><td>-3.204<br>(8.772)</td></tr><tr><td>ΔEBIT/Turnover<sub>f,t</sub></td><td></td><td>0.766*<br>(0.408)</td><td>0.420<br>(0.459)</td><td>-0.361<br>(0.414)</td></tr><tr><td>ΔCash holdings<sub>f,t</sub></td><td></td><td>0.400<br>(0.473)</td><td>-0.363<br>(0.544)</td><td>-0.743<br>(0.456)</td></tr><tr><td>Excess customer payment delays<sub>f,t-3</sub></td><td></td><td></td><td>-0.294***<br>(0.028)</td><td></td></tr><tr><td>Excess supplier payment delays<sub>f,t-3</sub></td><td></td><td></td><td></td><td>-0.166***<br>(0.027)</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>9286</td><td>9286</td><td>9286</td><td>9286</td><td>9787</td></tr><tr><td>R<sup>2</sup></td><td>0.457</td><td>0.461</td><td>0.495</td><td>0.312</td><td>0.097</td></tr></tbody></table>

The main variables are presented in table 2. The regressions include sector (NAF 2-digits) fixed-effects. The standard errors are clustered at the sector level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. In the last column, the first stage equation is estimated for the 2009-2010 period.
<table><caption>Table 5: Payment delays and firm-level outcomes.</caption><thead><tr><th rowspan="2"></th><th colspan="1">OLS</th><th colspan="3">IV 2SLS</th></tr><tr><th>ΔNet PD<sub>f,07-09</sub></th><th>ΔCash<sub>f,07-09</sub></th><th>ΔInv<sub>f,07-09</sub></th><th>ΔEmpl<sub>f,07-09</sub></th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f,t</sub> × 100</td><td>0.254<sup>***</sup><br>(0.091)</td><td>-0.143<sup>***</sup><br>(0.048)</td><td>0.037<sup>*</sup><br>(0.020)</td><td>-1.049<br>(0.925)</td></tr><tr><td>log(Total Assets)<sub>f,t-2</sub></td><td>0.005<br>(0.005)</td><td>-0.004<sup>***</sup><br>(0.007)</td><td>-0.002<sup>***</sup><br>(0.001)</td><td>0.185<sup>***</sup><br>(0.043)</td></tr><tr><td>Age<sub>f,t</sub></td><td>-0.011<br>(0.007)</td><td>0.000<br>(0.002)</td><td>0.001<br>(0.001)</td><td>-0.095<sup>**</sup><br>(0.038)</td></tr><tr><td>Group<sub>f,t</sub></td><td>-0.012<br>(0.008)</td><td>-0.010<sup>***</sup><br>(0.002)</td><td>-0.000<br>(0.001)</td><td>-0.000<br>(0.037)</td></tr><tr><td>Sales growth rates<sub>f,t</sub></td><td>-0.022<br>(0.032)</td><td>0.005<br>(0.007)</td><td>0.004<br>(0.003)</td><td>0.319<sup>**</sup><br>(0.139)</td></tr><tr><td>Long-term debt/Total assets<sub>f,t-2</sub></td><td>-0.017<br>(0.060)</td><td>-0.005<br>(0.016)</td><td>-0.088<sup>***</sup><br>(0.015)</td><td>0.870<sup>***</sup><br>(0.315)</td></tr><tr><td>Market power<sub>f,t</sub></td><td>0.254<br>(0.198)</td><td>-0.017<br>(0.033)</td><td>0.051<sup>**</sup><br>(0.025)</td><td>-0.129<br>(0.662)</td></tr><tr><td>ΔEBIT/Turnover<sub>f,t</sub></td><td>0.287<sup>***</sup><br>(0.062)</td><td>0.134<sup>***</sup><br>(0.013)</td><td>0.004<br>(0.005)</td><td>0.914<sup>***</sup><br>(0.262)</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>9267</td><td>9286</td><td>9286</td><td>9286</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include sector (NAF 2-digits) fixed-effects. The standard errors are clustered at the sector level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. ΔNet PD<sub>f,07-09</sub> is computed as the change in the ratio of customer receivables minus supplier debt over sales between 2007 and 2009.
<table><caption>Table 6: Effects of the change in payment delays on *Market entry*<sub>fm09</sub></caption><thead><tr><th></th><th>OLS</th><th>IV 2SLS (1)</th><th>IV 2SLS(2)</th><th>IV 2SLS (3)</th><th>First stage</th></tr><tr><th></th><th colspan="4">Export entry<sub>fm09</sub></th><th>ΔNet payment delays<sub>f09</sub></th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f07-09</sub> × 100</td><td>-0.006<br>(0.009)</td><td>-0.046***<br>(0.013)</td><td>-0.045***<br>(0.014)</td><td>-0.046***<br>(0.015)</td><td></td></tr><tr><td>log(Total Assets)<sub>f,07</sub></td><td>0.005***<br>(0.000)</td><td></td><td>0.005***<br>(0.000)</td><td>0.005***<br>(0.000)</td><td>0.002***<br>(0.001)</td></tr><tr><td>log(Age)<sub>f,09</sub></td><td>-0.003***<br>(0.001)</td><td></td><td>-0.003***<br>(0.001)</td><td>-0.003***<br>(0.001)</td><td>-0.001<br>(0.001)</td></tr><tr><td>Group<sub>f,09</sub></td><td>0.002*<br>(0.001)</td><td></td><td>0.001<br>(0.001)</td><td>0.002*<br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Sales growth rate<sub>f,07-09</sub></td><td>-0.005*<br>(0.003)</td><td></td><td>-0.003<br>(0.003)</td><td>-0.002<br>(0.003)</td><td>0.012**<br>(0.005)</td></tr><tr><td>Long-term debt/Total assets<sub>f,07</sub></td><td>0.018**<br>(0.008)</td><td></td><td></td><td>0.017**<br>(0.008)</td><td>-0.019***<br>(0.007)</td></tr><tr><td>Market power<sub>f,07</sub></td><td>0.065**<br>(0.027)</td><td></td><td></td><td>0.072***<br>(0.027)</td><td>0.092<br>(0.089)</td></tr><tr><td>ΔEBIT/Turnover<sub>f,07-09</sub></td><td>0.000<br>(0.003)</td><td></td><td></td><td>0.001<br>(0.003)</td><td>0.008*<br>(0.004)</td></tr><tr><td>ΔCash holdings<sub>f,07-09</sub></td><td>0.006<br>(0.004)</td><td></td><td></td><td>0.006<br>(0.004)</td><td>0.000<br>(0.005)</td></tr><tr><td>Excess net PD<sub>f,06</sub> × 100</td><td></td><td></td><td></td><td></td><td>-0.273***<br>(0.005)</td></tr><tr><td>Observations</td><td>350459</td><td>350459</td><td>350459</td><td>350459</td><td>350459</td></tr><tr><td>Firms</td><td>9136</td><td>9136</td><td>9136</td><td>9136</td><td>9136</td></tr><tr><td>Market FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R<sup>2</sup></td><td>0.013</td><td>-</td><td>-</td><td>-</td><td>0.431</td></tr><tr><td>Weak identification (KP stat)</td><td>-</td><td>1346.2</td><td>1153.2</td><td>1157.6</td><td>-</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification).
<table><caption>Table 7: Effects of the change in payment delays on *Market exit*<sub>fm09</sub> and
*ΔMarket exports*<sub>fm09</sub></caption><thead><tr><th colspan="6">Panel A: Market exit</th></tr><tr><th></th><th>OLS</th><th>IV 2SLS (1)</th><th>IV 2SLS(2)</th><th>IV 2SLS (3)</th><th>First stage</th></tr><tr><th></th><th colspan="4">Market exit<sub>fm09</sub></th><th>ΔNet payment delays<sub>f09</sub></th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f07-09</sub> × 100</td><td>-0.066<br>(0.061)</td><td>-0.055<br>(0.096)</td><td>-0.093<br>(0.101)</td><td>-0.096<br>(0.103)</td><td></td></tr><tr><td>log(Total Assets)<sub>f07</sub></td><td>-0.011***<br>(0.004)</td><td></td><td>-0.011**<br>(0.004)</td><td>-0.011***<br>(0.004)</td><td>0.233*<br>(0.122)</td></tr><tr><td>Age<sub>f09</sub></td><td>-0.080***<br>(0.016)</td><td></td><td>-0.081***<br>(0.016)</td><td>-0.080***<br>(0.016)</td><td>0.257<br>(0.403)</td></tr><tr><td>Group<sub>f09</sub></td><td>-0.025***<br>(0.006)</td><td></td><td>-0.024***<br>(0.006)</td><td>-0.025***<br>(0.006)</td><td>-0.083<br>(0.162)</td></tr><tr><td>Sales growth rate<sub>f07-09</sub></td><td>-0.008<br>(0.015)</td><td></td><td>-0.007<br>(0.017)</td><td>-0.006<br>(0.018)</td><td>3.215***<br>(0.557)</td></tr><tr><td>Long-term debt/Total assets<sub>f07</sub></td><td>0.021<br>(0.045)</td><td></td><td>0.021<br>(0.045)</td><td>0.021<br>(0.045)</td><td>0.339<br>(1.141)</td></tr><tr><td>Market power<sub>f07</sub></td><td>0.041<br>(0.147)</td><td></td><td>0.046<br>(0.152)</td><td>0.046<br>(0.152)</td><td>9.295<br>(10.175)</td></tr><tr><td>ΔEBIT/Turnover<sub>f07-09</sub></td><td>-0.016<br>(0.031)</td><td></td><td>-0.016<br>(0.031)</td><td>-0.016<br>(0.031)</td><td>0.028<br>(0.607)</td></tr><tr><td>ΔCash holdings<sub>f07-09</sub></td><td>-0.046<br>(0.030)</td><td></td><td>-0.046<br>(0.030)</td><td>-0.046<br>(0.030)</td><td>1.273**<br>(0.639)</td></tr><tr><td>Excess net PD<sub>f06</sub> × 100</td><td></td><td></td><td></td><td></td><td>-0.259***<br>(0.007)</td></tr><tr><td>Observations</td><td>119940</td><td>119940</td><td>119940</td><td>119940</td><td>119940</td></tr><tr><td>Firms</td><td>9096</td><td>9096</td><td>9096</td><td>9096</td><td>9096</td></tr><tr><td>Market FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R<sup>2</sup></td><td>0.078</td><td>-</td><td>-</td><td>-</td><td>0.379</td></tr><tr><td>Weak identification (KP stat)</td><td>-</td><td>757.9</td><td>641.3</td><td>671.6</td><td>-</td></tr></tbody></table><table><thead><tr><th colspan="6">Panel B: Evolution of the volume of transactions within a market</th></tr><tr><th></th><th>OLS</th><th>IV 2SLS (1)</th><th>IV 2SLS(2)</th><th>IV 2SLS (3)</th><th>First stage</th></tr><tr><th></th><th colspan="4">ΔMarket exports<sub>fm09</sub></th><th>ΔNet payment delays<sub>f09</sub></th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f07-09</sub> × 100</td><td>-0.045<br>(0.149)</td><td>0.781**<br>(0.306)</td><td>0.425<br>(0.333)</td><td>0.364<br>(0.320)</td><td></td></tr><tr><td>log(Total Assets)<sub>f07</sub></td><td>-0.007<br>(0.009)</td><td></td><td>-0.010<br>(0.010)</td><td>-0.007<br>(0.009)</td><td>0.002**<br>(0.001)</td></tr><tr><td>Age<sub>f09</sub></td><td>0.023<br>(0.046)</td><td></td><td>0.043<br>(0.050)</td><td>0.024<br>(0.046)</td><td>0.003<br>(0.005)</td></tr><tr><td>Group<sub>f09</sub></td><td>0.069***<br>(0.017)</td><td></td><td>0.070***<br>(0.017)</td><td>0.069***<br>(0.017)</td><td>-0.000<br>(0.002)</td></tr><tr><td>Sales growth rate<sub>f07-09</sub></td><td>0.290***<br>(0.049)</td><td></td><td>0.297***<br>(0.054)</td><td>0.261***<br>(0.053)</td><td>0.036***<br>(0.006)</td></tr><tr><td>Long-term debt/Total assets<sub>f07</sub></td><td>0.199*<br>(0.114)</td><td></td><td>0.197*<br>(0.114)</td><td>0.197*<br>(0.114)</td><td>0.001<br>(0.012)</td></tr><tr><td>Market power<sub>f07</sub></td><td>-0.338<br>(0.401)</td><td></td><td>-0.383<br>(0.406)</td><td>-0.383<br>(0.406)</td><td>0.051<br>(0.101)</td></tr><tr><td>ΔEBIT/Turnover<sub>f07-09</sub></td><td>0.729***<br>(0.083)</td><td></td><td>0.728***<br>(0.083)</td><td>0.728***<br>(0.083)</td><td>-0.004<br>(0.007)</td></tr><tr><td>ΔCash holdings<sub>f07-09</sub></td><td>0.337***<br>(0.080)</td><td></td><td>0.330***<br>(0.080)</td><td>0.330***<br>(0.080)</td><td>0.017**<br>(0.007)</td></tr><tr><td>Excess net PD<sub>f06</sub> × 100</td><td></td><td></td><td></td><td></td><td>-0.260***<br>(0.008)</td></tr><tr><td>Observations</td><td>58888</td><td>58888</td><td>58888</td><td>58888</td><td>58888</td></tr><tr><td>Firms</td><td>7472</td><td>7472</td><td>7472</td><td>7472</td><td>7472</td></tr><tr><td>Market FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R<sup>2</sup></td><td>0.066</td><td>-</td><td>-</td><td>-</td><td>0.380</td></tr><tr><td>Weak identification (KP stat)</td><td>-</td><td>564.5</td><td>480.1</td><td>486.5</td><td>-</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification).
<table><caption>Table 8: Effects of the change in payment delays on Customer base increase<sub>fm09</sub></caption><thead><tr><th></th><th>OLS</th><th>IV 2SLS (1)</th><th>IV 2SLS(2)</th><th>IV 2SLS (3)</th><th>First stage</th></tr><tr><th></th><th colspan="4">Customer base increase<sub>fm09</sub></th><th>ΔNet payment delays<sub>f09</sub></th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f07-09</sub> × 100</td><td>-0.008<br>(0.026)</td><td>-0.096**<br>(0.048)</td><td>-0.105**<br>(0.051)</td><td>-0.102**<br>(0.051)</td><td></td></tr><tr><td>log(Total Assets)<sub>f,07</sub></td><td>0.001<br>(0.002)</td><td>0.000<br>(0.001)</td><td>0.001<br>(0.002)</td><td>0.002**<br>(0.001)</td><td></td></tr><tr><td>Age<sub>f,09</sub></td><td>0.005<br>(0.007)</td><td>0.005<br>(0.007)</td><td>0.005<br>(0.007)</td><td>0.003<br>(0.004)</td><td></td></tr><tr><td>Group<sub>f,09</sub></td><td>0.007***<br>(0.003)</td><td>0.006**<br>(0.003)</td><td>0.007**<br>(0.003)</td><td>-0.000<br>(0.001)</td><td></td></tr><tr><td>Sales growth rate<sub>f07-09</sub></td><td>0.001<br>(0.008)</td><td>0.009<br>(0.009)</td><td>0.008<br>(0.009)</td><td>0.035***<br>(0.005)</td><td></td></tr><tr><td>Long-term debt/Total assets<sub>f,07</sub></td><td>0.003<br>(0.019)</td><td></td><td>0.003<br>(0.019)</td><td>0.002<br>(0.010)</td><td></td></tr><tr><td>Market power<sub>f,07</sub></td><td>-0.164**<br>(0.072)</td><td></td><td>-0.138*<br>(0.072)</td><td>0.192<br>(0.124)</td><td></td></tr><tr><td>ΔEBIT/Turnover<sub>f,07-09</sub></td><td>0.001<br>(0.009)</td><td></td><td>0.002<br>(0.009)</td><td>-0.002<br>(0.006)</td><td></td></tr><tr><td>ΔCash holdings<sub>f,07-09</sub></td><td>0.064***<br>(0.015)</td><td></td><td>0.065***<br>(0.015)</td><td>0.012*<br>(0.006)</td><td></td></tr><tr><td>Excess net PD<sub>f,06</sub> × 100</td><td></td><td></td><td></td><td>-0.260***<br>(0.007)</td><td></td></tr><tr><td>Observations</td><td>66724</td><td>66724</td><td>66724</td><td>66724</td><td>66724</td></tr><tr><td>Firms</td><td>8397</td><td>8397</td><td>8397</td><td>8397</td><td>8397</td></tr><tr><td>Market FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R<sup>2</sup></td><td>0.016</td><td>-</td><td>-</td><td>-</td><td>0.372</td></tr><tr><td>Weak identification (KP stat)</td><td>-</td><td>774.0</td><td>632.0</td><td>629.1</td><td>-</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification).
<table><caption>Table 9: Alternative specifications</caption><thead><tr><th>Measure of payment delays</th><th>(1)<br>Baseline</th><th>(2)<br>Derogations</th><th>(3)<br>Customer</th><th>(4)<br>Supplier</th><th>(5)<br>2008-2009</th></tr></thead><tbody><tr><td colspan="6"><strong>Panel A: Market entry</strong></td></tr><tr><td>Regression coefficient (× 100)</td><td>-0.046***<br>(0.015)</td><td>-0.025*<br>(0.014)</td><td>-0.034**<br>(0.015)</td><td>0.025<br>(0.024)</td><td>-0.119***<br>(0.039)</td></tr><tr><td>Observations</td><td>350459</td><td>350459</td><td>350459</td><td>350459</td><td>350459</td></tr><tr><td>KP statistic</td><td>1157.6</td><td>1157.2</td><td>1041.2</td><td>621.0</td><td>359.2</td></tr><tr><td colspan="6"><strong>Panel B: Market exit</strong></td></tr><tr><td>Regression coefficient (× 100)</td><td>-0.093<br>(0.102)</td><td>-0.104<br>(0.100)</td><td>-0.105<br>(0.099)</td><td>-0.013<br>(0.114)</td><td>-0.276<br>(0.303)</td></tr><tr><td>Observations</td><td>119940</td><td>119940</td><td>119940</td><td>119940</td><td>119940</td></tr><tr><td>KP statistic</td><td>639.4</td><td>569.6</td><td>359.1</td><td>317.8</td><td>164.7</td></tr><tr><td colspan="6"><strong>Panel C: Evolution of the volume of transaction within a market</strong></td></tr><tr><td>Regression coefficient (× 100)</td><td>0.348<br>(0.319)</td><td>0.420<br>(0.312)</td><td>0.400<br>(0.312)</td><td>0.039<br>(0.344)</td><td>0.956<br>(0.879)</td></tr><tr><td>Observations</td><td>58888</td><td>58888</td><td>58888</td><td>58888</td><td>58888</td></tr><tr><td>KP statistic</td><td>480.6</td><td>465.4</td><td>244.2</td><td>290.8</td><td>140.2</td></tr><tr><td colspan="6"><strong>Panel D: Evolution of the customer base</strong></td></tr><tr><td>Regression coefficient (× 100)</td><td>-0.102**<br>(0.051)</td><td>-0.109**<br>(0.051)</td><td>-0.086*<br>(0.049)</td><td>0.027<br>(0.063)</td><td>-0.261*<br>(0.134)</td></tr><tr><td>Observations</td><td>66724</td><td>66724</td><td>66724</td><td>66724</td><td>66724</td></tr><tr><td>KP statistic</td><td>629.1</td><td>646.7</td><td>398.8</td><td>381.5</td><td>185.1</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects and are all made according to the IV 2SLS (3) model. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification). In the *Derogations* column, the measure of excess payment delays takes into account the presence of derogations to the 60-rule (see Appendix A for the detail). In the *Customer* and *Supplier* columns, the ΔNet payment delays<sub>f,07-09</sub> are respectively replaced by ΔCustomer payment delays<sub>f,07-09</sub> and ΔSupplier payment delays<sub>f,07-09</sub>. In the *2008-2009* column, I consider only the change in payment delays between 2008 and 2009.
<table><caption>Table 10: Firm-level heterogeneity and Export entry<sub>fm09</sub></caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th colspan="3">Size of the group</th><th colspan="3">Sector</th></tr><tr><th></th><th>Small</th><th>Medium</th><th>Big</th><th>Agroalimentary, textile, chemistry</th><th>Metallurgy, machines, IT</th><th>Wholesale</th></tr></thead><tbody><tr><td colspan="7"><em>Customer base increase<sub>fm09</sub></em></td></tr><tr><td>ΔNPD<sub>f,07-09</sub> × 100</td><td>-0.102**<br>(0.052)</td><td>-0.175**<br>(0.075)</td><td>0.062<br>(0.179)</td><td>-0.346*<br>(0.208)</td><td>0.113<br>(0.074)</td><td>-0.024<br>(0.108)</td></tr><tr><td>Observations</td><td>66724</td><td>50856</td><td>14629</td><td>22814</td><td>23942</td><td>19775</td></tr><tr><td>Firms</td><td>8397</td><td>3935</td><td>927</td><td>2905</td><td>2989</td><td>2502</td></tr><tr><td colspan="7"><em>Market entry<sub>fm09</sub></em></td></tr><tr><td>ΔNPD<sub>f,07-09</sub> × 100</td><td>-0.044***<br>(0.015)</td><td>-0.120***<br>(0.035)</td><td>-0.047<br>(0.090)</td><td>-0.138***<br>(0.048)</td><td>0.035<br>(0.025)</td><td>-0.009<br>(0.032)</td></tr><tr><td>Observations</td><td>350459</td><td>219473</td><td>65010</td><td>106580</td><td>130470</td><td>113376</td></tr><tr><td>Firms</td><td>9136</td><td>4230</td><td>1107</td><td>3066</td><td>3350</td><td>2720</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects and are all made according to the IV 2SLS (3) model. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification). The first three columns display the results of the estimations made separately on the subsets of firms belonging to small, medium and large groups (see section 2.2). In the last three columns, firms are classified into three broad sub-sectors and the regressions are performed on each subset.
<table><caption>Table 11: Unobserved firm heterogeneity.</caption><thead><tr><th></th><th colspan="2">RZ index</th><th colspan="2">Idiosyncratic risk</th><th colspan="2">Market power</th></tr><tr><th></th><th>-</th><th>+</th><th>-</th><th>+</th><th>-</th><th>+</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-style: italic;">Customer base increase<sub>fm09</sub></td></tr><tr><td>ΔNPD&#773;<sub>f,07-09</sub> × 100</td><td>-0.129<sup>*</sup><br>(0.068)</td><td>-0.062<br>(0.082)</td><td>-0.049<br>(0.076)</td><td>-0.156<sup>**</sup><br>(0.074)</td><td>-0.185<sup>**</sup><br>(0.073)</td><td>-0.043<br>(0.071)</td></tr><tr><td>Observations</td><td>33179</td><td>33455</td><td>33240</td><td>33385</td><td>33331</td><td>33322</td></tr><tr><td>KP statistic</td><td>448.8</td><td>198.7</td><td>346.6</td><td>294.3</td><td>511.7</td><td>240.1</td></tr><tr><td colspan="7" style="text-align: center; font-style: italic;">Market entry<sub>fm09</sub></td></tr><tr><td>ΔNPD&#773;<sub>f,07-09</sub> × 100</td><td>-0.062<sup>***</sup><br>(0.022)</td><td>-0.039<sup>**</sup><br>(0.019)</td><td>0.016<br>(0.017)</td><td>-0.142<sup>***</sup><br>(0.027)</td><td>-0.036<sup>*</sup><br>(0.018)</td><td>-0.061<sup>***</sup><br>(0.023)</td></tr><tr><td>Observations</td><td>163912</td><td>186528</td><td>175187</td><td>175241</td><td>175227</td><td>175232</td></tr><tr><td>Firms</td><td>4056</td><td>5079</td><td>4975</td><td>4161</td><td>4595</td><td>4541</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects and are all made according to the IV 2SLS (3) model. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification). The construction of the *RZ index* and *Market power* variables are given in table 2. The firm-level measure of idiosyncratic risk is defined as the sales-weighted average of the sectoral standard deviation of the cash-flow to assets ratio (see Bates, Kahle and Stulz (2009)), the latter being computed by taking for each firm the standard deviation of the cash-flow to assets ratio between 2002 and 2006 and then averaging it at the level of the sector (only the firms that operated continuously during this period are included in the computation of the index). For each of this three variables, the sample is separated in two subsets, the symbol - (resp. +) indicating the first (resp. second) part of the distribution of the variable. The table displays the results of the regressions performed on the different sub-samples.
<table><caption>Table 12: Export market heterogeneity and Export entry<sub>fm09</sub></caption><thead><tr><th></th><th>(1)<br>Baseline</th><th>(2)<br>EU</th><th>(3)<br>Excl. EU</th><th>(4)<br>Europe</th><th>(5)<br>Asia</th><th>(6)<br>Africa</th><th>(7)<br>America</th></tr></thead><tbody><tr><td colspan="8"><em>Market entry<sub>fm09</sub></em></td></tr><tr><td>ΔNPD<sub>f,07-09</sub> × 100</td><td>-0.046<sup>***</sup><br>(0.015)</td><td>0.002<br>(0.022)</td><td>-0.097<sup>***</sup><br>(0.015)</td><td>-0.133<sup>***</sup><br>(0.037)</td><td>0.008<br>(0.039)</td><td>-0.149<sup>***</sup><br>(0.028)</td><td>-0.106<sup>***</sup><br>(0.017)</td></tr><tr><td>Observations</td><td>350459</td><td>177796</td><td>172663</td><td>26980</td><td>30740</td><td>31038</td><td>83905</td></tr><tr><td>Firms</td><td>9136</td><td>9066</td><td>9133</td><td>8683</td><td>8769</td><td>8908</td><td>9114</td></tr><tr><td colspan="8"><em>Market exit<sub>fm09</sub></em></td></tr><tr><td>ΔNPD<sub>f,07-09</sub> × 100</td><td>-0.093<sup>*</sup><br>(0.102)</td><td>-0.328<sup>***</sup><br>(0.125)</td><td>0.237<sup>*</sup><br>(0.132)</td><td>-0.048<br>(0.217)</td><td>0.420<sup>**</sup><br>(0.204)</td><td>0.611<sup>**</sup><br>(0.276)</td><td>0.179<br>(0.233)</td></tr><tr><td>Observations</td><td>119940</td><td>66564</td><td>53376</td><td>10203</td><td>14878</td><td>8365</td><td>15829</td></tr><tr><td>KP statistic</td><td>639.4</td><td>587.9</td><td>495.7</td><td>415.3</td><td>396.6</td><td>208.2</td><td>220.1</td></tr><tr><td colspan="8"><em>ΔMarket export<sub>fm09</sub></em></td></tr><tr><td>ΔNPD<sub>f,07-09</sub> × 100</td><td>0.348<br>(0.319)</td><td>0.335<br>(0.378)</td><td>0.300<br>(0.458)</td><td>1.988<sup>**</sup><br>(0.783)</td><td>-0.448<br>(0.720)</td><td>-2.091<br>(1.290)</td><td>1.044<br>(0.938)</td></tr><tr><td>Observations</td><td>58888</td><td>37393</td><td>21495</td><td>4535</td><td>5318</td><td>3578</td><td>6310</td></tr><tr><td>KP statistic</td><td>480.6</td><td>429.0</td><td>355.9</td><td>295.5</td><td>261.0</td><td>134.9</td><td>129.0</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects and are all made according to the IV 2SLS (3) model. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification). The table displays the results of the regressions performed separately on different geographic zones.

<table><caption>Table 13: Descriptive statistics</caption><thead><tr><th></th><th>Obs.</th><th>Mean</th><th>S.D.</th><th>P5</th><th>P25</th><th>P50</th><th>P75</th><th>P95</th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f,07-09</sub></td><td>9153</td><td>-0.24</td><td>35.11</td><td>-50.24</td><td>-16.11</td><td>-0.01</td><td>15.41</td><td>50.24</td></tr><tr><td>Excess net payment delays<sub>f,06</sub></td><td>9153</td><td>5.07</td><td>34.99</td><td>-43.51</td><td>-10.73</td><td>0.00</td><td>22.25</td><td>59.65</td></tr><tr><td>ΔCustomer payment delays<sub>f,07-09</sub></td><td>9153</td><td>-7.02</td><td>31.61</td><td>-53.51</td><td>-21.50</td><td>-5.93</td><td>5.98</td><td>38.88</td></tr><tr><td>Excess customer payment delays<sub>f,06</sub></td><td>9153</td><td>27.72</td><td>45.50</td><td>0.00</td><td>0.00</td><td>17.09</td><td>42.80</td><td>85.31</td></tr><tr><td>ΔSupplier payment delays<sub>f,07-09</sub></td><td>9153</td><td>-9.02</td><td>41.06</td><td>-64.17</td><td>-27.48</td><td>-9.15</td><td>6.80</td><td>48.54</td></tr><tr><td>Excess supplier payment delays<sub>f,06</sub></td><td>9153</td><td>37.63</td><td>388.49</td><td>0.00</td><td>0.00</td><td>20.98</td><td>47.02</td><td>98.37</td></tr></tbody></table>

Source: BRN-RSI, EAE, Customs data. Field: SMEs of the manufacturing and wholesale sector.
<table><caption>Table 14: Alternative measure of payment delays and firm-level outcomes</caption><thead><tr><th></th><th>ΔNet PD<sub>f,07-09</sub></th><th>ΔCustomer PD<sub>f,07-09</sub></th><th>ΔSupplier PD<sub>f,07-09</sub></th><th>ΔCash<sub>f,07-09</sub></th><th>ΔInvestment<sub>f,07-09</sub></th><th>ΔEmployment<sub>f,07-09</sub></th></tr></thead><tbody><tr><td>log(Total Assets)<sub>f,07</sub></td><td>0.007<br>(0.005)</td><td>-0.855<br>(0.563)</td><td>-1.393**<br>(0.550)</td><td>-0.004***<br>(0.002)</td><td>-0.002***<br>(0.001)</td><td>0.198***<br>(0.046)</td></tr><tr><td>Age<sub>f,09</sub></td><td>-0.006<br>(0.006)</td><td>-1.127**<br>(0.511)</td><td>-0.506<br>(0.715)</td><td>-0.002<br>(0.002)</td><td>0.001<br>(0.001)</td><td>-0.115***<br>(0.041)</td></tr><tr><td>Group<sub>f,09</sub></td><td>-0.010<br>(0.008)</td><td>-0.652<br>(0.668)</td><td>-0.248<br>(0.845)</td><td>-0.011***<br>(0.002)</td><td>-0.000<br>(0.001)</td><td>-0.014<br>(0.040)</td></tr><tr><td>Sectoral growth rates<sub>f,07-09</sub></td><td>-0.015<br>(0.031)</td><td>2.206<br>(2.920)</td><td>4.907*<br>(2.894)</td><td>-0.005<br>(0.007)</td><td>0.006**<br>(0.003)</td><td>0.219<br>(0.140)</td></tr><tr><td>Long-term debt/Total assets<sub>f,07</sub></td><td>-0.051<br>(0.058)</td><td>-5.696<br>(6.142)</td><td>-2.621<br>(7.476)</td><td>-0.006<br>(0.017)</td><td>-0.087***<br>(0.014)</td><td>0.825***<br>(0.317)</td></tr><tr><td>Market power<sub>f,07</sub></td><td>1.218<br>(0.809)</td><td>183.392**<br>(74.856)</td><td>218.410**<br>(86.038)</td><td>0.065<br>(0.193)</td><td>0.050<br>(0.067)</td><td>-2.338<br>(3.792)</td></tr><tr><td>ΔEBIT/Turnover<sub>f,07-09</sub></td><td>0.289***<br>(0.064)</td><td>-20.069***<br>(5.649)</td><td>-30.176***<br>(6.469)</td><td>0.171***<br>(0.018)</td><td>0.005<br>(0.006)</td><td>1.316***<br>(0.306)</td></tr><tr><td>Excess net payment delays<sub>f,06</sub></td><td>-0.174***<br>(0.023)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Excess customer delays<sub>f,06</sub></td><td></td><td>-0.122***<br>(0.034)</td><td></td><td></td><td></td><td></td></tr><tr><td>Excess supplier delays<sub>f,06</sub></td><td></td><td></td><td>-0.002<br>(0.002)</td><td></td><td></td><td></td></tr><tr><td>ΔNet payment delays<sub>f,07-09</sub></td><td></td><td></td><td></td><td>-0.107***<br>(0.019)</td><td>0.000<br>(0.007)</td><td>-1.219***<br>(0.255)</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>9153</td><td>9153</td><td>9153</td><td>9153</td><td>9153</td><td>9153</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects and are all made according to the IV 2SLS (3) model. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification). The table displays the results of the regressions performed separately on different geographic zones.
<table><caption>Table 15: Alternative measure of payment delays and market-level outcomes</caption><thead><tr><th></th><th>Market entry<sub>f,m,07-09</sub></th><th>Market exit<sub>f,m,07-09</sub></th><th>ΔMarket exports<sub>f,m,07-09</sub></th><th>Customer base increase<sub>fm09</sub></th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f,07-09</sub> × 100</td><td>0.027<br>(0.022)</td><td>-0.056<br>(0.049)</td><td>0.102<br>(0.104)</td><td>0.029<br>(0.023)</td></tr><tr><td>log(Total Assets)<sub>f,07</sub></td><td>0.016***<br>(0.002)</td><td>-0.017***<br>(0.004)</td><td>0.007**<br>(0.003)</td><td>0.001**<br>(0.001)</td></tr><tr><td>log(Age)<sub>f,09</sub></td><td>0.004*<br>(0.002)</td><td>-0.074***<br>(0.015)</td><td>-0.032*<br>(0.016)</td><td>-0.008*<br>(0.004)</td></tr><tr><td>Group<sub>f,09</sub></td><td>0.005*<br>(0.003)</td><td>-0.026***<br>(0.006)</td><td>0.021***<br>(0.007)</td><td>0.004**<br>(0.002)</td></tr><tr><td>Sales growth rate<sub>f,07-09</sub></td><td>-0.005<br>(0.008)</td><td>-0.010<br>(0.015)</td><td>0.066***<br>(0.017)</td><td>0.009<br>(0.006)</td></tr><tr><td>Long-term debt/Total assets<sub>f,07</sub></td><td>0.052**<br>(0.025)</td><td>0.018<br>(0.040)</td><td>0.063<br>(0.044)</td><td>0.006<br>(0.011)</td></tr><tr><td>Market power<sub>f,07</sub></td><td>0.340***<br>(0.104)</td><td>-0.111<br>(0.268)</td><td>-0.061<br>(0.187)</td><td>-0.040*<br>(0.022)</td></tr><tr><td>ΔEBIT/Turnover<sub>f,07-09</sub></td><td>-0.008<br>(0.013)</td><td>-0.005<br>(0.032)</td><td>0.242***<br>(0.040)</td><td>0.006<br>(0.006)</td></tr><tr><td>ΔCash holdings<sub>f,07-09</sub></td><td>0.049*<br>(0.025)</td><td>-0.093*<br>(0.052)</td><td>0.149**<br>(0.071)</td><td>0.014<br>(0.013)</td></tr><tr><td>Observations</td><td>441375</td><td>168719</td><td>85137</td><td>66788</td></tr><tr><td>Firms</td><td>5828</td><td>9500</td><td>8629</td><td>9094</td></tr><tr><td>Market FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Weak identification (KP stat)</td><td>20.2</td><td>12.6</td><td>6.3</td><td>26.0</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects and are all made according to the IV 2SLS (3) model. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification). The table displays the results of the regressions performed separately on different geographic zones.
<table><caption>Table 16: Alternative measure of payment delays and aggregate export outcomes</caption><thead><tr><th></th><th>ΔExports<sub>f,07-09</sub></th><th>log(#Entries)</th><th>log(#Exits)</th><th>log(#New customers)</th></tr></thead><tbody><tr><td>ΔNet payment delays<sub>f,07-09</sub></td><td>-0.120*<br>(0.068)</td><td>-0.287**<br>(0.143)</td><td>-0.455<br>(0.273)</td><td>0.283<br>(0.253)</td></tr><tr><td>log(Total Assets)<sub>f,07</sub></td><td>0.010<br>(0.007)</td><td>0.260***<br>(0.011)</td><td>0.304***<br>(0.025)</td><td>0.307***<br>(0.044)</td></tr><tr><td>Age<sub>f,09</sub></td><td>0.002<br>(0.007)</td><td>-0.041***<br>(0.013)</td><td>0.025*<br>(0.013)</td><td>0.068***<br>(0.023)</td></tr><tr><td>Group<sub>f,09</sub></td><td>0.006<br>(0.009)</td><td>0.026<br>(0.018)</td><td>0.100***<br>(0.032)</td><td>-0.003<br>(0.052)</td></tr><tr><td>Sectoral growth rates<sub>f,07-09</sub></td><td>0.105**<br>(0.045)</td><td>0.212***<br>(0.057)</td><td>0.188<br>(0.141)</td><td>0.781***<br>(0.147)</td></tr><tr><td>Long-term debt/Total assets<sub>f,07</sub></td><td>0.174**<br>(0.070)</td><td>0.364***<br>(0.141)</td><td>-0.017<br>(0.236)</td><td>0.645<br>(0.492)</td></tr><tr><td>Market power<sub>f,07</sub></td><td>-0.177<br>(0.752)</td><td>5.607***<br>(1.547)</td><td>4.297<br>(4.214)</td><td>14.982***<br>(5.031)</td></tr><tr><td>ΔEBIT/Turnover<sub>f,07-09</sub></td><td>0.476***<br>(0.045)</td><td>0.458***<br>(0.092)</td><td>0.109<br>(0.158)</td><td>0.054<br>(0.138)</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>9153</td><td>9153</td><td>9153</td><td>8168</td></tr></tbody></table>

Field: manufacturing and wholesale sector, excluding firms belonging to large groups. The main variables are presented in table 2. The regressions include market (country × industry) fixed-effects and are all made according to the IV 2SLS (3) model. All standard errors are clustered at the firm level. *, **, and *** denote statistical significance at 10, 5 and 1%. Standard errors are given in parentheses. The Kleibergen-Paap statistic (KP stat) tests for the presence of a weak instrument in presence of heteroscedasticity (high values suggest to reject the null hypothesis of weak identification). The table displays the results of the regressions performed separately on different geographic zones.
<table><tr><td>G 9001</td><td>J. FAYOLLE et M. FLEURBAEY<br>Accumulation, profitabilité et endettement des entreprises</td></tr><tr><td>G 9002</td><td>H. ROUSSE<br>Détection et effets de la multicolinéarité dans les modèles linéaires ordinaires - Un prolongement de la réflexion de BELSLEY, KUH et WELSCH</td></tr><tr><td>G 9003</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Indexation des salaires : la rupture de 1983</td></tr><tr><td>G 9004</td><td>D. GUELLEC et P. RALLE<br>Compétitivité, croissance et innovation de produit</td></tr><tr><td>G 9005</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Les conséquences de la désindexation. Analyse dans une maquette prix-salaires</td></tr><tr><td>G 9101</td><td>Équipe AMADEUS<br>Le modèle AMADEUS - Première partie - Présentation générale</td></tr><tr><td>G 9102</td><td>J.L. BRILLET<br>Le modèle AMADEUS - Deuxième partie - Propriétés variantielles</td></tr><tr><td>G 9103</td><td>D. GUELLEC et P. RALLE<br>Endogenous growth and product innovation</td></tr><tr><td>G 9104</td><td>H. ROUSSE<br>Le modèle AMADEUS - Troisième partie - Le commerce extérieur et l'environnement international</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></tr><tr><td>G 9106</td><td>B. CREPON<br>Innovation, taille et concentration : causalités et dynamiques</td></tr><tr><td>G 9107</td><td>B. AMABLE et D. GUELLEC<br>Un panorama des théories de la croissance endogène</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></tr><tr><td>G 9109</td><td>P. RALLE et alii<br>France - Allemagne : performances économiques comparées</td></tr><tr><td>G 9110</td><td>J.L. BRILLET<br>Micro-DMS<br>NON PARU</td></tr><tr><td>G 9111</td><td>A. MAGNIER<br>Effets accélérateur et multiplicateur en France depuis 1970 : quelques résultats empiriques</td></tr><tr><td>G 9112</td><td>B. CREPON et G. DUREAU<br>Investissement en recherche-développement : analyse de causalités dans un modèle d'accélérateur généralisé</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-économique</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></tr><tr><td>G 9202</td><td>J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE</td></tr></table>

<table><tr><td>G 9203</td><td>I. STAPIC<br>Les échanges internationaux de services de la France dans le cadre des négociations multilaterales du GATT<br>Juin 1992 (1ère version)<br>Novembre 1992 (version finale)</td></tr><tr><td>G 9204</td><td>P. SEVESTRE<br>L'économie sur données individuelles-temporelles. Une note introductive</td></tr><tr><td>G 9205</td><td>H. ERKEL-ROUSSE<br>Le commerce extérieur et l'environnement international dans le modèle AMADEUS (réestimation 1992)</td></tr><tr><td>G 9206</td><td>N. GREENAN et D. GUELLEC<br>Coordination within the firm and endogenous growth</td></tr><tr><td>G 9207</td><td>A. MAGNIER et J. TOUJAS-BERNATE<br>Technology and trade: empirical evidences for the major five industrialized countries</td></tr><tr><td>G 9208</td><td>B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHNEN<br>Cooperative, non cooperative R & D and optimal patent life</td></tr><tr><td>G 9209</td><td>B. CREPON et E. DUGUET<br>Research and development, competition and innovation: an application of pseudo maximum likelihood methods to Poisson models with heterogeneity</td></tr><tr><td>G 9301</td><td>J. TOUJAS-BERNATE<br>Commerce international et concurrence impartie : développements récents et implications pour la politique commerciale</td></tr><tr><td>G 9302</td><td>Ch. CASES<br>Durées de chômage et comportements d'offre de travail : une revue de la littérature</td></tr><tr><td>G 9303</td><td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique</td></tr><tr><td>G 9304</td><td>N. GREENAN - D. GUELLEC / G. BROUSSAUDIER - L. MIOTTI<br>Innovation organisationnelle, dynamisme technologique et performances des entreprises</td></tr><tr><td>G 9305</td><td>P. JAILLARD<br>Le traité de Maastricht : présentation juridique et historique</td></tr><tr><td>G 9306</td><td>J.L. BRILLET<br>Micro-DMS : présentation et propriétés</td></tr><tr><td>G 9307</td><td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td></tr><tr><td>G 9308</td><td>S. JACOBZONE<br>Les grands réseaux publics français dans une perspective européenne</td></tr><tr><td>G 9309</td><td>L. BLOCH - B. CŒURE<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td></tr><tr><td>G 9310</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les théories sur la structure optimale du capital : quelques points de repère</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></tr><tr><td>G 9312</td><td>L. BLOCH - B. CŒURE<br>Q de Tobin marginal et transmission des chocs financiers</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></tr><tr><td>G 9314</td><td>B. CREPON - E. DUGUET<br>Research & Development, competition and innovation</td></tr><tr><td>G 9315</td><td>B. DORMONT<br>Quelle est l'influence du coût du travail sur l'emploi ?</td></tr><tr><td>G 9316</td><td>D. BLANCHET - C. BROUSSE<br>Deux études sur l'âge de la retraite</td></tr><tr><td>G 9317</td><td>D. BLANCHET<br>Répartition du travail dans une population hétérogène : deux notes</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></tr><tr><td>G 9319</td><td>G. CETTE - Ph. CUNÉO - D. EYSSARTIER - J. GAUTIÉ<br>Les effets sur l'emploi d'un abaissement du coût du travail des jeunes</td></tr><tr><td>G 9401</td><td>D. BLANCHET<br>Les structures par âge importent-elles ?</td></tr><tr><td>G 9402</td><td>J. GAUTIÉ<br>Le chômage des jeunes en France : problème de formation ou phénomène de file d'attente ?<br>Quelques éléments du débat</td></tr><tr><td>G 9403</td><td>P. QUIRION<br>Les déchets en France : éléments statistiques et économiques</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></tr><tr><td>G 9405</td><td>V. MAILLARD<br>Theorie et pratique de la correction des effets de jours ouvrables</td></tr><tr><td>G 9406</td><td>F. ROSENWALD<br>La décision d'investir</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></tr><tr><td>G 9408</td><td>L. BLOCH, J. BOURDIEU, B. COLIN-SEDILLOT, G. LONGUEVILLE<br>Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté</td></tr><tr><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'aide au logement - quelques éléments d'évaluation</td></tr><tr><td>G 9410</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td></tr><tr><td>G 9411</td><td>C. DEFEUILLEY - Ph. QUIRION<br>Les déchets d'emballages ménagers : une analyse économique des politiques française et allemande</td></tr></table>

<table><tr><td>G 9412</td><td>J. BOURDIEU - B. CŒURÉ - B. COLIN-SEDILLOT<br>Investissement, incertitude et irréversibilité<br>Quelques développements récents de la théorie de l'investissement</td></tr><tr><td>G 9413</td><td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'élasticité emploi-salaire dépend-elle des structures de qualification ?</td></tr><tr><td>G 9414</td><td>I. KABLA<br>Le Choix de breveter une invention</td></tr><tr><td>G 9501</td><td>J. BOURDIEU - B. CŒURÉ - B. SEDILLOT<br>Irreversible Investment and Uncertainty: When is there a Value of Waiting?</td></tr><tr><td>G 9502</td><td>L. BLOCH - B. CŒURÉ<br>Imperfections du marché du crédit, investissement des entreprises et cycle économique</td></tr><tr><td>G 9503</td><td>D. GOUX - E. MAURIN<br>Les transformations de la demande de travail par qualification en France<br>Une étude sur la période 1970-1993</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></tr><tr><td>G 9505</td><td>D. GOUX - E. MAURIN<br>Persistence des hiérarchies sectorielles de salaires: un réexamen sur données françaises</td></tr><tr><td>G 9505<br>Bis</td><td>D. GOUX - E. MAURIN<br>Persistence of inter-industry wages differentials: a reexamination on matched worker-firm panel data</td></tr><tr><td>G 9506</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></tr><tr><td>G 9507</td><td>G. CETTE - S. MAHFOUZ<br>Le partage primaire du revenu<br>Constat descriptif sur longue période</td></tr><tr><td>G 9601</td><td>Banque de France - CEPREMAP - Direction de la Prévision - Érasme - INSEE - OFCE<br>Structures et propriétés de cinq modèles macro-économiques français</td></tr><tr><td>G 9602</td><td>Rapport d'activité de la DESE de l'année 1995</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></tr><tr><td>G 9604</td><td>A. TOPIOL-BENSAÎD<br>Les implantations japonaises en France</td></tr><tr><td>G 9605</td><td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie : peut-on parler d'une gestion globale du capital santé ?<br>Une modélisation microéconométrique empirique</td></tr><tr><td>G 9606</td><td>C. DOZ - F. LENGLART<br>Factor analysis and unobserved component models: an application to the study of French business surveys</td></tr><tr><td>G 9607</td><td>N. GREENAN - D. GUELLEC<br>La théorie coopérative de la firme</td></tr></table>
<table><tr><td>G 9608</td><td>N. GREENAN - D. GUELLEC<br>Technological innovation and employment<br>reallocation</td><td>G 9714</td><td>F. LEQUILLER<br>Does the French Consumer Price Index Over-<br>state Inflation?</td><td>G 9807</td><td>Bilan des activités de la Direction des Études<br>et Synthèses Économiques - 1997</td><td>Bis</td><td>Une estimation de l'élasticité de l'emploi peu<br>qualifié à son coût</td></tr><tr><td>G 9609</td><td>Ph. COUR - F. RUPPRECHT<br>L'intégration asymétrique au sein du continent<br>américain : un essai de modélisation</td><td>G 9715</td><td>X. BONNET<br>Peut-on mettre en évidence les rigidités à la<br>baisse des salaires nominaux ?<br>Une étude sur quelques grands pays de l'OCDE</td><td>G 9808</td><td>A. MOUROUGANE<br>Can a Conservative Governor Conduct an Ac-<br>comodative Monetary Policy?</td><td>G 9913</td><td>Division « Redistribution et Politiques Sociales »<br>Le modèle de microsimulation dynamique<br>DESTINIE</td></tr><tr><td>G 9610</td><td>S. DUCHENE - G. FORGEOT - A. JACQUOT<br>Analyse des évolutions récentes de la producti-<br>vité apparente du travail</td><td>G 9716</td><td>N. IUNG - F. RUPPRECHT<br>Productivité de la recherche et rendements<br>d'échelle dans le secteur pharmaceutique<br>français</td><td>G 9809</td><td>X. BONNET - E. DUBOIS - L. FAUVET<br>Asymétrie des inflations relatives et menus costs<br>: tests sur l'inflation française</td><td>G 9914</td><td>E. DUGUET<br>Macro-commandes SAS pour l'économie des<br>panels et des variables qualitatives</td></tr><tr><td>G 9611</td><td>X. BONNET - S. MAHFOUZ<br>The influence of different specifications of wages-<br>prices spirals on the measure of the NAIRU: the<br>case of France</td><td>G 9717</td><td>E. DUGUET - I. KABLA<br>Appropriation strategy and the motivations to use<br>the patent system in France - An econometric<br>analysis at the firm level</td><td>G 9810</td><td>E. DUGUET - N. IUNG<br>Sales and Advertising with Spillovers at the firm<br>level: Estimation of a Dynamic Structural Model<br>on Panel Data</td><td>G 9915</td><td>R. DUHAUTOIS<br>Évolution des flux d'emplois en France entre<br>1990 et 1996 : une étude empirique à partir du<br>fichier des bénéfices réels normaux (BRN)</td></tr><tr><td>G 9612</td><td>PH. COUR - E. DUBOIS, S. MAHFOUZ,<br>J. PISANI-FERRY<br>The cost of fiscal retrenchment revisited: how<br>strong is the evidence?</td><td>G 9718</td><td>L.P. PELÉ - P. RALLE<br>Âge de la retraite : les aspects incitatifs du régime<br>général</td><td>G 9811</td><td>J.P. BERTHIER<br>Congestion urbaine : un modèle de trafic de<br>pointe à courbe débit-vitesse et demande<br>élastique</td><td>G 9916</td><td>J.Y. FOURNIER<br>Extraction du cycle des affaires : la méthode de<br>Baxter et King</td></tr><tr><td>G 9613</td><td>A. JACQUOT<br>Les flexions des taux d'activité sont-elles seule-<br>ment conjoncturelles ?</td><td>G 9719</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique français-chinois,<br>chinois-français</td><td>G 9812</td><td>C. PRIGENT<br>La part des salaires dans la valeur ajoutée : une<br>approche macroéconomique</td><td>G 9917</td><td>B. CRÉPON - R. DESPLATZ - J. MAIRESSÉ<br>Estimating price cost margins, scale economies<br>and workers' bargaining power at the firm level</td></tr><tr><td>G 9614</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique Français-Chinois</td><td>G 9720</td><td>M. HOUDEBINE - J.L. SCHNEIDER<br>Mesurer l'influence de la fiscalité sur la locali-<br>sation des entreprises</td><td>G 9813</td><td>A.Th. AERTS<br>L'évolution de la part des salaires dans la valeur<br>ajoutée en France reflète-t-elle les évolutions<br>individuelles sur la période 1979-1994 ?</td><td>G 9918</td><td>Ch. GIANELLA - Ph. LAGARDE<br>Productivity of hours in the aggregate production<br>function: an evaluation on a panel of French firms<br>from the manufacturing sector</td></tr><tr><td>G 9701</td><td>J.L. SCHNEIDER<br>La taxe professionnelle : éléments de cadrage<br>économique</td><td>G 9721</td><td>A. MOUROUGANE<br>Crédibilité, indépendance et politique monétaire<br>A revenue de la littérature</td><td>G 9814</td><td>B. SALANIÉ<br>Guide pratique des séries non-stationnaires</td><td>G 9919</td><td>S. AUDRIC - P. GIVORD - C. PROST<br>Évolution de l'emploi et des coûts par quali-<br>fication entre 1982 et 1996</td></tr><tr><td>G 9702</td><td>J.L. SCHNEIDER<br>Transition et stabilité politique d'un système<br>redistributif</td><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<br>sectorielles</td><td>G 9901</td><td>S. DUCHÈNE - A. JACQUOT<br>Une croissance plus riche en emplois depuis le<br>début de la décennie ? Une analyse en compa-<br>raison internationale</td><td>G 2000/01</td><td>R. MAHIEU<br>Les déterminants des dépenses de santé : une<br>approche macroéconomique</td></tr><tr><td>G 9703</td><td>D. GOUX - E. MAURIN<br>Train or Pay: Does it Reduce Inequalities to En-<br>courage Firms to Train their Workers?</td><td>G 9723</td><td>P. AUGERAUD - J.E. CHAPRON<br>Using Business Accounts for Compiling National<br>Accounts: the French Experience</td><td>G 9902</td><td>Ch. COLIN<br>Modélisation des carrières dans Destinie</td><td>G 2000/02</td><td>C. ALLARD-PRIGENT - H. GUILMEAU -<br>A. QUINET<br>The real exchange rate as the relative price of<br>nontrables in terms of tradables: theoretical<br>investigation and empirical study on French data</td></tr><tr><td>G 9704</td><td>P. GENIER<br>Deux contributions sur dépendance et équité</td><td>G 9724</td><td>P. AUGERAUD<br>Les comptes d'entreprise par activités - Le pas-<br>sage aux comptes - De la comptabilité<br>d'entreprise à la comptabilité nationale - A<br>paraitre</td><td>G 9903</td><td>Ch. COLIN<br>Évolution de la dispersion des salaires : un essai<br>de prospective par microsimulation</td><td>G 2000/03</td><td>J.-Y. FOURNIER<br>L'approximation du filtre passe-bande proposée<br>par Christiano et Fitzgerald</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><td>G 9801</td><td>H. MICHAUDON - C. PRIGENT<br>Présentation du modèle AMADEUS</td><td>G 9904</td><td>B. CREPON - N. IUNG<br>Innovation, emploi et performances</td><td>G 2000/04</td><td>Bilan des activités de la DESE - 1999</td></tr><tr><td>G 9706</td><td>M. HOUDEBINE - A. TOPIOL-BENSAÏD<br>Les entreprises internationales en France : une<br>analyse à partir de données individuelles</td><td>G 9802</td><td>J. ACCARDO<br>Une étude de comptabilité générationnelle<br>pour la France en 1996</td><td>G 9905</td><td>B. CREPON - Ch. GIANELLA<br>Wages inequalities in France 1969-1992<br>An application of quantile regression techniques</td><td>G 2000/05</td><td>B. CREPON - F. ROSENWALD<br>Investissement et contraintes de financement : le<br>poids du cycle<br>Une estimation sur données françaises</td></tr><tr><td>G 9707</td><td>M. HOUDEBINE<br>Polarisation des activités et spécialisation des<br>départements en France</td><td>G 9803</td><td>X. BONNET - S. DUCHÈNE<br>Apports et limites de la modélisation<br>« Real Business Cycles »</td><td>G 9906</td><td>C. BONNET - R. MAHIEU<br>Microsimulation techniques applied to inter-<br>generational transfers - Pensions in a dynamic<br>framework: the case of France</td><td>G 2000/06</td><td>A. FLIPO<br>Les comportements matrimoniaux de fait</td></tr><tr><td>G 9708</td><td>E. DUGUET - N. GREENAN<br>Le biais technologique : une analyse sur données<br>individuelles</td><td>G 9804</td><td>C. BARLET - C. DUGUET -<br>D. ENCAOUA - J. PRADEL<br>The Commercial Success of Innovations<br>An econometric analysis at the firm level in<br>French manufacturing</td><td>G 9907</td><td>F. ROSENWALD<br>L'impact des contraintes financières dans la dé-<br>cision d'investissement</td><td>G 2000/07</td><td>R. MAHIEU - B. SÉDILLOT<br>Microsimulations of the retirement decision: a<br>supply side approach</td></tr><tr><td>G 9709</td><td>J.L. BRILLET<br>Analyzing a small French ECM Model</td><td>G 9805</td><td>P. CAHUC - Ch. GIANELLA -<br>D. GOUX - A. ZILBERBERG<br>Equalizing Wage Differences and Bargaining<br>Power - Evidence form a Panel of French Firms</td><td>G 9908</td><td>Bilan des activités de la DESE - 1998</td><td>G 2000/08</td><td>C. AUDENIS - C. PROST<br>Déficit conjoncturel : une prise en compte des<br>conjonctures passées</td></tr><tr><td>G 9710</td><td>J.L. BRILLET<br>Formalizing the transition process: scenarios for<br>capital accumulation</td><td>G 9806</td><td>J. ACCARDO - M. JLASSI<br>La productivité globale des facteurs entre 1975 et<br>1996</td><td>G 9909</td><td>J.P. ZOYEM<br>Contrat d'insertion et sortie du RMI<br>Évaluation des effets d'une politique sociale</td><td>G 2000/09</td><td>R. MAHIEU - B. SÉDILLOT<br>Équivalent patrimonial de la rente et souscription<br>de retraite complémentaire</td></tr><tr><td>G 9711</td><td>G. FORGEOT - J. GAUTIÉ<br>Insertion professionnelle des jeunes et processus<br>de déclassement</td><td>G 9911</td><td>G. LAROQUE - B. SALANIÉ<br>Une décomposition du non-emploi en France</td><td>G 9910</td><td>Ch. COLIN - FI. LEGROS - R. MAHIEU<br>Bilans contributifs comparés des régimes de<br>retraite du secteur privé et de la fonction publique</td><td>G 2000/10</td><td>R. DUHAUTOIS<br>Ralentissement de l'investissement : petites ou<br>grandes entreprises ? industrie ou tertiaire ?</td></tr><tr><td>G 9712</td><td>E. DUBOIS<br>High Real Interest Rates: the Consequence of a<br>Saving Investment Disequilibrium or of an in-<br>sufficient Credibility of Monetary Authorities?</td><td>G 9912</td><td>B. SALANIÉ<br>Une maquette analytique de long terme du<br>marché du travail</td><td>G 9912</td><td>B. SALANIÉ<br>Une maquette analytique de long terme du<br>marché du travail</td><td>G 2000/11</td><td>G. LAROQUE - B. SALANIÉ<br>Temps partiel féminin et incitations financières à<br>l'emploi</td></tr><tr><td>G 9713</td><td>Bilan des activités de la Direction des Études<br>et Synthèses Économiques - 1996</td><td>G 9912</td><td>Ch. GIANELLA<br>Local unemployment and wages</td><td>G 9912</td><td>Ch. GIANELLA<br>Local unemployment and wages</td><td>G 2000/12</td><td>Ch. GIANELLA<br>Local unemployment and wages</td></tr></table>
G2000/13 B. CREPON - Th. HECKEL
- Informatisation en France : une évaluation à partir de données individuelles
- Computerization in France: an evaluation based on individual company data

G2001/01 F. LEQUILLER
- La nouvelle économie et la mesure de la croissance du PIB
- The new economy and the measure ment of GDP growth

G2001/02 S. AUDRIC
La reprise de la croissance de l'emploi profite-t-elle aussi aux non-diplômés ?

G2001/03 I. BRAUN-LEMAIRE
Évolution et répartition du surplus de productivité

G2001/04 A. BEAUDU - Th. HECKEL
Le canal du crédit fonctionne-t-il en Europe ? Une étude de l'hétérogénéité des comportements d'investissement à partir de données de bilan agréées

G2001/05 C. AUDENIS - P. BISCOURP - N. FOURCADE - O. LOISEL
Testing the augmented Solow growth model: An empirical reassessment using panel data

G2001/06 R. MAHIEU - B. SÉDILLOT
Départ à la retraite, irréversibilité et incertitude

G2001/07 Bilan des activités de la DESE - 2000

G2001/08 J. Ph. GAUDEMET
Les dispositifs d'acquisition à titre facultatif d'annuités viagères de retraite

G2001/09 B. CRÉPON - Ch. GIANELLA
Fiscalité, coût d'usage du capital et demande de facteurs : une analyse sur données individuelles

G2001/10 B. CRÉPON - R. DESPLATZ
Évaluation des effets des dispositifs d'allégements de charges sociales sur les bas salaires

G2001/11 J.-Y. FOURNIER
Comparaison des salaires des secteurs public et privé

G2001/12 J.-P. BERTHIER - C. JAULENT
R. CONVENEVOLE - S. PISANI
Une méthodologie de comparaison entre consommations intermédiaires de source fiscale et de comptabilité nationale

G2001/13 P. BISCOURP - Ch. GIANELLA
Substitution and complementarity between capital, skilled and less skilled workers: an analysis at the firm level in the French manufacturing industry

G2001/14 I. ROBERT-BOBEE
Modelling demographic behaviours in the French microsimulation model Destinie: An analysis of future change in completed fertility

G2001/15 J.-P. ZOYEM
Diagnostic sur la pauvreté et calendrier de revenus : le cas du "Panel européen des ménages »

G2001/16 J.-Y. FOURNIER - P. GIVORD
La réduction des taux d'activité aux âges extrêmes, une spécificité française ?

G2001/17 C. AUDENIS - P. BISCOURP - N. RIEDINGER
Existe-t-il une asymétrie dans la transmission du prix du brut aux prix des carburants ?

G2002/01 F. MAGNIEN - J.-L. TAVERNIER - D. THESMAR
Les statistiques internationales de PIB par habitant en standard de pouvoir d'achat : une analyse des résultats

G2002/02 Bilan des activités de la DESE - 2001

G2002/03 B. SÉDILLOT - E. WALRAET
La cessation d'activité au sein des couples : y a-t-il interdépendance des choix ?

G2002/04 G. BRILHAULT
- Rétroposition des séries de FBCF et calcul du capital fixe en SEC-95 dans les comptes nationaux français

- Retroposition of the investment series (GFCF) and estimation of fixed capital stocks on the ESA-95 basis for the French balance sheets

G2002/05 P. BISCOURP - B. CRÉPON - T. HECKEL - N. RIEDINGER

How do firms respond to cheaper computers? Microeconometric evidence for France based on a production function approach

G2002/06 C. AUDENIS - J. DERDYON - N. FOURCADE
L'impact des nouvelles technologies de l'information et de la communication sur l'économie française - un bouclage macro-économique

G2002/07 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Évaluation de trois réformes du Régime Général d'assurance vieillesse à l'aide du modèle de microsimulation DESTINIE

G2002/08 J.-P. BERTHIER
Réflexions sur les différentes notions de volume dans les comptes nationaux : comptes aux prix d'une année fixe ou aux prix de l'année précédente, séries chaînées

G2002/09 F. HILD
Les soldes d'opinion résument-ils au mieux les réponses des entreprises aux enquêtes de conjoncture ?

G2002/10 I. ROBERT-BOBÉE
Les comportements démographiques dans le modèle de microsimulation Destinie - Une comparaison des estimations issues des enquêtes Jeunes et Carrières 1997 et Histoire Familiale 1999

G2002/11 J.-P. ZOYEM
La dynamique des bas revenus : une analyse des entrées-sorties de pauvreté

G2002/12 F. HILD
Prévisions d'inflation pour la France

G2002/13 M. LECLAIR
Réduction du temps de travail et tensions sur les facteurs de production

G2002/14 E. WALRAET - A. VINCENT
- Analyse de la redistribution intragénérationnelle dans le système de retraite des salariés du privé - Une approche par microsimulation

- Intragenerational distributional analysis in the french private sector pension scheme - A microsimulation approach

G2002/15 P. CHONE - D. LE BLANC - I. ROBERT-BOBEE
Offre de travail féminine et garde des jeunes enfants

G2002/16 F. MAUREL - S. GREGOIR
Les indices de compétitivité des pays : inter-prétation et limites

G2003/01 N. RIEDINGER - E.HAUVY
Le coût de dépollution atmosphérique pour les entreprises françaises : Une estimation à partir de données individuelles

G2003/02 P. BISCOURP et F. KRAMARZ
Création d'emplois, destruction d'emplois et internationalisation des entreprises industrielles françaises : une analyse sur la période 1986-1992

G2003/03 Bilan des activités de la DESE - 2002

G2003/04 P.-O. BEFFY - J. DERDYON - N. FOURCADE - S. GREGOIR - N. LAÏB - B. MONFORT
Évolutions démographiques et croissance : une projection macro-économique à l'horizon 2020

G2003/05 P. AUBERT
La situation des salariés de plus de cinquante ans dans le secteur privé

G2003/06 P. AUBERT - B. CRÉPON
Age, salaire et productivité
La productivité des salariés décline-t-elle en fin de carrière ?

G2003/07 H. BARON - P.O. BEFFY - N. FOURCADE - R. MAHIEU
Le ralentissement de la productivité du travail au cours des années 1990

G2003/08 P.-O. BEFFY - B. MONFORT
Patrimoine des ménages, dynamique d'allocation et comportement de consommation

G2003/09 P. BISCOURP - N. FOURCADE
Peut-on mettre en évidence l'existence de rigidités à la baisse des salaires à partir de données individuelles ? Le cas de la France à la fin des années 90

G2003/10 M. LECLAIR - P. PETIT
Présence syndicale dans les firmes : quel impact sur les inégalités salariales entre les hommes et les femmes ?

G2003/11 P.-O. BEFFY - X. BONNET - M. DARRACQ-PARIES - B. MONFORT
MZE: a small macro-model for the euro area

G2004/01 P. AUBERT - M. LECLAIR
La compétitivité exprimée dans les enquêtes trimestrielles sur la situation et les perspectives dans l'industrie

G2004/02 M. DUÉE - C. REBILLARD
La dépendance des personnes âgées : une projection à long terme

G2004/03 S. RASPILLER - N. RIEDINGER
Régulation environnementale et choix de localisation des groupes français

G2004/04 A. NABOULET - S. RASPILLER
Les déterminants de la décision d'investir : une approche par les perceptions subjectives des firmes

G2004/05 N. RAGACHE
La déclaration des enfants par les couples non mariés est-elle fiscalement optimale ?

G2004/06 M. DUÉE
L'impact du chômage des parents sur le devenir scolaire des enfants

G2004/07 P. AUBERT - E. CAROLI - M. ROGER
New Technologies, Workplace Organisation and the Age Structure of the Workforce: Firm-Level Evidence

G2004/08 E. DUGUET - C. LELARGE
Les brevets accroissent-ils les incitations privées à innover ? Un examen microéconomique

G2004/09 S. RASPILLER - P. SILLARD
Affiliating versus Subcontracting: the Case of Multinationals

G2004/10 J. BOISSINOT - C. L'ANGEVIN - B. MONFORT
Public Debt Sustainability: Some Results on the French Case

G2004/11 S. ANANIAN - P. AUBERT
Travailleurs âgés, nouvelles technologies et changements organisationnels : un réexamen à partir de l'enquête « REPONSE »

G2004/12 X. BONNET - H. PONCET
Structures de revenus et propensions différentes à consommer - Vers une équation de consommation des ménages plus robuste en prévision pour la France

G2004/13 C. PICART
Évaluer la rentabilité des sociétés non financières

G2004/14 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Les retraites du secteur public : projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE

G2005/01 S. BUFFETEAU - P. GODEFROY
Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945 à 1974

G2005/02 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G2005/03 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française

G2005/04 M. LECLAIR - S. ROUX
Mesure et utilisation des emplois instables dans les entreprises

G2005/05 C. L'ANGEVIN - S. SERRAVALLE
Performances à l'exportation de la France et de l'Allemagne - Une analyse par secteur et destination géographique

G2005/06 Bilan des activités de la Direction des Études et Synthèses Economiques - 2004

G2005/07 S. RASPILLER
La concurrence fiscale : principaux enseignements de l'analyse économique

G2005/08 C. L'ANGEVIN - N. LAÏB
Éducation et croissance en France et dans un panel de 21 pays de l'OCDE
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/peu 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
manufacturière

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. DUÉE
La modélisation des comportements démographiques dans le modèle de microsimulation
DESTINIE

G2005/16 H. RAOUI - S. ROUX
Étude de simulation sur la participation versée
aux salariés 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 implicites des
systèmes de retraite : chiffrages, propriétés
analytiques et réactions à des chocs
démographiques types

G2006/06 G. BIAU - O. BIAU - L. ROUVIERE
Nonparametric 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
coût 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, recentrage et poids des activités
de support dans les groupes (1993-2000)

G2007/01 D. SRAER
Allègements de cotisations patronales et
dynamique salariale

G2007/02 V. ALBOUY - L. LEQUIEN
Les rendements non monétaires de l'éducation :
le cas de la santé

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

G2008/03 Y. BARBESOL - A. BRIANT
Économies 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

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 - E. WALRAET
Social Security and Well-Being of the Elderly: the
Case of France

G2008/12 C. AFSA
Analyser les composantes 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?
A Real-Time Analysis on French Data

G2009/04 P. GIVORD - L. WILNER
Les contrats temporaires : trappe ou marchepied
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. D'HAULTFOEUILLE
Faut-il pondérer ?... Ou l'éternelle question de
l'économètre confronté à des données d'enquête

G2009/07 S. QUANTIN - S. RASPILLER - S. SERRAVALLE
Commerce intragroupe, fiscalité et prix de
transferts : une analyse sur données françaises

G2009/08 M. CLERC - V. MARCUS
Élasticités - prix 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 CACHEUX - V. MARCUS
Adjusted net savings and other approaches to
sustainability: some theoretical background

G2009/11 V. BELLAMY - G. CONSALES - M. FESSEAU - S. LE LAIDIER - É. 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
Discrimination à l'embauche : 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
révisées - 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, miroir 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 seuils de 10, 20 et 50 salariés : impact sur la
taille des entreprises françaises

G2010/06 R. AEBERHARDT - J. POUGET
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 trajectoires 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. LAPÈGUE - 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 degré public

G2010/13 D. BLANCHET - S. BUFFETEAU - E. CRENNER S. LE MINEZ Le modèle de microsimulation Destinie 2: principales caractéristiques et premiers résultats

G2010/14 D. BLANCHET - E. CRENNER Le bloc retraites du modèle Destinie 2: guide de l'utilisateur

G2010/15 M. BARLET - L. CRUSSON - S. DUPUCH - F. PUECH Des services échangés aux services échangeables : une application sur données françaises

G2010/16 M. BEFFY - T. KAMIONKA Public-private wage gaps: is civil-servant human capital sector-specific?

G2010/17 P.-Y. CABANNES - H. ERKEL-ROUSSE - 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: propriétés et mises en œuvre des modélisations de l'hétérogénéité 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. LAPÈGUE - 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: a quantile regression analysis

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 Projeter l'impact des réformes des retraites sur l'activité des 55 ans et plus : une comparaison de trois modèles

G2011/09 C. LOUVOT-RUNAVOT L'évaluation de l'activité dissimulée des entreprises sur la base des contrôles fiscaux et son insertion dans les comptes nationaux

G2011/10 A. SCHREIBER - A. VICARD La tertiairisation de l'économie française et le ralentissement 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'HAULTFOEUILLE - 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. LAPÈGUE - V. MARCUS La nouvelle version du modèle MZE, modèle macroéconométrique pour la zone euro

G2011/16 R. AEBERHARDT - I. BUONO - H. FADINGER Learning, Incomplete Contracts and Export Dynamics: Theory and Evidence form French Firms

G2011/17 C. KERDRAIN - V. LAPÈGUE 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. FÉVRIER - L. WILNER Do Consumers Correctly Expect Price Reductions? Testing Dynamic Behavior

G2012/04 M. GAINI - A. LEDUC - A. VICARD School as a shelter? School leaving-age and the business cycle in France

G2012/05 M. GAINI - A. LEDUC - A. VICARD A scarred generation? French evidence on young people entering into a tough labour market

G2012/06 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: An Unconditional Quantile Regression Approach

G2012/08 A. EIDELMAN - F. LANGUMIER - A. VICARD Prélèvements obligatoires reposant sur les ménages : des canaux redistributifs différents en 1990 et 2010

G2012/09 O. BARGAIN - A. VICARD Le RMI et son successeur le RSA découragent-ils certains jeunes de travailler ? 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 Destinie

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 allègements de charges : une estimation au niveau établissement de 1996 à 2008

G2012/13 X. D'HAULTFOEUILLE - 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-F1301 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-F1302 C. TREVIEN Habiter 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 on the French Labour Market During the Last Thirty Years

G2013/09 J-B. BERNARD - G. CLÉAUD 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

G2013/13 P.-Y. CABANNES - V. COTTET - Y. DUBOIS - C. LELARGE - M. SICSIC French Firms in the Face of the 2008/2009 Crisis

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 Destinie II: rebasage et projection

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 secteur privé en France depuis 1968

G2014/04 P. AUBERT Modalités d'application des réformes 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. SICSIC Wage Resilience in France since the Great Recession

G2014/12 F. ARNAUD - J. BOUSSARD - A. POISSONNIER - H. SOUAL 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 Euler 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 Q. LAFFÉTER - M. PAK
Élasticités des recettes 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'État

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. DORTET-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. POULHÈS
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 J. BARDAJI - J.-C. BRICONGNE - B. CAMPAGNE - G. GAULIER
Compared performances of French companies on the domestic and foreign markets

G2015/17 C. BELLÉGO - R. DE NIJS
The redistributive effect of online piracy on the box office performance of American movies in foreign markets

G2015/18 J.-B. BERNARD - L. BERTHET
French households financial wealth: which changes in 20 years?

G2015/19 M. POULHÈS
Fenêtre sur Cour ou Chambre avec Vue ?
Les prix hédoniques de l'immobilier parisien

G2016/01 B. GARBINTI - S. GEORGES-KOT
Time to smell the roses? Risk aversion, the timing of inheritance receipt, and retirement

G2016/02 P. CHARNOZ - C. LELARGE - C. TREVIEN
Communication Costs and the Internal Organization of Multi-Plant Businesses: Evidence from the Impact of the French High-Speed Rail

G2016/03 C. BONNET - B. GARBINTI - A. SOLAZ
Gender Inequality after Divorce: The Flip Side of Marital Specialization - Evidence from a French Administrative Database

G2016/04 D. BLANCHET - E. CAROLI - C. PROST - M. ROGER
Health capacity to work at older ages in France

G2016/05 B. CAMPAGNE - A. POISSONNIER
MELEZE: A DSGE model for France within the Euro Area

G2016/06 B. CAMPAGNE - A. POISSONNIER
Laffer curves and fiscal multipliers: lessons from Mélèze model

G2016/07 B. CAMPAGNE - A. POISSONNIER
Structural reforms in DSGE models: a case for sensitivity analyses

G2016/08 Y. DUBOIS et M. KOUBI
Relèvement de l'âge de départ à la retraite : quel impact sur l'activité des séniors de la réforme des retraites de 2010 ?

G2016/09 A. NAOUAS - M. ORAND - I. SLIMANI HOUTI
Les entreprises employant des salariés au Smic : quelles caractéristiques et quelle rentabilité ?

G2016/10 T. BLANCHET - Y. DUBOIS - A. MARINO - M. ROGER
Patrimoine privé et retraite en France

G2016/11 M. PAK - A. POISSONNIER
Accounting for technology, trade and final consumption in employment: an Input-Output decomposition

G2017/01 D. FOUGÈRE - E. GAUTIER - S. ROUX
Understanding Wage Floor Setting in Industry-Level Agreements: Evidence from France

G2017/02 Y. DUBOIS - M. KOUBI
Règles d'indexation des pensions et sensibilité des dépenses de retraites à la croissance économique et aux chocs démographiques

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

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

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