# Inter-regional highly skilled worker mobility and technological novelty

## Documents de travail

N° 2024-05- Mars 2024

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# Inter-regional highly skilled worker mobility and technological novelty*

JULIEN GIORGI‡, ANNE PLUNKET◊, FELIPE STAROSTA DE WALDEMAR◊

19 Mars 2024

Département des Études Économiques – Timbre G201

88, avenue Verdier – CS 70058 – 92541 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>* ‡INSEE, ◊ RITM, Université Paris Saclay</footnote>

<footnote>The authors thank participants at the 6th Geography of Innovation Conference (2022) in Milan, Clément Malgouyres who discussed the paper at the D2E INSEE Seminar, Matthieu Crozet and Miren Lafourcade for helpful comments and suggestions.</footnote>
# Mobilité inter-régionales de travailleurs hautement qualifiés et nouveauté technologique

Cet article propose une approche empirique pour étudier comment les régions introduisent une véritable nouveauté technologique en explorant l'effet des travailleurs géographiquement mobiles sur les nouvelles combinaisons de classes technologiques au sein des brevets déposés. Il a été démontré que les migrations augmentent l'activité d'innovation (quantité et entrée) à destination, mais la question de savoir si la mobilité des travailleurs permet de nouvelles combinaisons dans le portefeuille technologique est restée sans réponse. Empiriquement, nous cherchons à savoir si les travailleurs hautement qualifiés qui quittent des régions disposant d'un avantage comparatif révélé dans une classe technologique donnée suscitent de nouvelles combinaisons technologiques dans la région de destination. Nous testons cette hypothèse en utilisant à la fois des données administratives officielles sur la mobilité de la main-d'œuvre et des paires de combinaisons de codes technologiques présentes dans les données de brevets entre 1996 et 2017. Nous constatons que les travailleurs provenant de régions spécialisées dans une sous-classe technologique donnée stimulent la nouveauté technologique dans cette sous-classe dans la région de destination. Cet effet n'est présent que pour les sous-classes liées au portefeuille technologique local à destination et est présent à la fois pour les nouveautés de type réutilisation et création. Les résultats indiquent que la capacité d'absorption locale et la complémentarité entre les dynamiques internes et les moteurs exogènes sont importantes pour que les régions diversifient leur espace technologique.

**Mots-clés** : Nouveauté technologique, Mobilité de la main-d'œuvre, Connexité, Développement régional

**JEL Code** : O31, 033, J61, R11

# Inter-regional highly skilled worker mobility and technological novelty

This article proposes an empirical approach to study how regions introduce true technological novelty by exploring the effect of geographically mobile workers on new technological classes combinations within patents filled. Migration has been shown to raise innovation activity (quantity and entry) at destination, but the question whether workers' mobility allows new combinations in technological portfolio remained unanswered. Empirically, we investigate if high skilled workers moving from regions with a revealed comparative advantage in a given technological class spark new technological combinations in the destination region. We test this hypothesis using both official administrative labor mobility data and pairs of combination of technological codes present in patent data between 1996 and 2017. We find that workers coming from regions specialized in a given technology subclass drive technological novelty in this subclass at destination. This effect is present only for subclasses related to the local technological portfolio at destination and is both present for reuse and creation-type novelties. Results imply that local absorptive capacity and a complementarity between internal dynamics and exogenous drivers are important for regions to diversify their technological space.

**Keywords** : Technological novelty, Labor mobility, Relatedness, Regional development

**Code JEL** : O31, 033, J61, R11
# 1. Introduction

Technological diversification is key in the economic development of regions and makes it possible both to create economic systems that are more resilient to shocks, particularly sectoral, and to develop new growth prospects. However, the technological diversification observed locally tends to follow path and place dependent processes (Martin and Sunley, 2006; Heimeriks and Boschma, 2014; Henning et al., 2013), mainly favoring the development of incremental refinements in technological domains already explored locally and an entry in new technological activities driven by their similarity, also called relatedness, with the current regional set of activities (Hidalgo et al., 2018). Innovation domains do not emerge randomly and are instead related to the set of existing activities already carried out in that region, making technological diversification difficult. Moreover, beyond the emergence of new technological domains (or classes), regional technological diversification can designate the way in which different technological domains are combined in new ways within the innovations filed. Indeed, following the schumpetarian tradition of knowledge recombination, innovations are defined as a set of technologies and components that are combined to create new product. When unfamiliar components are combined they increase the opportunity of breakthrough inventions (Arts and Fleming, 2018). These new combinations of previously uncombined technology classes, also labeled technological novelty, can be an important driver of technological diversification leading to radical innovations and economic growth (Arthur, 2007; Epicoco et al., 2022). Understanding the mechanisms behind this technological novelty is therefore of utmost importance. Technological novelty, defined as new combinations of technology subclasses (Fleming, 2007; Verhoeven et al., 2016) may either build on regional determinants such as technological specialization or build on external sourcing of new technologies.

On the latter, a large literature has shown that migrants, both international and inter-regional, had an impact on the level and pace of innovation carried out at their destination. Moreover, as they carry knowledge with them, they also act as drivers of its diffusion. Thus mobile workers are seen as agents of technological change at destination (Miguelez and Morrison, 2022). In this paper, we explore the role of inter-regional migration on technological change modeled as technological novelty. More precisely, we ask whether inter-regional mobility of high skilled workers spark the emergence of new combinations of technological subclasses in their host region.

We empirically test the question above combining two major datasets. On the one hand, we use the OECD Regpat database which provides information on patents, the regional location of inventors and technological content. With these data, we measure technological novelty which is defined as a new combination of a technological subclasses occurring for the first time in a given patent in a given year and region. The technology subclass is a four-digit code based on the International Patent Classification (IPC) enabling to classify the technological content of patents filed. On the other hand, we use BTS ("Base tous salariés", All-Employee Basis) from INSEE (French national statistics office) which provides detailed information on
the employment for all workers and their location in years t and t-1. We compute the bilateral inter-regional worker flows by socio-professional categories available in BTS. The dataset is a panel built at the Subclass x Region x Year level (Class-Region-Year, CRY). The dependent variable is the number of pairwise subclass combinations associated with a given subclass C that appears for the first time in a given year Y and region R. Following Bahar et al., (2020), the main independent variable is, for each CRY, the sum of incoming qualified workers coming from regions specialized in that given subclass C. In our empirical approach, we control for other external drivers of novelty, internal determinants and fine grained fixed effects.

Our contribution is threefold. First, we combine the literature on recombinant novelty, focusing on how new combinations of technology class emerges (Verhoeven et al., 2016), with that on the international mobility of workers (Bahar et al., 2020), and we extend findings of Boschma et al. (2022) by exploring the interregional linkages that allow technological breakthroughs. To our knowledge, this is the first study that analyzes the effect of within country mobility of workers on innovation measured with patent data. Movement of workers occur more within country than across international borders. Our aim here is to capture more detailed information on the whole sample of workers' mobility to better measure the effect on a within country (so less heterogeneous) technological space. Our second contribution is to build a dataset on the mobility of all workers (and not only inventors), and focus on specific categories (high skilled workers) that might bring knowledge with them to their destination. We use official administrative data that measures all mobility of workers from year t-1 to year t when they change the location of their workplace. Third and last, we contribute to the novelty literature by focusing on a specific type of external contribution, i.e., the mobility of workers and the specific and knowledge they carry at a given technological subclass.

Our results extend previous knowledge in the aforementioned literature as we capture the effect of inter-regional mobility of skilled workers on novelty. We find that high skilled workers, such as scientists, engineers and technicians, when they carry an intensive technological knowledge, raise the creation of new combinations for this technological subclass in the region of their destination. This result is also true if the combinations are new to the country (France) or the World, and this result is robust when novelty is measured not as the number of new combinations but as their share or a dummy variable. Moreover, we find that the effect is higher when the destination region has a revealed comparative advantage in that technology, and when this technology is more related to the local technological space. This result highlights the fact that external drivers of novelty are complementary to and driven by the internal technological dynamic of the region.

The rest of the paper is organized as follows. In the next section we present our literature review and the hypothesis we test in the empirical section. The following section details the data we use, and section 4 presents our main variables. Section 5 unveils the main descriptive statistics and section 6 shows our empirical strategy. Section 7 presents the main
econometric results, including heterogeneous effects, and Section 8 presents the robustness checks. The last section concludes.

## 2. Literature review

In order to study the relationship between highly skilled internal migration and the dynamics of technological change and novelty in host regions, we integrate three streams of literature. First, we build on contributions documenting the role of migrants and mobile workers on knowledge transfers and their impact on the technological composition of their host destination. Second, we consider the literature on regional diversification and how migrants may act as agents of technological change driving regions to diversify into new technologies. And finally, we draw on the Schumpeterian tradition of knowledge recombination to further investigate the conditions under which the inflow of non-local knowledge workers may drive technological novelty by recombining existing or new technologies.

### 2.1. Migration of knowledge workers and innovation

This section reviews the literature investigating how migration affects innovation by focusing on knowledge diffusion mechanisms. Like firm collaborations and interpersonal networks, migration helps knowledge to move across geographical boundaries (Henderson et al., 2005; Singh and Marx, 2013). Accessing extra-regional knowledge is key to overcome lock-in and renew the local knowledge base. It is a solution to the fact that knowledge is geographically bounded and difficult to transfer across firms and cities as it is tacit and sticky (Polanyi and Knowledge, 1958; Cowan et al., 2000). This phenomenon is even reinforced by the fact that mobile workers are not likely to relocate in space, therefore, mobility occurs primarily within cities (Agrawal and Cockburn, 2003; Almeida and Kogut, 1999; Breschi and Lissoni, 2009).

When highly skilled workers move across space, they tend to increase the efficiency of the innovation process by providing a renewed and wider access to knowledge at a reduced cost. They thus enable knowledge to be reused instead of re-created elsewhere and enables its recombination with local sources of knowledge (Oettl and Agrawal, 2008). As knowledge is mostly tacit, it is attached to the individuals, firms, and regions in which they have been developed and produced. Thus, knowledge is more easily transferred throughout space if it is carried by workers coming from regions and countries in which they were initially developed and produced (Breschi et al., 2010; Breschi et al., 2020; Coe and Bunnell, 2003).

Once these migrant workers have moved to their new location, they positively contribute to the level and growth of innovation at destination. These movers prove to be more productive than non-movers (Gagliardi, 2015) and once they have transferred their knowledge to their host regions, they tend either to develop new technologies on their own by creating new ventures (Bettin et al. 2019) or through collaborations with local teams (Choudhury and Kim, 2019). The recent literature provides increasing evidence of the positive role of migration on innovation at the host regions and countries (Lissoni, 2018; Bosetti et al.,
2015; Kerr et al., 2016; Fassio et al., 2019; Cristelli and Lissoni, 2020): highly skilled migrants tend to patent proportionately more than native inventors (Kerr et al., 2016; Hunt and Gauthier-Loiselle, 2010; Fassio et al., 2019), their share in patenting is growing over time (Kerr and Lincoln, 2010) and they positively impact the patenting activity of natives (Kerr et al., 2016). They do also contribute to increase basic research as measured by the number of citations to published articles (Bosetti et al., 2015).

## 2.2. Migration, diversity, and technological evolution of regions

In addition to their impact on the level and pace of innovation, a more recent debate has questioned whether migrants modify the technological composition of economies by either reinforcing local specialization, or instead facilitating diversification of technological activities. This issue is part of a larger debate in the geography of innovation literature on how countries and regions diversify in new activities and industries. This capacity to diversify is key for the economic development to avoid technological lock-ins (Capone et al., 2019) and foster resilience (Frenken et al., 2007; Pasinetti et al., 1987), especially in case of a shock hitting specific activities (Boschma, 2017; Bathelt et al., 2004) or to enhance growth perspective (Pinheiro et al., 2018; Neffke et al., 2011). It is largely admitted that economies develop according to a branching process in which the entry of new activities are driven by their degree of relatedness to the existing set of activities (Hidalgo et al., 2007; Crespo et al., 2017; Hidalgo et al., 2018; Balland et al., 2019; Neffke et al., 2011). This dynamic has been first evidenced in the case of exports at the national level (Hidalgo et al., 2007) and then extended to the entry of industrial activities (Neffke et al. 2011) and new technological subclasses (Kogler et al., 2013; Rigby, 2015; Boschma et al., 2015). A similar pattern is also expected regarding the technological characteristics of skilled workers entering a region.

In this regard, the question is whether the knowledge transferred by migrants resemble or differ the local technological portfolio at destination. Two mechanisms not necessarily exclusive can be advanced to understand migrant's motivation and their impact at destination. First, migrants may be attracted to areas in which companies search for workers that have similar specialization as their region of origin in case of a shortage of highly skilled labor or rapid technological growth (Kerr and Lincoln, 2010). In this case, migration will tend to reinforce local specialization. Second, migrants may provide new and non-redundant knowledge thus increasing opportunities for technological diversification (Agrawal et al., 2003; Kc and Terwiesch, 2011; Breschi and Lissoni, 2009). In this case, they act as agents of structural change similarly to new firms entering a region and introducing new activities (Neffke et al., 2018; Miguelez and Morrison, 2022).

The recent literature tends to support this latter argument providing evidence that migrants affect the composition of receiving countries' technological portfolio. Bahar et al. (2020) find that the inflow of immigrant inventors coming from countries specialized in a given technology subclass<fnref n="1" /> contribute to boost patenting and gain technological advantages

<footnote n="1">¹ Technological subclasses are defined according to the international patent classification, https://www.wipo.int/classifications/ipc/en/ITsupport/Version20130101/transformations/viewer/index.htm</footnote>
in the same technology in the receiving countries. Concretely, migrants drive specialization when a country with no patent application in a technology gains a technological advantage in the subsequent years, i.e. when the share of innovations filed in the region belonging to this technology is higher than the average share this technology represents among all patents filed in all regions<fnref n="2" />. Akcigit et al. (2017) have also found that migration had modified the technological trajectory in the USA and Moser et al. (2014) found similar results for the specific technological field of chemistry in the USA. Bahar et al. (2020) generalize these findings by considering 95 countries and 651 technology subclasses.

Moving from the country to the regional level, Diodato et al., (2022) find similar results showing that migration shapes the technological evolution of cities. They use a historical database on US data over the period 1870 and 1940 and find that native inventors benefit from the inventive activity of migrants which in turn contribute to the rise of new and previously non existing technological fields in the regions to which they moved to. Results are confirmed by Miguelez and Morrison (2022) on European regions which find again that migrants coming from regions specialized in a given technology contribute to increase natives' patenting and help regions to develop new technological specialization in these subclasses.

In contrast, Caviggioli et al., (2020) provide evidence for the second mechanism. They find that highly skilled migrants reinforce local specialization, especially in those fields that represent particular niches, that is, fields that are not frequently found across geographical areas. They do also show that the technological distance between migrants and natives contributes to diversify unless distance becomes too important hindering knowledge integration. However, these results in opposition to the previous contribution may be explained by the fact that they do not directly investigate the direction of specialization. Based on these arguments and applying it to internal migration, we propose to test the following hypothesis:

**Hypothesis 1:** The number of highly skilled mobile workers tend to drive technological specialization of technologies in which their originating region is already specialized.

## 2.3. Migrants as agents of technological novelty

Despite their important contribution, most of these papers remain rather agnostic regarding the process through which migrants contribute to diversify or reinforce existing specialization. This last section further investigates the underlying mechanisms through which knowledge transferred by migrants gets reused and recombined with local knowledge. Knowledge may not be easily absorbed, integrated, and recombined if it is dissimilar, distant, and unrelated to the local technological specialization as explained by the Schumpeterian tradition of knowledge recombination (Plunket and Starosta de Waldemar, 2023).

<footnote n="2">² This index of the ratio of the share of a technology in a region to the share of this technology in all regions is called the Revealed Technological Advantage (RTA). See Appendix 1 for an example of technological novelty.</footnote>
Following this tradition, technological innovations can be defined as a set of technologies, components or artefacts that are combined to create new products (Martin, 1998; Fleming, 2001; Arthur, 2007; Verhoeven et al., 2016). Technological novelty corresponds to novel combinations of technological domains; they can be important drivers of radical innovations and economic growth (Arthur, 2007; Epicoco et al., 2022). When unfamiliar components are combined, they increase the risk of failure but also the opportunity of generating breakthrough inventions (Fleming, 2001; Arts and Fleming, 2018). Using patents to proxy innovation, novelty can be conceptualized as new combinations of technological subclasses. Moreover, following Carnabuci and Operti (2013) and applying their analysis to a regional portfolio, novelty can be characterized as recombinant reuse or recombinant creation. Recombinant reuse identifies new pairs of technological classes in which each of the two classes has already been used in the region in the past but for which the two classes have never been used in the same patent. Recombinant creation identifies new pairs of technology classes in which at least one of the two classes had never been used before in the region<fnref n="3" />.

Based on this framework and focusing on the impact of skilled immigration shocks in the United States at the firm level, Choudhury and Kim (2019) investigate knowledge recombination when ethnic migrant inventors differ from locals and transfer undiscovered knowledge from their home regions to American firms. They find that migrants rather reuse the knowledge they mastered before migration when they collaborate within teams only composed of migrant workers and instead engage in recombinant creation when they collaborate within mixed teams including both native and migrant inventors. This suggests a need for knowledge integration by local inventors in order to enable combination with local knowledge. Miguelez and Morrison (2022) confirm these findings to a regional frame. Their result is interesting but does not explicitly test the impact of migrants on knowledge and technology classes recombination, which is the focus of this paper.

Building on previous findings, we can hypothesize that inter-regional migrants contribute to the recombinant process occurring in their host region. They are a source of external and potentially non-redundant knowledge and as such they feed the processes of distant search and exploration and offer higher opportunities for cross-fertilization and new combinations. We test the following hypothesis:

**Hypothesis 2:** The number of highly skilled workers coming from regions specialized in a given technology may increase the number of novel recombination based on that technology.

Further investigating the underlying recombinant process, two situations may be distinguished. First, if migrants introduce new technologies, they will tend to drive recombinant creation, that is, they will tend to generate innovation combining subclasses that are not yet part of the local portfolio with existing subclasses. It is probable that the integration of such new technologies will be easier if they are somehow related to the technological portfolio of the destination region. Second, if migrants reinforce local specialization, they will instead contribute to recombinant reuse, that is, combining in a new

<footnote n="3">³ See Appendix 1 for an example.</footnote>
way technologies included in the host region's portfolio. Thus, we propose two additional hypotheses, that we test with our data:

**Hypothesis 2a:** The number of highly skilled mobile workers tend to drive recombinant creation if they bring new and non-redundant knowledge.

**Hypothesis 2b:** The number of highly skilled mobile workers tend to drive recombinant reuse if they reinforce local specialization.

**3. Data sources and sample construction**

The objective of this paper is to study the logic presiding over the degree of novelty of the technological subclasses in the French regions, especially in relation to the inter-regional migration flows of skilled workers and the patent filers collaboration networks. To this end, two datasets are used.

**3.1. Patent database**

The patent data used are the OECD REGPAT database and the EPO (European Patent Office) and PCT (Patent Cooperation Treaty) patent filing data. Patents are classified according to the technological subclasses they mobilize. The classification system used is the International Patent Classification<fnref n="4" /> (IPC) implemented by the World Intellectual Property Organization (WIPO). It is a nested classification system composed of 8 sections divided into classes, subclasses, groups and sub-groups. This study uses the 637 subclasses in order to define the technological components of each patent and characterize each regions technological portfolio. Subclasses are both disaggregated enough to identify heterogeneous patterns of technological specialization between French regions and aggregated enough to identify revealed technological comparative advantages. Using groups or sub-groups impede econometric estimations as patents within regions do not mobilize enough different IPC groups or sub-groups.

This study assigns patents to each French region based on the location of their inventors. When a patent includes inventors from multiple locations, the patent is assigned to each region. Considering the location of each inventor enables to map the inter-regional inventors' collaboration network. The geographic level used for the French regional breakdown uses the Nomenclature of Territorial Units for Statistics at its second level of disaggregation (NUTS2). The regions correspond to the 27 French administrative regions existing before the 2015 reform. For the purpose of this study, the 22 regions of metropolitan France have been selected.<fnref n="5" />

<footnote n="4">⁴ The International Patent Classification system came into force in 1975 with the objective of standardizing and facilitating the understanding of the technological information contained in patents filed and to enable analysis and comparisons at an international level. It is subject to periodic revisions and improvements in order to better mirror latest technological developments.</footnote>

<footnote n="5">⁵ The 5 overseas departments and regions (Guadeloupe, Reunion, Mayotte, Guyana, Martinique) are not retained because of their low contribution to inter-regional migration flows and their peculiar innovation dynamics, characterized by a low number of patents filed. We also show robustness checks taking out important regions at a time.</footnote>
Data cover the 1970 - 2017 period and thus provide historical depth relative to the technological development of regions.

## 3.2. Employment database

The employment data are extracted from the All-Employee Basis (BTS). These are administrative data provided by the French National Institute of Statistics and Economic Studies (INSEE). The data cover all French companies with employees. The information is provided at the establishment level. Establishments are production units corresponding to a single physical location of a firm.

The information provided encompasses the location of the establishment as well as the characteristics of each employee. For each year of BTS data, information on the employment situation in the previous year is provided. These data make it possible to quantify, year after year, the flows of occupational mobility of French employees. Worker flows are measured at the NUTS2 level and broken down by the socio-professional categories of employees. Employees socio-professional categories are based on the PCS-ESE classification (Professions and Socio-professional Categories of Salaried Employment of Private and Public Employers) at the second level of aggregation (29 categories). The employment data cover the 1996 – 2017 period.

Employment data are matched with the patent data so that the period of study corresponds to the years 1996 to 2017. Over this period, we identify new combinations of technology subclasses by relying on the whole history of European patent applications and their respective combinations since 1970.

# 4. Main variables

In order to study the impact of mobile workers on the evolution of each region's technological characteristics, we first need to develop a statistical indicator of regional technology spaces (i.e. the technological classes of filed patents). Thus, for each region, we determine their specialization and the subclass in which they have a comparative advantage. For each subclass in each region we determine whether it is related to the rest of the region's technological space. We also build a measure of similarity for each pair of existing technological subclasses. These indicators draw from the relatedness literature (Hidalgo et al., 2007; Hidalgo et al., 2018; Hidalgo, 2021; Boschma et al., 2015; Boschma, 2017).

## 4.1. Constructing technology spaces

### Revealed Technological Advantage

First, it is necessary to identify the technological classes in which a region has a relative technological advantage. To this end, we identify, for each region, the technological classes for which the share of patents filed in this class is higher than the average share observed in all regions. We do this computing the Revealed Technological Advantage (RTA) index which measures the patenting intensity and specialization of a given NUTS 2 region *r* in a given
subclass c as compared to the average intensity of patenting in this subclass in all the regions included in the sample<fnref n="6" /> based on all patents filed between over the period [t-5;t-1]. Formally:

$$RTA_{c,r,y} = 1 \left[ \frac{\sum_{r}^{\text{patent } s_{c,r,(y-5)-(y-1)}} \sum_{c}^{\text{patent } s_{c,r,(y-5)-(y-1)}}}{\sum_{r}^{\text{patent } s_{c,r,(y-5)-(y-1)}} \sum_{c}^{\text{patent } s_{c,r,(y-5)-(y-1)}}} \right] (1)$$

The region is considered to be specialized in a subclass if its RTA = 1 and 0 otherwise. We do also compute this variable in a narrower sense, as the frequency of patenting in a given subclass and region compared to the same patenting intensity in this technology in all French NUTS 2 regions only. We label this index $RTA_{FR_{c,r,y}}$.

## Relatedness

Using the RTA, it is possible to compute the relatedness $\phi_{c,c',y}$ for each pair of subclasses c and c'. Two subclasses are considered to be more related if regions with a RTA in one of these subclasses also had a RTA in the other one. The relatedness is computed by taking the minimum of the pair-wise conditional probabilities of regions having a comparative advantage in one technology subclass c, given that they have a comparative advantage in another technological subclass c' (Hidalgo et al. 2007, Boschma et al. 2015).

$$\phi_{c,c',y} = \min \left[ \frac{\sum_{r} (RT A_{c,r,y} * RT A_{c',r,y})}{\sum_{r} RT A_{c,r,y}}, \frac{\sum_{r} (RT A_{c,r,y} * RT A_{c',r,y})}{\sum_{r} RT A_{c',r,y}} \right] (2)$$

Relatedness is assigned to any pair of technology subclasses (c,c') and is therefore region-independent. The approach adopted in the construction of this indicator considers that if two technological subclasses are close or related, then they will tend to appear in patents filed in the same regions. More specifically, the relatedness measure relies on the assumption that related technologies will be used intensively in the same regions. Conversely, two unrelated technology subclasses are less likely to co-occur in the specialization bundles of many regions. The constructed indicator is based on this agnostic approach and is constructed in such a way that two IPC subclasses are all the closer as the regions having an RTA in one of these subclasses often have an RTA in the other subclass (Hidalgo et al. 2007). The average bilateral relatedness between IPC classes is 0.23, with a minimum at 0.007 and a maximum at 0.85. The probability density of the set of bilateral relatedness measured by kernel density estimation is shown in Figure 1.

<footnote n="6">⁶ 18 European countries and the United States.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1 - Estimated density of bilateral relatedness between IPC classes (2017)</figcaption></figure>

Source: OECD REGPAT database. EPO and PCT patent filing data.

## Relatedness Density

Finally, it is possible to measure the distance of any technology subclass from the specialization structure of a given region. Following the relatedness literature, we build the measure of *Relatedness Density*, specific to a technology subclass *c* in a region *r* in a year *y*. Regions are specialized in different technological subclasses, and every IPC subclass is more or less distant from the ones in which the region is already specialized. This distance between IPC classes is measured by the aforementioned pairwise proximity from equation (2). For a given class *c*, we therefore calculate its relatedness density to the technological portfolio of region *r* as the ratio of its pairwise relatedness with all the classes in which region *r* as a comparative advantage, divided by the sum of its pairwise proximities with all subclasses. Formally:

$$ \text{Relatedness Density}_{c,r,y} = \frac{\sum_{c' \in RT A_{c,y}, c' \neq c} \phi_{c, c', y}}{\sum_{c' \neq c} \phi_{c, c', y}} \quad (3) $$

With $RTA_{c,y}$ the set of subclasses in which region *r* has a RTA during year *y*.

This indicator increases when a class gets closer to the specialization space of region *r*. The relatedness density of an IPC subclass in a region is independent of whether this subclass is actually used in the region considered. An IPC subclass can have a very high relatedness density in a region because it is similar to the IPC subclasses the region is specialized in, even though the region does not file any patent mobilizing this subclass yet. For all technology classes in all French regions, the average relatedness density is 0.31. The distribution of the density set is represented by the estimated probability density in Figure 2. This figure shows the estimated probability density for the set of relatedness density measures for all
technology classes in all French regions. However, it should be noted that the distribution of relatedness densities of IPC classes varies from region to region.

<figure><img src="image_4.png" /><figcaption>Figure 2 – Estimated density of Relatedness Densities (All regions - 2017)</figcaption></figure>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France. Regional Relatedness Densities for all technology classes built using patent data from all countries.

## 4.2. Constructing technology spaces

In order to study the impact of highly skilled mobile workers on the production of technological novelty, we build three main dependent variables, Novelty, Recombinant creation and Recombinant reuse.

### 4.2.1: Patent Novelty

Novelty is the number of new combinations, that is, the number of pairwise subclass combinations associated with a given subclass that appears for the first time in a given year and region. A new combination occurs when a subclass pair is combined for the first time in a patent. This new combination can be new to the region, new to France or new to the world. Unless otherwise stated, the reference level used is that of novelty at the regional level. The technological novelty measure follows the literature on recombinant novelty (Arts and Fleming, 2018; Fleming, 2001, Verhoeven et al., 2016) and is applied at the NUTS 2 level.

$$ \text{Novelty}_{c,r,y} = \sum_{i=1}^{p_{c,r}} \left( \sum_{c_{i,c} \in n_{i,c}} 1_{[\text{NewCombination}_{c_{i,c},r,y}]} \right) (4) $$

With $p_{c,r}$ the total number of patents using subclass c in region r, $n_{i,c}$ the set of combinations in each patent i mobilizing the IPC subclass c. $1_{[\text{NewCombination}_{c_{i,c},r,y}]}$ takes the value 1 if combination $c_{i,c}$ from the set $n_{i,c}$ is new to the region r at year y.

In order to test the robustness of our estimates to the novelty indicator used, two other indicators are constructed and used in the robustness tests of our baseline estimations. Novelty Binary is a dummy variable which takes value 1 if at least one new combination is associated with a given subclass c in region r is observed in a given year y. Novelty Share is
the share of new combinations over the total combination associated with a given subclass in a given year and region.

### 4.2.2: Novelty reuse and novelty recombination

In order to further explore the underlying recombinant process, we introduce two dependent variables that provide information on the status of the classes that are combined together for the first time. Novelty reuse counts the number of new combinations using subclasses that are already part of the region's technological portfolio and recombinant creation counts the number of new combinations when at least one of the subclasses is new to the region.

### 4.2.3: Class entry and specialization gain

Finally, following the literature (Boschma et al. 2015), we also build two dependent variables that capture the entry of a technological class or the gain of a technological specialization in a class for a given region and year.

New $IPC_{c,r,y}$ takes the value 1 if a technology subclass $c$ appears in region $r$ for the first time in year $y$.

$Specialization_{c,r,y}$ models the gains in RTA in a given $c$ technology subclass in an $r$ region. It takes the value 1 when a subclass enter the specialization set of a region a given year. It enables to consider whether mobile workers help a region become specialized and gain comparative advantage in a given subclass. Following the literature and in order to reduce the statistical noise around the 0 to 1 RTA switches, $Specialization_{c,r,y}$ is set to 1 if $RTA_{c,r,y-1} = 0$ and $RTA_{c,r,y-2} = 0$ and if $RTA_{c,r,y} = 1$, $RTA_{c,r,y+1} = 1$ and $RTA_{c,r,y+2} = 1$. In other words, gains in comparative advantage are only considered if the region had no RTA in the two preceding years of observation and if this gain in specialization is persistent, i.e., if it is observed over a period of at least three years.

## 4.3. Independent variables: highly skilled mobile workers

The aim of this paper is to investigate whether the flow of highly skilled workers coming from regions specialized in a given technological subclass $c$ (RTA = 1) affects the production of new combinations using this technology. The socio-professional categories of the PCS-ESE nomenclature used to identify skilled workers correspond to professors and scientific professions (PCS-ESE 34), engineers and technical managers (PCS-ESE 38) and technicians other than service technicians (PCS-ESE 47)<fnref n="7" />. Thus, the flow of mobile workers coming from a region specialized in a given subclass is then computed as the sum of all highly skilled mobile workers $Mob_{r', r, y}$ coming from other French regions $r'$ moving into the region $r$ in year $y$ and weighted by the dummy variable whether RTA = 1 (Bahar and Rapoport, 2020).

$$ \text{High Skilled Mobility (RTA)}_{c,r,y} = \sum_{r' \neq r} \text{Mob}_{r', r, y} \times \text{RTA}_{c,r',y} \quad (7) $$

<footnote n="7">⁷ This definition is similar to the one used by Harrigan et al. (2023) and Mayda et al. (2022): their definition is based on PCS categories 38 and 47, while we assume that category 34 can be attribute to the high skilled workers.</footnote>
As a robustness check, we use also three other weighting variables: *RTA_FR* is used to consider specialization in a narrower sense, using French regions only; *Relatedness density* is used to control whether mobile workers are coming from regions specialized in technologies related to this given subclass; *Size* controls for the fact that workers are coming from regions with a large patenting activity in a given subclass. *Size* is the share of patents in a given subclass, region, and year $n_{c,r',y}$ over the total number of patents applied for in the region and year $N_{r',y}$, $Size = n_{c,r',y}/N_{r',y}$.

## 4.4. Control variables

As novelty may also be explained by variables characterizing the region's technological portfolio, several controls are added to the regressions. *Relatedness density*, described above, may explain the occurrence of new combinations given the relatedness a given subclass to the rest of the technology space in the region (Plunket and Starosta de Waldemar, 2023). *New subclass* takes value one if the combination includes at least one subclass that enters the region for the first time. By definition, any combination including this subclass would be new. *Number of patents in the subclass* is the number of patents in which the subclass occurs in a given year and region. It is an indication of its technological importance in the region. The panel is completely balanced thus it may be that some classes do not occur in any patent. To control for this fact, we include a variable *Missing subclass* which takes value 1 if the subclass is missing in a given year and region. The flow of external knowledge can come from highly skilled workers, but it may also be explained by the collaborations with extra-regional inventors. The variable *Share of extra-regional patents* that is the share of patents in region r and subclass c which includes extra-regional inventors over the past five years to control for the fact that external knowledge brought by migrants do not catch the impact of external collaboration.

# 5. Descriptive statistics

## 5.1. Patenting activities and IPC subclass combinations

Each year, more than 12,000 EPO patents are filed in France, a figure that has been steadily increasing since the beginning of the study period and seems to have stabilized since the beginning of the 2010s (Figure 3).
<figure><img src="image_5.png" /><figcaption>Figure 3 - Number of yearly patents files in France</figcaption></figure>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France patents.

However, patenting activity is not evenly distributed across French regions. In 2018, Île-de-France and the Rhône-Alpes region represented the two major innovation hubs in France with 5,088 and 2,489 patents filed respectively. The other French regions displayed very heterogeneous numbers of patents filed that same year, ranging from 66 filings for Limousin to 867 for the Midi-Pyrénées region.

The number of technological subclasses used on average in each region has grown strongly until the end of the 1980s before stabilizing. In 2018, 547 different technological subclasses were found in patents filed in France, namely nearly 86 % of all technological subclasses in the nomenclature (Figure 4). Not all of these subclasses are mobilized in all French regions. In 2018, the Île-de-France region (FR10) used 455 subclasses and the Rhône-Alpes region 405, while the other regions used an average of between 100 and 250 subclasses (Figure 5).

<caption>Figure 4 – Number of IPC subclasses used in France</caption>
<figure><img src="image_6.png" /></figure>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France patents.

<figure><img src="image_7.png" /><figcaption>Figure 5 - Number of IPC subclasses used by French regions</figcaption></figure>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France patents.

However, individual patents do not combine the same number of technology subclasses. Since 1996, half of the patents only use one IPC subclass (Table 1). Among the patents combining at least two subclasses and on which we focus, about 30% combine only two subclasses. At the other end of the distribution, 0.4% of patents are composed of at least 7 technology subclasses and thus have at least 21 pairwise IPC subclass combinations.
<table><caption>Table 1 – Number of IPC subclass by patent</caption><thead><tr><th></th><th colspan="7">Number of IPC subclasses (%)</th></tr><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7+</th></tr></thead><tbody><tr><th>Total</th><td>50.3</td><td>29.4</td><td>13.2</td><td>4.6</td><td>1.6</td><td>0.6</td><td>0.40</td></tr><tr><th>2013</th><td>52</td><td>30</td><td>12.3</td><td>3.9</td><td>1.3</td><td>0.4</td><td>0.20</td></tr><tr><th>2014</th><td>52</td><td>30.1</td><td>12</td><td>4</td><td>1.3</td><td>0.5</td><td>0.10</td></tr><tr><th>2015</th><td>51.6</td><td>30</td><td>12.7</td><td>3.9</td><td>1.2</td><td>0.4</td><td>0.20</td></tr><tr><th>2016</th><td>51.7</td><td>30.1</td><td>12.4</td><td>4.1</td><td>1</td><td>0.4</td><td>0.20</td></tr><tr><th>2017</th><td>51.9</td><td>31.2</td><td>11.8</td><td>3.5</td><td>1</td><td>0.4</td><td>0.10</td></tr></tbody></table>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France patents.

The number of technological combinations used each year in France is around 5,000, which corresponds to 2.5 % of all the possible pairs of technology subclasses which include 637 individual subclasses in the nomenclature.

However, the technological combinations used each year are not necessarily the same from one year to another. The Table 2 indicates that over the period, 31,263 distinct combinations were used in France. Once again, Île-de-France stands out, having mobilized 19,470 distinct combinations over the period, that is, 9.6 % of all theoretically possible combinations. Moving down the ranking, this figure decreases sharply. The Midi-Pyrénées region, fifth in the ranking of French regions having used the most distinct technological combinations since the 1970s, has exploited only 2.6 % of all theoretically possible technological combinations. While not all subclass combinations are comparable in practice, as the joint mobilization of highly unrelated domains would be complicated, it is noticeable that French regions still have a high potential to explore new technological domains by jointly mobilizing technological subclasses never before combined at the regional level.

<table><caption>Table 2 – Number of pairwise IPC combination used - Top 5 regions</caption><thead><tr><th>Région</th><th>Number of pairwise IPC combinations used (since 1970)</th><th>Share (%)</th></tr></thead><tbody><tr><td>Île-de-France</td><td>19 470</td><td>9.6</td></tr><tr><td>Rhône-Alpes</td><td>13 718</td><td>6.8</td></tr><tr><td>Provence-Alpes-Côte d'Azur</td><td>7 403</td><td>3.7</td></tr><tr><td>Alsace</td><td>5 348</td><td>2.6</td></tr><tr><td>Midi-Pyrénées</td><td>5 269</td><td>2.6</td></tr><tr><td><strong>Total (France)</strong></td><td><strong>31 263</strong></td><td><strong>15.4</strong></td></tr></tbody><thead><tr><th></th><th>Number of pairwise IPC combinations used yearly (2017)</th><th>Share (%)</th></tr></thead><tbody><tr><td>Île-de-France</td><td>2 341</td><td>1.2</td></tr><tr><td>Rhône-Alpes</td><td>1 389</td><td>0.7</td></tr><tr><td>Provence-Alpes-Côte d'Azur</td><td>597</td><td>0.3</td></tr><tr><td>Alsace</td><td>323</td><td>0.2</td></tr><tr><td>Midi-Pyrénées</td><td>594</td><td>0.3</td></tr></tbody></table>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France patents.
Across all technological classes, all French regions witness the emergence of new combinations (Table 3). The number of new combinations observed at the regional level in 2017 ranged from 35 (Limousin) to 315 (Île-de-France). New combinations at the national or global level are less common. For instance, in 2017, Île-de-France generated 187 new combinations at the national level and only 22 new combinations at the global level.

<table><caption>Table 3 – Technological novelty in French regions (2017)</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Distinct pairwise<br>IPC combinations</th><th colspan="3">Technological Novelty</th></tr><tr><th>New<br>combinations<br>(Region)</th><th>New<br>combinations<br>(France)</th><th>New<br>combinations<br>(World)</th></tr></thead><tbody><tr><td>Île-de-France</td><td>2341</td><td>315</td><td>187</td><td>22</td></tr><tr><td>Rhône-Alpes</td><td>1389</td><td>270</td><td>95</td><td>7</td></tr><tr><td>Provence-Alpes-Côte<br>d'Azur</td><td>597</td><td>176</td><td>54</td><td>6</td></tr><tr><td>Midi-Pyrénées</td><td>594</td><td>195</td><td>30</td><td>1</td></tr><tr><td>Aquitaine</td><td>496</td><td>169</td><td>45</td><td>3</td></tr><tr><td>Pays de la Loire</td><td>440</td><td>182</td><td>37</td><td>5</td></tr><tr><td>Alsace</td><td>323</td><td>84</td><td>11</td><td>0</td></tr><tr><td>Britanny</td><td>322</td><td>114</td><td>14</td><td>0</td></tr><tr><td>Nord-Pas-de-Calais</td><td>319</td><td>108</td><td>28</td><td>2</td></tr><tr><td>Auvergne</td><td>317</td><td>124</td><td>30</td><td>4</td></tr><tr><td>Centre-Val de Loire</td><td>291</td><td>108</td><td>22</td><td>4</td></tr><tr><td>Picardy</td><td>278</td><td>79</td><td>8</td><td>0</td></tr><tr><td>Upper Normandy</td><td>273</td><td>66</td><td>10</td><td>1</td></tr><tr><td>Lorraine</td><td>269</td><td>115</td><td>22</td><td>3</td></tr><tr><td>Languedoc-<br>Roussillon</td><td>239</td><td>75</td><td>16</td><td>1</td></tr><tr><td>Franche-Comté</td><td>205</td><td>89</td><td>13</td><td>1</td></tr></tbody></table>
<table><tr><td>Burgundy</td><td>157</td><td>60</td><td>6</td><td>0</td></tr><tr><td>Poitou-Charentes</td><td>136</td><td>66</td><td>8</td><td>2</td></tr><tr><td>Lower Normandy</td><td>95</td><td>44</td><td>6</td><td>1</td></tr><tr><td>Champagne-Ardenne</td><td>79</td><td>35</td><td>5</td><td>0</td></tr><tr><td>Limousin</td><td>58</td><td>31</td><td>7</td><td>1</td></tr></table>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France patents. Patent data from all countries used to build 'novel to the world' index.

## 5.2. Technological Specialization and worker flows

The $RTA_{c,r,y}$ constructed in equation (1) are used to indicate the technology subclasses in which regions have a revealed relative technological advantage. On average over the period, the top patenting regions displayed a RTA in a given subclass in about 200 different technology subclasses each year (Table 4). However, the domains in which regions have an RTA are evolving over time. Over the period, the most innovative regions developed an RTA at least one year in nearly 500 different technology subclasses. In other words, technology spaces evolve, and the specialization structures of regions changes over time.

<table><caption>Table 4 – Number of RTA - Top 5 regions</caption><thead><tr><td>Region</td><td>Total number of RTA (period)</td><td>Average yearly number of RTA</td></tr></thead><tbody><tr><td>Île-de-France</td><td>575</td><td>208</td></tr><tr><td>Rhône-Alpes</td><td>544</td><td>253</td></tr><tr><td>Provence-Alpes-Côte d'Azur</td><td>522</td><td>199</td></tr><tr><td>Alsace</td><td>481</td><td>170</td></tr><tr><td>Midi-Pyrénées</td><td>496</td><td>186</td></tr></tbody></table>

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France. RTA built using patent data from all countries.

For any year, the regions also present different specializations. Figures 6 to 9 compare, for example, the technological spaces of the Pays de la Loire and Brittany regions in 2017 by identifying 5 major technological sectors<fnref n="8" />. Each node represents an IPC subclass (figures 6 and 7) or a technological field (Figures 8 and 9) and each network's node's size represents the over-representation of this IPC class or technological field in the region under study compared with all patents filed in France. The networks are plotted using pairwise technological proximity for the edges. The graphic representation displays different specialization structure with the Pays de la Loire presenting a much more diversified technological space, with a marked specialization in mechanical engineering. Brittany, on the other hand, seems to specialize mainly in electrical engineering, with an important innovation cluster in chemicals as well. Figures 8 and 9 use the same graphical construction logic, aggregating IPC classes into 35 industrial sectors based on the CRIOS nomenclature. These two graphs provide a more synthetic visualization of regional technological spaces and

<footnote n="8">⁸ Chemistry, electrical engineering, instruments, mechanical engineering and other fields</footnote>
lead to similar observations.

<figure><img src="image_8.png" /><figcaption>Figure 7 – Brittany - 2017</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 8 – Pays de la Loire – (Fields, 2017)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure 9 – Brittany – (Fields, 2017)</figcaption></figure>

<figure><img src="image_11.png" /></figure>

Note: Each circle represents an IPC class (Figures 6 and 7) or a CRIOS technology field (figures 8 and 9). The size of the circles represents the over-representation of the class or technological field in the patents filed in the region under consideration compared with its average occurrence in all French patents. The color of the circles corresponds to the 5 sectors identified in the nomenclature. The distance of the segments between two circles corresponds to their bilateral relatedness measure as presented in section 4.1. The further apart two circles are, the more different the technology classes they represent. By comparing the graphs of 2 regions, we can visually identify differences in the specialization of their technological areas.

Source: OECD REGPAT database. EPO and PCT patent filing data.

Field: Metropolitan France. Bilateral relatedness measures built using patent data from all countries.
Finally, the flows of qualified mobile workers are significant. In 2017, one million salaried workers changed their regions of employment, among which around 161 000 high-skilled workers identified by the socio-professional categories used to build our High Skilled Mobility$_{c,r,y}$ variable. These mobility flows are particularly intertwined as shown in Figure 10. The most significant region in terms of mobility flows is Île-de-France, which had 39,107 incoming skilled employees and 32,646 outgoing skilled employees in 2017. Other regions show less significant and less balanced flows between inflows and outflows. For instance, in 2017, the inflow of skilled employees in Limousin was 1,564 individuals, while the outflow was 5,272 employees.

<caption>Figure 10 – Skilled worker flows between regions - 2017</caption>
<figure><img src="image_12.png" /></figure>

Note: The segments around the circle are proportional in width to the sum of incoming and outgoing flows received by each region. For a given region, outflows are the color associated with the region around the circle. Inflows to each region are colored according to their region of origin.

Source: BTS database.

Field: Metropolitan France patents.

Crossing these inter-regional mobility flows with the regions Revealed Comparative Advantages, both varying over time, provides class x region x year specific high-skilled workers flows with sufficient heterogeneity to study the effects of mobility flows from regions specialized in a technological subclass in a given year on the novelty of innovations mobilizing this subclass in the host regions. Detailed descriptive statistics for our main variables can be found in the appendix Table A2.

# 6. Empirical strategy

## 6.1. Estimated equation and identification strategy

Our aim is to estimate the impact of the inflow in a region *r* of highly skilled workers coming from all regions specialized in a given technological subclass *c* on the number of new
combinations (Novelty$_{c,r,y}$) associated with this given subclass (High Skilled Mobility$_{c,r,y-1}$). The basic econometric equation to be estimated is given by:

$$ \text{Novelty}_{c,r,y} = \beta_0 + \beta_1 \text{High Skilled Mobility}_{c,r,y-1} + \beta_2 X_{c,r,y-1} + \phi_{c,r} + \psi_{r,y} + \alpha_{c,y} + \epsilon_{c,r,y} \quad (8) $$

In our three-dimensional panel, $X_{c,r,y-1}$ is a set of control variables as defined in the previous section and constructed at the Class-Region-Year (CRY) level. We include fixed-effects $\phi_{c,r}$, $\psi_{r,y}$ and $\alpha_{c,y}$. Indeed, group-specific effects may exist at region × technological subclass, region × year or technological subclass × year levels. The inclusion of fixed effects enables us to control for inter-group variability by adding dummy variables that absorb the effects specific to each group considered. This identification strategy, based on high dimensional fixed effects, allows us to be confident in the estimation of our $\beta_1$ coefficient. Note that the inclusion of these three groups of high-dimensional fixed effects absorb much of the correlation between regressors and unobserved variables. In order to incorporate all these fixed effects, we use the high-dimensional fixed effects methodology proposed by Correia (2016; 2019) and based on the adaptation of the general solution proposed by Guimaraes and Portugal (2010) and Carneiro et al. (2012). $\epsilon_{c,r,y}$ are robust standard errors clustered at the fixed effects level (multiway).

Since the subclass × region × year panel used in the study is balanced, the number of observations is constant each year<fnref n="9" />, for each region and for each technological subclass, and the number of categories for each of these variables. To put it differently, the sample size does not depend on the number of patents filed and therefore the number of groups used to estimate the fixed effects is constant. Having a balanced panel therefore prevents inconsistency problems that might arise with high-dimensional fixed effects modeling in an unbalanced setting. Our independent variables are lagged one year, in order to diminish risks of reverse causality in the estimations.<fnref n="10" /> This is true for all variables except new subclass and missing subclass, which share contemporaneous information on the status of the subclass in that region and year.

Estimates are conducted in a linear framework. Nevertheless, the three versions of the independent variable proposed are the number of new combinations, a binary indicator and a proportion. Therefore, a Poisson regression for the count data, a Probit model for the binary indicator and a Tobit model could therefore intuitively be considered. However, with (high-dimensional) fixed-effects, the probit estimator is not well behaved because of the incidental parameter problem. Tobit model are also affected in a high-dimensional fixed-effects setting by the disturbance variance estimator (Greene, 2002). Therefore, in this setting, linear models are preferable. Regarding the Poisson model, incidental parameter

<footnote n="9">⁹ In total, the dataset contains 22 regions x 22 years x 637 classes: 308308 observations. Since we used lagged variables, the number of observations drops to 294 294 observations in our baseline estimations.</footnote>

<footnote n="10">¹⁰ One-year lagged are widely used in the literature (Balland et al., 2019; Caviggioli et al., 2020; Boschma et al. 2022; Miguelez and Morrison, 2022). In related but not similar literature, Wang and Hagedoorn (2014) show a significant effect of (y-1) internal R&D on patenting, and also long-run effects. They show that the effect of R&D activity on the number of patents at the firm level has a U-shaped lag structure: the share of more contemporaneous R&D accounts for 36% on the total R&D. We follow their approach and we add a previous lag to the (y-1) lag of our main variable of interest. We provide results in section 7.2. A robustness test is also presented in the appendix, including flows in y+1 to check for the absence of reverse causality.</footnote>
problems and caution about mobilizing fixed effects are less central. The use of the Poisson model with high-dimensional fixed-effects could therefore be considered. It is used in this paper as a robustness tool for the linear baseline regression using Poisson pseudo-maximum likelihood regressions (PPML) with multi-way fixed effects (Correia et. al., 2020). The result is presented in the appendix (Table A4).

An instrumentation strategy is employed to control for possible omitted variable or reverse causality problems. The instrumentation strategy and the subsequent results are used as robustness tests for the main estimates and are presented in section 8.2.

# 7. Results

## 7.1. Entry: new subclass to the region and on the specialization basket

Before addressing the impact on recombinant novelty, it is interesting to study simpler issues, namely whether mobility flows affect the occurrence of a new subclass (*New IPC*) or a new specialization.

In Table 5, column (1) reproduces the main model presented in equation (1) using the binary variable *New IPC*<sub>c,ry</sub> as the dependent variable. Results indicate that the occurrence of a new technological subclass in French regions is positively and significantly correlated with the inflow of highly skilled workers coming from regions specialized in this specific technological subclass *c* at year (y-1). The proximity of a technological subclass to a region portfolio, measured by its relatedness density in this region, is associated with a non-linear effect. It is only at a high relatedness density level (around 0.75) that an increase in the relatedness density of a class in a region has a positive effect on the propensity of this subclass to enter the region's technological portfolio.

<table><caption>Table 5 - Impact of labor mobility on the occurrence of new IPC classes and level of specialization</caption><thead><tr><th></th><th>New IPC</th><th>Specialization</th></tr></thead><tbody><tr><td></td><td>(1)</td><td>(2)</td></tr><tr><td>High Skilled Mobility (RTA) (y-1)</td><td>0.0016***<br>[0.0003]</td><td>0.0145***<br>[0.0008]</td></tr><tr><td>Relatedness density</td><td>-0.0150***<br>[0.0042]</td><td>-0.0359***<br>[0.0067]</td></tr></tbody></table>
<table><tr><td>Relatedness density sq</td><td>0.0103**<br/>[0.0043]</td><td>-0.0136+<br/>[0.0074]</td></tr><tr><td>New subclass</td><td></td><td>0.1504***<br/>[0.0034]</td></tr><tr><td># of patents in the subclass</td><td>-0.0087***<br/>[0.0004]</td><td>-0.0010<br/>[0.0012]</td></tr><tr><td>Missing subclass</td><td>-0.0696***<br/>[0.0023]</td><td>-0.0167***<br/>[0.0017]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0196***<br/>[0.0008]</td><td>-0.0380***<br/>[0.0019]</td></tr><tr><td>Constant</td><td>0.0585***<br/>[0.0028]</td><td>-0.0979***<br/>[0.0049]</td></tr><tr><td>Observations</td><td>294294</td><td>294294</td></tr><tr><td>R-Squared</td><td>.1517</td><td>.1916</td></tr></table>

Note: Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01. Estimation method: Linear regression with high-dimensional fixed effects.

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

Similarly, column (2) uses the variable Specialization<sub>c,r,y</sub> as the dependent variable in order to test whether mobility helps a region to gain a comparative advantage in a given subclass. This is a binary variable indicating a region's gain in specialization in a technological class. In other words, the variable indicates whether a region goes from a RTA of 0 to a stable RTA of 1 on year y. Again, the flow of skilled workers from regions specialized in a subclass is associated with a RTA gain in those same technology subclasses in the host regions. Relatedness density has again a non-linear effect, the share of extra-regional inventors is negative, whereas New subclasses has a positive effect.

These preliminary results confirm hypothesis 1 regarding the role of occupational mobility and are consistent with the existing literature (Bahar and Rapoport, 2020), showing previous results highlighted in the literature to be robust when considering intra-national occupational mobility.

## 7.2. Technological novelty: the number of new combinations

Beyond the emergence of new subclasses or specialization, the aim of this study is to investigate whether highly skilled mobile workers boost the production of new combinations in subclasses in which their regions of origins is already specialized.

Table 6 presents the result of the main equation with four different weights. The effect associated with the mobility flows weighted by the RTA (i.e. the variable is equal to zero when the originating region is not specialized in technology c, otherwise it is equal to the number of migrants) are statistically significant, both when the RTA is computed with reference to the regions in 18 countries (column 1, baseline specification) or uniquely all French regions (column 2). The inflow of workers from regions specialized in a technology subclass c is therefore associated with a higher number of novel combinations of technology subclasses involving this subclass in the host region. This result is robust to weighting by the size of the technology subclass in the region of origin (column 3). These results thus indicate that skilled worker mobility from regions in which innovation activity in a given technology
subclass is intense are strongly associated with more technological novelty in this specific subclass in the patents filed in the host region. This important result is corroborated by a number of robustness tests presented in the appendix.<fnref n="11" />

<table><caption>Table 6 - Impact of highly skilled labor mobility on novelty</caption><thead><tr><th></th><th colspan="4">Novelty (region)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA) (y-1)</td><td>0.0034***<br/>[0.0012]</td><td></td><td></td><td></td></tr><tr><td>High Skilled Mobility (RTA FR) (y-1)</td><td></td><td>0.0037**<br/>[0.0015]</td><td></td><td></td></tr><tr><td>High Skilled Mobility (Size) (y-1)</td><td></td><td></td><td>0.0127***<br/>[0.0033]</td><td></td></tr><tr><td>High Skilled Mobility (Related Density) (y-1)</td><td></td><td></td><td></td><td>0.0021<br/>[0.0019]</td></tr><tr><td>Relatedness density</td><td>-0.0111<br/>[0.0134]</td><td>-0.0111<br/>[0.0134]</td><td>-0.0113<br/>[0.0134]</td><td>-0.0113<br/>[0.0134]</td></tr><tr><td>Relatedness density sq</td><td>0.0076<br/>[0.0155]</td><td>0.0076<br/>[0.0155]</td><td>0.0075<br/>[0.0155]</td><td>0.0078<br/>[0.0155]</td></tr><tr><td>New subclass</td><td>0.2904***<br/>[0.0125]</td><td>0.2904***<br/>[0.0125]</td><td>0.2906***<br/>[0.0125]</td><td>0.2905***<br/>[0.0125]</td></tr><tr><td># of patents in the subclass</td><td>-0.0103***<br/>[0.0030]</td><td>-0.0103***<br/>[0.0030]</td><td>-0.0095***<br/>[0.0030]</td><td>-0.0103***<br/>[0.0030]</td></tr><tr><td>Missing subclass</td><td>-0.5774***<br/>[0.0049]</td><td>-0.5774***<br/>[0.0049]</td><td>-0.5773***<br/>[0.0050]</td><td>-0.5774***<br/>[0.0049]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0066***<br/>[0.0024]</td><td>-0.0066***<br/>[0.0024]</td><td>-0.0068***<br/>[0.0024]</td><td>-0.0067***<br/>[0.0024]</td></tr><tr><td>Constant</td><td>0.5807***<br/>[0.0083]</td><td>0.5782***<br/>[0.0097]</td><td>0.5843***<br/>[0.0059]</td><td>0.5900***<br/>[0.0106]</td></tr><tr><td>Observations</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td></tr><tr><td>R-Squared</td><td>.5552</td><td>.5552</td><td>.5552</td><td>.5552</td></tr></tbody></table>

Note: Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01. Novelty is measured as the number of distinct combinations associated with a given ipc code found in all patents in a year and region. Estimation method: Linear regression with high-dimensional fixed effects.

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

In contrast, when weighted by the relatedness density of class c in the region of origin, the result is not significant (column 4). Relatedness density indicates how close a technology subclass is to the core of a region's technological specialization, whether or not this subclass is used in the region. Therefore, the fact that a region is specialized in technological fields does not imply that labor mobility flows from this region would be associated with higher novelty indexes in related technological subclass in the host region if these subclasses are not used intensively in the sending region. Therefore, with workers flows, the key determinant of technological novelty in technology subclasses at destination lies in their intensive usage in

<footnote n="11">¹¹ Table A3 shows that the main result is robust when using alternative novelty indicators (novelty binary and novelty share). Table A4 shows that the result is robust when using a PPML regression with multi-ways fixed effects instead of a linear setting. Table A5 shows that the main result is robust to the removal of either one of the two largest patenting regions (Île-de-France, Rhône-Alpês) or the region with the lowest level of patenting activity (Corsica). Table A6 tackles the issue of bad controls (Angrist and Pischke, 2009) and shows that reproducing the benchmark specification including controls one by one does not alter the positive and statistically significant effect of our main independent variable. Table A7 reproduces Table 5 adding lag (y-2) in the regression following the methodology of Wang and Hagedoorn (2014). Our main result remains robust: the effect of lagged high skilled mobility occurs when there is a lag of one year, while the effect of the second lag is non-significant. Table A7bis reproduces equation from Table 5, including forward mobility flows (y+1) to test for the absence of reverse causality.</footnote>
the region of origin, rather than simply being related with the specialization portfolio of this region. These results confirm **hypothesis 2**.

Regarding variables controlling the technology space of the host region, relatedness density is associated with a negative and non-significant coefficient. The fact that a technology subclass is closer to the core of a region's technological specialization does not have an impact on the technological novelty produced in that technology subclass. This result may be related to the fact that regions do not explore diversification via novel combinations of technological subclasses in their inventive activity, especially in subclasses close to the fields in which the region is already specialized. This first regression table therefore suggests that mobility flows play a significant role in the dynamics of invention.

<table><caption>Table 7 - Technological novelty at national and international level</caption><thead><tr><th></th><th colspan="2">Novelty</th></tr><tr><th></th><th>France</th><th>World</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.0031***<br>[0.0009]</td><td>0.0015***<br>[0.0004]</td></tr><tr><td>Relatedness density</td><td>-0.0136<br>[0.0085]</td><td>-0.0048<br>[0.0038]</td></tr><tr><td>RelatedDens_sq</td><td>0.0190+<br>[0.0102]</td><td>0.0057<br>[0.0045]</td></tr><tr><td>New subclass</td><td>0.1164***<br>[0.0104]</td><td>0.0399***<br>[0.0052]</td></tr><tr><td># of patents in the subclass</td><td>0.0045**<br>[0.0020]</td><td>0.0011+<br>[0.0007]</td></tr><tr><td>Missing subclass</td><td>-0.1581***<br>[0.0037]</td><td>-0.0317***<br>[0.0014]</td></tr><tr><td>Share of extra-regional inventors</td><td>0.0010<br>[0.0014]</td><td>0.0006<br>[0.0006]</td></tr><tr><td>Constant</td><td>0.1510***<br>[0.0058]</td><td>0.0233***<br>[0.0025]</td></tr><tr><td>Observations</td><td>294294</td><td>294294</td></tr><tr><td>R-Squared</td><td>.3666</td><td>.1683</td></tr></tbody></table>

Note: Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01. Estimation method: Linear regression with high-dimensional fixed effects.

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

This influence of skilled worker flows on technological innovation is all the more important as the regional technological novelty they enable is not a mere imitation of what is being done in their region of origin or in other French regions. By adopting a broader definition of technological innovation to identify IPC subclasses combinations appearing for the first time, not only in the host region but also in France or globally, the results in Table 7 indicate that the mobility of skilled workers continues to have a significant and positive impact on the emergence of new technological combinations at their destination. The effect of mobility on technological novelty therefore enables the emergence of combinations of technological classes never before seen in the country, and sometimes in the world, underlining the pivotal importance of worker flows on innovation dynamics.
### 7.3. Worker flows and regional specialization complementarity

Previous results shows that on average, workers flows trigger technological novelty at the destination region in the IPC classes in which the workers' regions of origins are specialized. What remains unanswered is whether this effect is observed for every IPC class in every host region, regardless of the host region previous technological portfolio.

Table 8 explores the mechanisms through which mobile workers affect novelty given the characteristics of the regional technology space. Columns 1 and 2 estimate the impact of mobile workers when they come from regions specialized in a subclass and enter a region in which the given subclass relatedness density is lower (respectively higher) than the median. To put it differently, the first column is estimated as a sub-sample consisting of half of the technologies closest to the destination region's specialty fields, and the opposite for the second column. The effect appears higher and significant for subclasses characterized by high relatedness density at destination, that is, when the subclass is strongly related to subclasses in which the host region is already specialized. This result suggests a form of complementarity between the technologies conveyed by mobile workers and that embodied in the technological space at destination.

<table><caption>Table 8 - Disentangling the effects</caption><thead><tr><th rowspan="2"></th><th colspan="4">Novelty (region)</th><th rowspan="2">Recombinant<br>Reuse</th><th rowspan="2">Recombinant<br>Creation</th></tr><tr><th>(1)<br>RD &lt; med.</th><th>(2)<br>RD &gt; med.</th><th>(3)<br>RTA = 0</th><th>(4)<br>RTA = 1</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA) (y-1)</td><td>0.0022+<br>[0.0012]</td><td>0.0058**<br>[0.0029]</td><td>0.0003<br>[0.0014]</td><td>0.0130***<br>[0.0045]</td><td>0.0024+<br>[0.0012]</td><td>0.0015***<br>[0.0005]</td></tr><tr><td>Relatedness density</td><td>-0.0200</td><td>-0.0163</td><td>-0.0005</td><td>-0.0760+</td><td>0.0024</td><td>-0.0198***</td></tr></tbody></table>
<table><tbody><tr><td></td><td>[0.0171]</td><td>[0.0248]</td><td>[0.0135]</td><td>[0.0410]</td><td>[0.0133]</td><td>[0.0062]</td></tr><tr><td>Relatedness density sq</td><td>0.0146</td><td>0.0144</td><td>-0.0070</td><td>0.0681+</td><td>-0.0023</td><td>0.0137**</td></tr><tr><td></td><td>[0.0194]</td><td>[0.0272]</td><td>[0.0152]</td><td>[0.0410]</td><td>[0.0154]</td><td>[0.0064]</td></tr><tr><td>New subclass</td><td>0.2836***</td><td>0.2763***</td><td>0.2437***</td><td>0.0000</td><td></td><td></td></tr><tr><td></td><td>[0.0156]</td><td>[0.0206]</td><td>[0.0128]</td><td>[0.0000]</td><td></td><td></td></tr><tr><td># of patents in the subclass</td><td>-0.0123**</td><td>-0.0122***</td><td>-0.0016</td><td>-0.0118***</td><td>-0.0042</td><td>-0.0091***</td></tr><tr><td></td><td>[0.0050]</td><td>[0.0034]</td><td>[0.0042]</td><td>[0.0038]</td><td>[0.0030]</td><td>[0.0008]</td></tr><tr><td>Missing subclass</td><td>-0.6032***</td><td>-0.5651***</td><td>-0.6298***</td><td>-0.5020***</td><td>-0.5149***</td><td>-0.0962***</td></tr><tr><td></td><td>[0.0072]</td><td>[0.0053]</td><td>[0.0058]</td><td>[0.0057]</td><td>[0.0045]</td><td>[0.0034]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0044</td><td>-0.0097***</td><td>-0.0016</td><td>-0.0074</td><td>0.0060**</td><td>-0.0184***</td></tr><tr><td></td><td>[0.0038]</td><td>[0.0034]</td><td>[0.0032]</td><td>[0.0055]</td><td>[0.0024]</td><td>[0.0011]</td></tr><tr><td>Constant</td><td>0.6015***</td><td>0.5676***</td><td>0.6367***</td><td>0.4926***</td><td>0.5252***</td><td>0.0867***</td></tr><tr><td></td><td>[0.0088]</td><td>[0.0192]</td><td>[0.0092]</td><td>[0.0319]</td><td>[0.0081]</td><td>[0.0039]</td></tr><tr><td>Observations</td><td>138970</td><td>150890</td><td>212185</td><td>80482</td><td>294294</td><td>294294</td></tr><tr><td>R-Squared</td><td>.6309</td><td>.5467</td><td>.611</td><td>.5557</td><td>.5323</td><td>.1716</td></tr></tbody></table>

Note: RD < (resp. >) med is a split sample when Relatedness density is < (resp. >) median; RTA =1 (resp. = 0): split sample when RTA = 1 (resp. =0). Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01. Estimation method: Linear regression with high-dimensional fixed effects.

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

Columns 3 and 4 replicate the analysis by considering whether the region is or not already specialized in a given subclass. The impact is significant only for subclasses in which the region is already specialized confirming the results in column 2 and the relationship with the regional technology space. These results support hypothesis 2a rather than 2b. If the technological subclasses in which the originating region is specialized are too distant from the specialization of the host region, then the effects of workers' mobility flows between these regions will not be significant.

Does this mean that the inflow of workers only generate novelty recombining technological subclasses already used in the host region? To answer this question, we consider the characteristics of novelty. We remind that recombinant reuse are combinations that associate subclasses that are already in the region's portfolio whereas recombinant creation includes at least one new subclass. In both equations, the coefficients associated with skilled worker flows are significant, even though the magnitude of the elasticity is 1.6 times higher for recombinant reuse. From what precedes, the effect of specialized workers flows on recombinant reuse is rather intuitive. It shows that when skilled workers enter a new region, they help produce new technology combinations gathering in the same patents IPC classes that were previously already used in the host region but in separate patents.

Explaining the impact on recombinant creation seems more challenging. When mobile workers come from regions specialized in a given subclass, they enable the production of a combination which includes at least one new subclass at destination. Either this subclass is new to the region, or it is associated with a subclass that is new to the region. Technological complementarity between origin and destination regions can therefore be needed for only one of the two IPC subclasses of the new technology combinations observed: one subclass close to the innovation already carried out at destination, and the second one having never been used in the host region. Hypotheses 2a and 2b are therefore not opposed.
<table><caption>Table 9 - Reuse and Creation</caption><thead><tr><th rowspan="2"></th><th colspan="4">Recombinant Reuse (region)</th><th colspan="4">Recombinant Creation (region)</th></tr><tr><th>RD &lt; med.<br>(1)</th><th>RD &gt; med.<br>(2)</th><th>RTA = 0<br>(3)</th><th>RTA = 1<br>(4)</th><th>RD &lt; med.<br>(5)</th><th>RD &gt; med.<br>(6)</th><th>RTA = 0<br>(7)</th><th>RTA = 1<br>(8)</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.0013<br>[0.0012]</td><td>0.0050+<br>[0.0028]</td><td>-0.0002<br>[0.0014]</td><td>0.0128***<br>[0.0045]</td><td>0.0011+<br>[0.0006]</td><td>0.0013<br>[0.0009]</td><td>0.0005<br>[0.0007]</td><td>0.0000<br>[0.0010]</td></tr><tr><td>Relatedness density</td><td>-0.0008<br>[0.0169]</td><td>-0.0164<br>[0.0249]</td><td>0.0105<br>[0.0133]</td><td>-0.0742+<br>[0.0408]</td><td>-0.0267***<br>[0.0091]</td><td>-0.0011<br>[0.0093]</td><td>-0.0141+<br>[0.0075]</td><td>-0.0087<br>[0.0096]</td></tr><tr><td>Relatedness Density sq</td><td>-0.0026<br>[0.0192]</td><td>0.0174<br>[0.0272]</td><td>-0.0166<br>[0.0150]</td><td>0.0701+<br>[0.0408]</td><td>0.0230**<br>[0.0100]</td><td>-0.0045<br>[0.0090]</td><td>0.0121<br>[0.0083]</td><td>0.0022<br>[0.0096]</td></tr><tr><td># of patents in the subclass</td><td>-0.0014<br>[0.0050]</td><td>-0.0086**<br>[0.0034]</td><td>0.0047<br>[0.0043]</td><td>-<br>[0.0038]</td><td>-0.0167***<br>[0.0017]</td><td>-0.0055***<br>[0.0008]</td><td>-<br>[0.0013]</td><td>-0.0008<br>[0.0009]</td></tr><tr><td>Missing subclass</td><td>-0.5002***<br>[0.0063]</td><td>-0.5214***<br>[0.0051]</td><td>-<br>[0.0051]</td><td>0.5353***<br>[0.0057]</td><td>-0.1547***<br>[0.0062]</td><td>-0.0676***<br>[0.0030]</td><td>-<br>[0.0049]</td><td>-0.0331***<br>[0.0017]</td></tr><tr><td>Share of extra-regional inv.</td><td>0.0136***<br>[0.0039]</td><td>-0.0010<br>[0.0033]</td><td>0.0120***<br>[0.0032]</td><td>-0.0072<br>[0.0053]</td><td>-0.0259***<br>[0.0019]</td><td>-0.0127***<br>[0.0014]</td><td>-<br>[0.0015]</td><td>0.0003<br>[0.0016]</td></tr><tr><td>Constant</td><td>0.5053***<br>[0.0080]</td><td>0.5309***<br>[0.0190]</td><td>0.5496***<br>[0.0089]</td><td>0.4729***<br>[0.0318]</td><td>0.1460***<br>[0.0060]</td><td>0.0577***<br>[0.0065]</td><td>0.1280***<br>[0.0053]</td><td>0.0346***<br>[0.0073]</td></tr><tr><td>Observations</td><td>138970</td><td>150890</td><td>212185</td><td>80482</td><td>138970</td><td>150890</td><td>212185</td><td>80482</td></tr><tr><td>R-Squared</td><td>.5902</td><td>.5337</td><td>.5741</td><td>.5515</td><td>.2686</td><td>.2025</td><td>.234</td><td>.2614</td></tr></tbody></table>

Note: Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01. Estimation method: Linear regression with high-dimensional fixed effects.

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

To further explore the results of columns 5 and 6, we estimate, for each of our dependent variables (recombinant reuse and recombinant creation), the same split samples as in columns 1 to 4 in Table 9. The first 4 columns of Table 9 support the result that high skilled mobility supports technological novelty at destination only if the technological class is highly related to the technological space (column 2) or the technological specialization set at destination (column 4). Columns 5 to 8 sheds light on the fact that while there is evidence of an effect of high skilled mobility on recombinant creation, there is only an effect on classes which are below the median of the local relatedness density. This indicates that high skilled mobility indeed (also) bring new and non-redundant knowledge to the local economy.

# 8. Robustness checks

## 8.1. Placebo test: randomizing specializations

Three main additional robustness tests have been carried out. First, we perform a placebo test to see if the estimated coefficient associated with our main explanatory variable may have been a result of chance, namely a positive random effect of skilled labor on novelty. To verify that this is not the case, we performed a test that involves randomly assigning Revealed Technological Advantages (RTA) to origin regions while maintaining the same proportion of class x region dyad with an RTA. These randomly assigned RTA are then used to calculate the mobility flows of skilled employees weighted by the specialization of the region of origin using the formula presented in the methodology section. We then estimate our
benchmark specification (column 1 of Table 6) using these randomly assigned comparative
advantages to capture the coefficients, standard errors, and p-values associated with the
skilled labor flow built with the random RTAs. The simulation is replicated 1000 times. The
coefficients from the 1000 regressions are used to calculate the probability density of the
distribution of coefficients associated with the mobility variable when the technological
advantages of the regions are randomly distributed. The coefficient of our benchmark
equation from the result section is then compared to the estimated probability distribution
to assess the robustness of our main result.

Figure 11 below plots the probability density of the estimated coefficients in the 1000 placebos. This distribution is symmetrical and centered at 0. In contrast, the coefficient estimated in the reference equation is positive and of very large magnitude compared with the estimated distribution of coefficients estimated by Placebo. The value of this coefficient is greater than the maximum value observed in the 1000 placebo regressions. The result of the main analysis is therefore not statistically attributable to statistical chance. The weighting of mobility flows by the observed technological advantages leads to a positive effect of a magnitude that is out of all proportion to that of the estimated coefficients when the technological advantages are randomly attributed. In other words, the effect of mobility on technological novelty is indeed linked to the technological specialization of the host region and thus to the fact that mobile workers convey knowledge specific to their region of origin and that can be mobilized in the host regions to create technological novelty.

<caption>Figure 11 – Estimated density of Placebo coefficient distribution</caption>
<figure><img src="image_13.png" /></figure>

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

## 8.2. Instrumentation strategy

### Instrumentation strategy: method

In quantifying the effect of skilled labor mobility flows on novelty, panel estimates could still be biased as a consequence of endogeneity issues from two sources: omitted variables and reverse causality. We use an instrumentation strategy as robustness test of our main estimation presented in section 6.1.

First, an omitted variables issue could arise if unobserved class-region time-varying determinants (such as a productivity shock in a given CRY) shapes both the flow of workers and also the novelty activity. The question of causality is particularly relevant in the context of knowledge spillovers. The professional mobility of workers between different regions is not random. On the one hand, the size of flows studied may be partly correlated with the size of the population of the host regions. For example, structurally, the Île-de-France region receives larger flows of workers than the other regions because it represents the largest employment area in France. If the novelty indicator is correlated with the size of the region, an endogeneity problem may arise, since regions hosting more mobile workers will also have on average higher novelty indicators for all technological subclasses. The inclusion of three sets (class-region, region-year, class-year) of fixed effects mitigates much of this issue, but still some time-varying variable could be omitted, even when we take into account the standard determinants of novelty.

Second, there may also be a reverse causality, as mobility choices may be affected by the dynamics of specialization of regions in certain technological subclasses. For example, a region developing an innovation activity in one or more technological subclasses may resort to recruiting skilled workers with previous experience in the field. The recruitment of these workers may take place in firms already innovating in these technologies, and these firms are likely to be located in regions specializing in these technologies. This endogeneity issue is
widespread in the migration literature (Jaeger et al., 2018) and the identification strategy builds on a more canonical approach that is applied in different branches of the economic literature. This general approach is called shift-share instruments or Bartik instruments (Bartik, 1991), and it has become quite prevalent in the immigration and labor market literature following the work of Altonji and Card (1991) and Card (2001), and more recently on migration and innovation (Bahar et al. 2020; Diodato et al., 2022).

We propose an IV strategy to address the aforementioned issues by utilizing the shift-share methodology and the employment data, based on bilateral flow of skilled workers outlined above. The aim is to create a predicted flow of workers $\widehat{\text{MOB}}_{r',r,y}$ that is less dependent on the local demand for workers specialized in a subclass c, who are moving to a particular region r and year y. This would neutralize the reverse causality problem as in the shifts we take out the workers going from region r' to region r of the calculation. To do so, for each PCS category, we first calculate the percentage of workers who originated from a specific region r' and ended up in region r in the reference year $y^0$ (share). Next, for a given PCS category, we calculate the yearly sum of high-skilled workers in this PCS category who migrated from r' to every other r'' region (shift) than the region r we are focusing on. Using this information, we can determine an imputed measure $\widehat{\text{MOB}}_{r',r,y}$ that accurately reflects the migration patterns of high-skilled workers across different regions:

$$ \widehat{\text{MOB}}_{r',r,y} = \left( \frac{\text{Mob}_{r',r,y^0}}{\sum_{r' \neq r'} \text{Mob}_{r',r,y^0}} \right) * \left( \underbrace{\sum_{r' \neq r''} \text{Mob}_{r',r'',y^0}}_{\text{Shift}} \right) (9) $$

The idea behind the predicted flow of workers $\widehat{\text{Mob}}_{r',r,y}$ used in the instrument is to use the total mobility flows measured a given year arriving in region r, under the assumption that these total flows are not correlated with the region's specific needs in a technological class, on a given year y. Indeed, the reverse causality problem may be linked to the fact that the innovation dynamic observed in a given technology class induces temporary and targeted recruitment in regions with a comparative advantage in that technology class. Taking inflows from all regions cleanses the flow variables of this potential endogeneity problem. The bilateral flow proxy between r' and r is then calculated by weighting the total flows into region r by the share of flows into region r from region r' in a given reference year $y_0$. Taking a reference share distant in time helps to hedge against reverse causality, and is based on the assumption that the regions of origin of incoming flows are experiencing a stable trend, beyond the potential shocks that may arise when the region recruits specifically from regions specializing in the technological classes in which it files a patent.

The predicted bilateral measure $\widehat{\text{Mob}}_{r',r,y}$ is then weighted by the RTA in the origin region r' and then summed across all regions $r' \neq r$. The resulting imputed instrument High Skilled Mobility IV $\widehat{\text{MOB}}_{r',r,y}$ is formally computed as follows:
$$ \text{High Skilled Mobility IV}_{c,r,y} = \sum_{r' \neq r} \widehat{\text{MOB}}_{r', r, y} * \text{RTA}_{c, r', y} (10) $$

The variable calculated in equation 10 is then used as an instrument of the variable detailed in equation 7 in a two-stage least squares estimation. Tests for under-identification (Kleibergen-Paap), over-identification (Sargan-Hansen) and weak instruments (Cragg-Donald) are used to investigate the robustness of the identification strategy.

## Instrumentation strategy: results

The shift and share strategy used in a two-stage least squares does not alter the significance of the results. The coefficients associated with skilled worker flows remain positive and significant (Table 10, column 1).

<table><caption>Table 10 - Instrumentation</caption><thead><tr><th></th><th>Second Stage<br>Novelty</th><th>1<sup>st</sup> stage regression<br>High Skilled Mobility</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.0043**<br>[0.0018]</td><td></td></tr><tr><td>Relatedness density</td><td>-0.0250**<br>[0.0120]</td><td>-0.0035<br>[0.0081]</td></tr><tr><td>RelatedDens_sq</td><td>0.0063<br>[0.0136]</td><td>-0.0055<br>[0.0087]</td></tr><tr><td>New subclass</td><td>0.3499***<br>[0.0112]</td><td>0.0033<br>[0.0043]</td></tr><tr><td># of patents in the subclass</td><td>0.4328***<br>[0.0047]</td><td>-0.0024+<br>[0.0013]</td></tr><tr><td>Missing subclass</td><td>-0.1840***<br>[0.0049]</td><td>-0.0031**<br>[0.0015]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0023<br>[0.0023]</td><td>0.0064***<br>[0.0013]</td></tr><tr><td>External instrument</td><td></td><td>0.9411***<br>[0.0024]</td></tr><tr><td>Observations</td><td>2.78e+05</td><td></td></tr><tr><td>R-Squared</td><td>0.2777</td><td></td></tr><tr><td>Weak identification Kleibergen-Paap Wald F</td><td></td><td>1.52e+05</td></tr></tbody></table>

Note: Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05

***0.01. Estimation method: Linear regression with high-dimensional fixed effects.

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

## 8.3. Placebo tests: the effect of police forces and clergy on innovation

One last robustness is presented below. Indeed, our estimates focus on the effect of the arrival of skilled workers from regions specialized in a technological subclass. It is interesting to test whether these effects remain when the workers considered are not skilled workers but workers belonging to socio-professional categories from which no effect on innovation is expected. The mobility of the police force and the clergy is used as a placebo test, keeping the weighting by the RTAs. These flows are not associated with any significant effect as shown in Table 11 below. This result also reinforces the robustness of the previous results.
<table><caption>Table 11 - Identification strategy validity</caption><thead><tr><th></th><th>Second Stage<br>Novelty</th><th>1<sup>st</sup> stage<br>Cops and Clergy<br>Mobility</th></tr></thead><tbody><tr><td>Police and Clergy mobility</td><td>0.0021<br>[0.0014]</td><td></td></tr><tr><td>Relatedness density</td><td>-0.0170**<br>[0.0083]</td><td>-0.0137<br>[0.0145]</td></tr><tr><td>Relatedness density sq</td><td>0.0223**<br>[0.0097]</td><td>-0.0429***<br>[0.0156]</td></tr><tr><td>New subclass</td><td>0.1023***<br>[0.0088]</td><td>0.0180**<br>[0.0083]</td></tr><tr><td># of patents in the subclass</td><td>0.0038***<br>[0.0014]</td><td>-0.0013<br>[0.0015]</td></tr><tr><td>Missing subclass</td><td>-0.1558***<br>[0.0016]</td><td>-0.0035+<br>[0.0018]</td></tr><tr><td>Share of extra-regional inventors</td><td>0.0005<br>[0.0014]</td><td>-0.0045+<br>[0.0023]</td></tr><tr><td>External Instrument</td><td></td><td>0.4253***<br>[0.0023]</td></tr><tr><td>Observations</td><td>2.78e+05</td><td></td></tr><tr><td>R-Squared</td><td>0.0300</td><td></td></tr><tr><td>Weak identification Kleibergen-Paap Wald F</td><td></td><td>3.56e+04</td></tr></tbody></table>

Note: Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01. Estimation method: Linear regression with high-dimensional fixed effects.

Source: OECD REGPAT database. EPO and PCT patent filing data. BTS database.

Field: Metropolitan France patents and workers flows.

## 9. Conclusion

The literature analyzing the link between migration and innovation dynamics emphasizes the external knowledge source represented by migration, thereby contributing to the diversification of local knowledge bases. The transfer of knowledge made possible through worker mobility offers an opportunity to broaden the technological portfolio of host regions. This not only facilitates innovation in new technological fields but also enables specialization in previously untapped technological domains at the regional level. In essence, mobile workers serve as agents of structural technological change, promoting technological diversification by facilitating patenting in the specialized fields of their regions of origin. However, the integration and recombination of knowledge imported by mobile workers may prove challenging if it deviates significantly from the activities carried out within the host region. Recent literature also suggests that the utilization of external knowledge brought in by mobile workers varies depending on the composition of work teams. Teams comprising both local and migrant inventors tend to blend local knowledge with the newly acquired external knowledge, whereas teams consisting solely of migrant inventors drive unrelated diversification by relying exclusively on external knowledge.

Our study aims to comprehensively investigate the mechanisms through which knowledge transfers, facilitated by the inter-regional mobility of skilled workers, are integrated and recombined within their host regions and can lead to technological novelty. A first important contribution lies in adopting the lens of national inter-regional mobility to analyze the impact of professional mobilities on innovation. This approach acknowledges
that migration primarily occurs at the regional level and that technological specialization is
regional before being national. Our findings reveal that the mobility of skilled workers
fosters the emergence of novel technological combinations in the technological classes in
which the mobile workers' region of origin is specialized in. Furthermore, the influence of
inter-regional migration on technological novelty extends beyond mere replication of
existing technological combinations found in other regions, as new combinations are new
not only at the regional but also at the national and sometimes international level.

However, not all forms of knowledge are integrated the same way within host regions. If
mobile workers originate from regions with specialized areas that significantly differ from
the technological activities prevalent in the host region, their mobility will not have a
statistically significant effect on technological novelty. This suggests the presence of an
enhancement effect between worker inflows and the local technology space in the host
region. However, technological novelty enabled by workers mobility not only reinforces
specialization and encourages patenting in technological classes already used within host
regions but can also enable the introduction of new technological classes. The technological
novelty linked to migration is thus driven by the complementarity of the specialization
structures of both regions of departure and arrival but can also lead to the exploration of
technological classes that are new to the host region through the technological
combinations being observed.

Our study therefore enables us to study in greater detail the role of professional
mobility on the technological development of regions, taking into account the specificities
of the technological spaces of the regions from which mobile workers originate and those in
which they are received. This approach sheds light on the mechanisms of knowledge
transmission enabled by occupational mobility, looking specifically at the technological
fields in which they occur and the conditions under which they generate new technological
combinations.

However, our study suffers from some limitations. Focusing on the inflow of workers
coming from regions specialized in a technology impedes to study the characteristics of the
second class associated with the new combination. Our analysis shows that mobility does
indeed enable the emergence of both reuse and creation-type novelties, but it might be
interesting to extend the analysis by measuring the relatedness between the two classes
making up each new combination, as well as the relatedness density of each of these two
classes in relation to the technological space of the regions of departure and arrival. Indeed,
our study does not allow to conclude whether the new combinations favored by the arrival
of workers are subclasses that are distant from each other. It is possible that the necessary
condition allowing innovation leads to the emergence of new combinations in which one
class is close to the specialization of the host region and the other is only close to the
specialization of the region of departure. It is therefore possible that the complementarity
between technological areas can create combinations between distant domains. These
questions, which relate more to the characteristics of the technological innovation produced than to the mechanisms by which this innovation emerges, deserve to be characterized thoroughly and should be the focus of future research.
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# Appendix 1

The paper uses the International Patent Classification (IPC) to characterize the technology subclasses in which regions are specialized. Each patent is characterized and classified according to the technologies combined to form an invention. As an example, consider Patent EP19980933721 applied for by the company Pernod-Ricard in 1997 for "making a composition for producing corks", by two inventors, and one is located in the Paris region (FR10). The patent is assigned to three four-digit IPC codes B27J (Mechanical working of cork), B27N (Manufacture made from particles or fibers of wood) and C08L (Composition of macromolecular compounds)¹².

The novelty of a subclass or its combination is considered at the regional level, based on the location of inventors, and over the whole European patent history.

* Regarding single subclasses, B27J appears for the first time in the region in year 1997 and in this patent, whereas the others were already used in the past (see table below).

* Regarding combinations, the patent can be decomposed as three distinct pairwise combinations: (B27J;B27N), (B27J;C08L) and (B27N;C08L).

This patent has a high degree of novelty as each of the three combinations appear for the first time in the region. However, novelty can be characterized differently. (B27J;B27N) and (B27J;C08L) are combinations that include a subclass B27J previously unknown in the region, and thus they are characterized as "Recombinant creation" whereas (B27N;C08L) is characterized as "Recombinant reuse" as both subclasses are already part of the technological portfolio, that is, they have appeared previously in a patent but never together. Thus, this patent is the first to combine both of these subclasses. Recombinant reuse corresponds to the situation where both codes already exist in the region's portfolio but are combined for the first time. Recombinant creation is the situation in which at least one of the codes did not occur in any patent applied by local inventors and thus not present in the regions' portfolio.

<table><caption>**Table A1 - Characterizing novelty**</caption><thead><tr><th>IPC subclass</th><th>Year of IPC entry in the region</th><th>Combination</th><th>Year of novel combination in the region</th><th>Already existing combination</th><th>Recombinant reuse</th><th>Recombinant creation</th></tr></thead><tbody><tr><td colspan="7">Example 1: Patent EP19980933721 Priority year 1997</td></tr><tr><td>B27J</td><td>1997</td><td>B27J ; B27</td><td>1997</td><td></td><td></td><td></td></tr><tr><td>C08L</td><td>1978</td><td>B27J ; C08L</td><td>1997</td><td></td><td>B27J ; C08L</td><td>B27J ; B27</td></tr><tr><td>B27N</td><td>1978</td><td>B27N ; C08L</td><td>1997</td><td></td><td>B27N ; C08L</td><td></td></tr><tr><td colspan="7">Example 2: Patent EP19970401843 Priority year 1996</td></tr><tr><td>C07C</td><td>1978</td><td>C07C ; C12F</td><td>1978</td><td></td><td></td><td>C07C ; C12F</td></tr><tr><td>C11B</td><td>1978</td><td>C07C ; C11B</td><td>1978</td><td>C07C ; C11B</td><td></td><td></td></tr><tr><td>C12F</td><td>1996</td><td>C11B ; C12F</td><td>1996</td><td></td><td></td><td>C11B ; C12F</td></tr></tbody></table>

Another example is given by Patent applied for by Pernot Ricard by inventors located in the Paris Region and in year 1996 for "extracting 2-phenylethanol from residues obtained

<footnote>12</footnote>
during alcohol production by fermentation". Among the three combinations (see table below), two are new to the region and enter the category Recombinant creation as they combine subclass C12F which occurs for the first time in a patent in the region whereas the combination C07C; C11B has already been used in the region in 1978¹³.

## Appendix 2

<table><caption>Table A2 – descriptive statistics</caption><thead><tr><th></th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Novelty</td><td>0.38</td><td>1.07</td><td>0.00</td><td>25.00</td></tr><tr><td>Novelty (France)</td><td>0.11</td><td>0.48</td><td>0.00</td><td>18.00</td></tr><tr><td>Novelty (World)</td><td>0.02</td><td>0.15</td><td>0.00</td><td>6.00</td></tr><tr><td>Novelty Binary</td><td>0.17</td><td>0.38</td><td>0.00</td><td>1.00</td></tr><tr><td>Novelty Binary (France)</td><td>0.07</td><td>0.25</td><td>0.00</td><td>1.00</td></tr><tr><td>Novelty Binary (World)</td><td>0.01</td><td>0.11</td><td>0.00</td><td>1.00</td></tr><tr><td>Novelty Share</td><td>0.10</td><td>0.26</td><td>0.00</td><td>1.00</td></tr><tr><td>Novelty Share (France)</td><td>0.02</td><td>0.11</td><td>0.00</td><td>1.00</td></tr><tr><td>Novelty Share (World)</td><td>0.00</td><td>0.04</td><td>0.00</td><td>1.00</td></tr><tr><td>Novelty Reuse</td><td>0.35</td><td>1.02</td><td>0.00</td><td>25.00</td></tr><tr><td>Novelty Creation</td><td>0.03</td><td>0.26</td><td>0.00</td><td>18.00</td></tr><tr><td>New subclass</td><td>0.01</td><td>0.09</td><td>0.00</td><td>1.00</td></tr><tr><td>Specialization</td><td>0.03</td><td>0.16</td><td>0.00</td><td>1.00</td></tr><tr><td>High Skilled Mobility (RTA)</td><td>1165.30</td><td>1963.84</td><td>0.00</td><td>34235.00</td></tr><tr><td>High Skilled Mobility (RTA_FR)</td><td>1163.23</td><td>1849.68</td><td>0.00</td><td>30351.00</td></tr><tr><td>High Skilled Mobility (RD)</td><td>1095.46</td><td>1804.71</td><td>0.00</td><td>27477.00</td></tr><tr><td>High Skilled Mobility (Size)</td><td>10.61</td><td>40.58</td><td>0.00</td><td>3042.27</td></tr><tr><td>Relatedness density</td><td>0.27</td><td>0.26</td><td>0.00</td><td>1.00</td></tr><tr><td># of patents in the subclass</td><td>1.66</td><td>9.74</td><td>0.00</td><td>773.00</td></tr><tr><td>Missing subclass</td><td>0.71</td><td>0.45</td><td>0.00</td><td>1.00</td></tr><tr><td>Share of extra-regional inventors</td><td>0.23</td><td>0.35</td><td>0.00</td><td>1.00</td></tr><tr><td>Observations</td><td colspan="4">308 308</td></tr></tbody></table>

<footnote>13</footnote>
<table><caption>Table A3 - Robustness Checks : alternative novelty indicators</caption><thead><tr><th></th><th>Novelty (Binary)</th><th>Novelty (Share)</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.0019+<br>[0.0010]</td><td>0.0018**<br>[0.0007]</td></tr><tr><td>Relatedness density</td><td>-0.0157<br>[0.0106]</td><td>-0.0199**<br>[0.0077]</td></tr><tr><td>RelatedDens_sq</td><td>0.0143<br>[0.0123]</td><td>0.0105<br>[0.0086]</td></tr><tr><td>New subclass</td><td>0.2236***<br>[0.0088]</td><td>0.3551***<br>[0.0091]</td></tr><tr><td># of patents in the subclass</td><td>-0.0118***<br>[0.0022]</td><td>-0.0313***<br>[0.0015]</td></tr><tr><td>Missing subclass</td><td>-0.5724***<br>[0.0037]</td><td>-0.4401***<br>[0.0035]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0052***<br>[0.0020]</td><td>-0.0046***<br>[0.0017]</td></tr><tr><td>Constant</td><td>0.5751***<br>[0.0064]</td><td>0.4194***<br>[0.0051]</td></tr><tr><td>Observations</td><td>294294</td><td>294294</td></tr><tr><td>R-Squared</td><td>.5925</td><td>.5217</td></tr></tbody><tfoot><tr><td colspan="3">Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01</td></tr></tfoot></table>

<caption>Table A4 - Robustness Checks</caption>

Poisson pseudo-maximum likelihood regressions with high-dimensional fixed effects

<table><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.025**<br>[0.0103]</td></tr><tr><td>Relatedness density</td><td>-0.062<br>[0.0898]</td></tr><tr><td>RelatedDens_sq</td><td>0.017<br>[0.0931]</td></tr><tr><td>New subclass</td><td>0.2235***<br>[0.0325]</td></tr><tr><td># of patents in the subclass</td><td>-0.0414***<br>[0.0078]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0089<br>[0.0161]</td></tr><tr><td>Constant</td><td>-0.3725***<br>[0.077]</td></tr><tr><td>Observations</td><td>80630</td></tr><tr><td>R-Squared</td><td>0.1048</td></tr></tbody><tfoot><tr><td colspan="2">Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01</td></tr></tfoot></table>
<table><caption>Table A5 - Robustness checks</caption><thead><tr><th rowspan="2"></th><th colspan="3">Novelty</th></tr><tr><th>Excluding<br>Ile de France</th><th>Excluding<br>Rhône-Alpes</th><th>Excluding<br>Corsica</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.0023+<br>[0.0013]</td><td>0.0031**<br>[0.0013]</td><td>0.0037***<br>[0.0013]</td></tr><tr><td>Relatedness density</td><td>-0.0160<br>[0.0131]</td><td>-0.0094<br>[0.0136]</td><td>-0.0123<br>[0.0138]</td></tr><tr><td>RelatedDens_sq</td><td>0.0136<br>[0.0153]</td><td>0.0049<br>[0.0155]</td><td>0.0089<br>[0.0159]</td></tr><tr><td>New subclass</td><td>0.2874***<br>[0.0126]</td><td>0.2902***<br>[0.0127]</td><td>0.2981***<br>[0.0127]</td></tr><tr><td># of patents in the subclass</td><td>-0.0134***<br>[0.0030]</td><td>-0.0127***<br>[0.0030]</td><td>-0.0107***<br>[0.0030]</td></tr><tr><td>Missing subclass</td><td>-0.5811***<br>[0.0050]</td><td>-0.5783***<br>[0.0051]</td><td>-0.5771***<br>[0.0050]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0060**<br>[0.0024]</td><td>-0.0076***<br>[0.0024]</td><td>-0.0068***<br>[0.0024]</td></tr><tr><td>Constant</td><td>0.5863***<br>[0.0085]</td><td>0.5795***<br>[0.0086]</td><td>0.5796***<br>[0.0088]</td></tr><tr><td>Observations</td><td>280917</td><td>280917</td><td>280917</td></tr><tr><td>R-Squared</td><td>.5486</td><td>.55</td><td>.5532</td></tr></tbody></table>

Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01
<table><caption>Table A6 - Robustness check regarding the issue of bad controls</caption><thead><tr><th></th><th>0.0034***<br>[0.0012]</th><th>0.0075***<br>[0.0016]</th><th>0.0075***<br>[0.0016]</th><th>0.0057***<br>[0.0016]</th><th>0.0075***<br>[0.0016]</th><th>0.0040***<br>[0.0012]</th><th>0.0072***<br>[0.0016]</th><th>0.0072***<br>[0.0016]</th><th>0.0056***<br>[0.0016]</th><th>0.0039***<br>[0.0012]</th></tr></thead><tbody><tr><td>L.High Skilled Mobility (RTA)</td><td>0.0034***<br>[0.0012]</td><td>0.0075***<br>[0.0016]</td><td>0.0075***<br>[0.0016]</td><td>0.0057***<br>[0.0016]</td><td>0.0075***<br>[0.0016]</td><td>0.0040***<br>[0.0012]</td><td>0.0072***<br>[0.0016]</td><td>0.0072***<br>[0.0016]</td><td>0.0056***<br>[0.0016]</td><td>0.0039***<br>[0.0012]</td></tr><tr><td>Relatedness density</td><td>-0.0111<br>[0.0134]</td><td>-0.0108<br>[0.0164]</td><td></td><td></td><td></td><td></td><td>-0.0063<br>[0.0164]</td><td>0.0049<br>[0.0158]</td><td>0.0049<br>[0.0158]</td><td>-0.0154<br>[0.0135]</td></tr><tr><td>RelatedDens_sq</td><td>0.0076<br>[0.0155]</td><td>0.0036<br>[0.0183]</td><td></td><td></td><td></td><td></td><td>0.0020<br>[0.0183]</td><td>-0.0055<br>[0.0178]</td><td>0.0020<br>[0.0183]</td><td>0.0055<br>[0.0178]</td></tr><tr><td>New subclass</td><td>0.2904***<br>[0.0125]</td><td></td><td>0.8096***<br>[0.0128]</td><td></td><td></td><td></td><td></td><td>0.8089***<br>[0.0128]</td><td></td><td></td></tr><tr><td># of patents in the subclass</td><td>-0.0103***<br>[0.0030]</td><td></td><td></td><td>0.0036<br>[0.0033]</td><td></td><td></td><td>0.0033<br>[0.0033]</td><td>0.0088***<br>[0.0033]</td><td>-0.0128***<br>[0.0030]</td><td></td></tr><tr><td>Missing subclass</td><td>-0.5774***<br>[0.0049]</td><td></td><td></td><td></td><td>-0.5974***<br>[0.0051]</td><td></td><td></td><td></td><td>-0.5976***<br>[0.0051]</td><td></td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0066***<br>[0.0024]</td><td></td><td></td><td></td><td></td><td>-0.0270***<br>[0.0033]</td><td>-0.0267***<br>[0.0033]</td><td>-0.0095***<br>[0.0032]</td><td>-0.0123***<br>[0.0025]</td><td></td></tr><tr><td>Constant</td><td>0.5807***<br>[0.0083]</td><td>0.1424***<br>[0.0092]</td><td>0.1452***<br>[0.0095]</td><td>0.1465***<br>[0.0092]</td><td>0.1410***<br>[0.0092]</td><td>0.5861***<br>[0.0080]</td><td>0.1502***<br>[0.0092]</td><td>0.1506***<br>[0.0096]</td><td>0.1454***<br>[0.0096]</td><td>0.5977***<br>[0.0084]</td></tr><tr><td>Observations</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td><td>294294</td></tr><tr><td>R-Squared</td><td>.5552</td><td>.3798</td><td>.3798</td><td>.4044</td><td>.3798</td><td>.5521</td><td>.38</td><td>.38</td><td>.4045</td><td>.5522</td></tr></tbody></table>

Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01

Novelty is measured as the number of distinct combinations associated with a given ipc code found in all patents in a year and region
<table><caption>Table A7 - Impact of hilly skilled labor mobility on novelty including lag of y-2</caption><thead><tr><th></th><th colspan="4">Novelty</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.0043***<br/>[0.0015]</td><td></td><td></td><td></td></tr><tr><td>High Skilled Mobility (RTA) (lag 2)</td><td>-0.0016<br/>[0.0016]</td><td></td><td></td><td></td></tr><tr><td>High Skilled Mobility (RTA FR)</td><td></td><td>0.0035**<br/>[0.0017]</td><td></td><td></td></tr><tr><td>High Skilled Mobility (RTA FR) (lag 2)</td><td></td><td>0.0008<br/>[0.0016]</td><td></td><td></td></tr><tr><td>High Skilled Mobility (Size)</td><td></td><td></td><td>0.0108***<br/>[0.0032]</td><td></td></tr><tr><td>High Skilled Mobility (Size) (lag 2)</td><td></td><td></td><td>0.0088***<br/>[0.0029]</td><td></td></tr><tr><td>High Skilled Mobility (Related Density)</td><td></td><td></td><td></td><td>0.0024<br/>[0.0019]</td></tr><tr><td>High Skilled Mobility (Related Density) (lag 2)</td><td></td><td></td><td></td><td>-0.0010<br/>[0.0018]</td></tr><tr><td>Relatedness density</td><td>-0.0033<br/>[0.0138]</td><td>-0.0034<br/>[0.0138]</td><td>-0.0036<br/>[0.0138]</td><td>-0.0035<br/>[0.0138]</td></tr><tr><td>Relatedness density sq</td><td>-0.0004<br/>[0.0160]</td><td>-0.0003<br/>[0.0160]</td><td>-0.0002<br/>[0.0160]</td><td>-0.0002<br/>[0.0160]</td></tr><tr><td>New subclass</td><td>0.2976***<br/>[0.0130]</td><td>0.2975***<br/>[0.0129]</td><td>0.2978***<br/>[0.0130]</td><td>0.2977***<br/>[0.0130]</td></tr><tr><td># of patents in the subclass</td><td>-0.0113***<br/>[0.0030]</td><td>-0.0113***<br/>[0.0030]</td><td>-0.0108***<br/>[0.0030]</td><td>-0.0113***<br/>[0.0030]</td></tr><tr><td>Missing subclass</td><td>-0.5761***<br/>[0.0050]</td><td>-0.5761***<br/>[0.0050]</td><td>-0.5760***<br/>[0.0050]</td><td>-0.5762***<br/>[0.0050]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0068***<br/>[0.0025]</td><td>-0.0069***<br/>[0.0025]</td><td>-0.0071***<br/>[0.0025]</td><td>-0.0069***<br/>[0.0025]</td></tr><tr><td>Constant</td><td>0.5830***<br/>[0.0096]</td><td>0.5729***<br/>[0.0112]</td><td>0.5739***<br/>[0.0071]</td><td>0.5916***<br/>[0.0130]</td></tr><tr><td>Observations</td><td>280280</td><td>280280</td><td>280280</td><td>280280</td></tr><tr><td>R-Squared</td><td>.5561</td><td>.5561</td><td>.5562</td><td>.5561</td></tr></tbody></table>

Note: Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01

Novelty is measured as the number of distinct combinations associated with a given ipc code found in all patents in a year and region
<table><caption>TABLE A7bis - Impact of hilly skilled labor mobility on novelty including lag of y+1</caption><thead><tr><th></th><th colspan="2" style="text-align: center;">Novelty</th></tr><tr><th></th><th style="text-align: center;">(1)</th><th style="text-align: center;">(2)</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA) (y+1)</td><td style="text-align: center;">-0.0025<br>[0.0013]</td><td></td></tr><tr><td>High Skilled Mobility (RTA FR) (y+1)</td><td></td><td style="text-align: center;">0.0017<br>[0.0015]</td></tr><tr><td>Relatedness density</td><td style="text-align: center;">0.0214<br>[0.0136]</td><td style="text-align: center;">0.0215<br>[0.0155]</td></tr><tr><td>RelatedDens_sq</td><td style="text-align: center;">-0.0101<br>[0.0155]</td><td style="text-align: center;">-0.0101<br>[0.0155]</td></tr><tr><td>New subclass</td><td style="text-align: center;">0.2852***<br>[0.0122]</td><td style="text-align: center;">0.2853***<br>[0.0122]</td></tr><tr><td># of patents in the subclass</td><td style="text-align: center;">0.0292***<br>[0.0030]</td><td style="text-align: center;">0.0292***<br>[0.0030]</td></tr><tr><td>Missing subclass</td><td style="text-align: center;">-0.5744***<br>[0.0049]</td><td style="text-align: center;">-0.5745***<br>[0.0049]</td></tr><tr><td>Share of extra-regional inventors</td><td style="text-align: center;">0.0280***<br>[0.0028]</td><td style="text-align: center;">0.0281***<br>[0.0028]</td></tr><tr><td>Constant</td><td style="text-align: center;">0.5846***<br>[0.0083]</td><td style="text-align: center;">0.5800***<br>[0.0098]</td></tr><tr><td>Observations</td><td style="text-align: center;">294294</td><td style="text-align: center;">294294</td></tr><tr><td>R-Squared</td><td style="text-align: center;">.3666</td><td style="text-align: center;">.1683</td></tr></tbody><tfoot><tr><td colspan="3" style="text-align: left;">Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01<br>Estimation method: Linear regression with high-dimensional fixed effects.</td></tr></tfoot></table>
<table><caption>Table A8 - Disentangling the effects including lag y-2</caption><thead><tr><th rowspan="2"></th><th colspan="4">Novelty</th><th rowspan="2">Recombinant<br>Reuse</th><th rowspan="2">Recombinant<br>Creation</th></tr><tr><th>(1)<br>RD &lt; med.</th><th>(2)<br>RD &gt; med.</th><th>(3)<br>RTA = 0</th><th>(4)<br>RTA = 1</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>High Skilled Mobility (RTA)</td><td>0.0036**<br>[0.0016]</td><td>0.0055<br>[0.0034]</td><td>0.0018<br>[0.0015]</td><td>0.0110**<br>[0.0051]</td><td>0.0032**<br>[0.0015]</td><td>0.0013**<br>[0.0006]</td></tr><tr><td>High Skilled Mobility (RTA) (lag 2)</td><td>-0.0031+<br>[0.0018]</td><td>0.0034<br>[0.0033]</td><td>-0.0033**<br>[0.0016]</td><td>0.0069<br>[0.0047]</td><td>-0.0014<br>[0.0015]</td><td>0.0001<br>[0.0006]</td></tr><tr><td>Relatedness density</td><td>-0.0076<br>[0.0174]</td><td>-0.0154<br>[0.0255]</td><td>0.0051<br>[0.0139]</td><td>-0.0719+<br>[0.0424]</td><td>0.0072<br>[0.0138]</td><td>-0.0167***<br>[0.0061]</td></tr><tr><td>Relatedness density sq</td><td>0.0014<br>[0.0197]</td><td>0.0126<br>[0.0278]</td><td>-0.0125<br>[0.0155]</td><td>0.0631<br>[0.0424]</td><td>-0.0072<br>[0.0160]</td><td>0.0108+<br>[0.0063]</td></tr><tr><td>New subclass</td><td>0.2918***<br>[0.0160]</td><td>0.2879***<br>[0.0214]</td><td>0.2532***<br>[0.0133]</td><td>0.0000<br>[0.0000]</td><td></td><td></td></tr><tr><td># of patents in the subclass</td><td>-0.0118**<br>[0.0051]</td><td>-0.0140***<br>[0.0034]</td><td>-0.0024<br>[0.0043]</td><td>-0.0133***<br>[0.0039]</td><td>-0.0058+<br>[0.0030]</td><td>-0.0085***<br>[0.0008]</td></tr><tr><td>Missing subclass</td><td>-0.5992***<br>[0.0072]</td><td>-0.5650***<br>[0.0054]</td><td>-0.6265***<br>[0.0058]</td><td>-0.5021***<br>[0.0059]</td><td>-0.5156***<br>[0.0045]</td><td>-0.0935***<br>[0.0034]</td></tr><tr><td>Share of extra-regional inventors</td><td>-0.0043<br>[0.0039]</td><td>-0.0109***<br>[0.0035]</td><td>-0.0012<br>[0.0032]</td><td>-0.0085<br>[0.0056]</td><td>0.0054**<br>[0.0024]</td><td>-0.0181***<br>[0.0012]</td></tr><tr><td>Constant</td><td>0.6057***<br>[0.0101]</td><td>0.5482***<br>[0.0224]</td><td>0.6436***<br>[0.0104]</td><td>0.4613***<br>[0.0379]</td><td>0.5293***<br>[0.0092]</td><td>0.0840***<br>[0.0043]</td></tr><tr><td>Observations</td><td>132413</td><td>143584</td><td>201927</td><td>76747</td><td>280280</td><td>280280</td></tr><tr><td>R-Squared</td><td>.6324</td><td>.5477</td><td>.6127</td><td>.5569</td><td>.5339</td><td>.171</td></tr></tbody></table>

RD < (resp. >) med is a split sample when Relatedness density is < (resp. >) median; RTA =1 (resp. = 0): split sample when RTA =1 (resp. =0)

Robust standard errors clustered at fixed effects level (multiway) are shown in brackets. + 0.10 ** 0.05 ***0.01
## Liste des documents de travail de la Direction des Études et Synthèses Économiques

G 9001 J. FAYOLLE et M. FLEURBAEY
Accumulation, profitabilité et endettement des entreprises

G 9002 H. ROUSSE
Détection et effets de la multicolinéarité dans les modèles linéaires ordinaires - Un prolongement de la réflexion de BELSLEY, KUH et WELSCH

G 9003 P. RALLE et J. TOUJAS-BERNATE
Indexation des salaires : la rupture de 1983

G 9004 D. GUELLEC et P. RALLE
Compétitivité, croissance et innovation de produit

G 9005 P. RALLE et J. TOUJAS-BERNATE
Les conséquences de la désindexation. Analyse dans une maquette prix-salaires

G 9101 Équipe AMADEUS
Le modèle AMADEUS - Première partie - Présentation générale

G 9102 J.L. BRILLET
Le modèle AMADEUS - Deuxième partie - Propriétés variantielles

G 9103 D. GUELLEC et P. RALLE
Endogenous growth and product innovation

G 9104 H. ROUSSE
Le modèle AMADEUS - Troisième partie - Le commerce extérieur et l'environnement international

G 9105 H. ROUSSE
Effets de demande et d'offre dans les résultats du commerce extérieur manufacturé de la France au cours des deux dernières décennies

G 9106 B. CREPON
Innovation, taille et concentration : causalités et dynamiques

G 9107 B. AMABLE et D. GUELLEC
Un panorama des théories de la croissance endogène

G 9108 M. GLAUDE et M. MOUTARDIER
Une évaluation du coût direct de l'enfant de 1979 à 1989

G 9109 P. RALLE et alii
France - Allemagne : performances économiques comparées

G 9110 J.L. BRILLET
Micro-DMS **NON PARU**

G 9111 A. MAGNIER
Effets accélérateur et multiplicateur en France depuis 1970 : quelques résultats empiriques

G 9112 B. CREPON et G. DUREAU
Investissement en recherche-développement : analyse de causalités dans un modèle d'accélérateur généralisé

G 9113 J.L. BRILLET, H. ERKEL-ROUSSE, J. TOUJAS-BERNATE
"France-Allemagne Couplées" - Deux économies vues par une maquette macro-économétrique

G 9201 W.J. ADAMS, B. CREPON, D. ENCAOUA
Choix technologiques et stratégies de dissuasion d'entrée

G 9202 J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE
Macro-economic import functions with imperfect competition - An application to the E.C. Trade

G 9203 I. STAPIC
Les échanges internationaux de services de la France dans le cadre des négociations multilatérales du GATT
Juin 1992 (1ère version)
Novembre 1992 (version finale)

G 9204 P. SEVESTRE
L'économétrie sur données individuelles-temporelles. Une note introductive

G 9205 H. ERKEL-ROUSSE
Le commerce extérieur et l'environnement international dans le modèle AMADEUS (réestimation 1992)

G 9206 N. GREENAN et D. GUELLEC
Coordination within the firm and endogenous growth

G 9207 A. MAGNIER et J. TOUJAS-BERNATE
Technology and trade: empirical evidences for the major five industrialized countries

G 9208 B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHNEN
Cooperative, non cooperative R & D and optimal patent life

G 9209 B. CREPON et E. DUGUET
Research and development, competition and innovation: an application of pseudo maximum likelihood methods to Poisson models with heterogeneity

G 9301 J. TOUJAS-BERNATE
Commerce international et concurrence imparfaite : développements récents et implications pour la politique commerciale

G 9302 Ch. CASES
Durées de chômage et comportements d'offre de travail : une revue de la littérature

G 9303 H. ERKEL-ROUSSE
Union économique et monétaire : le débat économique

G 9304 N. GREENAN - D. GUELLEC / G. BROUSSAUDIER - L. MIOTTI
Innovation organisationnelle, dynamisme technologique et performances des entreprises

G 9305 P. JAILLARD
Le traité de Maastricht : présentation juridique et historique

G 9306 J.L. BRILLET
Micro-DMS : présentation et propriétés

G 9307 J.L. BRILLET
Micro-DMS - variantes : les tableaux

G 9308 S. JACOBZONE
Les grands réseaux publics français dans une perspective européenne

G 9309 L. BLOCH - B. CŒURÉ
Profitabilité de l'investissement productif et transmission des chocs financiers

G 9310 J. BOURDIEU - B. COLIN-SEDILLOT
Les théories sur la structure optimale du capital : quelques points de repère
G 9311 J. BOURDIEU - B. COLIN-SEDILLOT
Les décisions de financement des entreprises françaises : une évaluation empirique des théories de la structure optimale du capital

G 9312 L. BLOCH - B. CŒURÉ
Q de Tobin marginal et transmission des choos financiers

G 9313 Équipes Amadeus (INSEE), Banque de France, Métric (DP)
Présentation des propriétés des principaux modèles macroéconomiques du Service Public

G 9314 B. CREPON - E. DUGUET
Research & Development, competition and innovation

G 9315 B. DORMONT
Quelle est l'influence du coût du travail sur l'emploi ?

G 9316 D. BLANCHET - C. BROUSSE
Deux études sur l'âge de la retraite

G 9317 D. BLANCHET
Répartition du travail dans une population hétérogène : deux notes

G 9318 D. EYSSARTIER - N. PONTY
AMADEUS - an annual macro-economic model for the medium and long term

G 9319 G. CETTE - Ph. CUNÉO - D. EYSSARTIER - J. GAUTIÉ
Les effets sur l'emploi d'un abaissement du coût du travail des jeunes

G 9401 D. BLANCHET
Les structures par âge importent-elles ?

G 9402 J. GAUTIÉ
Le chômage des jeunes en France : problème de formation ou phénomène de file d'attente ?
Quelques éléments du débat

G 9403 P. QUIRION
Les déchets en France : éléments statistiques et économiques

G 9404 D. LADIRAY - M. GRUN-REHOMME
Lissage par moyennes mobiles - Le problème des extrémités de série

G 9405 V. MAILLARD
Théorie et pratique de la correction des effets de jours ouvrables

G 9406 F. ROSENWALD
La décision d'investir

G 9407 S. JACOBZONE
Les apports de l'économie industrielle pour définir la stratégie économique de l'hôpital public

G 9408 L. BLOCH, J. BOURDIEU, B. COLIN-SEDILLOT, G. LONGUEVILLE
Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté

G 9409 D. EYSSARTIER, P. MAIRE
Impacts macro-économiques de mesures d'aide au logement - quelques éléments d'évaluation

G 9410 F. ROSENWALD
Suivi conjoncturel de l'investissement

G 9411 C. DEFEUILLEY - Ph. QUIRION
Les déchets d'emballages ménagers : une

analyse économique des politiques française et allemande

G 9412 J. BOURDIEU - B. CŒURÉ - B. COLIN-SEDILLOT
Investissement, incertitude et irréversibilité
Quelques développements récents de la théorie de l'investissement

G 9413 B. DORMONT - M. PAUCHET
L'évaluation de l'élasticité emploi-salaire dépend-elle des structures de qualification ?

G 9414 I. KABLA
Le Choix de breveter une invention

G 9501 J. BOURDIEU - B. CŒURÉ - B. SEDILLOT
Irreversible Investment and Uncertainty: When is there a Value of Waiting?

G 9502 L. BLOCH - B. CŒURÉ
Imperfections du marché du crédit, investissement des entreprises et cycle économique

G 9503 D. GOUX - E. MAURIN
Les transformations de la demande de travail par qualification en France
Une étude sur la période 1970-1993

G 9504 N. GREENAN
Technologie, changement organisationnel, qualifications et emploi : une étude empirique sur l'industrie manufacturière

G 9505 D. GOUX - E. MAURIN
Persistance des hiérarchies sectorielles de salaires: un réexamen sur données françaises

G 9505 Bis
D. GOUX - E. MAURIN
Persistence of inter-industry wages differentials: a reexamination on matched worker-firm panel data

G 9506 S. JACOBZONE
Les liens entre RMI et chômage, une mise en perspective
NON PARU - article sorti dans Économie et Prévision n° 122 (1996) - pages 95 à 113

G 9507 G. CETTE - S. MAHFOUZ
Le partage primaire du revenu
Constat descriptif sur longue période

G 9601 Banque de France - CEPREMAP - Direction de la Prévision - Érasme - INSEE - OFCE
Structures et propriétés de cinq modèles macro-économiques français

G 9602 Rapport d'activité de la DESE de l'année 1995

G 9603 J. BOURDIEU - A. DRAZNIEKS
L'octroi de crédit aux PME : une analyse à partir d'informations bancaires

G 9604 A. TOPIOL-BENSAÏD
Les implantations japonaises en France

G 9605 P. GENIER - S. JACOBZONE
Comportements de prévention, consommation d'alcool et tabagie : peut-on parler d'une gestion globale du capital santé ?
Une modélisation microéconométrique empirique

G 9606 C. DOZ - F. LENGLART
Factor analysis and unobserved component models: an application to the study of French business surveys
G 9607 N. GREENAN - D. GUELLEC
La théorie coopérative de la firme

G 9608 N. GREENAN - D. GUELLEC
Technological innovation and employment
reallocation

G 9609 Ph. COUR - F. RUPPRECHT
L'intégration asymétrique au sein du continent
américain : un essai de modélisation

G 9610 S. DUCHENE - G. FORGEOT - A. JACQUOT
Analyse des évolutions récentes de la producti-
vité apparente du travail

G 9611 X. BONNET - S. MAHFOUZ
The influence of different specifications of wages-
prices spirals on the measure of the NAIRU: the
case of France

G 9612 PH. COUR - E. DUBOIS, S. MAHFOUZ,
J. PISANI-FERRY
The cost of fiscal retrenchment revisited: how
strong is the evidence?

G 9613 A. JACQUOT
Les flexions des taux d'activité sont-elles seule-
ment conjoncturelles ?

G 9614 ZHANG Yingxiang - SONG Xueqing
Lexique macroéconomique Français-Chinois

G 9701 J.L. SCHNEIDER
La taxe professionnelle : éléments de cadrage
économique

G 9702 J.L. SCHNEIDER
Transition et stabilité politique d'un système
redistributif

G 9703 D. GOUX - E. MAURIN
Train or Pay: Does it Reduce Inequalities to En-
courage Firms to Train their Workers?

G 9704 P. GENIER
Deux contributions sur dépendance et équité

G 9705 E. DUGUET - N. IUNG
R & D Investment, Patent Life and Patent Value
An Econometric Analysis at the Firm Level

G 9706 M. HOUDEBINE - A. TOPIOL-BENSAÏD
Les entreprises internationales en France : une
analyse à partir de données individuelles

G 9707 M. HOUDEBINE
Polarisation des activités et spécialisation des
départements en France

G 9708 E. DUGUET - N. GREENAN
Le biais technologique : une analyse sur données
individuelles

G 9709 J.L. BRILLET
Analyzing a small French ECM Model

G 9710 J.L. BRILLET
Formalizing the transition process: scenarios for
capital accumulation

G 9711 G. FORGEOT - J. GAUTIÉ
Insertion professionnelle des jeunes et processus
de déclassement

G 9712 E. DUBOIS
High Real Interest Rates: the Consequence of a
Saving Investment Disequilibrium or of an in-
sufficient Credibility of Monetary Authorities?

G 9713 Bilan des activités de la Direction des Études
et Synthèses Économiques - 1996

G 9714 F. LEQUILLER
Does the French Consumer Price Index Over-
state Inflation?

G 9715 X. BONNET
Peut-on mettre en évidence les rigidités à la
baisse des salaires nominaux ?
Une étude sur quelques grands pays de l'OCDE

G 9716 N. IUNG - F. RUPPRECHT
Productivité de la recherche et rendements
d'échelle dans le secteur pharmaceutique
français

G 9717 E. DUGUET - I. KABLA
Appropriation strategy and the motivations to use
the patent system in France - An econometric
analysis at the firm level

G 9718 L.P. PELÉ - P. RALLE
Âge de la retraite : les aspects incitatifs du ré-
gime général

G 9719 ZHANG Yingxiang - SONG Xueqing
Lexique macroéconomique français-chinois,
chinois-français

G 9720 M. HOUDEBINE - J.L. SCHNEIDER
Mesurer l'influence de la fiscalité sur la locali-
sation des entreprises

G 9721 A. Mourougane
Crédibilité, indépendance et politique monétaire
Une revue de la littérature

G 9722 P. AUGERAUD - L. BRIOT
Les données comptables d'entreprises
Le système intermédiaire d'entreprises
Passage des données individuelles aux données
sectorielles

G 9723 P. AUGERAUD - J.E. CHAPRON
Using Business Accounts for Compiling National
Accounts: the French Experience

G 9724 P. AUGERAUD
Les comptes d'entreprise par activités - Le pas-
sage aux comptes - De la comptabilité
d'entreprise à la comptabilité nationale - A
paraitre

G 9801 H. MICHAUDON - C. PRIGENT
Présentation du modèle AMADEUS

G 9802 J. ACCARDO
Une étude de comptabilité générationnelle
pour la France en 1996

G 9803 X. BONNET - S. DUCHÊNE
Apports et limites de la modélisation
« Real Business Cycles »

G 9804 C. BARLET - C. DUGUET -
D. ENCAOUA - J. PRADEL
The Commercial Success of Innovations
An econometric analysis at the firm level in
French manufacturing

G 9805 P. CAHUC - Ch. GIANELLA -
D. GOUX - A. ZILBERBERG
Equalizing Wage Differences and Bargaining
Power - Evidence from a Panel of French Firms
G 9806 J. ACCARDO - M. JLASSI
La productivité globale des facteurs entre 1975 et 1996

G 9807 Bilan des activités de la Direction des Études et Synthèses Économiques - 1997

G 9808 A. Mourougane
Can a Conservative Governor Conduct an Ac-comodative Monetary Policy?

G 9809 X. BONNET - E. DUBOIS - L. FAUVET
Asymétrie des inflations relatives et menus costs : tests sur l'inflation française

G 9810 E. DUGUET - N. IUNG
Sales and Advertising with Spillovers at the firm level: Estimation of a Dynamic Structural Model on Panel Data

G 9811 J.P. BERTHIER
Congestion urbaine : un modèle de trafic de pointe à courbe débit-vitesse et demande élastique

G 9812 C. PRIGENT
La part des salaires dans la valeur ajoutée : une approche macroéconomique

G 9813 A.Th. AERTS
L'évolution de la part des salaires dans la valeur ajoutée en France reflète-t-elle les évolutions individuelles sur la période 1979-1994 ?

G 9814 B. SALANIÉ
Guide pratique des séries non-stationnaires

G 9901 S. DUCHÊNE - A. JACQUOT
Une croissance plus riche en emplois depuis le début de la décennie ? Une analyse en comparaison internationale

G 9902 Ch. COLIN
Modélisation des carrières dans Destinie

G 9903 Ch. COLIN
Évolution de la dispersion des salaires : un essai de prospective par microsimulation

G 9904 B. CREPON - N. IUNG
Innovation, emploi et performances

G 9905 B. CREPON - Ch. GIANELLA
Wages inequalities in France 1969-1992
An application of quantile regression techniques

G 9906 C. BONNET - R. MAHIEU
Microsimulation techniques applied to inter-generational transfers - Pensions in a dynamic framework: the case of France

G 9907 F. ROSENWALD
L'impact des contraintes financières dans la décision d'investissement

G 9908 Bilan des activités de la DESE - 1998

G 9909 J.P. ZOYEM
Contrat d'insertion et sortie du RMI
Évaluation des effets d'une politique sociale

G 9910 Ch. COLIN - FI. LEGROS - R. MAHIEU
Bilans contributifs comparés des régimes de retraite du secteur privé et de la fonction publique

G 9911 G. LAROQUE - B. SALANIÉ
Une décomposition du non-emploi en France

G 9912 B. SALANIÉ
Une maquette analytique de long terme du marché du travail

G 9912 Bis Ch. GIANELLA
Une estimation de l'élasticité de l'emploi peu qualifié à son coût

G 9913 Division « Redistribution et Politiques Sociales »
Le modèle de microsimulation dynamique
DESTINIE

G 9914 E. DUGUET
Macro-commandes SAS pour l'économétrie des panels et des variables qualitatives

G 9915 R. DUHAUTOIS
Évolution des flux d'emplois en France entre 1990 et 1996 : une étude empirique à partir du fichier des bénéfices réels normaux (BRN)

G 9916 J.Y. FOURNIER
Extraction du cycle des affaires : la méthode de Baxter et King

G 9917 B. CRÉPON - R. DESPLATZ - J. MAIRESSE
Estimating price cost margins, scale economies and workers' bargaining power at the firm level

G 9918 Ch. GIANELLA - Ph. LAGARDE
Productivity of hours in the aggregate production function: an evaluation on a panel of French firms from the manufacturing sector

G 9919 S. AUDRIC - P. GIVORD - C. PROST
Évolution de l'emploi et des coûts par qualification entre 1982 et 1996

G 2000/01 R. MAHIEU
Les déterminants des dépenses de santé : une approche macroéconomique

G 2000/02 C. ALLARD-PRIGENT - H. GUILMEAU - A. QUINET
The real exchange rate as the relative price of nontrables in terms of tradables: theoretical investigation and empirical study on French data

G 2000/03 J.-Y. FOURNIER
L'approximation du filtre passe-bande proposée par Christiano et Fitzgerald

G 2000/04 Bilan des activités de la DESE - 1999

G 2000/05 B. CREPON - F. ROSENWALD
Investissement et contraintes de financement : le poids du cycle
Une estimation sur données françaises

G 2000/06 A. FLIPO
Les comportements matrimoniaux de fait

G 2000/07 R. MAHIEU - B. SÉDILLOT
Microsimulations of the retirement decision: a supply side approach

G 2000/08 C. AUDENIS - C. PROST
Déficit conjoncturel : une prise en compte des conjonctures passées

G 2000/09 R. MAHIEU - B. SÉDILLOT
Équivalent patrimonial de la rente et souscription de retraite complémentaire

G 2000/10 R. DUHAUTOIS
Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertiaire ?
G 2000/11 G. LAROQUE - B. SALANIÉ
Temps partiel féminin et incitations financières à l'emploi

G2000/12 Ch. GIANELLA
Local unemployment and wages

G2000/13 B. CREPON - Th. HECKEL
- Informatisation en France : une évaluation à partir de données individuelles
- Computerization in France: an evaluation based on individual company data

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

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

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

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

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

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

G2001/07 Bilan des activités de la DESE - 2000

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

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

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

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

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

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

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

G2001/15 J.-P. ZOYEM
Diagnostic sur la pauvreté et calendrier de

revenus : le cas du "Panel européen des ménages »

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

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

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

G2002/02 Bilan des activités de la DESE - 2001

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

G2002/04 G. BRILHAULT
- Rétropagation des séries de FBCF et calcul du capital fixe en SEC-95 dans les comptes nationaux français
- Retropagation of the investment series (GFCF) and estimation of fixed capital stocks on the ESA-95 basis for the French balance sheets

G2002/05 P. BISCOURP - B. CRÉPON - T. HECKEL - N. RIEDINGER
How do firms respond to cheaper computers? Microeconometric evidence for France based on a production function approach

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

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

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

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

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

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

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

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

G2002/14 E. WALRAET - A. VINCENT
- Analyse de la redistribution intragénérationnelle
dans le système de retraite des salariés du privé
- Une approche par microsimulation
- Intragenerational distributional analysis in the
french private sector pension scheme - A
microsimulation approach

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

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

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

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

G2003/03 Bilan des activités de la DESE - 2002

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2005/07 S. RASPILLER
La concurrence fiscale : principaux enseignements de l'analyse économique
G2005/08 C. L'ANGEVIN - N. LAÎB
Éducation et croissance en France et dans un panel de 21 pays de l'OCDE

G2005/09 N. FERRARI
Prévoir l'investissement des entreprises
Un indicateur des révisions dans l'enquête de conjoncture sur les investissements dans l'industrie.

G2005/10 P.-O. BEFFY - C. L'ANGEVIN
Chômage et boucle prix-salaires : apport d'un modèle « qualifiés/peu qualifiés »

G2005/11 B. HEITZ
A two-states Markov-switching model of inflation in France and the USA: credible target VS inflation spiral

G2005/12 O. BIAU - H. ERKEL-ROUSSE - N. FERRARI
Réponses individuelles aux enquêtes de conjoncture et prévision macroéconomiques : Exemple de la prévision de la production manufacturière

G2005/13 P. AUBERT - D. BLANCHET - D. BLAU
The labour market after age 50: some elements of a Franco-American comparison

G2005/14 D. BLANCHET - T. DEBRAND - P. DOURGNON - P. POLLET
L'enquête SHARE : présentation et premiers résultats de l'édition française

G2005/15 M. DUÉE
La modélisation des comportements démographiques dans le modèle de microsimulation
DESTINIE

G2005/16 H. RAOUI - S. ROUX
Étude de simulation sur la participation versée aux salariés par les entreprises

G2006/01 C. BONNET - S. BUFFETEAU - P. GODEFROY
Disparités de retraite de droit direct entre hommes et femmes : quelles évolutions ?

G2006/02 C. PICART
Les gazelles en France

G2006/03 P. AUBERT - B. CRÉPON - P. ZAMORA
Le rendement apparent de la formation continue dans les entreprises : effets sur la productivité et les salaires

G2006/04 J.-F. OUVRARD - R. RATHELOT
Demographic change and unemployment: what do macroeconometric models predict?

G2006/05 D. BLANCHET - J.-F. OUVRARD
Indicateurs d'engagements implicites des systèmes de retraite : chiffrages, propriétés analytiques et réactions à des chocs démographiques types

G2006/06 G. BIAU - O. BIAU - L. ROUVIERE
Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data

G2006/07 C. AFSA - P. GIVORD
Le rôle des conditions de travail dans les absences pour maladie

G2006/08 P. SILLARD - C. L'ANGEVIN - S. SERRAVALLE
Performances comparées à l'exportation de la France et de ses principaux partenaires
Une analyse structurelle sur 12 ans

G2006/09 X. BOUTIN - S. QUANTIN
Une méthodologie d'évaluation comptable du coût du capital des entreprises françaises : 1984-2002

G2006/10 C. AFSA
L'estimation d'un coût implicite de la pénibilité du travail chez les travailleurs âgés

G2006/11 C. LELARGE
Les entreprises (industrielles) françaises sont-elles à la frontière technologique ?

G2006/12 O. BIAU - N. FERRARI
Théorie de l'opinion
Faut-il pondérer les réponses individuelles ?

G2006/13 A. KOUBI - S. ROUX
Une réinterprétation de la relation entre productivité et inégalités salariales dans les entreprises

G2006/14 R. RATHELOT - P. SILLARD
The impact of local taxes on plants location decision

G2006/15 L. GONZALEZ - C. PICART
Diversification, recentrage et poids des activités de support dans les groupes (1993-2000)

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

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

G2007/03 D. BLANCHET - T. DEBRAND
Aspiration à la retraite, santé et satisfaction au travail : une comparaison européenne

G2007/04 M. BARLET - L. CRUSSON
Quel impact des variations du prix du pétrole sur la croissance française ?

G2007/05 C. PICART
Flux d'emploi et de main-d'œuvre en France : un réexamen

G2007/06 V. ALBOUY - C. TAVAN
Massification et démocratisation de l'enseignement supérieur en France

G2007/07 T. LE BARBANCHON
The Changing response to oil price shocks in France: a DSGE type approach

G2007/08 T. CHANEY - D. SRAER - D. THESMAR
Collateral Value and Corporate Investment Evidence from the French Real Estate Market

G2007/09 J. BOISSINOT
Consumption over the Life Cycle: Facts for France

G2007/10 C. AFSA
Interpréter les variables de satisfaction : l'exemple de la durée du travail

G2007/11 R. RATHELOT - P. SILLARD
Zones Franches Urbaines : quels effets sur l'emploi salarié et les créations d'établissements ?

G2007/12 V. ALBOUY - B. CRÉPON
Aléa moral en santé : une évaluation dans le cadre du modèle causal de Rubin
G2008/01 C. PICART
Les PME françaises : rentables mais peu dynamiques

G2008/02 P. BISCOURP - X. BOUTIN - T. VERGÉ
The Effects of Retail Regulations on Prices Evidence from the Loi Galland

G2008/03 Y. BARBESOL - A. BRIANT
Économies d'agglomération et productivité des entreprises : estimation sur données individuelles françaises

G2008/04 D. BLANCHET - F. LE GALLO
Les projections démographiques : principaux mécanismes et retour sur l'expérience française

G2008/05 D. BLANCHET - F. TOUTLEMONDE
Évolutions démographiques et déformation du cycle de vie active : quelles relations ?

G2008/06 M. BARLET - D. BLANCHET - L. CRUSSON
Internationalisation et flux d'emplois : que dit une approche comptable ?

G2008/07 C. LELARGE - D. SRAER - D. THESMAR
Entrepreneurship and Credit Constraints - Evidence from a French Loan Guarantee Program

G2008/08 X. BOUTIN - L. JANIN
Are Prices Really Affected by Mergers?

G2008/09 M. BARLET - A. BRIANT - L. CRUSSON
Concentration géographique dans l'industrie manufacturière et dans les services en France : une approche par un indicateur en continu

G2008/10 M. BEFFY - É. COUDIN - R. RATHELOT
Who is confronted to insecure labor market histories? Some evidence based on the French labor market transition

G2008/11 M. ROGER - E. WALRAET
Social Security and Well-Being of the Elderly: the Case of France

G2008/12 C. AFSA
Analyser les composantes du bien-être et de son évolution
Une approche empirique sur données individuelles

G2008/13 M. BARLET - D. BLANCHET - T. LE BARBANCHON
Microsimuler le marché du travail : un prototype

G2009/01 P.-A. PIONNIER
Le partage de la valeur ajoutée en France, 1949-2007

G2009/02 Laurent CLAVEL - Christelle MINODIER
A Monthly Indicator of the French Business Climate

G2009/03 H. ERKEL-ROUSSE - C. MINODIER
Do Business Tendency Surveys in Industry and Services Help in Forecasting GDP Growth? A Real-Time Analysis on French Data

G2009/04 P. GIVORD - L. WILNER
Les contrats temporaires : trappe ou marchepied vers l'emploi stable ?

G2009/05 G. LALANNE - P.-A. PIONNIER - O. SIMON
Le partage des fruits de la croissance de 1950 à 2008 : une approche par les comptes de surplus

G2009/06 L. DAVEZIES - X. D'HAULTFOEUILLE
Faut-il pondérer ?... Ou l'éternelle question de l'économètre confronté à des données d'enquête

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

G2009/08 M. CLERC - V. MARCUS
Élasticités-prix des consommations énergétiques des ménages

G2009/09 G. LALANNE - E. POULIQUEN - O. SIMON
Prix du pétrole et croissance potentielle à long terme

G2009/10 D. BLANCHET - J. LE CACHEUX - V. MARCUS
Adjusted net savings and other approaches to sustainability: some theoretical background

G2009/11 V. BELLAMY - G. CONSALES - M. FESSEAU - S. LE LAIDIER - É. RAYNAUD
Une décomposition du compte des ménages de la comptabilité nationale par catégorie de ménage en 2003

G2009/12 J. BARDAJI - F. TALLET
Detecting Economic Regimes in France: a Qualitative Markov-Switching Indicator Using Mixed Frequency Data

G2009/13 R. AEBERHARDT - D. FOUGÈRE - R. RATHELOT
Discrimination à l'embeuche : comment exploiter les procédures de testing ?

G2009/14 Y. BARBESOL - P. GIVORD - S. QUANTIN
Partage de la valeur ajoutée, approche par données microéconomiques

G2009/15 I. BUONO - G. LALANNE
The Effect of the Uruguay round on the Intensive and Extensive Margins of Trade

G2010/01 C. MINODIER
Avantages comparés des séries des premières valeurs publiées et des séries des valeurs révisées - Un exercice de prévision en temps réel de la croissance trimestrielle du PIB en France

G2010/02 V. ALBOUY - L. DAVEZIES - T. DEBRAND
Health Expenditure Models: a Comparison of Five Specifications using Panel Data

G2010/03 C. KLEIN - O. SIMON
Le modèle MÉSANGE réestimé en base 2000
Tome 1 – Version avec volumes à prix constants

G2010/04 M.-É. CLERC - É. COUDIN
L'IPC, miroir de l'évolution du coût de la vie en France ? Ce qu'apporte l'analyse des courbes d'Engel

G2010/05 N. CECI-RENAUD - P.-A. CHEVALIER
Les seuils de 10, 20 et 50 salariés : impact sur la taille des entreprises françaises

G2010/06 R. AEBERHARDT - J. POUGET
National Origin Differences in Wages and Hierarchical Positions - Evidence on French Full-Time Male Workers from a matched Employer-Employee Dataset
G2010/07 S. BLASCO - P. GIVORD
Les trajectoires professionnelles en début de vie active : quel impact des contrats temporaires ?

G2010/08 P. GIVORD
Méthodes économétriques pour l'évaluation de politiques publiques

G2010/09 P.-Y. CABANNES - V. LAPÈGUE - E. POULIQUEN - M. BEFFY - M. GAINI
Quelle croissance de moyen terme après la crise ?

G2010/10 I. BUONO - G. LALANNE
La réaction des entreprises françaises à la baisse des tarifs douaniers étrangers

G2010/11 R. RATHELOT - P. SILLARD
L'apport des méthodes à noyaux pour mesurer la concentration géographique - Application à la concentration des immigrés en France de 1968 à 1999

G2010/12 M. BARATON - M. BEFFY - D. FOUGÈRE
Une évaluation de l'effet de la réforme de 2003 sur les départs en retraite - Le cas des enseignants du second degré public

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

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

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

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

G2010/17 P.-Y. CABANNES - H. ERKEL-ROUSSE - G. LALANNE - O. MONSO - E. POULIQUEN
Le modèle Mésange réestimé en base 2000 Tome 2 - Version avec volumes à prix chaînés

G2010/18 R. AEBERHARDT - L. DAVEZIES
Conditional Logit with one Binary Covariate: Link between the Static and Dynamic Cases

G2011/01 T. LE BARBANCHON - B. OURLIAC - O. SIMON
Les marchés du travail français et américain face aux chocs conjoncturels des années 1986 à 2007 : une modélisation DSGE

G2011/02 C. MARBOT
Une évaluation de la réduction d'impôt pour l'emploi de salariés à domicile

G2011/03 L. DAVEZIES
Modèles à effets fixes, à effets aléatoires, modèles mixtes ou multi-niveaux : propriétés et mises en œuvre des modélisations de l'hétérogénéité dans le cas de données groupées

G2011/04 M. ROGER - M. WASMER
Heterogeneity matters: labour productivity differentiated by age and skills

G2011/05 J.-C. BRICONGNE - J.-M. FOURNIER V. LAPÈGUE - O. MONSO
De la crise financière à la crise économique

L'impact des perturbations financières de 2007 et 2008 sur la croissance de sept pays industrialisés

G2011/06 P. CHARNOZ - É. COUDIN - M. GAINI
Wage inequalities in France 1976-2004: a quantile regression analysis

G2011/07 M. CLERC - M. GAINI - D. BLANCHET
Recommendations of the Stiglitz-Sen-Fitoussi Report: A few illustrations

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

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

G2011/10 A. SCHREIBER - A. VICARD
La tertiairisation de l'économie française et le ralentissement de la productivité entre 1978 et 2008

G2011/11 M.-É. CLERC - O. MONSO - E. POULIQUEN
Les inégalités entre générations depuis le baby-boom

G2011/12 C. MARBOT - D. ROY
Évaluation de la transformation de la réduction d'impôt en crédit d'impôt pour l'emploi de salariés à domicile en 2007

G2011/13 P. GIVORD - R. RATHELOT - P. SILLARD
Place-based tax exemptions and displacement effects: An evaluation of the Zones Franches Urbaines program

G2011/14 X. D'HAULTFOEUILLE - P. GIVORD - X. BOUTIN
The Environmental Effect of Green Taxation: the Case of the French "Bonus/Malus"

G2011/15 M. BARLET - M. CLERC - M. GARNEO - V. LAPÈGUE - V. MARCUS
La nouvelle version du modèle MZE, modèle macroéconométrique pour la zone euro

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

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

G2012/01 P. GIVORD - S. QUANTIN - C. TREVIEN
A Long-Term Evaluation of the First Generation of the French Urban Enterprise Zones

G2012/02 N. CECI-RENAUD - V. COTTET
Politique salariale et performance des entreprises

G2012/03 P. FÉVRIER - L. WILNER
Do Consumers Correctly Expect Price Reductions? Testing Dynamic Behavior

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

G2012/05 M. GAINI - A. LEDUC - A. VICARD
A scarred generation? French evidence on young people entering into a tough labour market
G2012/06
P. AUBERT - M. BACHELET
Disparités de montant de pension et redistribution dans le système de retraite français

G2012/07
R. AEBERHARDT - P. GIVORD - C. MARBOT
Spillover Effect of the Minimum Wage in France: An Unconditional Quantile Regression Approach

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

G2012/09
O. BARGAIN - A. VICARD
Le RMI et son successeur le RSA découragent-ils certains jeunes de travailler ? Une analyse sur les jeunes autour de 25 ans

G2012/10
C. MARBOT - D. ROY
Projections du coût de l'APA et des caractéristiques de ses bénéficiaires à l'horizon 2040 à l'aide du modèle Destinie

G2012/11
A. MAUROUX
Le crédit d'impôt dédié au développement durable : une évaluation économique

G2012/12
V. COTTET - S. QUANTIN - V. RÉGNIER
Coût du travail et allègements de charges : une estimation au niveau établissement de 1996 à 2008

G2012/13
X. D'HAULTFOEUILLE - P. FÉVRIER - L. WILNER
Demand Estimation in the Presence of Revenue Management

G2012/14
D. BLANCHET - S. LE MINEZ
Joint macro/micro evaluations of accrued-to-date pension liabilities: an application to French reforms

G2013/01-F1301
T. DEROYON - A. MONTAUT - P-A PIONNIER
Utilisation rétrospective de l'enquête Emploi à une fréquence mensuelle : apport d'une modélisation espace-état

G2013/02-F1302
C. TREVIEN
Habiter en HLM : quel avantage monétaire et quel impact sur les conditions de logement ?

G2013/03
A. POISSONNIER
Temporal disaggregation of stock variables - The Chow-Lin method extended to dynamic models

G2013/04
P. GIVORD - C. MARBOT
Does the cost of child care affect female labor market participation? An evaluation of a French reform of childcare subsidies

G2013/05
G. LAME - M. LEQUIEN - P.-A. PIONNIER
Interpretation and limits of sustainability tests in public finance

G2013/06
C. BELLEGO - V. DORTET-BERNADET
La participation aux pôles de compétitivité : quelle incidence sur les dépenses de R&D et l'activité des PME et ETI ?

G2013/07
P.-Y. CABANNES - A. MONTAUT - P.-A. PIONNIER
Évaluer la productivité globale des facteurs en France : l'apport d'une mesure de la qualité du capital et du travail

G2013/08
R. AEBERHARDT - C. MARBOT
Evolution of Instability on the French Labour Market During the Last Thirty Years

G2013/09
J-B. BERNARD - G. CLÉAUD
Oil price: the nature of the shocks and the impact on the French economy

G2013/10
G. LAME
Was there a « Greenspan Conundrum » in the Euro area?

G2013/11
P. CHONÉ - F. EVAIN - L. WILNER - E. YILMAZ
Introducing activity-based payment in the hospital industry : Evidence from French data

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

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

G2013/14
A. POISSONNIER - D. ROY
Households Satellite Account for France in 2010. Methodological issues on the assessment of domestic production

G2013/15
G. CLÉAUD - M. LEMOINE - P.-A. PIONNIER
Which size and evolution of the government expenditure multiplier in France (1980-2010)?

G2014/01
M. BACHELET - A. LEDUC - A. MARINO
Les biographies du modèle Destinie II : rebasage et projection

G2014/02
B. GARBINTI
L'achat de la résidence principale et la création d'entreprises sont-ils favorisés par les donations et héritages ?

G2014/03
N. CECI-RENAUD - P. CHARNOZ - M. GAINI
Évolution de la volatilité des revenus salariaux du secteur privé en France depuis 1968

G2014/04
P. AUBERT
Modalités d'application des réformes des retraites et prévisibilité du montant de pension

G2014/05
C. GRISLAIN-LETRÉMY - A. KATOSSKY
The Impact of Hazardous Industrial Facilities on Housing Prices: A Comparison of Parametric and Semiparametric Hedonic Price Models

G2014/06
J.-M. DAUSSIN-BENICHOU - A. MAUROUX
Turning the heat up. How sensitive are households to fiscal incentives on energy efficiency investments?

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

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

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

G2014/10
B. GARBINTI - P. LAMARCHE
Les hauts revenus épargnent-ils davantage ?

G2014/11
D. AUDENAERT - J. BARDAJI - R. LARDEUX - M. ORAND - M. SICSIC
Wage Resilience in France since the Great Recession

G2014/12
F. ARNAUD - J. BOUSSARD - A. POISSONNIER - H. SOUAL
Computing additive contributions to growth and other issues for chain-linked quarterly aggregates

G2014/13 H. FRAISSE - F. KRAMARZ - C. PROST
Labor Disputes and Job Flows

G2014/14 P. GIVORD - C. GRISLAIN-LETRÉMY
- H. NAEGELE
How does fuel taxation impact new car purchases? An evaluation using French consumer-level dataset

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2015/16 J. BARDAJI - J.-C. BRICONGNE -
B. CAMPAGNE - G. GAULIER
Compared performances of French companies on the domestic and foreign markets

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2017/02 Y. DUBOIS - M. KOUBI
Règles d'indexation des pensions et sensibilité des dépenses de retraites à la croissance économique et aux chocs démographiques
G2017/03 A. CAZENAVE-LACROUTZ - F. GODET
L'espérance de vie en retraite sans incapacité sévère des générations nées entre 1960 et 1990 : une projection à partir du modèle Destinie

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

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

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

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

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

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

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

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

G2018/02 C.-M. CHEVALIER - A. LUCIANI
Computerization, labor productivity and employment: impacts across industries vary with technological level

G2018/03 R. MONIN - M. SUAREZ CASTILLO
L'effet du CICE sur les prix : une double analyse sur données sectorielles et individuelles

G2018/04 R. LARDEUX
Who Understands The French Income Tax? Bunching Where Tax Liabilities Start

G2018/05 C.-M. CHEVALIER
Financial constraints of innovative firms and sectoral growth

G2018/06 R. S.-H. LEE - M. PAK
Pro-competitive effects of globalisation on prices, productivity and markups: Evidence in the Euro Area

G2018/07 C.-M. CHEVALIER
Consumption inequality in France between 1995 and 2011

G2018/08 A. BAUER - B. GARBINTI -
S. GEORGES-KOT
Financial Constraints and Self-Employment in France, 1945-2014

G2018/09 P. BEAUMONT - A. LUCIANI
Prime à l'embauche dans les PME : évaluation à partir des déclarations d'embauche

G2018/10 C BELLÉGO - V. DORTET-BERNADET -
M. TÉPAUT
Comparaison de deux dispositifs d'aide à la R&D collaborative public-privé

G2018/11 R. MONIN - M. SUAREZ CASTILLO
Réplication et rapprochement des travaux d'évaluation de l'effet du CICE sur l'emploi en 2013 et 2014

G2018/12 A. CAZENAVE-LACROUTZ - F. GODET -
V. LIN
L'introduction d'un gradient social dans la mortalité au sein du modèle Destinie 2

G2019/01 M. ANDRÉ - A.-L. BIOTTEAU
Effets de moyen terme d'une hausse de TVA sur le niveau de vie et les inégalités : une approche par microsimulation

G2019/02 A. BOURGEOIS - A. BRIAND
Le modèle Avionic : la modélisation Input/Output des comptes nationaux

G2019/03 A. GODZINSKI - M. SUAREZ CASTILLO
Short-term health effects of public transport disruptions: air pollution and viral spread channels

G2019/04 L. AEBERHARDT - F. HATIER -
M. LECLAIR - B. PENTINAT - J.-D. ZAFAR
L'économie numérique fausse-t-elle le partage volume-prix du PIB ?

G2019/05 A. CAZENAVE-LACROUTZ - E. YILMAZ
Dans quelle mesure les incitations tarifaires et la procédure de mise sous accord préalable ont-elles contribué au développement de la chirurgie ambulatoire ?

G2019/06 J.-P. CLING - S. EGHBAL-TEHERANI -
M. ORZONI - C. PLATEAU
The Differences between EU Countries for Sustainable Development Indicators: It is (mainly) the Economy!

G2019/07 P. CHONÉ - L. WILNER
Competition on Unobserved Attributes: The Case of the Hospital Industry

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. SIREYJOL
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 matière et catégorie de produits

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

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

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

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

G2020/01 J. SILHOL - B. VENTELOU
Les zones d'intervention prioritaire 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

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

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

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

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

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

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

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

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

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

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

2021/03 N. BECHICHI - J. GRENET - G. THEBAULT
Ségrégation à l'entrée des études supérieures en France et en région parisienne : quels effets du passage à Parcoursup ?

2021/04 M. ANDRÉ - O. MESLIN
Et pour quelques appartements de plus : Étude de la propriété immobilière des ménages et du profil redistributif de la taxe foncière

2022/05 S. GEORGES-KOT – D. GOUX – E. MAURIN

The value of leisure synchronozation

2022/06 N. BECHICHI – M. FABRE – T. OLIVIA

Projections de la population active à l'horizon 2080

2022/11 S. QUANTIN – C. WELTER-MÉDÉE

Estimation des montants manquants de versements de TVA : exploitation des données du contrôle fiscal

2022/14 L. BLOCH – B. FAVETTO – A. LAGOUGE – F. SÉDILLOT

Inégalités de rendements et de patrimoine en France en 2017

2022/15 H. GENIN – S. SCOTT

Un portrait de la r'étention de main-d'oeuvre dans l'industrie française : analyse à partir des enquêtes mensuelles de conjoncture

2023/02 D. GOUX – E. MAURIN

On the seventh day you shall do not any work : the winners and losers of Sunday work deregulation

2023/03 P. AGHION – A. BERGEAUD – M. LEQUIEN – M. MELITZ – T. ZUBER

Opposing firm-level responses to the China shock: Output competition versus input supply

2023/04 L. GALIANA – L. WILNER

Private Wealth over the Life-Cycle: A Meeting between Microsimulation and Structural Approaches

2023/06 M. HILLION – E. MAUGENDRE

L'effet du « jour de carence » sur les absences pour maladie ordinaire, la santé perçue et le recours aux soins à court- terme

2023/07 M.ANDRÉ- J-M GERMAIN – M. SICSIC

'Do I get my money back?' : A Broader Approach to Inequality and Redistribution in France With a Monetary Valuation of Public Services

2023/08 R. LAFROGNE-JOUSSIER – J. MARTIN – I. MEJEAN

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

2023/09 J. SILHOL – L. WILNER

Teachers' desired mobility to disadvantaged schools: Do financial incentives matter?

2023/12 P. AGHION – C. ANTONIN – S. BUNEL – X. JARAVEL

Modern Manufacturing Capital, Labor Demand, and Product Market Dynamics: Evidence from France

2023/13 R. LAFROGNE-JOUSSIER – J. MARTIN – I. MEJEAN

Cost Pass-Through and the Rise of Inflation

2023/14 A. BOURGEOIS – F. GERVOIS – R. LAFROGNE-JOUSSIER

Forces et fragilités des tableaux internationaux entrées-sorties pour le calcul de l'empreinte carbone

2023/17 M. ADAM – O. BONNET – E. FIZE T.LOISEL – M. RAULT – L. WILNER

How does fuel demand respond to price changes?

Quasi-experimental evidence based on high-frequency data

2023/18 S. QUANTIN – C. WELTER-MÉDÉE

Estimation de l'activité dissimulée pour le changement de base 2020 des comptes nationaux

2023/19 T. LOISEL – M. SICSIC

La mobilité des individus le long de l'échelle des revenus en France sur la période 2003-2020

2023/20 D.BABET, O. GODECHOT, M. G. PALLANDINO

Retour sur AKM : expliquer la dynamique des inégalités salariales en France

2024/01 M. ANDRE, A. BOURGEOIS, M. LEQUIEN, E. COMBET, A POTTIER

Challenges in measuring the distribution of carbon footprints : the rôle of product and price heterogeneity

2024/02 C. LE THI, M.SUAREZ CASTILLO, V. COSTEMALLE

Residential mobility and air pollution inequalities: describing income disparities in lifelong air pollution exposure

2024/04 P. AGHION, A. BERGEAUD, T. GIGOUT, M. LEQUIEN, M. MELITZ
Exporting ideas: knowledge flows
from expanding trade in goods