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# Cost Pass-Through and the Rise of Inflation

Raphaël Lafrogne Joussier, Julien Martin, Isabelle Mejean

► To cite this version:

Raphaël Lafrogne Joussier, Julien Martin, Isabelle Mejean. Cost Pass-Through and the Rise of Inflation. 2023. [https://arxiv.org/abs/2305.05431](https://arxiv.org/abs/2305.05431)

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# Cost Pass-Through and the Rise of Inflation

## Documents de travail

N° 2023-13 – Mai 2023

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Institut national de la statistique et des études économiques

2023/13

**Cost Pass-Through and the Rise of Inflation**

Raphaël LAFROGNE JOUSSIER* Julien MARTIN** Isabelle MEJEAN***

Mai 2023

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 and CREST-Ecole Polytechnique, Email address: raphael.lafrogne-joussier@insee.fr</footnote>

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

<footnote>***Sciences Po and CEPR, Email address: isabelle.mejean@sciencespo.fr</footnote>

<footnote>We are grateful to Nicolas Carnot, Pauline Givord, Sophie Guilloux-Nefussi, Matthieu Lequien, Philippe Martin, and CAE members, as well as seminar participants at various institutions for helpful suggestions. The paper has greatly benefited from insightful discussions on the data with Camille Carré, Swann-Emilien Maillefert, Nicolas Studer, and Grégory Verriest. Mejean gratefully acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 714597).</footnote>
# Transmission des coûts et hausse de l'inflation

Nous utilisons les données de prix au niveau microéconomique sous-jacentes à l'indice des prix à la production français entre janvier 2018 et juillet 2022, ainsi que des mesures externes de l'exposition des entreprises aux intrants importés et aux chocs des coûts énergétiques, pour étudier le rôle des chocs externes dans l'inflation récente. Lors de la révision de leurs prix, les entreprises de notre échantillon répercutent 30 % des variations du prix des intrants importés et 100 % des variations des coûts énergétiques qui ont eu lieu depuis la dernière révision, conditionnellement à leur exposition à ces chocs. Pour l'entreprise moyenne de nos données, cela implique qu'une augmentation de 10% des coûts importés entraîne une hausse de 0,74% des prix de production, tandis qu'un choc de 10% des coûts de l'énergie induit une augmentation des prix de 0,73%. Nous étudions comment les taux de transmission varient d'une entreprise à l'autre en fonction de leur taille et de leur exposition aux chocs. Les taux de transmission sont asymétriques, les chocs de coûts positifs induisant une répercussion significativement plus importante que les chocs négatifs. L'hétérogénéité de l'exposition aux chocs externes, entre les entreprises et les secteurs, entraîne des différences importantes dans la dynamique de l'inflation. Pour illustrer cela, nous prédisons les variations de prix à partir des variations cumulées des intrants importés et des prix de l'énergie entre janvier 2021 et juillet 2022. 70 à 75 % de la variance des changements de prix prédits se produit au sein des industries, d'une entreprise à l'autre. Les industries chimiques et métallurgiques ont été les plus touchées par les chocs des coûts des importations et de l'énergie, qui ont contribué à une augmentation des prix à la production dans ces secteurs de 9 à 14 points de pourcentage.

**Mots-clés** : Inflation, Prix de l'énergie, Inflation importée, Transmission des coûts

# Cost Pass-Through and the Rise of Inflation

We use micro-level price data underlying the French producer price index from January 2018 to July 2022, along with external measures of firms' exposure to imported inputs and energy cost shocks, to study the role of external shocks in the recent inflation surge. Within our sample, firms pass through 30% of changes in the price of imported inputs and 100% of changes in energy costs when resetting their prices, conditional on their exposure to these shocks. For the average firm in our data, this implies that a 10% increase in foreign costs leads to a 0.74% rise in output prices, while a 10% energy cost shock induces prices to increase by 0.73%. We examine how pass-through rates vary across firms within and across industries, depending on their size and exposure to shocks. We find that pass-through rates are asymmetric, with positive cost shocks inducing significantly more pass-through than negative shocks. The heterogeneity in exposure to external shocks across firms and sectors drives important differences in inflation dynamics along firms' distribution. To illustrate this, we predict price changes from cumulative imported inputs and energy price changes between January 2021 and July 2022, and find that between 70% and 75% of the variance in predicted price changes happens within 2-digit industries, across firms. The chemical and metal industries are the most impacted by both imported and energy cost shocks, which contribute to an increase in producer prices in those sectors of at least 9% to 14%.

**Keywords** : Inflation, Energy prices, Imported Inflation, Cost Pass-Through

**Classification JEL** : F1, F4, L1
# 1 Introduction

After years of price moderation, inflation is back, characterized notably by a large increase in the price of physical goods (Lane, 2022). Since 2020, the dynamics of goods prices have often been explained by a combination of an increase in demand (relative to services) and an increase in costs - in the European context, mostly energy prices and foreign input prices (Seiler, 2022, di Giovanni et al., 2022).<fnref n="1" /> In this context, manufacturing firms are under scrutiny: they are directly exposed to energy and foreign input cost shocks, and their behavior may attenuate or amplify the downward transmission of these shocks to consumers.

This paper estimates the pass-through of imported inputs and energy costs shocks into producer prices and discusses the role of these external factors in the 2021-2022 inflation surge. We leverage the rich micro data underlying the French Producer Price Index (PPI) to study the behavior of manufacturing prices at the firm- and product level between January 2018 and July 2022. We tackle two questions: What are the pass-through rates of energy and foreign input shocks into manufacturing prices? What sectors and firms have been the most impacted by these two types of shocks in the post-pandemic period, and by how much?

We start by establishing four stylized facts about producer price-setting behavior in our data. First, micro-price movements are for the most part idiosyncratic: The degree of price synchronization across firms within a month, within industries, or narrow product markets, is very low. Second, the 2021-2022 rise in producer prices comes with both more frequent and larger price changes. Third, while larger firms tend to adjust their prices more frequently, either upward or downward, the pattern changes after 2021. The probability of upward price changes becomes similar across larger and smaller firms, whereas the differential in downward adjustments is now twice as large as before 2021. Last, output price volatility appears higher among firms that are more exposed to cost shocks.

We then estimate pass-through rates of energy and imported input cost shocks to manufacturing prices. We exploit a sample of 2,352 manufacturing firms that account for one-fifth of France's manufacturing production and report the monthly prices of 10,242 items sold domestically. The prices are observed from January 2018 to July 2022. We supplement the price dataset by incorporating

<footnote n="1">¹Labor shortages and wage pressures are also important factors, particularly in the US (see, e.g., Hobijn et al., 2022, Amiti et al., 2022).</footnote>
firm-level measures of exposure to external shocks, which are obtained from Customs and energy consumption data. We create two distinct firm samples, one consisting of firms for which we have information on exposure to imported input costs and the other consisting of firms for which we can measure exposure to energy price shocks.

To study the relationship between cost shocks and producer prices, we follow Burstein and Gopinath (2014) and estimate conditional pass-through rates: We estimate by how much prices respond to cost shocks, conditional on a price adjustment. To do so, we exploit the vast heterogeneity across firms within product markets and periods in their exposure to external shocks to control for any market- and time-specific unobserved determinants of price changes. Because imported inputs account for 32% of total variable costs on average in the sample of importers, whereas energy accounts for only 2.6% of costs, we apportion both types of shocks to total variable costs so that point estimates are comparable and can be interpreted as pass-through rates after a 1% cost shock. We find a conditional pass-through of foreign input prices into domestic prices of 30%. The pass-through of energy prices is higher, and not statistically different from full pass-through (100%). Unconditional on the share of foreign inputs in total costs, a 10% increase (decrease) in foreign costs leads to a .74% increase (decrease) in domestic prices. Similarly, a 10% increase (decrease) in energy prices leads to a .73% price increase (decrease).

We then study heterogeneity in pass-through behaviors. First, we complement our study of conditional pass-through rates by examining how external cost shocks affect the frequency of price adjustments. A 1% shock to imported input (energy) prices leads to a 1.6 (1.8) percentage point higher probability of adjusting prices. Such extensive adjustments can contribute to the increase in the frequency of price adjustments observed in the last year of the dataset.

Second, we explore non-linearities in pass-through rates, depending on the sign and size of cost shocks. We find strong evidence that the pass-through of negative and positive shocks is asymmetric: Firms pass through a larger share of cost increases to their customers. This asymmetry may suggest that the current and expected decline in energy prices should not contribute as much to inflation as during the energy price boom. However, as our period of study is characterized by more positive than negative cost shocks, it is unsure whether firms would behave similarly in an environment with mostly negative cost shocks. Besides, large cost shocks seem to be passed-through more, even though
the supplementary pass-through associated with large cost shocks is imprecisely estimated.

Last, we estimate pass-through rates along the firm size distribution. Previous literature suggests that firms with market power have a tendency to pass a lower share of cost shocks, thus gaining market shares over their competitors in a period of rising costs (Amiti et al., 2019). On the other hand, evidence in Brauning et al. (2022) suggests that cost pass-through may be magnified in more concentrated industries. We do not find strong evidence consistent with either of these findings. However, we may lack identification power in this specific exercise as the survey is restricted to the largest firms in each product market.

Whereas firms do not display much heterogeneity in their pass-through rates, they do differ substantially in their exposure to shocks. In the last section of the paper, we dig into the consequences of this heterogeneity. We use the predictions of our econometric model to quantify the direct transmission of external cost shocks to producer prices, across firms and sectors, and use the actual imported inputs and energy price changes that occurred between January 2021 and June 2022. Over the period, input prices increased for most firms – they decreased for less than 1% of firms in our sample – and energy costs rose by 72% on average – the average increase in costs due to energy costs is about 1.28%. In manufacturing industries, the average price increase from imported and energy cost shocks that our model predicts is about 1.90 and 1.62%, respectively; but it hides strong heterogeneity across firms. For the median firm in our sample, the two shocks increased output prices by about 0.93 and 0.81%.<fnref n="2" /> For the 1% of firms most exposed to each of these shocks, the impact on producer prices is 14.7% and up to 12.4%. Again, heterogeneity within industries matters: 73% (72%) of the variance in predicted price adjustments induced by imported inputs (energy) is across firms within the same 2-digit industry. The heterogeneity in exposures also matters for cross-sectoral predictions. The four industries most affected by imported inputs cost shocks in 2021-2022 are textile, chemical, paper, and metal products. In these sectors, imported input cost shocks may have directly increased industry-level prices by up to 8.2%. Metal products and the chemical industry were also exposed to the surge in energy prices. In the chemical industry, energy cost shocks may have directly contributed to an additional 5.7% of inflation.

<footnote n="2">²We focus on the direct incidence of the shocks and do not take into account further transmission through the production network.</footnote>
Our paper contributes to three strands of the literature. First, we participate in the growing efforts to understand the factors behind the 2021-2022 inflation surge. Several recent studies emphasize the dominant role of supply-side factors: for instance, energy prices (Bunn et al., 2022); foreign shocks and supply-chain disruptions (Amiti et al., 2022, di Giovanni et al., 2022); and stock-outs (Cavallo and Kryvtsov, 2021). Our paper uses micro-data on the pricing of manufacturing firms to shed light on the importance of both imported inputs and energy cost shocks and the role of their heterogeneity along the distribution of firms.

Furthermore, we contribute to the literature on cost pass-through.<fnref n="3" /> Previous papers using micro-price data have studied cost pass-through in the context of specific industries such as the coffee or beer industries (Nakamura and Zerom, 2010, Goldberg and Hellerstein, 2013). Closer to our work, Ganapati et al. (2020) estimated a pass-through rate of 70% of energy-driven cost shocks on manufacturing prices. We obtain slightly larger estimates using a different methodology - we exploit direct information on the energy use of firms - at a different level - we identify pass-through rates across firms within a product market in all manufacturing industries.

Martin (2011, chap.4) uses the micro data underlying the French PPI for an earlier period to study the pass-through of imported input shocks into domestic prices.<fnref n="4" /> We augment this work by considering pass-through behaviors during a high-inflation period, and we further explore the pass-through of energy cost shocks. In a related paper, Dedola et al. (2022) explore the extensive and intensive margins of price adjustments to cost shocks using price data underlying the Danish PPI. They find that selection issues have a small impact on estimated pass-through, and they document a strong heterogeneity in pass-through across firms and sectors. Our analysis in the French context covers a more recent period, which allows us to study the role of imported and energy cost shocks in the 2021-2022 inflation surge. In addition, we can study not only oil shocks but also any price shock on the energy bill.

Last, we contribute to the macro literature on price setting behavior of producers (Nakamura and Steinsson, 2008, Gautier, 2008, Loupias and Sevestre, 2013, Bhattarai and Schoenle, 2014). We

<footnote n="3">³More broadly, our paper relates to the literature on exchange rate pass-through (see, e.g., Gopinath and Itskhoki, 2010, Burstein and Gopinath, 2014).</footnote>

<footnote n="4">⁴About the transmission of foreign shocks to domestic prices see also Auer et al. (2019) and Goldberg and Campa (2010).</footnote>
highlight two factors behind the recent inflation surge: The frequency of price changes and their absolute magnitude have increased, and the probability of upward adjustments conditional on price changes has risen significantly. These new findings complement existing evidence on the role of the extensive margin of prices for inflation dynamics (Gagnon, 2006; Klenow and Kryvtsov, 2008; Nakamura et al., 2018; Alvarez et al., 2019).

The rest of the paper is organized as follows. In Section 2, we describe the data and provide novel stylized facts on the behavior of micro-level prices in the 2021-2022 inflation surge. Section 3 presents our strategy and results for the estimation of cost pass-through. We explore the heterogeneity of pass-through rates in Section 4. Then in Section 5 we run a quantification exercise to evaluate the role of foreign inflation and energy prices in the dynamics of producer prices in 2021-2022. Section 6 concludes.

# 2 Data and context

## 2.1 The French context

Since the beginning of the 2000s, France has experienced low and stable levels of inflation. In 16 years, from January 2005 to December 2020, producer prices have increased by 17% (an annual rate of 1%). The great price moderation stopped in the aftermath of the Covid-19 pandemic. From January 2021 to June 2022, producer prices have increased by 31%, a monthly rate of 1.6%. The recent inflation surge affects domestic and export prices, although the growth of export prices is slightly lower than the rise of domestic prices (Figure 1). The increase in manufacturing firms' producer prices then contributes to overall CPI inflation.

The recent acceleration of inflation is largely attributed to external factors, namely imported inflation and energy cost shocks (Bunn et al., 2022, di Giovanni et al., 2022). Both import prices and energy prices began to rise at an accelerating pace at the end of 2020 - beginning of 2021 (Figure 2).<fnref n="5" /> The surge in import prices has been fed by supply chain disruptions and rising freight costs following the first wave of Covid-19, as well as accelerating inflation in the US, after President Biden

<footnote n="5">⁵Here, the import price index is constructed from data on firms’ direct imports and does not include imported energy as a consequence.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 1: French PPI, January 2018 - July 2022</figcaption></figure>

*Notes*: This figure shows price indices, normalized to 100 in January 2018. "PPI - French Market" and "PPI - Foreign markets" are the producer price indices of the French manufacturing industry, for domestic and foreign sales, respectively. "CPI" is the French Consumer Price Index. Source: Insee.
<figure><img src="image_6.png" /><figcaption>Figure 2: External factors of inflation, January 2018- July 2022</figcaption></figure>

*Notes*: This figure shows price indices, normalized to 100 in January 2018. “Energy” is the producer price index of French energy sectors computed over their domestic sales. “Imports” is the price index of imported intermediate products. “Gas” is “Trade services of gas to final consuming businesses”, “Electricity” is “Electricity sold to final consuming businesses”, and “Oil products” is “Coke and petroleum products”. “Electricity (adjusted)” is the price index for Electricity, once seasonality is controlled for. Source: Insee.
announced a $1.9 trillion rescue plan in January 2021. The continuous depreciation of the euro against the dollar since the beginning of 2021 has further fueled imported inflation.

Cost inflation has also been fed by energy prices, which have increased by about 70% between January 2021 and July 2022. The rise in energy prices is particularly pronounced for oil products, whose prices have almost continuously increased since March 2020, but is also very steep on gas. Electricity prices have started to rise in 2021, although the trend is less clear due to important price seasonality in the European electricity market. Once seasonality is controlled for, the trend is clearer, although not as steep as for oil and gas.

How do these prices translate to firms' costs? Whereas oil prices are priced in spot markets, the diffusion of electricity and gas price shocks to firms is slowed down by the prevalence of long-term, fixed-price contracts, that smooth the transmission of energy cost shocks to manufacturing companies. Insee (2022) estimates that less than 20% of French manufacturing output is produced by firms whose electricity or gas contract is indexed on spot prices. Besides, 15% of manufacturing output is produced by relatively small producers that are offered a fixed regulated electricity price. In between these extremes, 40% of manufacturing firms benefit from long-term electricity contracts and 60% from long-run gas contracts. Among these firms, 48% (resp. 36%) will have their electricity contract (resp. gas contract) renegotiated before the end of 2022. Thus, when nationwide electricity and gas prices are increasing, only a subset of firms faces an actual rise in energy costs. Moreover, the high seasonality of electricity prices observed in Figure 2 is not expected to have consequences on manufacturing firms, beyond the small share which prices are indexed on spot prices. For this reason, our empirical analysis uses electricity prices which are adjusted for seasonality to measure firms' exposure to energy prices.

Whereas the role of external cost shocks on domestic prices is well-understood qualitatively, measuring its quantitative impact requires a finer view of firms' exposures to these shocks and their incidence on their pricing strategies. This paper brings together micro data on both dimensions to tackle these questions.
## 2.2 Data

**Individual prices.** We use micro-level data on firm- and product-level prices, collected by the French statistical institute (Insee) in the OPISE survey (Observation des Prix dans l'Industrie et les Services, Price Observation in Industry and Services). The data cover the period from January 2018 to July 2022.<fnref n="6" /> The survey's primary objective is to calculate producer price indices, which are predominantly used as deflators, such as for measuring industrial production in real terms and contract indexation clauses. The survey targets a group of companies that report their most representative product prices on a monthly basis. The data collected includes prices for domestic and export sales, as well as import prices for firms that source inputs from overseas. The survey also collects information on the total value of domestic sales, exports, or imports of the product. The survey covers both manufacturing and service products, but our study focuses on manufactured products.

The sample of surveyed firms is composed of relatively large firms, that are chosen via a cut-off method. The cut-off is performed within each market (domestic, export, and import) and “branche”, a “branche” gathering the production of all products within a 4-digit category (*Classification des Produits Français*, CPF4). For each one of the 240 4-digit categories, the largest firms in terms of their sales are included, until at least 40% of the product sales are covered (30% on exports and imports). Beyond the coverage, the sample also targets a minimum and a maximum number of units per product market. Once a firm is selected, it remains surveyed until the next renewal of the sample in the market, which happens once every five years on average.

In a typical year, around 5,000 firms are selected in manufacturing sectors. Once the sample is constituted, the firm and the statistician identify a list of its core products, selected to reflect at best the evolution of its output, export, and import prices. The firm is due to report the last four prices of the identified core products, mostly by filling out a web form. The raw data are then used to construct monthly price indices for each firm×product, correcting for non-response, quality changes, product substitution, or atypical evolutions. In 2021, the survey produces 28,000 price series, including 14,200 domestic prices and 9,400 import price series. All prices are defined before taxes. Import and export prices are converted to current euros when needed. Import prices include the cost of insurance and freight, up to the border of the importing country. Some of the export and

<footnote n="6">⁶Earlier vintages of this dataset have been used in Gautier (2008), Martin (2011).</footnote>
import price series can reflect intra-group transactions, as discussed in Martin (2011).

In the rest of the paper, we will call “items” the individual price series, that are firm- and product-specific. We study output price series in the manufacturing sector, excluding tobacco, energy products, extraction, water, and steam supply. We end up with 10,242 items, corresponding to 2,352 firms. When studying the pass-through of import cost shocks, we further restrict the sample to 250 firms that also report at least one import price.

**Energy consumption.** We use the Insee-EACEI survey to build a measure of exposure to energy cost shocks.<fnref n="7" /> The EACEI survey provides detailed plant-level information on the nominal and real consumption of energy, by type of energy, in a subset of manufacturing firms.<fnref n="8" /> The purpose of the survey is to compute aggregate energy consumption statistics at the industry and regional level, by categories of firms’ size. When we merge individual observations of the EACEI survey with the price data, we recover a dataset composed of 1,130 firms. We use energy consumption by energy types to measure firms’ heterogeneous exposure to various energy cost shocks. Table A1 in the appendix displays statistics about the relative share of various types of energy in firms’ real consumption. Electricity, gas, and oil products account for 99% of energy consumption.

**Other data.** We use the firm’s identifier to merge the price data with two additional micro-level datasets.<fnref n="9" /> First, we recover balance-sheet information over the firm using the Insee-FARE dataset. The dataset is constructed from the firm’s tax forms and contains information about the firm’s main activity, its production, value-added, and employment, as well as the structure of its costs. Throughout the paper, we measure total variable costs as the sum of the firm’s wage bill and intermediate consumption (raw materials, merchandises, and services). Variable costs are used in the denominator of all cost-share variables. Second, we retrieve information on firm-level imports using the French customs database (DGDDI, DEB, and DAU files). The ratio of nominal imports (excluding capital goods) over variable costs is used as a measure of the firm’s exposure to foreign

<footnote n="7">⁷EACEI stands for “Enquête Annuelle sur les Consommations d’Energie dans l’Industrie”. See, for instance, Fontagné et al. (2018), Aghion et al. (2020), Fontagné et al. (2023) for recent studies using the same dataset.</footnote>

<footnote n="8">⁸The activity of multi-establishment firms may not be fully covered by the survey as some plants of the firm are surveyed while others are not. We, therefore, include in our sample only firms that have all their manufacturing plants surveyed.</footnote>

<footnote n="9">⁹See details in Appendix A.</footnote>
input shocks. We also draw 2-digit industry-level labor costs and production levels from quarterly national accounts. These variables are used as controls in pass-through regressions.

<table><caption>Table 1: Summary statistics on the estimation sample</caption><thead><tr><th></th><th># Firms</th><th>Variable</th><th>5 pctl</th><th>Mean</th><th>Median</th><th>95 pctl</th><th>St.dev.</th></tr></thead><tbody><tr><td rowspan="2">Sample</td><td rowspan="2">2352</td><td>Empl.</td><td>23</td><td>257.9</td><td>146</td><td>767</td><td>430.8</td></tr><tr><td># Products</td><td>2</td><td>7.2</td><td>5</td><td>19</td><td>6.4</td></tr><tr><td rowspan="3">× Importers</td><td rowspan="3">250</td><td>Empl.</td><td>25</td><td>448.3</td><td>255</td><td>1453</td><td>722.9</td></tr><tr><td># Products</td><td>3</td><td>13.2</td><td>11</td><td>29</td><td>8.8</td></tr><tr><td>Imp. Share</td><td>.041</td><td>.321</td><td>.304</td><td>.624</td><td>.18</td></tr><tr><td rowspan="3">× Energy</td><td rowspan="3">1130</td><td>Empl.</td><td>32</td><td>227.1</td><td>148</td><td>652</td><td>314.7</td></tr><tr><td># Products</td><td>1</td><td>6.7</td><td>5</td><td>17</td><td>5.3</td></tr><tr><td>Energy Share</td><td>.003</td><td>.026</td><td>.015</td><td>.096</td><td>.037</td></tr><tr><td rowspan="2">× Superstar</td><td rowspan="2">144</td><td>Empl.</td><td>78</td><td>779.3</td><td>451.5</td><td>2434</td><td>1002.4</td></tr><tr><td># Products</td><td>2</td><td>13</td><td>10.5</td><td>33</td><td>11.7</td></tr></tbody></table>

*Notes:* This table reports statistics on the size, employment, number of products and cost shares of the population of firms in the sample, in the sub-sample of importers, in the sub-sample of firms that are also surveyed in the EACEI, and in the sub-sample of firms that we call “Superstar”.

Table 1 displays statistics on the employment, number of products, and cost shares in the sample of firms described above. Our sample is composed of large firms (258 employees on average), that report the prices of several products (7.2 domestic products per firm, on average). The 250 firms that also report imported input prices are larger, both in terms of employment and the number of output prices surveyed. On average, imported inputs account for 32% of their costs. The 1,130 firms for which we have energy usage information are on average smaller. Energy accounts for 2.6% of their costs, on average.

## 2.3 Stylized facts on micro-level producer prices

Before digging into the transmission of external shocks to producer prices, this section reviews several stylized facts on the recent inflation surge.
**Price setting behaviors are idiosyncratic.** We first decompose monthly price variations observed at the level of each item into a set of common and idiosyncratic components. Formally, we regress the (log of) price changes on period fixed effects and examine the (adjusted) R-squared ($R^2$). The higher this $R^2$, the less variation in price changes there is around the average price change in a given month. In other words, a high $R^2$ captures the general synchronization of prices across firms, and the residuals capture idiosyncratic (firm-level) movements in prices. Over the period between January 2018 and July 2022, we find a $R^2$ of 1.19% (see Appendix Table A3). We then replicate the exercise using alternatively product × time and industry × time fixed-effects, using 2- or 4-digit categories. In these regressions, the $R^2$ is informative of the degree of price synchronization across firms within an industry or a product market. 2-digit sector × time fixed effects explain 2.1% of the variance, while 4-digit product × time dummies explain 5 to 5.5%. The micro-level price data display massive heterogeneity, including across firms within a narrow product market. In the pass-through analysis, we will solely exploit this variation, using 4-digit product × time fixed effects to control for systematic trends across firms within a sector.

**The 2021-2022 inflation surge comes with more frequent and larger price changes.** Figure 3 illustrates the evolution over time of the frequency and size of price adjustments. Together, these graphs shed light on the forces underlying the recent rise in producer prices. Has the recent inflation surge been driven by an increase in the share of firms adjusting their price up, by an increase in the size of positive price changes, or both?<fnref n="10" />

In Figure 3, top panel, we see that the shares of positive and negative price changes have been stable until mid-2020.<fnref n="11" /> Every month, about 50% of prices change, with a roughly equal proportion of positive and negative price adjustments. These statistics are in line with the frequency of price changes documented by Goldberg and Hellerstein (2009) using US data over the 2005-2008 period. From September 2020, the share of negative price changes has declined, from 25 to 20%, while the

<footnote n="10">¹⁰The empirical literature on this question provides mixed answers. Nakamura et al. (2018) find that the frequency of price changes is strongly correlated with inflation whereas the size of price changes is flatter. Instead, Klenow and Kryvtsov (2008) attribute a larger share of inflation to changes in the size of price adjustments. In theoretical models of price rigidities, menu cost models point to firms being more likely to adjust their price in periods of high inflation whereas models à la Calvo assume the frequency of price adjustments is constant and the size of the adjustment is the main driver of inflation.</footnote>

<footnote n="11">¹¹Following Gautier (2008), price adjustments are restricted to variations in absolute values above .1%.</footnote>
<caption>Figure 3: The margins of price changes</caption>

Panel A: Frequency of price changes

<figure><img src="image_7.png" /></figure>

Panel B: Size of price changes

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

*Notes*: Panel A shows the share of prices increasing or decreasing in a given month, with respect to the last month. Panel B displays the average size of positive and negative price adjustments. Price changes concern only items in our sample. The inflation rate is the monthly percentage change of the price index of these items.
share of positive price changes increased, reaching about 45% in July 2022. Overall, the frequency of price adjustments has thus increased, although moderately. The bottom panel of Figure 3 focuses instead on the intensive margin of inflation, namely the average size of positive and negative price adjustments, over time. Whereas average price changes are roughly constant until 2021, around .75% for positive adjustments and -.6% when prices are adjusted down, the mean price increase starts rising in 2021, reaching around 2% in 2022.

**“Superstar” firms adjust their prices more frequently.** An important question that micro-level data can help us address is the extent to which pricing behaviors vary along the distribution of firms’ size. Is the price-setting behavior of large firms different from other firms? To what extent is the recent surge in inflation led by relatively large firms adjusting their prices more frequently and/or by a larger amount? Addressing these questions with the data at hand is not trivial as the sample of surveyed firms is biased towards relatively large firms. It is however possible to identify a subset of firms that are large among surveyed firms. To do so, we first select a subset of firms that belong to the top quartile of their (4-digit) industry in terms of employment. This selects 661 firms in the population of 2,352 firms surveyed over domestic prices. Because these firms may not be equally prominent in the various product markets that they serve, we then select the largest sellers in each product market. These are the firms whose total sales in the 4-digit product category account for at least 20% of the cumulated sales of firms in the survey. This criterion is thus specific to a “branche” in which the firm is producing. Using the double criteria, we end up with 144 firm×product pairs that we call “superstar”.<fnref n="12" />

We then investigate the extent to which superstar firms’ pricing behaviors differ from the rest of the sample using the following statistical framework:

$$y_{fpt} = \beta \times 1_{fp}^{\text{Super}} + \gamma \times 1_{fp}^{\text{Super}} \times 1_{t}^{2021} + \alpha_{t} + \mu_{s} + \epsilon_{fpt}, \quad (1)$$

where $y_{fpt}$ is a monthly firm×product-level price outcome, $1_{fp}^{\text{Super}}$ is a dummy identifying superstar firms, $1_{t}^{2021}$ is a dummy which is set to one starting from 2021. Finally, $\alpha_{t}$ and $\mu_{s}$ are period and 4-digit product fixed effects, respectively. In this equation, the $\beta$ coefficient identifies any systematic

<footnote n="12">¹²These 153 items correspond to 144 single firms. 9 firms are “superstar” in two product markets.</footnote>
different price behaviors are driven by heterogeneous exposure to shocks in the 2021-2022 wave of inflation, or whether they instead reveal different pass-through behaviors.

**Price volatility is higher among firms exposed to cost shocks.** Finally, Figure 4 provides suggestive evidence of firms' heterogeneous price strategies in response to external shocks. We compare the dynamics of prices across firms with low and high exposures to imported inputs (top panel) and energy cost shocks (bottom panel). In general, average producer prices in the top quintile of exposures are more volatile than in the rest of the sample, due to less smoothing across firms. However, the figures confirm that, after 2021, monthly growth rates are almost systematically larger in the sub-sample of most exposed firms. This is consistent with the view that their exposure to external shocks has generated upward price pressures in a period of rising costs. We examine this conjecture in the rest of the paper.
<figure><img src="image_9.png" /><figcaption>Figure 4: Heterogeneous price dynamics and heterogeneous exposure to external shocks</figcaption></figure>

*Notes*: Panel A shows the monthly growth rate of the producer price index of firms exposed to imported inputs in our sample. Firms are split between highly exposed firms and others. Panel B shows the monthly growth rate of the producer price index of firms exposed to energy prices in our sample. Firms are split between highly exposed firms and others. In both cases, exposure is measured by the share of the corresponding inputs in total variable costs.
# 3 The role of external factors

In the current context, a natural question is the extent to which the surge in inflation is driven by external factors. Specifically, we examine two potential sources of producer price inflation: inflation in foreign markets and the rise in energy prices. To tackle this question, we first estimate the pass-through of foreign input price shocks and energy costs onto producer prices. We begin by describing our cost-shock variables (Section 3.1). We then estimate the pass-through of cost shocks into producer prices in Section 3.2. Whereas these estimates reflect pass-through conditional on price changes, Section 3.3 reports the impact of cost shocks on the extensive margin, that is the decision to change prices.

## 3.1 Measuring firm-level cost shocks

**Imported input shocks.** Our data offer a unique opportunity to match producer price adjustments with shocks to import prices. Among producers surveyed on domestic output, we use the sub-sample of 250 firms that also report import prices. For these firms, we build an import price shock between any two periods $t$ and $t'$ as:

$$ \Delta_{t',t} p_{ft}^{M} = \sum_{m} w_{f0}^{m} \Delta_{t',t} p_{fmt}^{M} $$

where $m$ indexes the firm's imported product, and $\Delta_{t',t} p_{fmt}^{M}$ denotes the (log) change in the price of this input between period $t$ and $t'$. The weights $w_{f0}^{m}$ are firm-level import shares of product $m$, collected when firm $f$ enters the survey and constant throughout the period. The $\Delta_{t',t} p_{ft}^{M}$ variable is an input price shock that varies at the firm level but is common to all products sold by the firm.

In some of the regressions, we will further normalize the import price shock by the size of imported inputs in the firm's overall costs. To this aim, we combine the customs and balance sheet data to measure the ratio of the firm's nominal imports (excluding capital goods) over variable costs.<fnref n="13" /> Normalizing $\Delta_{t',t} p_{ft}^{M}$ by this ratio amounts to measuring the marginal price adjustment that firm $f$ is facing as a consequence of the price of foreign inputs changing. Figure A1 (Top panel) in Appendix shows the distribution of these shares in the estimation sample. Our sample is biased

<footnote n="13">¹³We use the average ratio over 2014-2017, to maximize coverage.</footnote>
towards relatively large manufacturing firms which explains that exposure to foreign inputs is high, on average, at 32%. There is however substantial heterogeneity across firms, with a standard deviation of 18% and two-thirds of the dispersion across firms within the same 4-digit industry.

**Energy cost shocks.** We combine firm-level energy consumption by type of energy with the nationwide evolution of energy prices to build a measure of energy costs that varies a the firm level. We use monthly price indices reflecting changes in the price of energy faced by French firms over the observation period (see Figure 2). Firms’ exposure to various sources of energy is recovered from the EACEI survey on energy consumption. For each firm $f$, the energy cost shock computed between period $t$ and $t'$ is defined as a shift-share:

$$ \Delta_{t',t} p_{ft}^E = \sum_e w_{f0}^e \Delta_{t',t} p_t^e $$

$w_{f0}^e$ is the share of energy $e$ in firm $f$'s consumption of energy, in real terms (tons of oil equivalent). It is calculated as an average over 2014-2017 to maximize coverage while using pre-sample data. $\Delta_{t',t} p_t^e$ is the growth of the price index for energy $e$ between $t$ and $t'$. The shock on energy prices is calculated based on variations in the price of electricity, natural gas, and oil products, which are the three main sources of energy consumption (Table A1).

As discussed in Section 2.1, the price index of electricity is strongly affected by the seasonality of prices in European markets (Figure 2). This, together with evidence that a majority of firms are covered by long-run, fixed-term contracts, implies that the evolution of electricity prices recovered from the raw electricity price index may be a bad proxy of the actual price changes that manufacturing firms face. To deal with this issue, we remove the (monthly) seasonal component of the series of electricity prices.<fnref n="14" />

In several specifications, we normalize the energy cost shock by the firm’s exposure to these shocks. Exposure is measured as the ratio of the firm’s nominal consumption of energy over variable costs.<fnref n="15" /> The bottom panel of Figure A1 shows the distribution of these shares in the estimation sample. In comparison with import shares, exposure to energy cost shocks is severely dampened by

<footnote n="14">¹⁴Gas may also be subject to contracts, but we do not detect seasonality in the gas price series.</footnote>

<footnote n="15">¹⁵We use the average ratio over 2014-2017 to maximize coverage.</footnote>
the limited contribution of energy to overall costs. On average, electricity, gas, and oil purchases account for 2.6% of the value of a firm's variable costs in our sample. There is however substantial heterogeneity across firms, with a standard deviation of 3.7% and 80% of the dispersion across firms within the same 4-digit industry.

## 3.2 Estimating cost pass-through

Armed with these firm-level measures of cost shocks, we follow Burstein and Gopinath (2014) and estimate the pass-through of cost shocks conditional on a price adjustment:

$$ \Delta_{t,\tau} p_{fpt} = \alpha \Delta_{t,\tau} z_{ft} + \beta X_{fpt} + FE + \epsilon_{fpt} \quad (2) $$

where $\Delta_{t,\tau} p_{fpt}$ is the price adjustment in period $t$ for product $p$ of a firm $f$ that had not adjusted its price since period $\tau$. $\Delta_{t,\tau} z_{ft}$ is the corresponding cost shock (imported input or energy) that cumulates all price adjustments between $\tau$ and $t$. $X_{fpt}$ stands for a set of controls: the growth in labor costs and output between $\tau$ and $t$, measured at the 2-digit industry level. $FE$ denotes a set of fixed effects. $\alpha$ is a parameter indicating the extent to which firms when resetting their prices, pass through cost changes that have occurred since the last revision.

We discard items whose prices remain constant in the sample. We also remove censored spells; that is, we work on products for which at least two new prices were observed in the sample. Both conditional price adjustments and cost shock variables are positive on average (see Appendix Table A4). They are also significantly dispersed, easing the identification of the $\alpha$ coefficient. Last, starting periods and duration of price spells vary across firms, as opposed to a setting where year-to-year changes in prices are studied. However, the dispersion in the duration of price spells is rather small: the median number of periods between $\tau$ and $t$ is one month, and the average duration is 1.14 months (Nakamura and Steinsson, 2008).

Our preferred specification has product×period fixed effects, i.e. we identify pass-through rates solely from the cross-section of firms within a product market. Such a demanding identification strategy exploits heterogeneity across firms within the same sector in the magnitude of cost shocks since the last price adjustment. The fixed effects absorb the share of the variance that comes from
the common component of cost shocks across firms within a product market. For energy cost shocks, as we use nationwide price level, fixed effects imply that identification comes from heterogeneity in both energy mixes across firms and the timing of price adjustments. We also present the results of a less demanding specification that combines period and firm×product fixed effects and thus further uses heterogeneity across firms from different sectors.

**Imported input cost shocks.** The results of the estimation of equation (2) for the transmission of imported input cost shocks are presented in Table 3, columns (1) to (4). We start with a simple specification in which we estimate the pass-through of input cost shocks into producer prices, unconditional on the weight of imported inputs in firms’ costs. The unconditional pass-through is estimated between 7.4% and 9% depending on the structure of fixed effects (columns (1) and (3)). We however expect that this pass-through rate varies widely across firms because their exposure (the weight of imported inputs in total costs) is heterogeneous. We thus estimate the pass-through in specifications in which we normalize each shock by the contribution of the corresponding imported inputs to the firm’s overall costs (columns (2) and (4)). We find that the pass-through of import input cost shocks into prices lies between 30 and 33% and is precisely estimated. The imported input cost share for the average importing firm in our sample is 32.2%, which implies that for the average importing firm an increase in imported input costs by 10% leads to an increase in prices between .98% and 1.07%.<fnref n="16" />

**Energy cost shocks.** We present the pass-through of energy cost shocks into producer prices in columns (5)-(8) of Table 3.<fnref n="17" /> The unconditional pass-through of energy cost shocks into producer prices is estimated at 7.3%, and 4.7% in the less demanding specification with firm×product and period fixed effects. Unconditional pass-through rates are thus in the same ballpark as those recovered from imported input cost shocks, despite the smaller exposure of firms to energy (Figure A1). The reason is that *conditional* pass-through rates are significantly larger for energy cost shocks (Columns (6) and (8)). We cannot reject full pass-through of energy cost shocks to producer prices. For the average firm in the data, full pass-through implies that a 10% increase in the cost of energy leads to

<footnote n="16">¹⁶The figures are obtained by multiplying the coefficients estimated in columns (2) and (4) by 32.2%.</footnote>

<footnote n="17">¹⁷In the Appendix, Table A5, we also report results obtained with energy cost shocks computed from raw electricity price series. The comparison confirms the importance of controlling for the seasonality of electricity prices.</footnote>
<table><caption>Table 3: Pass-through of imported input and energy cost shocks</caption><thead><tr><th></th><th colspan="4">Imports</th><th colspan="4">Energy</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Δ Cost</td><td>0.074<sup>***</sup><br>(0.025)</td><td>0.090<sup>***</sup><br>(0.020)</td><td>0.073<sup>***</sup><br>(0.007)</td><td>0.047<sup>***</sup><br>(0.006)</td><td>0.073<sup>***</sup><br>(0.007)</td><td>0.047<sup>***</sup><br>(0.006)</td><td>0.073<sup>***</sup><br>(0.007)</td><td>0.047<sup>***</sup><br>(0.006)</td></tr><tr><td>Δ Cost × Share</td><td></td><td>0.304<sup>***</sup><br>(0.070)</td><td>0.333<sup>***</sup><br>(0.058)</td><td></td><td>1.075<sup>***</sup><br>(0.147)</td><td></td><td>0.913<sup>***</sup><br>(0.111)</td><td></td></tr><tr><td>Fixed effects</td><td>pt</td><td>pt</td><td>t + i</td><td>t + i</td><td>pt</td><td>pt</td><td>t + i</td><td>t + i</td></tr><tr><td>Controls</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td># Items</td><td>993</td><td>993</td><td>993</td><td>993</td><td>3,884</td><td>3,884</td><td>3,884</td><td>3,884</td></tr><tr><td>Obs.</td><td>22,611</td><td>22,611</td><td>22,611</td><td>22,611</td><td>93,535</td><td>93,535</td><td>93,535</td><td>93,535</td></tr></tbody></table>

*Notes:* This table reports the results of the estimation of equation (2) on imported input cost shocks (Columns (1) to (4)) and energy price shocks (Columns (5) to (8)). Controls are changes in labor costs and in output at the 2-digit industry level. Share is the firm-level ratio of imported inputs or energy consumption to total variable costs. pt, t and i stand for product×period, period, and item fixed effects, respectively. Robust standard errors in parenthesis. * *p* < 0.10 ** *p* < 0.05 *** *p* < 0.01

a .26% price adjustment.

**Differences in imported inputs and energy pass-through.** Conditional pass-through rates of imported inputs and energy prices differ markedly. The result may arise from a difference in the nature of the shock, as energy cost shocks have a stronger common component across firms within a product market, and might thus be easier to pass onto producer prices without significant competitiveness consequences. Similarly, if idiosyncratic imported input cost shocks are detrimental to competitiveness, firms may delay the pass-through of these shocks.

However, two methodological issues force us to take the difference in estimated pass-through with caution. First, import prices are more subject to endogeneity concerns than energy cost shocks. Idiosyncratic demand shocks may for instance trigger a simultaneous rise in producer and import prices, without the latter causing the former. Second, import price adjustments combine intra-firm and third-party price variations that we cannot separate in the data. Martin (2011) shows that the pass-through of import price shocks onto producer prices is close to zero in intra-firm trade. As both the endogeneity bias and the measurement issue are expected to push the estimated pass-through towards zero, we cannot exclude that the difference in pass-through rates estimated on imported input and energy cost shocks is in part driven by econometric considerations.
## 3.3 Extensive adjustments

Whereas the previous section focuses on pass-through rates conditional on a price adjustment, we also examine the impact of cost shocks on the probability of adjusting prices, using an ordered probit model on monthly price changes (Loupias and Sevestre, 2013):

$$1_{fpt}(\Delta_{t,\tau}p_{fpt}>=<0) = \alpha\Delta_{t,\tau}z_{ft} + \beta X_{fpt} + FE + \epsilon_{fpt} \quad (3)$$

The notations and controls are the same as in equation (2) but the left-hand side variable is now a dummy equal to -1 if the firm adjusts its price downwards, 1 if it adjusts the price up, and 0 if the price is left unchanged. Unfortunately, the model does not handle a large number of fixed effects as the linear model in equation (2) does. As a consequence, we use (2-digit) product and period fixed effects and augment the list of controls with a proxy for competitor prices that absorbs the common component of shocks at the product×period level.

We estimate the impact of cost shocks on the probability of price adjustment in four different specifications: using either imported inputs or energy cost shocks or cost shocks conditional on the firm's exposure. Table 4 summarizes our results. For each specification, the first two columns present the estimated coefficients and the associated Z-statistics whereas the last two columns correspond to the marginal impact of the shock on the probability of a negative and a positive price adjustment. We compute marginal effects for each observation and report the (simple) average. All coefficients are positive and significant, although estimates are less precise for energy cost shocks. The positive coefficient implies that larger cost shocks are associated with a significantly larger probability of the firm adjusting its price. In quantitative terms, a 1% increase in costs due to imported input costs (resp. energy costs) leads to a 1.56 p.p. increase (resp. 1.8 p.p. increase) in the probability that the firm adjusts its price up. As the average probability that a given firm adjusts its price upwards is around 30%, a 1% increase in imported input costs (resp. energy costs) increases the probability that the firm adjusts its price upwards by 5.2% (6%). The model constrains positive and negative cost shocks to have the same marginal effect on price adjustment probabilities. Hence, the marginal effects of negative cost shocks are identical. We question this assumption in Appendix Table A6, where we estimate different coefficients for positive and negative cost shocks. We find that a 1%
increase in costs due to imported inputs increases the probability of upwards price adjustments by 1.9 pp, while the same but negative shock increases the probability of downward price adjustments by 1 pp. The same positive shock but originating in energy costs has a marginal effect of 0.93 pp on the probability of a price increase and a 1% negative energy cost shock increases the probability of a price decrease by 3 pp. These results reinforce those recovered from the pass-through conditional on a price adjustment: In periods of upward pressures on external costs, firms adjust their prices more frequently and with larger upwards adjustments. Both margins can contribute to explaining the upward trend in producer prices observed in Figure 3 from mid-2021 onwards.

<table><caption>Table 4: Impact of external cost shocks on adjustment probabilities</caption><thead><tr><th rowspan="2"></th><th rowspan="2">X</th><th rowspan="2">Coefficient</th><th rowspan="2">Z-stat</th><th colspan="2">Marginal Effects (pp) on a Price</th></tr><tr><th>Decrease</th><th>Increase</th></tr></thead><tbody><tr><td rowspan="2">Imported inputs</td><td>Δ Cost</td><td>1.24</td><td>7.07</td><td>-.4</td><td>.44</td></tr><tr><td>Δ Cost × Share</td><td>4.38</td><td>9.17</td><td>-1.42</td><td>1.56</td></tr><tr><td rowspan="2">Energy</td><td>Δ Cost</td><td>.35</td><td>3.24</td><td>-.1</td><td>.12</td></tr><tr><td>Δ Cost × Share</td><td>5.23</td><td>2.82</td><td>-1.55</td><td>1.8</td></tr></tbody></table>

*Notes:* This table shows results of the estimation of the ordered probit model (Equation 3), using either imported input or energy costs shocks as right-hand side variables, either normalized by the corresponding cost shares or not. The first column reports the estimated coefficient, the second column shows the associated Z-statistic. The marginal effects give the probability change associated with the occurrence of a 1 percent increase in X, setting the other covariates at their sample mean. All models control for period and 2-digit product fixed effects and changes in competitors prices, 2-digit industry-level labor costs, and 2-digit industry output.
# 4 Heterogeneity in pass-through rates

We now examine the heterogeneous response of firms to imported input and energy cost shocks. Results are reported in Tables 5 and 6. In columns (2) and (5) of Table 5, we test for asymmetries in pass-through rates of negative and positive cost shocks. We find that firms adjust their prices more on positive than negative cost shocks. Firms transmit 50% of positive shocks on their imported inputs but do not adjust their prices when imported inputs drop. Positive energy cost shocks are more than fully passed into prices, whereas only 60% of energy cost drops are passed into prices.<fnref n="18" />

The more-than-full pass-through rate of positive energy cost shocks is not statistically different from a 100% pass-through. In periods of high inflation, if firms, in addition to current cost shocks, expect additional energy cost shocks in the near future, they might decide to smooth their output price changes by passing-through more today. Moreover, as energy cost shocks have a macroeconomic component, energy cost shocks and other inputs cost shocks may be correlated, as suppliers also face the energy shock. In that case, the pass-through rate we estimate compounds both the direct effect from energy costs and the indirect effect from other material costs and may be larger than 100%. Overall, these findings are in line with the early results of Peltzman (2000) on the asymmetric response of US consumer and producer prices to positive and negative shocks.

In columns (3) and (6), we further test for non-linearities by adding an interaction of the cost shock variable with a dummy for large positive shocks. Large positive shocks are those falling into the last decile of the distribution of (positive) cost shocks. Even though the coefficient describing the supplementary pass-through associated with large positive shocks is imprecisely estimated, it is positive and relatively high, which suggests that large positive cost shocks may be passed into prices at a higher rate.

In Table 6, we test for heterogeneity across firms of different sizes.<fnref n="19" /> The pass-through literature has long recognized that optimal pass-through rates vary depending on the firm's perceived demand elasticity. Under oligopolistic competition, large firms are willing to absorb a larger share of cost shocks through markup adjustments (see Atkeson and Burstein, 2008, Amiti and Konings, 2007,

<footnote n="18">¹⁸In both cases we can reject the equality of coefficients on positive and negative cost shocks (t-statistics at 8.79 and 5.67).</footnote>

<footnote n="19">¹⁹In this table, we do not normalize the shock by the corresponding cost shares as the shares are themselves correlated with the interaction terms.</footnote>
<table><caption>Table 5: Pass-through: Heterogeneity along the distribution of shocks</caption><thead><tr><th></th><th colspan="3">Imports</th><th colspan="3">Energy</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Δ Cost × Share</td><td>0.304***<br>(0.070)</td><td></td><td></td><td>1.075***<br>(0.147)</td><td></td><td></td></tr><tr><td>Max( - × -,0)</td><td></td><td>0.469***<br>(0.109)</td><td>0.302**<br>(0.120)</td><td></td><td>1.273***<br>(0.174)</td><td>1.017***<br>(0.323)</td></tr><tr><td>Max() × High positive change</td><td></td><td></td><td>0.189<br>(0.140)</td><td></td><td></td><td>0.300<br>(0.364)</td></tr><tr><td>Min( - × -,0)</td><td></td><td>0.060<br>(0.073)</td><td>0.067<br>(0.073)</td><td></td><td>0.578**<br>(0.226)</td><td>0.585***<br>(0.227)</td></tr><tr><td>Fixed effects</td><td>pt</td><td>pt</td><td>pt</td><td>pt</td><td>pt</td><td>pt</td></tr><tr><td># Items</td><td>993</td><td>993</td><td>993</td><td>3,884</td><td>3,884</td><td>3,884</td></tr><tr><td>Int. Obs</td><td></td><td></td><td>1,010</td><td></td><td></td><td>5,712</td></tr><tr><td>Obs.</td><td>22,611</td><td>22,611</td><td>22,611</td><td>93,535</td><td>93,535</td><td>93,535</td></tr></tbody></table>

Notes: This table reports the results of the estimation of equation (2) with interactions of the cost shock variable with dummies for the sign and size of the shock. In columns (2) and (6) we estimate different coefficients on positive and negative cost shocks. In columns (3) and (7) we interact positive cost shocks with a high positive cost shock dummy that identifies the shock in the last decile of the distribution. pt stands for a product×period fixed effect. Robust standard errors in parenthesis. * $p < 0.10$ ** $p < 0.05$ *** $p < 0.01$

among others). Our sample is not best suited to test for such asymmetries as the survey is restricted to relatively large firms and the number of firms within a product market is low. In columns (1) and (5), we try to check that superstar firms do not behave differently than their peers in the same sector. However, our sample is biased toward large firms and the superstar definition is demanding.<fnref n="20" /> The absence of significant differences in the pricing of the largest firms in our sample should thus not be interpreted as a definitive finding on the heterogeneity in pass-through between large and small firms. In columns (2) and (6), we thus propose an alternative in which cost shocks are interacted with a measure of the firm's market share in the product market. Whereas the specification is more comparable to the literature on the topic, it comes with the caveat that market shares are measured on surveyed firms. Heterogeneous coverages across product markets complicate the interpretation.

Overall, we do not find significant heterogeneity in pass-through rates along the distribution of (relatively large) firms in our sample. In all but one specification, the coefficient on the interaction is negative but not significantly different from zero.<fnref n="21" />

<footnote n="20">²⁰28 et 44 firms are superstars in the importers and the energy sample respectively.</footnote>

<footnote n="21">²¹The coefficient on the interaction in column (1) is positive and significant. However, it is identified on very few</footnote>
However, the absence of significant heterogeneity in pass-through rates does not rule out strategic complementarities between firms. In columns (3) and (6), we add a measure of changes in firms' competitor prices.²²,²³ The associated coefficient is positive and significant, consistent with the view that large firms react to changes in their competitors' prices in oligopolistic markets.²⁴

<table><caption>Table 6: Pass-through: Heterogeneity across firms</caption><thead><tr><th></th><th colspan="4">Imports</th><th colspan="4">Energy</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Δ Cost</td><td>0.053*<br>(0.027)</td><td>0.074<br>(0.058)</td><td></td><td></td><td>0.075***<br>(0.007)</td><td>0.096***<br>(0.015)</td><td></td><td></td></tr><tr><td>Δ Cost × Superstar</td><td>0.147***<br>(0.053)</td><td></td><td></td><td></td><td>-0.013<br>(0.018)</td><td></td><td></td><td></td></tr><tr><td>Δ Cost × Market Share</td><td></td><td>-0.014<br>(0.088)</td><td></td><td></td><td></td><td>-0.030*<br>(0.017)</td><td></td><td></td></tr><tr><td>Δ Cost × Share</td><td></td><td></td><td>0.294***<br>(0.060)</td><td>0.294***<br>(0.060)</td><td></td><td></td><td>0.722***<br>(0.123)</td><td>0.726***<br>(0.122)</td></tr><tr><td>Competitors price change</td><td></td><td></td><td>0.315***<br>(0.026)</td><td>0.304***<br>(0.030)</td><td></td><td></td><td>0.404***<br>(0.017)</td><td>0.411***<br>(0.022)</td></tr><tr><td>– × HHI</td><td></td><td></td><td></td><td></td><td>0.089<br>(0.187)</td><td></td><td></td><td>-0.064<br>(0.137)</td></tr><tr><td>Fixed effects</td><td>pt</td><td>pt</td><td>t + i</td><td>t + i</td><td>pt</td><td>pt</td><td>t + i</td><td>t + i</td></tr><tr><td>Controls</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td># Items</td><td>970</td><td>970</td><td>970</td><td>970</td><td>3,791</td><td>3,791</td><td>3,791</td><td>3,791</td></tr><tr><td>Int. Obs</td><td>3,681</td><td></td><td></td><td></td><td>5,894</td><td></td><td></td><td></td></tr><tr><td>Obs.</td><td>22,111</td><td>22,111</td><td>22,111</td><td>22,111</td><td>91,156</td><td>91,156</td><td>91,157</td><td>91,157</td></tr></tbody></table>

*Notes:* The definition of Superstar firms is based on employment size and market shares. Market Share corresponds to the weight of the firm in the product market, see the text. HHI is the Herfindahl-Hirschman index of the 4-digit industry of the firm. Specifications include the interacted variable – Superstar, Market Share, or HHI – in levels. Controls are changes in labor costs and in output at the 2-digit industry level. pt, t and i stand for branche, product×period, period and item fixed effects, respectively. Robust standard errors in parenthesis. \* *p* < 0.10 \*\* *p* < 0.05 \*\*\* *p* < 0.01

firms and sectors. Moreover, endogeneity concerns regarding import prices are particularly acute at the very top of the distribution.

²²The variable is constructed from price changes observed since the firm's last price adjustment in the sample of firms active in the same product market. The variable is thus collinear with product×period fixed effects which explains that we need to change the structure of fixed effects. As a consequence, the variable also absorbs part of the common component of shocks, affecting in turn the level of the pass-through coefficient.

²³This variable does not take into account the prices of foreign competitors. A later version of the document will do so.

²⁴In columns (4) and (8), we further interact the variable with the sectoral Herfindahl index, a proxy for market concentration. Results suggest that the strength of the competition within sectors does not affect strategic complementarities.
# 5 Contribution of cost shocks to inflation across firms and sectors

In this section, we use the pass-through estimates to examine the impact of imported inputs and energy cost shocks on producer price inflation. We limit the analysis to the industries on which we estimated pass-through rates, that is all manufacturing industries except energy sectors, tobacco, and extraction. Due to variations in the intensity and mix of inputs across firms and sectors, even homogeneous conditional pass-through rates can generate significant dispersion in predicted price adjustments. To illustrate this heterogeneity, we build expected price changes induced by the cumulated changes in imported input or energy costs between the first quarter of 2021 (2021Q1) and the second quarter of 2022 (2022Q2). We do so conditional on price changes – that is we do not predict the extensive margin – and use the asymmetric pass-through rates estimated in Columns (2) and (5) of Table 5: 47% and 6% for positive and negative imported cost shocks, and 127% for energy cost shocks (which are all positive). We focus on the direct transmission of these changes and do not estimate further transmission through the production network. As such, our estimates can be viewed as lower bounds of the overall increase in producer prices due to a given shock to imported costs and energy prices.

Whereas 80% of firms are importers according to Customs data, not all of them are surveyed on their import prices. The reason is that the import price index is itself constructed from a representative sample of importers in the manufacturing sector. Omitting the pass-through of import prices to output prices for firms that import but are not surveyed would tend to under-evaluate the role of foreign inflation in PPI movements. To circumvent this issue we assign them the average import price change in their 2-digit industry, computed from firms for which we do observe import prices. The prediction of output price changes from energy cost shocks suffers from the same issue, if not more, as virtually each manufacturing firm in the PPI sample uses energy whereas data on energy consumption are recovered from a survey. Here as well, we extrapolate our results for firms that are not covered by the energy consumption survey, assuming they display the average energy intensity of firms in the same 2-digit industry and face their average energy cost shock.<fnref n="25" />

<footnote n="25">²⁵See Appendix B for more details.</footnote>
**Imported input cost shocks.** Although the last two years have been characterized by overall rising import prices, some firms experienced a decrease in import prices. There is indeed significant variation across firms in the size and sign of import price changes over the period. This heterogeneity translates into a distribution of predicted price changes, which is described in the first line of Table 7. At the median of the distribution, the firm-level price change that our model attributes to imported input cost shocks is positive and equal to 0.93%. This number however hides strong heterogeneity, as 10% of firms experience an increase above 7.8%, and 1% above 14.7%. We estimate that imported cost shocks can explain around 11.5% of the variance of the observed output price increase across those firms.

We then aggregate firm-level predicted price changes at the 2-digit industry level using PPI weights at the firm level, normalized to one in each 2-digit industry.<fnref n="26" /> Importantly, we include non-importing firms, for which the model predicts a zero output price change. As illustrated in Figure 5, firms in the pharmaceutical and apparel industries are somewhat insulated from imported inputs cost shocks in the post-pandemic period. On the other side of the distribution, some industries have been hurt particularly strongly by imported inflation, most notably producers of chemicals and metals. In the metal industry, the rise in producer prices solely attributed to imported input cost increase is 6.2%.<fnref n="27" /> Overall, we estimate that imported cost shocks contributed to increase the PPI in the manufacturing sector by 1.90% over the period (Figure 5). Moreover, there is substantial heterogeneity across firms within the same 2-digit industry in predicted price increases. In Figure A2, Panel A, we display the 10th and 90th percentiles of the predicted price increase distribution within 2-digit industries. The span of the predicted price changes is large and in most industries, the 10th percentile is close to or equal to zero. In the metal and the chemical industry, the 10% most impacted firms experience a price increase larger than 12%. Overall, around 73% of the dispersion in predicted prices is across firms within the same 2-digit industry. This confirms the importance of digging into micro-level data.

**Energy cost shocks.** We also observe substantial heterogeneity in predicted price changes from

<footnote n="26">²⁶The PPI weights are formally defined at the item level. We add up these weights to the firm level, before normalizing the sum across firms within an industry.</footnote>

<footnote n="27">²⁷Although large in absolute value, this number needs to be put in perspective with the industry's PPI increase over the same period, equal to 46.5%.</footnote>
energy prices. Here, the heterogeneity comes from differences across firms in their dependence on energy and their energy mix. For the median firm in our sample, the 2021-2022 energy cost shock leads to a .81% increase in prices. The predicted impact of energy cost shocks rises to 12.4% for firms in the top percentile. We estimate that the energy price shock can explain around 6.4% of the variance of the observed output price increase across those firms. Overall, we estimate that energy cost shocks contributed to increase the PPI in the manufacturing sector by 1.62% over the period (Figure 5).

Industries vary greatly in their exposure to the energy crisis, with some of the sectors that are most vulnerable to imported input shocks also being among the most affected. For instance, in the chemical industry, a cumulative increase in energy and imported input prices from 2021 to 2022 could have raised the price index by up to 14%. The predicted price changes also show significant heterogeneity within industries, with around 72% of the variance being within an industry, across firms with varying levels of exposure to energy cost shocks. In the chemical industry, this heterogeneity is particularly pronounced, with 90% of firms experiencing moderate cost shocks leading to price increases below 0.29%, while a few large and highly exposed firms are greatly impacted by the rise in energy costs, resulting in a 5.7% increase in the average sectoral price.

<table><caption>Table 7: Distribution of predicted price changes attributable to imported input and energy cost shocks</caption><thead><tr><th></th><th colspan="7">Percentile</th></tr><tr><th></th><th>10</th><th>25</th><th>50</th><th>75</th><th>90</th><th>95</th><th>99</th></tr></thead><tbody><tr><th>Imported inputs</th><td>.01</td><td>.3</td><td>.93</td><td>2.4</td><td>5.2</td><td>7.8</td><td>14.7</td></tr><tr><th>Energy</th><td>.24</td><td>.44</td><td>.81</td><td>1.5</td><td>4.7</td><td>5.3</td><td>12.4</td></tr><tr><th>Observed increase</th><td>0</td><td>3.2</td><td>9.4</td><td>22.6</td><td>43.2</td><td>60.5</td><td>101.5</td></tr></tbody></table>

Notes: This table reports the percentiles of the predicted and observed price changes (in %) for firms in our sample between 2021Q1 and 2022Q2. Predicted price changes are based on asymmetric pass-through rates of 47% (6) and 127% for positive (negative) imported input and energy cost shocks respectively.

The heterogeneity of firms in their energy dependence and energy mix has significant implications for the insights we can glean from microdata, as opposed to industry-level data. As discussed in Appendix B, these sources of heterogeneity and their correlation with firms' size can lead to
<figure><img src="image_10.png" /><figcaption>Figure 5: Sectoral impact of imported input and energy price shocks</figcaption></figure>

*Notes*: The figure shows the predicted rise in sectoral PPI attributed by the model to imported input shocks (green circles) and energy costs shocks (blue circles), in percent. We first use the model to predict the impact of the cumulated shocks observed between 2021Q1 and 2022Q2. We then build each point as the sales-weighted average of firm-level predicted price increases. Vertical lines represent aggregate predicted increases. Predictions are based on asymmetric pass-through rates: 47% (6) and 127% for positive (negative) imported inputs and energy costs shocks respectively. Details on the method in Appendix B.
systematic discrepancies between sector-level predictions obtained from the microdata and those
obtained from industry-level data. We estimate that the surge in energy prices has led to a 1.62%
increase in prices across industries. Ignoring heterogeneity in firms’ energy dependence and their
energy mix leads to underestimating the aggregate impact of the energy price surge, by .26 percentage
points. The difference is mostly explained by a composition effect, as large firms use gas more
intensively in their energy mix (see Appendix Figure A3), and the price of gas has increased relatively
more during the period of observations. If electricity prices surge, the direction of the bias will reverse
since relatively small firms are more reliant on this source of energy. The effect of large firms’ exposure
to gas prices is partially offset by the fact that these firms are typically less dependent on energy
overall.

# 6 Conclusion

We exploit micro-level data on French manufacturing firms’ prices and their exposure to foreign
shocks to study the pass-through of imported input and energy costs shocks and their role in the
2021 inflation surge. Following the first wave of the Covid pandemic, producer prices have started
to rise in France, due to both more frequent and larger price increases, conditional on prices being
adjusted. We quantify the role of imported input and energy cost shocks. First, we estimate the
pass-through of these two categories of shocks on firms’ prices using unique data on firm-level import
prices and their dependence on various types of energy. Firms in our samples pass through 30% of
imported input prices and 100% of energy costs onto their own prices, conditional on their exposure
to these cost shocks. Firms’ adjustment to these shocks is asymmetric, with positive cost shocks
inducing significantly more pass-through than negative shocks. Heterogeneity in exposure to external
shocks, across firms and sectors, drives important differences in the dynamics of inflation across firms.
In the chemical and metal industry, imported and energy cost shocks have contributed to an increase
in the producer price index by at least 9.5 and 13.9%.
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# Appendix

## A Data

Opise is a survey of French firms (“unités légales”). However, some firms do not report directly to the pollster, and a “fournisseur” supplies the data for them. The unit in Opise is thus a “fournisseur”, identified by a unique identifier called IDFOUR. IDFOUR is different from the SIREN identifier, which corresponds to legal units and is used in other firm-level datasets. In this paper, we choose to restrict to units for which there is a one-to-one matching between IDFOUR and SIREN, which is the case for 94.5% IDFOUR.

When selecting products that will enter the survey, the pollster tries to select a specific transaction of a specific product, rather than an average price of a product mix, to limit statistical biases.

## B Quantification exercise

### B.1 Methodology

In this section, we describe our procedure to predict firm- and industry-level output price changes using pass-through estimates. We use asymmetric pass-through rates.

**Firm-level predicted price changes from imported input cost shocks** We predict output price changes using specification (2) of Table 5 and set the estimated pass-through rates to $\hat{\alpha}^M = 0.47$ for positive shocks and $\hat{\alpha}^M = 0.06$ for negative shocks. For firms for which we have import price data, we compute:

$$ \Delta \hat{p}_f^M = \hat{\alpha}^M \times \Delta p_f^M \times S_f^M $$

For firms for which we do not have import price data, we compute:

$$ \Delta \hat{p}_f^M = \hat{\alpha}^M \times \Delta p_s^M \times S_s^M $$

where $S_f^M$ is the firm-level share of imported inputs in overall costs, which we recover from customs and balance-sheet data. The ratio is equal to 0 for firms that source all of their inputs domestically. $\Delta p_f^M$ is the firm-level imported input cost shock, which we observe over the sub-sample of firms surveyed in OPISE. $\Delta p_s^M$ is the 2-digit industry average change in import prices, defined as $\Delta p_s^M = \sum_{f \in s} w_f^M \Delta p_f^M$, where $w_f^M$ is the weight of firm f in the import price index, normalized so that $\sum_{f \in s} w_f^M = 1$.28

**Firm-level predicted price changes from energy cost shocks** We predict output price changes using specification (5) of Table 5 and set the estimated pass-through rate to that of positive energy cost shocks, as energy prices only rose over the period: $\hat{\alpha}^E = 1.27$. We use the same notations as for imported inputs. For firms for which we have energy data, we compute:

$$ \Delta \hat{p}_f^E = \hat{\alpha}^E \times \Delta p_f^E \times S_f^E $$

<footnote n="28">28 The original weights are defined at the item level. We add up these weights to the firm level, before normalizing by the sectoral share.</footnote>
For firms for which we do not have energy data, we compute:

$$ \Delta \hat{p}_f^E = \hat{\alpha}^E \times \Delta p_s^E \times S_s^E $$

where $\Delta p_s^E$ is the 2-digit industry average change in energy prices. More specifically, we compute $\Delta p_s^E = w_s^{elec} \times \Delta p^{elec} + w_s^{gas} \times \Delta p^{gas} + w_s^{oil} \times \Delta p^{oil}$, where $w_s^{elec} + w_s^{gas} + w_s^{oil} = 1$ are the shares of the cost of energy e in total energy costs among firms for which we have energy data. Every firm in our Opise sample is given an energy consumption level, via $S_f^E$. $S_s^E$ is the 2-digit industry average share of energy costs in total variable costs. It is computed as the ratio between total energy bill and total variable costs among firms for which we have energy data.

**Sectoral predicted price changes** Based on the distribution of firm-level predicted price changes, we can compute the sectoral inflation which the model attributes to either imported input or energy cost shocks. Namely:

$$ \Delta \hat{p}_s^t = \sum_{f \in s} w_f^{ppi} \Delta \hat{p}_f^t, \quad t = M, E \quad (4) $$

where $w_f^{ppi}$ is the weight of firm f in the OPISE survey, normalized so that weights sum to one within a sector.

## B.2 From micro to macro

For energy cost shocks, we can compare the sectoral predictions recovered from equation (4) with the counterpart obtained from sectoral data. The difference is informative about the additional information one can gather from micro-level sources.

The industry-level prediction recovered from micro data writes as follows:

$$ \Delta \hat{p}_s^{micro} = \hat{\alpha}^E \sum_{f \in s} w_f^{ppi} \times \Delta p_f^E \times S_f^E \quad (5) $$

The prediction of output price change recovered from sectoral data is computed from the following formula:

$$ \Delta \hat{p}_s^{Macro} = \hat{\alpha}^E \times \Delta p_s^E \times S_s^E $$

with:

$$ S_s^E = \frac{EnergyBill_s}{Costs_s} $$

$$ \Delta p_s^E = \sum_e w_s^e \Delta p^e, \text{ with } e = \text{elec, gas, oil} $$

Differences between $\Delta \hat{p}_s^{micro}$ and $\Delta \hat{p}_s^{Macro}$ arise from heterogeneity in the energy mix ($w_s^e$ for all f, e) and from different expositions to energy costs ($S_f^E$ for all f). This can be seen from the following
decomposition:<fnref n="29" />

$$ 
\begin{aligned}
\Delta \hat{p}_s^{\text{micro}} &= \hat{\alpha}^E \sum_{f \in s} w_f^{ppi} \times (\Delta p_f^E - \Delta p_s^E) \times S_s^E \\
&\quad + \hat{\alpha}^E \sum_{f \in s} w_f^{ppi} \times (S_f^E - S_s^E) \times \Delta p_s^E \\
&\quad + \hat{\alpha}^E \sum_{f \in s} w_f^{ppi} \times (\Delta p_f^E - \Delta p_s^E) \times (S_f^E - S_s^E) \\
&\quad + \Delta \hat{p}_s^{\text{Macro}} \\
&= (a) + (b) + (c) + \Delta \hat{p}_s^{\text{Macro}}
\end{aligned}
\quad (6) $$

The difference between the predicted micro-based and industry-based price predictions comes from heterogeneity in the energy mix (a),<fnref n="30" /> heterogeneity in the energy cost share (b), and the correlation between the two. To get insights about the size of these sources of heterogeneity, and their impact at the sector level, we proceed as follows:

* (a) is the difference between micro and macro predictions when there is no heterogeneity in energy cost shares. Then

$$ (a) = \Delta \hat{p}_s^m(S_f^E = S_s^E) - \Delta \hat{p}_s^{\text{Macro}}, $$

where $\Delta \hat{p}_s^m(S_f^E = S_s^E)$ is the industry-level prediction recovered from micro data ignoring heterogeneity in cost shares.

* Similarly, (b) is the difference between micro and macro predictions when there is no heterogeneity in energy mixes. Then

$$ (b) = \Delta \hat{p}_s^m(w_f^e = w_s^e) - \Delta \hat{p}_s^{\text{Macro}}. $$

* Armed with these two counterfactuals, we can compute

$$ (c) = \Delta \hat{p}_s^{\text{micro}} + \Delta \hat{p}_s^{\text{Macro}} - \Delta \hat{p}_s^m(w_f^e = w_s^e) - \Delta \hat{p}_s^m(S_f^E = S_s^E) $$

Table A7 displays $\Delta \hat{p}_s^{\text{Macro}}$, $\Delta \hat{p}_s^{\text{micro}}$, (a), and (b) for each industry and at the aggregate level. In the overall manufacturing sector, heterogeneity in the energy mix contributes to inflating the aggregate predicted price increase (+0.36 p.p.), whereas differences in energy cost shares across firms have a small dampening effect (-.11 p.p.). The large role played by the energy mix has to do with the nature of the shock: The surge in energy prices between 2021Q1 and 2022Q2 largely comes from gas and oil prices (+110% and 148% from our price data, respectively). In our sample, relatively large firms (with a large $w_f^{ppi}$) rely relatively more on gas and have thus been exposed to a larger energy cost shock over the period. By ignoring the heterogeneity, industry-based predictions underestimate the overall impact of the shock. By contrast, large firms display relatively low energy cost shares which pushes the sector-level prediction down, in comparison with the industry-based counterpart that ignores the heterogeneity. Finally, the residual term (c) is small in the aggregate, around 0.01 p.p. In some industries, it contributes to increasing micro-level predictions.

<footnote n="29">²⁹The decomposition ignores that industry-based predictions implicitly aggregate the shadow firm-level price predictions using cost-based rather than sales-based weights ($S_s^E = \sum_{f \in s} \frac{Costs_f}{Costs_s} S_f^E$). Whereas the difference in the aggregation weights could in theory drive both predictions further away from each other, the difference is small in the aggregate in the aggregate (lower than 0.01%) because sales and cost shares are strongly correlated.</footnote>

<footnote n="30">³⁰(a) = $\hat{\alpha} \sum_{f \in s} w_f^{ppi} \times \sum_e \Delta p^e(w_f^e - w_s^e) \times S_s^E$:</footnote>
Figure A3 illustrates the correlation between firms’ size and their exposure to energy cost shocks, in the overall dataset. The figure shows the strong correlation between a firm’s size and its exposure to gas prices and the negative correlation with its overall energy dependence that triggers the results just mentioned. Note however that the strength and direction of these correlations are not uniform across sectors. The correlation between a firm’s size and its energy exposure through the firm’s energy mix is particularly pronounced in chemicals, as a consequence of large chemical producers being highly dependent on oil. In this sector, heterogeneity in energy cost shares instead has a strong dampening effect, because large chemical producers display relatively low energy dependence. In a few industries such as wood products, printing, or pharmaceuticals, both sources of heterogeneity reinforce each other because relatively large firms are more exposed to the crisis through their energy mix and more dependent on energy on average.
C Additional Tables

<table><caption>Table A1: Energy consumption statistics</caption><thead><tr><th></th><th>Variables</th><th># Firms</th><th>10 pctl</th><th>Mean</th><th>Median</th><th>90 pctl</th><th>St.dev.</th></tr></thead><tbody><tr><td></td><td>Energy share</td><td>1,130</td><td>.3</td><td>2.7</td><td>1.5</td><td>10</td><td>4.1</td></tr><tr><td></td><td>Elec.+Gas+Oil</td><td>1,130</td><td>99.2</td><td>99.4</td><td>100</td><td>100</td><td>5.4</td></tr><tr><td rowspan="3">Share of energy consumption</td><td>Elec.</td><td>1,130</td><td>18.6</td><td>61.2</td><td>61.8</td><td>100</td><td>26</td></tr><tr><td>Gas</td><td>870</td><td>0</td><td>31.8</td><td>29.2</td><td>79.1</td><td>27.5</td></tr><tr><td>Oil</td><td>645</td><td>0</td><td>6.4</td><td>.1</td><td>38.3</td><td>15.1</td></tr></tbody></table>

*Notes*: This table reports energy usage statistics among firms in our sample. Energy Share is the cost of energy over variable costs (in percentage points). The next 4 rows report statistics on the share of each type of energy in firms' real consumption (in toe).

<table><caption>Table A2: Size of the sample of firms</caption><thead><tr><th rowspan="2"></th><th colspan="4">Share of...</th></tr><tr><th>PPI (%)</th><th>Production (%)</th><th>Imports (%)</th><th>Exports (%)</th></tr></thead><tbody><tr><td>Sample</td><td>36</td><td>22.66</td><td>22.73</td><td>25.15</td></tr><tr><td>Importers</td><td>6</td><td>5</td><td>7.02</td><td>7.22</td></tr><tr><td>Energy</td><td>16</td><td>9.48</td><td>10.48</td><td>11.18</td></tr><tr><td>Superstar</td><td>7</td><td>4.17</td><td>3.99</td><td>4.48</td></tr></tbody></table>

*Notes*: This table reports the share of PPI weights, of production (Source: FARE), imports and exports (Source: French Customs) that firms in the sample account for. 2018.
<table><caption>Table A3: Within-industry and between-industry variation in monthly price changes</caption><thead><tr><th></th><th>Period</th><th>Product×<br>Period</th><th>Product (2D)×<br>Period</th><th>Industry×<br>Period</th><th>Industry (2D)×<br>Period</th></tr></thead><tbody><tr><td>Variance explained (%)</td><td>1.19</td><td>5.53</td><td>2.12</td><td>5.06</td><td>2.12</td></tr></tbody></table>

*Notes:* This table reports the adjusted R-squared of five regressions of monthly item (log) price changes on various fixed effects: period (calendar time), 4-digit product × period, 2-digit product × period, 4-digit industry × period, 2-digit industry × period. The regressions use domestic prices over Jan. 2018 - July 2022.

<table><caption>Table A4: Distributions of shocks and price changes</caption><thead><tr><th></th><th></th><th>5 pctl</th><th>Mean</th><th>Median</th><th>95 pctl</th><th>St.dev.</th></tr></thead><tbody><tr><td rowspan="3">Importers</td><td>Δ Cost</td><td>-3.91</td><td>.49</td><td>0</td><td>6.13</td><td>4.24</td></tr><tr><td>Δ Cost × Share</td><td>-1.2</td><td>.15</td><td>0</td><td>2.01</td><td>1.59</td></tr><tr><td>Δ Price</td><td>-6.6</td><td>.34</td><td>.26</td><td>7.67</td><td>5.61</td></tr><tr><td rowspan="3">Energy</td><td>Δ Cost</td><td>-5.7</td><td>1.37</td><td>.85</td><td>9.68</td><td>5.59</td></tr><tr><td>Δ Cost × Share</td><td>-.12</td><td>.03</td><td>.01</td><td>.25</td><td>.26</td></tr><tr><td>Δ Price</td><td>-6</td><td>.54</td><td>.31</td><td>7.73</td><td>5.46</td></tr></tbody></table>

*Notes:* This table reports statistics on the conditional changes in output prices and cost shocks in our two estimation samples. In %.
<table><caption>Table A5: Pass-through of energy costs shocks, by type of energy</caption><thead><tr><th rowspan="2"></th><th colspan="2">Baseline</th><th colspan="2">Raw Electricity Price</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Δ Energy cost × Energy Share</td><td>1.075<sup>***</sup><br>(0.147)</td><td></td><td>0.745<sup>***</sup><br>(0.118)</td><td></td></tr><tr><td>Δ Electricity cost × Electricity Share</td><td></td><td>1.334<sup>***</sup><br>(0.380)</td><td></td><td>0.070<br>(0.172)</td></tr><tr><td>Δ Gas cost × Gas Share</td><td></td><td>1.290<sup>***</sup><br>(0.272)</td><td></td><td>1.398<sup>***</sup><br>(0.269)</td></tr><tr><td>Δ Oil cost × Oil Share</td><td></td><td>0.938<sup>***</sup><br>(0.246)</td><td></td><td>0.953<sup>***</sup><br>(0.248)</td></tr><tr><td>Fixed Effects</td><td>pt</td><td>pt</td><td>pt</td><td>pt</td></tr><tr><td># Items</td><td>3,884</td><td>3,884</td><td>3,884</td><td>3,884</td></tr><tr><td>Obs.</td><td>93,535</td><td>93,535</td><td>93,535</td><td>93,535</td></tr></tbody></table>

*Notes:* The table reports the results of the estimation of equation (2) using our measure of energy cost shocks as right-hand side variable. Columns (2) and (4) decompose energy cost shocks into the sum of electricity, gas, and oil cost shocks. Columns (3) and (4) report pass-through estimates when electricity prices are not adjusted for seasonality. *pt* stands for product×period fixed effects. Robust standard errors in parenthesis. \* *p* < 0.10 \*\* *p* < 0.05 \*\*\* *p* < 0.01

<table><caption>Table A6: Impact of external cost shocks on adjustment probabilities: Asymmetric shocks</caption><thead><tr><th rowspan="2"></th><th rowspan="2">X</th><th rowspan="2">Coefficient</th><th rowspan="2">Z-stat</th><th colspan="2">Marginal Effects (pp) on a Price</th></tr><tr><th>Decrease</th><th>Increase</th></tr></thead><tbody><tr><td rowspan="4">Imported inputs</td><td>Δ Cost ≥ 0</td><td>2.08</td><td>8.54</td><td>-.68</td><td>.74</td></tr><tr><td>Δ Cost ≤ 0</td><td>-.07</td><td>-.25</td><td>.02</td><td>-.03</td></tr><tr><td>Δ Cost ≥ 0 × Share</td><td>5.29</td><td>8.12</td><td>-1.71</td><td>1.88</td></tr><tr><td>Δ Cost ≤ 0 × Share</td><td>-3.05</td><td>-3.84</td><td>.99</td><td>-1.09</td></tr><tr><td rowspan="4">Energy</td><td>Δ Cost ≥ 0</td><td>.28</td><td>1.6</td><td>-.08</td><td>.1</td></tr><tr><td>Δ Cost ≤ 0</td><td>-.41</td><td>-2.56</td><td>.12</td><td>-.14</td></tr><tr><td>Δ Cost ≥ 0 × Share</td><td>2.72</td><td>1.17</td><td>-.81</td><td>.93</td></tr><tr><td>Δ Cost ≤ 0 × Share</td><td>-10</td><td>-3.05</td><td>2.97</td><td>-3.43</td></tr></tbody></table>

*Notes:* This table shows results of the estimation of the ordered probit model (Equation 3), using either positive or negative imported input or energy costs shocks as right-hand side variables, either normalized by the corresponding cost shares or not. Negative cost shocks are in absolute value. The first column reports the estimated coefficient, the second column shows the associated Z-statistic. The marginal effects give the probability change associated with the occurrence of a 1 percent increase in X, setting the other covariates at their sample mean. All models control for period and 2-digit product fixed effects and changes in competitors prices, 2-digit industry-level labor costs, and 2-digit industry output.
<table><caption>Table A7: Predictions of output price change</caption><thead><tr><th>Industry</th><th>NAF2</th><th>(1)<br>Δp̂<sup>Macro</sup><br>Δp̂<sup>Micro</sup></th><th>(2)</th><th>(3)<br>(a)</th><th>(4)<br>(b)</th><th>(5)<br>(c)</th><th>(6)<br>PPI</th></tr></thead><tbody><tr><td>10</td><td>Food</td><td>1.23</td><td>1.20</td><td>0.08</td><td>-0.15</td><td>0.04</td><td>16.7</td></tr><tr><td>11</td><td>Beverages</td><td>0.55</td><td>0.50</td><td>-0.03</td><td>-0.01</td><td>-0.01</td><td>4.28</td></tr><tr><td>13</td><td>Textile</td><td>1.47</td><td>1.55</td><td>0.16</td><td>-0.10</td><td>0.02</td><td>19.21</td></tr><tr><td>14</td><td>Apparel</td><td>0.19</td><td>0.46</td><td>0.29</td><td>-0.01</td><td>-0.01</td><td>1.97</td></tr><tr><td>15</td><td>Leather</td><td>0.22</td><td>0.50</td><td>0.24</td><td>0.00</td><td>0.04</td><td>2.46</td></tr><tr><td>16</td><td>Wood products</td><td>0.82</td><td>1.57</td><td>0.53</td><td>0.04</td><td>0.18</td><td>27.88</td></tr><tr><td>17</td><td>Paper</td><td>3.12</td><td>2.51</td><td>-0.11</td><td>-0.59</td><td>0.08</td><td>26.72</td></tr><tr><td>18</td><td>Printing</td><td>0.71</td><td>1.73</td><td>0.70</td><td>0.12</td><td>0.21</td><td>6.87</td></tr><tr><td>20</td><td>Chemicals</td><td>5.27</td><td>5.75</td><td>1.96</td><td>-1.64</td><td>0.16</td><td>56.47</td></tr><tr><td>21</td><td>Pharma.</td><td>0.46</td><td>1.46</td><td>0.70</td><td>0.12</td><td>0.18</td><td>5.00</td></tr><tr><td>22</td><td>Rubber and plastic</td><td>0.80</td><td>0.97</td><td>0.09</td><td>0.05</td><td>0.03</td><td>18.2</td></tr><tr><td>23</td><td>Mineral products</td><td>4.50</td><td>5.39</td><td>1.03</td><td>-0.48</td><td>0.35</td><td>15.79</td></tr><tr><td>24</td><td>Metals</td><td>3.01</td><td>3.31</td><td>-0.47</td><td>0.36</td><td>0.41</td><td>46.44</td></tr><tr><td>25</td><td>Metal products</td><td>0.74</td><td>1.30</td><td>0.46</td><td>0.01</td><td>0.09</td><td>14.66</td></tr><tr><td>26</td><td>Computers. etc.</td><td>0.21</td><td>0.33</td><td>0.15</td><td>-0.02</td><td>-0.02</td><td>2.68</td></tr><tr><td>27</td><td>Electrical products</td><td>0.46</td><td>0.64</td><td>0.15</td><td>0.01</td><td>0.02</td><td>9.79</td></tr><tr><td>28</td><td>Machinery</td><td>0.39</td><td>0.55</td><td>0.14</td><td>0.03</td><td>0.00</td><td>9.61</td></tr><tr><td>29</td><td>Automotive indus.</td><td>0.80</td><td>0.40</td><td>-0.40</td><td>-0.01</td><td>0.00</td><td>7.39</td></tr><tr><td>30</td><td>Transport equip.</td><td>0.17</td><td>0.20</td><td>0.03</td><td>0.00</td><td>0.00</td><td>2.02</td></tr><tr><td>31</td><td>Furnitures</td><td>0.48</td><td>0.62</td><td>0.26</td><td>-0.07</td><td>-0.05</td><td>13.58</td></tr><tr><td>32</td><td>Other manuf.</td><td>0.31</td><td>0.59</td><td>0.21</td><td>0.04</td><td>0.03</td><td>3.92</td></tr><tr><td></td><td>All</td><td>1.36</td><td>1.62</td><td>0.36</td><td>-0.11</td><td>0.01</td><td>17.46</td></tr></tbody></table>

Notes: This table reports the industry level predicted output price change from energy cost changes and factors of the decomposition in Equation (6). Column (1) uses industry-level energy costs. Columns (2) uses firms' cost share and energy mix. Column (3) reports the contribution of heterogeneity in energy mix across firms within industries to the difference between (1) and (2). Column (4) reports the contribution of heterogeneity in energy cost shares across firms within industries to the difference between (1) and (2). Column (5) reports the contribution of the correlation between heterogeneity in energy cost shares and heterogeneity in energy mix across firms within industries to the difference between (1) and (2). Column (6) reports the observed increase in output prices at the industry level. The last row ("All") is the aggregate from all the industries above. All columns in %.
D Additional Figures

<figure><img src="image_11.png" /><figcaption>Figure A1: Distribution of cost shares</figcaption></figure>

Notes: Panel A shows the distribution of imported inputs shares, defined as the ratio of imports over output using data for 2018. Panel B shows energy cost shares, defined as the ratio of (nominal) energy consumption over output, also using data from 2018. Energy consumption is restricted to electricity, gas, and oil.
<figure><img src="image_12.png" /><figcaption>Figure A2: Predicted price increases in 2-digit industries: 10th and 90th percentiles</figcaption></figure>

*Notes*: The figure shows the predicted rise in sectoral PPI and the 10th and 90th percentiles of the firm-level predicted price increase distribution within 2-digit industries attributed by the model, from imported input shocks (Panel A) and energy costs shocks (Panel B). We first use the model to predict the impact of the cumulated shocks observed between 2021Q1 and 2022Q2. We then build each point as the sales-weighted average of firm-level predicted price increases. Vertical lines represent aggregate predicted increases. Predictions are based on asymmetric pass-through rates: 47% (6) and 127% for positive (negative) imported inputs and energy costs shocks respectively. Changes are in %.
<figure><img src="image_13.png" /><figcaption>Figure A3: Correlations between PPI weights, energy exposure, and energy mix</figcaption></figure>

Notes: This figure displays binscatters of $S_f^E$ (top left), $w_f^{elec}$ (top right), $w_f^{gas}$ (bottom left), $w_f^{oil}$ (bottom right) on PPI weights. The red dashed line represents the OLS fit, which estimated coefficient is below each panel. Variables in log.
## Liste des documents de travail de la Direction des Études et Synthèses Économiques

<table><tr><td>G 9001</td><td>J. FAYOLLE et M. FLEURBAEY<br>Accumulation, profitabilité et endettement des entreprises</td><td>G 9202</td><td>J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE<br>Macro-economic import functions with imperfect competition - An application to the E.C. Trade</td></tr><tr><td>G 9002</td><td>H. ROUSSE<br>Détection et effets de la multicolinearité dans les modèles linéaires ordinaires - Un prolongement de la réflexion de BELSLEY, KUH et WELSCH</td><td>G 9203</td><td>I. STAPIC<br>Les échanges internationaux de services de la France dans le cadre des negotiations multilatérales du GATT<br>Jun 1992 (1ère version)<br>Novembre 1992 (version finale)</td></tr><tr><td>G 9003</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Indexation des salaires : la rupture de 1983</td><td>G 9204</td><td>P. SEVESTRE<br>L'économie sur données individuelles-temporelles. Une note introductive</td></tr><tr><td>G 9004</td><td>D. GUELLEC et P. RALLE<br>Comptabilité, croissance et innovation de produit</td><td>G 9205</td><td>H. ERKEL-ROUSSE<br>Le commerce extérieur et l'environnement international dans le modèle AMADEUS (réestimation 1992)</td></tr><tr><td>G 9005</td><td>P. RALLE et J. TOUJAS-BERNATE<br>Les conséquences de la désindexation. Analyse dans une maquette prix-salaires</td><td>G 9206</td><td>N. GREENAN et D. GUELLEC<br>Coordination within the firm and endogenous growth</td></tr><tr><td>G 9101</td><td>Équipe AMADEUS<br>Le modèle AMADEUS - Première partie - Présentation générale</td><td>G 9207</td><td>A. MAGNIER et J. TOUJAS-BERNATE<br>Technology and trade: empirical evidences for the major five industrialized countries</td></tr><tr><td>G 9102</td><td>J.L. BRILLET<br>Le modèle AMADEUS - Deuxième partie - Propriétés variabilaires</td><td>G 9208</td><td>B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHON<br>Cooperative, non cooperative R & D and optimal patent life</td></tr><tr><td>G 9103</td><td>D. GUELLEC et P. RALLE<br>Endogenous growth and product innovation</td><td>G 9209</td><td>B. CREPON et E. DUGUET<br>Research and development, competition and innovation: an application of pseudo maximum likelihood methods to Poisson models with heterogeneity</td></tr><tr><td>G 9104</td><td>H. ROUSSE<br>Le modèle AMADEUS - Troisième partie - Le commerce extérieur et l'environnement international</td><td>G 9301</td><td>J. TOUJAS-BERNATE<br>Commerce international et concurrence impartie - développements récents et implications pour la littérature commerciale</td></tr><tr><td>G 9105</td><td>H. ROUSSE<br>Effets de demande et d'offre dans les résultats du commerce extérieur manufacturé de la France au cours des deux dernières décennies</td><td>G 9302</td><td>Ch. CASES<br>Cherces de chômage et comportements d'offre de travail : une revue de la littérature</td></tr><tr><td>G 9106</td><td>B. CREPON<br>Innovation, taille et concentration : causnels et dynamiques</td><td>G 9303</td><td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique et monétaire : le débat</td></tr><tr><td>G 9107</td><td>B. AMABLE et D. GUELLEC<br>Un panorama des théories de la croissance endogène</td><td>G 9304</td><td>N. GREENAN - D. GUELLEC / G. BROUSSAUDIER - L. MIOTTI<br>Innovation organisationnelle, dynamisme technologique et performances des entreprises</td></tr><tr><td>G 9108</td><td>M. GLAUDE et M. MOUTARDIER<br>Une évaluation du coût direct de l'enfant de 1979 à 1989</td><td>G 9305</td><td>P. JAILLARD<br>Le traité de Maastricht : présentation juridique et historique</td></tr><tr><td>G 9109</td><td>P. RALLE et alii<br>France - Allemagne : performances économiques comparées</td><td>G 9306</td><td>J.L. BRILLET<br>Micro-DMS - présentation et propriétés</td></tr><tr><td>G 9110</td><td>J.L. BRILLET<br>Micro-DMS<br>NON PARU</td><td>G 9307</td><td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td></tr><tr><td>G 9111</td><td>A. MAGNIER<br>Effets acélérateur et multiplicateur en France depuis 1970: quelques résultats empiriques</td><td>G 9308</td><td>S. GUEBZONE<br>Les grands réseaux publics français dans une perspective européenne</td></tr><tr><td>G 9112</td><td>B. CREPON et G. DUREAU<br>Investissement en recherche-développement : analyse de causnels dans un modèle d'accélérateur généralisé</td><td>G 9309</td><td>L. BLOCH - B. CŒURÉ<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td></tr><tr><td>G 9113</td><td>J.L. BRILLET, H. ERKEL-ROUSSE, J. TOUJAS-BERNATE<br>"France-Allemagne Coupées" - Deux économies vues par une maquette macro-économétrique</td><td>G 9310</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les théories sur la structure optimale du capital : quelques points de repère</td></tr><tr><td>G 9201</td><td>W.J. ADAMS, B. CREPON, D. ENCAOUA<br>Choix technologiques et stratégies de dissuasion d'entrée</td><td>G 9311</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les décets d'emballages ménagers : une analyse économique des politiques française et allemande</td></tr></table>

<table><tr><td>G 9202</td><td>J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE<br>Macro-economic import functions with imperfect competition - An application to the E.C. Trade</td><td>G 9311</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les décets de financement des entreprises françaises: une évaluation empirique des théories de la structure optimale du capital</td></tr><tr><td>G 9203</td><td>I. STAPIC<br>Les échanges internationaux de services de la France dans le cadre des negotiations multilatérales du GATT<br>Jun 1992 (1ère version)<br>Novembre 1992 (version finale)</td><td>G 9312</td><td>L. BLOCH - B. CŒURÉ<br>Q de Tobin marginal et transmission des chocs financiers</td></tr><tr><td>G 9204</td><td>P. SEVESTRE<br>L'économie sur données individuelles-temporelles. Une note introductive</td><td>G 9313</td><td>Équipes Amadeus (INSEE), Banque de France, Métric (DP)<br>Presentation des propriétés des principaux modèles macroéconomiques du Service Public</td></tr><tr><td>G 9205</td><td>H. ERKEL-ROUSSE<br>Le commerce extérieur et l'environnement international dans le modèle AMADEUS (réestimation 1992)</td><td>G 9314</td><td>B. CREPON - E. DUGUET<br>Research & Development, competition and innovation</td></tr><tr><td>G 9206</td><td>N. GREENAN et D. GUELLEC<br>Coordination within the firm and endogenous growth</td><td>G 9315</td><td>B. DORMONT<br>Quelle est l'influence du coût du travail sur l'emploi ?</td></tr><tr><td>G 9207</td><td>A. MAGNIER et J. TOUJAS-BERNATE<br>Technology and trade: empirical evidences for the major five industrialized countries</td><td>G 9316</td><td>D. BLANCHET - C. BROUSSE<br>Deux études sur l'âge de la retraite</td></tr><tr><td>G 9208</td><td>B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHON<br>Cooperative, non cooperative R & D and optimal patent life</td><td>G 9317</td><td>D. BLANCHET<br>Répartition du travail dans une population héritière: deux notes</td></tr><tr><td>G 9209</td><td>B. CREPON et E. DUGUET<br>Research and development, competition and innovation: an application of pseudo maximum likelihood methods to Poisson models with heterogeneity</td><td>G 9318</td><td>D. EYSSARTIER - N. PONTY<br>AMADEUS - an annual macro-economic model for the medium and long term</td></tr><tr><td>G 9301</td><td>J. TOUJAS-BERNATE<br>Commerce international et concurrence impartie - développements récents et implications pour la littérature commerciale</td><td>G 9319</td><td>G. CETTE - Ph. CUNÉO - D. EYSSARTIER - J. GAUTIÉ<br>Les effets sur l'emploi d'un abaissement du coût du travail des jeunes</td></tr><tr><td>G 9302</td><td>Ch. CASES<br>Cherces de chômage et comportements d'offre de travail : une revue de la littérature</td><td>G 9401</td><td>D. BLANCHET<br>Les structures par âge importantes ?</td></tr><tr><td>G 9303</td><td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique et monétaire : le débat</td><td>G 9402</td><td>J. GAUTIÉ<br>Le chômage des jeunes en France: problème de formation ou phénomène de file d'attente ?<br>Quelques éléments du débat</td></tr><tr><td>G 9304</td><td>N. GREENAN - D. GUELLEC / G. BROUSSAUDIER - L. MIOTTI<br>Innovation organisationnelle, dynamisme technologique et performances des entreprises</td><td>G 9403</td><td>P. QUIRION<br>Les décets en France: éléments statistiques et économiques</td></tr><tr><td>G 9305</td><td>P. JAILLARD<br>Le traité de Maastricht : présentation juridique et historique</td><td>G 9404</td><td>D. LADIRAY - M. GRUN-REHOMME<br>Lissage par moyennes mobiles - Le problème des extrêmes de série</td></tr><tr><td>G 9306</td><td>J.L. BRILLET<br>Micro-DMS - présentation et propriétés</td><td>G 9405</td><td>V. MAILLARD<br>Theorie et pratique de la correction des effets de jours ouvrables</td></tr><tr><td>G 9307</td><td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td><td>G 9406</td><td>F. ROSENWALD<br>La décision d'investir</td></tr><tr><td>G 9308</td><td>S. GUEBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique du l'ôpital public</td><td>G 9407</td><td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique du l'ôpital public</td></tr><tr><td>G 9309</td><td>L. BLOCH - B. CŒURÉ<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td><td>G 9408</td><td>L. BLOCH, J. BOURDIEU, G. LONGUEVILLE<br>Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté</td></tr><tr><td>G 9310</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les théories sur la structure optimale du capital : quelques points de repère</td><td>G 9409</td><td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'ade au logement - quelques éléments d'évaluation</td></tr><tr><td>G 9311</td><td>J. BOURDIEU - B. COLIN-SEDILLOT<br>Les théories sur la structure optimale du capital : quelques points de repère</td><td>G 9410</td><td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td></tr><tr><td>G 9312</td><td>B. CREPON et G. DUREAU<br>Investissement en recherche-développement : analyse de causnels dans un modèle d'accélérateur généralisé</td><td>G 9411</td><td>C. DEFEUILLE - Ph. QUIRION<br>Les décets d'emballages ménagers : une analyse économique des politiques française et allemande</td></tr></table>

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

<table><tr><td>G 9412</td><td>J. BOURDIEU - B. CŒURÉ - B. COLIN-SEDILLOT<br>Investissement, incertitude et irréversibilité<br>Quelques développements récents de la théorie de l'investissement</td><td>G 9413</td><td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'élaborité emploi-salaire dépend-elle des structures de qualification ?</td></tr><tr><td>G 9414</td><td>I. KABLA<br>Le Chois de breveter une invention</td><td>G 9413</td><td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'élaborité emploi-salaire dépend-elle des structures de qualification ?</td></tr><tr><td>G 9501</td><td>J. BOURDIEU - B. CŒURÉ - B. SEDILLOT<br>Irreversible Investment and Uncertainty:<br>When is there a Value of Waiting?</td><td>G 9504</td><td>L. BLOCH - B. CŒURÉ<br>Impairments du marché du crédit, investissement des entreprises et cycle économique</td></tr><tr><td>G 9502</td><td>D. BLANCHET - E. MAURIN<br>Les transformations de la demande de travail par qualification en France</td><td>G 9503</td><td>D. BLANCHET - E. MAURIN<br>Les transformations de la demande de travail par qualification en France</td></tr><tr><td>G 9504</td><td>N. GREENAN<br>Technologie, changement organisationnel, qualification et emploi, une étude empirique sur l'industrie manufacturière</td><td>G 9505</td><td>D. BLANCHET - E. MAURIN<br>Les études sur la période 1970-1993</td></tr><tr><td>G 9505</td><td>D. GOUX - E. MAURIN<br>Persistance des hiérarchies sectorielles de salaires: un réexamen sur données françaises</td><td>G 9506</td><td>D. GOUX - E. MAURIN<br>Persistance de inter-industry wages differentials: a reexamination on matched worker-firm panel data</td></tr><tr><td>G 9506</td><td>S. JACOBZONE<br>Les liens entre RMI et chômage, une mise en perspective<br>NON PARU - article sorti dans Économie et Prévision n° 122 (1996) - pages 95 à 113</td><td>G 9507</td><td>G. CETTE - S. MAHFOUZ<br>Le pâtage primaire du revenu<br>Constat descriptif sur longue période</td></tr><tr><td>G 9601</td><td>Banque de France - CEPREMAP - Direction de la Prévision - Érasme - INSEE - OFE<br>Structures et propriétés de cinq modèles macro-économiques français</td><td>G 9602</td><td>Banque de France - CEPREMAP - Direction de la Prévision - Érasme - INSEE - OFE<br>Structures et propriétés de cinq modèles macro-économiques français</td></tr><tr><td>G 9602</td><td>Rapport d'activité de la DESE de l'année 1995</td><td>G 9603</td><td>Rapport d'activité de la DESE de l'année 1995<br>J. BOURDIEU - A. DRAZNIKIE<br>L'octroi de crédit aux PME: une analyse à partir d'informations bancaires</td></tr><tr><td>G 9603</td><td>A. TOPIAL-BENSAID<br>The implantations japonaises en France</td><td>G 9604</td><td>A. TOPIAL-BENSAID<br>The implantations japonaises en France</td></tr><tr><td>G 9605</td><td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie: peut-on parier d'une gestion globale du capital santé ?<br>Une modélisation microéconométrique empirique</td><td>G 9605</td><td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie: peut-on parier d'une gestion globale du capital santé ?<br>Une modélisation microéconométrique empirique</td></tr><tr><td>G 9606</td><td>C. DOZ - F. LENGLART<br>Factor analysis and unobserved component models: an application to the study of French business surveys</td><td>G 9606</td><td>C. DOZ - F. LENGLART<br>Factor analysis and unobserved component models: an application to the study of French business surveys</td></tr></table>
<table><tr><td>G 9607</td><td>N. GREENAN - D. GUELLEC</td><td>La théorie coopérative de la firme</td></tr><tr><td>G 9608</td><td>N. GREENAN - D. GUELLEC</td><td>Technological innovation and employment reallocation</td></tr><tr><td>G 9609</td><td>Ph. COUR - F. RUPPRECHT</td><td>Ph. COUR - F. RUPPRECHT</td></tr><tr><td>G 9610</td><td>S. DUCHENE - G. FORGEOT - A. JACQUOT</td><td>Ph. COUR - F. RUPPRECHT</td></tr><tr><td>G 9611</td><td>X. BONNET - S. MAHFOUZ</td><td>Analyse des évolutions récentes de la productivité apparente du travail</td></tr><tr><td>G 9612</td><td>PH. COUR - E. DUBOIS, S. MAHFOUZ, J. PISANI-FERRY</td><td>The cost of fiscal retrenchment revisited: how strong is the evidence?</td></tr><tr><td>G 9613</td><td>A. JACQUOT</td><td>A. JACQUOT</td></tr><tr><td>G 9614</td><td>ZHANG Yingxiang - SONG Xueqing</td><td>Les flexions des taux d'activité sont-elles seule-ment conjoncturelles?</td></tr><tr><td>G 9615</td><td>Lexique macroéconomique Français-Chinois</td><td>La taxe professionnelle : éléments de cadrage économique</td></tr><tr><td>G 9701</td><td>J.L. SCHNEIDER</td><td>J.L. SCHNEIDER</td></tr><tr><td>G 9702</td><td>J.L. SCHNEIDER</td><td>Transition et stabilité politique d'un système redistributif</td></tr><tr><td>G 9703</td><td>D. GOUX - E. MAURIN</td><td>D. GOUX - E. MAURIN</td></tr><tr><td>G 9704</td><td>P. GENIER</td><td>Train or Pay: Does it Reduce Inequalities to Encourage Firms to Train their Workers?</td></tr><tr><td>G 9705</td><td>E. DUGUET - N. IUNG</td><td>R & D Investment: Patent Life and Patent Value</td></tr><tr><td>G 9706</td><td>M. HOUBEBINE - A. TOPIOL-BENSAID</td><td>An Econometric Analysis at the Firm Level</td></tr><tr><td>G 9707</td><td>M. HOUBEBINE</td><td>Les entreprises internationales en France : une analyse à partir de données individuelles</td></tr><tr><td>G 9708</td><td>E. DUGUET - N. GREENAN</td><td>P. GENIER</td></tr><tr><td>G 9709</td><td>J.L. BRILLET</td><td>Deux contributions sur dépendance et équité</td></tr><tr><td>G 9710</td><td>J.L. BRILLET</td><td>Formalizing the transition process: scenarios for capital accumulation</td></tr><tr><td>G 9711</td><td>G. FORGEOT - J. GAUTIÉ</td><td>Insertion professionnelle des jeunes et processus de déclassement</td></tr><tr><td>G 9712</td><td>E. DUBOIS</td><td>High Real Interest Rates: The Consequences of a Saving Investment Disequilibrium or of an insufficient Credibility of Monetary Authorities?</td></tr></table>

<table><tr><td>G 9713</td><td>Bilan des activités de la Direction des Études et Synthèses Économiques - 1996</td></tr><tr><td>G 9714</td><td>F. LEQUILLER</td><td>Does the French Consumer Price Index Over-state Inflation?</td></tr><tr><td>G 9715</td><td>X. BONNET</td><td>Peut-on mettre en évidence les rigidités à la baisse des salaires nominaires ?</td></tr><tr><td>G 9716</td><td>N. IUNG - F. RUPPRECHT</td><td>Une étude sur quelques grands pays de l'OCDE</td></tr><tr><td>G 9717</td><td>E. DUGUET - I. KABLA</td><td>Produitivité de la recherche et rendements d'échelle dans le secteur pharmaceutique français</td></tr><tr><td>G 9718</td><td>L.P. PELÉ - P. RALLE</td><td>Appropriation strategy and the motivations to use the patent system in France - An econometric analysis at the firm level</td></tr><tr><td>G 9719</td><td>ZHANG Yingxiang - SONG Xueqing</td><td>Le système intermédiaire d'entreprises</td></tr><tr><td>G 9720</td><td>M. HOUBEBINE - J.L. SCHNEIDER</td><td>Les données comptables d'entreprises</td></tr><tr><td>G 9721</td><td>A. MOUROUGANE</td><td>Le système intermédiaire d'entreprises</td></tr><tr><td>G 9722</td><td>P. AUGERAUD - L. BRIOT</td><td>Les données comptables d'entreprises</td></tr><tr><td>G 9723</td><td>P. AUGERAUD - J.E. CHAPRON</td><td>Using Business Accounts for Compiling National Accounts: the French Experience</td></tr><tr><td>G 9724</td><td>P. AUGERAUD</td><td>Les comptes d'entreprise par activités - Le passage aux comptes - De la comptabilité d'entreprise à la comptabilité nationale - A paraître</td></tr><tr><td>G 9801</td><td>H. MICHAUDON - C. PRIGENT</td><td>Presentation du modèle AMADEUS</td></tr><tr><td>G 9802</td><td>J. ACCARDO</td><td>Une étude de comptabilité générale pour la France en 1996</td></tr><tr><td>G 9803</td><td>X. BONNET - S. DUCHÈNE</td><td>Apports et limites de la modélisation « Real Business Cycles »</td></tr><tr><td>G 9804</td><td>C. BARLET - C. DUGUET - D. ENCAOUA - J. PRADEL</td><td>The Commercial Success of Innovations</td></tr><tr><td>G 9805</td><td>P. CAHUC - Ch. GIANELLA - D. GOUX - A. ZILBERBERG</td><td>An econometric analysis at the firm level in French manufacturing</td></tr><tr><td>G 9806</td><td>J. ACCARDO</td><td>P. CAHUC - Ch. GIANELLA - D. GOUX - A. ZILBERBERG</td></tr><tr><td>G 9807</td><td>F. ROSENWALD</td><td>Une étude de comptabilité générale pour la France en 1996</td></tr><tr><td>G 9808</td><td>J.P. ZOYEM</td><td>Contrat d'insertion et sortie du RMI</td></tr><tr><td>G 9809</td><td>Ch. COLIN - FI. LEGROS - R. MAHIEU</td><td>Évaluation des effets d'une politique sociale</td></tr><tr><td>G 9910</td><td>Bilan des activités de la DESE - 1998</td><td>Évaluation des effets d'une politique sociale</td></tr><tr><td>G 9911</td><td>G. LAROQUE - B. SALANIÉ</td><td>Une décomposition du non-emploi en France</td></tr></table>

<table><tr><td>G 9806</td><td>J. ACCARDO - M. JLASSI</td><td>La productivité globale des facteurs entre 1975 et 1996</td></tr><tr><td>G 9807</td><td>Bilan des activités de la Direction des Études et Synthèses Économiques - 1997</td><td>La productivité globale des facteurs entre 1975 et 1996</td></tr><tr><td>G 9808</td><td>A. MOUROUGANE</td><td>Can a Conservative Governor Conduct an Ac-comodative Monetary Policy?</td></tr><tr><td>G 9809</td><td>X. BONNET - E. DUBOIS - L. FALVET</td><td>Asymétrie des inflations relatives et menus costs - tests sur l'inflation française</td></tr><tr><td>G 9810</td><td>E. DUGUET - N. IUNG</td><td>Sales and Advertising with Spillovers at the firm level: Estimation of a Dynamic Structural Model on Panel Data</td></tr><tr><td>G 9811</td><td>J.P. BERTHIER</td><td>Congestion urbaine : un modèle de trafic de pointe à courbe débit-vitesse et demande élastique</td></tr><tr><td>G 9812</td><td>C. PRIGENT</td><td>La part des salaires dans la valeur ajoutée : une approche macroéconomique</td></tr><tr><td>G 9813</td><td>A.Th. AERTS</td><td>L'évolution de la part des salaires dans la valeur ajoutée en France refiletée-telle les évolutions individuelles sur la période 1979-1994 ?</td></tr><tr><td>G 9814</td><td>B. SALANIÉ</td><td>Guide pratique des séries non-stationnaires</td></tr><tr><td>G 9901</td><td>S. DUCHÈNE - A. JACQUOT</td><td>Une croissance plus riche en emplois depuis le début de la dédenne ? Une analyse en comparaison internationale</td></tr><tr><td>G 9902</td><td>Ch. COLIN</td><td>Modélisation des carrières dans Destinie</td></tr><tr><td>G 9903</td><td>Ch. COLIN</td><td>Ch. COLIN</td></tr><tr><td>G 9904</td><td>B. CREPON - N. IUNG</td><td>Évolution de la dispersion des salaires : un essai de prospective par microsimulation</td></tr><tr><td>G 9905</td><td>B. CREPON - Ch. GIANELLA</td><td>Innovation, emploi et performances</td></tr><tr><td>G 9906</td><td>B. CREPON - Ch. GIANELLA</td><td>Wages inequalities in France 1969-1992</td></tr><tr><td>G 9907</td><td>F. ROSENWALD</td><td>An application of quantile regression techniques</td></tr><tr><td>G 9908</td><td>J.P. ZOYEM</td><td>Contrat d'insertion et sortie du RMI</td></tr><tr><td>G 9909</td><td>Ch. COLIN - FI. LEGROS - R. MAHIEU</td><td>Évaluation des effets d'une politique sociale</td></tr><tr><td>G 9910</td><td>Bilan des activités de la DESE - 1998</td><td>Évaluation des effets d'une politique sociale</td></tr><tr><td>G 9911</td><td>G. LAROQUE - B. SALANIÉ</td><td>Une décomposition du non-emploi en France</td></tr></table>

<table><tr><td>G 9912</td><td>B. SALANIÉ</td><td>Une maquette analytique de long terme du marché du travail</td></tr><tr><td>G 9912 BIs</td><td>Ch. GIANELLA</td><td>Une estimation de l'élasticité de l'emploi peu qualifié à son coût</td></tr><tr><td>G 9913</td><td>Division « Redistribution et Politiques Sociales »</td><td>Division « Redistribution et Politiques Sociales »</td></tr><tr><td>G 9914</td><td>E. DUGUET</td><td>Le modèle de microsimulation dynamique DESTINÉ</td></tr><tr><td>G 9914</td><td>MACRO-Commandes SAS pour l'économie des panels et des variables qualitatives</td><td>E. DUGUET</td></tr><tr><td>G 9915</td><td>R. DUHAUTOIS</td><td>Évolution des flux d'emplois en France entre 1990 et 1996 : une étude empirique à partir du fichier des bénéfices réels normaux (BRN)</td></tr><tr><td>G 9916</td><td>J.Y. FOURNIER</td><td>Extraction du cycle des affaires : la méthode de Baxter et King</td></tr><tr><td>G 9917</td><td>B. CRÉPON - R. DESPLATZ - J. MAIRESSE</td><td>Estimating price cost margins, scale economies and workers' bargaining power at the firm level</td></tr><tr><td>G 9918</td><td>Ch. GIANELLA - Ph. LAGARDE</td><td>Productivity of hours in the aggregate production function: an evaluation on a panel of French firms from the manufacturing sector</td></tr><tr><td>G 9919</td><td>S. AUDRIC - P. GIVORD - C. PROST</td><td>Évolution de l'emploi et des coûts par qualification entre 1982 et 1996</td></tr><tr><td>G 2000/01</td><td>R. MAHIEU</td><td>Les déterminants des dépenses de santé : une approche macroéconomique</td></tr><tr><td>G 2000/02</td><td>C. ALLARD-PRIGENT - H. GUILMEAU - A. QUINET</td><td>The real exchange rate as the relative price of nontrables in terms of tradables: theoretical investigation and empirical study on French data</td></tr><tr><td>G 2000/03</td><td>J.-Y. FOURNIER</td><td>L'approximation du titre passe-bande proposée par Christiano et Fitzgerald</td></tr><tr><td>G 2000/04</td><td>B. CREPON - F. ROSENWALD</td><td>Investissement et contraintes de financement : le poids du cycle</td></tr><tr><td>G 2000/05</td><td>Bilan des activités de la DESE - 1999</td><td>Une estimation sur données françaises</td></tr><tr><td>G 2000/06</td><td>A. FLIPO</td><td>Les comportements matrimoniaux de fait</td></tr><tr><td>G 2000/07</td><td>R. MAHIEU - B. SÉDILLOT</td><td>R. MAHIEU - B. SÉDILLOT</td></tr><tr><td>G 2000/08</td><td>C. AUDENIS - C. PROST</td><td>Deficit conjointuel : une prise en compte des conjonctures passées</td></tr><tr><td>G 2000/09</td><td>R. MAHIEU - B. SÉDILLOT</td><td>R. MAHIEU - B. SÉDILLOT</td></tr><tr><td>G 2000/10</td><td>R. DUHAUTOIS</td><td>Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertiaire ?</td></tr></table>
V

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
- Informalisation in 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 of GDP growth

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

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

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

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

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

G2001/07 Bilan des activités de la DESE - 2000

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

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

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

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

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

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

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

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

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

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

G2001/01 F. MAGNIER - J.-L. TAVERNIER - D. THESMAR
Les statistiques internationales de PIB par habitant en standard de pouvoir d'achat : une analyse des résultats

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

G2002/02 Bilan des activités de la DESE - 2001

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

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

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

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

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

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

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

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

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

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

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

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

G2002/15 A. NABOULET - S. RASPILLER
Les déterminants de la décision d'investir : une approche par les perceptions subjectives des firmes

G2002/16 N. RAGACHE
La déclaration des enfants par les couples non marés est-elle fiscalement optimale ?

G2002/17 M. DUÈE
L'impact du chômage des parents sur le devenir scolaire des enfants

G2002/18 P. AUBERT - E. CAROLI - M. ROGER

G2002/19 New Technologies, Workplace Organisation and the Age Structure of the Workforce: Firm-Level Evidence

G2002/20 E. DUGUET - C. LELARGE
Les brevets accroissent-ils les incitations privées à innover ? Un examen microéconométrique

G2002/21 S. RASPILLER - P. SILLARD
Affiliating versus Subcontracting: the Case of Multinational

G2002/22 S. ANANIAN - P. AUBERT
Travaillateurs âgés, nouvelles technologies et changements organisationnels, un réexamen à partir de l'enquête « REPONSE »

G2002/23 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

G2002/24 C. PICART
Évalueur la rentabilité des sociétés non financières

G2002/25 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Les retraites du secteur public : projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE

G2002/26 S. BUFFETEAU - P. GODEFROY
Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945-à1974

G2002/27 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G2002/28 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française

G2002/29 M. LECLAIR - S. ROUX
Mesure et utilisation des emplois instables dans les entreprises

G2002/30 C. L'ANGEVIN - S. SERRAVALLE
Performances à l'exportation de la France et de l'Allemagne - Une analyse par secteur et destination géographique

G2002/31 Bilan des activités de la Direction des Études et Synthèses Economiques - 2004

G2002/32 S. RASPILLER
La concurrence fiscale : principaux enseignements de l'analyse économique

vi

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

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

G2002/01 F. MAUREL - S. GREGOIR
Les indices de compétitivité des pays : interprétation et limites

G2002/07 N. RIEDINGER - E. HAUVY
Le coût de dépollution atmosphérique pour les entreprises françaises : Une estimation à partir de données individuelles

G2002/08 P. BISCOURP e.f. F. KRAMARZ
Création d'emplois, destruction d'emplois et internationalisation des entreprises industrielles françaises : une analyse sur la période 1986-1992

G2002/09 P. BISCOURP e.f. 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. DEROYNIER - N. FOURCADE - S. GREGOIR - N. LAIB - B. MONFORT

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

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

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

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

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

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

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

G2003/12 P. AUBERT - M. LECLAIR
La compétitivité exprimée dans les enquêtes trimestrielles sur la situation et les perspectives dans l'industrie

G2003/13 M. DUÈE - C. REBILLARD
La dépendance des personnes âgées : une projection à long terme

G2003/14 S. RASPILLER - N. RIEDINGER
Régulation environnementale et choix de localisation des groupes français

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

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

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

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

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

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

G2004/10 G. BOISSINOT - S. ANGEVIN - B. MONFORT
Public Debt Sustainability: Some Results on the French Case

G2004/11 S. ANANIAN - P. AUBERT
Travaillateurs 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
Évalueur 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

G2004/15 S. ANANIAN - P. AUBERT
Travaillateurs âgés, nouvelles technologies et changements organisationnels, un réexamen à partir de l'enquête « REPONSE »

G2004/16 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

G2004/17 C. PICART
Évalueur la rentabilité des sociétés non financières

G2004/18 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Les retraites du secteur public : projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE

G2004/19 S. BUFFETEAU - P. GODEFROY
Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945-à1974

G2004/20 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G2004/21 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française

G2004/22 M. LECLAIR - S. ROUX
Mesure et utilisation des emplois instables dans les entreprises

G2004/23 C. L'ANGEVIN - S. SERRAVALLE
Performances à l'exportation de la France et de l'Allemagne - Une analyse par secteur et destination géographique

G2004/24 Bilan des activités de la Direction des Études et Synthèses Economiques - 2004

G2004/25 S. RASPILLER
La concurrence fiscale : principaux enseignements de l'analyse économique

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

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

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

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

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

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

G2004/10 G. BOISSINOT - S. ANGEVIN - B. MONFORT
Public Debt Sustainability: Some Results on the French Case

G2004/11 S. ANANIAN - P. AUBERT
Travaillateurs 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
Évalueur 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

G2004/15 S. BUFFETEAU - P. GODEFROY
Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945-à1974

G2004/16 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G2004/17 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française

G2004/18 M. LECLAIR - S. ROUX
Mesure et utilisation des emplois instables dans les entreprises

G2004/19 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G2004/20 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française

G2004/21 M. LECLAIR - S. ROUX
Mesure et utilisation des emplois instables dans les entreprises

G2004/22 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G2004/23 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française
<table><tr><td>G2005/08</td><td>C. L'ANGEVIN - N. LAÎB<br/>Éducation et croissance en France et dans un panel de 21 pays de l'OCDE</td></tr><tr><td>G2005/09</td><td>N. FERRARI<br/>Prévoir l'investissement des entreprises<br/>Un indicateur des révolutions dans l'enquête de conjoncture sur les investissements dans l'industrie.</td></tr><tr><td>G2005/10</td><td>P.-O. BEFFY - C. L'ANGEVIN<br/>Chomage et boucle prix-salaires : apport d'un modèle « qualifiés/peu qualifiés »</td></tr><tr><td>G2005/11</td><td>B. HEITZ<br/>A two-states Markov-switching model of inflation in France and the USA: credible target VS inflation spiral</td></tr><tr><td>G2005/12</td><td>O. BIAU - H. ERKEL-ROUSSE - N. FERRARI<br/>Réponses individuelles aux enquêtes de conjoncture et prévision macroéconomiques : Exemple de la prévision de la production manufacturière</td></tr><tr><td>G2005/13</td><td>P. AUBERT - D. BLANCHET - D. BLAU<br/>The labour market after age 50: some elements of a France-American partnership</td></tr><tr><td>G2005/14</td><td>D. BLANCHET - T. DEBRAND - P. DOUGRON - P. POLLET<br/>L'enquête SHARE : présentation et premiers résultats de l'édition française</td></tr><tr><td>G2005/15</td><td>M. DUÉE<br/>La modélisation des comportements démographiques dans le modèle de microsimulation DESTINIE</td></tr><tr><td>G2005/16</td><td>H. RAOUI - S. ROUX<br/>Étude de simulation sur la participation versée aux salariés par les entreprises</td></tr><tr><td>G2006/01</td><td>C. BONNET - S. BUFFETEAU - P. GODDEFROY<br/>Disparités de retraite de droit direct entre hommes et femmes : quelles évolutions ?</td></tr><tr><td>G2006/02</td><td>C. PICART<br/>Les gazelles en France</td></tr><tr><td>G2006/03</td><td>P. AUBERT - B. CRÉPON - P. ZAMORA<br/>Le rendement apparent de la formation continue dans les entreprises : effets sur la productivité et les salaires</td></tr><tr><td>G2006/04</td><td>J.-F. OUVRARD - R. RATHELOT<br/>Demographic change and unemployment: what do macroeconomic models predict?</td></tr><tr><td>G2006/05</td><td>D. BLANCHET - J.-F. OUVRARD<br/>Indicateurs d'engagements implicites des systèmes de retraite : chiffrages, propriétés analytiques et réactions à des chocs démographiques types</td></tr><tr><td>G2006/06</td><td>G. BIAU - O. BIAU - L. ROLVIERE<br/>Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data</td></tr><tr><td>G2006/07</td><td>C. AFSA - P. GIVORD<br/>Le rôle des conditions de travail dans les absences pour malade</td></tr><tr><td>G2006/08</td><td>P. SILLARD - C. L'ANGEVIN - S. SERRAVALLE<br/>Performances comparées à l'exportation de la France et de ses principaux partenaires<br/>Une analyse structurelle sur 12 ans</td></tr></table>

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

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

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

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

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

G2010/10 I. BUONO - G. I. LALANNE La réaction des entreprises françaises à la baisse des 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 : principes caractéristiques et premiers résultats

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

G2010/15 M. BARLET - L. CRUSSON - S. DUPUCH - F. PUECH Des services échangés aux services 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 Mesange réestimé en base 2000

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

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

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

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

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

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

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

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

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

G2011/09 C. LOUVOT-RUNAVOT L'évaluation de l'activité dissimulée des 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 Evaluation 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 "BonusMalus"

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

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

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

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

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

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

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

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

G2012/06 P. AUBERT - M. BACHELET Disparités de montant de pension et redistribution dans le système de retraite français

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

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

G2012/09 O. BARGAIN - A. VICARD Le RMI et son successeur le RSA découragellés certains jeunes de travailier ? 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 réelle

G2012/12 V. COTTET - S. QUANTIN - V. RÉGNIER Coût du travail et allégrements 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. MONTAULT - P.-A. PIONNIER Utilisation rétrospective de l'enquête Emploi à une fréquence mensuelle : apport d'une modélisation espace-élat

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

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

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

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

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

G2013/07 P.-Y. CABANNES - A. MONTAULT - 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. EYAIN - L. WILNER - E. YILMAZ Introducing activity-based payment in the hospital industry: Evidence from French data

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

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

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

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

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

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

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

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

G2014/05 C. GRISLAIN-LETREMY - A. KATOSKY 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. LAME Credit Growth and Capital Requirements: Binding or Not?

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

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

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

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

G2014/12 F. ARNAUD - J. BOUSSARD - A. POISSONNIER - H. SOUAL
<table><tr><td>Computing additive contributions to growth and other issues for chain-linked quarterly aggregates</td><td>G2015/15</td><td>C. GEAY - M. KOUBI - G de LAGASNERIE</td></tr><tr><td>H. FRAISSE - F. KRAMARZ - C. PROST</td><td>G2015/16</td><td>J. BARDAJI - J.-C. BRICONGNE-G</td></tr><tr><td>Labor Disputes and Job Flows</td><td>G2015/16</td><td>B. CAMPAGNE - G. GAULIER</td></tr><tr><td>P. GIVORD - C. GRISLAIN-LETRÉMY - H. NAEGELE</td><td>G2015/17</td><td>C. BELLÉGO - R. DE NUS</td></tr><tr><td>How does fuel taxation impact new car purchases? An evaluation using French consumer-level dataset</td><td>G2015/17</td><td>The redistributive effect of online piracy on the box office performance of American movies in foreign markets</td></tr><tr><td>P. AUBERT - S. RABATÉ</td><td>G2015/18</td><td>J.-B. BERNARD - L. BERTHET</td></tr><tr><td>Durée passée en carrière et durée de vie en retraite : quel partage des gains d'espérance de vie?</td><td>G2015/18</td><td>French households financial wealth: which changes in 20 Years?</td></tr><tr><td>A. POISSONNIER</td><td>G2015/19</td><td>M. POULHÉS</td></tr><tr><td>The walking dead Euler equation Addressing a challenge to monetary policy models</td><td>G2015/19</td><td>Fenêtre sur Cour ou Chambre avec Vue ? Les prix hédoniques de l'immobilier parisien</td></tr><tr><td>Y. DUBOIS - A. MARINO</td><td>G2016/01</td><td>B. GARBINTI - S. GEORGES-KOT</td></tr><tr><td>Indicateurs de rendement du système de retraite français</td><td>G2016/02</td><td>Time to smell the roses? Risk aversion, the timing of inheritance receipt, and retirement</td></tr><tr><td>T. MAYER - C. TREVIEU</td><td>G2016/02</td><td>P. CHARNOZ - C. LELARGE - C. TREVIEN</td></tr><tr><td>The impacts of Urban Public Transportation: Evidence from the Paris Region</td><td>G2016/02</td><td>Communication Costs and the Internal Organization of Multi-Plant Businesses: Evidence from the Impact of the French High-Speed Rail</td></tr><tr><td>S. T. LY - A. RIEGERT</td><td>G2016/03</td><td>C. BONNET - B. GARBINTI - A. SOLAZ</td></tr><tr><td>Measuring Social Environment Mobility</td><td>G2016/03</td><td>Gender Inequality after Divorce: The Flip Side of Marital Specialization - Evidence from a French Administrative Database</td></tr><tr><td>M. A. BEN HALIMA - V. HYAFIL-SOLELHAC</td><td>G2016/04</td><td>D. BLANCHET - E. CAROLI - C. PROST - M. ROGER</td></tr><tr><td>Quel est l'impact du système d'indemnisation maladie sur la durée des arrêts de travail pour maladie?</td><td>G2016/04</td><td>Health capacity to work at older ages in France</td></tr><tr><td>Y. DUBOIS - A. MARINO</td><td>G2016/05</td><td>B. CAMPAGNE - A. POISSONNIER</td></tr><tr><td>Disparités de rendement du système de retraite dans le secteur privé : approches intergénérationnelle et intragénérationnelle</td><td>G2016/05</td><td>MELEZE: A DSGE model for France within the Euro Area</td></tr><tr><td>B. CAMPAGNE - V. ALHENC-GELAS - J.-B. BERNARD</td><td>G2016/06</td><td>B. CAMPAGNE - A. POISSONNIER</td></tr><tr><td>No evidence of financial accelerator in France</td><td>G2016/07</td><td>Laffer curves and fiscal multipliers: lessons from Mélèze model</td></tr><tr><td>Q. LAFFÉTER - M. PAK</td><td>G2016/07</td><td>B. CAMPAGNE - A. POISSONNIER</td></tr><tr><td>Elastiques des recettes fiscales au cycle économique - étude de trois impôts sur la période 1979-2013 en France</td><td>G2016/08</td><td>Structural reforms in DSGE models: a case for sensitivity analyses</td></tr><tr><td>J.-M. DAUSSIN-BENICHOU, S. IDMACHICHE, A. LEDUC et E. POULIQUEN</td><td>G2016/08</td><td>Y. DUBOIS et M. KOUBI</td></tr><tr><td>Les déterminants de l'attractivité de la fonction publique de l'État</td><td>G2016/09</td><td>Relevement de l'âge de départ à la retraite: quel impact sur l'activité des séniors de la reforme des retraites de 2010?</td></tr><tr><td>P. AUBERT</td><td>G2016/09</td><td>A NAQUAS - M. ORAND - I. SUMANI HOUTTI</td></tr><tr><td>La modulation du montant de pension selon la durée de carrière et l'âge de la retraite : quelles disparités entre assurés?</td><td>G2016/10</td><td>Les entreprises employant des salarés au Smic : quelles caractéristiques et quelle rentabilité?</td></tr><tr><td>V. DORTET-BERNADET - M. SICISC</td><td>G2016/10</td><td>T. BLANCHET - Y. DUBOIS - A. MARINO - M. ROGER</td></tr><tr><td>Effet des aides publiques sur l'emploi en R&D dans les petites entreprises</td><td>G2016/11</td><td>Patrimoine privé et retraite en France</td></tr><tr><td>S. GEORGES-KOT</td><td>G2016/11</td><td>M. PAK - A. POISSONNIER</td></tr><tr><td>Annual and lifetime incidence of the value-added tax in France</td><td>G2016/12</td><td>Accounting for technology, trade and final consumption in employment: an Input-Output decomposition</td></tr><tr><td>M. POULHÉS</td><td>G2017/01</td><td>D. FOUGÈRE - E. GAUTIER - S. ROUX</td></tr><tr><td>Are Enterprise Zones Benefits Capitalized into Commercial Property Values? The French Case</td><td>G2017/02</td><td>Understanding Wage Floor Setting in Industry-Level Agreements: Evidence from France</td></tr><tr><td>J.-B. BERNARD - Q. LAFFÉTER</td><td>G2017/02</td><td>Y. DUBOIS - M. KOUBI</td></tr><tr><td>Effet de l'activité et des prix sur le revenu salarial des différentes catégories socioprofessionnelles</td><td>G2017/14</td><td>Règles d'indexation des pensions et sensibilité des dépenses de retraites à la croissance économique et aux chocs démographiques</td></tr></table>

<table><tr><td>A. CAZENAVE-LACROUTZ - F. GODET</td><td>G2017/03</td><td>A. BAUER - B. GARBINTI - S. GEORGES-KOT</td></tr><tr><td>L'espérance de vie en retraite sans incapacité severe des générations nés entre 1960 et 1990: une projection à partir du modèle Destinie</td><td>G2017/04</td><td>P. BEAUMONT - A. LUCIANI</td></tr><tr><td>J. BARDAJI - B. CAMPAGNE - M.-B. KHDER - Q. LAFFÉTER - O. SIMON (Insee)</td><td>G2017/04</td><td>Prime à l'embauche dans les PME: évaluation à partir des déclarations d'embauche</td></tr><tr><td>A-S. DUFERNEZ - C. ELEZAAR - P. LEBLANC - E. MASSON - H. PARTOUCHE (DG-Trésor)</td><td>G2017/05</td><td>C. BELLÉGO - V. DORTET-BERNADET - M. TEAULT</td></tr><tr><td>Le modèle macroéconomique Mésange : réestimation et nouveautés</td><td>G2017/05</td><td>R. MONIN - M. SUAREZ CASTILLO</td></tr><tr><td>J. BOUSSARD - B. CAMPAGNE</td><td>G2017/06</td><td>Réplication et rapprochement des travaux d'évaluation de l'effet du CICE sur l'emploi en 2013 et 2014</td></tr><tr><td>Fiscal Policy Coordination in a Monetary Union at the Zero-Lower-Bound</td><td>G2017/06</td><td>A. CAZENAVE-LACROUTZ - V. LIN</td></tr><tr><td>A. CAZENAVE-LACROUTZ - A. GODZINSKI</td><td>G2018/12</td><td>L'introduction d'un gradient social dans la mortalité au sein du modèle Destinie 2</td></tr><tr><td>Effects of the one-day waiting period for sick leave on health-related absences in the French central civil service</td><td>G2018/12</td><td>M. ANDRÉ - A.-L. BIOTTEAU</td></tr><tr><td>P. CHARNOZ - M. ORAND</td><td>G2019/01</td><td>Effets de moyen terme d'une hausse de TVA sur le niveau de vie et les inégalités: une approche par microsimulation</td></tr><tr><td>Qualification, progrès technique et marchés du travail locaux en France, 1990-2011</td><td>F1901</td><td>A. BOURGEOIS - A. BRIAND</td></tr><tr><td>K. MILIN</td><td>G2019/02</td><td>Le modèle Avionic : la modélisation Input/Output des comptes nationaux</td></tr><tr><td>Modélisation de l'inflation en France par une approche macrosecteuriale</td><td>G2019/03</td><td>A. GODZINSKI - M. SUAREZ CASTILLO</td></tr><tr><td>C.-M. CHEVALIER - L. RARDEUX</td><td>G2019/04</td><td>Short-term health effects of public transport disruptions: air pollution and viral spread channels</td></tr><tr><td>Homeownership and labor market outcomes: disentangling externality and composition effects</td><td>F1903</td><td>L. AEBERHARDT - F. HATIER - M. LECLAIR - B. PENTINAT - J.-D. ZAFAR</td></tr><tr><td>P. BEAUMONT</td><td>G2019/04</td><td>L'économie numérique fausse-t-elle le partage volume-prix du PIB ?</td></tr><tr><td>Time is Money: Cash-Flow Risk and Export Market Behavior</td><td>F1903</td><td>A. CAZENAVE-LACROUTZ - E. YILMAZ</td></tr><tr><td>S. ROUX - F. SAVIGNAC</td><td>G2019/05</td><td>Dans quelle mesure les incitations tarifaires et la procédure de mise sous accord préalable ont-elles contribué au développement de la chirurgie ambulatoire ?</td></tr><tr><td>SMES' financing: Divergence across Euro area countries?</td><td>G2019/06</td><td>J.-P. CLING - S. EGBHAL-TEHERANI - M. ORZONI - C. PLATEAU</td></tr><tr><td>C.-M. CHEVALIER - A. LUCIANI</td><td>G2019/06</td><td>The Differences between EU Countries for Sustainable Development Indicators: It is (mainly) the Economy!</td></tr><tr><td>Computerization, labor productivity and employment impacts across industries vary with technological level</td><td>G2019/06</td><td>P. CHONÉ - L. WILNER</td></tr><tr><td>RMONIN - M. SUAREZ CASTILLO</td><td>G2019/07</td><td>Competition on Unobserved Attributes: The Case of the Hospital Industry</td></tr><tr><td>L'effet du CICE sur les prix une double analyse sur donnés sectorielles et individuelles</td><td>G2019/08</td><td>G. PORA - L. WILNER</td></tr><tr><td>R. LARDEUX</td><td>G2019/08</td><td>Child Penalties and Financial Incentives: Exploiting Variation along the Wage Distribution</td></tr><tr><td>Who Understands The French Income Tax?</td><td>G2019/09</td><td>E. GAUTIER - S. ROUX - M. SUAREZ CASTILLO</td></tr><tr><td>Bunching Where Tax Liabilities Start</td><td>G2019/09</td><td>Do Minimum Wages make Wages more Rigid? Evidence from French Micro Data</td></tr><tr><td>C.-M. CHEVALIER</td><td>G2019/10</td><td>M. ANDRÉ - A. SIREYJOL</td></tr></table>

<table><tr><td>A. BAUER - B. GARBINTI - S. GEORGES-KOT</td><td>G2018/08</td><td>A. BAUER - B. GARBINTI - S. GEORGES-KOT</td></tr><tr><td>Financial Constraints and Self-Employment in France, 1945-2014</td><td>G2018/09</td><td>P. BEAUMONT - A. LUCIANI</td></tr><tr><td>Prime à l'embauche dans les PME: évaluation à partir des déclarations d'embauche</td><td>G2018/10</td><td>C. BELLÉGO - V. DORTET-BERNADET - M. TEAULT</td></tr><tr><td>Comparaison de deux dispositifs d'aide à la R&D collaborative public-privé</td><td>G2018/11</td><td>R. MONIN - M. SUAREZ CASTILLO</td></tr><tr><td>Réplication et rapprochement des travaux d'évaluation du CICE sur l'emploi en 2013 et 2014</td><td>G2018/11</td><td>A. CAZENAVE-LACROUTZ - F. GODET - V. LIN</td></tr><tr><td>Le modèle macroéconomique Mésange : réestimation et nouveautés</td><td>G2018/12</td><td>L'introduction d'un gradient social dans la mortalité au sein du modèle Destinie 2</td></tr><tr><td>J. BOUSSARD - B. CAMPAGNE</td><td>G2019/01</td><td>M. ANDRÉ - A.-L. BIOTTEAU</td></tr><tr><td>Fiscal Policy Coordination in a Monetary Union at the Zero-Lower-Bound</td><td>F1901</td><td>Effets de moyen terme d'une hausse de TVA sur le niveau de vie et les inégalités: une approche par microsimulation</td></tr><tr><td>A. CAZENAVE-LACROUTZ - A. GODZINSKI</td><td>G2019/02</td><td>A. BOURGEOIS - A. BRIAND</td></tr><tr><td>Modélisation de l'inflation en France par une approche macrosecteuriale</td><td>G2019/03</td><td>Le modèle Avionic : la modélisation Input/Output des comptes nationaux</td></tr><tr><td>C.-M. CHEVALIER - L. RARDEUX</td><td>G2019/04</td><td>A. GODZINSKI - M. SUAREZ CASTILLO</td></tr><tr><td>Homeownership and labor market outcomes: disentangling externality and composition effects</td><td>F1903</td><td>Short-term health effects of public transport disruptions: air pollution and viral spread channels</td></tr><tr><td>P. BEAUMONT</td><td>G2019/04</td><td>L. AEBERHARDT - F. HATIER - M. LECLAIR - B. PENTINAT - J.-D. ZAFAR</td></tr><tr><td>Time is Money: Cash-Flow Risk and Export Market Behavior</td><td>F1903</td><td>L'économie numérique fausse-t-elle le partage volume-prix du PIB ?</td></tr><tr><td>S. ROUX - F. SAVIGNAC</td><td>G2019/05</td><td>A. CAZENAVE-LACROUTZ - E. YILMAZ</td></tr><tr><td>SMES' financing: Divergence across Euro area countries?</td><td>G2019/05</td><td>Dans quelle mesure les incitations tarifaires et la procédure de mise sous accord préalable ont-elles contribué au développement de la chirurgie ambulatoire ?</td></tr><tr><td>C.-M. CHEVALIER - A. LUCIANI</td><td>G2019/06</td><td>J.-P. CLING - S. EGBHAL-TEHERANI - M. ORZONI - C. PLATEAU</td></tr><tr><td>Computerization, labor productivity and employment impacts across industries vary with technological level</td><td>G2019/06</td><td>The Differences between EU Countries for Sustainable Development Indicators: It is (mainly) the Economy!</td></tr><tr><td>R. MONIN - M. SUAREZ CASTILLO</td><td>G2019/07</td><td>P. CHONÉ - L. WILNER</td></tr><tr><td>Who Understands The French Income Tax?</td><td>G2019/07</td><td>Competition on Unobserved Attributes: The Case of the Hospital Industry</td></tr><tr><td>Bunching Where Tax Liabilities Start</td><td>G2019/08</td><td>G. PORA - L. WILNER</td></tr><tr><td>C.-M. CHEVALIER</td><td>G2019/08</td><td>Child Penalties and Financial Incentives: Exploiting Variation along the Wage Distribution</td></tr><tr><td>Financial constraints of innovative firms and sectoral growth</td><td>G2019/08</td><td>E. GAUTIER - S. ROUX - M. SUAREZ CASTILLO</td></tr><tr><td>R. S-H. LEE - M. PAK</td><td>G2019/09</td><td>Do Minimum Wages make Wages more Rigid? Evidence from French Micro Data</td></tr><tr><td>Pro-competitive effects of globalisation on prices, productivity and markups: Evidence in the Euro Area</td><td>G2019/09</td><td>M. ANDRÉ - A. SIREYJOL</td></tr><tr><td>C.-M. CHEVALIER</td><td>G2019/10</td><td>Consumption inequality in France between 1995 and 2011</td></tr></table>
<table><tr><td>Imposition des couples et des familles : effets budgétaires et redistributifs de l'impôt sur le revenu</td><td>G2020/11</td><td>P. AGHION – A. BERGEAUD<br/>M. LEQUIEN – M. J. MELITZ<br/>The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports</td></tr><tr><td>G2019/11</td><td>G2020/12</td><td>L. GALIANA – B. SAKAROVITCH<br/>F. SÉMÉCURBE – Z. SMOREDA<br/>Residential segregation, daytime segregation and spatial frictions : an analysis</td></tr><tr><td>G2019/12</td><td>G2020/13</td><td>A. BAUER – J. BOUSSARD<br/>Market Power and Labor Share</td></tr><tr><td>G2019/13</td><td>G2020/14</td><td>A. BAUER – J. BOUSSARD – D. LASHKARI<br/>Information Technology and Returns to Scale</td></tr><tr><td>G2019/14</td><td>G2020/15</td><td>V. LIN – O. MESLIN<br/>Hausse des prix immobiliers et mesure du niveau de vie</td></tr><tr><td>G2019/15</td><td>G2021/01</td><td>N. BECHICHI – G. THEBAULT<br/>Students' Preferences, Capacity Constraints and Post-Secondary Achievements in a Non-Selective System</td></tr><tr><td>G2020/02</td><td>G2021/02</td><td>B. BUREAU – A. DUQUERROY<br/>J. GIORGI – M. LÉ – S. SCOTT – F. VINAS<br/>Une année de crise COVID : impact sur la dymanique des entreprises en France<br/>Une évaluation sur données individuelles</td></tr><tr><td>G2020/03</td><td>G2021/03</td><td>B. BUREAU – A. DUQUERROY<br/>J. GIORGI – M. LÉ – S. SCOTT – F. VINAS<br/>L'impact de la crise sanitaire sur la situation financière des entreprises en 2020 : une analyse sur données individuelles</td></tr><tr><td>G2020/04</td><td>G2021/04</td><td>A. GODZINSKI – M. SUAREZ CASTILLO<br/>Disentangling the effects of air pollutants with many instruments</td></tr><tr><td>G2020/05</td><td>G2021/01</td><td>S. QUANTIN – S. BUNEL – C.LENOIR<br/>Évaluation du dispositif Jeune entreprise innovante (JEI) Un exemple d'application du modèle d'analyse de sensibilité de Rosenbaum</td></tr><tr><td>G2020/06</td><td>2021/02</td><td>L. GOBILLON – D. MEURS – S. ROUX<br/>Differences in positions along a hierarchy : Counterfactuals based on an assignment model</td></tr><tr><td>G2020/07</td><td>2021/03</td><td>N. BECHICHI – J. GRENET – G.<br/>THEBAULT<br/>Ségregation à l'entrée des études supérieures en France et en région parisienne : quels effets du passage à Parcoursup ?</td></tr><tr><td>G2020/08</td><td></td><td>L. WILNER<br/>The persistence of subjective well-being: permanent happiness, transitory misery?</td></tr><tr><td>G2020/09</td><td></td><td>J-M. GERMAIN – T. LELLOUCH<br/>Comptabilité économique de la soutenabilité climatique</td></tr><tr><td>G2020/10</td><td></td><td>A. BAUER – M. ROTEMBERG<br/>Tax avoidance in French Firms: Evidence from the Introduction of a Tax Notch</td></tr></table>

<table><tr><td>G2020/11</td><td>P. AGHION – A. BERGEAUD<br/>M. LEQUIEN – M. J. MELITZ<br/>The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports</td></tr><tr><td>G2020/12</td><td>L. GALIANA – B. SAKAROVITCH<br/>F. SÉMÉCURBE – Z. SMOREDA<br/>Residential segregation, daytime segregation and spatial frictions : an analysis</td></tr><tr><td>G2020/13</td><td>A. BAUER – J. BOUSSARD<br/>Market Power and Labor Share</td></tr><tr><td>G2020/14</td><td>A. BAUER – J. BOUSSARD – D. LASHKARI<br/>Information Technology and Returns to Scale</td></tr><tr><td>G2020/15</td><td>V. LIN – O. MESLIN<br/>Hausse des prix immobiliers et mesure du niveau de vie</td></tr><tr><td>G2021/01</td><td>N. BECHICHI – G. THEBAULT<br/>Students' Preferences, Capacity Constraints and Post-Secondary Achievements in a Non-Selective System</td></tr><tr><td>G2021/02</td><td>B. BUREAU – A. DUQUERROY<br/>J. GIORGI – M. LÉ – S. SCOTT – F. VINAS<br/>Une année de crise COVID : impact sur la dymanique des entreprises en France<br/>Une évaluation sur données individuelles</td></tr><tr><td>G2021/03</td><td>B. BUREAU – A. DUQUERROY<br/>J. GIORGI – M. LÉ – S. SCOTT – F. VINAS<br/>L'impact de la crise sanitaire sur la situation financière des entreprises en 2020 : une analyse sur données individuelles</td></tr><tr><td>G2021/04</td><td>A. GODZINSKI – M. SUAREZ CASTILLO<br/>Disentangling the effects of air pollutants with many instruments</td></tr><tr><td>G2021/01</td><td>S. QUANTIN – S. BUNEL – C.LENOIR<br/>Évaluation du dispositif Jeune entreprise innovante (JEI) Un exemple d'application du modèle d'analyse de sensibilité de Rosenbaum</td></tr><tr><td>2021/02</td><td>L. GOBILLON – D. MEURS – S. ROUX<br/>Differences in positions along a hierarchy : Counterfactuals based on an assignment model</td></tr><tr><td>2021/03</td><td>N. BECHICHI – J. GRENET – G.<br/>THEBAULT<br/>Ségregation à l'entrée des études supérieures en France et en région parisienne : quels effets du passage à Parcoursup ?</td></tr></table>

<table><tr><td>2021/04</td><td>M. ANDRÉ – O. MESLIN<br/>Et pour quelques appartenances de plus : Étude de la propriété immobilière des ménages et du profil redistributif de la taxe foncière</td></tr><tr><td>2022/05</td><td>S. GEORGES-KOT – D. GOUX – E. MAURIN<br/>The value of leisure synchronisation</td></tr><tr><td>2022/06</td><td>N. BECHICHI – M. FABRE – T. OLIVIA<br/>Projections de la population active à l'horizon 2080</td></tr><tr><td>2022/11</td><td>S. QUANTIN – C. WELTER-MÉDÈEE<br/>Estimation des montants manquants de versements de TVA: exploitation des données du contrôle fiscal</td></tr><tr><td>2022/14</td><td>L. BLOCH – B. FAVETTO – A. LAGOUGE – F. SÉDILLOT<br/>Inégalités de rendements et de patrimoine en France en 2017</td></tr><tr><td>2022/15</td><td>H. GENIN – S. SCOTT<br/>Un portrait de la r'ention de main-d'oeuvre dans l'industrie française : analyse à partir des enquêtes mensuelles de conjoncture</td></tr><tr><td>2023/02</td><td>D. GOUX – E. MAURIN<br/>On the seventh day you shall do not any work : the winners and losers of Sunday work deregulation</td></tr><tr><td>2023/03</td><td>P. AGHION – A. BERGEAUD – M. LEQUIEN – M. MELITZ – T. ZUBER<br/>Opposing firm-level responses to the China shock: Output competition versus input supply</td></tr><tr><td>2023/04</td><td>L. GALIANA – L. WILNER<br/>Private Wealth over the Life-Cycle: A Meeting between Microsimulation and Structural Approaches</td></tr><tr><td>2023/06</td><td>M. HILLION – E. MAUGENDRE<br/>L'effet du « jour de carence » sur les absences pour maladie ordinaire, la santé perçue et le recours aux soins à court terme</td></tr><tr><td>2023/07</td><td>M. ANDRÉ – J-M. GERMAIN – M. SICSIC<br/>‘Do I get my money back’?: A Broader Approach to Inequality and Redistribution in France With a Monetary Valuation of Public Services</td></tr><tr><td>2023/08</td><td>R. LAFROGNE-JOUSSIER – J. MARTIN – I. MEJEAN<br/>Supply shocks in supply chains: Evidence from the early lockdown in China</td></tr></table>

<table><tr><td>2023/09</td><td>J. SILHOL – L. WILNER<br/>Teachers' desired mobility to disadvantaged schools: Do financial incentives matter?</td></tr><tr><td>2023/12</td><td>P. AGHION – C. ANTONIN – S. BUNEL – X. JARAVEL<br/>Modern Manufacturing Capital, Labor Demand, and Product Market Dynamics: Evidence from France</td></tr><tr><td>2023/13</td><td>R. LAFROGNE-JOUSSIER – J. MARTIN – I. MEJEAN<br/>Cost Pass-Through and the Rise of Inflation</td></tr></table>