# Supply Shocks in Supply Chains: Evidence from the Early Lockdown in China

Raphael Lafrogne-Joussier, Julien Martin, Isabelle Mejean

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Raphael Lafrogne-Joussier, Julien Martin, Isabelle Mejean. Supply Shocks in Supply Chains: Evidence from the Early Lockdown in China. 2023. hal-05354484

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# Supply shocks in supply chains: Evidence from the early lockdown in China

## Documents de travail

N° 2023-08 – Avril 2023

<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /></figure>
Institut national de la statistique et des études économiques

2023/08

# Supply Shocks in Supply Chains: Evidence from the Early Lockdown in China

Raphael LAFROGNE-JOUSSIER* Julien Martin** Isabelle MEJEAN***

Avril 2023

Département des Études Économiques – Timbre G201
88, avenue Verdier – CS 70 058 – 92 541 MONTROUGE CEDEX – France

Tél. : 33 (1) 87 69 59 54 – E-mail : d3e-dg@insee.fr – Site Web Insee : http://www.insee.fr

Ces documents de travail ne reflètent pas la position de l'Insee et n'engagent que leurs auteurs.
Working papers do not reflect the position of INSEE but only their author's views.

The paper was prepared for the 2021 Jacques Polak Annual Research Conference where we received insightful comments from Rudolf Bems (our discussant), Gita Gopinath, and Andrei Levchenko (the editor). We are also grateful to two anonymous referees which comments helped deepen the analysis. Finally, the paper has beneted from comments of participants in various seminars and conferences, most notably Lionel Fontagné, Fadi Hassan, Maxime Bonelli and Frank Pisch, who discussed early versions of the paper. The project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 714597). The authors thank Robin Lenoir and Pierre Coster for excellent research assistance.

<footnote>* CREST-Ecole Polytechnique and INSEE, Email address: raphael.lafrogne-joussier@polytechnique.edu</footnote>

<footnote>** Université du Québec à Montréal and CEPR, Email address: martin.julien@uqam.ca.</footnote>

<footnote>***Sciences Po and CEPR, Email address: isabelle.mejean@sciencespo.fr.</footnote>
# Chocs d'offre dans les chaînes de valeur : Enseignements du confinement en Chine

## Résumé

Comment les entreprises des chaînes de valeur mondiales réagissent-elles aux pénuries d'intrants ? Nous examinons les ajustements à des chocs de chaînes de fournisseurs, en s'appuyant sur le cas de la pandémie de Covid-19. Les entreprises françaises qui se fournissent en intrants en Chine juste avant le confinement de janvier 2020 dans ce pays ont vu leurs importations diminuer de plus en plus entre février et avril 2020, relativement à celles qui se fournissent auprès d'autres pays. Ce choc sur les achats d'intrants se transmet au reste de la chaîne de valeur à travers les ventes des entreprises touchées. Leurs exportations et leurs ventes sur le marché français ont baissé de 5% relativement aux entreprises non exposées. La baisse des ventes à l'étranger est entièrement attribuable à un retrait temporaire des marchés occasionnels. Nous explorons ensuite l'hétérogénéité de la transmission parmi les entreprises touchées. La diversification des sources d'approvisionnement avant le choc ne semble pas permettre d'atténuer l'impact du choc. En revanche, les entreprises ayant un niveau relativement élevé de stocks ont réussi à absorber la pénurie d'intrants.

**Mots-clés** : Pandémie de Covid-19, Disruptions de chaînes de valeur, Transmission de chocs, Pénurie d'intrants

# Supply Shocks in Supply Chains: Evidence from the Early Lockdown in China

## Abstract

How do firms in global value chains react to input shortages? We examine micro-level adjustments to supply chain shocks, building on the COVID-19 pandemic as a case study. French firms sourcing inputs from China just before the early lockdown in the country experienced a relative drop in imports that increases from February to April 2020. This shock on input purchases transmits to the rest of the supply chain through exposed firms' domestic and export sales. Between February and June, firms exposed to the Chinese early lockdown experienced a 5.5% drop in domestic sales and a 5% drop in exports, in relative terms with respect to comparable non-exposed firms. The drop in foreign sales is entirely attributable to a lower volume of exports driven by a temporary withdrawal from occasional markets. We then dig into the heterogeneity of the transmission across treated firms. Whereas the ex-ante geographic diversification of inputs does not seem to mitigate the impact of the shock, firms with relatively high inventories have been able to absorb the supply shock better.

**Keywords** : Covid-19 pandemic, Supply chain disruptions, Transmission of shocks, Global Value Chains

**Classification JEL** : F1, F6
# 1 Introduction

International flows of intermediate inputs constitute as much as two-thirds of international trade and half of global trade is embodied in global value chains (GVCs) (Johnson, 2014, Antràs, 2020). In this context, international production processes appear as a key channel of transmission of shocks across countries (di Giovanni et al., 2018, Boehm et al., 2019). The Covid-19 pandemic offers plenty of anecdotal evidence of firms’ vulnerability to shocks affecting their international supply chain. However, there is little quantitative evidence on the reaction of firms in GVCs to input shortages. This paper makes two contributions. First, it provides evidence of a firm-level transmission of shocks on imported inputs to the firm’s domestic and export sales. Second, it evaluates how the diversification of the firm’s supply chain and its inventory management can help mitigate the transmission of adverse shocks affecting its supply chain.

The empirical analysis exploits the January 2020 lockdown in China as a natural experiment of a shock to French firms’ supply chain. Six weeks separated the lockdown in China and lockdowns in France’s other main trading partners. This paves the way for a comparison of firms sourcing inputs from China with those that do not to explore the consequences of being exposed to supply chain disruptions. We study the real transmission of the shock using detailed data on French firms’ foreign and domestic activity. Firms relying on Chinese inputs before the beginning of the pandemic experienced a 5% decline in their exports and a 5.5% decline in domestic sales between February and June 2020, in relative terms with respect to similar firms involved in GVCs that were not exposed to Chinese inputs. The drop in firm-level exports is almost entirely driven by the extensive margin: exposed exporters temporarily stopped serving some of their occasional foreign partners, while they maintained their level of exports to other partners. Whereas the ex-ante geographic diversification of inputs does not seem to influence the transmission of the shock, we provide evidence that holding inventories offers a buffer for firms exposed to such temporary supply shocks.

We organize the paper in three parts. First, we describe the data and present evidence that the Chinese lockdown has caused a shortage of inputs for French firms importing from China. Our analysis builds on French customs data that cover the universe of French importers and exporters, merged with domestic sales recovered from VAT statements. The final dataset
contains transaction-level imports and exports as well as domestic sales, at the monthly fre-
quency, before and during the pandemic. The monthly frequency of the data combined with
information on the geography of firms’ input purchases allow us to exploit the timing of the
pandemic to identify the propagation of a supply shock downstream in the value chain. In
early 2020, when the world was to a large extent ignorant of the pandemic risk, China adopted
stringent measures to contain the spread of SARS-CoV-2, which led to shuttering factories in
the aftermath of the Chinese new year. In February 2020, French imports from China had
already dropped by more than 10% and they reached a minimum in March, one month before
imports from the rest of the world. The drop in imports from China immediately after the
lockdown is more severe than the usual seasonal slowdown. We thus interpret it as a supply
shock for French importers of Chinese inputs.

Our main analysis covers firms that participate in global value chains (GVCs). These firms
both import intermediate inputs and export some of their output (WDR, 2020). We split
this sample into two groups: a treatment group composed of firms exposed to China through
imports of intermediate inputs, and a control group that was not importing from China when
the Covid crisis started<fnref n="1" />. Having established that these groups display similar import patterns
before the pandemic and significantly divergent import patterns in the aftermath of the Chinese
early lockdown, we examine the within-firm downstream propagation of the supply shock. We
estimate the strength of the propagation using firms’ domestic and foreign sales as outcome
variables. We find firms exposed to Chinese inputs incurred a 7.4% drop in imports and a 5%
drop in exports, in comparison with the control group, in the five months following the Chinese
lockdown. A similar relative adjustment is observed in domestic sales, which confirms that the
trade adjustment is not attributable to substitution away from foreign markets. The relative
drop peaks in April 2020 at -15% for exports and -12% for domestic sales. In June 2020, both
groups have converged to the same contraction in sales, in comparison with their January level.
Interestingly, the firm-level adjustment is mainly driven by the extensive margin. The average
treated firm serves 10.3% fewer products and 9.2% fewer destinations in April, in comparison
with the control group. Likewise, the (relative) recovery in May and June 2020 mostly involves
(relative) extensive margin adjustments. Conditional on keeping serving a market, firms do not

<footnote n="1">¹The control group may be indirectly affected via input-output linkages. This would attenuate our results, while leaving results qualitatively unchanged.</footnote>
reduce their exports subsequently to the supply shock. We provide evidence of a temporarily
higher probability for treated firms to stop serving some of the markets they were serving in
the pre-shock period, in particular the firm’s secondary and/or occasional markets. To our
knowledge, we are the first to provide evidence of within-firm temporary extensive margin
adjustments in response to a shock.

We then investigate the extent to which risk management strategies can help mitigate the transmission of the supply shock downstream in the value chain. First, we explore the role played by the supply-structure of the firm. Given the vulnerability of input-output structures to localized shocks, diversifying the supply chain in the spatial dimension should be an efficient resilience strategy. One should thus expect the impact of being exposed to the Chinese early lockdown to be muted for firms with a diversified supply chain, that can increase their demand for non-Chinese inputs when the shock kicks in. To test this assumption, we quantify the extent to which geographic diversification of imported inputs prior to the shock helped firms withstand the supply disruption. We do not find evidence that diversified firms performed better. It appears that exposed firms that were not diversified ex-ante have managed to find new suppliers. As a result, imports and exports of exposed firms, both diversified and undiversified, followed a similar trajectory. However, there are significant measurement challenges in measuring diversification using firm-level trade data, and the lack of results may also be due to poorly measured diversification opportunities.

We then ask whether stock-piling can offer firms a buffer against short-lived supply chain disruptions. Formally, we investigate how the export performances of firms with more inventories differ from the performances of firms with just-in-time production strategies. The level of inventories is recovered from balance-sheet data covering firms’ activity prior to the shock and is normalized to take into account systematic differences in the level of inventories across sectors. Among firms exposed to the Chinese lockdown, those holding more inventories ex-ante performed better in the aftermath of the shock. The dynamics of their exports is not significantly different than in the control group and the baseline relative drop in exports is thus entirely attributable to treated firms with relatively low levels of inventories. Holding stocks has thus been a useful buffer in the early stages of the 2020 crisis.<fnref n="2" />

<footnote n="2">²Even if holding inventories has proved useful in the early stages of the Covid crisis, the long-lasting nature of the crisis implies that such buffers are not sufficient in the long-run, as proved by the historically low level of inventories in the manufacturing sector observed in 2021, after 18 months of the Covid crisis (INSEE, *Enquête*</footnote>
**Related literature.** A series of paper examine how trade adjusted during the pandemic.<fnref n="3" /> Our contribution to the trade and covid-19 literature is to trace out the quick transmission of supply chain disruptions induced by the pandemic at the firm-level, and analyze heterogeneity in this transmission across firms with different risk management strategies.

We participate to the broad literature on GVCs (see, e.g., Antràs and Chor, 2013, Baldwin and Lopez-Gonzalez, 2015, Johnson, 2018, Antràs, 2020). We connect exogenous changes in input purchases to firms’ exports. In this respect, our work relates to the literature showing how imported inputs affect domestic (Goldberg et al., 2010, Huneeus, 2018) and export performances (Halpern et al., 2015, Feng et al., 2016, Bas and Strauss-Kahn, 2015, Amiti et al., 2014). High-frequency data makes it possible to dig into the dynamics of the adjustment to a large but relatively short-lived supply-side shock.<fnref n="4" /> Whereas this literature mostly focuses on the structure and geography of global value chains, we instead study the consequences of this structure for firms’ exposure to localized shocks.

In doing so, we contribute to the recent literature measuring the transmission of shocks along supply chains.<fnref n="5" /> As in Boehm et al. (2019), we exploit the monthly frequency of firm-level trade data to trace the dynamics of firms’ adjustment to supply chain shocks. New to this strand of the literature is our investigation of geographic diversification of input purchases and inventories as buffers against adverse shocks.<fnref n="6" /> Several papers have highlighted the role of inventories for firms engaged in international trade (Alessandria et al., 2010b, Khan and Khederlarian, 2021), notably during the 2008 Trade Collapse (Alessandria et al., 2010a). Here, we show inventories mitigate the international propagation of shocks along supply chains.<fnref n="7" /> Instead, we do not find

<footnote>mensuelle de conjoncture dans l'industrie).</footnote>

<footnote n="3">³Using product- or firm-level data, Heise (2020), Liu et al. (2021), Bas et al. (2021), Berthou and Stumpner (2021), Bricongne et al. (2021) show how containment policies had an adverse effect on trade. More directly related to our work, several papers, mostly based on input-output tables, study the transmission of shocks along GVCs during the Covid pandemic (see, e.g. Bonadio et al., 2020, Eppinger et al., 2021, Gerschel et al., 2020, Meier et al., 2020).</footnote>

<footnote n="4">⁴Throughout the paper, we refer to the shock as being short-lived, even though the pandemic has had long-lasting consequences and firms may not have perceived it as temporary. The reason is that the identification exploits the one- to two-month delay between the productivity slowdown in China and in the rest of the world. See Freund et al. (2021) for an analysis of the long-run adjustments of GVCs to a supply shock.</footnote>

<footnote n="5">⁵See, e.g., Alessandria et al. (2010b), Barrot and Sauvagnat (2016), Boehm et al. (2019), Carvalho et al. (2020), Gopinath and Neiman (2014) on the transmission of natural disasters or large currency crisis.</footnote>

<footnote n="6">⁶See Grossman et al. (2021) for a discussion of the theoretical conditions under which promoting input diversification is desirable. Elliott et al. (2020) and Jiang et al. (2021) also investigate firms’ incentive to build robust supply chains. Unlike Kramarz et al. (2020) and Esposito (2020), we focus on the geographic diversification of inputs rather than sales.</footnote>

<footnote n="7">⁷Buffer stocks are not widespread among importers as shown by Pisch (2020) who documents (and then models) the prevalence of just-in-time supply chains in France.</footnote>
evidence that diversification helped firms weather this particular shock.

The paper is organized as follows. Section 2 describes the data and shows the Chinese lockdown has induced a shortage of inputs for French firms sourcing these inputs from China. Section 3 provides evidence of the within-firm transmission of the Chinese shock to firms' downstream partners. Section 4 examines differences in adjustments to shocks across firms with heterogeneous risk management strategies. Section 5 concludes.

# 2 Data and empirical strategy

This section presents the firm-level data used throughout the analysis and the definition of firms' involvement in GVCs. We then provide evidence that the Chinese lockdown has severely reduced productivity, with consequences for firms importing their inputs from China.

## 2.1 Data

The main source of data for our empirical analysis is provided by the French customs. The dataset covers each unique transaction involving a French company (unité légale, identified by a unique SIREN number) and a non-French partner. For each export and import transaction, we have information on the French company that originated the trade flow, the product category, the partner country, the value and quantity of the shipment, the mode of transport, and the date of the transaction, at the monthly level.<fnref n="8" /> As discussed in Section 2.2, the monthly frequency is particularly useful as it captures the timing of the pandemic and its heterogeneous impact on bilateral trade.

We merge the estimation sample with an other two firm-level datasets (SIREN-level). The INSEE-FARE dataset, available up to 2018, provides balance-sheet information on French firms, collected for tax purposes. Based on the balance-sheet data, we measure the ratio of imports over intermediate consumption and the share of exports in aggregate sales, which we use as proxies for the firm's degree of vertical specialization. The balance-sheet data are also used to recover information on firms' inventories. Second, we merge the data with monthly information on French firms domestic and overall sales, available in their VAT (Value Added

<footnote n="8">⁸Formally, the dataset is constructed from four sets of files, all collected by the French customs, namely export and import files, for intra-EU and extra-EU trade. We construct the final dataset following Bergounhon et al. (2018). Details are provided in Appendix A.1.</footnote>
Tax) statements. These data can be used to compare the dynamics of foreign and domestic sales in the aftermath of the early lockdown in China.

Our objective is to identify the diffusion of supply chain disruptions induced by the Chinese lockdown on GVCs, using French firms as reference. We follow the World Development Report (WDR, 2020) and consider that a firm is engaged in GVCs if it both imports some of its inputs and exports part of its output. In the rest of the analysis, we thus restrict our attention to firms that display strictly positive exports of any good *and* strictly positive imports of intermediate goods at least once between September 2019 and January 2020, where the definition of intermediates follows the UN-BEC classification of products. Of course, there is ample heterogeneity in this sample regarding the intensity of these firms' international activity. 49.4% of the firms export more than 10% of their output and 60.5% purchase more than 10% of their inputs from abroad. Our baseline results pool all firms together but we show in appendix A.4 that results are not very different across firms displaying heterogeneous degrees of trade exposure.

Table 1 shows descriptive statistics on the sample of firms under study. These approximately 30,000 firms account for about 40% of French firms' domestic sales and three quarters of the value of French exports and imports. These numbers are consistent with the view that, in tradable sectors, large firms tend to be involved into two-way trade (Bernard et al., 2018). Among these firms, 45% import some of their inputs from China, accounting for 7.4% of their imported inputs, and 14% have interacted with Chinese producers on a monthly basis between September 2019 and January 2020.<fnref n="9" /> In the rest of the analysis, firms importing from China constitute our treatment group while firms that import part of their inputs but did not imported from Chinese firms in the months before the shock are used as controls. In Appendix A.3, we consider an alternative definition of the treatment restricted to firms with monthly interactions with China.

The treated firms are about three times larger than other importers, in terms of the average value of their imports and exports. This size gap is not surprising because importing from

<footnote n="9">⁹A standard issue while working on Chinese trade data has to do with the status of trade with Hong Kong. Throughout the analysis, we decided to focus on direct trade with mainland China. We have also reproduced all results based on a dataset that considers imports from Hong Kong into the treatment group. The results obtained from this alternative definition (and available upon request) are unchanged because the volume of imports that is recorded in the customs flows as originating from Hong Kong is very limited. Adding firms importing from Hong Kong into the baseline treatment group thus moves 229 French firms from the control to the treatment groups.</footnote>
China involves substantial fixed and variable costs that only the largest firms can afford.<fnref n="10" />

China is one of the largest suppliers to French firms, which explains that 54% of imports and 58% of exports originate from firms importing from China.

<table><caption>Table 1: Summary Statistics on the estimation sample</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Nb. of firms</th><th colspan="3">Avg.</th><th colspan="3">Share of aggregate</th></tr><tr><th>Imports</th><th>Dom.Sales</th><th>Exports</th><th>Imports</th><th>Dom.Sales</th><th>Exports</th></tr></thead><tbody><tr><td>All firms</td><td>30,420</td><td>6.4</td><td>54.2</td><td>12.4</td><td>76.5</td><td>43.5</td><td>77.1</td></tr><tr><td>Importers from</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>China</td><td>13,701</td><td>10.0</td><td>77.7</td><td>20.7</td><td>54.1</td><td>28.1</td><td>58.3</td></tr><tr><td>Elsewhere</td><td>16,719</td><td>3.4</td><td>34.9</td><td>5.5</td><td>22.3</td><td>15.4</td><td>18.9</td></tr><tr><td>Monthly importers from</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>China</td><td>4,228</td><td>19.0</td><td>138.1</td><td>38.1</td><td>31.6</td><td>15.4</td><td>33.0</td></tr><tr><td>Elsewhere</td><td>9,697</td><td>5.6</td><td>47.0</td><td>7.9</td><td>21.3</td><td>12.0</td><td>15.8</td></tr></tbody></table>

Note: Firms in our sample account for 76.5% of aggregate imports. Treated firms account for 54.1% of aggregate imports. Summary statistics computed in 2019. Statistics on imports are restricted to intermediate goods. Average sales are in millions euros. Shares in %. Source: Customs and INSEE-VAT data.

## 2.2 Empirical strategy

Supply chain disruptions have been at the heart of policy debates since the Covid-19 pandemic started. However, their actual impact on the economy is difficult to establish. From the Spring of 2020, many countries have simultaneously adopted lockdown strategies that affected both supply and demand. Since then, the world has experienced a series of epidemic waves followed by rapid economic recoveries that have disorganized world trade. To isolate the effect of a supply shock, we exploit the timing and geography of the early phases of the pandemic. The pandemic started in China and the Chinese government has been the first to implement lockdown measures at the end of January 2020, at a time when the rest of the world was not contaminated yet. As a result, industrial production in China collapsed, together with the volume of the country's exports.<fnref n="11" /> It is only in the first half of March 2020 that the virus really started to spread to the rest of the world. In France, a national lockdown started in March 17th, which was partially released on May 11th. Our empirical strategy uses the monthly dynamics

<footnote n="10">¹⁰In the rest of the analysis, we control for systematic differences between firms with and without exposure to China using fixed effects. In a robustness exercise, we also rely on a matching algorithm to compare firms exposed to Chinese inputs with similar controls in terms of their trade activity.</footnote>

<footnote n="11">¹¹Appendix A.2 provides a longer discussion of the timing of the shock and its consequences on worldwide trade.</footnote>
of sales to provide evidence of the transmission of the productivity consequences of the early lockdown in China along the value chain of exposed French firms.

Figure 1 provides indicative evidence that the early lockdown in China has had consequences on French firms exposed to China through their input purchases. We compare the evolution of overall imports before and after the Chinese lockdown for firms directly exposed to the Chinese lockdown in comparison with the control group.<fnref n="12" /> Before the lockdown in February, there is no significant difference in the evolution of imports for firms in the treatment and the control groups, except in November due to the specific seasonality of Chinese exports<fnref n="13" />. We observe a relative drop in imports in the treatment group in the month that followed the Chinese lockdown. The effect seems to be transitory with a peak in April before a rebound. In June, the level of imports is only marginally lower in the treatment than in the control group.<fnref n="14" /> The dynamics, recovered from a narrow comparison of firm-level imports in a treated and a control group, is in line with the behavior of aggregate imports displayed in Figure A.3. Overall, the level of imports between February and June 2020 has been 7.4% lower in the treatment group than for firms in the control group (Table 2, columns (1) and (2)).

Results in Figure 1 show that total imports of firms exposed to the Chinese lockdown have dropped after January 2020. These results thus justify our interpretation of the early lockdown in China as a (temporary) shock to French firms’ input purchases. In the next section, we investigate the propagation of this supply shock along GVCs by studying the dynamics of domestic and export sales, as a function of firms’ exposure to the Chinese lockdown. We will do so in the context of a differences-in-differences estimation, using the early lockdown in China

$$ \ln \text{Imports}_{ft} = \sum_{l=-4}^{5} \beta^l \text{Treated}_f \times \text{Time}_{lt} + \text{FE}_f + \text{FE}_t + \varepsilon_{ft}, $$

with *Imports*<sub>*ft*</sub> the value of total import purchases of firm *f* at time *t*, *Treated*<sub>*f*</sub> a dummy equal to one if the firm is in the treatment group, *Time*<sub>*lt*</sub> a dummy equal to one *l* periods before/after the shock, and *FE*<sub>*f*</sub> and *FE*<sub>*t*</sub> that respectively denote firm- and time- fixed effects. The above estimation thus compares the dynamics of imports before and after the Chinese lockdown, for firms directly exposed to the shock and those that are not. Any difference in firm-level characteristics that is constant over time is captured by the firm-level fixed effects. Coefficients are normalized to zero in January 2020.

<footnote n="12">¹²The Figure is recovered from a dynamic differences-in-differences design:</footnote>

<footnote n="13">¹³We also find this pattern in November 2018. US imports from China also display this seasonality (Meier et al., 2020).</footnote>

<footnote n="14">¹⁴Figure A.12 in the Appendix further shows that the relative drop of imports is observed earlier among the sub-sample of French firms that rely on air freight for their imports from China. Instead, the relative drop in imports is not significant before March 2020 for firms that rely on sea freight. Given the one-month delay incurred by firms that ship their imports from China, the time lag further supports our interpretation of the relative drop as being a consequence of the productivity slowdown in China.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 1: Impact of the Chinese lockdown on firm-level imports</figcaption></figure>

Notes: The figure shows the dynamics of imports before and after the Chinese lockdown, for treated firms in comparison with the control group. Treated firms are those importing from China prior to the shock. Control firms are importers not exposed to China. The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

as a quasi-natural experiment. Several factors complicate identification of the transmission of the supply shock. First of all, the “control” firms are not necessarily immune from the shock. Their suppliers may have been exposed to supply chain disruptions from China. By the same token, firms we dubbed as treated may as well be also indirectly exposed. Since the empirical strategy focuses on a relatively short window, the analysis is implicitly based on the premise that a direct exposure has earlier and stronger consequences on firms’ input purchases than any indirect exposure. As such, our estimates recovered from the comparison of firms directly hit by the Chinese slowdown with firms that may be indirectly affected should be interpreted as a lower bound of the actual impact of the shock. Second, control firms are also affected by subsequent supply shocks affecting other input providers. Our identification strategy is indeed based on the timing of these shocks but one should keep in mind that all firms ultimately end up being “treated” one way or another. These subsequent shocks affecting control firms more than treated ones, they should play against us finding any difference in the dynamics of sales in the differences-in-differences framework. Third, the Covid crisis entails a combination of supply and demand shocks. Our identification uses the timing of the supply shocks but
firms in our sample also had to cope with a series of bad demand shocks. The extent to which concurrent demand shocks impose a threat to the identification strategy is not entirely clear though, because treated and control firms are both exposed to these shocks, which in turn depend on their portfolio of clients around the world. As explained later, we will use the granularity of our data to control for heterogeneous demand shocks using fixed effects.<fnref n="15" />

# 3 Firm-level transmission along the supply chain

This section shows that the shortage of Chinese inputs, which followed China's early lockdown, had a negative impact on the domestic and export sales of French firms that rely on these inputs. We first discuss the economic magnitude of the effect, before digging into the mechanisms of the adjustment. Sections A.3 and A.4 in the Appendix present a systematic robustness analysis to alternative specifications and discuss the heterogeneity of the transmission among treated firms.

## 3.1 Baseline results

We compare the evolution of firm-level exports before and after the Chinese lockdown for firms directly exposed to the Chinese lockdown and firms in the control group. We use the same exposure variable as in the previous section, which takes the value of one for any French firm having imported inputs from China between September 2019 and January 2020. The control group is composed of French exporting firms that also imported inputs in this period but not from China. To investigate the dynamics of the adjustment of exposed firms, we use a dynamic differences-in-differences design, using firm-level (log) exports as the dependent variable and control for firm and time specific trends using fixed effects.

The time pattern of the relative (log) level of exports of treated firms is presented in Figure 2, red line. We cannot distinguish between the export trends of treated and control firms before the Chinese lockdown. Exports of exposed firms then dropped abruptly relative to the control group in March and April 2020. The effect is transitory and the difference between the two

<footnote n="15">¹⁵Whereas demand shocks in general are unlikely to affect treated and control firms in a systematically different way, demand shocks originating from China stand out. As treated firms are 14 p.p. more likely to export to China, they were also exposed earlier to the demand shock induced by local lockdowns than control firms. To rule out that this early exposure to demand shocks is driving our baseline results, we excluded from the analysis exports to the Chinese market and checked that results were unaffected.</footnote>
groups' exports is no longer significant from May 2020. In unreported results, we have extended the sample until December 2020 but did not find any sign of the two groups of firms diverging again later in the year. For comparison purposes, the dynamics of exports before and after the early lockdown in China is compared with the dynamics observed one year before (blue line). The comparison confirms that the dynamics observed post-lockdown is not due to the specific seasonality of exports for firms sourcing their inputs from China.

<figure><img src="image_6.png" /><figcaption>Figure 2: Impact of the Chinese lockdown on firm-level exports</figcaption></figure>

Notes: The figure shows the dynamics of exports before and after the Chinese lockdown, for treated firms in comparison with the control group. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%. Treated firms are those importing intermediate inputs from China prior to the shock. Control firms are importers not exposed to China. The estimated equation has firm and period fixed effects. The red line covers the period from September 2019 to June 2020. The blue line reproduces the same specification based on data from September 2018 to June 2019.

We confirm the adverse impact of the Chinese lockdown on exports in various differences-in-differences estimations in Table 2. For comparison purposes, we first estimate the differences-in-differences model using the firm's imports as left-hand side variable, before quantifying the propagation to the firm's downstream partners. Column (3) thus corresponds to the DiD specification surrounding the results in Figure 2. Overall, between February and June 2020, exports of exposed firms declined by 5% more than in the control group.

Column (4) further exploits the granularity of the data by estimating the effect of the treatment on exports at the firm-product-destination level. The upside of this specification is
<table><caption>Table 2: Impact of the Chinese lockdown on various firm-level outcomes: Differences-in-difference results</caption><thead><tr><th></th><th colspan="2">Imports</th><th colspan="2">Dep. Var: log of</th><th rowspan="2">Dom sales</th><th rowspan="2">Export price</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Treatment × Post</td><td>-0.074<sup>a</sup><br>(0.010)</td><td>-0.074<sup>a</sup><br>(0.006)</td><td>-0.050<sup>a</sup><br>(0.011)</td><td>-0.035<sup>a</sup><br>(0.005)</td><td>-0.055<sup>a</sup><br>(0.007)</td><td>-0.007<sup>a</sup><br>(0.002)</td></tr><tr><td>Firm FE</td><td>Yes</td><td>× Product</td><td>Yes</td><td>× Product</td><td>Yes</td><td>× Product</td></tr><tr><td>Time FE</td><td>Yes</td><td>× Product</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Product × Destination × Period</td><td></td><td></td><td></td><td>Yes</td><td></td><td>Yes</td></tr><tr><td># Treated</td><td>12,086</td><td>10,852</td><td>12,086</td><td>11,542</td><td>12,086</td><td>11,542</td></tr><tr><td># Control</td><td>13,563</td><td>24,174</td><td>13,563</td><td>13,566</td><td>13,563</td><td>13,566</td></tr><tr><td>R<sup>2</sup></td><td>0.857</td><td>0.869</td><td>0.856</td><td>0.735</td><td>0.901</td><td>0.864</td></tr><tr><td># Obs.</td><td>211,818</td><td>2,196,743</td><td>202,622</td><td>6,712,916</td><td>236,121</td><td>6,344,742</td></tr></tbody></table>

Note: The table reports results of difference-in-difference estimations based on various firm-level outcome variables, estimated based on data from September 2019 to June 2020. The generic estimated equation writes:

$$ \ln X_{ft} = Post_t + Treated_f + Treated_f \times Post_t + FE + \varepsilon_{ft} $$

where $X_{ft}$ is an outcome variable, that can further have the product dimension as in column (2) or the product and country dimensions as in columns (4) and (6). $Treated_f$ is a dummy equal to one for firms that were exposed to China in the pre-shock period, through their input purchases. In column (2), the “treatment” is defined at the firm and product level. $Post_t$ is equal to one for periods between February and June 2020. The fixed effects FE include firm and period fixed effects in columns (1), (3) and (5), product × firm and product × period fixed effects in column (2) and product × firm and product × destination × period fixed effects in columns (4) and (6). $^a, ^b$ and $^c$ denote significance at the 1, 5 and 10% level respectively. Standard errors are clustered at the firm-level, and firm × product level in columns (2)-(4)-(6).

that it allows us to use product-destination-time fixed effects to control for monthly demand shocks in each market that are common to control and treated firms. For instance, differences in the rise of cases or in the adoption of containment measures may induce heterogeneity in the dynamics of exports across destinations. For firms sourcing inputs from China, export sales of a given product within a given destination have dropped by 3.5% after the Chinese lockdown, a smaller drop than in the baseline specification (-5%). One possible interpretation of the dampening is that the firm-level results capture extensive adjustments (the drop of destination-product pairs), which are considered as missing when working at the firm-product-destination level. We come back to this issue when discussing the different adjustment margins of firm-level exports in Section 3.2. One may wonder the extent to which the export adjustment comes from a substitution away from foreign markets, to preserve the firm’s domestic market share. In column (5), we thus investigate how the supply chain disruption affects domestic sales, recovered from the firms’ VAT statements.<fnref n="16" />,<fnref n="17" /> The general pattern is very similar for domestic

<footnote n="16">¹⁶Whereas the VAT statements provide valuable information on domestic sales at the monthly level, the rest of the analysis uses export sales as an outcome variable. The reason is that the monthly frequency in VAT data is less reliable than in the export flows collected by the customs. Reporting delays are tolerated as long as the tax administration does not observe the exact date of the invoicing and the payment. The tolerance is likely to be particularly accurate during the lockdown in France.</footnote>

<footnote n="17">¹⁷Results in column (5) of Table 2 are complemented with the corresponding event study graph in figure A.13 in the Appendix.</footnote>
sales as for foreign sales. Between February and June 2020, domestic sales have dropped by 5.5% in the treatment group in comparison with the control, with a negative peak in April. In unreported results, we found that the domestic to overall sales ratio did not adjust after the shock. Together, these results suggest that the drop in exports is not mostly attributable to a substitution away from foreign markets to maintain the firm's domestic market share.

A natural question, that is particularly salient given inflationary pressures observed in 2021-2022, is the extent to which supply-chain disruptions translate into higher prices. We investigate this question using export prices<fnref n="18" /> as an outcome variable. Results are reported in the last column of Table 2. In contrast with what would be expected, export prices of exposed firms have *decreased* after January 2020, although the average effect is very small, at -0.7%.<fnref n="19" /> In Section A.4 in the Appendix, we dig deeper into this result and show that the average (small) effect hides important heterogeneities across sectors.

Overall, these results thus confirm a significant transmission of the shock to input purchases on firms' exports and domestic sales. Section A.3 in the Appendix provides an extensive robustness analysis of these baseline results. We first discuss how results vary with alternative definitions of the treatment and control groups. We then show that an alternative estimation based on a matching algorithm gives similar results. Finally, we conduct two placebo exercises. These are meant to provide support for our interpretation of the relative drop in exports as being the result of the transmission of the shock along the supply chain rather than the consequence of a global shock or country-specific seasonal patterns in trade data. These results support our interpretation that the dynamic of export sales following the early lockdown in China is consistent with a transmission of the shock along the firm's value chain.

In Section A.4, we dig more into the heterogeneity of the transmission across treated firms. More specifically, we test for heterogeneous treatment effects depending on the firm's sector of activity, size bin or degree of exposure to Chinese inputs. We do not find evidence that more exposed firms, as measured by the contribution of Chinese inputs to the firm's overall input purchases, display stronger adjustment patterns. This result is consistent with anecdotal evidence that even a relatively marginal component can induce severe productive disturbances

<footnote n="18">¹⁸We proxy export prices by unit values (value over quantity). Because quantities are sometimes unreported, we restrict all regressions in Table 2 to observations on which we have both value and quantity.</footnote>

<footnote n="19">¹⁹This result is in contrast with the dynamics of *import* prices which have increased in relative terms for treated firms after the lockdown, by 1% on average. The corresponding regression is not reported here and is available upon request.</footnote>
when missing. What appears to be affecting the level of the adjustment is the degree of ex-
posure to products which production is concentrated in the Hubei, the first region entering
into a stringent lockdown. Results in Section A.4 also suggest that the transmission to down-
stream partners is exacerbated for large and export-intensive firms. This result echoes those
in Bricongne et al. (2021) who show large firms have played a specific role during the Covid-
induced trade collapse. Finally, adjustments are found larger for final consumption producers,
most notably in the apparel and footwear and the perfumes and soaps industries. A possible
reason is that trade in intermediates is more strongly gathered within long-term contractual
relationships. Still, the adjustment is also significant for firms that sell intermediate products,
which points to second-tier propagation to downstream firms.

3.2 Adjustment margins

**The importance of the extensive margin.** Having discussed the magnitude of the shock transmission to firms’ downstream partners, we now dig into the mechanism of the adjustment. Table 3 decomposes the adjustment of firms’ exports after the Chinese lockdown into different margins. In columns (2) and (3), exports are broken down into the value of exports per destination and the number of destinations. In columns (4) and (5), the decomposition involves the value of exports per product and the number of products. Finally, columns (6) and (7) respectively display results based on the value of exports per product-destination and the number of product-destination pairs.

All specifications point toward the same direction. Export adjustments occur along the extensive margin, whereas the effect of the treatment is not significant at the intensive margin. Firms sourcing inputs from China have reduced the number of products and the number of destinations they serve after the Chinese lockdown. The result on extensive adjustments at the product margin is consistent with the literature on multi-product firms showing that firms adjust to shocks by changing their product mix (see, e.g., Mayer et al., 2021). To our knowledge, this paper is however the first one to show evidence of adjustments to *temporary* supply shocks through the extensive margin.

The last column in Table 3 complements the analysis with a model investigating extensive
margin adjustments at the *firm* level. Up to now, the analysis has indeed been restricted to
firm × periods with strictly positive exports. We estimate the probability that firms remain active on export markets before and after the shock.<fnref n="20" /> The effect is positive but not significant suggesting that treated firms are equally likely to drop out of export than firms in the control group. As shown in Figure A.14 (bottom panel), coefficients estimated period by period are never significant.

<table><caption>Table 3: Margins decomposition of differences-in-differences results</caption><thead><tr><th></th><th>Baseline</th><th colspan="2">Destination</th><th colspan="2">Products</th><th colspan="2">Product×Destinations</th><th>Firm</th></tr><tr><th></th><th>(1)</th><th>Int</th><th>Ext</th><th>Int</th><th>Ext</th><th>Int</th><th>Ext</th><th>(8)</th></tr><tr><th>Treatment × Post</th><th>-0.050<sup>a</sup><br>(0.011)</th><th>-0.010<br>(0.010)</th><th>-0.040<sup>a</sup><br>(0.004)</th><th>-0.003<br>(0.010)</th><th>-0.046<sup>a</sup><br>(0.005)</th><th>0.004<br>(0.009)</th><th>-0.054<sup>a</sup><br>(0.005)</th><th>0.004<br>(0.003)</th></tr></thead><tbody><tr><th>Firm FE</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Time FE</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th># Treated</th><td>12086</td><td>12086</td><td>12086</td><td>12086</td><td>12086</td><td>12086</td><td>12086</td><td>12086</td></tr><tr><th># Control</th><td>13563</td><td>13563</td><td>13563</td><td>13563</td><td>13563</td><td>13563</td><td>13563</td><td>13563</td></tr><tr><th># Obs.</th><td>202,622</td><td>202,622</td><td>202,622</td><td>202,622</td><td>202,622</td><td>202,622</td><td>202,622</td><td>256,490</td></tr><tr><th>R<sup>2</sup></th><td>0.856</td><td>0.784</td><td>0.918</td><td>0.824</td><td>0.899</td><td>0.785</td><td>0.927</td><td>0.481</td></tr></tbody></table>

Note: Columns (1)-(7) use the log of the firm's exports (1), or one of its component (2-7), as left-hand side variable. “Int” denotes the intensive margin whereas “Ext” is the extensive margin. Column (8) corresponds to a linear probability model of the likelihood that the firm exports. All variables are at the firm and period level. All specifications include firm fixed effects and time fixed effects. The treatment group is made of firms importing from China at least once before the treatment. <sup>a</sup>, <sup>b</sup> and <sup>c</sup> denote significance at the 1, 5 and 10% level respectively. Standard errors in parenthesis are clustered at the firm-level.

**A short-lived effect.** Figure 3 shows the dynamics surrounding the specifications in Columns (6) and (7).<fnref n="21" /> Consistent with the differences-in-differences results, there is little action along the intensive margin. We find that the adverse impact of the shock on the treatment group has been short-lived on the extensive margin, in the sense that there is no statistical difference in the number of markets served by control and treated firms from June 2020. While the numbers are indicative of the *relative* persistence of the shock, we verified in separate regressions on treated and control firms that the various margins rebounded to their pre-shock *level* by June 2020.<fnref n="22" />

$$1_{ft} = \beta \text{Treated}_f \times \text{Post}_t + FE_f + FE_t + \varepsilon_{ft}$$

with $1_{ft}$ a dummy that is equal to one when firm f displays strictly positive exports in period t.

<footnote n="20">²⁰We estimate a linear probability model of the following form:</footnote>

<footnote n="21">²¹The corresponding figures for destinations (Columns (2)-(3)) and products (Columns (4)-(5)) are reproduced in Figure A.15.</footnote>

<footnote n="22">²²See Figure A.16 in the Appendix. For each subset of firms, we regressed the log of exports (top panel) or the number of markets served (bottom panel) on firm and period fixed effects and reported the estimated fixed effects on the graph. Of course, since we lose the differences-in-differences structure, the results are not fully</footnote>
<figure><img src="image_7.png" /><figcaption>Figure 3: Impact of the Chinese lockdown on firm-level exports: Intensive versus extensive adjustments</figcaption></figure>

Note: The figure shows the results of the event-study estimation, using the intensive and extensive components of firms' exports as left-hand side variable. The corresponding difference-in-differences estimates are summarized in Table 3, columns (6) and (7). All specifications include firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

**Heterogeneity across markets.** We now explore heterogeneity in these extensive adjustments along the distribution of markets composing a firm's portfolio. More specifically, we distinguish core and secondary markets and occasional versus frequent markets. For each firm, a “Core” market is defined as the main destination of exports in terms of market share in the five months before the shock. A “Frequent” market is instead a market served each single month during this period. Like above, market is used as a broad term to designate products, destinations, and product×destination pairs. To test whether the above extensive adjustments are driven by some specific markets, we estimated triple differences linear probability models.23 The double difference dimension is similar to the baseline model: we estimate the probability that a particular market stops being served after the beginning of the Chinese lockdown for treated versus control firms. The triple interaction term allows us to estimate whether the relative probability to remain active in a market in the post period, for treated relative to control firms, varies within a firm, across its core vs secondary markets, or across its frequent versus occasional markets.

comparable with our baseline results but are informative about the level impact of the shocks.

$$1_{f_{mt}} = \beta Treated_f \times Post_t + \gamma Treated_f \times Core_{fm} \times Post_t + \zeta Core_{fm} \times Post_t + FE_{fm} + FE_{mt} + \varepsilon_{f_{mt}}$$

with $1_{f_{mt}}$ a dummy that is equal to one when firm f displays strictly positive exports in period t in market m. $Core_{fm}$ is one if m is the core market of firm f. The analysis is restricted to firm×market pairs that are active at least once in the pre-shock period and firms that stay active in at least one market in the post-shock period.

<footnote>23Formally, the estimated equation is:</footnote>
<table><caption>Table 4: Digging into the extensive adjustments</caption><thead><tr><th rowspan="2"></th><th colspan="2">Destinations</th><th colspan="2">Products</th><th colspan="2">Product×Destinations</th></tr><tr><th>X = Core<br>(1)</th><th>Frequent<br>(2)</th><th>X = Core<br>(3)</th><th>Frequent<br>(4)</th><th>X = Core<br>(5)</th><th>Frequent<br>(6)</th></tr></thead><tbody><tr><td>Post × X</td><td>-0.066<sup>a</sup><br>(0.002)</td><td>-0.106<sup>a</sup><br>(0.002)</td><td>-0.150<sup>a</sup><br>(0.003)</td><td>-0.093<sup>a</sup><br>(0.003)</td><td>-0.169<sup>a</sup><br>(0.003)</td><td>-0.137<sup>a</sup><br>(0.004)</td></tr><tr><td>Treated × Post</td><td>-0.012<sup>a</sup><br>(0.002)</td><td>-0.006<sup>a</sup><br>(0.002)</td><td>-0.012<sup>a</sup><br>(0.003)</td><td>-0.003<br>(0.002)</td><td>-0.011<sup>a</sup><br>(0.004)</td><td>-0.004<br>(0.003)</td></tr><tr><td>Treated × Post × X</td><td>0.012<sup>a</sup><br>(0.003)</td><td>0.013<sup>a</sup><br>(0.003)</td><td>0.020<sup>a</sup><br>(0.005)</td><td>0.003<br>(0.005)</td><td>0.010<sup>b</sup><br>(0.005)</td><td>-0.007<br>(0.007)</td></tr><tr><td>Firm×Market FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Market×Time FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td># Treated firms</td><td>12,732</td><td>12,732</td><td>12,732</td><td>12,732</td><td>12,709</td><td>12,709</td></tr><tr><td># Control firms</td><td>15,026</td><td>15,026</td><td>15,026</td><td>15,026</td><td>14,977</td><td>14,977</td></tr><tr><td>R<sup>2</sup></td><td>0.476</td><td>0.477</td><td>0.489</td><td>0.490</td><td>0.483</td><td>0.484</td></tr><tr><td># Obs.</td><td>3,101,445</td><td>3,101,445</td><td>6,006,836</td><td>6,006,836</td><td>20,425,493</td><td>20,425,493</td></tr><tr><td># Obs. Treated</td><td>1,853,612</td><td>1,853,612</td><td>3,979,596</td><td>3,979,596</td><td>15,108,404</td><td>15,108,404</td></tr><tr><td># Obs. Treated × Post</td><td>917,173</td><td>917,173</td><td>1,971,304</td><td>1,971,304</td><td>7,524,070</td><td>7,524,070</td></tr><tr><td># Obs. Treated × Post × X</td><td>48,988</td><td>245,733</td><td>49,058</td><td>337,426</td><td>47,414</td><td>888,311</td></tr></tbody></table>

Note: The table shows results of triple-differences linear probability models of the form:

$$1_{fmt} = \beta Treated_f \times Post_t + \gamma Treated_f \times X_{fm} \times Post_t + \zeta X_{fm} \times Post_t + FE_{fm} + FE_{mt} + \varepsilon_{fmt}$$

with $1_{fmt}$ a dummy that is equal to one when firm f displays strictly positive exports in period t in market m. A market can be defined as a destination in columns (1)-(2), a product in columns (3)-(4) or a destination×product pair in columns (5)-(6). $X_{fm}$ is one if m is either the core market of firm f (columns (1), (3), (5)) or a market served every month in the pre-shock period (columns (2), (4), (6)). The analysis is restricted to firm×market pairs that are active at least once in the pre-shock period and firms that stay active in at least one market in the post-shock period. $^a$, $^b$ and $^c$ denote significance at the 1, 5 and 10% level respectively. Standard errors are clustered at the firm level.

The corresponding results are reported in Table 4. The negative coefficients on the Treated×Post interaction confirm that treated firms were less likely to remain active on a market after the shock. However, the extensive margin adjustment is more pronounced in secondary than in core markets, as shown by the positive coefficient on the Treated×Post×Core. Whereas we do find some evidence that the extensive adjustment is also more pronounced on occasional than on frequent markets, the result is not robust across the definition of what a market is.<fnref n="24" /> Overall, the finding that extensive margin adjustments are more pronounced towards treated firms' secondary markets is consistent with a model in which firms facing input shortages try to preserve their core relationships. Another (complementary) explanation is that input shortages have affected only a subset of products that are sold on secondary markets.

<footnote n="24">²⁴A possible explanation for the lack of robustness is that product-specific definitions of a market have more of an impact on the structure of the sample than the definition that is based on the country dimension only. Indeed, the sample contains a small number of firms that export many different products abroad and digging into the product dimension of exports mechanically distorts the structure of the sample towards these firms.</footnote>
# 4 Weathering supply shocks: diversification and inventories

Section 3 has established a statistically significant impact of being exposed to the Chinese lockdown through upstream suppliers on the dynamics of firm-level sales between February and June 2020. Extensive adjustments identified on treated firms are consistent with disruptions in input purchases forcing firms to ration their exports and delay the delivery of some markets. The granularity of our data makes it possible to go beyond this result and examine whether the effect is similar for firms having different strategies in the management of their value chain. The vulnerability of modern value chains to localized supply shocks is often argued to be attributable to mostly two properties of these production organizations: i) the lack of diversification of production networks and ii) the absence of inventory buffers in organizations that to a large extent produce just-in-time (Pisch, 2020). We now consider these two arguments in turn, testing whether more diversified firms and firms with more inventories have been able to weather the supply chain disruption in the aftermath of the Chinese lockdown.

## 4.1 Diversification to hedge against localized supply chain disruptions

A popular argument in the literature discussing the vulnerability of global value chains is that the lack of diversification of production networks is at the root of the amplification of localized shocks. In this section, we investigate this claim, asking whether the geographic diversification of supply sources helps firms perform better when hit by the Chinese lockdown shock.

We first examine whether firms that source inputs whose production is concentrated in China have been more severely hit by the shock. We consider that production is concentrated in China if at least 60% of world exports in this product category originate from China.<fnref n="25" /> We then define a firm as locked-in with China if at least 10% of the value of its imports from China consist of products whose production is concentrated in China. About 5% of treated firms would face major issues to diversify away from China based on this definition. Figure 4 shows that firms importing inputs from China that display such high level of concentration have

<footnote n="25">²⁵Among the 5,000 product categories of the HS classification, 205 products display a market share for China above 60%.</footnote>
experienced a more severe drop in their exports than firms importing more diversifiable inputs.
This heterogeneity in the strength of the treatment is consistent with the stronger decline in
imports of firms purchasing inputs with a high concentration of Chinese production, relative
to others (see Figure A.17).

<figure><img src="image_8.png" /><figcaption>Figure 4: Impact of the Chinese lockdown on firm-level exports: Diversifiable and non-diversifiable firms</figcaption></figure>

Notes: The figure shows the dynamics of exports for treated firms in comparison with the control group. The treatment group is separated into two sub-samples. The “Non Diversifiable” group is composed of firms which imports from China include at least 10% on products for which China represents more than 60% of world exports. The “Diversifiable” group is made of firms that import inputs from China that they could source from elsewhere. The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

The analysis presented in Figure 4 shows that firms importing inputs that are difficult to diversify due to the large share of China in worldwide production have been more strongly impacted by the shock. However, 95% of products can be sourced from other countries than China. A natural question is whether firms whose sourcing of inputs is geographically diversified have performed better than the others. To test for this, we define a treated firm as being diversified if it imports its inputs from China and at least one other country prior to the shock. We first tag an input as diversified if it is imported by the firm from more than one country between September 2019 and January 2020. A firm is then diversified if its main inputs (accounting for more than 1% of firm-level imports) are diversified.<fnref n="26" /> In the baseline sample,

<footnote n="26">²⁶We put the 1% threshold to abstract from secondary goods that are imported infrequently or in tiny</footnote>
slightly more than 40% of treated firms are diversified according to our definition. To test for a
role of diversification strategies, we reproduce the baseline estimation, distinguishing between
diversified and non-diversified treated firms.

Figure 5 shows the coefficients of this dynamic specification. It appears that among firms exposed to the Chinese shock, ex-ante diversified firms did not perform better than non-diversified.<fnref n="27" /> Consistent with this result, we found no effect of ex-ante diversification on the adjustment of firm-level imports to the Chinese lockdown (see Figure A.18).

<figure><img src="image_9.png" /><figcaption>Figure 5: Impact of the Chinese lockdown on firm-level exports: Diversified and non-diversified firms</figcaption></figure>

Notes: Baseline regression after splitting the treatment group into two sub-samples. Treated firms are labeled “diversified” if all their main inputs imported from China are also sourced from elsewhere in the pre-period. Main inputs are products accounting for at least 1% of the firm’s imports in the pre-period. The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

At first glance, this result contradicts the premise that the geographic diversification of
input purchases can be a useful strategy to insure against localized shocks hitting firms’ supply
chain. We now consider several potential reasons for this result.

First, we may not be able to properly identify ex-ante diversified firms. Here, our implicit
quantities and are not likely to be key for the production process. Relaxing this threshold does not affect our
qualitative results.

<footnote n="27">²⁷We have tried a variety of alternative metrics of diversification that all lead to the same result. We increased the threshold of 1% of firm-level imports to 5 and 10%. We have also computed the share of overall imports that are diversified, and tried various thresholds to split firms into a diversified and a non-diversified sub-samples, along this continuous measure.</footnote>
assumption is that a firm that has interacted in the past with two input suppliers of the same product will be able to increase its demand to non-Chinese suppliers in response to the Chinese input shortage. Implicitly, products sold by Chinese and non-Chinese suppliers are thus considered as substitutes, once we condition on a particular (8-digit) category. In table A2, column (2), we show results recovered from the triple-difference estimation that defines diversified firms based on the diversification of inputs that are classified as non-differentiated by Rauch (1999). Among this subset of inputs, the assumption that inputs from different origins are substitutable is more likely to hold. When diversified products are restricted to homogenous products based on the Rauch classification, results go in the expected direction. The relative drop in exports is found larger for treated firms that are not diversified than for diversified firms. However, the focus on non-differentiated products strongly narrows the set of firms that we consider as being potentially diversified and the result is statistically weaker as a consequence.

Another possibility is that the pandemic has constrained firms in their ability to substitute away from China, even when knowing alternative sourcing partners from before. To test whether this could explain our results, we define a third dummy for "diversified" firms based on the sub-sample of diversified firms from Figure 5 with former partners in the EU15. The intuition behind is that it was probably easier to reshore input sourcing to partners in the EU at a time when the pandemic started disrupting value chains outside of China. Results are reported in table A2, column (3). However, the triple interaction is still non-significant meaning that ex-ante diversified firms with partners in the EU15 have not performed better ex-post than other treated firms.

Finally, it is also possible that firms that do no diversify ex-ante can benefit from some form of ex-post diversification, by switching to new suppliers once the shock hits. Selection in diversification may in fact explain the (lack of) result in Figure 5 if firms that do no diversify know that the type of inputs they are sourcing from China is easy to purchase in other countries in case of a shock. Again, it is difficult to formally test for this possibility although the results in Figure 6 provide some support for this interpretation. Namely, Figure 6 examines differences in extensive margin adjustments by diversified and non-diversified treated firms relative to the control group. We now work at the firm×product level and consider the number of countries
from which firms import a given product before and after the shock. We see a surge in the
number of sourcing countries for the ex-ante non-diversified firms when the number drops for
diversified firms. Some of the firms that were not diversified ex-ante thus managed to diversify
in the aftermath of the shock. For this reason, the ex-ante diversification is not associated with
significantly better trade performances in the aftermath of the shock.

The ex-post diversification of some of the treated firms raises several questions. First, one may wonder if it means that new relationships are easier to set up for these firms. To approach this point, we have considered an extension of the estimation in which we compare the diversification of products sold on spot markets with the diversification of more differentiated products.<fnref n="28" /> The rational is again that it is likely to be easier for firms to substitute away from China for products sold on spot markets. We do not find that the ex-post diversification is more pronounced for products traded on spot markets. Another interpretation is that firms have accepted to pay high sunk costs to find new suppliers because they anticipated that the shock would be long lasting. Measuring firms’ expectations about the length of the disruption is not possible given the data at hands. But results from business surveys suggest that at least some firms have anticipated a huge increase in the downside risk on their annual sales, which suggests the shock was perceived as long-lasting (Bunn et al., 2020). The same surveys also show a strong dispersion in firms’ perception of the risk. Whether grounded on unobserved characteristics of the firms or due to different levels of risk aversion, the heterogeneity in the diversification structure of individual firms and their reaction to the shock would be worth exploring in future research.

<footnote n="28">²⁸More specifically, we reproduced the estimation in Figure 6 allowing the coefficients to differ across products that are sold in organized markets according to Rauch (1999) classification and products that are more differentiated. Results available upon request do not show any difference between both groups.</footnote>
<figure><img src="image_10.png" /><figcaption>Figure 6: Ex-ante diversification and the number of firms’ suppliers</figcaption></figure>

Notes: Baseline at the firm×product-level with treated firm×product pairs split into a “diversified” and “non-diversified” sub-samples. The diversified sample corresponds to firms importing the product from China and some- where else whereas the non-diversified sub-sample includes firms that solely import from China. The outcome here is the (log-) number of countries the firm sources the product from. We perform a Poisson regression to account for the extensive margin at its full extent. The estimated equation includes firm×product and product×period fixed effects. Standard errors are clustered at the firm×product-level. Confidence intervals are defined at 5%.
## 4.2 Inventories as a buffer against input shortages

We now investigate the role of inventories in offering a buffer against input shortages. We merge the estimation sample with balance-sheet information provided by the French National Institute of Statistics and Economic Studies from tax administrative data (Insee-DGFiP FARE dataset). The dataset is exhaustive and contains information on the value of firms' inventories at the end of the accounting year. Using the variable, normalized by the value of the firm's activity, we obtain a proxy for the average level of inventories held by the firm. There are three caveats associated with the use of these data. First, the last year of data availability is 2018 and we will thus focus on firms in the estimation dataset that were already active in 2018 – more than 90% of the sample. Second, the inventory variable does not distinguish between inputs and output.<fnref n="29" /> Third, inventories are measured at the end of the accounting year (December for 3/5 of the firms, March, June or September for the rest), and they may not be representative of inventories during the rest of the year.

We first define a dummy for firms displaying a relatively high level of inventories in 2018. Under the assumption that inventory strategies are relatively smooth and persistent over time, these firms should also be less exposed to disruptions induced by input shortages in early 2020 thanks to their inventory buffer. The dummy variable is defined into two steps. First, we construct a measure of the level of inventories, defined by the value of inventories at the end of the accounting year, divided by the value of the firms' 2018 yearly sales, times 365. The ratio can be interpreted as the average daily production held in inventories. Figure A.19 in Appendix shows the distribution of this variable in the estimation sample. Heterogeneity in the level of inventories is important, in particular across firms in different sectors.<fnref n="30" /> In the analysis, we focus on the heterogeneity within a sector and define as high-inventory a firm which ratio of inventories over sales falls in the fifth quintile of its sector-specific distribution.<fnref n="31" />

<footnote n="29">²⁹We exploit two variables called “stocmpp” and “stocmar”. “stocmpp” measures the stock of inventories for raw materials and output whereas “stocmar” measures the inventory stock of merchandises. Our baseline analysis uses the sum of the two variables in the numerator of the stock ratio described in the text.</footnote>

<footnote n="30">³⁰Among the sectors with the largest level of inventories, one can cite the manufacture of sparkling wines (NAF: 1102A), the nuclear fuel enrichment industry (NAF: 2013A) or the manufacture of basic pharmaceutical products (NAF: 2110Z), with medians at 162, 144 and 92 days of inventories, respectively. At the other side of the distribution, the manufacture of bread; fresh pastry goods and cakes (NAF: 1071C) or the manufacture of industrial gases (NAF: 2011Z) for example display very low levels of inventories, with medians at 5 and 14 days respectively. These statistics are computed on all French firms. Firms in the estimation sample on average display higher levels of inventories than purely domestic firms.</footnote>

<footnote n="31">³¹We have checked the robustness of the results to this definition. In unreported results, we define as high-inventory any firm with more than 45 days of sales in inventories. Results obtained with this definition are</footnote>
<figure><img src="image_11.png" /><figcaption>Figure 7: Impact of the Chinese lockdown on firm-level exports: Low- and high-inventory firms</figcaption></figure>

Notes: The figure shows the results of a dynamic differences-in-differences estimation, distinguishing between firms with high inventories, as defined by a ratio of inventories over sales larger falling in the fifth quintile of the firm's sector-specific distribution, and the rest of the estimation sample. All coefficients interpret in relative terms with respect to firms in the control group that would display comparable inventory-to-sales ratios. The estimated equation has firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

Results are displayed in Figure 7. They are based on a variant of the dynamic differences-in-differences specification, using either the log of imports (upper panel) or the log of exports qualitatively similar although the difference between low- and high-inventory firms is less significant.
(bottom panel) as left-hand side variable and distinguishing between the dynamics of trade of high-inventory and low-inventory firms. The dynamics of imports is not significantly different in both groups, and is very similar to Figure 1, which suggests that firms holding larger inventories were not the most exposed to the shock. We expect the role of inventories to materialize into an heterogeneous transmission of the shock to the rest of the value chain as firms with more inventories can keep on serving their downstream partners, even when facing an input shortage. It is indeed the dynamics observed in the bottom panel of Figure 7. For firms with a high level of inventories, the dynamics of exports is not significantly different in the treatment and the control groups. Instead, firms exposed to the Chinese lockdown displaying low levels of inventories see their exports decline in relative terms with respect to unexposed firms. Although the period-by-period estimates are a bit noisy, a triple difference estimation confirms that the relative drop in exports among treated firms with low and high inventories is significantly different (coefficient estimated at .0532 with a standard error at .0268 implying significance at 5%).

To our knowledge, such evidence of an heterogeneous transmission of the supply chain shock to the rest of the value chain among firms with different levels of inventories is new. These results offer empirical support to the statement that holding more inventories can be an efficient strategy to cover against (short-lived) supply chain disruptions.

What is the external validity of this result? The early Chinese lockdown starts at the end of January 2020 and we cannot exclude that firms hit by the crisis were by chance particularly well-equipped to handle the supply chain disruption *because of* a relatively high level of inventories during this period. This concern is particularly legitimate given the seasonality of imports from China discussed in Section A.2. Since Chinese exports tend to slow down at the beginning of the year, firms importing from China may be used to accumulate inventories around these dates. Whereas this possibility cannot be ruled out in the absence of high-frequency data on inventories, it is unlikely that the seasonality of inventories entirely explains the above-mentioned results. First, firms had no incentives to accumulate inventories beyond what was expected to be optimal given the seasonality of Chinese shipments. Second, the result of inventories offering an efficient buffer against the shock is recovered from the comparison of treated firms with relatively high or low levels of average inventories, in 2018. The comparison conveys useful information beyond and above the overall impact of importing from China on
firms' inventory management.

# 5 Conclusion

This paper uses detailed firm-level data to gauge the transmission of supply shocks along global value chains. We find French firms sourcing inputs from China just before the early lockdown in the country experienced a drop in imports between February and June 2020 that is 7% larger than firms sourcing their inputs from elsewhere. This shock on input purchases transmits to the rest of the supply chain through exposed firms' domestic and export sales. Between February and June, firms exposed to the Chinese early lockdown experienced a 5% drop in exports and a 5.5% drop in domestic sales, relative to French firms importing from other countries. The relative drop in export sales is entirely driven by the volume of exports, whereas export prices do not seem to adjust. Moreover, the adjustment is attributable to the extensive margin with firms temporarily rationing their exports in some (secondary) markets.

We then assess the role of risk management strategies in mitigating such supply shocks. We find firms diversifying the sources of their inputs before the shock have not performed better than others. Indeed, firms that were not diversified managed to find new suppliers in the aftermath of the shock. Unlike diversification, we find firms holding more inventories before the shock performed better than other firms exposed to the same supply chain disruption. This result confirms the popular idea that stockpiling may be an efficient buffer against supply chain disruptions.

Trade disruptions in GVCs such as the one induced by the early lockdown in China are not anecdotal. Semiconductors shortages have started to affect GVCs by the end of 2020, and supply chains disruptions are now reported for other critical materials.<fnref n="32" /> Whereas less easy to trace out, understanding how firms adjusted to this accumulation of input shortages during a long-lasting crisis that saw a surge in uncertainties is likely to be especially informative regarding the functioning of Global Value Chains, from both a positive and a normative points of view.

<footnote n="32">³²The shortage of semi conductors is expected to last through 2022 according to the chief financial officer of Fiat Chrysler and Peugeot PSA, quoted by CNBC: https://www.cnbc.com/2021/05/07/chip-shortage-is-starting-to-have-major-real-world-consequences.html. Plastic shortage is driven by constraints on raw chemicals, see details in https://hbr.org/2021/03/the-latest-supply-chain-disruption-plastics.</footnote>
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# Appendix

## A.1 Data Appendix

The empirical analysis mostly uses French customs data between September 2019 and June 2020. The dataset is constructed from the raw customs forms filled by French firms and made available to us by the French customs. The final dataset is constructed from four types of customs forms depending on whether the firm declares an export or an import flow and whether the partner country belongs to the EU or not. The combination and treatment of these files is described in details in Bergounhon et al. (2018).

An important technical step in the process of constructing the final dataset concerns the treatment of imports originating from extra-EU countries that are intermediated by a third EU country before entry into France. This step is particularly important quantitatively as 80% of transactions accounting for almost half of the value of French imports from China are recovered from intra-EU customs forms. When the product enters Europe through another European country, say the Netherlands or Belgium, two countries that host major cargo ports, it is fairly common that two customs forms are filled. A first customs form, which is not part of our database, records the trade flow from China to the point of entry. A second customs form covers the intra-EU flow up to France and is thus included in our data. Thankfully, the second form keeps information on the origin of the good, which makes it possible to count the second flow as imports from China. Throughout the analysis, we treat all import forms so that the country of origin is systematically the first country at the root of the trade flow, China in our example. Figure A.1 shows the importance of this treatment across bilateral trade flows for 2019.

Whereas the raw data offer a solution to treat the problem of intermediated trade flows, it is still tricky to measure the mode of transportation for the corresponding trade flows. The reason is that the only recorded transport mode corresponds to the last segment of the product journey. As is the case for the vast majority of intra-EU trade flows, the corresponding import flow is likely to be associated with a road transport mode. When the product enters France from Belgium or the Netherlands, it is quite likely that the good was shipped from China to Europe on a cargo. There is more uncertainty regarding the mode of transportation when
<figure><img src="image_12.png" /><figcaption>Figure A.1: Share of transactions and the value of imports from non-European countries recovered from intra-EU trade data</figcaption></figure>

Notes: The figure shows the share of transactions and of the value of imports from one of the 15 largest non-European partners of France that is recovered from intra-EU trade data.

the product enters France through Germany, the third most likely point of entry. Germany hosts large logistic companies that may intermediate trade using both maritime and aeronautic modes of transportation. However, one would expect that a product that has been imported from China to Germany by air would also travel from Germany to France by air, in which case the recorded transportation mode is still correct. Given this uncertainty, the best we can do is to keep information on goods entering France by air, whether directly or indirectly. The vast majority of goods that do not fly from China to France are shipped on cargos, with a delay of roughly one month between the time when the products are put onto the cargo and the date of the customs clearance in Europe.<fnref n="33" />

## A.2 The early lockdown in China as a natural experiment

Figure A.2 illustrates the discrepancy in the rise of confirmed covid cases across countries. Whereas most countries have been hit by the pandemic in the first half of March, China has been hit earlier in January 2020. As a consequence, China has been the first country to impose a severe lockdown, in the Hubei region from January 23rd. In other countries, government

<footnote n="33">³³Another possibility is that the good has travelled using the new transcontinental train line that links Chengdu to Rotterdam, which is faster than sea freight but still takes around two weeks.</footnote>
responses came later, at the end of February or the beginning of March.<fnref n="34" /> The rise in the number of cases and the containment policies have led to early production disruptions in China. Like Eppinger et al. (2021), we exploit this one-month lag to separate in the data the impact of the productivity slowdown in China from the general drop in productivity induced by the pandemic.

<figure><img src="image_13.png" /><figcaption>Figure A.2: Spread of the pandemic: number of confirmed cases for a selection of countries</figcaption></figure>

Source: Oxford COVID-19 Government Response Tracker.

A first hint that this one-month delay has had consequences on French firms is illustrated in Figure A.3, which compares the monthly evolution of French imports from China and from the rest of the world.<fnref n="35" /> Whereas the value of imports from the rest of the world was stable

<footnote n="34">³⁴The Oxford Blavatnik School of Government systematically collects daily information on policy responses to the pandemic, which they aggregate into a “Government Response Index”. For each country, it is possible to identify the first important adjustment in this index. China has been the first to adopt containment measures, whereas most countries have adopted similar measures four to five weeks later.</footnote>

<footnote n="35">³⁵Throughout the analysis, we exclude imports of Covid-related products, namely masks, anti-epidemic goods, medical equipments, medical supplies and medicines using the list of Covid-related products provided by the WTO. Covid-related products do not affect the dynamics of trade prior to March, when the number of cases was still very small in France. In particular, the one month delay between the drop in imports from China and from the rest of the world is the same whether Covid-related products are included or not. However, the dynamics of trade after April 2020 is strongly affected by imports of Covid-related products. Namely, the dynamics of imports sourced in China and in the rest of the world are very similar once Covid-related products are removed from the estimation sample. Instead, the value of imports from China is 20% higher in June than in January, when Covid-related products are included. See Bown (2021) for a more detailed discussion of trade in Covid-related products during the pandemic.</footnote>
<figure><img src="image_14.png" /><figcaption>Figure A.3: Value of French imports from China and from the rest of the world</figcaption></figure>

Source: French customs, import files. The figure shows the evolution in the value of French imports from China and from the rest of the world, between September 2019 and June 2020. Both time series are normalized to 100 in January 2020. COVID products are excluded using the list of HS6 products produced by the WTO.

in February 2020, it decreased by almost 10% for imports originating from China. Imports from the rest of the world instead started to decrease in March, when imports from China were already close to their lowest level. During the Spring 2020, the evolution of imports from China and from the rest of the world is more synchronized. It is only in the Fall that the two series start diverging again, due to the second wave affecting most European and American countries when the situation was much more under control in China. Importantly, the early contraction of imports from China is not innocuous from the point of view of the French economy as China is the second most important source of imports.<fnref n="36" />

International trade displays important seasonal patterns, and the seasonality is in part country-specific. One may thus wonder the extent to which the relative drop in imports from China observed in February and March 2020 is not a consequence of the specific seasonality of trade with China. Figure A.4 compares the monthly growth of imports from China in 2020, against the seasonal component of monthly growth estimated using data from 2012 to 2019.<fnref n="37" /> The relative drop in imports observed in February 2020 may be attributable to the

<footnote n="36">³⁶In 2019, France imported 542.8 billion euros from abroad, 9.3% of which was imported from China. About 35.9% of French imports from China are final products, whereas intermediate goods and capital goods account for 26.7 and 37.1 percent of imports, respectively.</footnote>

<footnote n="37">³⁷More specifically, we estimate a fixed effect model using monthly growth rates of imports from China between 2012 and 2019. Month-specific fixed effects capture the seasonal component of trade. Figure A.4</footnote>
<figure><img src="image_15.png" /><figcaption>Figure A.4: Actual versus predicted monthly growth of Chinese imports</figcaption></figure>

Source: French customs, import files. The figure shows the monthly growth of imports from China in 2020 (blue bars) and the mean monthly growth estimated based on data over 2012-2019.

normal seasonality of trade. The 20% drop observed between February and March is instead
significantly larger than historical seasonal variations, as is the recovery in March and April.

<footnote>compares the actual growth during the Covid episode (blue bars) with this seasonal component (grey area).</footnote>
## A.3 Robustness

In this section, we test the robustness of our main findings. We first discuss how results vary with alternative definitions of the treatment and the control groups. We then test robustness to the estimation method, using a matching algorithm as an alternative. Finally, we conduct two placebo exercises. These are meant to provide support for our interpretation of the relative drop in exports as being the result of the transmission of the shock along the supply chain rather than the consequence of a global shock or country-specific seasonal patterns in trade data.

First, we replicated all results with an alternative definition of the treatment. In the baseline case, a firm is treated whenever it has imported from China at least once between September 2019 and January 2020. In the alternative definition, we keep the subset of importers that interacted at least once every month during this period. The control group is then composed of similar firms that also display regular ties with their input suppliers, but do not import from China. If any, results recovered from this “T2 Treatment” are stronger than our baseline results (Figure A.5). Overall between February and June 2020, the relative drop in these firms’ exports is estimated at 6.3%.

Second, one may worry that firms in the control group are exposed to systematically different supply shocks through their import portfolio. To deal with this issue, we exclude from the control group firms that solely import inputs from EU15 countries. The corresponding firms are small on average and given the degree of integration of the single market in these countries, the extent to which these firms participate to GVCs may be questionable. This restriction removes about six thousands firms from the control group. In an alternative exercise we restrict the control group to firms importing some of their inputs from less-developed and emerging countries.38 The corresponding control group contains 6,673 firms which imports and exports on average represent 70 and 47% of the average treated firm’s pre-shock trade, respectively. Here as well, the objective is to move the average control firms closer to treated firms, in terms of their import activities. Finally, we also tested with a completely different control group

<footnote>38The list of countries considered as “emerging” is the following: Algeria, Argentina, Bahrein, Bangladesh, Brazil, Brunei, Cambodia, Chile, Colombia, Egypt, Ecuador, India, Indonesia, Iran, Iraq, Israel, Jordan, Kuwait, Lao PDR, Lebanon, Libya, Malaysia, Mexico, Morocco, Oman, Paraguay, the Philippines, Qatar, Russia, Saudi Arabia, South Africa, Sri Lanka, Syria, Thailand, Tunisia, Turkey, United Arab Emirates, Uruguay, Venezuela, Vietnam, Yemen plus the Eastern European countries that joined the EU after 2000.</footnote>
<figure><img src="image_16.png" /><figcaption>Figure A.5: Impact of the Chinese lockdown on firm-level exports: Monthly importers</figcaption></figure>

Notes: The figure shows the dynamics of exports before and after the Chinese lockdown for treated firms in comparison with the control group. The treatment is based on monthly imports from China between September 2019 and January 2020 (T2) and the control corresponds to monthly importers from a third country and not importing from China. The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

composed of 33,645 non-importers. Here, the rational is to compare treated firms with a group that was not directly exposed to international supply chain disruptions, either in January nor later.

Results of these three exercises are summarized in Figure A.6. Results obtained excluding firms solely importing from EU countries look very similar to those in Figure 2, which confirms that the identified transmission of the shock is not attributable to extra-EU imports being more strongly affected by the world trade shock than intra-EU imports. Focusing on firms importing from developing countries as in the bottom panel of Figure A.6 is costly in terms of the precision of the estimates. The relative drop in exports of treated firms in April 2020 is still significantly negative, although slightly lower. Finally, results recovered using non-importers as controls look very similar as in the baseline.

We then depart from the baseline specification and instead use a matching estimator. We back out propensity scores from a probit model in which we estimate the probability of being treated using the level of imports, the level of exports, the number of destination countries
<figure><img src="image_17.png" /><figcaption>Figure A.6: Impact of the Chinese lockdown on firm-level exports: Alternative control groups</figcaption></figure>

Notes: The figure shows the dynamics of exports before and after the Chinese lockdown for treated firms in comparison with the control group. The treatment is based on imports from China between September 2019 and January 2020. The control group is based on i) importers from other countries excluding firms that solely imports from the EU15 (Top Left Panel, 11,720 controls), ii) importers from other emerging countries (Top right Panel, 6,673 controls) and iii) non-importers (Bottom Panel, 33,645 controls). The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.
and the number of exported products in each month in the pre-period, as well as the 2-digit
industry code of the firm. We keep units with a propensity score between .1 and .9 to ensure
sufficient overlap in covariates distribution between treated and controls (see Crump et al.,
2009). Armed with these scores, we can match each treated firm with a synthetic “control”
based on its nearest neighbor in the population of control firms. We then use a simple inference
method based on a generalized differences-in-differences to build the average treatment effect
and use subsampling to construct confidence intervals.<fnref n="39" /> The results presented in Figure A.7
confirm the negative impact of the Chinese lockdown on the exports of firms importing from
China.<fnref n="40" /> In unreported regressions, we show this result is robust if one compares treated firms
to their 4-nearest neighbors, or if we use covariates matching using Mahalanobis' metric rather
than propensity score matching.

These results thus confirm that the estimated impact of the Chinese lockdown on exposed
firms' exports is robust to changes in the definition of the control group. Although the stability
is reassuring, identification in these exercises still relies on the comparison of firms that source
inputs from China with firms that do not, which may be worrisome in light of the country-
specific seasonal patterns characterizing trade data. Although we have already argued that this
seasonality is unlikely to fully explain the large relative drop in imports observed on treated
firms, one may still argue that it may have consequences for the relative drop in exports.
Unfortunately, ruling out this possibility is not straightforward as matching exposed units with
firms facing the same seasonality is statistically complex. Our strategy to deal with this issue
thus relies on a placebo constructed from pre-Covid data. If seasonality was important in
driving the relative drop in exports observed in the treatment group after January 2020, then
the exact same pattern would be observed in a placebo treatment group after January 2019.
Figure A.8, left panel shows that it is not the case. In 2018-2019 data, the dynamics of exports
is the same before and after January, for firms importing from China in relative terms with
respect to firms importing from elsewhere. This finding confirms that the dynamics identified

<footnote n="39"><sup>39</sup>More specifically, the average treatment effect $k \in [-5, 5]$ months after the shock is the sample average among treated of $Y_{i,k} - \hat{Y}_{i,k} - (Y_{i,-1} - \hat{Y}_{i,-1})$, where $\hat{Y}_{i,k}$ denotes the outcome for the firm chosen as control for treated firm $i$. As bootstrap cannot help for inference in this setting (Abadie and Imbens (2008)), we use subsampling instead. See Politis and Romano (1994) for the theory, and Alfaro-Urena et al. (2020), Deryugina et al. (2020) for recent applications.</footnote>

<footnote n="40"><sup>40</sup>The DiD coefficient is only significant at 10%. As Figure A.7 illustrates, this explains by standard errors being relatively large in this specification as well as December displaying a negative drop in treated firms' exports (although smaller in magnitude than the relative drop observed in March and April 2020.</footnote>
<figure><img src="image_18.png" /><figcaption>Figure A.7: Impact of the Chinese lockdown on firm-level exports: Robustness based on propensity score matching and subsampling</figcaption></figure>

Note: Results based on propensity score matching and subsampling. The effect $k \in [-5, 5]$ months after the shock is the sample average over treated firms of $Y_{i,k} - \hat{Y}_{i,k} - (Y_{i,-1} - \hat{Y}_{i,-1})$, where $Y_{i,k}$ is the (observable) outcome for treated firm $i$ and $\hat{Y}_{i,k}$ is the average outcome among firms chosen as control $i$. The nearest neighbor is selected by the propensity score matching. Inference is conducted using subsampling, using 500 repetitions with a tuning parameter $R = 3$ (Politis and Romano, 1994). Confidence intervals are defined at 5%.

in Figure 2 is specific to the Covid crisis period in early 2020.

Finally, one may also suspect that the identified effect is attributable to the Covid crisis quickly disturbing production processes in complex value chains, which may produce the dynamics in Figure 2 if firms importing from China are systematically more likely to have sophisticated supply chain structures. Whereas the use of various control groups, including those based on propensity score matching, is meant to control for this possibility, we ran another placebo exercise in which we defined the treatment as importing from the US. In early 2020, US production was still immune from Covid-related problems. If the results displayed in Figure 2 is indeed attributable to supply chain disruptions after the early Chinese lockdown, we should not see any difference between treated and control firms once treated firms are defined based on importing from the US. Instead, if the dynamics of exports is driven by the worldwide disruption of complex value chains in the early stages of the Covid crisis, we shall see a similar pattern in this placebo test as in the baseline case. The right panel in Figure A.8 shows that it is not the case. Firms importing from the US do not display a different dynamics of exports
Figure A.8: Impact of the Chinese lockdown on firm-level exports: Placebo tests

<figure><img src="image_19.png" /></figure>

Notes: The figure shows the dynamics of exports for treated firms in comparison with the control group. In the left panel, the treatment is based on imports from China between September 2018 and January 2019 and the placebo date of the treatment is considered to be February 2019. IN the right panel, the treatment is based on import from the US instead of China. The estimated equations include firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

than other firms in the first semester of 2020. If any, these firms' trade patterns start diverging
in June 2020, when the Covid crisis was hitting the US much more severely.
## A.4 Heterogeneity across treated firms

This section investigates the extent of the heterogeneity in the transmission, across treated firms from different sectors, size bins, or degrees of exposure to Chinese inputs. To this aim, we first run a number of difference-in-difference specifications in which the post-treatment-on-treated coefficient is interacted with a firm-specific variable that captures heterogeneity in the treatment among treated firms. Results are summarized in Table A1.

<table><caption>Table A1: Impact of the Chinese lockdown on firm-level exports: Heterogeneity across treated firms</caption><thead><tr><th></th><th colspan="8">Dep. Var: log of exports</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Treatment × Post</td><td>-0.060<sup>a</sup><br>(0.013)</td><td>-0.024<br>(0.016)</td><td>-0.052<sup>a</sup><br>(0.014)</td><td>-0.031<sup>b</sup><br>(0.014)</td><td>-0.048<sup>a</sup><br>(0.011)</td><td>-0.015<br>(0.014)</td><td>-0.063<sup>a</sup><br>((0.019)</td><td>-0.027<sup>b</sup><br>(0.013)</td></tr><tr><td>... x 25% most exposed</td><td>0.022<br>(0.016)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>... x Im. Intensity</td><td></td><td>0.085<sup>c</sup><br>(0.031)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>... x Ex. Intensity</td><td></td><td></td><td>-0.108<sup>a</sup><br>(0.029)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>... x Im. Intensity x Ex. Intensity</td><td></td><td></td><td></td><td>-0.014<br>(0.048)</td><td></td><td></td><td></td><td></td></tr><tr><td>... x Largest 25%</td><td></td><td></td><td></td><td></td><td>-0.054<sup>a</sup><br>(0.003)</td><td></td><td></td><td></td></tr><tr><td>... x Hubei</td><td></td><td></td><td></td><td></td><td></td><td>-0.074<sup>a</sup><br>(0.016)</td><td></td><td></td></tr><tr><td>Firm FE</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Time FE</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td># Treated</td><td>12,086</td><td>10,973</td><td>11,516</td><td>10,973</td><td>11,709</td><td>12,086</td><td>6,462</td><td>10,176</td></tr><tr><td># Control</td><td>13,563</td><td>11,918</td><td>12,510</td><td>11,918</td><td>12,792</td><td>13,563</td><td>5635</td><td>11003</td></tr><tr><td>Export Sample</td><td>All</td><td>All</td><td>All</td><td>All</td><td>All</td><td>All</td><td>Final</td><td>Interm.</td></tr><tr><td>R<sup>2</sup></td><td>0.856</td><td>0.868</td><td>0.867</td><td>0.868</td><td>0.868</td><td>0.856</td><td>0.864</td><td>0.863</td></tr><tr><td># Obs.</td><td>202,622</td><td>164,496</td><td>172,349</td><td>164,496</td><td>175,651</td><td>202,622</td><td>85,580</td><td>158,531</td></tr></tbody></table>

Note: The table reports results of difference-in-difference estimations on exporting firms. "T1" means that control group are firms that import inputs from abroad outside of China whereas treated firms are those exposed to Chinese inputs in the five months before the pandemic. The date of treatment is February 2020 and the DiD compares the evolution of imports between September 2019 and January 2020 (pre-treatment period) and between February 2020 and June 2020 (post-treatment period). Estimates are on firm-level exports and 'units' are firms. Column (1) augments the baseline specification with a categorical interaction between the "Treatment" dummy, the "Post" dummy and a dummy equal to 1 if the firm falls into the fourth quartile of the distribution of the share of Chinese inputs into the firm's imports of intermediates. Columns (2)-(4) add a continuous interaction to capture the treatment intensity based on three variables, all comprised between 0 and 1: import intensity (Total intermediate imports / Total intermediate purchases), export intensity (Total exports / Total sales) and vertical specialization (Total intermediate imports × Total exports / Total intermediate purchases × Total sales). Purchases, sales, total imports and exports are based on 2018 data. In column (5), the interaction involves a dummy which is equal to 1 if the firm's 2018 income is in the top quartile. In column (6), the interaction captures the likelihood that the firm imports intermediates from the Hubei region. As we do not observe the regional origin of imports in our data, we use 2014 product level export data from China, at the regional level to define as Hubei product a product whose Balassa ratio is above 1. A firm for which the triple term equals 1 would be a firm that imports a Hubei product at least once in the pre-treatment period. Finally, columns (7) and (8) reproduce the baseline regression on final goods exports (column (7)) and on intermediate goods (column (8)). Standard errors are clustered at the firm-level. <sup>a</sup>, <sup>b</sup> and <sup>c</sup> denote significance at the 1, 5 and 10% level respectively.

The baseline specifications implicitly assume that any pre-Covid import of intermediates from China signaled an exposure to supply chain disruptions once the Covid has hit the country. Moreover, we did not differentiate between firms that were exposed on a tiny fraction of their
imported inputs and firms that mostly import inputs from China. The rational behind is
that complementarities between inputs would imply that even a small exposure can disrupt
the whole value chain. Column (1) in Table A1 provides support for this assumption. The
treatment variable is interacted with a dummy that takes a value of one for the 25% of treated
firms with the highest share of Chinese products in their imported inputs. The coefficient on
the interaction is not significant which is consistent with our prior that the intensity of the
exposure does not matter much, conditional on being exposed.<fnref n="41" />

In columns (2)-(4), we then investigate heterogeneity across treated firms, depending on the intensity of their integration in Global Value Chains. More specifically, we measure the share of imported inputs in the firm's intermediate consumption ("Import Intensity") and the share of exports in its overall sales ("Export Intensity"), the product of the two being akin to a measure of vertical specialization at firm-level (Hummels et al., 2001). Each variable is then interacted with the *Treatment × Post* dummy. Results suggest the transmission to downstream partners is exacerbated for firms that export a larger share of their output (column (3)). The impact of the firm's import intensity is instead unclear. Firms that source a larger share of their inputs from abroad may be slightly less prone to transmitting the shock downstream but the effect is only marginally significant (column (2)). Given these ambiguous results, it is not surprising that the interaction between the firm's import and export intensities does not identify firms with a significantly different propensity to propagate the shock downstream (column (4)). Instead, the impact of the shock is shown larger among the largest firms in terms of their total sales (column (5)). This result is consistent with Bricongne et al. (2021) who show large firms have played a specific role during the Covid-induced trade collapse.

In column (6), we try to control for the heterogeneity in the severity of the treatment.
Hubei is the first region hit by the pandemic, that has been under lockdown from January
23rd, 2020. Other Chinese regions have instead entered into lockdown later in February. By
considering as treated in February all firms that were importing from China before the Covid
crisis, we are de facto pooling firms that were treated at heterogeneous dates. To dig into the
consequences of the pooling, we ran an additional specification where we distinguish between
firms that are exposed to Chinese inputs and those importing products that Hubei is specialized

<footnote n="41">⁴¹In unreported results, we have checked that this result is robust to our measure of the intensity of the treatment, that can be measured using the share of Chinese products in overall imports or using the number of products that the firm imports from China.</footnote>
<figure><img src="image_20.png" /><figcaption>Figure A.9: Impact of the Chinese lockdown on the value and unit value of exports, across sectors</figcaption></figure>

Notes: The figure shows the DiD coefficient estimated using as left-hand side variable the log of exports (black bars) or the log of export unit values (grey bars), for the 20 biggest HS2 chapters in export flows over the sample period. Stars on top of bars indicates significance of the effect: ***, ** and * denote significance at the .1, 1 and 5% level respectively. Standard errors are clustered at the firm×product level.

into.<fnref n="42" /> Results go in the expected direction. The relative drop in exports in the treatment group during the post-treatment period is almost entirely driven by firms that import products that Hubei is specialized into.<fnref n="43" />

Finally, we conclude the analysis of the heterogeneity of the treatment effect by digging into the heterogeneity across exported products. In columns (7) and (8) of Table A1, we show that the impact of the treatment is larger for final goods than for intermediates. In Figure A.9, we further breakdown the effect across good categories (HS2 chapters). For each HS2 chapter, we run two DiD regressions, using either the value of exports (black bars) or the unit value of exports (grey bars) as left-hand-side variable. The figure confirms the stronger transmission of the shock in sectors that mostly produce final goods (apparel and footwear, perfumes and

<footnote n="42">⁴²Our dataset does not allow to identify the regional origin of imports. To overcome this limitation, we used 2014 regional Chinese export data to identify the products most likely to originate from Hubei. We consider that Hubei has a comparative advantage in a product and is thus likely to supply it to French importers if the Balassa ratio for this product — computed as the ratio of a given product's share in Hubei's total exports to the same product's share of China's overall exports — is greater than one. Statistics on Chinese exports by regions have been kindly provided to us by Sandra Poncet, based on data used in Gourdon et al. (2021).</footnote>

<footnote n="43">⁴³The corresponding event-study graph is shown in Figure A.10.</footnote>
<figure><img src="image_21.png" /><figcaption>Figure A.10: Impact of the Chinese lockdown on firm-level exports: Heterogeneity between Hubei's and other provinces' comparative advantages</figcaption></figure>

Notes: The figure shows the dynamics of exports for treated firms in comparison with the control group. The treatment group is separated into two sub-samples. The “Hubei” group is composed of firms that are exposed to Chinese inputs which the Hubei region is specialized into whereas the “Non-Hubei” group is composed of the rest of the treated sample. Hubei’s specialization patterns are measured using data on Chinese exports to France, by region, in 2014. A product is considered a comparative advantage of Hubei if its share in Hubei’s exports is larger than its share in Chinese’s exports (Balassa ratio larger than 1). The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

soaps). A possible reason is that trade in intermediates is more strongly gathered within long-term contractual relationships. Interestingly, while the majority of price effects is not statistically different from 0, goods that are more impacted tend to also experience a significant drop in their export price. We interpret this drop as a competition effect: as they lose market share, French exporters need to decrease their selling price. The corresponding figure recovered on imported values and unit prices, Figure A.11, shows that these relative price adjustments are in contrast with what is observed on foreign input prices, that either stay stable or increase after the shock. Together, these results suggest that some firms may have had to reduce their margin to compensate for increased production costs.
<figure><img src="image_22.png" /><figcaption>Figure A.11: Impact of the Chinese lockdown on the value and unit value of imports, across sectors</figcaption></figure>

Notes: The figure shows the DiD coefficient estimated using as left-hand side variable the log of imports (black bars) or the log of import unit values (grey bars), for the 20 biggest HS2 chapters in import flows over the sample period. Stars on top of bars indicates significance of the effect: ***, ** and * denote significance at the .1, .1 and 5% level respectively. Standard errors are clustered at the firm×product level.
## A.5 Additional results

<figure><img src="image_23.png" /><figcaption>Figure A.12: Impact of the Chinese lockdown on firm-level imports: By transport mode</figcaption></figure>

Notes: The figure shows the dynamics of imports before and after the Chinese lockdown, for treated firms in comparison with the control group. The estimated equation reads:

$$ \ln \text{Imports}_{ft} = \sum_{l=-4}^{5} \beta^l \text{Treated}_f \times \text{Time}_{lt} \times (1 - \text{Air}_f) + \sum_{l=-4}^{5} \gamma^l \text{Treated}_f \times \text{Time}_{lt} \times \text{Air}_f + \text{FE}_f + \text{FE}_t + \varepsilon_{ft} $$

with Time$_{lt}$ a dummy equal to one $l$ periods before/after the shock and Air$_f$ equals to one if the firm uses air transport for at least 25% of its imports from China before the shock. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.
<figure><img src="image_24.png" /><figcaption>Figure A.13: Impact of the Chinese lockdown on firm-level domestic sales</figcaption></figure>

Notes: The figure shows the dynamics of domestic sales before and after the Chinese lockdown, for treated firms in comparison with the control group. The estimated equation has firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.
<figure><img src="image_25.png" /><figcaption>Figure A.14: Impact of the Chinese lockdown on the probability of staying as an...</figcaption></figure>

Note: Same specification as in Column (7) of Table 4 of the paper. The estimated equation reads:

$$1_{ft} = \sum_{l=-4}^{5} \beta^l \text{Treated}_f \times \text{Time}_{lt} + FE_f + FE_t + \varepsilon_{ft},$$

with $1_{ft}$ that is equal to one when firm $f$ displays strictly positive imports (Top Panel) or exports (Bottom Panel) in period $t$. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.
<figure><img src="image_26.png" /><figcaption>Figure A.15: Intensive versus extensive adjustments: Product, destinations and markets mar-
gins</figcaption></figure>

Note: The figure shows the results of the event-study estimation, using the intensive
and extensive components of firms’ exports as left-hand side variable. The correspond-
ing difference-in-differences estimates are summarized in Table 3. All specifications
include firm and period fixed effects. Standard errors are clustered at the firm-level.
Confidence intervals are defined at 5%.
<figure><img src="image_27.png" /><figcaption>Figure A.16: Dynamics of firm-level exports: Treated versus control firms</figcaption></figure>

Notes: The figure shows the dynamics of exports (top panel) and the number of markets served (bottom panel) in the treated and control groups, separately (in red and in blue, respectively). All regressions control for firm fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.
<figure><img src="image_28.png" /><figcaption>Figure A.17: Impact of the Chinese lockdown on firm-level imports: Diversifiable and non-diversifiable firms</figcaption></figure>

Notes: Baseline regression after splitting the treatment group into two sub-samples. Treated firms are labeled “diversifiable” if more than 90% of the value of their imports from China cover products for which China displays a world market share below 60%. The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.
<figure><img src="image_29.png" /><figcaption>Figure A.18: Impact of the Chinese lockdown on firm-level imports: Diversified and non-diversified firms</figcaption></figure>

Notes: Baseline equation in (1) with the treatment group split into two groups. Treated firms are labeled “diversified” if all their main inputs imported from China are also sourced from elsewhere in the pre-period. Main inputs are products amounting to at least 1% of the firm’s imports in the pre-period. The estimated equation includes firm and period fixed effects. Standard errors are clustered at the firm-level. Confidence intervals are defined at 5%.

<figure><img src="image_30.png" /><figcaption>Figure A.19: Distribution of inventory ratios in the estimation sample</figcaption></figure>

Notes: The figure shows the distribution of firms’ inventories-to-sales ratios, in the estimation sample, for treated and control firms. Source: INSEE-FARE for 2018, merged with the customs data.
<table><caption>Table A2: Impact of the Chinese lockdown on firm-level exports: Diversified and non-diversified firms</caption><thead><tr><th></th><th colspan="3">Dep. Var: log of exports</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Treatment × Post</td><td>-0.046<sup>a</sup><br>(0.014)</td><td>-0.055<sup>a</sup><br>(0.011)</td><td>-0.044<sup>a</sup><br>(0.013)</td></tr><tr><td>-×-×Div</td><td>-0.009<br>(0.016)</td><td>0.086<sup>b</sup><br>(0.034)</td><td>-0.020<br>(0.016)</td></tr><tr><td>Firm FE</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Time FE</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td># Treated</td><td>12,732</td><td>12,732</td><td>12,732</td></tr><tr><td># Control</td><td>15,026</td><td>15,026</td><td>15,026</td></tr><tr><td># Diversified</td><td>5422</td><td>557</td><td>4011</td></tr><tr><td>R<sup>2</sup></td><td>0.853</td><td>0.853</td><td>0.853</td></tr><tr><td># Obs.</td><td>215,530</td><td>215,530</td><td>215,530</td></tr></tbody></table>

Note: The table reports results of differences-in-differences estimations on exporting firms. Here treated firms are split into a group of “diversified” and a group of “non-diversified” firms. In column (1), diversified firms are those that import all of their main inputs from at least two countries during the pre-treatment period. In column (2), we focus on inputs classified as “non-differentiated” by Rauch (1999) and call a firm “diversified” if all of its main inputs sourced from China are non-differentiated and sourced from at least two countries in the pre-treatment period. In column (3), “diversified” firms are those that source all of their main inputs from China and an other country of the European Union (EU15), in the pre-crisis period. Standard errors are clustered at the firm-level. <sup>a</sup>, <sup>b</sup> and <sup>c</sup> denote significance at the 1, 5 and 10% level respectively.
## Liste des documents de travail de la Direction des Études et Synthèses Économiques

<table><tr><td>G 9001</td><td>J. FAYOLLE et M. FLEURBAEY<br>Accumulation, profitabilité et endettement des entreprises</td><td>G 9202</td><td>J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE<br>Macro-economic import functions with imperfect competition - An application to the E.C. Trade</td></tr><tr><td>G 9002</td><td>H. ROUSSE<br>Détection et effets de la multiclinéarité dans les modèles linéaires ordinaires - Un prolongement de la réflexion de BELSLEY, KUH et WELSCH</td><td>G 9203</td><td>I. STAPIC<br>Les échanges internationaux de services de la France dans le cadre des negotiations multilatérales du GATT<br>Jun 1992 (1ère version)<br>Novembre 1992 (version finale)</td></tr><tr><td>G 9003</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Indexation des salaires : la rupture de 1983</td><td>G 9204</td><td>P. SEVESTRE<br>L'économie sur données individuelles-temporelles. Une note introductive</td></tr><tr><td>G 9004</td><td>D. GUELLEC et P. RALLE<br>Comptabilité, croissance et innovation de produit</td><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 9005</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Les conséquences de la désindexation. Analyse dans une maquette prix-salaires</td><td>G 9206</td><td>N. GREENAN et D. GUELLEC<br>Coordination within the firm and endogenous growth</td></tr><tr><td>G 9101</td><td>Équipe AMADEUS<br>Le modèle AMADEUS - Première partie - Présentation générale</td><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 9102</td><td>J.L. BRILLET<br>Le modèle AMADEUS - Deuxième partie - Propriétés variabilaires</td><td>G 9208</td><td>B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHON<br>Cooperative, non cooperative R & D and optimal patent life</td></tr><tr><td>G 9103</td><td>D. GUELLEC et P. RALLE<br>Endogenous growth and product innovation</td><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 9104</td><td>H. ROUSSE<br>Le modèle AMADEUS - Troisième partie - Le commerce extérieur et l'environnement international</td><td>G 9301</td><td>J. TOUJAS-BERNATE<br>Commerce international et concurrence impartie - développements récents et implications pour la littérature commerciale</td></tr><tr><td>G 9105</td><td>H. ROUSSE<br>Effets de demande et d'offre dans les résultats du commerce extérieur manufacturé de la France au cours des deux dernières décennies</td><td>G 9302</td><td>Ch. CASES<br>Cherces de chômage et comportements d'offre de travail : une revue de la littérature</td></tr><tr><td>G 9106</td><td>B. CREPON<br>Innovation, taille et concentration : causnels et dynamiques</td><td>G 9303</td><td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique et monétaire : le débat</td></tr><tr><td>G 9107</td><td>B. AMABLE et D. GUELLEC<br>Un panorama des théories de la croissance endogène</td><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 9108</td><td>M. GLAUDE et M. MOUTARDIER<br>Une évaluation du coût direct de l'enfant de 1979 à 1989</td><td>G 9305</td><td>P. JAILLARD<br>Le travail de Maastricht : présentation juridique et historique</td></tr><tr><td>G 9109</td><td>P. RALLE et alii<br>France - Allemagne : performances économiques comparées</td><td>G 9306</td><td>J.L. BRILLET<br>Micro-DMS - présentation et propriétés</td></tr><tr><td>G 9110</td><td>J.L. BRILLET<br>Micro-DMS<br>NON PARU</td><td>G 9307</td><td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td></tr><tr><td>G 9111</td><td>A. MAGNIER<br>Effets accélérateur et multiplicateur en France depuis 1970, quelques résultats empiriques</td><td>G 9308</td><td>S. JACOBZONE<br>Les grands réseaux publics français dans une perspective européenne</td></tr><tr><td>G 9112</td><td>B. CREPON et G. DUREAU<br>Investissement en recherche-développement : analyse de causnels dans un modèle d'accélérateur généralisé</td><td>G 9309</td><td>L. BLOCH - B. CŒURÉ<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td></tr><tr><td>G 9113</td><td>J.L. BRILLET, H. ERKEL-ROUSSE, J. TOUJAS-BERNATE<br>"France-Allemagne Coupées" - Deux économies vues par une maquette macro-économétrique</td><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><tr><td>G 9201</td><td>W.J. ADAMS, B. CREPON, D. ENCAOUA<br>Choix technologiques et stratégies de dissuasion d'entrée</td><td></td><td></td></tr></table>

<table><tr><td>G 9202</td><td>J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE<br>Macro-economic import functions with imperfect competition - An application to the E.C. Trade</td><td>G 9203</td><td>I. STAPIC<br>Les échanges internationaux de services de la France dans le cadre des negotiations multilatérales du GATT<br>Jun 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><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><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. MOHON<br>Cooperative, non cooperative R & D and optimal patent life</td><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 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><td>G 9301</td><td>J. TOUJAS-BERNATE<br>Commerce international et concurrence impartie - développements récents et implications pour la littérature commerciale</td></tr><tr><td>G 9302</td><td>Ch. CASES<br>Cherces de chômage et comportements d'offre de travail : une revue de la littérature</td><td>G 9303</td><td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique et monétaire : le débat</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><td>G 9305</td><td>P. JAILLARD<br>Le travail de Maastricht : présentation juridique et historique</td></tr><tr><td>G 9305</td><td>P. JAILLARD<br>Le travail de Maastricht : présentation juridique et historique</td><td>G 9306</td><td>J.L. BRILLET<br>Micro-DMS - présentation et propriétés</td></tr><tr><td>G 9306</td><td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td><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><td>G 9309</td><td>L. BLOCH - B. CŒURÉ<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td></tr><tr><td>G 9309</td><td>L. BLOCH - B. CŒURÉ<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9401</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</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><td>G 9402</td><td>C. DEFEUILLE - Ph. QUIRION<br>Les déchets d'emballages ménagers : une</td></tr><tr><td>G 9403</td><td>P. QUIRION<br>Les déchets en France : éléments statistiques et économiques</td><td>G 9404</td><td>D. LADIRAY - M. GRUN-REHOMME<br>Lissage par moyennes mobiles - Le problème des extrêmes 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><td>G 9405</td><td>F. ROSENWALD<br>La décision d'investir</td></tr><tr><td>G 9406</td><td>F. ROSENWALD<br>La décision d'investir</td><td>G 9406</td><td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique du l'ôpital public</td></tr><tr><td>G 9407</td><td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique du l'ôpital public</td><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 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><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'ade au logement - quelques éléments d'évaluation</td></tr><tr><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'ade au logement - quelques éléments d'évaluation</td><td>G 9409</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td></tr><tr><td>G 9410</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td><td>G 9410</td><td>C. DEFEUILLE - Ph. QUIRION<br>Les déchets d'emballages ménagers : une</td></tr><tr><td>G 9411</td><td>C. DEFEUILLE - Ph. QUIRION<br>Les déchets d'emballages ménagers : une</td><td>G 9411</td><td></td></tr></table>

<table><tr><td>G 9311</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les décisions de financement des entreprises françaises : une évaluation empirique des théories de la structure optimale du capital</td><td>G 9312</td><td>L. BLOCH - B. CŒURÉ<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><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><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 héritière : deux notes</td><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><td>G 9401</td><td>D. BLANCHET<br>Les structures par âge importantes ?</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><td>G 9402</td><td>J. GAUTIÉ<br>Les structures par âge importantes ?</td></tr><tr><td>G 9403</td><td>P. QUIRION<br>Les déchets en France : éléments statistiques et économiques</td><td>G 9404</td><td>D. LADIRAY - M. GRUN-REHOMME<br>Lissage par moyennes mobiles - Le problème des extrêmes 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><td>G 9405</td><td>F. ROSENWALD<br>La décision d'investir</td></tr><tr><td>G 9406</td><td>F. ROSENWALD<br>La décision d'investir</td><td>G 9406</td><td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique du l'ôpital public</td></tr><tr><td>G 9407</td><td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique du l'ôpital public</td><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 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><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'ade au logement - quelques éléments d'évaluation</td></tr><tr><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'ade au logement - quelques éléments d'évaluation</td><td>G 9409</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td></tr><tr><td>G 9410</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td><td>G 9410</td><td>C. DEFEUILLE - Ph. QUIRION<br>Les déchets d'emballages ménagers : une</td></tr><tr><td>G 9411</td><td>C. DEFEUILLE - Ph. QUIRION<br>Les déchets d'emballages ménagers : une</td><td>G 9411</td><td></td></tr></table>

<table><tr><td>G 9412</td><td>J. BOURDIEU - B. CŒURÉ - B. COLIN-SEDILLOT<br>Investissement, incertitude et risque-variabilité<br>Quelques développements récents de la théorie de l'investissement</td><td>G 9413</td><td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'élaboréiste emploi-salaire dépend-elle des structures de qualification ?</td></tr><tr><td>G 9414</td><td>I. KABLA<br>Le Chôx de breveter une invention</td><td>G 9414</td><td>L. BLOCH - B. CŒURÉ<br>Implications du marché du crédit, investissement des entreprises et cycle économique</td></tr><tr><td>G 9501</td><td>J. BOURDIEU - B. CŒURÉ - B. SEDILLOT<br>Irreversible Investment and Uncertainty:<br>When is there a Value of Waiting?</td><td>G 9502</td><td>D. GOUX - E. MAURIN<br>The transformations de la demande de travail par qualification en France<br>Une étude sur la période 1970-1993</td></tr><tr><td>G 9503</td><td>L. BLOCH - B. CŒURÉ<br>Implications du marché du crédit, investissement des entreprises et cycle économique</td><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>Persistance des hiérarchies sectorielles de salaires: un réexamen sur données françaises</td><td>G 9505</td><td>D. GOUX - E. MAURIN<br>Persistance 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 Présision n° 122 (1996) - pages 95 à 113</td><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 Présision n° 122 (1996) - pages 95 à 113</td></tr><tr><td>G 9507</td><td>G. CETTE - S. MAHFOUZ<br>La pâtage primaire du revenu</td><td>G 9507</td><td>G. CETTE - S. MAHFOUZ<br>La pâtage 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><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<br>J. BOURDIEU - A. DRAZNIKIE<br>L'octroi de crédit aux PME : une analyse à partir d'informations bancaires</td><td>G 9602</td><td>J. BOURDIEU - A. DRAZNIKIE<br>L'octroi de crédit aux PME : une analyse à partir d'informations bancaires</td></tr><tr><td>G 9603</td><td>A. TOPIAL-BENSAID<br>The implantations japonaises en France</td><td>G 9604</td><td>A. TOPIAL-BENSAID<br>The 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 : peu-on parier d'une gestion globale du capital santé ?<br>Une modélisation microéconométrique empirique</td><td>G 9605</td><td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie : peu-on parier 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><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></table>
<table>
  <tbody>
    <tr>
      <td>G 9607</td>
      <td>N. GREENAN - D. GUELLEC</td>
      <td>La théorie coopérative de la firme</td>
    </tr>
    <tr>
      <td>G 9608</td>
      <td>N. GREENAN - D. GUELLEC</td>
      <td>Technological innovation and employment reallocation</td>
    </tr>
    <tr>
      <td>G 9609</td>
      <td>Ph. COUR - F. RUPPRECHT</td>
      <td>Ph. COUR - F. RUPPRECHT</td>
    </tr>
    <tr>
      <td>G 9610</td>
      <td>S. DUCHENE - G. FORGEOT - A. JACQUOT</td>
      <td>Ph. COUR - F. RUPPRECHT</td>
    </tr>
    <tr>
      <td>G 9611</td>
      <td>X. BONNET - S. MAHFOUZ</td>
      <td>Analyse des évolutions récentes de la productivité apparente du travail</td>
    </tr>
    <tr>
      <td>G 9612</td>
      <td>PH. COUR - E. DUBOIS, S. MAHFOUZ, J. PISANI-FERRY</td>
      <td>The cost of fiscal retrenchment revisited: how strong is the evidence?</td>
    </tr>
    <tr>
      <td>G 9613</td>
      <td>A. JACQUOT</td>
      <td>A. JACQUOT</td>
    </tr>
    <tr>
      <td>G 9614</td>
      <td>ZHANG Yingxiang - SONG Xueqing</td>
      <td>Les flexions des taux d'activité sont-elles seule-ment conjoncturelles?</td>
    </tr>
    <tr>
      <td>G 9705</td>
      <td>M. HOUEBINE - A. TOPIOL-BENSAID</td>
      <td>Train or Pay: Does it Reduce Inequalities to Encourage Firms to Train their Workers?</td>
    </tr>
    <tr>
      <td>G 9706</td>
      <td>M. HOUEBINE - A. TOPIOL-BENSAID</td>
      <td>An Econometric Analysis at the Firm Level</td>
    </tr>
    <tr>
      <td>G 9707</td>
      <td>M. HOUEBINE</td>
      <td>Les entreprises internationales en France : une analyse à partir de données individuelles</td>
    </tr>
    <tr>
      <td>G 9708</td>
      <td>E. DUGUET - N. GREENAN</td>
      <td>Polarisation des activités et spécialisation des départements en France</td>
    </tr>
    <tr>
      <td>G 9709</td>
      <td>J. BRILLET</td>
      <td>Le biais technologique : une analyse sur données individuelles</td>
    </tr>
    <tr>
      <td>G 9710</td>
      <td>J.L. BRILLET</td>
      <td>Analyzing a small French ECM Model</td>
    </tr>
    <tr>
      <td>G 9711</td>
      <td>G. FORGEOT - J. GAUTIÉ</td>
      <td>Formalizing the transition process: scenarios for capital accumulation</td>
    </tr>
    <tr>
      <td>G 9712</td>
      <td>E. DUBOIS</td>
      <td>Insertion professionnelle des jeunes et processus de déclassement</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>G 9713</td>
      <td>Bilan des activités de la Direction des Études et Synthèses Économiques - 1996</td>
    </tr>
    <tr>
      <td>G 9714</td>
      <td>F. LEQUILLER</td>
    </tr>
    <tr>
      <td>G 9715</td>
      <td>Does the French Consumer Price Index Over-state Inflation?</td>
    </tr>
    <tr>
      <td>G 9716</td>
      <td>X. BONNET</td>
    </tr>
    <tr>
      <td>G 9717</td>
      <td>Peut-on mettre en évidence les rigidités à la baisse des salaires nominaires ?</td>
    </tr>
    <tr>
      <td>G 9718</td>
      <td>Une étude sur quelques grands pays de l'OCDE</td>
    </tr>
    <tr>
      <td>G 9719</td>
      <td>N. IUNG - F. RUPPRECHT</td>
    </tr>
    <tr>
      <td>G 9720</td>
      <td>Productivité de la recherche et rendements d'échelle dans le secteur pharmaceutique français</td>
    </tr>
    <tr>
      <td>G 9721</td>
      <td>E. DUGUET - I. KABLA</td>
    </tr>
    <tr>
      <td>G 9722</td>
      <td>Appropriation strategy and the motivations to use the patent system in France - An econometric analysis at the firm level</td>
    </tr>
    <tr>
      <td>G 9723</td>
      <td>P. PELÉ - P. RALLE</td>
    </tr>
    <tr>
      <td>G 9724</td>
      <td>ZHANG Yingxiang - SONG Xueqing</td>
    </tr>
    <tr>
      <td>G 9725</td>
      <td>Lexique macroéconomique français-chinois, chinois-français</td>
    </tr>
    <tr>
      <td>G 9726</td>
      <td>M. HOUEBINE - J.L. SCHNEIDER</td>
    </tr>
    <tr>
      <td>G 9727</td>
      <td>Meduser l'influence sur la fiscalité sur la localisation des entreprises</td>
    </tr>
    <tr>
      <td>G 9728</td>
      <td>A. MOUROUGANE</td>
    </tr>
    <tr>
      <td>G 9729</td>
      <td>Creditibilité, indépendance et politique monétaire</td>
    </tr>
    <tr>
      <td>G 9730</td>
      <td>Une revue de la littérature</td>
    </tr>
    <tr>
      <td>G 9731</td>
      <td>P. AUGERAUD - L. BRIOT</td>
    </tr>
    <tr>
      <td>G 9732</td>
      <td>Les données comptables d'entreprises</td>
    </tr>
    <tr>
      <td>G 9733</td>
      <td>Le système intermédiaire d'entreprises</td>
    </tr>
    <tr>
      <td>G 9734</td>
      <td>Passage des données individuelles aux données sectorielles</td>
    </tr>
    <tr>
      <td>G 9735</td>
      <td>P. AUGERAUD - J.E. CHAPRON</td>
    </tr>
    <tr>
      <td>G 9736</td>
      <td>Using Business Accounts for Compiling National Accounts: the French Experience</td>
    </tr>
    <tr>
      <td>G 9737</td>
      <td>P. AUGERAUD</td>
    </tr>
    <tr>
      <td>G 9738</td>
      <td>Les comptes d'entreprise par activités - Le passage aux comptes - De la comptabilité d'entreprise à la comptabilité nationale - A paraître</td>
    </tr>
    <tr>
      <td>G 9739</td>
      <td>H. MICHAUDON - C. PRIGENT</td>
    </tr>
    <tr>
      <td>G 9740</td>
      <td>P. ACCARDO</td>
    </tr>
    <tr>
      <td>G 9741</td>
      <td>Une étude de comptabilité générationnelle pour la France en 1996</td>
    </tr>
    <tr>
      <td>G 9742</td>
      <td>X. BONNET - S. DUCHÈNE</td>
    </tr>
    <tr>
      <td>G 9743</td>
      <td>Apports et limites de la modélisation « Real Business Cycles »</td>
    </tr>
    <tr>
      <td>G 9744</td>
      <td>C. BARLET - C. DUGUET - D. ENCAOUA - J. PRADEL</td>
    </tr>
    <tr>
      <td>G 9745</td>
      <td>The Commercial Success of Innovations</td>
    </tr>
    <tr>
      <td>G 9746</td>
      <td>An econometric analysis at the firm level in French manufacturing</td>
    </tr>
    <tr>
      <td>G 9747</td>
      <td>P. CAHUC - Ch. GIANELLA - D. GOUX - A. ZILBERBERG</td>
    </tr>
    <tr>
      <td>G 9748</td>
      <td>Equalizing Wage Differences and Bargaining Power - Evidence form a Panel of French Firms</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>G 9806</td>
      <td>J. ACCARDO - M. JLASSI</td>
      <td>La productivité globale des facteurs entre 1975 et 1996</td>
    </tr>
    <tr>
      <td>G 9807</td>
      <td>Bilan des activités de la Direction des Études et Synthèses Économiques - 1997</td>
      <td></td>
    </tr>
    <tr>
      <td>G 9808</td>
      <td>A. MOUROUGANE</td>
      <td>Can a Conservative Governor Conduct an Ac-comodative Monetary Policy?</td>
    </tr>
    <tr>
      <td>G 9809</td>
      <td>X. BONNET - E. DUBOIS - L. FALVET</td>
      <td>Asymétrie des inflations relatives et menus costs : tests sur l'inflation française</td>
    </tr>
    <tr>
      <td>G 9810</td>
      <td>E. DUGUET - N. IUNG</td>
      <td>Sales and Advertising with Spillovers at the firm level: Estimation of a Dynamic Structural Model on Panel Data</td>
    </tr>
    <tr>
      <td>G 9811</td>
      <td>J.P. BERTHIER</td>
      <td>Congestion urbaine : un modèle de trafic de pointe à courbe débit-vitesse et demande élastique</td>
    </tr>
    <tr>
      <td>G 9812</td>
      <td>C. PRIGENT</td>
      <td>La part des salaires dans la valeur ajoutée : une approche macroéconomique</td>
    </tr>
    <tr>
      <td>G 9813</td>
      <td>A.Th. AERTS</td>
      <td>L'évolution de la part des salaires dans la valeur ajoutée en France refiletée-telle les evolutions individuelles sur la période 1979-1994 ?</td>
    </tr>
    <tr>
      <td>G 9814</td>
      <td>B. SALANIE</td>
      <td>Guide pratique des séries non-stationnaires</td>
    </tr>
    <tr>
      <td>G 9901</td>
      <td>S. DUCHÈNE - A. JACQUOT</td>
      <td>Une croissance plus riche en emplois depuis le début de la dédenne ? Une analyse en comparaison internationale</td>
    </tr>
    <tr>
      <td>G 9902</td>
      <td>Ch. COLIN</td>
      <td>Modélisation des carrières dans Destinie</td>
    </tr>
    <tr>
      <td>G 9903</td>
      <td>Ch. COLIN</td>
      <td>Évolution de la dispersion des salaires : un essai de prospective par microsimulation</td>
    </tr>
    <tr>
      <td>G 9904</td>
      <td>B. CREPON - N. IUNG</td>
      <td>Innovation, emploi et performances</td>
    </tr>
    <tr>
      <td>G 9905</td>
      <td>B. CREPON - Ch. GIANELLA</td>
      <td>Wages inequalities in France 1969-1992</td>
    </tr>
    <tr>
      <td>G 9906</td>
      <td>C. BONNET - R. MAHIEU</td>
      <td>An application of quantile regression techniques</td>
    </tr>
    <tr>
      <td>G 9907</td>
      <td>C. BONNET - R. MAHIEU</td>
      <td>Microsimulation techniques applied to inter-generational transfers - Pensions in a dynamic framework: the case of France</td>
    </tr>
    <tr>
      <td>G 9908</td>
      <td>F. ROSENWALD</td>
      <td>L'impact des contraintes financières dans la décision d'investissement</td>
    </tr>
    <tr>
      <td>G 9909</td>
      <td>J.P. ZOYEM</td>
      <td>Contrat d'insertion et sortie du RMI</td>
    </tr>
    <tr>
      <td>G 9910</td>
      <td>Ch. COLIN - F. LEGROS - R. MAHIEU</td>
      <td>Evaluation des effets d'une politique sociale</td>
    </tr>
    <tr>
      <td>G 9911</td>
      <td>G. LAROQUE - B. SALANIE</td>
      <td>Équivalent patrimonial de la rente et souscription de retraite complémentaire</td>
    </tr>
    <tr>
      <td>G 9912</td>
      <td>R. DUHAUTOIS</td>
      <td>Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertière ?</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>G 9913</td>
      <td>B. SALANIÉ</td>
      <td>Une maquette analytique de long terme du marché du travail</td>
    </tr>
    <tr>
      <td>G 9914</td>
      <td>Ch. GIANELLA</td>
      <td>Une estimation de l'élasticité de l'emploi peu qualifié à son coût</td>
    </tr>
    <tr>
      <td>G 9915</td>
      <td>Division « Redistribution et Politiques Sociales »</td>
      <td>« Redistribution et Politiques Sociales »</td>
    </tr>
    <tr>
      <td>G 9916</td>
      <td>DESTINÉ</td>
      <td>« Redistribution et Politiques Sociales »</td>
    </tr>
    <tr>
      <td>G 9917</td>
      <td>E. DUGUET</td>
      <td>Macro-commandes SAS pour l'économie des panels et des variables qualitatives</td>
    </tr>
    <tr>
      <td>G 9918</td>
      <td>R. DUHAUTOIS</td>
      <td>« Redistribution et Politiques Sociales »</td>
    </tr>
    <tr>
      <td>G 9919</td>
      <td>S. AUDRIC - P. GIVORD - C. PROST</td>
      <td>Évolution de l'emploi et des coûts par qualification entre 1982 et 1996</td>
    </tr>
    <tr>
      <td>G 2000/01</td>
      <td>R. MAHIEU</td>
      <td>Les déterminants des dépenses de santé : une approche macroéconomique</td>
    </tr>
    <tr>
      <td>G 2000/02</td>
      <td>C. ALLARD-PRIGENT - H. GUILMEAU - A. QUINET</td>
      <td>The real exchange rate as the relative price of nontrables in terms of tradables: theoretical investigation and empirical study on French data</td>
    </tr>
    <tr>
      <td>G 2000/03</td>
      <td>J.-Y. FOURNIER</td>
      <td>L'approximation du titre passe-bande proposée par Christiano et Fitzgerald</td>
    </tr>
    <tr>
      <td>G 2000/04</td>
      <td>B. CREPON - F. ROSENWALD</td>
      <td>Investissement et contraintes de financement : le poids du cycle</td>
    </tr>
    <tr>
      <td>G 2000/05</td>
      <td>Bilan des activités de la DESE - 1999</td>
      <td>Une estimation sur données françaises</td>
    </tr>
    <tr>
      <td>G 2000/06</td>
      <td>A. FLIPO</td>
      <td>Les comportements matrimoniaux de fait</td>
    </tr>
    <tr>
      <td>G 2000/07</td>
      <td>R. MAHIEU - B. SÉDILLOT</td>
      <td>Microsimulations of the retirement decision: a supply side approach</td>
    </tr>
    <tr>
      <td>G 2000/08</td>
      <td>C. AUDENIS - C. PROST</td>
      <td>Déficit conjointuel : une prise en compte des conjonctures passées</td>
    </tr>
    <tr>
      <td>G 2000/09</td>
      <td>R. MAHIEU - B. SÉDILLOT</td>
      <td>Équivalent patrimonial de la rente et souscription de retraite complémentaire</td>
    </tr>
    <tr>
      <td>G 2000/10</td>
      <td>R. DUHAUTOIS</td>
      <td>Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertière ?</td>
    </tr>
  </tbody>
</table>
v

G 2000/11
Temps partiel féminin et incitations financières à l'emploi

G2000/12
Ch. GIANELLA

Local unemployment and wages

G2000/13
B. CREPON - Th. HECKEL
- Informalisation in France : une évaluation à partir de données individuelles

- Computerization in France : an evaluation 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 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'ethroégalité des comportements d'investissement à partir de données de bilan agrégées

G2001/05
C. AUDENS - 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'annulés viagériens de retraite

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

G2001/10
B. CREPON - R. DESPLATZ
Évaluation des effets des dispositifs d'allègements

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

G2001/12
J.-P. BERTHIER - C. JAULENT
R. CONVÈNEVOLE - 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
Modeling 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. AUDENS - P. BISCOURP - N. RIEDINGER
- Existe-t-il uneasy metrie dans la transmission du prix du brut aux prix des carburants ?

G2002/01
F. MAGNIER - 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. BRILHALLT
- Rétrogradation 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? Microeconomic evidence for France based on a production function approach

G2002/06
C. AUDENS - J. DEROYON - 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 viellieuse 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 Familière 1999

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

G2002/12
F. HILD
Previsions 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 salarés du privé

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

G2002/16
F. MAUREL - S. GREGOIR
Les indices de compétitivité des pays : interprétation 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. DEROYON - N. FOURCADE - S. GREGOIR - N. LAIB - B. MONFORT

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

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

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 menages, dynamique d'allocation et comportement de consommation

G2003/09
P. BISCOURP - N. FOURCADE
Peut-on mettre en evidence 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. DUEE - 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 marés est-elle fiscalement optimale ?

G2004/06
M. DUÈE
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 incitats privées à innover ? Un examen microéconométrique

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

G2004/10
G. BOISSINOT - S. 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 organisatoires, 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-à1974

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

vi
<table><tr><td>G2005/08</td><td>C. L'ANGEVIN - N. LAÎB<br/>Éducation et croissance en France et dans un panel de 21 pays de l'OCDE</td></tr><tr><td>G2005/09</td><td>N. FERRARI<br/>Prévoir l'investissement des entreprises<br/>Un indicateur des révolutions dans l'enquête de conjoncture sur les investissements dans l'industrie.</td></tr><tr><td>G2005/10</td><td>P.-O. BEFFY - C. L'ANGEVIN<br/>Chômage et boucle prix-salaires : apport d'un modèle « qualifiés/peu qualifiés »</td></tr><tr><td>G2005/11</td><td>B. HEITZ<br/>A two-states Markov-switching model of inflation in France and the USA: credible target VS inflation spiral</td></tr><tr><td>G2005/12</td><td>O. BIAU - H. ERKEL-ROUSSE - N. FERRARI<br/>Réponses individuelles aux enquêtes de conjoncture et prévision macroéconomiques : Exemple de la prévision de la production manufacturière</td></tr><tr><td>G2005/13</td><td>P. AUBERT - D. BLANCHET - D. BLAU<br/>The labour market after age 50: some elements of a Franco-American comparison</td></tr><tr><td>G2005/14</td><td>D. BLANCHET - T. DEBRAND - P. DOUGRON - P. POLLET<br/>L'enquête SHARE : présentation et premiers résultats de l'édition française</td></tr><tr><td>G2005/15</td><td>M. DUÉE<br/>La modélisation des comportements démographiques dans le modèle de microsimulation DESTINIE</td></tr><tr><td>G2005/16</td><td>H. RAOUI - S. ROUX<br/>Étude de simulation sur la participation versée aux salariés par les entreprises</td></tr><tr><td>G2006/01</td><td>C. BONNET - S. BUFFETEAU - P. GODDEFROY<br/>Disparités de retraite de droit direct entre hommes et femmes : quelles évolutions ?</td></tr><tr><td>G2006/02</td><td>C. PICART<br/>Les gazelles en France</td></tr><tr><td>G2006/03</td><td>P. AUBERT - B. CRÉPON - P. ZAMORA<br/>Le rendement apparent de la formation continue dans les entreprises : effets sur la productivité et les salaires</td></tr><tr><td>G2006/04</td><td>J.-F. OUVRARD - R. RATHELOT<br/>Demographic change and unemployment: what do macroeconomic models predict?</td></tr><tr><td>G2006/05</td><td>D. BLANCHET - J.-F. OUVRARD<br/>Indicateurs d'engagements implicites des systèmes de retraite : chiffrages, propriétés analytiques et réactions à des chocs démographiques types</td></tr><tr><td>G2006/06</td><td>G. BIAU - O. BIAU - L. ROLVIERE<br/>Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data</td></tr><tr><td>G2006/07</td><td>C. AFSA - P. GIVORD<br/>Le rôle des conditions de travail dans les absences pour maladie</td></tr><tr><td>G2006/08</td><td>P. SILLARD - C. L'ANGEVIN - S. SERRAVALLE<br/>Performances comparées à l'exportation de la France et de ses principaux partenaires<br/>Une analyse structurelle sur 12 ans</td></tr></table>

<table><tr><td>G2006/09</td><td>X. BOUTIN - S. QUANTIN<br/>Une méthodologie d'évaluation comptable du coût du capital des entreprises françaises : 1984-2002</td></tr><tr><td>G2006/10</td><td>C. AFSA<br/>L'estimation d'un coût implicite de la pénibilité du travail chez les travailleurs âgés</td></tr><tr><td>G2006/11</td><td>C. LEI ARGE<br/>Les entreprises (industrielles) françaises sont-elles à la frontière technologique ?</td></tr><tr><td>G2006/12</td><td>O. BIAU - N. FERRARI<br/>Théorie de l'opinion<br/>Fauil-lil ponderer les réponses individuelles ?</td></tr><tr><td>G2006/13</td><td>A. KOUBI - S. ROUX<br/>Une réinterprétation de la relation entre productivité et inégalités salariales dans les entreprises</td></tr><tr><td>G2006/14</td><td>R. RATHELOT - P. SILLARD<br/>The impact of local taxes on plants location decision</td></tr><tr><td>G2006/15</td><td>L. GONZALEZ - C. PICART<br/>Diversification, recentrage et poids des activités de support dans les groupes (1993-2000)</td></tr><tr><td>G2007/01</td><td>D. SRAER<br/>Allégements de cotisations patronales et dynamique salariale</td></tr><tr><td>G2007/02</td><td>V. ALBOUY - L. LEQUIEN<br/>Les rendements non monétaires de l'éducation : le cas de la santé</td></tr><tr><td>G2007/03</td><td>D. BLANCHET - T. DEBRAND<br/>Aspiration à la retraite, santé et satisfaction au travail : une comparaison européenne</td></tr><tr><td>G2007/04</td><td>M. BARLET - L. CRUSSON<br/>Quel impact des variations du prix du pétrole sur la croissance française ?</td></tr><tr><td>G2007/05</td><td>C. PICART<br/>Flux d'emploi et de main-d'œuvre en France : un réexamen</td></tr><tr><td>G2007/06</td><td>V. ALBOUY - C. TAYAN<br/>Massification et démocratisation de l'enseignement supérieur en France</td></tr><tr><td>G2007/07</td><td>T. LE BARBANCHON<br/>The Changing response to oil price shocks in France: a DSGE type approach</td></tr><tr><td>G2007/08</td><td>T. CHANEY - D. SRAER - D. THESMAR<br/>Collateral Value and Corporate Investment<br/>Evidence from the French Real Estate Market</td></tr><tr><td>G2007/09</td><td>J. BOISSINOT<br/>Consumption over the Life Cycle: Facts for France</td></tr><tr><td>G2007/10</td><td>C. AFSA<br/>Interpréter les variables de satisfaction : l'expression de la durée du travail</td></tr><tr><td>G2007/11</td><td>R. RATHELOT - P. SILLARD<br/>Zones Franches Urbanes: quelles effets sur l'emploi salarié et les créations d'établissements ?</td></tr><tr><td>G2007/12</td><td>V. ALBOUY - B. CRÉPON<br/>Aléa moral en santé : une évaluation dans le cadre du modèle causal de Rubin</td></tr></table>

<table><tr><td>G2008/01</td><td>C. PICART<br/>Les PME françaises : rentables mais peu dynamiques</td></tr><tr><td>G2008/02</td><td>P. BISCOURP - X. BOUTIN - T. VERGÈ<br/>The Effects of Retail Regulations on Prices Evidence form the Loi Galland</td></tr><tr><td>G2008/03</td><td>Y. BARBESOL - A. BRIANT<br/>Économies d'agglomération et productivité des entreprises : estimation sur données individuelles françaises</td></tr><tr><td>G2008/04</td><td>D. BLANCHET - F. LE GALLO<br/>Les projections démographiques : principaux mécanismes et retour sur l'expérience française</td></tr><tr><td>G2008/05</td><td>D. BLANCHET - F. TOUTLEMONDE<br/>Évolutions démographiques et deformation du cycle de vie active : quelles relations ?</td></tr><tr><td>G2008/06</td><td>M. BARLET - D. BLANCHET - L. CRUSSON<br/>Internationalisation et flux d'emplois : que dit une approche comptable ?</td></tr><tr><td>G2008/07</td><td>C. LELARGE - D. SRAER - D. THESMAR<br/>Entrepreneurship and Credit Constraints - Evidence from a French Loan Guarantee Program</td></tr><tr><td>G2008/08</td><td>X. BOUTIN - L. JANIN<br/>Are Prices Really Affected by Mergers?</td></tr><tr><td>G2008/09</td><td>M. BARLET - A. BRIANT - L. CRUSSON<br/>Concentration géographique dans l'industrie manufacturière et dans les services en France : une approche par un indicateur en continu</td></tr><tr><td>G2008/10</td><td>M. BEFFY - É. COUDIN - R. RATHELOT<br/>Who is confronted to insecure labor market histories? Some evidence based on the French labor market transition</td></tr><tr><td>G2008/11</td><td>M. ROGER - E. WALRAET<br/>Social Security and Well-Being of the Elderly: the Case of France</td></tr><tr><td>G2008/12</td><td>C. AFSA<br/>Analyser les composantes du bien-être et de son évolution<br/>Une approche empirique sur données individuelles</td></tr><tr><td>G2008/13</td><td>M. BARLET - D. BLANCHET - T. LE BARBANCHON<br/>Microsimplifier le marché du travail : un prototype</td></tr><tr><td>G2009/01</td><td>P.-A. PIONNIER<br/>Le partage de la valeur ajoutée en France, 1949-2007</td></tr><tr><td>G2009/02</td><td>Laurat CLAYEL - Christelle MINODIER<br/>A Monthly Indicator of the French Business Climate</td></tr><tr><td>G2009/03</td><td>H. ERKEL-ROUSSE - C. MINODIER<br/>Do Business Tendency Surveys in Industry and Services Help in Forecasting GDP Growth?</td></tr><tr><td>G2009/04</td><td>P. GIVORD - L. WILNER<br/>Les contrats temporaires : trappe ou marchéied vers l'emploi stable ?</td></tr><tr><td>G2009/05</td><td>G. LALANNE - P.-A. PIONNIER - O. SIMON<br/>Le partage des fruits de la croissance de 1950 à 2008 : une approche par les comptes de surplus</td></tr></table>

<table><tr><td>G2009/06</td><td>L. DAVEZIES - X. D'HAULTFŒUILLE<br/>Fauil-lil ponder ?... Ou l'éternelle question de l'économètre confronté à des données d'enquête</td></tr><tr><td>G2009/07</td><td>S. QUANTIN - S. RASPILLER - S. SERRAVALLE<br/>Commerce Intragroupe, fiscalité et prix de transferts, une analyse sur données françaises</td></tr><tr><td>G2009/08</td><td>M. CLERC - V. MARCUS<br/>Élastiques-prix des consommations énergétiques des ménages</td></tr><tr><td>G2009/09</td><td>G. LALANNE - E. POULIQUEN - O. SIMON<br/>Prix du pétrole et croissance potentielle à long terme</td></tr><tr><td>G2009/10</td><td>D. BLANCHET - J. LE CACHEUX - V. MARCUS<br/>Adjusted net savings and other approaches to sustainability, some theoretical background</td></tr><tr><td>G2009/11</td><td>V. BELLAMY - G. CONSALES - M. FESSEAU - S. LE LAIDIER - É. RAYNAUD<br/>Une décomposition du compte des ménages de la comptabilité nationale par catégorie de ménage en 2003</td></tr><tr><td>G2009/12</td><td>J. BARDAJI - F. TALLET<br/>Defecting Economic Regimes in France: a Qualitative Markov-Switching Indicator Using Mixed Frequency Data</td></tr><tr><td>G2009/13</td><td>R. AEBERHARDT - D. FOUGÈRE - R. RATHELOT<br/>Discrimination à l'embrache : comment exploiter les procédures de testing ?</td></tr><tr><td>G2009/14</td><td>Y. BARBESOL - P. GIVORD - S. QUANTIN<br/>Partage de la valeur ajoutée, approche par données microéconomiques</td></tr><tr><td>G2009/15</td><td>I. BUONO - G. LALANNE<br/>The Effect of the Uruguay round on the Intensive and Extensive Margins of Trade</td></tr><tr><td>G2010/01</td><td>C. MINODIER<br/>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</td></tr><tr><td>G2010/02</td><td>V. ALBOUY - L. DAVEZIES - T. DEBRAND<br/>Health Expenditure Models: a Comparison of Five Specifications using Panel Data</td></tr><tr><td>G2010/03</td><td>C. KLEIN - O. SIMON<br/>Le modèle MÉSANGE réestimé en base 2000 Tome 1 - Version avec volumes à prix constants</td></tr><tr><td>G2010/04</td><td>M.-É. CLERC - É. COUDIN<br/>L'IFC, miroir de l'évolution du coût de la vie en France ? Ce qu'apporte l'analyse des courbes d'Engel</td></tr><tr><td>G2010/05</td><td>N. CECL-RENAUD - P.-A. CHEVALIER<br/>Les seuls de 10, 20 et 50 salarés : impact sur la taille des entreprises françaises</td></tr><tr><td>G2010/06</td><td>R. AEBERHARDT - J. POUGET<br/>National Origin Differences in Wages and Hierarchical Positions - Evidence on French Full-Time Male Workers from a matched Employer-Employee Dataset</td></tr></table>
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. LAPÉGUE - E. POULIQUEN - M. BEFFY - M. GAINI Quelle croissance de moyens terme après la crise ?

G2010/10 I. BUONO - G. I. LALANNE La réaction des entreprises françaises à la baisse des taufs 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 : principes 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 échan-geables : une application sur données françaises

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

G2010/17 P.-Y. CABANNES - H. ERKEL-ROUSSE - G. LALANNE - O. MONSO - E. POULIQUEN Le modèle Mesange réestimé en base 2000

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 conjoncturels 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'égénération 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. LAPÉGUE - M. MONSO De la crise financière à la crise économique

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-Fousssi Report: A few illustrations

G2011/08 M. BACHELET - M. BEFFY - D. BLANCHET Projet : l'impact des réformes des retraites sur l'activité des 55 ans et plus : une comparaison de trois modèles

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

G2011/10 A. SCHREIBER - A. VICARD La tertiarisation 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 Evaluation of the 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 "BonusMalus"

G2011/15 M. BARLET - M. CLERC - M. GARNEO - V. LAPÉGUE - M. MARÇO Le nouvelle version du modèle MZE, modèle macroéconomique pour la zone euro

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

G2011/17 C. KERDRAIN - V. LAPÉGUE Restrictive Fiscal Policies in Europe: What are the Likely Effects?

G2012/01 P. GIVORD - S. QUANTIN - C. TREVIE 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. LANGUNIER - A. VICARD Prélevements 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 décourag- ilis certains jeunes de travailer ? 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 allégements 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-étal

G2013/02- F1302 C. TREVIE 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 incidence 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 global 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. EYAIN - L. WILNER - E. YILMAZ Introducing activity-based payment in the hospital industry : Evidence from French data

G2013/12 C. GRISLAIN-LETREMY Natural Disasters: Exposure and Underinsurance

G2013/13 P.-Y. CABANNES - V. COTTET - Y. DUBOIS - C. LELARGE - M. SICSI

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. BACHELOT - A. LEDUC - A. MARINO Les biographies du modèle Destin II : rebasse 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 G. AUBERT Modalités d'application des réformes des retraites et prévisibilité du montant de pension

G2014/05 C. GRISLAIN-LETREMY - A. KATOSKYY 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 in fiscal incentives on energy? efficiency investments?

G2014/07 C. LABONNE - G. LAME Credit Growth and Capital Requirements: Binding or Not?

G2014/08 C. GRISLAIN-LETREMY et C. TREVIE The Impact of Housing Subsidies on the Rental Sector: the French Example

G2014/09 G. LEQUIEN et A. MONTAUG Croissance potentielle en France et en zone euro : un tour d'horizon des méthodes d'estimation

G2014/10 B. GARBINTI - P. LAMARCHERE Les hauts revenus épartementaux-là davantage ?

G2014/11 D. AUDENAERT - J. BARDAIJ - R. LARDEUX - M. ORAND - M. SICSI 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. NAEGLE
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. TREVIAN
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

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

G2015/08

Q. LAFFÉTER - M. PAK
Élastiques 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. DORDET-BERNADET - M. SICISC
Effet des aides publiques sur l'emploi en R&D dans les petites entreprises

G2015/12

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

G2015/13

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

G2015/14

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

G2015/15

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

G2015/16

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 NUS
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. POULHÉS
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. TREVIAN
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
Rélevement de l'âge de départ à la retraite : quel impact sur l'activité des séniors de la reforme des retraites de 2010 ?

G2016/09

A. NAQUAS - M. ORAND - I. SUMANI HOUTTI
Les entreprises employant des salarés 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 severe des générations nées entre 1960 et 1990 : une projection à partir du modèle Destinie

G2017/04

J. BARDAJI - B. CAMPAGNE - M.-B. KHIDER - Q. LAFFÉTER - O. SIMON (Insee)
A-S DUFERNEZ - C. ELEZAAR - P. LEBLANC - E. MASSON - H. PARTOUCHE (DG-Trésor)

Le modèle macroéconomique 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

G2018/01

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

G2018/02

C.-M. CHEVALIER - A. LUCIANI
Computerization, labor productivity and employment impacts across industries vary with technological level

G2018/03

R. MONIN - M. SUAREZ CASTILLO
L'effet du CICE sur les prix une double analyse sur donnés sectorielles et individuelles

G2018/04

R. LARDEUX
Who Understands The French Income Tax?
Bunching Where Tax Liabilities Start

G2018/05

C.-M. CHEVALIER
Financial constraints of innovative firms and sectoral growth

G2018/06

R. S-H. LEE - M. PAK
Pro-competitive effects of globalisation on prices, productivity and markups: Evidence in the Euro Area

G2018/07

C.-M. CHEVALIER
Consumption inequality in France between 1995 and 2011

A. BAUER - B. GARBINTI - S. GEORGES-KOT

Financial Constraints and Self-Employment in France, 1945-2014

P. BEAUMONT - A. LUCIANI

Prime à l'embauche dans les PME :
Évaluation à partir des déclarations d'embauche

C. BELLÉGO - V. DORDET-BERNADET - M. TEAUPT

Comparaison de deux dispositifs d'aide à la R&D collaborative public-privé

R. MONIN - M. SUAREZ CASTILLO

Réplication et rapprochement des travaux d'évaluation du effet du CICE sur l'emploi en 2013 et 2014

A. CAZENAVE-LACROUTZ - F. GODET - V. LIN

L'introduction d'un gradient social dans la mortalité au sein du modèle Destinie 2

M. ANDRÉ - A.-L. BIOTTEAU

Effets de moyen terme d'une hausse de TVA sur le niveau de vie et les inégalités : une approche par microsimulation

A. BOURGEOIS - A. BRIAND

Le modèle Avionic : la modélisation Input/Output des comptes nationaux

A. GODZINSKI - M. SUAREZ CASTILLO

Short-term health effects of public transport disruptions: air pollution and viral spread channels

L. AEBERHARDT - F. HATIER - M. LECLAIR - B. PENTINAT - J.-D. ZAFAR

L'économie numérique fausse-t-elle le partage volume-prix du PIB ?

A. CAZENAVE-LACROUTZ - E. YILMAZ

Dans quelle mesure les incitations tarifaires et la procédure de mise sous accord préalable ont-elles contribué au développement de la chirurgie ambulatoire ?

J.-P. CLING - S. EGBHAL-TEHERANI - M. ORZONI - C. PLATEAU

The Differences between EU Countries for Sustainable Development Indicators: It is (mainly) the Economy!

P. CHONÉ - L. WILNER

Competition on Unobserved Attributes: The Case of the Hospital Industry

G2019/08

Child Penalties and Financial Incentives: Exploiting Variation along the Wage Distribution

G2019/09

E. GAUTIER - S. ROUX - M. SUAREZ CASTILLO
Do Minimum Wages make Wages more Rigid ? Evidence from French Micro Data

G2019/10

G2018/08
Imposition des couples et des familles : effets budgétaires et redistributifs de l'impôt sur le revenu

K. MOHKAM – O. SIMON
L'empreinte matière de l'économie française : une analyse par matière et catégorie de produits

G2019/12 S. BUNEL – B. HADJIBEYLI
Évaluation du crédit d'impôt innovation

G2019/13 C. BONNET – F. GODET – A. SOLAZ
Gendered economic determinants of couple formation over 50 in France

G2019/14 P. GIVORD – M. SUAREZ CASTILLO
Excellence for all? Heterogeneity in high schools' value-added

G2019/15 G. CETTE – L. KOEHL – T. PHILIPPON
Labor Share in Some Advanced Countries

G2020/01 J. SILHOL – B. VENTELOU
Les zones d'intervention prioritaire reflétent- elles des écarts de pratiques des médecins généralistes ?

G2020/02 B. BOUCHTENIK – R. LARDEUX
The Take-Up of Unemployment Benefit Extensions

G2020/03 J.-M. GERMAIN
A Welfare Based Estimate of "Real Feel GDP" for Europe and the USA

G2020/04 J. BOUSSARD – R. LEE
Competition, Profit Share and Concentration

G2020/05 P. PORA
Keep Working and Spend Less? Collective Childcare and Parental Earnings in France

G2020/06 R. MONIN – M. SUAREZ CASTILLO
Product Switching, market power and distance to core competency

G2020/07 L. WILNER
How do citizens perceive centralization reforms? Evidence from the merger of French regions

G2020/08 L. WILNER
The persistence of subjective well-being: permanent happiness, transitory misery?

G2020/09 J.-M. GERMAIN – T. LELLOUCH
Comptabilité économique de la soutenabilité climatique

G2020/10 A. BAUER – M. ROTEMBERG
Tax avoidance in French Firms: Evidence from the Introduction of a Tax Notch

G2020/11 P. AGHION – A. BERGEAUD
M. LEQUIEN – M. J. MELITZ
The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports

G2020/12 L. GALIANA – B. SAKAROVITCH
F. SÉMÉCURBE – Z. SMOREDA
Residential segregation, daytime segregation and spatial frictions : an analysis

G2020/13 A. BAUER – J. BOUSSARD
Market Power and Labor Share

G2020/14 A. BAUER – J. BOUSSARD – D. LASHKARI
Information Technology and Returns to Scale

G2020/15 V. LIN – O. MESLIN
Hausse des prix immobiliers et mesure du niveau de vie

G2021/01 N. BECHICHI – G. THEBAULT
Students' Preferences, Capacity Constraints and Post-Secondary Achievements in a Non-Selective System

G2021/02 B. BUREAU – A. DUQUERROY
J. GIORGI – M. LÉ – S. SCOTT – F. VINAS
Une année de crise COVID : impact sur la dymanique des entreprises en France

G2021/03 B. BUREAU – A. DUQUERROY
J. GIORGI – M. LÉ – S. SCOTT – F. VINAS
L'impact de la crise sanitaire sur la situation financière des entreprises en 2020 : une analyse sur données individuelles

G2021/04 A. GODZINSKI – M. SUAREZ CASTILLO
Disentangling the effects of air pollutants with many instruments

2021/01 S. QUANTIN – S. BUNEL – C.LENOIR
Évaluation du dispositif Jeune entreprise innovante (JEI) Un exemple d'application du modèle d'analyse de sensibilité de Rosenbaum

2021/02 L. GOBILLON – D. MEURS – S. ROUX
Differences in positions along a hierarchy: Counterfactuals based on an assignment model

2021/03 N. BECHICHI – J. GRENET – G.
THEBAULT

2021/04 M. ANDRÉ – O. MESLIN
Ségregation à l'entrée des études supérieures en France et en région parisienne : quels effets du passage à Parcoursup ?

2022/05 S. GEORGES-KOT – D. GOUX – E. MAURIN
The value of leisure synchronozation

2022/06 N. BECHICHI – M. FABRE – T. OLIVIA
Projections de la population active à l'horizon 2080

2022/11 S. QUANTIN – C. WELTER-MÉDÈE
Estimation des montants manquants de versements de TVA, exploitation des données du contrôle fiscal

2022/14 L. BLOCH – B. FAVETTO – A. LAGOUGE – F. SÉDILLOT
Inégalités de rendements et de patrimoine en France en 2017

2022/15 H. GENIN – S. SCOTT
Un portrait de la r'ention de main-d'œuvre dans l'industrie française : analyse à partir des enquêtes mensuelles de conjoncture

2023/02 D. GOUX – E. MAURIN
On the seventh day you shall do not any work : the winners and losers of Sunday work deregulation

2023/03 P. AGHION – A. BERGEAUD – M. LEQUIEN – M. MELITZ – T. ZUBER
Opposing firm-level responses to the China shock: Output competition versus input

2023/04 L. GALIANA – L. WILNER
Private Wealth over the Life-Cycle: A Meeting between Microsimulation and Structural Approaches

2023/06 M. HILLION – E. MAUGENDRE
L'effet du « jour de carence » sur les absences pour maladie ordinaire, la santé perçue et le recours aux soins à court- terme

2023/07 M. ANDRÉ – J-M. GERMAIN – M. SICSIC
'Do I get my money back?' : A Broader Approach to Inequality and Redistribution in France With a Monetary Valuation of Public Services

2023/08 R. LAFROGNE-JOUSSIER – J. MARTIN – I. MEJEAN
Supply shocks in supply chains: Evidence from the early lockdown in China