# The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports

Philippe Aghion, Antonin Bergeaud, Matthieu Lequien, Marc J. Melitz

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

## To cite this version:

Philippe Aghion, Antonin Bergeaud, Matthieu Lequien, Marc J. Melitz. The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports. 2020. hal-05381964

HAL Id: hal-05381964

https://insee.hal.science/hal-05381964v1

Preprint submitted on 25 Nov 2025

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

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

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

Distributed under a Creative Commons Attribution 4.0 International License
# The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports

Philippe AGHION - Antonin BERGEAUD

Matthieu LEQUIEN - Marc J. MELITZ
<figure><img src="image_3.png" /></figure>
Institut national de la statistique et des études économiques

G2020/11

# The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports

Philippe AGHION* - Antonin BERGEAUD*
Matthieu LEQUIEN* - Marc J. MELITZ**

Novembre 2020

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.

<footnote>* Collège de France, LSE et PSE</footnote>

<footnote>* Banque de France et CEP</footnote>

<footnote>* Insee-Dese – Département des études économiques – Division « Études Macroéconomiques », Banque de France et PSE</footnote>

<footnote>** Harvard et NBER</footnote>
# L'effet hétérogène du commerce international sur l'innovation

Nous analysons comment les conditions de la demande auxquelles est confrontée une entreprise sur ses marchés d'exportation influent sur ses décisions d'innovation. Pour s'assurer du sens de la causalité de la demande vers l'innovation, nous exploitons un choc sur la demande adressée aux exports de l'entreprise, mais qui est indépendant des décisions prises par l'entreprise. En utilisant des données exhaustives sur le secteur manufacturier français, nous montrons que les entreprises françaises brevettent davantage lorsque leurs marchés d'export croissent plus fortement de manière exogène. Cette réponse est entièrement imputable aux entreprises initialement les plus productives. La réponse du brevet est perceptible 3 à 5 ans après un choc de demande, mettant en évidence le temps nécessaire pour innover. En revanche, les ventes et l'emploi augmentent en même temps que le choc de demande, et ceci de façon similaire pour les entreprises quel que soit leur niveau de productivité. Nous montrons que cette réponse différente de l'innovation à des chocs de demande communs découle naturellement d'un modèle d'innovation et de concurrence endogène avec une hétérogénéité des entreprises. L'augmentation de la taille du marché pousse toutes les entreprises à innover davantage en augmentant les rentes d'innovation; cependant, en induisant plus d'entrées et donc plus de concurrence, elle décourage également l'innovation des entreprises à faible productivité.

**Mots-clés:** Innovation, exportations, chocs de demande, brevets

# The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports

We analyze how demand conditions faced by a firm in its export markets impact its innovation decisions. To disentangle the direction of causality between export demand and innovation, we construct a firm-level export demand shock which responds to aggregate conditions in a firm's export destinations but is exogenous to firm-level decisions. Using exhaustive data covering the French manufacturing sector, we show that French firms respond to exogenous growth shocks in their export destinations by patenting more; and that this response is entirely driven by the subset of initially more productive firms. The patent response arises 2 to 5 years after a demand shock, highlighting the time required to innovate. In contrast, the demand shock raises contemporaneous sales and employment for all firms, without any notable differences between high and low productivity firms. We show that this finding of a skewed innovation response to common demand shocks arises naturally from a model of endogenous innovation and competition with firm heterogeneity. The market size increase drives all firms to innovate more by increasing the innovation rents; yet by inducing more entry and thus more competition, it also discourages innovation by low productivity firms.

**Keywords:** Innovation, export, demand shocks, patents

**Classification JEL:** D21, F13, F14, F41, O30, O47
# 1 Introduction

Among its many impacts, the Covid crisis dramatically shrinks international trade flows. Other events or policies such as trade wars, though clearly not as extreme, also impact trade. Beyond their immediate impacts, these trade shocks can have long-run consequences, in particular on firms’ innovation, one of the main driver of long-run economic growth.<fnref n="1" /> The economic magnitude of this link is substantial. In our more conservative specification, we find that a 1 percent expansion/contraction in export demand leads to 52 additional/fewer priority patents (corresponding to the first patent publication for an invention) in the French manufacturing sector – a .64 aggregate elasticity. We analyze how the quantity and quality of this innovation response unfold over time and varies across firms with different initial levels of productivity.

In order to analyze those patenting responses, we merge comprehensive patent records with exhaustive firm-level production and customs data, which cover the whole population of French manufacturing firms. The combined use of these datasets has been made possible by a new algorithm developed in Lequien et al. (2019) that matches a French firm’s name with its unique identifier (Siren) used in all French administrative business records and allows us to link the innovation activities of a firm with the other firm data sources.

We measure innovation by the flow of *priority* patent applications. All subsequent filings of the same intellectual property (in particular if they are filed at patent authorities in other countries) are secondary filings. We focus on priority patents for two reasons. First because our goal is not to measure a response in patenting but a response in innovation. By focusing on priority patents, we concentrate on patents that correspond to new inventions. Second because we want to avoid capturing the fact that firms that are more involved in international trade are more likely to patent many secondary filings so as to protect their invention in their sales’ destinations.

Our first finding is that on average firms respond to a positive export demand shock by innovating more. In other words, we find a significant *market size effect* of export demand shocks on French firms’ innovation. Since our specifications always control for sector-year effects, this innovation response must be driven by differences in firm-level innovation responses to demand shocks within each sector. This stands in sharp contrast to the literature measuring sector-wide innovation responses – whether across sectors or for a given sector over time.<fnref n="2" />

Our second finding is that the innovation response to a positive export demand shock takes 2 to 5 years to materialize. In contrast, we find that the response of sales and employment is immediate. We interpret this difference as a confirmation that the response to export demand shocks captures a market size effect.

Our third finding is that the impact of a positive export demand shock on innovation is entirely driven by French firms with above median productivity levels (in an initial period prior to the demand shocks). This heterogeneous response could simply reflect the fact that the demand shock only affects the most productive firms. We check that this is

<footnote n="1">¹For a survey on the short-run costs of the 2018 trade war, see Amiti et al. (2019).</footnote>

<footnote n="2">²In an influential study, Acemoglu and Linn (2004) measure the sector-wide innovation response of the pharmaceutical industry to changes in demand over time.</footnote>
not the case by allowing for a different impact of the export demand shocks on sales or
employment depending upon initial productivity levels. We find that in contrast to what
we observe for innovation, there is no heterogeneous response of sales or employment to a
demand shock for low versus high productivity firms. Thus, similar demand shocks only
lead to future innovation responses by relatively more productive firms.

These results provide some additional context to the recent literature documenting
the rise of superstar firms: the skewed innovation response is likely to generate further
increases in market share for the best performing firms leading to increases in market
concentration. Indeed, Autor et al. (2017) document that this growth in concentration is
most apparent in industries with above average growth in patent-intensity.

Our identification strategy relies on the construction of firm-level demand shocks that respond to aggregate conditions in a firm’s export destinations but are independent of firm-level decisions (including the concurrent decisions for export-market participation and the forward looking innovation response). Following Hummels et al. (2014), this type of export demand shock has been used extensively in the recent empirical trade literature. It leverages detailed information on the set of products exported to specific destinations by a firm at a prior given date (prior to any changes in innovation that we analyze in our sample). Focusing on this export-driven measure of market size means that we are abstracting from the potential effects of domestic market demand on firms’ innovation. For this market, we cannot separate out the causal effects of domestic market size on innovation from the reverse effect of innovation on domestic demand and market size.

We show that our results using this identification strategy are robust to many different
specifications including variations in the measure of and functional forms for innovation.
We also perform placebo tests that independently confirm that our causation inference
from increases in market size to innovation are well founded.

While several explanations might be entertained to explain why the effect of export
on innovation should be skewed towards more frontier firms, we show that this outcome
arises naturally from a model of exports and innovation with endogenous innovation and
markups. In this setting, a positive export demand shock induces not only a direct
market size effect – which increases innovation for all firms – but also a competition
effect. The idea is that an increase in market size in any export destination will attract
new firms into the export market as more firms find it profitable to sell there. And indeed
we find a positive correlation between our export demand shocks and various measures
of firm entry into the corresponding destination markets. With endogenous markups
 linked to endogenous price elasticities), this competition effect associated with entry
impinges disproportionately on the market share of the less productive firms, reducing
their incentives to innovate. Overall, this combination of the direct market size effect and
of its induced competition effect leads to a skewed innovation response between more and
less productive firms. Firms closest to the technological frontier increase innovation the
most, while the combined effect can even be negative for the least productive firms.

Our analysis relates to several strands of literature. There is first the theoretical
literature on trade, innovation and growth (see Grossman and Helpman, 1991a,b, Aghion
and Howitt, 2009, chapter 13, and more recently Akcigit et al., 2018).<fnref n="3" /> Our paper also

<footnote n="3">³ Akcigit et al. (2018) develop and calibrate a new dynamic trade model where firms from different coun-</footnote>
relates to the recent empirical literature on firm-level trade and innovation. In particular both Lileeva and Trefler (2010) and Bustos (2011) highlight a clear relationship between R&D efforts and export status. Our analysis contributes to this literature in two main respects: (i) this literature focuses on the *extensive* margin of export markets (i.e. whether a firm exports or not to a particular market or set of markets) whereas we consider instead the effect of the *intensive* margin of exports (i.e. of the size of export markets) on innovation;<fnref n="4" /> (ii) we use innovation outcomes - the flow of priority patent filings - instead of R&D spending as our main measure of innovation, whereas these papers consider the causal impact of new export markets on R&D spending.<fnref n="5" />

There is also a recent literature on trade and innovation that focuses on the impact of import competition on domestic firms (see Bloom et al., 2016; Iacovone et al., 2011; Autor et al., 2016; Bombardini et al., 2017). These papers investigate whether import competition induces firms to innovate more in order to escape competition as in Aghion et al. (2005). Empirically, our work is quite distinct as we examine the market expansion channel related to exports. Our theoretical model therefore does not feature an escape competition channel: reductions in market share generate reductions in innovations, though disproportionately so for low productivity firms.

Finally, our work contributes to the empirical literature on market size and innovation, starting with Acemoglu and Linn (2004). We add to this literature in three main respects: (i) by providing evidence of a widespread (manufacturing) firm-level market size effect that is not driven by any sector-level dynamics; (ii) by showing that this market size effect is skewed and mainly driven by the most productive French firms; (iii) by looking at the time dynamics of the market size effect of expanded export markets on firm-level innovation: in particular we show that while a positive export demand shock immediately increases the firm's sales, the innovation response takes several additional years to materialize in new patents. However, one should keep in mind that our analysis is grounded in the market size variations arising from export destinations, which means that we do not use variations coming from the domestic markets. Thus we leave open the question as to whether the domestic market size affects firms in a similar way as the export market size effect that we document.

The remaining part of the paper is organized as follows. Section 2 presents the data and shows some descriptive statistics on export and innovation. Section 3 describes our estimation methodology. Sections 4, 5 and 6 present our empirical results respectively regarding the effect of market size on innovation, its heterogeneous impact with productivity and falsification tests. Section 7 develops a model of export and innovation featuring both

<footnote>tries compete strategically for leadership in domestic import and export markets. Their model predicts that trade openness encourages innovation in advanced sectors and discourages it in backward sectors. Dhingra (2013) and Impullitti and Licandro (2018) also develop theoretical models with endogenous firm innovation and endogenous competition (via endogenous markups). Dhingra (2013) focuses on the firm-level trade-offs between innovation and product variety, whereas Impullitti and Licandro (2018) focuses on the consequences of innovation for growth and welfare.</footnote>

<footnote n="4">⁴Restricting attention to the extensive margin makes it somewhat more difficult to analyze the details of how the market size channel operates: one reason being that several aspects are changing for a firm as it makes the big step of becoming an exporter.</footnote>

<footnote n="5">⁵In related work, Coelli et al. (2016) document the patenting response of firms in response to the Uruguay round of tariff levels.</footnote>
a direct market size and an induced competition effect, which predicts that the innovation response to a positive export shock is skewed towards the more productive firms. Section 8 concludes.

# 2 Exporters and innovators: data and descriptive statistics

In this section, we briefly present our datasets and show some descriptive evidence. Further details about data construction can be found in Appendix A.

## 2.1 Data sources

Our goal is to explore information on French firms’ exports to capture variations in their market size that we can connect to innovation (patenting) outcomes. We also want to look at how this relationship varies across firms with different levels of productivity. Toward this goal, we build a database covering all French firms by linking export, production and innovation data from 1994 to 2012. Our database draws from three sources: (i) French customs, which reports yearly export flows at a very disaggregated product level (representing over 10,000 manufacturing products) by destination; (ii) administrative fiscal datasets (FICUS and FARE from Insee-DGFiP), which provide extensive production and financial information for all firms operating in France; (iii) the Spring 2016 vintage of PATSTAT patent dataset from the European Patent Office, which contains detailed information on all patent applications from many patent offices in the world. In our analysis we will focus on patent applications by French firms, regardless of the origin of the patent office (see below and Appendix A for details).

**Matching patents and firms:** Although each French firm has a unique identifying number (Siren) across all French databases, patent offices do not identify firms applying for patents using this number but instead use the firm's name. This name may sometime carry inconsistencies from one patent to another and/or can contain typos. Various algorithms have been developed to harmonize assignees' names (see Morrison et al., 2017 for a review) but none of those have been applied specifically to French firms. One notable exception is the rigorous matching algorithm developed in Lequien et al. (2019) to link each patent application with the corresponding French firms' Siren numbers, for all firms with more than 10 employees. This new method, based on supervised learning and described in Appendix A.4, provides significant performance improvements relative to previous methods used in the empirical patent literature: based on a verification sample similar to the learning sample, its recall rate (share of all the true matchings that are accurate) is 86.1% and its precision rate (share of the identified matches that are accurate) is 97.0%. This is the matching procedure we use for our empirical analysis in this paper.

**Measure of innovation:** Our main measure of innovation consists of a count of *priority* patent applications. This corresponds to the first patent publication that describes an invention. All subsequent filings of the same intellectual property in other jurisdictions
(for example in order to extend the geographical coverage of the protection) are secondary filings. We make this restriction for two reasons. First because our goal is not to measure a response in patenting but a response in innovation. By focusing on priority patents, concentrate directly on patents that correspond to new inventions. Second because we want to avoid capturing the fact that firms that are more involved in international trade are more likely to patent many secondary filings so as to protect their invention in the markets they export to. Priority patents correspond to 35% of the total set of patents but 95% of innovative firms (firms that hold any patent, whether a priority or a secondary filing) in our sample hold at least one priority patent. This suggests that most of the patents we observe in the data are successive secondary filings of the same innovation by the same firm, and legitimate the use of priority applications as our main measure of innovation. Appendix A provides additional details on the construction of our patent measures. For robustness, we report all of our main results using an alternative patent measure based on citation weights for all patent applications by a firm (citations received within a 5 year window). Following Hall et al. (2005), this measure has been widely used in the literature to more accurately capture the innovative relevance of patents. We have also confirmed that our results are robust to a much wider set of patent measures in Appendix C (see in particular Figures C1).

**Capturing variations in market size:** Finally, to capture variations in firms’ market size, we use CEPII’s BACI database of bilateral trade flows at the HS6 product level (covering more than 5,000 manufacturing products, see Gaulier and Zignago, 2010) to construct measures of demand shocks across export destinations. These data cover the period 1995-2012.

**Sample restrictions:** Although our main firm-level administrative data source is comprehensive, with more than 46.8 million observations spanning nearly 7.5 million different firms from 1995 to 2012, we restrict our data sample for several reasons. First, we restrict our attention to private business corporations (legal category 5 in the INSEE classification). We thus drop state-owned firms, self-employed businesses, and non-profit organizations as we focus on profit-maximizing firms. Second, we drop firms with less than 10 employees since our matching to the patent data is substantially less complete for those firms (as we previously described). These two restrictions substantially reduce the number of firms in our sample. Yet, the bulk of aggregate employment (77%), sales (80%), and exports (92%) remain in our sample. Those firms are matched with 460,000 patents in PATSTAT, including 170,000 priority patents. Lastly, since our detailed customs trade data only covers goods trade (and not services), we will further restrict our sample to the manufacturing sector.<fnref n="6" /> This reduces our working sample to 66,679 firms. Nevertheless French aggregate exports and innovation are still concentrated in manufacturing covering 55% of aggregate exports and 43% of patents. Table 1 summarizes these successive sample restrictions and also shows the average number of firms operating in any given year of our sample. For our manufacturing sample, we see that this represents 42,924 firms on average per year between 1995-2012.

<footnote n="6">⁶Although the customs data also covers the wholesale sector, we also exclude those firms as they do not produce the goods that they export.</footnote>
<table><caption>Table 1: Successive restriction of the sample</caption><thead><tr><th></th><th>Total Firms</th><th>Firms per Year</th><th>Employment</th><th>Sales</th><th>Exports</th><th>Patents</th></tr></thead><tbody><tr><td>Full</td><td>7,474,147</td><td>2,597,852</td><td>100</td><td>100</td><td>100</td><td></td></tr><tr><td>Private business Corp.</td><td>2,888,647</td><td>1,114,651</td><td>88</td><td>90</td><td>97</td><td></td></tr><tr><td>More than 10 emp</td><td>400,662</td><td>260,386</td><td>77</td><td>80</td><td>92</td><td>100</td></tr><tr><td>Manufacturing</td><td>66,679</td><td>42,924</td><td>19</td><td>20</td><td>55</td><td>43</td></tr></tbody></table>

Notes: This Table gives the number of distinct firms and average number of firms per year as well as the share of employment, sales, exports and patents in each sample as compared to the Full (raw) firm level dataset (in %). All columns except the first consider yearly average over the period 1995-2012. Full correspond to our complete sample of firms based on administrative data (see Section 2). “Private Business Corp.” corresponds to this sample restricted to firms that are in Legal category (“*catégorie juridique*”) number 5. “More than 10 emp” further reduces the sample to firms that are at least once over 10 employees over the period of observation. “Manufacturing” restricts to firms that are always classified in a manufacturing sector.

**The case of multinational groups:** Our dataset does not allow us to properly take into account the case of multinational groups, an issue which often arises when dealing with national firm level data. The presence of multinational groups tends to break the relationship between export shocks and patenting since these groups may locate their R&D activities in different countries than the location of production. In particular, the R&D activity for production based in France may be located elsewhere under a different entity of a multinational’s group. In this case, we will not record the appropriate link between the export shocks for this producer and an induced innovation (patents). This measurement issue works against our obtained results of a positive response of patenting to export shocks that is increasing with a firm’s proximity to its industry frontier. Thus, we conjecture that our results would be strengthened if we had the needed information to exclude broken production/R&D links amongst the multinational groups in our sample.

## 2.2 Sector breakdown and skewness

Starting from our sample of manufacturing firms from Table 1, Table 2 shows how those firms are distributed across sectors, along with their average employment and sales per firm over our sample period from 1995-2012 – shown as yearly averages.<fnref n="7" /> Table 2 also shows the proportion of exporters and innovators (firms with at least one patent) in each sector (again, averaged over our sample years) – along with the average exports per exporter (firms with positive exports) and the average number of patents and priority patents per innovator. We clearly see that innovators represent a small minority of manufacturing firms. Only 2.7% of firms introduce any new patents in any given year (on average). Looking across years, 9.7% of firms have at least one patent in one of those years. This is the set of firms we will classify as innovators in our ensuing analysis. Although a minority of firms, they nevertheless represent 37% of employment, 45% of sales, and 60% of exports for the manufacturing sector. In Table 3, we report the same statistics for employment, sales, exports, and patents as sector-level shares. We see that priority patents are concentrated in the computer and electronic, machinery and equipment, and motor vehicles sectors, jointly accounting for 44.4% of the priority patents in manufacturing.

Table 2 reveals that the number of patents introduced each year by innovators can be substantial – especially in some sectors. There is a huge amount of dispersion underlying

<footnote n="7">⁷Throughout, we define sectors at the 2-digit level of the European NACE rev2 classification. We also eliminate the tobacco sector (# 12) as it only contains two firms.</footnote>
<table><caption>Table 2: EXPORTS AND INNOVATION IN THE MANUFACTURING SECTOR</caption><thead><tr><th rowspan="2">Sector</th><th rowspan="2">Description</th><th rowspan="2">Firms</th><th colspan="2">Mean per Firm</th><th rowspan="2">% Exporter</th><th colspan="2">Mean per Exporter</th><th rowspan="2">Innov.</th><th colspan="2">Mean per Innovator</th></tr><tr><th>Employment</th><th>Sales</th><th>Exports</th><th>Patents</th><th>Priority</th></tr></thead><tbody><tr><td>10</td><td>Food products</td><td>6,612</td><td>49</td><td>13</td><td>25</td><td>7</td><td>0.4</td><td>5.4</td><td>2.2</td></tr><tr><td>11</td><td>Beverages</td><td>397</td><td>70</td><td>34</td><td>71</td><td>14</td><td>*</td><td>*</td><td>*</td></tr><tr><td>13</td><td>Textiles</td><td>1,613</td><td>42</td><td>6</td><td>63</td><td>3</td><td>2.3</td><td>3.5</td><td>1.8</td></tr><tr><td>14</td><td>Wearing apparel</td><td>1,579</td><td>39</td><td>5</td><td>54</td><td>3</td><td>0.5</td><td>1.8</td><td>1.4</td></tr><tr><td>15</td><td>Leather</td><td>491</td><td>60</td><td>8</td><td>60</td><td>5</td><td>1.0</td><td>1.7</td><td>1.1</td></tr><tr><td>16</td><td>Wood</td><td>1,922</td><td>30</td><td>4</td><td>41</td><td>2</td><td>0.7</td><td>1.5</td><td>1.2</td></tr><tr><td>17</td><td>Paper</td><td>2,385</td><td>52</td><td>11</td><td>49</td><td>5</td><td>1.6</td><td>4.7</td><td>2.0</td></tr><tr><td>18</td><td>Printing</td><td>1,361</td><td>26</td><td>4</td><td>26</td><td>1</td><td>0.5</td><td>3.1</td><td>1.9</td></tr><tr><td>19</td><td>Coke</td><td>112</td><td>338</td><td>738</td><td>71</td><td>168</td><td>7.8</td><td>57.9</td><td>17.3</td></tr><tr><td>20</td><td>Chemicals</td><td>978</td><td>106</td><td>37</td><td>80</td><td>18</td><td>6.0</td><td>9.9</td><td>4.1</td></tr><tr><td>21</td><td>Basic pharmaceutical</td><td>298</td><td>224</td><td>91</td><td>79</td><td>42</td><td>11.8</td><td>15.5</td><td>3.0</td></tr><tr><td>22</td><td>Rubber and plastic</td><td>2,367</td><td>78</td><td>13</td><td>64</td><td>5</td><td>5.0</td><td>5.2</td><td>2.8</td></tr><tr><td>23</td><td>Other non-metallic</td><td>1,615</td><td>67</td><td>14</td><td>42</td><td>5</td><td>2.7</td><td>11.3</td><td>3.3</td></tr><tr><td>24</td><td>Basic metals</td><td>1,125</td><td>91</td><td>24</td><td>54</td><td>17</td><td>3.0</td><td>5.2</td><td>2.1</td></tr><tr><td>25</td><td>Fabricated metal</td><td>7,655</td><td>34</td><td>5</td><td>39</td><td>2</td><td>1.7</td><td>3.2</td><td>1.9</td></tr><tr><td>26</td><td>Computer and electronic</td><td>2,318</td><td>89</td><td>18</td><td>59</td><td>11</td><td>7.6</td><td>9.0</td><td>4.6</td></tr><tr><td>27</td><td>Electrical equipment</td><td>527</td><td>156</td><td>33</td><td>69</td><td>17</td><td>8.6</td><td>18.0</td><td>9.2</td></tr><tr><td>28</td><td>Machinery and equipment</td><td>3,263</td><td>93</td><td>27</td><td>63</td><td>10</td><td>7.4</td><td>5.7</td><td>3.2</td></tr><tr><td>29</td><td>Motor vehicles</td><td>941</td><td>126</td><td>40</td><td>55</td><td>27</td><td>4.1</td><td>22.8</td><td>21.9</td></tr><tr><td>30</td><td>Other transport equipment</td><td>422</td><td>192</td><td>58</td><td>59</td><td>42</td><td>7.7</td><td>20.2</td><td>10.1</td></tr><tr><td>31</td><td>Furniture</td><td>985</td><td>38</td><td>5</td><td>41</td><td>1</td><td>1.1</td><td>1.9</td><td>1.5</td></tr><tr><td>32</td><td>Other manufacturing</td><td>1,008</td><td>47</td><td>8</td><td>54</td><td>7</td><td>4.0</td><td>19.1</td><td>9.5</td></tr><tr><td>33</td><td>Repair of machinery</td><td>2,952</td><td>27</td><td>3</td><td>23</td><td>1</td><td>1.0</td><td>4.0</td><td>1.9</td></tr></tbody><tfoot><tr><td colspan="2">All Manufacturing</td><td>42,924</td><td>58</td><td>14</td><td>45</td><td>8</td><td>2.7</td><td>8.0</td><td>4.1</td></tr></tfoot></table>

Notes: This table presents the number of firms, average employment, sales, employment and exports (sales and exports are in million of Euros, employment in number of employees), the share of exporters, the total number of patents and of priority patents in the sector and the share of innovators. The data presented represents the yearly averages from 1995 to 2012. Cells with too few observations to ensure data confidentiality are replaced with *.

<table><caption>Table 3: RELATIVE IMPORTANCE OF EACH SECTOR</caption><thead><tr><th rowspan="2">NAF</th><th rowspan="2">Description</th><th colspan="6">Share of total (in %)</th></tr><tr><th>Firms</th><th>Employment</th><th>Sales</th><th>Exports</th><th>Patents</th><th>Priority</th></tr></thead><tbody><tr><td>10</td><td>Food products</td><td>15.5</td><td>13.0</td><td>14.3</td><td>7.7</td><td>1.5</td><td>1.1</td></tr><tr><td>11</td><td>Beverages</td><td>0.9</td><td>1.1</td><td>2.2</td><td>2.5</td><td>*</td><td>*</td></tr><tr><td>13</td><td>Textiles</td><td>3.7</td><td>2.7</td><td>1.6</td><td>2.3</td><td>1.4</td><td>1.4</td></tr><tr><td>14</td><td>Wearing apparel</td><td>3.6</td><td>2.4</td><td>1.2</td><td>1.4</td><td>0.1</td><td>0.2</td></tr><tr><td>15</td><td>Leather</td><td>1.1</td><td>1.2</td><td>0.6</td><td>0.8</td><td>0.1</td><td>0.1</td></tr><tr><td>16</td><td>Wood</td><td>4.5</td><td>2.3</td><td>1.4</td><td>0.8</td><td>0.2</td><td>0.3</td></tr><tr><td>17</td><td>Paper</td><td>5.4</td><td>4.4</td><td>3.6</td><td>3.2</td><td>1.5</td><td>1.4</td></tr><tr><td>18</td><td>Printing</td><td>3.3</td><td>1.5</td><td>0.8</td><td>0.2</td><td>0.2</td><td>0.3</td></tr><tr><td>19</td><td>Coke</td><td>0.3</td><td>1.3</td><td>8.7</td><td>5.5</td><td>4.0</td><td>2.8</td></tr><tr><td>20</td><td>Chemicals</td><td>2.3</td><td>4.2</td><td>6.0</td><td>8.8</td><td>6.6</td><td>4.7</td></tr><tr><td>21</td><td>Basic pharmaceutical</td><td>0.7</td><td>2.6</td><td>4.2</td><td>5.7</td><td>5.6</td><td>1.9</td></tr><tr><td>22</td><td>Rubber and plastic</td><td>5.5</td><td>7.4</td><td>5.1</td><td>4.9</td><td>6.6</td><td>7.1</td></tr><tr><td>23</td><td>Other non-metallic</td><td>3.8</td><td>4.3</td><td>3.6</td><td>2.3</td><td>5.1</td><td>3.1</td></tr><tr><td>24</td><td>Basic metals</td><td>2.6</td><td>3.8</td><td>3.8</td><td>5.9</td><td>1.8</td><td>1.4</td></tr><tr><td>25</td><td>Fabricated metal</td><td>18.0</td><td>10.6</td><td>5.9</td><td>4.0</td><td>4.6</td><td>5.4</td></tr><tr><td>26</td><td>Computer and electronic</td><td>5.3</td><td>7.7</td><td>6.1</td><td>9.1</td><td>15.2</td><td>15.1</td></tr><tr><td>27</td><td>Electrical equipment</td><td>1.3</td><td>3.1</td><td>2.8</td><td>4.1</td><td>7.7</td><td>8.3</td></tr><tr><td>28</td><td>Machinery and equipment</td><td>7.5</td><td>12.1</td><td>15.1</td><td>13.5</td><td>14.5</td><td>16.4</td></tr><tr><td>29</td><td>Motor vehicles</td><td>2.2</td><td>4.5</td><td>5.0</td><td>6.6</td><td>7.5</td><td>12.9</td></tr><tr><td>30</td><td>Other transport equipment</td><td>1.0</td><td>3.1</td><td>4.0</td><td>6.9</td><td>7.0</td><td>6.8</td></tr><tr><td>31</td><td>Furniture</td><td>2.3</td><td>1.5</td><td>0.8</td><td>0.4</td><td>0.2</td><td>0.4</td></tr><tr><td>32</td><td>Other manufacturing</td><td>2.4</td><td>1.9</td><td>1.4</td><td>2.6</td><td>7.2</td><td>7.5</td></tr><tr><td>33</td><td>Repair of machinery</td><td>7.0</td><td>3.3</td><td>1.6</td><td>0.6</td><td>1.3</td><td>1.2</td></tr></tbody></table>

Notes: This table presents the share of value added, employment, export and patents (all patents and priority patents) accounted for by each 2-digit manufacturing sector as well as the share of firms in each sector. Data are averaged over the period 1995-2012. Cells with too few observations to ensure data confidentiality are replaced with *.
that average number of patents. To highlight this skewness, we show the Lorenz curve for the distribution of those patents in Figure 1, along with the Lorenz curves for exports, sales, and employment in one of our sample years (2007). Figure 1 confirms the previously reported finding that firm-level exports are significantly more skewed than sales and employment (e.g. see Mayer and Ottaviano, 2008 and Bernard et al., 2016): 1% of firms account for 70% of aggregate exports in 2007, whereas the top 1% of firms based on total size account for 51% of sales (ranked by sales) and 33% of employment (ranked by employment). But Figure 1 also shows that patenting is even significantly more skewed than exporting: 1% of all firms account for 91% of priority patents in 2007. (Although we don't show the Lorenz curve for citations, it is even more skewed than that for patenting: all the 5-year citations are owned by the top 1.6% of firms). Yet, these univariate statistics for patenting and exporting do not capture the massive overlap between these two activities across firms – which we investigate in more detail below.

<figure><img src="image_4.png" /><figcaption>Figure 1: LORENZ CURVES FOR PRIORITY PATENTS, EXPORTS, SALES AND EMPLOYMENT</figcaption></figure>

Notes: Lorenz curves plot cumulative distribution function for priority patents, employment, export and sales. Data are for manufacturing firms and for the year 2007.

## 2.3 The nexus between innovation and exports

Looking across our sample years (1995-2012), Table 4 reports different size-related performance measures (averages per firm) based on their exporter and innovator classification. As we previously discussed, we classify firms as innovators if they introduced at least one patent during those sample years. From here on out, we classify exporters in a similar way as a firm with positive exports in at least one of our sample years. This raises the proportion of exporting firms to 61% of our manufacturing sample (45% of firms export on average in any given year, c.f. Table 2). Table 4 confirms the well-documented size differential in favor of exporters. However, several new salient features regarding innovators pop-out from this table. First, innovating firms are massively concentrated among exporters: only 5% of innovators do not report any exporting. Second, non-exporting innovators do not look very different from non-exporting non-innovators, and the various measures of firm size (employment, sales, value-added) respectively for innovators
<table><caption>Table 4: EXPORTERS AND INNOVATORS ARE BIGGER</caption><thead><tr><th rowspan="2"></th><th colspan="2">Non-exporter</th><th colspan="2">Exporter</th><th rowspan="2">Total</th></tr><tr><th>Non-innovator</th><th>Innovator</th><th>Non-innovator</th><th>Innovator</th></tr></thead><tbody><tr><td>Firms</td><td>13,266</td><td>173</td><td>25,045</td><td>4,440</td><td>42,924</td></tr><tr><td>Employment</td><td>20</td><td>19</td><td>51</td><td>215</td><td>58</td></tr><tr><td>Sales</td><td>4.2</td><td>2.4</td><td>10.7</td><td>62.3</td><td>14.1</td></tr><tr><td>Value Added</td><td>0.8</td><td>0.9</td><td>2.7</td><td>14.7</td><td>3.4</td></tr><tr><td>Export</td><td>0</td><td>0</td><td>2.4</td><td>20.8</td><td>3.6</td></tr><tr><td>Countries</td><td>0</td><td>0</td><td>4.8</td><td>17.3</td><td>4.6</td></tr><tr><td>Products</td><td>0</td><td>0</td><td>5.0</td><td>16.1</td><td>4.6</td></tr></tbody></table>

Notes: This table presents basic descriptive statistics across four categories of manufacturing firms whether they innovate, export, both or none. Employment is given in full-time equivalent on average over the year and exports, sales and value added are in million euros. Countries is the number of destination countries for exports. Employment, Sales, Value Added, Age, Exports, Countries and Patents are taken as a yearly average over the whole period 1995-2012.

and non-innovators among non-exporters remain close to each other;<fnref n="8" /> and third, these same measures of firm size differ markedly between innovators and non-innovators among exporters: innovators employ on average 4.5 times more workers and produce 7-8 times more output and value-added than non-innovating exporters. They export almost 10 times more than non-innovators and reach more than three times the number of export destinations. These size differentials are several times larger than those between exporters and non-exporters. In the aggregate, this small subset of innovators accounts for over half of French manufacturing exports.

In order to compare exporters to non-exporters and innovators to non-innovators, within specific groups, we compute export and innovation premia (in log points). Consider first the exporter premia reported in the top panel of Table 5. These premia are generated by regressing the performance measure of interest (listed in the rows) on our exporter indicator – with each cell representing a separate regression. Column 1 includes no other controls; Column 2 adds a 2-digit sector fixed effect (see Table 2); and Column 3 controls for firm employment, in addition to the sector fixed effect. Since we are using a broad cross-year definition for exporter status, we expect these premia to be lower than measures based on current-year exporter status since firms who drop in and out of export markets tend to be substantially smaller than year in year out exporters. This is the case for the premia in column 1 compared to similar numbers reported by Bernard et al. (2016) for U.S. firms in 2007. Yet, once we control for sectors in column 2, the reported premia become much more similar. In particular, we find that even within sectors, exporters are substantially larger than non-exporters. And we also find that large differences in productivity and wages in favor of exporters persist even after further controlling for firm employment.

In the bottom panel, we focus on the subset of exporters from the top panel, and report

<footnote n="8">⁸This is not the case outside of the manufacturing sector. In those other sectors, non-exporting innovators are substantially bigger than their non-exporting and non-innovating counterparts. We conjecture that this is driven by the fact that exporting no longer serves the same performance screening function outside of manufacturing.</footnote>
<table><caption>Table 5: EXPORT AND INNOVATION PREMIA</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>Obs.</th><th>Firms</th></tr></thead><tbody><tr><td colspan="6"><strong>Panel 1: Premium for being an exporter (among all manufacturing firms)</strong></td></tr><tr><td>log Employment</td><td>0.865</td><td>0.843</td><td></td><td>754,008</td><td>66,563</td></tr><tr><td>log Sales</td><td>1.361</td><td>1.344</td><td>0.463</td><td>764,372</td><td>66,601</td></tr><tr><td>log Wage</td><td>0.122</td><td>0.100</td><td>0.113</td><td>752,774</td><td>66,548</td></tr><tr><td>log Value Added per Worker</td><td>0.209</td><td>0.184</td><td>0.183</td><td>744,076</td><td>66,119</td></tr><tr><td colspan="6"><strong>Panel 2: Premium for being an innovator (among all exporting manufacturing firms)</strong></td></tr><tr><td>log Employment</td><td>1.001</td><td>0.976</td><td></td><td>519,463</td><td>42,023</td></tr><tr><td>log Sales</td><td>1.270</td><td>1.239</td><td>0.205</td><td>525,674</td><td>42,042</td></tr><tr><td>log Wage</td><td>0.118</td><td>0.096</td><td>0.111</td><td>518,682</td><td>42,019</td></tr><tr><td>log Value Added per Worker</td><td>0.207</td><td>0.183</td><td>0.185</td><td>512,040</td><td>41,795</td></tr><tr><td>log Export Sales (Current period exporters)</td><td>2.015</td><td>1.897</td><td>0.790</td><td>346,273</td><td>41,659</td></tr><tr><td>Number of destination countries</td><td>12.55</td><td>11.47</td><td>6.95</td><td>530,729</td><td>42,082</td></tr></tbody></table>

Notes: This table presents results from an OLS regression of firm characteristics (rows) on a dummy variable for exporting (upper table) or patenting (lower table) from 1994 to 2012. Column 1 uses no additional covariate, column 2 adds a 2-digit sector fixed effect, column 3 adds a control for the log of employment to column 2. All firm characteristic variables are taken in logs. All results are significant at the 1 percent level. Upper table uses all manufacturing firms whereas lower table focuses on exporting manufacturing firms.

the *additional* premia in favor of innovators within this subset. As with the top panel, those premia are calculated by running separate regressions on our innovator indicator. Even within this subset of bigger and better performing firms, innovators stand out: they are substantially bigger, more productive, and have larger total wage bill. They also export substantially more (and to more destinations) than non-innovative exporters. All these differences persist within sectors and controlling for firm employment.

Even these large premia do not fully reflect the concentration of innovative and exporting activities within the more restricted subset of firms that are both exporters and innovators. Figure 2 plots the share of innovating firms for each percentile of the firm export distribution. We see that the innovative firms are highly concentrated within the top percentiles of the export distribution. At the 80th percentile of the export distribution, 30% of the firms have some patenting experience. And the increase in the share of innovative firms with the percentile of the export distribution is highly convex. Above the 95th percentile of the export distribution, a majority of firms are innovators; in the top percentile, 68% of the firms are innovators. Those firms in the top export percentile account for 41% of the aggregate share of French patents.<fnref n="9" />

<footnote n="9">⁹Of course part of the relationship in Figure 2 could be driven by a scale effects: large firms tend to export more and are more likely to innovate. When we rank firms in percentile of export intensity (instead of absolute export) we still find a near monotonic increase in the share of innovators for export intensity in the 5-95% range. After this threshold, the relationship becomes negative as the last 5 percentiles of export intensity are dominated by unusual small firms that export virtually all of their sales.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 2: THE SHARE OF INNOVATORS JUMPS AT THE TOP OF THE EXPORT DISTRIBUTION</figcaption></figure>

Notes: Percentiles of exports are computed each year from 1995 to 2012 separately and then pooled together. For each percentile, we compute the share of innovators. Each percentile contains the same number of firms, except for percentile 0 that contains all the firms with no export. Manufacturing firms only.

# 3 Empirical Framework

## 3.1 Firm level export demand shocks

We have just documented a strong correlation between exports and innovation in the cross-section of French manufacturing firms. However, this correlation does not say much about the direction of causation: from innovation to exports (a major innovation leads to growth in export demand and entry into new export markets), or from exports to innovation. Moreover, other firm-level changes could generate concurrent changes in both innovation and exports (for example, a new management team). Thus, to identify the causal relationship from exports to innovation, we need to identify a source of variation in firm exports that is exogenous to changes within the firm (and in particular to the innovation activity of the firm). We follow Mayer et al. (2016) in building an exogenous firm-level measure of export demand shocks.

To construct these export demand shocks, consider a French exporter *f* who exports a product *s* to destination *j* at an initial date *t*₀. Let *M*<sub>*j*,s,t</sub> denote the aggregate import flow in product *s* into country *j* from all countries except France at time *t* > *t*₀. *M*<sub>*j*,s,t</sub> reflects the size of the (*s*, *j*) export market at time *t*. We then sum over the *M*<sub>*j*,s,t</sub> across destinations *j* and products *s* weighted by the relative importance of each market (*s*, *j*) in firm *f*'s exports at the initial date *t*₀. The underlying idea is that subsequent changes in destination *j*'s imports of product *s* from the world (excluding France) will be a good proxy for the change in export demand faced by this firm. By excluding French exports
to this destination, we seek to exclude sources of variation that originate in France and may be correlated with changes for the firm.<fnref n="10" />

We then scale the weighted export demand variable by the firm's initial export intensity (at $t_0$) so that our demand shock scales proportionately with a firm's total production (as a firm's export intensity goes to zero, so does the impact of any export shock on total production).

Formally, $t_0$ is the first year with positive exports in both customs (to compute destination market shares) and production data (to compute export intensity).<fnref n="11" /> $X_{f,j,s,t_0}$ denotes firm $f$'s export flow to market $(j, s)$ at time $t_0$. The export demand shock for firm $f$ between $t$ and $t-1$ is then constructed as:

$$ \Delta D_{f,t} = \sum_{j,s} w_{f,j,s,t_0} \left( \frac{M_{j,s,t} - M_{j,s,t-1}}{\frac{1}{2}(M_{j,s,t} + M_{j,s,t-1})} \right), \quad (1) $$

where the weight $w_{f,j,s,t_0} \equiv (X_{f,t_0}^*/S_{f,t_0}^*)(X_{f,j,s,t_0}/X_{f,t_0})$ represents firm $f$'s initial share of sales of product $s$, at the HS6 level, to destination $j$ and $X_{f,t_0} = \sum_{j,s} X_{f,j,s,t_0}$ represents the firm's total exports at date $t_0$. The asterisks on firm $f$'s initial export intensity $X_{f,t_0}^*/S_{f,t_0}^*$ indicate that the underlying data for total exports $X_{f,t_0}$ and sales $S_{f,t_0}$ come from the production data (as opposed to customs data which we use to calculate the destination/product specific market shares).<fnref n="12" />

There are some clear outliers in the distribution of this demand shock $\Delta D_{f,t}$ across firms. They typically involve firms that export a small number of often highly specialized products to small destinations (such as yachts to Seychelles and Maldives). In order to deal with these outliers in a consistent way, we trim our demand shock $\Delta D_{f,t}$ at 2.5% (eliminating those trade shocks below/above the 2.5th and 97.5th percentiles in each year). We report our main results on the response of innovation to this trade shock using trimming thresholds between 0-5% in Appendix C (Figures C3).

**Demand shock as a shift share instrument:** We note that the time variation in our demand shock $\Delta D_{f,t}$ only stems from the variation in the world export flow $M_{j,s,t}$ and not in the firm-level weights, which are fixed at their value in the initial export period $t_0$. We expect that a firm's innovation response at time $t > t_0$ will induce changes to its pattern of exports at time $t$ and beyond, including both intensive margin responses (changes in exports for a previously exported product $s$ to a destination $j$) and extensive margin responses (changes in the set of products $s$ sold across destinations $j$). By fixing the firm-level weights in the initial period $t_0$ (including the extensive margin set of products and destinations), we exclude this subsequent endogenous variation in exports from our demand shock. This is quite similar to a standard shift-share or “Bartik” (Bartik, 1991)

<footnote n="10">¹⁰One potential source of endogeneity may arise in markets where a French firm has a dominant position. We check that our results are robust to dropping firm-destination pairs whenever the firm's market share in the destination exceeds 10%. See Figure C2 in Appendix C.</footnote>

<footnote n="11">¹¹This year is 1994 for about half of the firms.</footnote>

<footnote n="12">¹²Total exports reported by customs and in the production data do not always exactly match, though they are highly correlated. One potential source of difference comes from small exports towards other European Union countries which are not reported in customs data (see Appendix A for more details).</footnote>
setting in which aggregate shocks are combined with measures of shock exposure. In our case the sum of exposure weights $w_{f,j,s,t_0}$ across $(s, j)$'s is different from 1 and varies across firm. We follow Borusyak et al. (2018) who argue that in such “incomplete shift-share” case with panel data, one needs to control for this sum interacted with a time dummy in our regressions.

## 3.2 Estimation strategy

Here we spell out the baseline regression equations of French firm's innovation on the export demand shock variables $\Delta D_{f,t}$. Our identifying assumption is that after controlling for any sector-level variation by year and firm characteristics at and prior to $t_0$, subsequent variations in the firm-level export demand shock are uncorrelated with firm-specific shocks to innovation.

As we have no presumption regarding the timing of this innovation response to demand shocks, we include a full set of lags and leads for the demand shock $\Delta D_{f,t}$ in our regressions. Our identification strategy nevertheless relies on the fact that our shock is independent of previous innovation decisions and we will check that the response of innovation to future shocks remains insignificant – in other words, no pre-trends. We restrict our analysis to the subset of innovating firms (i.e. firms with at least one patent between 1985 and 2012), and check that entry into innovation subsequent to 1994 does not bias our sample.<fnref n="13" /> Out of our sample of 66,679 manufacturing firms (see Table 1), there are 4,785 such innovators. Not all of them are active throughout our sample period. On average across those years there are 1,159 innovators in our sample (2.7% of 42,924 manufacturing firms operating in a given year).

Our main estimation strategy is described by:

$$ 
\begin{aligned}
\Delta Y_{f,t} &= \left( \sum_{\tau=-k'}^{k} \alpha_{\tau} \Delta D_{f,t-\tau} \right) + \gamma \cdot Z_{f,t_0} + \tilde{\gamma} \cdot (\tilde{Z}_{f,t_0} \times \chi_t) + \varepsilon_{f,t} \\
&= \alpha \cdot \Delta_k D_{f,t} + \gamma \cdot Z_{f,t_0} + \tilde{\gamma} \cdot (\tilde{Z}_{f,t_0} \times \chi_t) + \varepsilon_{f,t},
\end{aligned}
\quad (2) $$

where $\Delta Y_{f,t}$ is firm $f$'s outcome of interest between $t$ and $t-1$; $Z_{f,t_0}$ is a vector of controls for firm $f$ at $t_0$; and $\tilde{Z}_{f,t_0}$ is a subset of that vector, which is interacted with year interval fixed-effects $\chi_t$. The second equation uses the vector notation $\Delta_k D_{f,t} = [\Delta D_{f,t+k', \tau}, \Delta D_{f,t+k'-1, \tau}, \dots, \Delta D_{f,t, \tau}, \dots, \Delta D_{f,t-k, \tau}]$ and $\alpha = [\alpha_{-k', \tau}, \dots, \alpha_k]$. As we previously discussed, we include a sector indicator and the firm's prior export intensity (at $t_0$) in the subset $\tilde{Z}_{f,t_0}$ of $Z_{f,t_0}$, so those are also interacted with the year dummies.

Our specification in first-difference eliminates any bias that would be generated by a correlation between non time-varying firm characteristics (likely to affect current and future innovation) and the level of the demand shock shock $D_{f,t}$.<fnref n="14" /> We additionally want

<footnote n="13">¹³In Appendix C, Figure C4 shows that our main results are essentially unchanged when we further restrict the sample to firms who innovated before 1994. Our sample also includes firms for which we can define a $t_0$, i.e. firms that exported at least once since 1994. $t_0$ is used as a reference year and can be any year from 1994. Figure C5 shows that our results hold if we restrict to firms for which $t_0 = 1994$.</footnote>

<footnote n="14">¹⁴As discussed in Borusyak et al. (2018), this would require a firm fixed-effect control for a specification in levels.</footnote>
to control for a potential correlation between those firm characteristics and future changes
in the demand shock ΔD<sub>f,t</sub>. Following Blundell et al. (1999) and Blundell et al. (2002),
we use a control function approach based on firm performance variables measured at t<sub>0</sub>.
We use the levels and growth rates of sales and employment as controls, which we include
in the vector Z<sub>f,t<sub>0</sub></sub>. In addition, we include controls for the firm's past and current rate of
innovation at t<sub>0</sub> whenever we use an innovation measure as the dependent outcome. We
describe the functional form for those additional controls in more detail in the following
section. We note that this type of correlation between changes in the demand shock ΔD<sub>f,t</sub>
and firm characteristics is substantially less likely than a correlation with the level of the
demand shock D<sub>f,t</sub>. We have checked that there is indeed a strong correlation between
that demand shock in levels and the firm characteristics in our control function (better
performing firms tend to export to destinations with higher levels of demand). However,
there is no correlation between those variables and changes in demand ΔD<sub>f,t</sub>.

Lastly, Borusyak et al. (2018) and Goldsmith-Pinkham et al. (2018) point out that even
when such a correlation between firm characteristics and future demand shocks remains,
the induced bias disappears as the number of shocks (our combination of destination-
product pairs) grows large.

4 Market Size and Innovation

We first show that our constructed export demand shock has a strong and contemporaneous impact on a firm’s market size. We thus run our estimating equation (2) using the growth rate of sales and employment as our outcome variable ΔY<sub>f,t</sub> on the left-hand-side. We compute the average growth rate ΔY<sub>f,t</sub> = (Y<sub>f,t</sub> − Y<sub>f,t−1</sub>)/[.5(Y<sub>f,t</sub> + Y<sub>f,t−1</sub>)] in the same way that we constructed the export demand shock ΔD<sub>f,t</sub>.<sup>15</sup> The results for our key estimated coefficients α<sub>τ</sub> (large darker dot) and their confidence intervals (95% as bar and 99% as dots) are represented graphically in Figure 3 for τ = −4, . . . , 5. The α<sub>τ</sub> coefficients for τ > 0 represent a response of the outcome variable ΔY<sub>f,t</sub> to a demand shock Δ<sub>f,t−τ</sub> τ years earlier; and conversely the coefficients for τ < 0 represent a response of the outcome variable to a demand shock −τ years later.<sup>16</sup> It clearly shows a strong and contemporaneous response in both sales and employment to the export demand shock. As one would expect, the contemporaneous (τ = 0) employment elasticity is lower than the one for sales; but it nevertheless becomes strongly positive (and significant beyond the 1% level) in the same time interval as the demand shock. This highlights that this shock induces “real” growth for the firm (and that the increase in sales is not just associated with higher prices). As is also expected given the sluggish nature of employment adjustments, the response is longer-lasting than the one for sales and still significant one year following the demand shock. None of the pre-trend coefficients (τ < 0) are significant except for

<footnote n="15">¹⁵Using this average growth rate computation is important for the trade shock in order to accommodate the substantial number of import flow changes to/from zero. It is inconsequential for our measurement of the growth rate of sales and employment: our results are nearly identical when we compute the growth rate using the log difference instead.</footnote>

<footnote n="16">¹⁶From here on out, we set this timing window for the demand shock ΔD<sub>f,t</sub> to 4 leads and 5 lags. We have experimented with longer and shorter windows; this does not qualitatively affect our results. See Figures C6 for a longer window and C7 for a semi-dynamic specification without pre-trends.</footnote>
the response of sales one year prior to the demand shock. This is entirely explained by the reporting lag between the booking of an order (when it shows up in the firm's sales accounting data) and the delivery of the exported goods (when it shows up in the export customs data) – that can potentially occur in different calendar years.<fnref n="17" />

<figure><img src="image_6.png" /><figcaption>Figure 3: OLS: AVERAGE RESPONSE TO A DEMAND SHOCK</figcaption></figure>

**Notes:** Estimates of coefficients $\alpha_{\tau}$ for $\tau = -4...5$ from equation (2) are reported graphically with the growth rate of sales (left-hand panel) and employment (right-hand panel) as the dependent variable. The x-axis represents the value of $\tau$, the darker dots the point estimates of $\alpha_{\tau}$, the bar the 95% confidence intervals and the smaller dots the boundaries of the 99% confidence intervals. These estimations are obtained from an OLS regression with standard errors clustered at the 2-digit sector level and robust to heteroskedasticity. Number of observations: 21,421. Time period for $t$: 2000-2008.

We now investigate how the firm's innovation responds to the same export demand shock using the same estimation strategy. We are left with a choice of functional form for a firm's patent response $\Delta Y_{f,t}$ between $t$ and $t-1$. We do not think that the growth rate of a firm's full (over time) patent stock $P_{f,t}$ would be appropriate – because this puts too much weight on patents that may have been accumulated very far in the past and may not be relevant for more recent patents (reflecting current innovation success). Instead of dividing the change in patent stock $\Delta P_{f,t}$ — new patents introduced between $t$ and $t-1$ — by the average stock in those 2 periods (the Davis-Haltiwanger growth rate), we directly control for the average rate of new patent introductions $\Delta P_{f,0}$ during our pre-sample time interval from 1985-1994 (prior to $t_0$). Given the very large dispersion across firms in new patents $\Delta P_{f,t}$, including the prevalence of zeros in many years (and for many firms, most years), we use the functional form $\log(1+\Delta P_{f,t})$ with $\log(1+\Delta P_{f,0})$ in our control vector $Z_{f,t_0}$ for our OLS specification (2). We also address the zeros and over-dispersion in $\Delta P_{f,t}$ using a negative binomial specification where we can then use $\Delta P_{f,t}$ directly on the left-hand-side:

$$ \mathbb{E}_{Z}[\Delta P_{f,t}] = \exp \left[ \alpha \cdot \Delta_k D_{f,t} + \gamma \cdot Z_{f,t_0} + \tilde{\gamma} \cdot (\tilde{Z}_{f,t_0} \times \chi_t) \right], \quad (3) $$

where the expectation $\mathbb{E}_Z$ is taken conditional on $Z_{f,t}$ and on past and future values of $\Delta D_{f,t}$. We keep the same functional form $\log(1+\Delta P_{f,0})$ in $Z_{f,t_0}$ to control for the

<footnote n="17">¹⁷In Appendix B, we use the monthly customs export data to show that this discrepancy is explained by shipments that arrive at the beginning of a new calendar year. It also mostly affects firms with volatile sales: the significant pre-trend coefficient disappears when we exclude those firms with sales growth rates above ±50%.</footnote>
average rate of new patent introductions during our pre-sample years.<fnref n="18" /> We choose a negative binomial (NB) specification as it is best suited (especially compared to Poisson) for the over-dispersion in the empirical distribution of new patents $\Delta P_{f,t}$, which standard deviation is 10.9, an order of magnitude higher than the 0.9 mean.

The graphical results for our OLS specification with the $log(1 + \Delta P_{f,t})$ functional form are presented in Figure 4 with the innovation response $\Delta P_{f,t}$ measured both as new priority patents as well as our alternative measure based on citations received within five years. The graphical results for our negative binomial specification (3) are presented in Figure 5 with the same two options for the innovation response $\Delta P_{f,t}$.

<figure><img src="image_7.png" /><figcaption>Figure 4: OLS: AVERAGE RESPONSE TO A DEMAND SHOCK</figcaption></figure>

Notes: Estimates of coefficients $\alpha_{\tau}$ for $\tau = -4...5$ from equation (2) are reported graphically. The two panels differ in the dependent variable: the left-hand side panel considers the log of the number of new priority patents + 1 and the right-hand side panel considers the log of the number of accumulated citations received within 5 years + 1. The x-axis represents the value of $\tau$, the darker dots the point estimates of $\alpha_{\tau}$, the bar the 95% confidence intervals and the smaller dots the boundaries of the 99% confidence intervals. These estimations are obtained from an OLS regression with standard errors clustered at the 2-digit sector level and robust to heteroskedasticity. Number of observations: 22,175. Time period for t: 2000-2008.

All four figures (across different functional form specifications and new patent measures) show a significant and sustained response of patenting activity starting 3 years after the export shock. The pre-trends are centered around zero and do not show any sign that the patenting activity precedes the change in export demand. We thus find a significant aggregate *market size* effect of export demand shocks on French firms' innovation. Since our specifications include sector-year fixed effects, this innovation response cannot be explained by any sector-wide innovation changes. Rather, it must be driven by the firm-level innovation responses to demand shocks.

Table 6 summarizes our results from Figures 3-5 for the response of both market size (scale) and innovation to the export demand shock. The dynamic leads and lags are cumulated (the coefficients are summed) into a pre-period (1 to 4 years prior to the shock), a current period (concurrent and 1 year after the shock), and a future period (2

<footnote n="18">¹⁸This control is then defined for firms with zero new patents during some pre-sample years. We have also experimented with using $log \Delta P_{f,0}$ directly in $Z_{f,t}$ – hence a control for $\Delta P_{f,0}$ outside of the exponential in (3) – along with an indicator variable when $P_{f,0}$ is zero. This does not qualitatively affect our results. See Blundell et al. (1999) and Aghion et al. (2016) for a use of this type of control function in a similar specification.</footnote>
<figure><img src="image_8.png" /><figcaption>Figure 5: NEGATIVE BINOMIAL: AVERAGE RESPONSE TO A DEMAND SHOCK</figcaption></figure>

Notes: Estimates of coefficients $\alpha_{\tau}$ for $\tau = -4...5$ from equation (3) are reported graphically. The two panels differ in the dependent variable: the left-hand side panel considers the number of new priority patents and the right-hand side panel considers the number of accumulated citations received within 5 years. The x-axis represents the value of $\tau$, the darker dots the point estimates of $\alpha_{\tau}$, the bar the 95% confidence intervals and the smaller dots the boundaries of the 99% confidence intervals. These estimations are obtained from a negative binomial regression with standard errors clustered at the 2-digit sector level and robust to heteroskedasticity. Number of observations: 22,175. Time period for t: 2000-2008.

to 5 years after the shock). Even when cumulated, there is no evidence of pre-trends for either scale (sales and employment) or innovation. Table 6 also highlights how the response of scale occurs concurrently with the shock while the response of innovation is delayed to the future period. This cumulative response is significant beyond the 1% level in our OLS specifications, and significant around the 5% level (a bit stronger for the patents; and weaker when measured as citations) in our negative binomial specification.<fnref n="19" />

The economic magnitude of those cumulated innovation responses are substantial. On average, there are 3,321 firms (the innovators in our sample) operating in the future period 2-5 years following a demand shock in 1999, 2000, ..., 2003. Those firms introduced 8,176 priority patents (on average, in that same future period), which generated 27,982 citations. The future period coefficients for innovation in Table 6 imply that a 1 point export demand shock would induce 52 (OLS) - 166 (NB) new priority patent associated with 82 (OLS) - 1,344 (NB) citations during that same future period (again, on average for demand shocks in 1999, 2000, ..., 2003.) This represents an aggregate (macro) elasticity of .64-2.0 for patents to an aggregate export demand shock; and an elasticity of .29-4.8 in terms of citations. The economic magnitude of that innovation response to demand shocks in export markets is therefore substantial.<fnref n="20" />

<footnote n="19">¹⁹As the discussion of the economic magnitudes below makes clear, this is due to very large but imprecisely estimated coefficients in the negative binomial specification.</footnote>

<footnote n="20">²⁰We have chosen throughout to report the magnitudes of the innovation responses in terms of the export demand shock. We could alternatively consider an instrumental variable specification in order to report those innovation magnitudes in terms of a shock to scale (market size or employment), using our scale regression as a first stage. Our innovation regressions can be viewed as the reduced form for that instrumental variable specification. Since the magnitude of those reduced form coefficients have a natural and direct interpretation, we stick to this specification.</footnote>
<table><caption>Table 6: CUMULATIVE RESPONSE TO DEMAND SHOCK</caption><thead><tr><th rowspan="3"></th><th colspan="2">Scale</th><th colspan="4">Innovation</th></tr><tr><th colspan="2">Sales</th><th colspan="2">Priority Patents</th><th colspan="2">Citations</th></tr><tr><th>OLS</th><th>OLS</th><th>OLS</th><th>NB</th><th>OLS</th><th>NB</th></tr></thead><tbody><tr><th>Pre-Trend</th><td>0.088 (0.134)</td><td>-0.010 (0.062)</td><td>-0.199 (0.328)</td><td>-0.799 (1.588)</td><td>-0.357 (0.448)</td><td>-1.696 (2.755)</td></tr><tr><th>Current</th><td>0.500*** (0.073)</td><td>0.272*** (0.065)</td><td>0.046 (0.257)</td><td>-1.029 (1.580)</td><td>0.121 (0.474)</td><td>1.632 (2.874)</td></tr><tr><th>Future</th><td>0.139 (0.094)</td><td>0.160 (0.113)</td><td>1.662*** (0.534)</td><td>7.002** (3.112)</td><td>2.341*** (0.718)</td><td>9.765* (5.262)</td></tr></tbody></table>

Notes: This table reports point estimate and standard errors (under parentheses) for different linear combinations of coefficients from various estimations of equations (2) and (3). Pre-Trend corresponds to the estimate of $\alpha_4 + \alpha_3 + \alpha_2 + \alpha_1$, current to $\alpha_0 + \alpha_1$ and Future to $\alpha_2 + \alpha_3 + \alpha_4 + \alpha_5$. Column 1 corresponds to the results displayed in Figure 3a, column 2 to Figure 3b, column 3 to Figure 4a, column 4 to Figure 5a, column 5 to Figure 4b and column 6 to Figure 5b. ***, ** and * indicate p-value below 0.01, 0.05 and 0.1 respectively.

# 5 Heterogeneous Impact: Distance to Frontier

We now investigate whether this innovation response varies across firms based on their distance to their sector's frontier. We use labor productivity (value-added per worker) as our metric for this distance. Just as we did with the firm-level export shares, we use the initial year $t_0$ to generate a distance measure that does not subsequently vary over time $t > t_0$. We partition firms into those with productivity above their 2-digit sector median (in year $t_0$), $a_{f,t_0} \ge \bar{a}_{t_0}$ (represented by indicator dummy $1_a^+$), and those with productivity below the sector median, $a_{f,t_0} < \bar{a}_{t_0}$ (represented by indicator dummy $1_a^-$). More specifically, we consider the following regression equation:

$$ \Delta Y_{f,t} = \alpha_H \cdot (\Delta_k D_{f,t} \times 1_a^+) + \alpha_L \cdot (\Delta_k D_{f,t} \times 1_a^-) + \gamma \cdot Z_{f,t_0} + \tilde{\gamma} \cdot (\tilde{Z}_{f,t_0} \times \chi_t) + \varepsilon_{f,t}. \quad (4) $$

Since the firm's initial productivity level $a_{f,t_0}$ is now used to construct our two different trade shocks on the right-hand-side, we add that variable to the control vectors $Z_{f,t_0}$ and $\tilde{Z}_{f,t_0}$. We use the same functional form $\Delta Y_{f,t} = \log(1 + \Delta P_{f,t})$ for our OLS specification (adding $\log(1 + \Delta P_{f,t_0})$ to our control vector $Z_{f,t_0}$). And we also estimate a negative binomial specification with the 'untransformed' new patent measure $\Delta P_{f,t}$ on the left-hand side, along with a control for $\Delta P_{f,t_0}$ in $Z_{f,t_0}$:

$$ \mathbb{E}_{Z}[\Delta P_{f,t}] = \exp \left[ \alpha_H \cdot (\Delta_k D_{f,t} \times 1_a^+) + \alpha_L \cdot (\Delta_k D_{f,t} \times 1_a^-) + \gamma \cdot Z_{f,t_0} + \tilde{\gamma} \cdot (\tilde{Z}_{f,t_0} \times \chi_t) \right], \quad (5) $$

where the expectation $\mathbb{E}_Z$ is again taken conditional on $Z_{f,t}$ and on past and future values of $\Delta D_{f,t}$.

The graphical results for both our OLS and negative binomial specifications are presented in Figures 6 and 7, once again using both priority patents and the accumulated citations as our measure of new patent activity $\Delta P_{f,t}$. All four figures show a significant and sustained response of patenting activity starting 3 years after the export shock – but only for firms that are initially closer to their sector's frontier (with labor productivity above the median level, in orange). In Appendix C, we return to the full battery of robustness checks that we previously described for the analysis of the non-heterogeneous responses. The main messages from Figures 6 and 7 remain unchanged (See Figures C1-C7).

Could this heterogeneous response simply reflect the fact that the demand shock only affects the most productive firms? To check that this is not the case, we replicate the results shown in Figures 3a and 3b: that is, we allow for a different impact of the export demand shocks on sales or employment depending upon initial productivity levels. Looking at Figures 8a and 8b, we see that in contrast to what we observe for innovation, there
<figure><img src="image_9.png" /><figcaption>Figure 6: OLS: HETEROGENEOUS RESPONSE TO A DEMAND SHOCK</figcaption></figure>

**Notes:** Estimates of coefficients $\alpha_{H,\tau}$ and $\alpha_{L,\tau}$ for $\tau = -4...5$ from equation (4) are presented graphically, respectively in orange and blue. The two panels differ in the dependent variable: the left-hand side panel considers the log of the number of new priority patents + 1 and the right-hand side panel considers the log of the number of accumulated citations received within 5 years + 1. The x-axis represents the value of $\tau$, the darker dots the point estimates of $\alpha_{\tau}$, the bar the 95% confidence intervals and the smaller dots the boundaries of the 99% confidence intervals. These estimations are obtained from an OLS regression with standard errors clustered at the 2-digit sector level and robust to heteroskedasticity. Number of observations: 22,175. Time period for t: 2000-2008.

<figure><img src="image_10.png" /><figcaption>Figure 7: NEGATIVE BINOMIAL: HETEROGENEOUS RESPONSE TO A DEMAND SHOCK</figcaption></figure>

**Notes:** Estimates of coefficients $\alpha_{H,\tau}$ and $\alpha_{L,\tau}$ for $\tau = -4...5$ from equation (5) are presented graphically, respectively in orange and blue. The two panels differ in the dependent variable: the left-hand side panel considers the number of new priority patents and the right-hand side panel considers the number of accumulated citations received within 5 years. The x-axis represents the value of $\tau$, the darker dots the point estimates of $\alpha_{\tau}$, the bar the 95% confidence intervals and the smaller dots the boundaries of the 99% confidence intervals. These estimations are obtained from a negative binomial regression with standard errors clustered at the 2-digit sector times productivity group level and robust to heteroskedasticity. Number of observations: 22,175. Time period for t: 2000-2008.

is no heterogeneous response of sales or employment to a demand shock for low versus high productivity firms. The responses for both sets of firms match the magnitudes of the average response that we previously documented.<fnref n="21" />

We summarize once again our dynamic results in Figures 6-8 by cumulating the coef-

<footnote n="21">²¹As can be seen in Figure 8a, the growth rate of the sales response for the below median firms fluctuates up and down following the trade shock. This effect is driven by firms with volatile sales: it disappears when we exclude those firms with sales growth rates above ±50%.</footnote>
<figure><img src="image_11.png" /><figcaption>Figure 8: HETEROGENEOUS RESPONSE TO A DEMAND SHOCK - SALES AND EMPLOYMENT</figcaption></figure>

Notes: This Figure replicates Figure 3 but allowing for heterogeneity based on the initial productivity level as described in equation (4). Number of observations: 21,421. Time period for t: 2000-2008.

Coefficients into pre-trend, current, and future periods just as we previously reported in Table 6 for the case without the heterogeneous impact by productivity: pre-trend for 1-4 years prior to the export demand shock; current for 0-1 year following the shock; and future for 2-5 years following the shock. Those coefficients are reported in Table 7 for the above and below median firm productivity groups. In addition, we now report a significance test for their difference across those two groups.

<table><caption>Table 7: CUMULATIVE HETEROGENEOUS RESPONSE TO DEMAND SHOCK</caption><thead><tr><th rowspan="3"></th><th colspan="2">Scale</th><th colspan="4">Innovation</th></tr><tr><th>Sales</th><th>Employment</th><th colspan="2">Priority Patents</th><th colspan="2">Citations</th></tr><tr><th>OLS</th><th>OLS</th><th>OLS</th><th>NB</th><th>OLS</th><th>NB</th></tr></thead><tbody><tr><th colspan="7">Pre-Trend</th></tr><tr><th>Below Median</th><td>0.192 (0.152)</td><td>0.006 (0.080)</td><td>-0.247 (0.328)</td><td>0.658 (2.016)</td><td>-0.954* (0.484)</td><td>-7.999** (3.662)</td></tr><tr><th>Above Median</th><td>0.022 (0.175)</td><td>-0.030 (0.087)</td><td>-0.323 (0.381)</td><td>-2.074 (1.619)</td><td>-0.162 (0.665)</td><td>-0.675 (2.741)</td></tr><tr><th>Difference</th><td>-0.170 (0.200)</td><td>-0.036 (0.087)</td><td>-0.077 (0.433)</td><td>-2.732 (2.537)</td><td>0.792 (0.694)</td><td>7.325* (4.025)</td></tr><tr><th colspan="7">Current</th></tr><tr><th>Below Median</th><td>0.408*** (0.108)</td><td>0.231*** (0.076)</td><td>0.004 (0.251)</td><td>-0.292 (1.401)</td><td>0.096 (0.299)</td><td>2.105 (2.753)</td></tr><tr><th>Above Median</th><td>0.582*** (0.081)</td><td>0.303*** (0.105)</td><td>0.090 (0.300)</td><td>-1.167 (1.824)</td><td>0.124 (0.783)</td><td>2.126 (2.701)</td></tr><tr><th>Difference</th><td>0.174 (0.117)</td><td>0.072 (0.119)</td><td>0.087 (0.272)</td><td>-0.875 (1.523)</td><td>0.028 (0.701)</td><td>0.020 (2.549)</td></tr><tr><th colspan="7">Future</th></tr><tr><th>Below Median</th><td>0.347** (0.149)</td><td>0.168* (0.097)</td><td>0.569 (0.353)</td><td>1.761 (3.046)</td><td>0.788 (0.681)</td><td>-1.820 (3.788)</td></tr><tr><th>Above Median</th><td>-0.030 (0.156)</td><td>0.160 (0.146)</td><td>2.491*** (0.713)</td><td>10.223*** (3.004)</td><td>3.550*** (0.881)</td><td>15.685*** (4.880)</td></tr><tr><th>Difference</th><td>-0.377* (0.205)</td><td>-0.008 (0.157)</td><td>1.922*** (0.589)</td><td>8.461*** (2.725)</td><td>2.762*** (0.954)</td><td>17.505*** (3.375)</td></tr></tbody></table>

Notes: This table reports point estimate and standard errors (under parentheses) for different linear combinations of coefficients from various estimations of equations (4) and (5). Pre-Trend corresponds to the estimate of $\alpha_{X_{-4}} + \alpha_{X_{-3}} + \alpha_{X_{-2}} + \alpha_{X_{-1}}$, current to $\alpha_{X_0} + \alpha_{X_1}$ and Future to $\alpha_{X_2} + \alpha_{X_3} + \alpha_{X_4} + \alpha_{X_5}$ where $X = H$ for lines "Above Median" and $X = L$ for lines "Below Median". The lines "Difference" corresponds to the difference between the corresponding above and below median linear combinations. Column 1 corresponds to the results displayed in Figure 8a, column 2 to Figure 8b, column 3 to Figure 6a, column 4 to Figure 7a, column 5 to Figure 6b and column 6 to Figure 7b. ***, ** and * indicate p-value below 0.01, 0.05 and 0.1 respectively.

As can also be seen in Figures 6b and 7b, the response of innovation in terms of citations is negative in the pre-trend period for below-median productivity firms. There is no evidence of pre-trends in any of the other dependent variables for either scale of innovation among either subset of more or less productive firms. Table 7 also highlights the strong contemporaneous response of the scale variables to the demand shock that we previously emphasized. Importantly, there is no evidence of any significant differences in the responses of those scale variables across the two productivity groups. On the other hand, Table 7 makes clear that the strong future response of innovation for the above-median firms is statistically distinguishable from the response for the below-median firms.
Not only are the future response coefficients for those below-median firms insignificant, the coefficient difference in favor of the relatively more productive firms is statistically significant beyond the 1% level in all of our specifications. This strongly supports our main finding that the innovation response is entirely concentrated within the subset of relatively more productive firms.

The economic magnitude of the innovation response for those above-median firms corresponds roughly to a similar aggregate response as the one we reported for the case without firm heterogeneity – except that this response is now concentrated more intensely and exclusively within the top-half of relatively more productive firms. The OLS coefficients in Table 7 imply that an aggregate 1 point increase in the export demand shock would induce 44 patents associated with 82 citations amongst the above-median firms. Those numbers are slightly lower than the 52 patents and 88 citations we previously recorded for the aggregate response without firm heterogeneity. The NB coefficient for the patent response implies a slightly higher number of patents from the 1 point increase in the export demand shock: 197 patents relative to 166 for the case without firm heterogeneity. The NB coefficient for the citation response implies a substantially larger response relative to the case without heterogeneity (almost double). But both NB coefficients for citations (and especially the one for the above-median firms) have a large standard error, so there is still a wide overlap between our predictions for the aggregate response in terms of citations with and without firm heterogeneity.

Once again, we find that the economic magnitude of the innovation response – concentrated within the subset of relatively more productive firms – is substantial.

# 6 Falsification Tests

In order to reinforce our finding of a causal impact for market size (via our demand shocks) on innovation by above-median productivity firms, we develop a falsification test that highlights that those innovation responses cannot be explained by a firm-level trend: that is, that those firms observed to increase innovation would have done so anyway absent an increase in export demand.<fnref n="22" /> This test also provides a further check on our control that the innovation response is not explained by a firm’s prior exposure to export markets (since we use prior export intensity to construct our trade shocks).<fnref n="23" /> Our test involves the construction of placebo demand shocks for each firm and then showing that firm innovation does not respond to this placebo shock.

In our first placebo construction, we allocate products to firms randomly (based on their empirical distribution across firms) and compute the demand shocks that each firm would have experienced had it actually exported those products at $t_0$. In our second placebo construction, we instead allocate the export destinations randomly across firms (again, based on the empirical distribution of destinations across firms).<fnref n="24" />

<footnote n="22">²²A similar approach has been implemented by Chetty et al. (2009) and Malgouyres et al. (2019).</footnote>

<footnote n="23">²³Our main check is to add export intensity interacted with the year fixed effects as controls.</footnote>

<footnote n="24">²⁴To be more precise, each placebo demand shock is the outcome of a random permutation across firms from either the empirical distribution of products, or the empirical distribution of destinations.</footnote>
We construct 2000 different placebo demand shocks using both methods, and then estimate our baseline OLS specification (4) each time with the response of priority patents on the left-hand side. Figure 9 shows the cumulative distribution for the coefficient $\alpha_{H,4}$ and its t-statistic for the response by firms with above-median productivity 4 years after the shock. Against those distributions, we show (red vertical line) the coefficient value and t-statistic for $\alpha_{H,4}$ that we reported in Figure 6a using the ‘true’ demand shocks. We immediately see that the value and significance of the demand shock coefficient we previously obtained are clear outliers in those distributions (well beyond the 100th percentile for the coefficient values; and at the 98.5 and 96 percentiles for the associated t-statistics). We can thus easily reject the hypothesis that a similar innovation response by the above-median productivity firms would have been observed absent the impact of the “true” demand shock. We have repeated this falsification test summing the coefficients representing 2 to 5 years after the shock (instead of just year 4), along with its associated t-statistic. In all those cases, our reported coefficients (and their t-statistics) are again clear outliers in the simulated cdf: above the 95th percentile of the distribution in all cases (and above the 100th percentile in a few). We have also repeated this exercise, with similar results, with citations as the dependent variable.

<figure><img src="image_12.png" /><figcaption>Figure 9: OLS: FALSIFICATION TESTS</figcaption></figure>

**Notes:** This figure plots the cumulative distribution of the point estimates (top panels) and the associated t-stat (bottom panels) and for the $\alpha_{H,4}$ coefficient when equation (4) is estimated 2000 times with a placebo shock, randomly switching the products exported at $t_0$ (left panel) or randomly switching the export countries at $t_0$ (right panel). $\alpha_{H,4}$ coefficient and t-stat from Figure 6a in red line.
# 7 A model

In this section, we show that our main finding of a skewed innovation response to common demand shocks arises naturally from a model of endogenous innovation and competition with firm heterogeneity. Our model features a “standard” market size effect that increases innovation for all firms. But it also embodies an endogenous competition effect that discourages innovation by low productivity firms. This skewed induced competition effect captures the idea that the expanded market for exports will attract new firms into the export market as more firms find it profitable to sell their products there; this in turn will raise competition for exporters into that market. Due to the nature of competition between firms – featuring endogenous markups – this effect gradually dissipates as productivity (and resulting market share) increases. This competition effect is thus more salient for smaller French firms with initially lower productivity, as they lose market share to larger more productive firms.

The model we present is highly parametrized. However, we show in a companion paper (Aghion et al., 2018) that an increase in market size triggers a skewed competition effect under more general cost (including the return to innovation) and demand conditions. In particular, we show that the main skewness result holds for a broad class of preferences under monopolistic competition that satisfy Marshall’s Second Law of Demand (MSLD), i.e. lead to residual (firm-level) inverse demands that become more inelastic as consumption increases. Instead, a model with monopolistic competition and CES preferences (and hence exogenous markups) would not generate a skewed induced competition effect of increased market size. The recent empirical trade literature provides mounting evidence for the relevance of endogenous markups associated with MSLD demand.<fnref n="25" />

Finally, we stress that our empirical work and results in the previous sections are not meant to specifically test whether the heterogeneous impact of increased market size on innovation is due to the skewed competition effect with endogenous markups that we model in this section. We are just showing that this evidence is consistent with – and easily explained by – a competition channel highlighted by our model. Our model also illustrates the fact that very few assumptions are needed beyond MSLD demand to generate a skewed innovation response to increased market size.

## 7.1 Motivating evidence

To provide supporting evidence of an induced competition effect, a natural place to start is to look at the correlation between a local demand shock and measures of ensuing product entry into that destination. For this purpose, we expand our data on trade flows to incorporate all exporters selling into a given destination. We use the same BACI database, which reports all bilateral trade flows at a disaggregated product level (HS6). We define the set of products sold in a destination at the most disaggregated level possible for this

<footnote n="25">²⁵See Melitz (2018) for a summary of this evidence and how it is connected to endogenous markups and MSLD demand. This evidence for endogenous markups adjustments would also be consistent with oligopoly models where the elasticity of substitution between products remain constant. Such a model would nevertheless feature endogenous price elasticities that respond in a very similar way to those in a model of monopolistic competition with MSLD demand.</footnote>
database: the total number of HS6-exporter pairs observed selling into a destination *i*. We use the HS2 product classification to further partition those products into destination-sector pairs (*i*, *S*). For each of those 20,859 pairs and each year, we construct a demand shock using exports from all countries excluding France, in the same way that we constructed the product-level demand shock in our previous analysis. We then calculate a net entry rate for each pair and year by dividing the net change in the number of products sold by the number of existing products.

Figure 10a shows a bin-scatter plot for this net entry rate in year *t* against the demand shock in the same destination-sector pair (*i*, *S*) in year *t* − 1 (so the observations are triplets (*i*, *S*, *t*) with *t* varying between 2000 and 2007). It clearly shows a strong positive correlation between net product entry and demand shocks. Figure 10b repeats this exercise absorbing a year fixed-effect. The correlation remains strong and positive. In Appendix C (Figures C8), we report an event study regression specification for the cumulative product entry response using leads and lags of the demand shock. Those figures show a large contemporaneous and persistent jump in the number of new products in a destination-sector pair in response to the demand shock.

<figure><img src="image_13.png" /><figcaption>Figure 10: DEMAND SHOCK AND NET ENTRY - CORRELATION</figcaption></figure>

**Notes:** These Figures present the correlation between net entry rate in year *t* and the previous year demand shock. The level of observation is a destination-sector pair (*i*, *S*) and year *t*. Net entry rate is defined as the relative variation in the number of pairs (*hs*6, *j*), where *j* is an exporting country and *hs*6 a product, that are exported to market (*i*, *S*). Left-hand side panel does not absorb any fixed effects while right-hand side panel absorbs a year fixed effect. Data are from 2000 to 2007. Number of observations: 146,013.

7.2 Basic setup

French firms exporting to some export market destination *D* are competing with local firms producing in *D*. We let *L* denote the number of consumers in that destination. This indexes market size. These consumers have preferences over all varieties available in *D*. There is a continuum of differentiated varieties indexed by *i* ∈ [0, *M*], where *M* is the measure of available products. Suppose that the demand for variety *q*ᵢ is generated by a representative consumer in country *D* with additively separable preferences with
sub-utility:<fnref n="26" />

$$u(q_i) = \alpha q_i - \frac{\beta q_i^2}{2},$$

where $\alpha > 0$ and $\beta > 0$.

Those preferences do not differentiate between French or locally produced varieties. Thus, the output, profit and revenues for the French exporters and local producers have the same expression. For simplicity, we assume that both types of firms have access to the same innovation technology, which leads to similar innovation decisions.

### 7.2.1 Consumer optimization

This representative consumer facing prices $p_i$ solves:

$$\max_{q_i \ge 0} \int_0^M u(q_i) di \quad \text{s.t.} \quad \int_0^M p_i q_i di = 1.$$

This yields the inverse residual demand function (per consumer):

$$p(q_i) = \frac{u'(q_i)}{\lambda} = \frac{\alpha - \beta q_i}{\lambda}, \qquad (6)$$

where $\lambda = \int_0^M u'(q_i)q_i di > 0$ is the corresponding Lagrange multiplier, also equal to the marginal utility of income. Given the assumption of separable preferences, this marginal utility of income $\lambda$ is the unique endogenous aggregate demand shifter. Higher $\lambda$ shifts all residual demand curves downwards; we thus interpret this as an increase in competition for a given exogenous level of market size $L$.

### 7.2.2 Firm optimization

Consider a (French or domestic) firm with marginal cost $c$ facing competition $\lambda$. This firm chooses the output per consumer $q(c; \lambda)$ to maximize operating profits $L[p(q)q - cq]$. The corresponding first order condition yields

$$q(c; \lambda) = \frac{\alpha - c\lambda}{2\beta}, \qquad (7)$$

so long as the firm's cost is below $\alpha/\lambda$; the remaining firms with higher cost do not produce. This output choice in turn leads to the maximized profit per consumer

$$\pi(c; \lambda) = \frac{(\alpha - c\lambda)^2}{4\beta\lambda}.$$

In particular, we see that both output and profit are decreasing in both firm level cost $c$ and the endogenous competition measure $\lambda$. More productive firms (with lower cost $c$) are larger and earn higher profits than their less productive counterparts; and an increase in competition $\lambda$ lowers production levels and profits for all firms.

<footnote n="26">²⁶As we previously discussed, our analysis can be extended to a broader class of preferences that satisfy Marshall's Second Law of Demand (such that residual demand becomes more inelastic as consumption increases).</footnote>
### 7.2.3 Innovation choice

A firm is characterized by its baseline cost $\tilde{c}$. It can reduce its marginal cost of production $c$ below its baseline cost by investing in innovation. More formally, we assume that

$$c = \tilde{c} - \varepsilon k,$$

where $k$ is the firm's investment in innovation and $\varepsilon > 0$; and we assume that the cost of innovation is quadratic in $k$, equal to $c_I k + \frac{1}{2} c_{I2} k^2$.<fnref n="27" />

Thus a firm with baseline cost $\tilde{c}$ will choose its optimal R&D investment $k(\tilde{c}; \lambda)$ so as to maximize total profit:

$$\Pi(\tilde{c}, k; \lambda) = L\pi(\tilde{c} - \varepsilon k; \lambda) - c_I k - \frac{1}{2} c_{I2} k^2.$$

The optimal R&D investment $k(\tilde{c}; \lambda)$, if positive, satisfies the first order condition:

$$\varepsilon Q(\tilde{c}, k; \lambda) = c_{I2} k + c_I, \qquad (\text{FOC})$$

where

$$Q(\tilde{c}, k; \lambda) \equiv Lq(\tilde{c} - \varepsilon k; \lambda) = L[\alpha - (\tilde{c} - \varepsilon k)\lambda]/2\beta$$

is the total firm output (across consumers) produced by a firm with baseline cost $\tilde{c}$ and innovation $k$. We assume that the baseline cost $\tilde{c}$ is bounded below by $\tilde{c}_{\min}$ such that $\tilde{c}_{\min} - \varepsilon k(\tilde{c}_{\min}; \lambda) = 0$, or equivalently

$$\tilde{c}_{\min} = \frac{\varepsilon}{c_{I2}} \left( \frac{\varepsilon L \alpha}{2\beta} - c_I \right).$$

This in turn ensures that the post-innovation marginal cost is bounded away from zero, even for the most productive firms.

Figure 11 depicts the optimal innovation choice at the intersection between the marginal cost (MC, right-hand side of FOC) and the marginal benefit of innovation (MB, left-hand side of FOC). As long as the marginal benefit is above the marginal cost of investing in R&D, the firm wants to increase innovation, because the marginal profit made by investing one more unit of R&D exceeds its marginal cost. We assume that the second order condition holds so that the slope of the marginal cost is strictly larger than the slope of the marginal gain:

$$c_{I2} > \varepsilon \frac{\partial Q}{\partial k} = \frac{\varepsilon^2 \lambda L}{2\beta}. \qquad (\text{SOC})$$

This ensures a smooth innovation response to productivity differences.

When comparing a more productive firm (with lower baseline cost, depicted by the blue curve) and a less productive firm (with higher baseline cost, depicted by the red curve), we see that both firms face the same marginal cost curve and their marginal gain

<footnote n="27">²⁷Since we only consider a single sale destination $D$ for our firms, we are implicitly assuming that the innovation is directed at the delivered cost to consumers in $D$. We should thus think of innovation as specific to the appeal/cost trade-off to consumers in $D$. Our companion paper describes how our main skewness result holds for more general functional forms for the cost and return to innovation.</footnote>
<figure><img src="image_14.png" /><figcaption>Figure 11: Optimal innovation is higher for more efficient firms</figcaption></figure>

curves have the same slope. Only the zero intercepts of the two marginal gain curves are different: the lower $\tilde{c}$ firms have a higher intercept, thus a higher marginal gain, and therefore invest more in R&D. Firms with sufficiently high baseline costs do not innovate, as the zero intercept of their marginal gain curves falls below $c_I$, so that even their first innovation unit would not be worth its cost. These are firms with baseline costs above the baseline cost of the marginal innovator, which is equal to:

$$ \hat{C}_I = \frac{1}{\lambda} \left( \alpha - \frac{2\beta c_I}{\varepsilon L} \right). \qquad (8) $$

In the next subsection we analyze how the optimal innovation choice $k(\tilde{c}; \lambda)$ responds to a positive demand shock, i.e. to an increase in market size $L$.

## 7.3 The market size and competition effects

We first analyze the direct effect of an increase in $L$, holding the competition level $\lambda$ constant. At each firm's current innovation choice $k(\tilde{c}; \lambda)$, this triggers a proportional increase in firm output, and an upward shift in the marginal benefit of innovation, inducing all firms to increase innovation.

Figure 12 shows this innovation response for firms with different baseline costs. Both the intercept and the slope of the marginal gain curve increase. We see how this leads to higher innovation for all firms. Given our assumptions on the benefits and costs of innovations, this leads to higher innovation responses for more productive firms:

$$ \frac{\partial^2 k}{\partial L \partial \tilde{c}} < 0. $$

This increase in market size also induces some firms to begin R&D (higher $\hat{C}_I$, see 8).

We now consider the effect of an increase in competition $\lambda$, holding market size $L$ constant. At each firm's current innovation choice $k(\tilde{c}; \lambda)$, this triggers a decrease in firm output (see equation (7)). However, unlike the case of a change in market size $L$, this output response is no longer proportional across firms: high cost firms bear the brunt of the competition increase and disproportionately lose market share. Even though all firms
<figure><img src="image_15.png" /><figcaption>Figure 13: Competition effect (increase in λ)</figcaption></figure>

<figure><img src="image_16.png" /></figure>

respond by reducing innovation, this reduction in innovation is most pronounced (larger) for those high cost firms:

$$ \frac{\partial^2 k}{\partial \lambda \partial \tilde{c}} < 0. $$

This contrasts with the case of a market size decrease (leading to proportional output decreases), which would lead to bigger innovation reductions for low cost firms instead. In the limit for the most efficient firms (with baseline cost approaching $\tilde{c}_{min}$), the negative impact of increased competition on innovation dissipates completely (see FOC).

Figure 13 shows this innovation response for firms with different baseline costs. The increase in competition decreases the marginal benefit of innovation, but substantially more for the high cost firm – because the intercept decrease is larger (recall that the slope of the marginal benefit curve does not change with the firm’s baseline cost).<fnref n="28" /> Thus, the high cost firm’s reduction in innovation is most pronounced. The competition increase also induces some firms to stop R&D (lower $\hat{C}_I$, see 8).

<footnote n="28">²⁸The new dotted marginal benefit curve remains below the old one at least until it meets the marginal cost curve, even though an increase in competition increases the slope of the marginal benefit curve.</footnote>
## 7.4 The heterogeneous innovation response to an export shock

How can our model generate the skewness we observed in firms’ innovation response to a positive export demand shock? In the Appendix we endogenize the equilibrium competition level $\lambda$ in country $D$ and we show that it increases with $L$. The intuition is that an increase in market size $L$ induces entry on the export market $D$ by new firms; this in turn increases the elasticity of the inverse demand curve faced by each French exporter to $D$ and an increase in $\lambda$. It then follows that an increase in market size $L$ will have two effects on firms’ innovation incentives: (a) a *direct* - positive - *market size effect*, whereby the increase in $L$ induces all firms to increase innovation; this effect was shown above to be more positive for more frontier firms (i.e. for firms with lower initial production cost $\tilde{c}$); (b) an *induced* - negative - *competition effect* whereby the increase in $L$ increases competition $\lambda$ which in turns reduces firms’ innovation incentives; as we saw above, the effect of an increase in $\lambda$ on firms’ innovation is more negative for less productive firms (i.e. for firms with higher initial production cost $\tilde{c}$). The overall effect of an increase in market size $L$ on innovation – which combines the direct market size effect and the induced competition effect – will be unambiguously more positive for more frontier firms; moreover, this overall effect can turn out to be negative for the least productive firms - depending on the relative magnitude of the direct and indirect impacts. This heterogeneous response is fully consistent with our empirical analysis: we showed that the most productive half of the firms increase their innovation when their market size expands, while the response for the least productive half of the firms is essentially muted.

## 8 Conclusion

In this paper we used exhaustive data covering the French manufacturing sector to analyze the impact of export demand shocks on patenting by French exporting firms. To disentangle the direction of causality between export demand and innovation, we constructed a firm-level export demand shock which responds to aggregate conditions in a firm’s export destinations but is exogenous to firm-level decisions.

We first showed that French firms respond to exogenous growth shocks in their export destinations by patenting more. Second, we showed that this positive impact of market size on innovation is skewed and entirely driven by French firms with above-median initial labor productivity within their sector. Third, we showed that the innovation response arises 2 to 5 years after a demand shock, whereas the same demand shock raises contemporaneous sales and employment for all firms. And lastly, we developed a simple theoretical model with endogenous innovation and endogenous markups which rationalizes the skewed innovation response to increases in export demand.

Our paper contributes to the existing literature on innovation and market size in several respects: To our knowledge, we are the first to identify a causal impact of firm-level market size on innovation that is independent of any sector-level dynamics (controlling for arbitrary sector level year-on-year changes) and widespread across the entire manufacturing sector. Given the detailed timing of the changes in demand, we are also able to precisely measure the time-lag required before the ensuing patenting activity is recorded. And lastly, we have showed that this innovation response is highly skewed and dominated by relatively more productive firms within each sector.
References

**Acemoglu, Daron and Joshua Linn**, “Market size in innovation: theory and evidence from the pharmaceutical industry,” *The Quarterly journal of economics*, 2004, *119* (3), 1049–1090.

**Aghion, Philippe and Peter Howitt**, *The economics of growth*, Cambridge, Massachusetts. London, England: MIT Press, 2009. OCLC: 610950320.

_, Antoine Dechezleprêtre, David Hemous, Ralf Martin, and John Van Reenen, “Carbon taxes, path dependency, and directed technical change: Evidence from the auto industry,” *Journal of Political Economy*, 2016, *124* (1), 1–51.

_, Antonin Bergeaud, Matthieu Lequien, and Marc J Melitz, “The Impact of Exports on Innovation: Theory and Evidence,” Working Paper 24600, National Bureau of Economic Research May 2018.

_, Nicholas Bloom, Richard Blundell, Rachel Griffith, and Peter Howitt, “Competition and Innovation: an Inverted-U Relationship,” *The Quarterly Journal of Economics*, 2005, *120* (2), 701–728.

**Akcigit, Ufuk, Sina T. Ates, and Giammario Impullitti**, “Innovation and Trade Policy in a Globalized World,” NBER Working Paper 24543, National Bureau of Economic Research 2018.

**Amiti, Mary, Stephen J Redding, and David E Weinstein**, “The impact of the 2018 tariffs on prices and welfare,” *Journal of Economic Perspectives*, 2019, *33* (4), 187–210.

**Autor, David, David Dorn, Gordon Hanson, Gary Pisano, and Pian Shu**, “Foreign Competition and Domestic Innovation: Evidence from U.S. Patents,” Technical Report 22879, National Bureau of Economic Research 2016. DOI: 10.3386/w22879.

_, _ , Lawrence F Katz, Christina Patterson, and John Van Reenen, “The Fall of the Labor Share and the Rise of Superstar Firms,” Working Paper 23396, National Bureau of Economic Research May 2017.

**Bartik, Timothy J.**, *Who Benefits from State and Local Economic Development Policies?* number wbsle. In ‘Books from Upjohn Press.’, W.E. Upjohn Institute for Employment Research, 1991.

**Bernard, Andrew B., Bradford J. Jensen, Stephen J. Redding, and Peter K. Schott**, “Global Firms,” NBER Working Paper 22727, National Bureau of Economic Research 2016.

**Bloom, Nicholas, Mirko Draca, and John Van Reenen**, “Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT and Productivity,” *The Review of Economic Studies*, 2016, *83* (1), 87–117.

**Blundell, Richard, Rachel Griffith, and Frank Windmeijer**, “Individual effects and dynamics in count data models,” *Journal of econometrics*, 2002, *108* (1), 113–131.
—, —, and **John van Reenen**, "Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms," *Review of Economic Studies*, 1999, **66** (3), 529–554.

**Bombardini, Matilde, Bingjing Li, and Ruoying Wang**, "Import Competition and Innovation: Evidence from China," Technical Report 2017.

**Borusyak, Kirill, Peter Hull, and Xavier Jaravel**, "Quasi-Experimental Shift-Share Research Designs," Working Paper 24997, National Bureau of Economic Research September 2018.

**Bustos, Paula**, "Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of MERCOSUR on Argentinian Firms," *American Economic Review*, 2011, *101* (1), 304–340.

**Chetty, Raj, Adam Looney, and Kory Kroft**, "Salience and taxation: Theory and evidence," *American economic review*, 2009, **99** (4), 1145–77.

**Coelli, Federica, Andreas Moxnes, and Karen Helene Ulltveit-Moe**, "Better, Faster, Stronger: Global Innovation and Trade Liberalization," Working Paper 22647, National Bureau of Economic Research September 2016.

de Rassenfosse, Gaetan, Helene Dernis, Dominique Guellec, Lucio Picci, and Bruno van Pottelsberghe de la Potterie, "The worldwide count of priority patents: A new indicator of inventive activity," *Research Policy*, 2013, **42** (3), 720–737.

**Dhingra, Swati**, "Trading Away Wide Brands for Cheap Brands," *American Economic Review*, October 2013, **103** (6), 2554–2584.

**Gaulier, Guillaume and Soledad Zignago**, "BACI: International Trade Database at the Product-Level. The 1994-2007 Version," Working Papers 2010-23, CEPII research center October 2010.

**Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift**, "Bartik Instruments: What, When, Why, and How," Working Paper 24408, National Bureau of Economic Research March 2018.

**Grossman, Gene M. and Elhanan Helpman**, *Innovation and growth in the global economy*, MIT Press, 1991.

— and —, "Quality Ladders in the Theory of Growth," *The Review of Economic Studies*, 1991, **58** (1), 43.

**Hall, Bronwyn, Adam Jaffe, and Manuel Trajtenberg**, "Market Value and Patent Citations," *RAND Journal of Economics*, 2005, **36** (1), 16–38.

**Hummels, David, Rasmus Jørgensen, Jakob Munch, and Chong Xiang**, "The wage effects of offshoring: Evidence from Danish matched worker-firm data," *American Economic Review*, 2014, **104** (6), 1597–1629.

**Iacovone, Leonardo, Wolfgang Keller, and Ferdinand Rauch**, "Innovation Responses to Import Competition," 2011. Unpublished manuscript.
Impullitti, Giammario and Omar Licandro, “Trade, firm selection, and innovation: the competition channel,” *The Economic Journal*, February 2018, *128*, 189–229.

Insee, “NAF REV. 2 et CPF REV. 2 : Guide d’utilisation,” 2016.

Lequien, Matthieu, Martin Mugnier, Loriane Py, and Paul Trichelair, “Linking patents to firms: insights with French firms,” 2019. Mimeo Banque de France.

Lileeva, Alla and Daniel Trefler, “Improved access to foreign markets raises plant-level productivity... for some plants,” *The Quarterly journal of economics*, 2010, *125* (3), 1051–1099.

Malgouyres, Clément, Thierry Mayer, and Clément Mazet-Sonilhac, “Technology-induced Trade Shocks? Evidence from Broadband Expansion in France,” CEPR Discussion Papers 13847, C.E.P.R. Discussion Papers 2019.

Mayer, Thierry and Gianmarco I. P. Ottaviano, “The Happy Few: The Internationalisation of European Firms,” *Intereconomics - Review of European Economic Policy*, 2008, *43* (3), 135–148.

_ , Marc J. Melitz, and Gianmarco I. P. Ottaviano, “Market Size, Competition, and the Product Mix of Exporters,” *American Economic Review*, 2014, *104* (2), 495–536.

_ , _ , and _ , “Product Mix and Firm Productivity Responses to Trade Competition,” NBER Working Papers 22433, National Bureau of Economic Research, Inc July 2016.

Melitz, Marc J., “Competitive effects of trade: theory and measurement,” *Review of World Economics*, February 2018, *154* (1), 1–13.

Morrison, Greg, Massimo Riccaboni, and Fabio Pammolli, “Disambiguation of patent inventors and assignees using high-resolution geolocation data,” *Nature Scientific Data*, 2017, *4*.
# APPENDIX

## A Data description

### A.1 Patent data

Our first database is PATSTAT Spring 2016 which contains detailed information about patent applications from every patent office in the world. Each patent can be exactly dated to the day of application, which is sometimes referred to as the “filing date”.

**Counting patent applications** Each French firm is associated with a number of patent applications by that firm each year (see section A.4). If the firm shares a patent with some other firms, then we only allocate a corresponding share of this patent to the firm. This raises the well-documented issue of truncation bias (Hall et al. (2005)). Indeed as we come closer to the end of the sample, we observe a smaller fraction of all patents since many of them are not yet granted.<fnref n="29" /> In addition, there is a legal obligation to wait 18 months before publication in PATSTAT. With our version of Spring 2016 this implies that we can assume the data to be reasonably complete up to 2012. The sector-time fixed effects also deal with the truncation bias in our regressions. An alternative solution could be to use the year of granting instead of the year of application. However, the former is less relevant than the latter as it is affected by administrative concerns and also by potential lobbying activities that have little to do with the innovation itself. In order to be as close to the time of the innovation as possible, we follow the literature and consider the filing date. We consider every patent owned by a French firm, regardless of the patent office that granted the patent rights. Here we need to be aware of the differences in regulations across intellectual property offices. Some patent offices, especially those of Japan and Korea, offer less breadth to a patent, which implies that more patents are needed to protect a given invention than in other patent offices (see de Rassenfosse et al., 2013). Since we only consider French firms, this would become an issue only if some French firms patent relatively more in countries like Japan or Korea, which would induce an upward bias in the number of patents held by those firms. However, we use a count of priority patent applications only, which is immune to this potential bias.

**Priority patent applications** The fact that an inventor might want to patent its invention in different countries (or through supranational patent offices like PCT or EPO) makes it impossible to consider that one patent is equal to one invention. For this reason, patents are associated with a family which gather different patents which are more or less related to the same invention. More precisely, during a 12-month period following the filing of an application, the applicant has the right of priority. During this period, the applicant can file a similar patent in a different patent office and claim the priority of

<footnote n="29">²⁹The time between patent application and patent granting is a little more than 2 years on average but the distribution of this lag is very skewed with few patent applications still waiting for patent granting many years after the application.</footnote>
the first application when filing this subsequent application. If the priority claim is valid, the date of filing of the first application is considered to be the effective date of filing for the subsequent applications. This first application corresponds to the priority patent. All subsequent filings of the same intellectual property (in particular if they are in other countries) are secondary filings.

**Citations** We also use PATSTAT information on citations received by patents owned by French firms. Citations are often used to address the problem that all patents are not of equal quality and that simply counting the number of patent applications provides a noisy measure of the true innovation performance of a firm. However, the truncation bias issue is even worse with citations than with patent count. Patents from say 2010 have less time to be cited than patents from 1980 regardless of their respective qualities. Comparing different cohorts of patents can thus lead to misinterpreting what is reflected by the total number of citations received by a firm. To address this problem, we consider the number of citations received within a certain time window after the application date (usually 3 or 5 years). Using sector times year fixed effects in the regressions also helps to alleviate this concern.

## A.2 Firm-level accounting data

Our second data source, provided by the DGFiP-Insee and called FICUS and FARE, provides us with accounting data for French firms. The corresponding data are drawn from compulsory reporting of firms and income statements to fiscal authorities in France. Since every firm needs to report every year to the tax authorities, the coverage of the data is all French firms from 1994 to 2012 with no limiting threshold in terms of firm size or sales. This dataset provides us with information on the turnover, employment, value-added, the four-digit sector the firm belongs to ... This corresponds to around 47 million observations and the number of observations per year increases from 1.9m to 3.9m over the period we consider.

The manufacturing sector is defined as category C of the first level of the NAF (Nomenclature d'Activités Française), the first two digits of which are common to both NACE (Statistical Classification of Economic Activities in the European Community) and ISIC (International Standard Industrial Classification of All Economic Activities). Insee provides each firm with a detailed principal activity code (APE) with a top-down approach: it identifies the 1-digit section with the largest value added. Among this section, it identifies the 2-digit division with the largest value-added share, and so on until the most detailed 5-digit APE code (Insee, 2016). It is therefore possible that another 5-digit code shows a larger value-added share than the APE identified, but one can be sure that the manufacturing firms identified produce a larger value-added in the manufacturing section than in any other 1-digit section, which is precisely what we rely on to select the sample of most of our regressions. The 2-digit NAF sector, which we rely intensively on for our fixed effects, then represents the most important activity among the main section of the firm. Employment each year is measured on average within the year and may therefore be a non-integer number.

A unique 9-digit identifier called *Siren number* is associated to each firm, this number is given to the firm until it disappears and cannot be assigned to another firm in the
future. When a firm merges with another firm, or is acquired by another firm, or makes
significant changes in its organization, this number may change over time. Hence, new
entrant *Sirens* in our database do not necessary correspond to new firms.

## A.3 Trade data

**Customs data for French firms** Detailed data on French exports by product and country of destination for each French firm are provided by the French Customs. These are the same data as in Mayer et al. (2014) but extended to the whole 1994-2012 period. Every firm must report its exports by destination country and by very detailed product (at a level finer than HS6). However administrative simplifications for intra-EU trade have been implemented since the Single Market, so that when a firm annually exports inside the EU less than a given threshold, these intra-EU flows are not reported and therefore not in our dataset. The threshold stood at 250 000 francs in 1993, and has been periodically reevaluated (650 000 francs in 2001, 100 000 euros in 2002, 150 000 euros in 2006, 460 000 euros in 2011). Furthermore flows outside the EU both lower than 1 000 euros in value and 1 000 kg in weight are also excluded until 2009, but this exclusion was deleted in 2010.

**Country-product bilateral trade flows** CEPII's database BACI, based on the UN database COMTRADE, provides bilateral trade flows in value and quantity for each pair of countries from 1995 to 2015 at the HS6 product level, which covers more than 5,000 products.

## A.4 Matching

Our paper is the first to merge those three very large - patent, administrative, and customs - datasets covering exporting French firms. Merging administrative firm-level data from FICUS/FARE and Customs data is fairly straightforward as a firm can be identified by its *Siren* identifier in both datasets.<fnref n="30" /> Thus the main challenge is to match either of these two datasets with PATSTAT. Indeed, PATSTAT only reports the name of the patent applicant(s). Not only can this name be slightly different from the name reported in the other two databases, but it may also change over time, for example because of spelling mistakes. We thus relied on the work of Lequien et al. (2019) who developed a matching algorithm to map patents with the corresponding French firms.

Lequien et al. (2019) proceed in three main steps to merge PATSTAT and SIRENE:

1. For each *Siren* number from SIRENE, find a small subset of applicant firms in Patstat with phonetic similarities:

* perform cleaning, splitting and phonetic encoding on firms' name in both databases. Too common words are deleted (THE, AND, CO, FRANCAISE ...).

<footnote n="30">³⁰Although one must keep track of the different definitions of firms across these two datasets.</footnote>
* sort each name by least frequent encoding in SIRENE. The more often a word appears in the database, the less information it can convey to identify firms.

* for each SIRENE firm, the first (ie least frequent) cleaned word of the firm's name is compared with every PATSTAT name. All the PATSTAT names containing this word form a first subset of possible matches. Then the second word of the firm's name is compared with every name in this subset, reducing it further. This procedure stops before arriving at a null subset, and yields a set of likely PATSTAT matches for each SIRENE name. Very often this set is null because the majority of firms do not patent. On average, this subset contains 10 applicants, reducing a lot the computationally intensive comparisons.

## 2. Computation of parameters on these possible matches

* Comparison of the names (raw names, and cleaned names), using Levenshtein distances and an inclusion parameter (all the words in one name are included in the name from the other database)

* zip code comparison (code postal)

* date comparisons (a firm cannot have patented before its creation)

## 3. Matching with supervised learning

* Sample from INPI (*Institut National de la Propriété Intellectuelle*) with 15,000 true matches between Siren number and PATSTAT person id (and in total 170,000 pairs, with the corresponding known mismatches).

* This sample is randomly split into a learning sample and a verification sample (this procedure is repeated 10 times, and the recall and precision measures are averaged over them, so that the choice of the sample does not alter the results). This allows to choose the relevant variables and estimate the parameters.

* apply this model on all the possible matches identified in the previous step.

* in 90% of cases, unique matching. In the remaining 10% of cases, filter further with a decision tree (is the date of creation of the firm lower than the first filing of the applicant?, which couple has the minimum Levenshtein distance between raw names, between cleaned names, is one of the names included in the other?, which firm has the maximum number of employees?)

Based on the (rotating) verification sample taken from INPI data, the recall rate (share of all the true matchings that are accurate) is at 86.1% and the precision rate (share of the identified matches that are accurate) is at 97.0%.
## B Time lag in exports reporting between production and customs data

The different timing for recording the export transaction between tax and customs authorities materializes in the annual data in particular when the transaction occurs at the end of a year $t$ – it is recorded in the tax data for year $t$ – but the shipment occurs at the beginning of the following year, in which case it is recorded in the customs data in year $t+1$. Because part of the January $t+1$ (customs) exports is recorded as year $t$ (tax) exports, a firm with larger (customs) exports in January of year $t+1$ is expected to show a larger discrepancy between tax and customs exports in year $t$. Figure B1 reports the bin-scatter of the ratio of customs over production exports in year $t$ (y axis) versus the share of January $t+1$ exports over exports in year $t$ (both from the customs data, x axis), absorbing firm fixed effects. It shows that when January $t+1$ (customs) exports represent a bigger share of year $t$ (customs) exports, then the customs data falls shorter than the production data for year $t$.

<figure><img src="image_17.png" /><figcaption>Figure B1: CUSTOMS/PRODUCTION DISCREPANCY IN YEAR $t$ VERSUS $t+1$ JANUARY SHARE OF YEAR $t$ EXPORTS</figcaption></figure>

Notes: This Figure reports the bin-scatter of the ratio of customs over production exports for year $t$ (y axis) against the ratio of January $t+1$ exports over year $t$ exports (both taken from customs data). Firm fixed effects are absorbed. Number of observations: 53,287. Years: 1994-2012

We extend this analysis over the last months of year $t$ and the first months of year $t+1$ with the following regression:

$$X_{f,t}/X_{f,t}^* = \sum_{m=-6}^{6} \alpha_m \frac{X_{f,m}}{X_{f,t}} + \mu_{s(f,t),t} + \nu_f + \varepsilon_{f,t} \quad (B1)$$

where $X_{f,m}$ is (customs) exports for month $m$ of year $t+1$ if $m > 0$, or month $12+m$
of year $t$ if $m \le 0$. $0$ corresponds to December $t$, 1 to January $t + 1$. We control for firm fixed effects and the sector of the firm. We keep in the regression only observations where $X_{f,t}/X_{f,t}^* \le 10$ and where each share $\frac{X_{f,m}}{X_{f,t}} \le 1$. Figure B2 reports the coefficients $\alpha_m$ along with their 95 and 99 confidence intervals (standard errors are clustered at the firm level). Everything else equal, if the first months of year $t+1$ represent a larger share of year $t$ exports, then the ratio of yearly exports from customs to production data is smaller. Conversely if the last months of year $t$ represent a larger share of yearly exports, then the customs yearly figure is bigger relative to the production figure.

<figure><img src="image_18.png" /><figcaption>Figure B2: DIFFERENCE IN REPORTING TIMING BETWEEN CUSTOMS AND PRODUCTION SOURCES</figcaption></figure>

**Notes:** This Figure reports the coefficients $\alpha_m$ and corresponding 95% and 99% confidence intervals from equation (B1). Number of observations: 58,027. Years: 1994-2012
C Additional Empirical results

<figure><img src="image_19.png" /><figcaption>Figure C1: OLS: OTHER VARIABLES</figcaption></figure>

Notes: These Figures replicate Figures 4a and 6a but using different measures of innovation as the dependent variable: respectively counting citations received within a 3 year window, counting the number of patents among the 10% most cited in the year and counting any patent (whether priority or secondary filing). Number of observations: 22,175.
<figure><img src="image_20.png" /><figcaption>Figure C2: OLS: REMOVING THE MARKETS WHERE A FRENCH FIRM IS A LEADER</figcaption></figure>

Notes: These Figures replicate Figures 4 and 6 but dropping firm-destination pairs whenever the firm's market share in the destination exceeds 10% in the construction of the demand shock variable. Number of observations: 21,859
<figure><img src="image_21.png" /><figcaption>Figure C3: DIFFERENT TRIMMING THRESHOLDS - OLS: PRIORITY PATENTS</figcaption></figure>

Notes: These Figures replicate Figure 6a but use a different trimming of the demand shocks, respectively: 0, 1, 2, 3, 4 and 5%. Number of observations: 26,954; 24,866; 23,087; 21,381; 19,828 and 18,475 respectively.
<figure><img src="image_22.png" /><figcaption>Figure C4: OLS: SAMPLE OF FIRMS THAT INNOVATED BEFORE 1994</figcaption></figure>

**Notes:** These Figures replicate Figures 4 and 6 but restricting to firms that innovated before 1994 (i.e. with a patent application with filing date between 1985 and 1994). Number of observations: 6,866
<figure><img src="image_23.png" /><figcaption>Figure C5: OLS: SAMPLE OF FIRMS WITH $t_0$ = 1994</figcaption></figure>

Notes: These Figures replicate Figures 4 and 6 but restricting to firms that first exported before 1994 ($t_0$ = 1994 in equations (2) and (4)). Number of observations: 15,742
<figure><img src="image_24.png" /><figcaption>Figure C6: OLS: 6 LAGS</figcaption></figure>

**Notes:** These Figures replicate Figures 4 and 6 but using 6 lags and 4 leads (therefore defining *k* = 6 and *k'* = 4 in equations (2) and (4)). Number of observations: 18,707
<figure><img src="image_25.png" /><figcaption>Figure C7: OLS: NO PRE-TREND</figcaption></figure>

Notes: These Figures replicate Figures 4 and 6 but without using any lead (therefore defining $k = 5$ and $k' = 0$ in equations (2) and (4)). Number of observations: 25,237

<figure><img src="image_26.png" /><figcaption>Figure C8: DEMAND SHOCK AND NET ENTRY - EVENT STUDY - DEFINITION 2</figcaption></figure>

Notes: This Figure reports regression coefficients $\alpha_k$ as well as 95% confidence intervals from an OLS estimation of the following equation:

$$N_{S,i,t} = \sum_{k=-3}^{3} \alpha_k DM_{S,i,t+k} + \beta X_{S,i,t} + \varepsilon_{S,i,t},$$

denoting $DM_{S,i,t}$ the trade shock faced by market $(S, i)$ (a sector-destination pair, see Section 7.1) at $t$ and $N_{S,i,t}$ the cumulative net entry rate into market $(S, i)$ at $t$ since 2000, $X_{S,i,t}$ controls for (the log of) the number of products exported to $(S, i)$ during year $t$ and for a country times year fixed effects. Left hand side panel does not weight the observations while right hand side panel weights the observations by the size of the export market. Number of observations: 19,058
# D Theoretical appendix

We describe how the equilibrium competition level $\lambda$ in destination $D$ is endogenously determined and show that $\lambda$ increases with $L$. Although this equilibrium involves all the firms operating in $D$, including both the French exporters to $D$ along with the domestic producers in $D$, we show that the equilibrium competition level $\lambda$ is determined independently of the export supply to $D$ (which then only impacts the number of domestic entrants and producers).

Let $\Gamma_D(\tilde{c})$ denote the cumulative distribution of baseline costs $\tilde{c}$ among domestic producers in $D$. We assume that $\Gamma_D(\tilde{c})$ has support on $[\tilde{c}_{0D}, +\infty)$ with $\tilde{c}_{0D} > \tilde{c}_{\min}$. Let $F_D$ denote the fixed production cost faced by those domestic firms in $D$. Since a firm's operating profit is monotonic in its baseline cost $\tilde{c}$, producing for the domestic market $D$ is profitable only for domestic firms with a baseline cost $\tilde{c}$ below a cutoff value $\tilde{C}_D$ defined by the zero profit condition:

$$ \Pi(\tilde{C}_D, 0; \lambda) = F_D, \qquad (\text{ZCP}) $$

where we have assumed that $\tilde{C}_D > \tilde{C}_I$ so that the firm with the cutoff cost $\tilde{C}_D$ does not innovate (and hence does not incur any innovation cost). Entry is unrestricted subject to a sunk entry cost $F_D^E$. In equilibrium, the expected profit of a prospective entrant will be equalized with this cost, yielding the free-entry condition:

$$ \int_{\tilde{c}_{0D}}^{\tilde{C}_D} [\Pi(\tilde{c}, k(\tilde{c}; \lambda); \lambda) - F_D] d\Gamma_D(\tilde{c}) = F_D^E. \qquad (\text{FE}) $$

**Proposition 1** The two conditions (ZCP) and (FE) jointly determine a unique pair $(\lambda, \tilde{C}_D)$.

**PROOF Uniqueness:** in $(\tilde{C}_D, \lambda)$ space, the (ZCP) condition is strictly downward-sloping while the (FE) condition is strictly upward-sloping, ensuring uniqueness of the equilibrium if such an equilibrium exists. More precisely: (a) an increase in competition from $\lambda$ to $\lambda + d\lambda$ reduces the profit of firms with baseline cost $\tilde{C}_D(\lambda)$, so that those firms no longer operate; this means that $\tilde{C}_D(\lambda + d\lambda) < \tilde{C}_D(\lambda)$, which proves that the (ZCP) curve is strictly downward-sloping; (b) an increase in competition from $\lambda$ to $\lambda + d\lambda$ reduces the profit of all firms (the envelope theorem ensures that at the optimal innovation level $\frac{\partial \Pi}{\partial k} = 0$ so that $\frac{d\Pi}{d\lambda} = \frac{\partial \Pi}{\partial \lambda} < 0$); this in turn means that $\tilde{C}_D$ has to strictly increase for the (FE) condition to hold, which proves that the (FE) curve is strictly upward-sloping.

**Existence:** We show that the (FE) curve lies below the (ZCP) curve for values of $\tilde{C}_D$ close to $\tilde{c}_{0D}$, and that the (FE) curve ends up above the (ZCP) curve for high values of $\tilde{C}_D$. As $\tilde{C}_D$ becomes close to $\tilde{c}_{0D}$, (ZCP) implies a value for $\lambda$ which is positive and bounded away from zero, whereas (FE) requires $\lambda$ to become arbitrarily small, because the integrand must go to $+\infty$ for the integral over a very small interval to remain equal to $F_D^E$. Next, recall that the (ZCP) curve must remain below the $\lambda = \frac{\alpha}{\tilde{C}_D}$ curve. Given that $\frac{\alpha}{\tilde{C}_D} \to 0$ when $\tilde{C}_D \to +\infty$, the $\frac{\alpha}{\tilde{C}_D}$ curve must cross the (FE) curve at some point. At this point, the (ZCP) curve lies below the (FE) curve.

For simplicity, we have abstracted from any export profits for the domestic firms. This is inconsequential for our prediction that the equilibrium competition level $\lambda$ increases
with market size $L$, so long as destination $D$ is small relative to the size of the global export market.<fnref n="31" />

**Proposition 2** An increase in market size $L$ in $D$ leads to an increase in competition $\lambda$.

**PROOF** We prove this proposition by contradiction. If $\lambda$ were to decrease, then the cutoff $\hat{C}_D$ would have to increase (see (ZCP)). Since $\pi(c; \lambda)$ is decreasing in $\lambda$, then $\Pi(\tilde{c}, k; \lambda)$ must also increase for any given innovation level $k$ when $\lambda$ decreases. Given the optimization principle, $\Pi(\tilde{c}, k(\tilde{c}; \lambda); \lambda)$ must also increase. This, together with an increase in the cutoff $\hat{C}_D$, represents a violation of the (FE) condition. Thus competition $\lambda$ must increase when $L$ increases.

$$ \int_{\tilde{c}_{0D}}^{\hat{C}_D} [\Pi(\tilde{c}, k(\tilde{c}; \lambda); \lambda) - F_D + \Pi_{-D}(\tilde{c}, k; \{\lambda_{-D}\})] d\Gamma_D(\tilde{c}) = F_D^E, $$

where $\{\lambda_{-D}\}$ denotes the vector of competition levels in countries other than $D$. So long as these competition levels $\{\lambda_{-D}\}$ do not respond to changes in $D$, the export profits shift up the marginal benefit of innovation in (FOC) by an amount that does not depend on $\lambda$ or $L$. This marginal benefit curve will remain an increasing function of innovation $k$ and will shift up with any market-wide change in $D$ that increases firm output $Q(\tilde{c}, k; \lambda)$ at fixed innovation $k$.

<footnote n="31">³¹More precisely, the free entry condition can be extended to incorporate the (net) export profits $\Pi_{-D}$ earned in other destinations:</footnote>
## 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,<br>J. TOUJAS-BERNATE<br>Macro-economic import functions with imperfect competition - An application to the E.C. Trade</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><td>G 9410</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td></tr><tr><td>G 9002</td><td>H. ROUSSE<br>Détection et effets de la multicolinearité dans les modèles linéaires ordinaires - Un prolongement de la 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 négociations multilatérales du GATT<br>Junin 1992 (tête version)<br>Novembre 1992 (version finale)</td><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 9411</td><td>C. DEFEUILLEY - Ph. QUIRION<br>Les déchets d'emballages ménagers : une analyse économique des bagages française et allemande</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 9312</td><td>L. BLOCH - B. CŒURÉ<br>Q de Tobin marginal et transmission des choos financiers</td><td>G 9313</td><td>Équipes Amadeus (INSEE), Banque de France, Métric (DP)<br>Présentation des propriétés des principaux modèles macroéconomiques du Service Public</td><td>G 9413</td><td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'elasticité emploi-salaire dépend-elle des structures de qualification ?</td></tr><tr><td>G 9004</td><td>D. GUELEC et P. RALLE<br>Coopérativité, croissance et innovation de produit</td><td>G 9204</td><td>P. SEVESTRE<br>L'économie sur données individuelles-temporaires. Une note introdutive</td><td>G 9313</td><td>Équipes Amadeus (INSEE), Banque de France, Métric (DP)<br>Présentation des propriétés des principaux modèles macroéconomiques du Service Public</td><td>G 9414</td><td>I. KABLA<br>Le Chois de breveter une invention</td></tr><tr><td>G 9005</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Les consequences de la désindexation. Analyse dans une maquette prix-salaires</td><td>G 9205</td><td>H. ERKEL-ROUSSE<br>Le commerce extérieur et l'environnement international dans le modèle AMADEUS (réestimation 1992)</td><td>G 9314</td><td>G. CREPON - E. DUGUET<br>Research & Development, competition and innovation</td><td>G 9504</td><td>J. BOURDIEU - B. CŒURÉ - B. SEDILLOT<br>Irreversible investment and Uncertainty:<br>When is there a Value of Waiting?</td></tr><tr><td>G 9101</td><td>Équipe AMADEUS<br>Le modèle AMADEUS - Première partie -Présentation générale</td><td>G 9206</td><td>N. GREENAN et D. GUELLEC<br>Coordination within the firm and endoge nous growth</td><td>G 9315</td><td>B. DORMONT<br>Quelle est l'influence du coût du travail sur l'emploi ?</td><td>G 9502</td><td>L. BLOCH - B. CŒURÉ<br>Impfections du marché du crédit, investissement des entreprises et cycle économique</td></tr><tr><td>G 9102</td><td>J.L. BRILLET<br>Le modèle AMADEUS - Deuxième partie -Propriétés variantielles</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><td>G 9316</td><td>G. BLANCHET - C. BROUSSE<br>Deux études sur l'âge de la retraite</td><td>G 9503</td><td>D. GOUX - E. MAURIN<br>Les transformations de la demande de travail par qualification en France</td></tr><tr><td>G 9103</td><td>D. GUELEC et P. RALLE<br>Endogenous growth and product innovation</td><td>G 9208</td><td>B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHINEN<br>Le modèle AMADEUS - Troisième partie - Le commerce extérieur et l'environnement international</td><td>G 9317</td><td>D. BLANCHET<br>Répartition du travail dans une population hétérogène : deux notes</td><td>G 9503</td><td>Une étude sur la période 1970-1993</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 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 9318</td><td>D. EYSSARTIER - N. PONTY<br>AMADEUS - an annual macro-economic model for the medium and long term</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 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 9301</td><td>B. CREPON<br>Innovation, taille et concentration : causalités et dynamiques</td><td>G 9319</td><td>G. CETTE - Ph. CUNÉO - D. EYSSARTIER<br>J. GAUTIÉ<br>Les effets sur l'emploi d'un abaissement du coût du travail des jeunes</td><td>G 9505</td><td>D. GOUX - E. MAURIN<br>Persistance des hérarchies sectorielles de salaires: un réexamen sur données françaises</td></tr><tr><td>G 9106</td><td>B. CREPON<br>Innovation, taille et concentration : causalités et dynamiques</td><td>G 9302</td><td>Ch. CASES<br>Durées de chômage et comportements d'offre de travail : une revue de la littérature</td><td>G 9401</td><td>D. BLANCHET<br>Les structures par âge importent-elles ?</td><td>G 9505<br>Bis</td><td>D. GOUX - E. MAURIN<br>Persistance des hérarchies sectorielles de salaires: un réexamen sur données françaises</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 9303</td><td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique</td><td>G 9402</td><td>J. GAUTIÉ<br>Le chômage des jeunes en France: problème de formation ou phénomène de file d'attente ?<br>Les éléments du débat</td><td>G 9506</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 9108</td><td>M. GLAUDE et M. MOUTARDIER<br>Une évaluation du coût direct de l'enfant de 1979 à 1989</td><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 9404</td><td>P. QUIRION<br>Les déchets en France : éléments statistiques et économiques</td><td>G 9507</td><td>S. JACOBZONE<br>Les liens entre RMI et chômage, une mise en perspective<br>NON PARU - article sorti dans Économie et Prévision n° 122 (1996) - pages 95 à 113</td></tr><tr><td>G 9109</td><td>P. RALLE et alii<br>France - Allemagne : performances économiques comparées</td><td>G 9305</td><td>P. JALLARD<br>Le traité de Maastricht : présentation juridique et historique</td><td>G 9405</td><td>D. LADIRAY - M. GRUN-REHOMME<br>Lissage par moyennes mobiles - Le problème des extrémités de série</td><td>G 9601</td><td>Banque de France - CEPREMAP - Direction de la Prévision - Érasme - INSEE - OFCE<br>Structures et propriétés de cinq modèles macroéconomiques français</td></tr><tr><td>G 9110</td><td>J.L. BRILLET<br>Micro-DMS<br>NON PARU</td><td>G 9306</td><td>J.L. BRILLET<br>Micro-DMS - présentation et propriétés</td><td>G 9406</td><td>V. MAILLARD<br>Theorie et pratique de la correction des effets de jours ouvrables</td><td>G 9602</td><td>Rapport d'activité de la DESE de l'année 1995</td></tr><tr><td>G 9111</td><td>A. MAGNIER<br>Effets accéléré et multiplicateur en France depuis 1970 : quelques résultats empiriques</td><td>G 9307</td><td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td><td>G 9407</td><td>F. ROSENWALD<br>La décision d'investir</td><td>G 9603</td><td>J. BOURDIEU - A. DRAZNIKS<br>L'octroi de crédit aux PME : une analyse à partir d'informations bancaires</td></tr><tr><td>G 9112</td><td>B. CREPON et G. DUREAU<br>Investissement en recherche-developpement : analyse de causalités dans un modèle d'accélérateur généralisé</td><td>G 9308</td><td>S. JACOBZONE<br>Les grands réseaux publics français dans une perspective européenne</td><td>G 9408</td><td>L. BLOCH, J. BOURDIEU,<br>G. COLIN-SEDILLOT, G. LONGUEVILLE<br>Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté</td><td>G 9604</td><td>A. TOPIOL-BENSAID<br>Les implantations japonaises en France</td></tr><tr><td>G 9113</td><td>J.L. BRILLET, H. ERKEL-ROUSSE, J. TOUJAS-BERNATE<br>"France-Allemagne Couleurs" - Deux économies vues par une maquette macro-économétique</td><td>G 9309</td><td>L. BLOCH - B. CŒURÉ<br>Profitabilité de l'investissement productif et transmission des choos financiers</td><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'aide au logement - quelques éléments d'évaluation</td><td>G 9605</td><td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie, peut-on paier d'une gestion globale du capital santé ?<br>Une modélisation microéconométrique empirique</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><td></td><td></td><td></td><td></td></tr></table>
<table><tr><td>G 9606</td><td>C. DOZ - F. LENGLART<br>Factor analysis and unobserved compo<br>models; an application to the study of French<br>business surveys</td></tr><tr><td>G 9607</td><td>N. GREENAN - D. GUELLEC<br>La théorie coopérative de la firme</td></tr><tr><td>G 9608</td><td>N. GREENAN - D. GUELLEC<br>Technological innovation and employment<br>reallocation</td></tr><tr><td>G 9609</td><td>Ph. COUR - F. RUPPRECHT<br>L'intégration asymptérique au sein du continent<br>américain: un essai de modélisation</td></tr><tr><td>G 9610</td><td>S. DUCHENE - G. FORGEOT - A. JACQUOT<br>Analysis des évolutions récentes de la producti-<br>vité apparente du travail</td></tr><tr><td>G 9611</td><td>X. BONNET - S. MAHFOUZ<br>The influence of different specifications of<br>wages-prices spirals on the measure of the<br>NAIRU: the case of France</td></tr><tr><td>G 9612</td><td>PH. COUR - E. DUBOIS, S. MAHFOUZ,<br>J. PISANI-FERRY<br>The cost of fiscal retrenchment revisited: how<br>strong is the evidence?</td></tr><tr><td>G 9613</td><td>A. JACQUOT<br>Les flexions des taux d'activité sont-elles seule-<br>ment conjonctuelles ?</td></tr><tr><td>G 9614</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique Français-Chinois</td></tr><tr><td>G 9701</td><td>J.L. SCHNEIDER<br>La taxe professionnelle : éléments de cadrage<br>économique</td></tr><tr><td>G 9702</td><td>J.L. SCHNEIDER<br>Transition et stabilité politique d'un système<br>redistribuit</td></tr><tr><td>G 9703</td><td>D. GOUX - E. MAURIN<br>Train or Pay: Does it Reduce Inequalities to En-<br>courage Firms to Train their Workers?</td></tr><tr><td>G 9704</td><td>P. GENIER<br>Deux contributions sur dépendance et équité</td></tr><tr><td>G 9705</td><td>E. DUGUET - N. IUNG<br>R & D investment, Patent Life and Patent Value<br>An Econometric Analysis at the Firm Level</td></tr><tr><td>G 9706</td><td>M. HOUDEBINE - A. TOPIOL-BENAID<br>Les entreprises internationales en France: une<br>analyse à partir de données individuelles</td></tr><tr><td>G 9707</td><td>M. HOUDEBINE<br>Polarisation des activités et spécialisation des<br>départements en France</td></tr><tr><td>G 9708</td><td>E. DUGUET - N. GREENAN<br>Le biais technologique : une analyse sur don-<br>nées individuelles</td></tr><tr><td>G 9709</td><td>J.L. BRILLET<br>Analysing a small French ECM Model</td></tr><tr><td>G 9710</td><td>J.L. BRILLET<br>Formalizing the transition process: scenarios for<br>capital accumulation</td></tr><tr><td>G 9711</td><td>G. FORGEOT - J. GAUTIÉ<br>Insertion professionnelle des jeunes et proces-<br>sus de déclassement</td></tr></table>

<table><tr><td>G 9712</td><td>E. DUBOIS<br>High Real Interest Rates: the Consequence of a<br>Saving Investment Disequilibrium or of an in-<br>sufficient Credibility of Monetary Authorities?</td></tr><tr><td>G 9713</td><td>N. GREENAN - D. GUELLEC<br>La théorie coopérative de la firme</td></tr><tr><td>G 9714</td><td>F. LEQUILLER<br>Does the French Consumer Price Index Over-<br>state Inflation?</td></tr><tr><td>G 9715</td><td>X. BONNET<br>Peut-on mettre en évidence les rigidités à la<br>baisse des salaires nominale ?<br>Une étude sur quelques grands pays de l'OCDE</td></tr><tr><td>G 9716</td><td>N. IUNG - F. RUPPRECHT<br>Productivité de la recherche et rendements<br>d'échelle dans le secteur pharmaceutique<br>français</td></tr><tr><td>G 9717</td><td>E. DUGUET - I. KABLA<br>Appropriation strategy and the motivations to use<br>the patent system in France - An econo metric<br>analysis at the firm level</td></tr><tr><td>G 9718</td><td>L.P. PELÉ - P. RALLE<br>Àge de la retraite : les aspects incitatifs du ré-<br>gime général</td></tr><tr><td>G 9719</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique français-chinois,<br>chinois-français</td></tr><tr><td>G 9720</td><td>M. HOUDEBINE - J.L. SCHNEIDER<br>Mesurer l'influence de la fiscalité sur la locali-<br>sation des entreprises</td></tr><tr><td>G 9721</td><td>A. MOUROUGANE<br>Crédibilité, indépendance et politique monétaire<br>Une redevance à la littérature</td></tr><tr><td>G 9722</td><td>P. AUGERAUD - L. BRIOT<br>Les données comptables d'entreprises<br>Le système intermédiaire d'entreprises<br>Passage des données individuelles aux données<br>sectorielles</td></tr><tr><td>G 9723</td><td>P. AUGERAUD - J.E. CHAPRON<br>Using Business Accounts for Compiling National<br>Accounts: the French Experience</td></tr><tr><td>G 9724</td><td>P. AUGERAUD<br>Les comptes d'entreprise par activités - Le pas-<br>sage aux comptes - De la comptabilité - A<br>parlaire</td></tr><tr><td>G 9801</td><td>H. MICHAUDON - C. PRIGENT<br>Presentation du modèle AIMADEUS</td></tr><tr><td>G 9802</td><td>J. ACCARDO<br>Une étude de comptabilité générationnelle<br>pour la France en 1996</td></tr><tr><td>G 9803</td><td>X. BONNET - S. DUCHÉNE<br>Apports et limites de la modélisation<br>« Real Business Cycles »</td></tr><tr><td>G 9804</td><td>C. BARLET - C. DUGUET -<br>D. ENCAOUA - J. PRADEL<br>The Commercial Success of Innovations<br>An econometric analysis at the firm level in<br>French manufacturing</td></tr></table>

<table><tr><td>G 9805</td><td>P. CAHUC - Ch. GIANELLA -<br>D. GOUX - A. ZILBERBERG<br>Equalizing Wage Differences and Bargain ing<br>Power - Evidence form a Panel of French Firms</td></tr><tr><td>G 9806</td><td>J. ACCARDO - M. JLASSI<br>La productivité globale des facteurs entre 1975<br>et 1996</td></tr><tr><td>G 9807</td><td>Bilan des activités de la Direction des Études<br>et Synthèses Économiques - 1997</td></tr><tr><td>G 9808</td><td>X. MOUROUGANE<br>Can a Conservative Governor Conduct an Ac-<br>comodative Monetary Policy?</td></tr><tr><td>G 9809</td><td>X. BONNET - E. DUBOIS - L. FAUVET<br>Asymétrie des inflations relatives et menus<br>costs : tests sur l'infographique</td></tr><tr><td>G 9810</td><td>E. DUGUET - N. IUNG<br>Sales and Advertising with Spillovers at the firm<br>level: Estimation of a Dynamic Structural Model<br>on Panel Data</td></tr><tr><td>G 9811</td><td>J.P. BERTHIER<br>Congestion urbaine : un modèle de trafic de<br>pointe à courbe débît-vitesse et demande<br>élastique</td></tr><tr><td>G 9812</td><td>C. PRIGENT<br>La part des salaires dans la valeur ajoutée : une<br>approche macroéconomique</td></tr><tr><td>G 9813</td><td>A.Th. AERTS<br>L'évolution de la part des salaires dans la valeur<br>ajoutée en France reflète-elle les évolutions<br>individuelles sur la période 1979-1994 ?</td></tr><tr><td>G 9814</td><td>B. SALANIÉ<br>Guide pratique des séries non-stationnaires</td></tr><tr><td>G 9901</td><td>S. DUCHENE - A. JACQUOT<br>Une croissance plus riche en emplois depuis le<br>début de la décennie ? Une analyse en compa-<br>raison internationale</td></tr><tr><td>G 9902</td><td>Ch. COLIN<br>Modélisation des carrières dans Destine</td></tr><tr><td>G 9903</td><td>Ch. COLIN<br>Évolution de la dispersion des salaires : un essai<br>de prospective par microsimulation</td></tr><tr><td>G 9904</td><td>B. CREPON - N. IUNG<br>Innovation, emploi et performances</td></tr><tr><td>G 9905</td><td>B. CREPON - Ch. GIANELLA<br>Wages inequalities in France 1969-1992</td></tr><tr><td>G 9906</td><td>C. BONNET - R. MAHIEU<br>Microsimulation techniques applied to inter-<br>generational transfers - Pensions in a dy namic<br>framework: the case of France</td></tr><tr><td>G 9907</td><td>F. ROSENWALD<br>L'impact des contraintes financières dans la dé-<br>cision d'investissement</td></tr><tr><td>G 9908</td><td>Bilan des activités de la DESE - 1998</td></tr><tr><td>G 9909</td><td>J.P. ZOYEM<br>Contrat d'insertion et sortie du RMI<br>Évaluation des effets d'une politique sociale</td></tr><tr><td>G 9910</td><td>Ch. COLIN - FI. LEGROS - R. MAHIEU<br>Bilans contribués comparés des régimes de<br>retraite du secteur privé et de la fonction<br>publique</td></tr></table>

<table><tr><td>G 9911</td><td>G. LAROQUE - B. SALANIÉ<br>Une décomposition du non-emploi en France</td></tr><tr><td>G 9912</td><td>B. SALANIÉ<br>Une maquette analytique de long terme du<br>marché du travail</td></tr><tr><td>G 9912</td><td>Ch. GIANELLA<br>Une estimation de l'élasticité de l'emploi peu<br>qualifié à son coût</td></tr><tr><td>G 9913</td><td>Division « Redistribution et Politiques Sociales »<br>Le modèle de microsimulation dynamique<br>DESTINÉE</td></tr><tr><td>G 9914</td><td>E. DUGUET<br>Macro-commandes SAS pour l'économétrie des<br>panels et des variables qualitatives</td></tr><tr><td>G 9915</td><td>R. DUHAUTOIS<br>Évolution des flux d'emplois en France entre<br>1990 et 1996 : une étude empirique à partir du<br>fichier des bénéfices réels normaux (BRN)</td></tr><tr><td>G 9916</td><td>J.Y. FOURNIER<br>Extraction du cycle des affaires : la méthode de<br>Baxter et King</td></tr><tr><td>G 9917</td><td>B. CRÉPON - R. DESPLATZ - J. MAIRESSE<br>Estimating price cost margins, scale economies<br>and workers' bargaining power at the firm level</td></tr><tr><td>G 9918</td><td>Ch. GIANELLA - Ph. LAGARDE<br>Productivity of hours in the aggregate pro duction<br>function: an evaluation on a panel of French<br>firms from the manufacturing sector</td></tr><tr><td>G 9919</td><td>S. AUDRIC - P. GIVORD - C. PROST<br>Évolution de l'emploi et des coûts par quali-<br>fication entre 1982 et 1996</td></tr><tr><td>G 2000/01</td><td>R. MAHIEU<br>Les déterminants des dépenses de santé : une<br>approche macroéconomique</td></tr><tr><td>G 2000/02</td><td>C. ALLARD-PRIGENT - H. GUILMEAU -<br>A. QUINET<br>The real exchange rate as the relative price of<br>nontrables in terms of tradables: theoretical<br>investigation and empirical study on French data</td></tr><tr><td>G 2000/03</td><td>J.-Y. FOURNIER<br>L'approximation du filtre passe-bande proposée<br>par Christiano et Fitzgerald</td></tr><tr><td>G 2000/04</td><td>Bilan des activités de la DESE - 1999</td></tr><tr><td>G 2000/05</td><td>B. CREPON - F. ROSENWALD<br>Investment et contraintes de financement : le<br>poids du cycle</td></tr><tr><td>G 2000/06</td><td>Une estimation sur données françaises<br>A. FLIPO<br>Les comportements matrimoniaux de fait</td></tr><tr><td>G 2000/07</td><td>R. MAHIEU - B. SÉDILLOT<br>Microsimulations of the retirement decision: a<br>supply side approach</td></tr><tr><td>G 2000/08</td><td>C. AUDENS - C. PROST<br>Déficit conjoncturel : une prise en compte des<br>conjonctures passées</td></tr><tr><td>G 2000/09</td><td>R. MAHIEU - B. SÉDILLOT<br>Équivalent patrimonial de la rente et souscription<br>de retraite complémentaire</td></tr></table>
<table><tr><td>G 2000/10</td><td>R. DUHAUTOIS</td><td>Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertiaire ?</td></tr><tr><td>G 2000/11</td><td>G. LAROQUE - B. SALANIÉ</td><td>Temps partiel féminin et incitatives financières à l'emploi</td></tr><tr><td>G 2000/12</td><td>Ch. GIANELLA</td><td>Local unemployment and wages</td></tr><tr><td>G 2000/13</td><td>B. CREPON - Th. HECKEL</td><td>Informalité en France : une évaluation à partir de données individuelles</td></tr><tr><td>G 2001/01</td><td>F. LEQUILLER</td><td>- La nouvelle économie et la mesure de la croissance du PIB<br/>- The new economy and the measure of GDP growth</td></tr><tr><td>G 2001/02</td><td>S. AUDRIC</td><td>La reprise de la croissance de l'emploi profite-t-elle aussi aux non-riploèmes ?</td></tr><tr><td>G 2001/03</td><td>I. BRAJUN-LEMAIRE</td><td>Évolution et répartition du surplus de productivité</td></tr><tr><td>G 2001/04</td><td>A. BEAUDU - Th. HECKEL</td><td>Le canal du crédit fonctionne-t-il en Europe ?<br/>Une étude de l'haprofondité des comportements d'investissement à partir de données de bilan agrégées</td></tr><tr><td>G 2001/05</td><td>C. AUDENIS - P. BISCOURP - N. FOURCADE - O. LOISEL</td><td>Testing the augmented Solow growth model: An empirical reassessment using panel data</td></tr><tr><td>G 2001/06</td><td>R. MAHIEU - B. SÉDILLOT</td><td>Départ à la retraite, irréversibilité et incertitude</td></tr><tr><td>G 2001/07</td><td>J. Ph. GAUDEMET</td><td>Bilan des activités de la DESE - 2000</td></tr><tr><td>G 2001/08</td><td>B. CRÉPON - Ch. GIANELLA</td><td>Les dispositifs d'acquisition à titre facultatif d'annuïtés viagères de retraite</td></tr><tr><td>G 2001/09</td><td>B. CRÉPON - Ch. GIANELLA</td><td>Fiscalité, coût d'usage du capital et demande de facteurs : une analyse sur données individuelles</td></tr><tr><td>G 2001/10</td><td>B. CRÉPON - R. DESPLATZ</td><td>Évaluation des effets des dispositifs d'aliègements</td></tr><tr><td>G 2001/11</td><td>J.-Y. FOURNIER</td><td>Comparaison des salaires des secteurs public et privé</td></tr><tr><td>G 2001/12</td><td>J.-P. BERTHIER - C. JAULENT</td><td>R. CONVENEVOLE - S. PISANI</td></tr><tr><td>G 2001/13</td><td>P. BISCOURP - Ch. GIANELLA</td><td>Substitution and complementarity between capital, skilled and less skilled workers: an analysis at the firm level in the French manufacturing industry</td></tr><tr><td>G 2001/14</td><td>I. ROBERT-BOBEE</td><td>Modelling demographic behaviours in the French</td></tr></table>

<table><tr><td>G2002/13</td><td>M. LECLAIR</td><td>Réduction du temps de travail et tensions sur les facteurs de production</td></tr><tr><td>G2002/14</td><td>E. WALRAET - A. VINCENT</td><td>Évaluation de la redistribution intragénérationnelle dans le système de retraite des salariés du privé - Une approche par microsimulation</td></tr><tr><td>G2002/15</td><td>P. CHONE - D. LE BLANC - I. ROBERT-BOBEE</td><td>Offre de travail féminine et garde des jeunes enfants</td></tr><tr><td>G2002/16</td><td>F. MAUREL - S. GREGOIR</td><td>Les indices de compétitivité des pays : interprétation et limites</td></tr><tr><td>G2003/01</td><td>N. RIEDINGER - E.HAUVY</td><td>Le coût de dépollution atmosphérique pour les entreprises françaises: Une estimation à partir de données individuelles</td></tr><tr><td>G2003/02</td><td>P. BISCOURP et F. KRAMARZ</td><td>Création d'emplois, destruction d'emplois et internationalisation des entreprises industrielles françaises: une analyse sur la période 1986-1992</td></tr><tr><td>G2003/03</td><td>Bilan des activités de la DESE - 2002</td><td></td></tr><tr><td>G2003/04</td><td>P.-O. BEFFY - J. DEROYON - N. FOURCADE - S. GREGOIR - N. LAIB - B. MONFORT</td><td>Évolutions démographiques et croissance : une projection macro-économique à l'horizon 2020</td></tr><tr><td>G2003/05</td><td>P. AUBERT</td><td>La situation des salariés de plus de cinquante ans dans le secteur privé</td></tr><tr><td>G2003/06</td><td>P. AUBERT - B. CRÉPON</td><td>Age, salaire et productivité</td></tr><tr><td>G2003/07</td><td>H. BARON - P.O. BEFFY - N. FOURCADE - R. MAHIEU</td><td>La productivité des salariés décline-t-elle en fin de carrière?</td></tr><tr><td>G2003/08</td><td>P. O. BEFFY - B. MONFORT</td><td>Patrimoine des ménages, dynamique d'allocation et comportement de consommation</td></tr><tr><td>G2003/09</td><td>P. BISCOURP - N. FOURCADE</td><td>Peut-on mettre en évidence l'existence de rigidités à la baisse des salaires à partir de données individuelles ? Le cas de la France à la fin des années 90</td></tr><tr><td>G2003/10</td><td>M. LECLAIR - P. PETIT</td><td>Présence syndicale dans les firmes : quel impact sur les inégalités salariales entre les hommes et les femmes?</td></tr><tr><td>G2003/11</td><td>P. O. BEFFY - X. BONNET - M. DARRACO-PARIES - B. MONFORT</td><td>Les comportements démographiques dans le modèle de microsimulation DESTINIE - Une comparaison des estimations issues des enquêtes Jeunes et Carrières 1997 et Histoire Famillelle 1999</td></tr><tr><td>G2003/12</td><td>J.-P. ZOYEM</td><td>La dynamique des bas revenus : une analyse des entrées-sorties de pauvreté</td></tr><tr><td>G2003/13</td><td>F. HILD</td><td>Prévisions d'inflation pour la France</td></tr></table>

<table><tr><td>G2002/13</td><td>M. LECLAIR</td><td>Réduction du temps de travail et tensions sur les facteurs de production</td></tr><tr><td>G2002/14</td><td>E. WALRAET - A. VINCENT</td><td>Évaluation de la redistribution intragénérationnelle dans le système de retraite des salariés du privé - Une approche par microsimulation</td></tr><tr><td>G2002/15</td><td>P. CHONE - D. LE BLANC - I. ROBERT-BOBEE</td><td>Offre de travail féminine et garde des jeunes enfants</td></tr><tr><td>G2002/16</td><td>F. MAUREL - S. GREGOIR</td><td>Les indices de compétitivité des pays : interprétation et limites</td></tr><tr><td>G2003/01</td><td>N. RIEDINGER - E.HAUVY</td><td>Le coût de dépollution atmosphérique pour les entreprises françaises: Une estimation à partir de données individuelles</td></tr><tr><td>G2003/02</td><td>P. BISCOURP et F. KRAMARZ</td><td>Création d'emplois, destruction d'emplois et internationalisation des entreprises industrielles françaises: une analyse sur la période 1986-1992</td></tr><tr><td>G2003/03</td><td>Bilan des activités de la DESE - 2002</td><td></td></tr><tr><td>G2003/04</td><td>P.-O. BEFFY - J. DEROYON - N. FOURCADE - S. GREGOIR - N. LAIB - B. MONFORT</td><td>Évolutions démographiques et croissance : une projection macro-économique à l'horizon 2020</td></tr><tr><td>G2003/05</td><td>P. AUBERT</td><td>La situation des salariés de plus de cinquante ans dans le secteur privé</td></tr><tr><td>G2003/06</td><td>P. AUBERT - B. CRÉPON</td><td>Age, salaire et productivité</td></tr><tr><td>G2003/07</td><td>H. BARON - P.O. BEFFY - N. FOURCADE - R. MAHIEU</td><td>La productivité des salariés décline-t-elle en fin de carrière?</td></tr><tr><td>G2003/08</td><td>P. O. BEFFY - B. MONFORT</td><td>Patrimoine des ménages, dynamique d'allocation et comportement de consommation</td></tr><tr><td>G2003/09</td><td>P. BISCOURP - N. FOURCADE</td><td>Peut-on mettre en évidence l'existence de rigidités à la baisse des salaires à partir de données individuelles ? Le cas de la France à la fin des années 90</td></tr><tr><td>G2003/10</td><td>M. LECLAIR - P. PETIT</td><td>Présence syndicale dans les firmes : quel impact sur les inégalités salariales entre les hommes et les femmes?</td></tr><tr><td>G2003/11</td><td>P. O. BEFFY - X. BONNET - M. DARRACO-PARIES - B. MONFORT</td><td>Les comportements démographiques dans le modèle de microsimulation DESTINIE - Une comparaison des estimations issues des enquêtes Jeunes et Carrières 1997 et Histoire Famillelle 1999</td></tr><tr><td>G2003/12</td><td>J.-P. ZOYEM</td><td>La dynamique des bas revenus : une analyse des entrées-sorties de pauvreté</td></tr><tr><td>G2003/13</td><td>F. HILD</td><td>Prévisions d'inflation pour la France</td></tr></table>

<table><tr><td>G2004/02</td><td>M. DUÈE - C. REBILLARD</td><td>La dépendance des personnes âgées : une projection à long terme</td></tr><tr><td>G2004/03</td><td>S. RASPILLER - N. RIEDINGER</td><td>Réglation environnementale et choix de localisation des groupes français</td></tr><tr><td>G2004/04</td><td>A. NABOULET - S. RASPILLER</td><td>Les déterminants de la décision d'investir: une approche par les perceptions subjectives des firmes</td></tr><tr><td>G2004/05</td><td>N. RAGACHE</td><td>La déclaration des enfants par les couples non mariés est-elle fiscalement optimale?</td></tr><tr><td>G2004/06</td><td>M. DUÈE</td><td>L'impact du chômage des parents sur le devenir scolaire des enfants</td></tr><tr><td>G2004/07</td><td>P. AUBERT - E. CAROLI - M. ROGER</td><td>New Technologies, Workplace Organisation and the Age Structure of the Workforce: Firm-Level Evidence</td></tr><tr><td>G2004/08</td><td>E. DUGUET - C. LELARGE</td><td>Les brevets acquis-ils les incitations privées à innover? Un examen microéconométique</td></tr><tr><td>G2004/09</td><td>S. RASPILLER - P. SILLARD</td><td>Affiliating versus Subcontracting: the Case of Multinationals</td></tr><tr><td>G2004/10</td><td>J. BOISSINOT - C. L'ANGEVIN - B. MONFORT</td><td>Public Debt Sustainability: Some Results on the French Case</td></tr><tr><td>G2004/11</td><td>S. ANANIAN - P. AUBERT</td><td>Travailleurs âgés, nouvelles technologies et changements organisationalistes: un réexamen à partir de l'enquête «REPONSE»?</td></tr><tr><td>G2004/12</td><td>X. BONNET - H. PONCET</td><td>Structures de revenus et propensions différentes à consommer - Vers une équation de consommation des ménages plus robuste en prévision pour la France</td></tr><tr><td>G2004/13</td><td>C. PICART</td><td>Évaluer la rentabilité des sociétés non financières</td></tr><tr><td>G2004/14</td><td>J. BARDAJI - B. SÉDILLOT - E. WALRAET</td><td>Les retraites du secteur public: projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE</td></tr><tr><td>G2005/01</td><td>S. BUFFETEAU - P. GODEFROY</td><td>Conditions de départ en retraite selon l'âge de fin d'études: analyse prospective pour les générations 1945 à 1974</td></tr><tr><td>G2005/02</td><td>C. AFSA - S. BUFFETEAU</td><td>L'évolution de l'activité féminine en France: une approche par pseudo-panel</td></tr><tr><td>G2005/03</td><td>P. AUBERT - P. SILLARD</td><td>Délocalisations et réductions d'effectifs dans l'industrie française</td></tr><tr><td>G2005/04</td><td>M. LECLAIR - S. ROUX</td><td>Mesure et utilisation des emplois instables dans les entreprises</td></tr><tr><td>G2005/05</td><td>C. L'ANGEVIN - S. SERRAVALLE</td><td>Performances à l'exportation de la France</td></tr></table>
<table><tr><td>G2005/06</td><td>Bilan des activités de la Direction des Études et Synthèses Économiques - 2004</td></tr><tr><td>G2005/07</td><td>S. RASPILLER<br>La concurrence fiscale : principaux enseignements de l'analyse économique</td></tr><tr><td>G2005/08</td><td>C. L'ANGEVIN - N. LAIB<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 révisions 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>Chromage et boucle prix-salaires :<br>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 enjuvés de conjoncture et prévision macroéconomiques :<br>Exemple de la prévision de la production<br>manufacture</td></tr><tr><td>G2005/13</td><td>P. AUBERT - D. BLANCHET - D. BLAU<br>The labour market after 50: some elements of a Franco-American comparison</td></tr><tr><td>G2005/14</td><td>D. BLANCHET - T. DEBRAND -<br>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<br>DESTINE</td></tr><tr><td>G2005/16</td><td>H. RAOUI - S. ROUX<br>Étude de simulation sur la participation versée aux salaires par les entreprises</td></tr><tr><td>G2006/01</td><td>C. BONNET - S. BUFFETEAU - P. GODEFROY<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:<br>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. ROUVIERE<br>Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data</td></tr></table>

<table><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><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. LELARGE<br>Les entreprises (industrielles) françaises sont-elles à la frontière technologique ?</td></tr><tr><td>G2006/12</td><td>O. BIAU - N. FERRARI<br>Theorie de l'opinion<br>Faut-il pondérer 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, recensement et poids des activités de support dans les groupes (1995-2000)</td></tr><tr><td>G2007/01</td><td>D. SRAER<br>Allégrons 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 :<br>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. TAVAN<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'exemple de la durée du travail</td></tr></table>

<table><tr><td>G2007/11</td><td>R. RATHELOT - P. SILLARD<br>Zones Franches Urbaines : quels 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><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. VERGÉ<br>The Effects of Retail Regulations on Prices<br>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 d'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 -<br>T. LE BARBANCHON<br>Microsimumer 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>Laurent CLAVEL - 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?<br>A Real-Time Analysis on French Data</td></tr></table>

<table><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><tr><td>G2009/06</td><td>L. DAVEZIES - X. D'HAULTFOUEUILLE<br>Faut-il pondérer ?... 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 -<br>S. LE LAIDET - 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>Detecting 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 -<br>R. RATHELOT<br>Discrimination à l'embauche : 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<br>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<br>Tome 1 - Version avec volumes à prix constants</td></tr><tr><td>G2010/04</td><td>M. É. CLERC - É. COUDIN<br>L'IPC, 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. CECI-RENAUD - P.-A. CHEVALIER<br>Les seuils de 10, 20 et 50 salariés : impact sur la taille des entreprises françaises</td></tr></table>
<table><tr><td>G2014/10</td><td>B. GARBINTI - P. LAMARCHE<br>Les hauts revenus epargnent-ile davantage ?</td></tr><tr><td>G2014/11</td><td>D. AUDENAERT - J. BARDAJI - R. LARDEUX -<br>M. ORAND - M. SICNIC<br>Wage Resilience in France since the Great<br>Recession</td></tr><tr><td>G2014/12</td><td>F. ARNAUD - J. BOUSSARD - A. POISSONNIER -<br>H. SOAL<br>Computing additive contributions to growth and<br>other issues for chain-linked quarterly<br>aggregates</td></tr><tr><td>G2014/13</td><td>H. FRAISSE - F. KRAMARZ - C. PROST<br>Labor Disputes and Job Flows</td></tr><tr><td>G2014/14</td><td>P. GIVORD - C. GRISLAIN-LETRÉMY<br>- H. NAEGELE<br>How does fuel taxation impact new car<br>purchases? An evaluation using French<br>consumer-level dataset</td></tr><tr><td>G2014/15</td><td>P. AUBERT - S. RABATÉ<br>Durée passée en carrière et durée de vie en<br>retraite : quel partage des gains d'espérance de<br>vie ?</td></tr><tr><td>G2015/01</td><td>A. POISSONNIER<br>The walking dead Euler equation<br>Addressing a challenge to monetary policy<br>models</td></tr><tr><td>G2015/02</td><td>Y. DUBOIS - A. MARINO<br>Indicateurs de rendement du système de retraite<br>français</td></tr><tr><td>G2015/03</td><td>T. MAYER - C. TREVIEN<br>The impacts of Urban Public Transportation:<br>Evidence from the Paris Region</td></tr><tr><td>G2015/04</td><td>S.T. LY - A. RIEGERT<br>Measuring Social Environment Mobility</td></tr><tr><td>G2015/05</td><td>M. A. BEN HALINA - V. HYAFIL-SOLELHAC<br>M. KOUBI - C. REGAERT<br>Quel est l'impact du système d'indemnisation<br>maladie sur la durée des arrêts de travail pour<br>maladie ?</td></tr><tr><td>G2015/06</td><td>Y. DUBOIS - A. MARINO<br>Disparités de rendement du système de retraite<br>dans le secteur privé : approches intergénéra-<br>tionnelle et intragénérationnelle</td></tr><tr><td>G2015/07</td><td>B. CAMPAGNE - V. ALHENC-GELAS -<br>J.-B. BERNARD<br>No evidence of financial accelerator in France</td></tr><tr><td>G2015/08</td><td>Q. LAFFÉTER - M. PAK<br>Élasticités des recettes fiscales au cycle<br>économique : étude de trois impôts sur la<br>période 1979-2013 en France</td></tr><tr><td>G2015/09</td><td>J.-M. DUASSIN-BENICHOU, S. IDMACHICHE,<br>A. LEDUC et E. POULIQUEN<br>Les déterminants de l'attractivité de la fonction<br>publique de l'État</td></tr><tr><td>G2015/10</td><td>P. AUBERT<br>La modulation du montant de pension selon la<br>durée de carrière et l'âge de la retraite : quelles<br>disparités entre assurés ?</td></tr><tr><td>G2015/11</td><td>V. DORTET-BERNADET - M. SICNIC<br>Effet des aides publiques sur l'emploi en R&D<br>dans les petites entreprises</td></tr></table>

<table><tr><td>G2015/12</td><td>S. GEORGES-KOT<br>Annual and lifetime incidence of the value-added<br>tax in France</td></tr><tr><td>G2015/13</td><td>M. POULHÉS<br>Are Enterprise Zones Benefits Capitalized into<br>Commercial Property Values? The French Case</td></tr><tr><td>G2015/14</td><td>J.-B. BERNARD - Q. LAFFÉTER<br>Effet de l'activité et prix sur le revenu salarial<br>des différentes catégories socioprofessionnelles</td></tr><tr><td>G2015/15</td><td>C. GEAY - M. KOUBI - G de LAGASNERIE<br>Projections des dépenses de soins de ville,<br>construction d'un module pour Destinine</td></tr><tr><td>G2015/16</td><td>J. BARDAJI - J.-C. BRICONGNE -<br>B. CAMPAGNE - G. GAULIER<br>Compared performances of French companies<br>on the domestic and foreign markets</td></tr><tr><td>G2015/17</td><td>C. BELLÉGO - R. DE NUJ<br>The redistributive effect of online piracy on the<br>box office performance of American movies in<br>foreign markets</td></tr><tr><td>G2015/18</td><td>J.-B. BERNARD - L. BERTHET<br>French households financial wealth: which<br>changes in 20 years?</td></tr><tr><td>G2015/19</td><td>M. POULHÉS<br>Fenêtre sur Cour ou Chambre avec Vue ?<br>Les prix hédoniques de l'immobilier parisien</td></tr><tr><td>G2016/01</td><td>B. GARBINTI - S. GEORGES-KOT<br>Time to smell the roses? Risk aversion, the<br>timing of inheritance receipt, and retirement</td></tr><tr><td>G2016/02</td><td>P. CHARNOZ - C. LELARGE - C. TREVIEN<br>Communication Costs and the Internal<br>Organization of Multi-Plant Businesses: Evidence<br>from the Impact of the French High-Speed Rail</td></tr><tr><td>G2016/03</td><td>C. BONNET - B. GARBINTI - A. SOLAZ<br>C. Gender inequality after Divorce: The Flip Side of<br>Marital Specialization - Evidence from a French<br>Administrative Database</td></tr><tr><td>G2016/04</td><td>D. BLANCHET - E. CAROLI - C. PROST -<br>M. ROGER<br>Health capacity to work at older ages in France</td></tr><tr><td>G2016/05</td><td>B. CAMPAGNE - A. POISSONNIER<br>MELEZE: A DSGE model for France within the<br>Euro Area</td></tr><tr><td>G2016/06</td><td>B. CAMPAGNE - A. POISSONNIER<br>Laffer curves and fiscal multipliers: lessons from<br>Mélèze model</td></tr><tr><td>G2016/07</td><td>B. CAMPAGNE - A. POISSONNIER<br>Structural reforms in DSGE models: a case for<br>sensitivity analyses</td></tr><tr><td>G2016/08</td><td>Y. DUBOIS et M. KOUBI<br>Relevement de l'âge de départ à la retraite : quel<br>impact sur l'activité des seniors de la réforme des<br>retraites de 2010 ?</td></tr><tr><td>G2016/09</td><td>A. NAQUAS - M. ORAND - I. SLIMANI HOUTI<br>Les entreprises employant des salaires au Smic:<br>quelles caractéristiques et quelle rentabilité ?</td></tr><tr><td>G2016/10</td><td>T. BLANCHET - Y. DUBOIS - A. MARINO -<br>M. ROGER<br>Patrimoine privé et retraite en France</td></tr><tr><td>G2016/11</td><td>M. PAK - A. POISSONNIER<br>Accounting for technology, trade and final</td></tr></table>

<table><tr><td>G2017/01</td><td>D. FOUGÈRE - E. GAUTIER - S. ROUX<br>Understanding Wage Floor Setting in Industry-<br>Level Agreements: Evidence from France</td></tr><tr><td>G2017/02</td><td>Y. DUBOIS - M. KOUBI<br>Regles d'indexation des pensions et sensibilité<br>des dépenses de retraites à la croissance<br>économique et aux chocs démographiques</td></tr><tr><td>G2017/03</td><td>A. CAZENAVE-LACROUTZ - F. GODET<br>L'espérance de vie en retrait sans incapacité<br>sèvère des générations nées entre 1960 et<br>1990 : une projection à partir du modèle Destinine</td></tr><tr><td>G2017/04</td><td>J. BARDAJI - B. CAMPAGNE -<br>M.-B. KHDER - Q. LAFFÉTER - O. SIMON<br>(Insee)<br>A-S) DUFERNEZ - C. ELEZAAR -<br>P. LEBLANC - E. MASSON -<br>H. PARTOUCHE (DG-Trésor)<br>Le modèle macroéconométrique Mésange :<br>rés estimation et nouveautés</td></tr><tr><td>G2017/05</td><td>J. BOUSSARD - B. CAMPAGNE<br>Fiscal Policy Coordination in a Monetary<br>Union at the Zero-Lower-Bound</td></tr><tr><td>G2017/06</td><td>A. CAZENAVE-LACROUTZ -<br>A. GODZINSKI<br>Effects of the one-day waiting period for sick<br>leave on health-related absences in the<br>French central civil service</td></tr><tr><td>G2017/07</td><td>P. CHARNOZ - M. ORAND<br>Qualification, prog ress technique et marchés<br>du travail locaux en France, 1990-2011</td></tr><tr><td>G2017/08</td><td>K. MILIN<br>Modélisation de l'inflation en France par une<br>approche macrosectorielle</td></tr><tr><td>G2017/09</td><td>C.-M. CHEVALIER - R. LARDEUX<br>Homeownership and labor market outcomes:<br>disentangling externality and composition<br>effects</td></tr><tr><td>G2017/10</td><td>P. BEAUMONT<br>Time is Money: Cash-Flow Risk and Export<br>Market Behavior</td></tr><tr><td>G2018/01</td><td>S. ROUX - F. SAVIGNAC<br>SMES' financing: Divergence across Euro<br>area countries?</td></tr><tr><td>G2018/02</td><td>C.-M. CHEVALIER - A. LUCIANI<br>Computerization, labor productivity and<br>employment: impacts across industries vary<br>with technological level</td></tr><tr><td>G2018/03</td><td>R. MONIN - M. SUAREZ CASTILLO<br>L'effet du CICE sur les prix : une double<br>analyse sur données sectorielles et<br>individuelles</td></tr><tr><td>G2018/04</td><td>R. LARDEUX<br>Who Understands The French Income Tax?<br>Bunching Where Tax Liabilities Start</td></tr></table>

<table><tr><td>G2018/05</td><td>C.-M. CHEVALIER<br>Financial constraints of innovative firms and<br>sectoral growth</td></tr><tr><td>G2018/06</td><td>R. S.-H. LEE - M. PAK<br>Pro-competitive effects of globalisation on<br>prices, productivity and markups: Evidence<br>in the Euro Area</td></tr><tr><td>G2018/07</td><td>C.-M. CHEVALIER<br>Consumption inequality in France between<br>1995 and 2011</td></tr><tr><td>G2018/08</td><td>A. BAUER - B. GARBINTI -<br>S. GEORGES-KOT<br>Financial Constraints and Self-Employment<br>in France, 1945-2014</td></tr><tr><td>G2018/09</td><td>P. BEAUMONT - A. LUCIANI<br>Prime à l'embauche dans les PME :<br>évaluation à partir des déclarations<br>d'embauche</td></tr><tr><td>G2018/10</td><td>C. BELLÉGO - V. DORTET-BERNADET -<br>M. TÉPAUT<br>Collaboration de deux dispositifs d'aide à la<br>R&D collaborative public-privé</td></tr><tr><td>G2018/11</td><td>R. MONIN - M. SUAREZ CASTILLO<br>Replication et rapprochement des travaux<br>d'évaluation de l'affet du CICE sur l'emploi<br>en 2013 et 2014</td></tr><tr><td>G2018/12</td><td>A. CAZENAVE-LACROUTZ - F. GODET -<br>V. LIN<br>L'introduction d'un gradient social dans la<br>maltrité au sein du modèle Destologie 2</td></tr><tr><td>G2019/01</td><td>M. ANDRÉ - A.-L. BIOTTEAU<br>Effets de moyen terme d'une hausse de TVA<br>sur le niveau de vie et les inégalités : une<br>approche par microsimulation</td></tr><tr><td>G2019/02</td><td>A. BOURGEOIS - A. BRIAND<br>Le modèle Avionic : la modélisation<br>Input/Output des comptes nationaux</td></tr><tr><td>G2019/03</td><td>A. GODZINSKI - M. SUAREZ CASTILLO<br>Short-term health effects of public transport<br>disruptions: air pollution and viral spread<br>channels</td></tr><tr><td>G2019/04<br>F1903</td><td>L. AEBERHARDT - F. HATIER -<br>M. LECLAIR - B. PENTINAT - J.-D. ZAFAR<br>L'économie numérique fausse-t-elle le<br>partage volume-prix du PIB?</td></tr><tr><td>G2019/05</td><td>A. CAZENAVE-LACROUTZ - E. YILMAZ<br>Dans quelle mesure les incitations tarifaires<br>et la procédure de mise sous accord<br>préalable ont-elles contribué au<br>développement du chirurgie ambulatoire?</td></tr><tr><td>G2019/06</td><td>J.-P. CLING - S. EGHBAL-TEHERANI -<br>M. ORZONI - C. PLATEAU<br>The Differences between EU Countries for<br>Sustainable Development Indicators:<br>It is (mainly) the Economy!</td></tr><tr><td>G2019/07</td><td>P. CHONÉ - L. WILNER<br>Competition on Unobserved Attributes: The<br>Case of the Hospital Industry</td></tr></table>
G2019/08
P. PORA - L. WILNER
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
M. ANDRÉ - A. SIREY JOL
Imposition des couples et des familles: effets budgétaires et redistributifs de l'impôt sur le revenu

G2019/11
K. MOHKAM - O. SIMON
L'empreinte matière de l'économie française: une analyse par matérière 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 reflètent-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