# Do Minimum Wages Make Wages more Rigid? Evidence from French Micro Data

Erwan Gautier, Sébastien Roux, Milena Suarez Castillo

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# Do Minimum Wages Make Wages more Rigid? Evidence from French Micro Data

Erwan GAUTIER - Sébastien ROUX

Milena SUAREZ CASTILLO
<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

Institut national de la statistique et des études économiques

G2019/09

# Do Minimum Wages Make Wages more Rigid? Evidence from French Micro Data

Erwan GAUTIER* Sébastien ROUX**
Milena SUAREZ CASTILLO***

Novembre 2019

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

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

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

The authors would like to thank our discussants in seminars and conferences Robert Anderton, Alexander Hijzen, Yoon J. Jo, Ana Lamo, Dominique Goux and Jeremy Tanguy for helpful comments. We are grateful to Susanto Basu, Gilbert Cette, Andrea Garnero, Hervé Le Bihan, and participants to the Insee research seminar (Paris, 2017), the conference "Understanding Recent Wage Dynamics" (Paris, 2017), the 24th International Panel Data Conference (Seoul, 2018), the JMA conference (Bordeaux, 2018), the AFSE conference (Paris, 2018), the Banque de France research seminar (Paris, 2018), the ECB workshop on France: Structural challenges and Reforms (Frankfurt, 2018), the ETEPP Winter School Workshop (Aussois, 2018), the members of the French Minimum Wage Commission (Paris, 2018), the T2M conference (Nuremberg, 2019), the Royal Economic Society Conference (Warwick, 2019), the Annual Conference of the International Association for Applied Econometrics (Nicosia, 2019), for comments and suggestions. We are also grateful to the Chaire Sécurisation des Parcours Professionnels for nancing and the CASD (Centre d'Accès Sécurisé Distant) for access to the data. The views expressed in this paper are those of the authors and do not necessarily represent those of Banque de France or Insee.

<footnote>* Banque de France et Université de Nantes</footnote>

<footnote>** Insee-Dese Département des études économiques – Ined – Crest</footnote>

<footnote>*** Insee-Dese Département des études économiques – Crest</footnote>
# Les salaires minima rendent-ils les salaires plus rigides?
## Résultats à partir de salaires individuels en France

### Résumé

Les salaires minima (SMIC et minima de branche) affectent-ils la dynamique agrégée des salaires dont les changements individuels sont irréguliers ? Dans cette étude, nous documentons de nouveaux résultats empiriques sur les effets des salaires minima sur la rigidité des salaires. Pour cela, nous utilisons des données individuelles de salaire appariées avec les salaires minima de branche. Un modèle micro économétrique de rigidité des salaires est estimé prenant en compte la dynamique des salaires minima. Nous utilisons ensuite des simulations pour déduire les implications macroéconomiques de la rigidité nominale au niveau individuel. Nos principales conclusions sont les suivantes. Les salaires minima (SMIC ou minima de branche) ont un impact sur le calendrier et l'ampleur des relèvements salariaux. Au niveau agrégé, les salaires minima contribuent à amplifier, par un facteur de 1,7, la réponse des salaires à l'inflation passée. Ignorer les salaires minima conduit à sous-estimer la vitesse de l'ajustement des salaires agrégés d'environ un an. Les élasticités des salaires par rapport à l'inflation passée, au SMIC et aux minima de branche sont respectivement de 0,42, 0,17 et 0,16. Enfin, une hausse du SMIC se diffuse aux salaires plus élevés via les minima de branche.

**Mots-clés** : rigidité des salaires, salaire minimum, négociation collective

# Do Minimum Wages Make Wages more Rigid?
## Evidence from French Micro Data

### Abstract

How do minimum wages (MW) shape the aggregate wage dynamics when wage adjustment is lumpy? In this paper, we document new empirical findings on the effects of MW on wage rigidity using quarterly micro wage data matched with sectoral bargained MW. We first estimate a micro empirical model of wage rigidity taking into account minimum wage dynamics. We then use a simulation method to investigate implications of lumpy micro wage adjustment for the aggregate wage dynamics. Our main findings are the following. Both national and sectoral MW have a large effect on the timing and on the size of wage adjustments. At the aggregate level, MW contribute to amplify, by a factor of 1.7, the response of wages to past inflation. Ignoring MW leads to underestimate the speed of aggregate wage adjustment by about a year. The elasticities of wages with respect to past inflation, the national MW and industry-level MW are respectively 0.42, 0.17 and 0.16. Finally, there are significant spillover effects of the NMW on higher wages transiting through industry-level MW.

**Keywords:** Wage Rigidity, Minimum Wage, Collective Bargaining

**Classification JEL** : E24, E52, J31, J50
# 1 Introduction

In standard macro models, wage stickiness is one of the key ingredients to generate fluctuations in employment or real effects of monetary policy (Erceg et al. [2000] and Smets and Wouters [2003]).<fnref n="1" /> Micro empirical evidence on wage rigidity is thus highly relevant for macro models and a recent literature has documented new stylised facts on wage stickiness (Taylor [2016] for a survey). However, this literature often disregards minimum wage as a potential source of wage rigidity while, in most European countries, minimum wage policies potentially affect a large majority of workers.<fnref n="2" /> This paper aims at filling this gap and provides new micro evidence on how minimum wages (set by law or bargained with unions) can shape the aggregate wage dynamics.

For that, we introduce a new empirical methodology to investigate implications of the lumpiness in micro wage adjustment for the aggregate wage dynamics. We first estimate a standard microeconometric wage rigidity model where the timing and the size of wage adjustments depend on inflation or unemployment but also on national or sectoral wage floors. We then show that the aggregate wage response to a shock cannot be easily derived from parameter estimates of the micro wage rigidity model for at least two reasons. First, in this set-up, since wages are rigid at the micro level, a shock has long-lasting effects on the aggregate wage dynamics and micro estimates do not provide any direct information on the speed of adjustment to a shock.<fnref n="3" /> Second, the transmission of a shock to wages is complicated by the multi-level wage-setting system. A shock can affect minimum wages which then affect individual wages, leading to potential indirect effects of the initial shock on workers’ wages. One novelty of this paper is that we use simulations of wages and minimum wages (using parameter estimates of our micro models) and we then aggregate

<footnote n="1">¹Christiano et al. [2005] show that wage stickiness is even more important than price rigidity to explain the dynamic responses of real macro variables after a monetary policy shock.</footnote>

<footnote n="2">²Díez-Catalán and Villanueva [2014], Martins [2014], and Guimaraes et al. [2017] describe how the existence of sectoral wage floors affect employment in Portugal and Spain. See also Magruder [2012] for similar evidence in South Africa.</footnote>

<footnote n="3">³See also Berger et al. [2019] for analytical results on the ability of macro empirical models to capture aggregate persistence when micro adjustment is lumpy.</footnote>
all these simulations to describe how wages respond to a macro shock. This simulation
exercize allows us: (i) to assess the aggregate wage persistence due to micro lumpiness
in wage changes; (ii) to investigate how minimum wages shape but also amplify the
transmission of a shock to aggregate wages.

When measuring the impact of minimum wages on wage rigidity, one important empir-
ical challenge is to link wage trajectories and sectoral minimum wages at the micro level.
In this paper, we use a large data set of micro wages collected at a quarterly frequency
by the French Ministry of Labour over the period 2005Q4 - 2015Q4. We match this data
set with quarterly data on bargained sectoral wage floors for more than 350 industries
(covering almost all workers in the private sector) but also with data on firm-level wage
agreements.

We document three sets of new empirical findings. First, micro wage stickiness trans-
lates into a delayed aggregate wage response to a shock: a 1%-increase in inflation will
take a little less than 4 years to be fully incorporated into wages. Taking into account
state-dependent factors modifies the aggregate dynamics response to a shock compared to
a set-up where we assume exogenous frequency of wage adjustment. Second, we estimate
medium-run direct effects of the main drivers of aggregate wages. After 5 years, a 1%
increase in inflation has a direct effect of +0.24 pp on aggregate wage increase whereas
unemployment has only a small negative effect. One novelty of the paper is also to esti-
mate the direct effects of minimum wages on the aggregate wage dynamics. We find that
after 5 years, a 1%-increase in NMW (National Minimum Wage) or sectoral wage floors
have a impact of respectively 0.13 pp and 0.16 pp, each effect representing more than
half the effect of inflation. Third, minimum wages do amplify the effect of inflation on
aggregate wages. Once we allow NMW and sectoral minimum wages to react to aggregate
shocks, the overall effect of inflation on aggregate wages raises to 0.42 pp and the effect
of NMW to 0.17 pp. Besides, ignoring the multi-level system of wage setting leads us to
underestimate the speed of adjustment of aggregate wages by about a year.
Our paper is a contribution to the empirical literature documenting patterns of nom-
inal wage rigidity. The very first papers calibrating the degree of wage rigidity used firm-
level wage agreement data for the United States and Canada (Christofides and Wilton
[1983], Taylor [1983], Cecchetti [1987], Christofides [1987]), or Sweden (Fregert and Jo-
nung [1998]) and more recently for France (Avouyi-Dovi et al. [2013] and Fougère et
al. [2018]). On the other hand, a recent growing literature has documented new facts
on wage rigidity using administrative sources of wage data (Barattieri et al. [2014] or
Grigsby et al. [2018] for the United States, Le Bihan et al. [2012] for France, Sigurdsson
and Sigurdardottir [2016] for Iceland or Lunneman and Wintr [2015] for Luxemburg).
Our contribution is here to fill the gap between these two types of literature and to re-
late infrequent wage adjustments to the way minimum wages adjust in sectoral collective
agreements. Moreover, the most recent wage rigidity literature usually investigates the
main drivers of wage adjustments by estimating wage rigidity microeconometric models.
In this paper, we go a step further: we use simulation exercizes to derive implications
of micro wage rigidity for the aggregate wage dynamics (in particular for the speed of
aggregate wage adjustment). To our knowledge, this is the first attempt to derive em-
pirically the aggregate wage response to shocks from estimates of a micro wage rigidity
model. Using these simulation exercizes, we are also able to identify quite precisely how
national or sectoral minimum wages contribute to shape the aggregate wage dynamics in
response to a shock.

We also contribute to the empirical literature assessing the pass-through of minimum
wages to other wages. Several empirical studies find that the NMW affects not only
wages close to the NMW but have also spillover effects to higher wages (see for instance
Grossman [1983], Card and Krueger [1995], Neumark et al. [2004], and Autor et al.
[2016], or Givord et al. [2016]). In France, sectoral minimum wages set by industry-
level agreements can be a relevant channel through which the NMW can affect higher
wages. In France - as in most European countries - every industry defines wage floors for
representative occupations and wage floors cannot be set below the NMW. Thus, when the NMW adjusts, industries have to update thousands of industry-level wage floors to keep them above the NMW. In addition, the NMW increase is considered as the fair value for sectoral minimum wage negotiations or the norm and might be transmitted to the whole scale of wage floors.<fnref n="4" /> Then, wage floors affect individual wages and are a possible channel of NMW spillover to higher wages (see Dittrich et al. [2014] for experimental evidence). Our contribution is here to quantify the empirical relevance of sectoral wage floors as a channel for spillover effects of NMW to higher wages. Doing so, we can also better identify NMW pass-through to other wages.

The remainder of the paper is organized as follows. In Section 2, we set up a wage rigidity model at the micro level where wages depend on minimum wages and derive some implications for the aggregate wage persistence. Section 3 presents our micro data sets and documents stylised facts relating wage and minimum wage nominal rigidities. In Section 4, we describe our empirical exercise presenting estimation results of our micro-conometric model and the simulation-aggregation exercise. In Section 5, we document empirical results on how aggregate wages respond to shocks. Section 6 concludes.

## 2 Aggregate Implications of Lumpy Wage Changes with Minimum Wages

In this section, we first set up a quite general model of staggered wage adjustment at the micro level to examine implications for the aggregate wage persistence.<fnref n="5" /> Then, we allow the wage adjustment process to depend on minimum wage changes and describe possible consequences for the adjustment of aggregate wages to shocks.

<footnote n="4">⁴Falk et al. [2006] show that the introduction of a MW can increase reservation wages (even if the MW is not binding) because the MW affects the workers perception of a fair wage offer whereas Knell and Stiglbauer [2012] show that sectoral wage floors play an important role as norms for individual wages.</footnote>

<footnote n="5">⁵In a recent contribution, Berger et al. [2019] provide evidence on how the estimated persistence in linear time series can be downward biased because of underlying lumpiness in the micro adjustment.</footnote>
## 2.1 A Simple Model of Wage Rigidity

Most macro models assume that wages do not adjust at every period, this can be rationalized by different theoretical models. Taylor [1980] and Calvo [1983] assume that wages remain constant for a certain period of time whereas state-dependent models assume that wages can not adjust continuously because wage changes entail some negotiation costs, costs of performance appraisal, or administrative costs of payrolls for instance (Kahn [1997] and Fehr and Goette [2005]). In all these models, when wages do not adjust, there is a gap between the wage that would have been observed in absence of any friction ($w_{it}^*$) and the actual wage ($w_{it}$) whereas when wages adjust, the new wage $w_{it}$ is equal to $w_{it}^*$. Overall, we can write:

$$w_{i,t} = R_{i,t}w_{i,t}^* + (1 - R_{i,t}) w_{i,t-1} \quad (1)$$

where $R_{i,t}$ is a dummy variable equal to 1 in case of wage update and 0 otherwise. By recurrence, it comes that $w_{it-1} = w_{i,\tau_{it}}^*$, $\tau_{it}$ being the last time the wage of worker $i$ was adjusted (i.e. $\tau_{it} = \max_s [s < t, R_{is} = 1]$). Hence, we have:

$$w_{it} - w_{it-1} = R_{it} (w_{it}^* - w_{i\tau_{it}}^*) \quad (2)$$

The occurrence of a wage update $R_{it}$ is a Bernoulli variable and the probability of wage change $P_{it}$ can then be written as:

$$P_{it} = P(R_{it} = 1) = P(R_{it}^* > 0) \quad (3)$$

where $R_{it}^*$ is the propensity to update wages and depends on ($w_{it}^* - w_{i\tau_{it}}^*$) the cumulated change in the frictionless wage since the last wage adjustment but also on the elapsed duration since the last wage adjustment. This model allows us to encompass predictions of both time- and state-dependent wage rigidity models. In a typical Taylor model, the probability of a wage adjustment will only depend on the elapsed duration whereas in
the adjustment cost model, this probability depends on $(w_{it}^* - w_{i\tau_{it}}^*)$. Finally, in a Calvo model, the probability of wage change is constant.

In this set-up, a shock on a variable affecting the frictionless wage will not be transmitted instantaneously to individual wages. At the date of the shock $t_0$, only wages that adjust will incorporate the shock. However, after $t_0$, wages that have not yet adjusted will keep track of this shock through $(w_{it}^* - w_{i\tau_{it}}^*)$ (i.e. the cumulative change in frictionless wage since the last wage adjustment). Thus, they will incorporate the shock later, when they can adjust. Similarly, a shock will affect the probability of wage change at the date of the shock but also later as far as this probability depends on $w_{it}^*$.

## 2.2 Implications for the Aggregate Wage Dynamics

From this simple micro wage rigidity model, we can now derive implications for the aggregate wage dynamics. Let us denote $W_t$ the aggregate wage at date $t$, computed as a simple average of all individual wages. The aggregate wage change (between date $t$ and $t-1$) can be written in expectation as:

$$ 
\begin{aligned}
E(\Delta W_t) &= E(w_{it} - w_{it-1}) = E(R_{it}(w_{it}^* - w_{i\tau_{it}}^*)) \\
&= \sum_{\tau=-\infty}^{t-1} \pi_{t,\tau} p_{t,\tau} E(w_{it}^* - w_{i\tau}^* | R_{it} = 1, \tau_{it} = \tau)
\end{aligned} 
\quad (4) $$

where $p_{t,\tau} = P(R_{it} = 1 | \tau_{it} = \tau)$ is the probability of a wage update at date $t$ given the date of the last wage update equal to $\tau$ and $\pi_{t,\tau} = P(\tau_{it} = \tau)$ is the distribution across workers of the dates of last wage changes before date $t$. This distribution results from the past probability of wage updates and can be derived by recurrence:

$$ 
\begin{aligned}
\pi_{t+1,\tau} &= \pi_{t,\tau} (1 - p_{t,\tau}), \quad \tau < t \\
\pi_{t+1,t} &= \sum_{\tau=-\infty}^{t-1} \pi_{t,\tau} p_{t,\tau}
\end{aligned} 
\quad (5) $$
How do aggregate wages respond to a macro shock in this set-up? A shock $S$ affecting the frictionless wage at date $t_0$ will take time to be incorporated to aggregate wages since a proportion of wages cannot adjust immediately to the shock, leading to persistence in aggregate wages. In Equation (4), the shock will affect the probability of wage change at $t_0$ but also later (and so the distribution of dates of last wage adjustments before date $t$) and the size of wage changes.

We can easily show that if the shock does not affect the probability of wage change (like in a Calvo or a model), the aggregate response to a shock will only come from the response of the size of wage adjustment (third term in Equation (4)). The duration before a full transmission to aggregate wages will fully depend on the distribution of dates since the last adjustment and the probability of a wage adjustment. In a menu-cost model, the shock will also modify the probability of adjustment (and so the distribution of dates since the last wage adjustment) (the term $\pi_{t,\tau}p_{t,\tau}$ in Equation (4)). A positive shock will lead to a quicker aggregate wage adjustment.<fnref n="6" />

## 2.3 How Do Minimum Wages Affect the Aggregate Wage Dynamics?

In France as in many European countries, workers' wages depend on minimum wages set either at the national level or at the industry level. The existence of minimum wages can modify the response of wages to shocks for at least two reasons. First, minimum wage adjustments might be affected by the same macro shocks as the ones hitting individual wages (like unemployment, inflation...) (see Fougère et al. [2018] for evidence on bargained sectoral wage floors).<fnref n="7" /> Thus, minimum wages can be an additional channel

<footnote n="6">⁶As an illustration, we report in the Appendix A some calibrations of a stylised model of wage rigidity similar to the one presented above. We also report calibrations on how the aggregate response to a shock depends on the parameters used in the micro model.</footnote>

<footnote n="7">⁷National minimum wages might also depend more or less explicitly on past wage increases or past inflation. In France, this dependence is explicit through a legal formula (Cette et al. [2011]). In Germany, the MW commission often mentions past negotiated wage increases in unionized sectors as one of the determinants for the NMW increase.</footnote>
through which macro shocks will affect individual wages and might amplify the wage
response to a given shock. Second, because of negotiation costs, minimum wage adjust-
ments are infrequent, meaning that a shock will take some time to be transmitted to
minimum wages and much more time to be transmitted to individual wages. This would
add some delays in the reaction of wages to a given shock.

However, the overall effect of the shock on the aggregate wage change will be a non-
trivial composition of the direct response of individual wages and the indirect responses
of individual wages transiting through minimum wages. The aggregate implications of
the existence of minimum wages are thus hard to derive analytically. As a simple illus-
tration and to give intuition behind the aggregate dynamics in this case, we present some
calibrations of a simple model where wages and minimum wages adjust infrequently and
wages depend on minimum wages, we also assume that a shock can affect both minimum
wages and actual wages (Appendix A for a full description). Figure 1 plots the impulse
response functions of aggregate wages where we allow the shock to affect MW through
the probability of MW adjustment (top panel) or through the frictionless MW (bottom
panel). When the shock only affects the probability of MW adjustment, the aggregate
wage response is different from the one obtained in a model without any MW (red line):
it first accelerates the transmission of the shock but it also takes more time to converge to
the long run effect. When the shock only affects the frictionless MW (bottom panel), the
long-run effect of MW on aggregate wages is a little larger. The long-run effects of the
shock increase with the size of the shock in the frictionless MW because of second-round
effects transiting through MW.

In the rest of the paper, we will use micro data on wages and minimum wages to
first estimate the main determinants of infrequent wage and minimum wage adjustments.
Then, using micro estimates from these models, we will simulate individual wage trajec-
tories aggregate them to assess the aggregate wage dynamics of shocks when we allow or
not minimum wages to respond to these shocks.
# 3 Wage Micro Data

In this study, we use three quarterly data sets containing individual wages, sectoral wage floors set in industry-level wage agreements and information on collective wage agreements at the firm level.

## 3.1 Wages

Our first data set consist of individual wages collected in the ACEMO survey at a quarterly frequency over the period 2005Q1-2015Q4.<fnref n="8" /> This survey is carried out by the Ministry of Labour to compute official aggregate base wage indices. These are key economic indicators since the aggregate growth of base wages is one of the two inputs in the legal rule updating every year the NMW (see section 4.3). Every quarter, data are collected in about 40,000 different firms with at least 10 employees (in the private non-farm market sector); firms are sampled to be representative of the French economy. The survey collects individual monthly base wages, excluding bonuses, allowances, performance-related compensations or overtime payments. Base wages represent about 85% of total labour earnings (Sanchez [2014]). In a given firm, wage data are collected for workers who hold representative job positions within the firm: at first, depending on their size, firms define 1 to 12 different representative job positions (3 different occupations in 4 broad job categories: blue-collar workers, white-collar workers, technicians and managers); then, every quarter, firms report individual base wages for all their representative occupations. Using this data set, we are able to track individual wage trajectories of representative occupations within firms and so, we can compute base wage changes at a quarterly frequency for a given occupation in a given firm. By construction, we focus on wage dynamics of job stayers and we cannot track wage adjustments due to job mobility. However, the effects of collective wage agreements on the wage dynamics might be concentrated on insiders’

<footnote n="8">⁸Le Bihan et al. [2012] used micro data from this survey over the period 1998Q4-2005Q4.</footnote>
wages.<fnref n="9" />

Table 1 documents stylised facts on wage changes. First, the average wage change (q-o-q) is about 0.5%. Every quarter, 27% of base wages adjust (which implies an average duration between two wage changes of about one year)<fnref n="10" /> and the average non-zero wage change is 1.8%. Figure 2 plots the average wage growth (q-o-q), the frequency of wage changes and the average non-zero size of wage changes over time. The main time variations of the average wage growth come from strong seasonal movements. Quarterly wage growth is much higher on the first quarter (0.9% on average versus less than 0.5% for the other quarters (Table 1)). This strong seasonality comes mainly from the seasonality of the frequency of wage adjustments: 45% of all wages adjust in the first quarter versus only 20% on average in the other quarters. Moreover, the distribution of durations between two wage changes shows a large peak at durations exactly equal to one year (Figure D in Appendix C). The seasonality in the size of non-zero wage changes is much weaker and is mainly due to the fact that wage changes in the first quarter are associated with longer wage durations.<fnref n="11" /> Over a longer horizon, we also find that wage growth was much weaker in 2010 and during the low inflation period (2013-2015). When looking at the cross section distribution of wage changes (Figure 3), only 2% of all non-zero wage changes are negative (representing less than 0.5% of all wage changes) and about two thirds of all non-zero wage changes are between 0 and 2%.<fnref n="12" />

<footnote n="9">⁹See also Appendix B for a discussion on measurement issues and for details on the data treatment.</footnote>

<footnote n="10">¹⁰By comparison, using the same French survey data, Le Bihan et al. [2012] obtain a much higher frequency of 38% but their data set cover the period of workweek reduction which implied a lot of wage changes. For the US, Barattieri et al. [2014] and Grigsby et al. [2018] find quarterly frequencies of wage change between 20 and 25% whereas for Iceland, Sigurdsson and Sigurdardottir [2016] document a monthly frequency of 13% and a typical wage duration of 7 months.</footnote>

<footnote n="11">¹¹Tables A and B in Appendix C provide additional results on the heterogeneity of wage adjustments by firm size and wage level. Wage changes are a little more frequent but smaller in large firms compared to small firms whereas wage changes are less frequent but larger at the top of the wage distribution compared to wages close to the NMW.</footnote>

<footnote n="12">¹²Figure C in Appendix C plots the distribution of wage changes when inflation is close 2% and when inflation is much below. The distribution shifts to the left and is less dispersed when inflation is low.</footnote>
## 3.2 Collective Bargaining and Minimum Wages

In France, as in many European countries, different levels of wage regulation coexist. At the national level, a binding and uniform National Minimum Wage (NMW, in French SMIC for *Salaire Minimum Interprofessionnel de Croissance*) is set by the Ministry of Labour and its value is updated once a year (in January since 2010) following a legal rule (see below). The NMW is binding for all workers but only 10 to 15% of workers are directly concerned by NMW increases. At the industry level, collective agreements define sector- and job-specific wage floors which should be higher than the NMW. At the firm level, unions and firms can negotiate on collective wage agreements but wages cannot be set below sectoral wage floors or the NMW. We match our sample of individual wage data with information on sectoral wage floors and on firm-level wage agreements (Appendix B for details on the matching procedure).

Our first data source on collective bargaining consists of industry-level wage floors over the period 2005-2015.<fnref n="13" /> At the industry level, collective wage agreements define wage floors for several representative occupations within the industry. Every industry defines a specific classification of jobs using criteria such as worker skills, job requirements, or experience. All workers within an industry are then assigned to one position of the job classification and their wage cannot be set below the wage floor associated to their job position. A new wage agreement sets updated values for wage floors. By law, industries must open negotiations on wages every year but have no obligation to reach an agreement. In absence of any new agreement, wage floors remain unchanged until the next agreement and there is no explicit contract duration.<fnref n="14" /> Besides, industry-level wage agreements are automatically and quickly extended by decision of the Ministry of Labor to all workers covered by the industry and firms cannot opt out from these wage agreements. We have here collected wage floors contained in more than 3,000 wage agreements covering more

<footnote n="13">¹³This data set is described in full details in Fougère et al. [2018].</footnote>

<footnote n="14">¹⁴If some wage floors are below the NMW, in particular because of delays in reaching a new agreement in a given industry, the NMW applies.</footnote>
than 360 bargaining industries (i.e. about 90% of wage observations collected by the ACEMO survey). The main variables are the following: the identifier of the industry, the date at which the agreement comes into force, the scale of wage floors for all representative occupations and a broad category for job occupations (blue-collar workers, employees, technicians, managers). Wage floors can be defined as hourly, monthly, or yearly base wages (in euros), bonuses and other fringe benefits are excluded. Their definition is close to the one used to define base wages in the ACEMO survey. Using this data set, we track wage floor trajectories for typical job occupations in a given industry and we calculate the growth rate of wage floors between two wage agreements.

Our second data source on collective bargaining is an administrative data set con-
taining comprehensive information on firm-level agreements. At the firm level, employers
and unions must also open wage negotiation at least once a year<fnref n="15" /> but without any obli-
gation to reach an agreement. In most firm-level wage agreements, unions and employers
bargain on wage increases that can be the same for all workers or different from a job cat-
egory to another. On average, the share of workers covered by firm-level wage agreements
is between 15% and 20% of the total labour force and this proportion has been rather
stable for several years. By law, French firms must report to the Ministry of Labour all
collective agreements. Information contained in these agreements is standardized by the
Ministry of Labour to build a longitudinal firm-level research data set. Available vari-
ables include for each agreement: a firm identifier, the date and the main topics of the
agreement. Firm-level agreements cover a wide range of topics including wages, bonuses,
employment, hours, union rights, labour conditions, on-the-job training... We here re-
strict the data set to firm-level agreements that deal with wage policy.<fnref n="16" /> Wages are the
most frequent topic of firm-level agreements (about 70% of all firm-level agreements deal
with wages and bonuses, Carluccio et al. [2015]). Information on the size of the negoti-

<footnote n="15"><sup>15</sup>This obligation is enforced only for firms with a union representative (i.e. firms with at least 50 employees).</footnote>

<footnote n="16">¹⁶We cannot distinguish agreements dealing with annual base wage increase and agreements dealing with bonuses or performance-related compensations.</footnote>
ated wage increase or on categories of workers covered by the agreement is not available.
We here use a dummy variable equal to one if a firm-level wage agreement is signed in a
given quarter.

Overall, our estimation sample contains about 2 millions of individual wage observa-
tions for more than 45,000 different firms. The simple aggregation of all individual wage
changes of our sample turns out to be very close to the aggregate growth of base wage
published by the Ministry of Labour (Figure B in Appendix C).<fnref n="17" />

Two main stylized facts emerge when relating wage agreements to the wage dynamics.
First, there is a strong common seasonality between NMW updates, increases in sectoral
MW, the frequency of firm-level agreements and the aggregate wage growth (Figure 4):
they all usually increase in the first quarter of the year (Table 1) and to a lesser extent in
the second quarter for firm-level agreements. This might suggest that wage agreements
are at least partly driving the timetable of actual wage changes.<fnref n="18" /> The second main fact
is the strong similarities between the distribution of wage changes and the distribution
of sectoral minimum wage changes (Figure 3). Besides, the average wage change is much
larger when there is a wage agreement: the average wage change is 0.3% when there is
no wage agreement, 0.7% if there is either an industry-level or a firm-level agreement and
1.1% if there are both a firm- and an industry-level agreements (Table 2). Wage changes
are both more frequent and larger when there is a wage agreement either at the industry-
or firm-level.<fnref n="19" />

<footnote n="17">¹⁷Some small differences are observed in the beginning of the sample period where the number of observations in our sample is smaller. Our weighting scheme also slightly differs from the one used by the Ministry of Labour, which can explain deviations between the two series.</footnote>

<footnote n="18">¹⁸Moreover, the distribution of durations between two wage changes (Figure D in Appendix C) also shows that wage durations of exactly one year are much more frequent when there is a wage agreement at the same time.</footnote>

<footnote n="19">¹⁹In presence of a sectoral wage agreements or a firm-level wage agreement, the whole distribution of wage changes shifts to the right (Figure E in Appendix C)</footnote>
# 4 Empirical Micro Model of Wage Rigidity and the Aggregate Wage Dynamics

In this section, we present our empirical strategy to investigate aggregate wage response to shocks when micro wage adjustment is lumpy. We first present the estimates of the micro empirical wage rigidity models (estimated separately on individual wages and sectoral wage floors) which then will be used as data generating processes to simulate micro wage trajectories. Then, we present the simulation and aggregation exercise which is used to derive the macro dynamic wage response to a shock.

## 4.1 Empirical Model of Wage Rigidity

Our empirical model can be easily derived from the model presented in section 2.1. We estimate determinants of a joint process of wage adjustment: first, the decision to change wages $R$ and second, the size of wage adjustment conditional on observing a wage change $\Delta W$. For a given worker $j$ in firm $i$ at date $t$, the model can then written as follows:

$$ 
\begin{aligned}
R_{ijt} &= 1 (R_{ijt}^* \ge 0) \\
\Delta_{(t,\tau_{ijt})} W_{ij} &= R_{ijt} \times \Delta_{(t,\tau_{ijt})} W_{ij}^*
\end{aligned}
$$

where $\Delta_{(t,\tau_{ijt})}$ is the log difference operator between date $t$ and the date of the last wage change $\tau_{ijt}$, $R_{ijt}^*$ is the propensity to adjust wages and $\Delta_{(t,\tau_{ijt})} W_{ij}^*$ the frictionless wage adjustment. The use of cumulative variables can be justified by predictions of state-dependent models of wage rigidity (see for instance Le Bihan et al. [2012] or Sigurdsson and Sigurdardottir [2016]).<fnref n="20" /> Our empirical model is a type II Tobit model. The first equation of the model is a Probit model for the decision of wage adjustment $R$ where $R^*$ depends on the cumulative change in explanatory variables between date $t$ and the date

<footnote n="20">²⁰Our approach can be related to the adjustment hazard model developed by Caballero and Engel [1999]. The probability of a wage change is a function of the gap between wage at date $t$ and a static frictionless optimal wage. This gap is the relevant state variable, so that even if an optimization problem underlies the decision rule, no expectation term is explicitly included.</footnote>
of the last wage adjustment $\tau_{ijt}$, as follows:

$$R_{ijt}^* = \beta \Delta_{(t,\tau_{ijt})} X + \sum_d \gamma_d 1(t - \tau_{ijt} = d) + \mu_{ij} + \lambda_q + \epsilon_{ijt} \quad (6)$$

where $X$ include the French headline CPI, the nominal NMW, the industry- and job-specific wage floor, a dummy variable equal to one if a firm-level wage agreement has been signed in a firm $j$ since the last wage change, and the local unemployment rate. $1(t - \tau_{ijt} = d)$ are duration dummies controlling for Taylor contracts and $\lambda_q$ are quarter dummies capturing the seasonality of wage adjustments.<fnref n="21" /> We also include firm and worker controls $\mu_{ij}$ like dummy variables for the size class and sector of the firm, and dummy variables for the wage position in the wage distribution (by deciles). Our second equation relates the frictionless wage adjustment to some similar determinants:

$$\Delta_{(t,\tau_{ijt})} W_{ij}^* = b \Delta_{(t,\tau_{ijt})} X + v_{ij} \times (t - \tau_{ijt}) + u_{ijt} \quad (7)$$

where $X$ are the same variables as in the Probit equation and $v_{ij}$ are the same worker and firm controls interacted with duration (in quarters) since the last wage adjustment. We here assume that duration dummies and quarter-specific dummies do not affect the size of wage adjustment but only the wage change decision.<fnref n="22" />

The Tobit model is estimated using a two-step Heckman estimation procedure. Standard errors are obtained using pair cluster (firm) bootstrap simulations.<fnref n="23" /> Two identifica-

<footnote n="21">²¹We also run different robustness specifications where $\lambda_t$ are date dummies or quarter dummies in interaction with a post 2010 dummy (since the usual quarter of NMW adjustment was modified in 2010 (from Q3 to Q1)). We also run a specification where we include only duration dummies (and no quarter dummies) and another one where we include only quarter dummies (and no duration dummies).</footnote>

<footnote n="22">²²We still control for elapsed duration by introducing duration as a linear trend (and interacting with size, decile or sector, $v_{ij} \times (t - \tau_{ijt})$), doing so we capture all other potential unobserved determinants of the size of wage changes. Besides, there is no constant term in this equation, which is consistent with the model's prediction that only cumulative shocks since the last wage adjustment will affect the size of wage changes.</footnote>

<footnote n="23">²³Maximum likelihood estimation would require to specify a rather complex covariance matrix for residuals. Resorting to bootstrap simulations allows us to have a very flexible covariance matrix without specifying it explicitly (see also Fougère et al. [2018]).</footnote>
tion issues should be addressed. First, we here use macro variables like CPI or NMW that
might lack of individual variability. By using cumulated changes in macro variables since
the last wage adjustment, we here expand the support of the distribution of changes in
macro variables. Cumulated variations are now specific to each individual, which should
help us to identify the effect of macro variables.<fnref n="24" /> Second, the identification of the Tobit
parameters comes from the assumption that the duration and quarter dummies have no
direct effect on the size of the wage changes besides the impact of cumulated macro vari-
ables introduced in the model.<fnref n="25" /> We argue that these two sets of variables correspond
to calendar or seasonal effects, independent of the decision about the size of wage ad-
justments (once taken into account the cumulated evolution of macro variables). These
calendar effects may be related to negotiation costs or legal constraints (e.g. the yearly le-
gal obligation to negotiate wages at the firm and industry levels). These time regularities
in wage adjustment can be also related to similar time regularities of other firm decisions
like price setting. Synchronized price changes in a given quarter or fixed-period duration
of price changes (typically one year) are well documented by Nakamura and Steinsson
[2008] for producer prices in the US or Gautier [2008] in France. These calendar effects
would also be consistent with predictions of the Taylor wage contracts model.

## 4.2 Estimation Results

In Table 3, columns (1a) and (1b) report results of the Tobit model without any variables related to wage bargaining (NMW, industry or firm-level agreements). One first finding is the strong degree of time-dependence of wage changes: the probability of a wage change increases by about 40 pp if the duration since the last wage change is exactly one year. In addition, the probability of a wage change is much smaller (by about 10 pp or more) in other quarters than Q1. Inflation and local unemployment have also a significant effect

<footnote n="24"><sup>24</sup>A similar identification method has been used by Fougère et al. [2010] or Le Bihan et al. [2012].</footnote>

<footnote n="25"><sup>25</sup>In the Appendix D, we check the robustness of our results when using various specifications of calendar effects (e.g. date dummies).</footnote>
on the probability of a wage change: their marginal effects are respectively +4.6 and -0.1 pp. The size of wage changes is also positively correlated with inflation and negatively with unemployment. Overall, Taylor-type time-dependence seems to play a key role on the probability of wage changes but macro variables like inflation or unemployment have still a significant contribution. These results are very in line with the ones provided by Le Bihan et al. [2012] for France or Sigurdsson and Sigurdardottir [2016] for Iceland.

When we include the NMW, sectoral wage floors and firm-level agreements in our regression (columns (2a) and (2b) and columns (3a) and (3b)), results are somewhat modified: inflation has now a smaller effect on both the probability and on the size of a wage adjustment whereas the effect of unemployment is larger; second, duration effects are now weaker, marginal effects of duration dummies decrease by about 2 pp when including the NMW and by 6 to 10 pp when we include wage bargaining variables; finally, the strong firm size effects on the probability of wage change almost disappear (Figure F in Appendix C). Besides, wage-setting institutions have a significant direct effect on both the probability and the size of wage changes. First, a 1%-increase in the NMW or in sectoral wage floors raises the probability of a wage adjustment by about 2 pp whereas a firm-level agreement raises this probability by 11 pp. NMW and sectoral MW have also a direct effect on the size of wage changes, respectively +0.11 and +0.14 and a firm-level wage agreement increases the average wage change by 0.33 pp.<fnref n="26" />

## 4.3 Minimum Wage Adjustments

In our simulation exercise, we will allow (national and sectoral) minimum wages and the occurrence of firm-level agreements to respond to the same shocks as the ones considered for wages. For that, we first define the data generating processes for these variables.

<footnote n="26">²⁶In Appendix, we report several robustness exercises including or not quarter/duration dummies, date dummies (see Tables C and D in Appendix D), results are quite robust. We also run a type 1 Tobit on annual wage growth to be able to control for annual productivity growth (Appendix D for more details on this model). We find only a small effect of firm-level productivity growth on individual wage changes whereas the impact of wage floors or firm-level wage agreements remain unchanged (Table E in Appendix D).</footnote>
First, the data generating process of the NMW is given by the legal formula and the legal calendar: the NMW adjusts automatically every year (in July until 2009, then in January since 2010) according to an explicit formula linking NMW increase to the past inflation rate and the past real wage increase of blue-collar workers:

$$ \Delta NMW_t = Max(0, \Delta CPI_{t-1}) + \frac{1}{2} Max(0, \Delta W_{t-1} - \Delta CPI_{t-1}) + \epsilon_t \quad (8) $$

where $\Delta NMW_t$ is the NMW increase in year $t$, $\Delta CPI_{t-1}$ is the inflation rate since the last NMW update, $\Delta W_{t-1}$ is the increase of the blue-collar hourly base wage since the last NMW update and $\epsilon_t$ is a possible discretionary governmental increase.<fnref n="27" />

At the industry level, we assume that sectoral wage floors follow a similar two-stage process as the one assumed for individual wages (Fougère et al. [2018]). Results are reported in Appendix D Table F. Like for individual wages, we find large time-dependence effects on the probability of a minimum wage adjustment (for instance, the probability of a wage change is 33 pp higher when a sectoral wage floor has not adjusted for exactly one year) and small but significant effects of state-dependent variables (inflation, NMW or past aggregate wage change) on the probability of wage floor adjustments. Moreover, we find that a 1% increase in inflation, NMW or past aggregate wage growth has a significant positive effect on the size of wage adjustment (respectively 0.25, 0.24 and 0.31).

Finally, we also estimate a model for the occurrence of a wage agreement at the firm level (Table G in Appendix D). Firm size and duration effects are the main drivers of the probability of a wage agreement whereas minimum wages have only small negative effects. The negative effects of minimum wages might suggest the presence of crowding-out effects.<fnref n="28" />

<footnote n="27">²⁷If between two NMW adjustments, the cumulated inflation is larger than 2%, the NMW is automatically and immediately adjusted (it was the case in May 2008 and in Dec. 2011).</footnote>

<footnote n="28">²⁸Like for wage floors, it is likely that inflation and NMW may play a role on the size of wage changes set in the firm-level agreements. Indirect effects of NMW or inflation might however come mainly through the size of negotiated wages, affecting mostly large firms. This is left for further research since information on the size of wage change in firm-level agreements is not available.</footnote>
4.4 Simulation Exercize

As shown in Section 2.2, if wages are sticky at the micro level, the transmission of a shock
to aggregate wages can take several quarters. To investigate the speed of transmission
of shocks to aggregate wages, we resort to simulation exercizes using estimates of micro
models as data generating processes (DGP). Our simulation exercize is the following.<fnref n="29" />
We simulate four variables: the NMW trajectory using as DGP the legal formula; job-
specific wage floors and individual base wages using as DGP our Tobit model estimates;
and occurrence of firm-level agreements using as DGP our Probit estimates. We use
as inputs for all simulations: parameter estimates, initial values of simulated variables,
exogenous variables (like inflation, unemployment,...) and simulated variables when they
enter as inputs in micro-econometric models (for instance, wage floors for base wages). We
run simulations of wage trajectories only for individuals observed at the date of the shock
and we keep the sample composition fixed for the rest of the simulation period (i.e. there
is no entry/exit during the simulations).<fnref n="30" /> Using the simulated base wage trajectories, we
then compute the average wage change at every period, defined as: $\Delta W_t^0 = \frac{1}{N_t} \sum_i \Delta W_{it}^0$
where $N_t$ is the number of individuals at t. This average aggregate wage change computed
without any exogenous shock will be used as a benchmark.

Then, we redo the same simulation exercize but introducing a shock at a given date
(Q12010 in our baseline simulations). For instance, we consider that the CPI is now
1% higher after Q12010 (compared to its actual value). All our simulated variables will
respond to this shock since they all depend on inflation. Besides, since some simulated
variables are used as inputs of others (like wage floors for base wages), it leads to possible
additional indirect effects of shocks on base wages (see below for a description of the
different cases). At the end, we compute the average wage change for this new set of
simulations ($\Delta W_t^1 = \frac{1}{N_t} \sum_i \Delta W_{it}^1$).

<footnote n="29">²⁹Appendix G for a full description.</footnote>

<footnote n="30">³⁰We run several simulations using bootstrapped values of our parameter estimates to provide standard errors of aggregate simulated responses to shocks.</footnote>
Overall, the average aggregate response to a shock is given by the difference between average wage change with the shock and the same average without the shock ($\Delta W_t^1 - \Delta W_t^0$). We will report the cumulative response to a shock as the cumulative sum of this difference over time. We will consider different simulation exercizes to decompose the impact of a shock on aggregate base wages in several channels.

In the first exercize, the shock can only affect base wages (and not the NMW, wage floors and firm-level agreements). Simulated trajectories of NMW, wage floors and firm-level agreements do not include the shock but are still used as inputs for simulations of base wages. In the rest of the paper, the cumulative aggregate response obtained in this exercize will be called the direct effect of a shock on base wages. In a second exercize, we allow base wages but also wage floors and firm-level agreements to respond to the shock. For instance, an exogenous increase in CPI will lead wage floors to adjust, which would in turn affect workers' wages. We are then able to estimate the indirect effect of a given shock on base wages coming through wage floor adjustment process. This effect will be referred as the indirect effect of the shock (Figure K in Appendix F for a diagram). In a third exercize, we assume that base wages, sectoral wage floors and firm-level agreements but also the NMW can respond to the shock. NMW adjustment depends on two factors: past inflation and past aggregate wage change. In our set-up, a positive shock is going to raise individual wages (due to direct or indirect effects), translating into increases in aggregate wages. Since past aggregate wage change is one input of the NMW legal formula, this increase in aggregate wage will lead to raise NMW (with some delays), which might increase again individual wages and wage floors.<fnref n="31" /> In our simulation exercize, we will allow such feedback loop effects from past increase of actual wages (calculated as the sum of all simulated changes in micro wage trajectories) on NMW or industry-level wage floors. In the rest of the paper, feedback loop effects refer to this channel (Figure L in

<footnote n="31">³¹We do not consider possible feedback loop effects coming from the response of inflation and unemployment to a shock even if they other potential channels for feedback loop effects (see for instance Fougère et al. [2010] on prices and MW). However, it would require modelling and linking at the firm-level responses from prices and employment, this research is left for future work.</footnote>
Appendix F for a diagram). The sum of indirect and feedback loop effects is referred as second-round effects of a shock on base wages.

# 5 Aggregate Wage Response to Shocks

In this section, we describe the main results of the different simulation and aggregation exercizes. In particular, we provide findings on the speed of adjustment of aggregate wages in response to a shock (allowing or not minimum wages to respond) and on the medium-term effects of shocks (over a 5-year horizon). We also document heterogeneity of the effects along the wage distribution.

## 5.1 Aggregate Direct Effects

We first describe how aggregate wages directly respond to different shocks (introduced separately): a 1%-shock in CPI inflation, NMW, sectoral MW and unemployment. Figure 5 plots the aggregate response of base wages to different shocks. The red line is the aggregate response when the shock affects both the probability and the size of wage changes (our baseline model) whereas the dashed black line is the aggregate response when the shock only affects the size of wages changes (i.e. the probability of wage changes remains unchanged (exogenous to the shock) like in a time-dependent model). First, in our baseline model, it takes about 4 years for aggregate wages to fully adjust to the shock versus 3 years in a time-dependent model (see Table 4 for statistics on the duration before full adjustment). In our baseline model allowing state-dependence, aggregate adjustment is first a little quicker than in the model without state-dependence (75% of the long term effect after 2 quarters versus 58% in the model with exogenous frequency) since wage changes are much more frequent with the shock. However, after some quarters, wage adjustments are less frequent in our baseline model since firms which have already incorporated the shock are then less likely (compared to the case without
shock) to update their wages again.

In Table 5, we have reported the cumulative effects of shocks after 5 years.<fnref n="32" /> The first column reports direct effects, we find that the medium-run or long-run effects of a 1% shock in inflation on aggregate base wages is 0.24 pp. The cumulative effects of minimum wages on base wages are substantial: after 5 years, a 1% increase in sectoral wage floors leads to an increase of base wages of 0.16 pp whereas the same increase in the NMW leads to an increase of 0.13 pp in aggregate base wages. Each of this effect represents more than half the overall effect of inflation. We can also note that in our baseline model, these cumulative effects of shocks are a little larger than the estimates of the second equation in the Tobit model since they include the effects on both the size and the frequency of wage adjustments.<fnref n="33" />

## 5.2 Minimum Wages and Aggregate Wage Dynamics

To which extent do minimum wage adjustments modify the aggregate wage response to shocks? We here present results of simulations where we allow minimum wages to react to changes in macro variables (i.e. CPI inflation, NMW and past aggregate wages for sectoral MW and inflation and past aggregate wages for the NMW).

Figure 6 plots the overall effect of CPI and NMW shocks on aggregate wages. The solid blue line corresponds to the overall cumulative response of aggregate wages including second-round effects whereas the red dashed line represents the direct effect of the shock. The maximum cumulative effect of a shock is obtained after two years (more than 0.5% for CPI and a little more than 0.2% for the NMW) but the convergence to the medium-run effect is also longer than in the case when we allow only for direct effects (Table 4). Overall, it takes about 5 years for a shock to be fully transmitted to aggregate wages (versus 4 years for the direct effect). This higher degree of persistence in the reaction

<footnote n="32">³²We measure cumulative effects until the end of the sample period Q4 2015. Standard errors are obtained using bootstrap simulations.</footnote>

<footnote n="33">³³See Appendix H showing in a simplified framework how the long-term effect, in our set-up can be decomposed in three terms.</footnote>
of aggregate wages to shocks can be explained by the fact that the reaction of minimum wages to shocks is also persistent (Figure G in Appendix E for the aggregate response of wage floors to a 1% increase in the NMW and inflation).<fnref n="34" />

The second and third columns of Table 5 report cumulative effects of inflation and NMW shocks after 5 years when we account for indirect effects (through wage floor adjustments) and also second-round effects (feedback loop effects). First, effects of shocks are much larger when taking into account second-round effects. A 1%-increase in NMW now raises base wages by 0.17 pp (versus 0.13 pp only for direct effects).<fnref n="35" /> The amplification effect is mainly driven by the response of wage floors to NMW (about +0.03 pp) whereas the feedback loop effects are much smaller (0.01 pp). Overall, the response of sectoral minimum wages amplifies the wage response to NMW increases by a factor of 1.3. The degree of inflation indexation of base wages is also amplified by minimum wages. A 1%-increase in inflation now raises wages by 0.42 pp when we allow minimum wages to respond to the inflation shock (versus 0.22 when we do not allow this possibility). The indirect effect of inflation coming from sectoral wage floors is estimated close to 0.05 pp whereas the feedback loop due in particular to the reaction of NMW to the inflation shock is 0.16 pp. This strong reaction of NMW to inflation can be explained by the legal formula for NMW where NMW adjusts fully to past inflation. Overall, wage indexation to past inflation is augmented by a factor 1.7 when we take into account interactions with wage-setting institutions.

What do we miss if we do not include minimum wages as possible determinants of wage adjustments? In Table 5, we report cumulative effects 5 years after the shock obtained in models with only NMW or without any minimum wage variable. In those models, CPI inflation effects are a little lower and might capture part of the minimum wage effect. Figure 7 plots the cumulative response function to a 1%-increase in inflation

<footnote n="34">³⁴The contribution of the response of firm-level agreements to the shock is close to zero since the probability of a firm-level agreement depends only weakly on macro variables.</footnote>

<footnote n="35">³⁵The NMW shock should be interpreted as a discretionary exogenous increase decided by the government.</footnote>
and NMW with the different specifications. Excluding all bargaining variables, we find a quicker response of wages to inflation (by about 2 to 3 quarters, Table H in Appendix for further statistics on duration before full adjustment). When we include the NMW, the cumulative impulse response function is much closer to the aggregate response obtained with NMW and sectoral minimum wages.

We also test the robustness of aggregate responses to shocks according to the quarter and the year of the shock. First, some papers argue that seasonality of wage changes may affect the effects of monetary policy (see Olivei and Teynrero [2010], Juillard et al. [2013], and Bjorklund et al. [2018]). We here run simulations where the shock is introduced either in Q1, Q2, Q3 or Q4. We find that the duration before full adjustment to a CPI shock is a little longer when the shock is introduced in Q1 whereas a shock has less persistent effect when introduced in Q4 (Figure 8 and also Table J in Appendix E). This is due to the strong seasonality of minimum wages: if the shock is introduced in Q1, it takes more time for wages and minimum wages to adjust since they usually adjust in Q1. However, cumulative effects 5 years after the shock are of similar magnitude. For a NMW shock, the overall effect is stronger in Q1 where the marginal effects of NMW increase is larger leading to more frequent wage changes (direct effect) whereas a NMW shock introduced in Q2 has a smaller effect (Table I in Appendix E reports results of long-term effect of CPI and NMW shock according to the quarter of the shock). Finally, we have run robustness exercizes with respect to the year of the introduction of the shock. Cumulative effects of shocks vary only a little.<fnref n="36" />

## 5.3 Heterogeneity Along the Wage Distribution

We now investigate to which extent cumulative effects of shocks are heterogeneous along the wage distribution. Following the empirical literature on minimum wage spillover

<footnote n="36">³⁶We also provide results of robustness exercizes where we modify the specification of the Probit model in the Tobit regression (including or not time/quarter controls). We find that cumulative effects 5 years after the shock are quite robust to the different specifications (see Table K in Appendix E).</footnote>
effects, we might expect in particular some heterogeneity in the transmission of NMW
increases along the wage distribution. Moreover, our simulation exercizes allow us to in-
vestigate whether spillover effects might come from second round effects. In this exercize,
we have first estimated Tobit model on base wages where our main exogenous variables
interact with 10 different positions of wages in the wage distribution (these positions
correspond to deciles of base wages).<sup>37</sup> We have run the same estimation for industry-
level wage floor process including interactions with positions along the wage distribution.
Finally, we have run the same simulation exercize as previously described.

Figure 9 plots the cumulative effects 5 years after a 1%-shock on NMW for the 10 deciles of the wage distribution. First, looking at overall effects of a 1%-NMW shock (black line), we find a decreasing effect of the NMW along the wage distribution. The overall effect is about 0.4 pp for wages close to the NMW (decile 1) and then falls to about 0.2 for wages between 1.04 and 1.2 × the NMW (deciles 2 and 3). For wages higher than 1.3 × the NMW (deciles 4 to 10), the overall effect of NMW is still positive and significant (about 0.1 pp) and increases for wages higher 2×NMW.<sup>38</sup> This overall effect can be broken down into three components: direct effects from NMW to wages, indirect effects coming from the reaction of wage floors and feedback loop effects coming from the response of aggregate wages. For wages close to the NMW, we find a large contribution of direct effects but this direct effects decreases quickly along the wage distribution. Indirect effects of NMW transiting through wage floors contribute mostly to the overall effects on the highest wages (last 4 deciles) and represent half of the overall effects at the top of the wage distribution. Sectoral minimum wages do contribute to NMW spillovers to wages

<footnote n="37"><sup>37</sup>The deciles of the distribution are the following: 1.04\*NMW, 1.12\*NMW, 1.2\*NMW, 1.3 NMW, 1.5\*NMW, 1.6\*NMW, 1.9\*NMW, 2.2\*NMW, 2.9\*NMW. We have dropped wage observations when base wage is below 0.97\*NMW and above 8\*NMW. Each individual is assigned to the decile of the wage distribution at the date of entry of the individual in the sample (i.e. for a given wage trajectory, the decile remains the same all over the sample period)</footnote>

<footnote n="38"><sup>38</sup>Figure I in Appendix E plots robustness analysis with models including different time effects. When we include time dummies in the model, the overall effect of NMW is close to 0 for wages higher than the median wage (above 1.5\*NMW) since time dummies might capture a large share of the NMW effect. The other specifications including duration/quarter dummies or not deliver very similar results along the wage distribution.</footnote>
higher than the NMW.<fnref n="39" /> Finally, feedback loop effects are positive and concentrated on wages below 1.6\*NMW (on this part of the distribution, these feedback effects are about 0.04 pp). By comparison, using different administrative French data sources at annual frequency, Givord et al. [2016] find that spillover effects affect wages until 2 \* NMW (see also Goarant and Muller [2011]).

If we consider the impact of indexation to past inflation along the wage distribution (Figure 10), we find that the impact of CPI inflation is rather homogenous along the wage distribution. This small degree of heterogeneity in the overall effect is the result of two opposite effects: first, direct effects of CPI inflation are increasing along the wage distribution, their contribution is rather small for NMW earners whereas they are about 0.25 pp for wages higher than 1.1 times the NMW; second, feedback loop effects are very large for wages close to the NMW (about +0.2 pp) but decrease along the wage distribution and are close to 0.1 pp for higher wages. After a CPI inflation shock, the NMW adjusts accordingly, leading to wage increases concentrated on low wages. Finally, indirect effects coming from wage floor adjustments after the CPI inflation shock are significant over the whole wage distribution but larger for the highest deciles. Overall, the dynamics of minimum wages contribute to increase the degree of indexation to past inflation for the whole distribution of wages.

# 6 Conclusion

In this paper, we have documented how a multi-level system of minimum wages can shape the aggregate wage dynamics. For that, we have matched comprehensive French data sets of millions of quarterly base wages, industry-level wage floors for more than 350 different industries and thousands of firm-level wage agreements over the period 2005-2015.

<footnote n="39">³⁹Metalworking, Construction and Public Works industries covering managers at the national level (total of 500,000 employees) contribute a lot to explain this increase. In Appendix E, Figure J plots the same estimates but excluding these industries from our sample. The overall effect of NMW is much lower for the highest deciles.</footnote>
First, we have provided new stylised facts on how wage bargaining institutions has an impact on the degree of micro wage rigidity. Time schedules of wage agreements and actual wage changes are highly synchronized: most wages changes are observed during the first quarter of the year when a vast majority of both industry- and firm-wage agreements are signed. The typical duration between two wage changes is one year which corresponds to the usual duration of wage agreement. This finding is quite consistent with predictions of Taylor [1980] model. We also show that the size of wage adjustments depends not only on inflation and unemployment but also on NMW and sectoral wage floors increases.

Second, using simulation exercizes, we have investigated how micro wage stickiness translates into a delayed aggregate wage response to a shock. A typical 1% increase in inflation would take between 4 and 5 years to be fully incorporated to aggregate wages. We also provide new evidence on the empirical relevance of state-dependent factors for the micro wage dynamics but also for the aggregate wage response to shocks. Finally, minimum wages contribute to delay by about one year the transmission of a given shock to wages.

Third, we have estimated direct effects of the main drivers of aggregate wages. Minimum wages have a large effect on the aggregate wage dynamics: a 1% increase in NMW or sectoral wage floors have a cumulative impact (over a 5-year horizon) of respectively 0.13 pp and 0.16 pp, more than half the effect of inflation. Besides, minimum wages do amplify the effect of inflation on aggregate wages. Once we allow NMW and sectoral wage floors to react to shocks, the overall effect of inflation on aggregate wages raises to 0.42 pp and the effect of NMW to 0.17 pp. This amplification effect is not homogeneous along the wage distribution: the NMW pass-through to higher wages is mainly due to sectoral wage floors for the highest deciles of the wage distribution whereas feedback loop effects play a major role for the lowest deciles of the wage distribution.
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Tables

<table><caption>Table 1: Aggregate Moments of Wage Changes</caption><thead><tr><th rowspan="2"></th><th colspan="3">Base Wage changes</th><th colspan="3">Collective wage agreements</th></tr><tr><th>Average (%)</th><th>Freq.</th><th>Size (%)</th><th>Average (%)</th><th>Freq.</th><th>Size (%)</th><th>Firm Freq.</th></tr></thead><tbody><tr><td>Overall</td><td>0.47</td><td>0.27</td><td>1.75</td><td>0.38</td><td>0.21</td><td>1.91</td><td>0.15</td></tr><tr><td>Overall (unw.)</td><td>0.45</td><td>0.24</td><td>1.90</td><td>0.37</td><td>0.21</td><td>1.91</td><td>0.11</td></tr><tr><td>Q1</td><td>0.84</td><td>0.45</td><td>1.87</td><td>0.90</td><td>0.47</td><td>1.93</td><td>0.21</td></tr><tr><td>Q2</td><td>0.49</td><td>0.27</td><td>1.82</td><td>0.31</td><td>0.17</td><td>1.92</td><td>0.20</td></tr><tr><td>Q3</td><td>0.37</td><td>0.23</td><td>1.62</td><td>0.24</td><td>0.14</td><td>1.71</td><td>0.10</td></tr><tr><td>Q4</td><td>0.21</td><td>0.14</td><td>1.50</td><td>0.15</td><td>0.08</td><td>1.93</td><td>0.09</td></tr><tr><td>2006</td><td>0.47</td><td>0.27</td><td>1.75</td><td>0.43</td><td>0.21</td><td>1.95</td><td>0.14</td></tr><tr><td>2007</td><td>0.59</td><td>0.30</td><td>1.97</td><td>0.53</td><td>0.22</td><td>2.43</td><td>0.14</td></tr><tr><td>2008</td><td>0.70</td><td>0.35</td><td>2.05</td><td>0.60</td><td>0.27</td><td>2.32</td><td>0.15</td></tr><tr><td>2009</td><td>0.43</td><td>0.25</td><td>1.75</td><td>0.44</td><td>0.21</td><td>1.99</td><td>0.13</td></tr><tr><td>2010</td><td>0.39</td><td>0.25</td><td>1.55</td><td>0.23</td><td>0.16</td><td>1.61</td><td>0.14</td></tr><tr><td>2011</td><td>0.53</td><td>0.28</td><td>1.86</td><td>0.50</td><td>0.25</td><td>1.95</td><td>0.15</td></tr><tr><td>2012</td><td>0.52</td><td>0.30</td><td>1.78</td><td>0.47</td><td>0.22</td><td>2.09</td><td>0.15</td></tr><tr><td>2013</td><td>0.38</td><td>0.24</td><td>1.59</td><td>0.40</td><td>0.23</td><td>1.79</td><td>0.15</td></tr><tr><td>2014</td><td>0.33</td><td>0.22</td><td>1.50</td><td>0.19</td><td>0.15</td><td>1.16</td><td>0.19</td></tr><tr><td>2015</td><td>0.30</td><td>0.21</td><td>1.41</td><td>0.14</td><td>0.15</td><td>1.02</td><td>0.15</td></tr></tbody></table>

Note: Moments are calculated using the data set matching ACEMO individual data, firm-level and industry-level wage agreements data sets. The first column contains the average quarterly wage changes for all workers of our data set. The second column is the proportion of workers whose wage is modified in a given quarter compare to the previous quarter. The third column is the average wage change conditional on observing a wage change. Columns 4-5-6 are the same statistics but calculated for sectoral minimum wage changes in industry-level agreements. The last column is the proportion of workers covered in a given quarter by a firm-level wage agreement. Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<table><caption>Table 2: Aggregate Moments of Wage Changes and Wage Agreements</caption><thead><tr><th rowspan="2">Level of wage agreement</th><th colspan="3">Wage changes</th></tr><tr><th>Average (%)</th><th>Freq.</th><th>Size (%)</th></tr></thead><tbody><tr><td><em>All</em></td><td></td><td></td><td></td></tr><tr><td>No Agreement</td><td>0.34</td><td>0.20</td><td>1.70</td></tr><tr><td>Firm OR Industry</td><td>0.70</td><td>0.40</td><td>1.77</td></tr><tr><td>Firm AND Industry</td><td>1.08</td><td>0.54</td><td>1.98</td></tr><tr><td><em>Wage Inflation Close to 2%</em></td><td></td><td></td><td></td></tr><tr><td>No Agreement</td><td>0.40</td><td>0.22</td><td>1.80</td></tr><tr><td>Firm OR Industry</td><td>0.78</td><td>0.41</td><td>1.91</td></tr><tr><td>Firm AND Industry</td><td>1.27</td><td>0.59</td><td>2.15</td></tr><tr><td><em>Wage Inflation Below 2%</em></td><td></td><td></td><td></td></tr><tr><td>No Agreement</td><td>0.25</td><td>0.17</td><td>1.52</td></tr><tr><td>Firm OR Industry</td><td>0.56</td><td>0.37</td><td>1.50</td></tr><tr><td>Firm AND Industry</td><td>0.76</td><td>0.47</td><td>1.63</td></tr></tbody></table>

Note: Moments are calculated using the data set matching ACEMO individual data, firm-level and industry-level wage agreements data sets. Moments are calculated according to the coverage in a given quarter by a firm- or an industry-level wage agreement. About 70% of observations are not concerned by any wage agreement in a given quarter, 25% by a firm- OR an industry-level agreement and about 5% by at the same quarter an industry and a firm-level agreements. Column (2) contains the average quarterly wage changes in a given bargaining regime. Column (3) is the proportion of workers whose wage is modified in a given quarter compared to the previous quarter for a given wage agreement regime. Column (4) is the average wage change conditional on observing a wage change by wage agreement regimes. We report the same statistics for two different subperiods: years 2006-2009, 2011-2012 where wage inflation was close to 2% or above on average and years 2010, 2013-2015 where wage inflation was below 2%. Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<table><caption>Table 3: Determinants of Wage Changes: Tobit Estimates</caption><thead><tr><th rowspan="2"></th><th colspan="3">Probability of wage change</th><th colspan="3">Size of wage change</th></tr><tr><th>(1a)</th><th>(2a)</th><th>(3a)</th><th>(1b)</th><th>(2b)</th><th>(3b)</th></tr></thead><tbody><tr><td>CPI Inflation</td><td>0.046***<br>(0.000)</td><td>0.031***<br>(0.001)</td><td>0.025***<br>(0.001)</td><td>0.330***<br>(0.003)</td><td>0.267***<br>(0.003)</td><td>0.217***<br>(0.003)</td></tr><tr><td>Unemployment</td><td>-0.001**<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.012***<br>(0.001)</td><td>-0.043***<br>(0.004)</td><td>-0.062***<br>(0.004)</td><td>-0.089***<br>(0.004)</td></tr><tr><td>NMW</td><td></td><td>0.022***<br>(0.000)</td><td>0.020***<br>(0.000)</td><td></td><td>0.124***<br>(0.002)</td><td>0.114***<br>(0.002)</td></tr><tr><td>Wage floors</td><td></td><td></td><td>0.023***<br>(0.000)</td><td></td><td></td><td>0.136***<br>(0.002)</td></tr><tr><td>Firm agreement</td><td></td><td></td><td>0.109***<br>(0.001)</td><td></td><td></td><td>0.327***<br>(0.005)</td></tr><tr><td>Duration</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1 quarter</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td></td><td></td><td></td></tr><tr><td>2 quarters</td><td>0.031***<br>(0.001)</td><td>0.029***<br>(0.001)</td><td>0.015***<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>3 quarters</td><td>0.012***<br>(0.001)</td><td>0.002*<br>(0.001)</td><td>-0.026***<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>1 year</td><td>0.387***<br>(0.001)</td><td>0.365***<br>(0.001)</td><td>0.311***<br>(0.002)</td><td></td><td></td><td></td></tr><tr><td>5 quarters</td><td>0.069***<br>(0.002)</td><td>0.048***<br>(0.002)</td><td>-0.003*<br>(0.002)</td><td></td><td></td><td></td></tr><tr><td>6 quarters</td><td>-0.043***<br>(0.002)</td><td>-0.059***<br>(0.002)</td><td>-0.099***<br>(0.002)</td><td></td><td></td><td></td></tr><tr><td>7 quarters</td><td>-0.064***<br>(0.002)</td><td>-0.085***<br>(0.002)</td><td>-0.123***<br>(0.002)</td><td></td><td></td><td></td></tr><tr><td>2 years</td><td>0.054***<br>(0.004)</td><td>0.020***<br>(0.003)</td><td>-0.042***<br>(0.003)</td><td></td><td></td><td></td></tr><tr><td>&gt;2 years</td><td>-0.100***<br>(0.002)</td><td>-0.122***<br>(0.002)</td><td>-0.157***<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>Q1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td></td><td></td><td></td></tr><tr><td>Q2</td><td>-0.097***<br>(0.001)</td><td>-0.092***<br>(0.001)</td><td>-0.087***<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>Q3</td><td>-0.102***<br>(0.001)</td><td>-0.115***<br>(0.001)</td><td>-0.103***<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>Q4</td><td>-0.170***<br>(0.001)</td><td>-0.174***<br>(0.001)</td><td>-0.162***<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>Mills ratio</td><td></td><td></td><td></td><td>0.763***<br>(0.003)</td><td>0.752***<br>(0.003)</td><td>0.751***<br>(0.003)</td></tr><tr><td>Time linear trend</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td colspan="3">1,986,531</td><td colspan="3">466,585</td></tr></tbody></table>

Note: We report in this table the marginal effects calculated from the estimation of the Probit model and the parameter estimates obtained from the second step of the Tobit model. Determinants are calculated as cumulative variable since the last wage adjustment. Duration is a dummy variable for durations since the last wage changes. Q1-Q4 are dummy variables for every quarter of the year. Sector, size and wage deciles controls are introduced in all specifications. In the second equation of the Tobit model, time linear trends are interacted with sector, size and wage deciles.*p<0.1; **p<0.05; ***p<0.01.
<table><caption>Table 4: Duration Before Aggregate Wage Adjustment</caption><thead><tr><th rowspan="2"></th><th colspan="3">Duration (in Q) Before Full Adjustment</th><th colspan="3">% of Long-Term Effect At Date:</th></tr><tr><th>90%</th><th>95%</th><th>98%</th><th>t</th><th>t+1</th><th>t+2</th></tr></thead><tbody><tr><td><em>Inflation</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Exogenous Freq.</td><td>8</td><td>10</td><td>13</td><td>0.40</td><td>0.58</td><td>0.70</td></tr><tr><td>Direct effect</td><td>13</td><td>15</td><td>18</td><td>0.54</td><td>0.75</td><td>0.88</td></tr><tr><td>Overall effect</td><td>17</td><td>19</td><td>&gt; 20</td><td>0.34</td><td>0.49</td><td>0.58</td></tr><tr><td><em>NMW</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Exogenous Freq.</td><td>8</td><td>10</td><td>13</td><td>0.40</td><td>0.58</td><td>0.70</td></tr><tr><td>Direct effect</td><td>15</td><td>17</td><td>19</td><td>0.59</td><td>0.82</td><td>0.95</td></tr><tr><td>Overall effect</td><td>18</td><td>&gt; 20</td><td>&gt; 20</td><td>0.52</td><td>0.74</td><td>0.88</td></tr></tbody></table>

Note: this table reports results on the dynamic aggregate effect of a shock on wages. In the first three columns we report the number of quarters before the cumulative effect is equal to 90, 95 or 98% of the long term effect (i.e. 5 years after the shock) of a shock on aggregate wages. Our criterion is the following: the first date at which the cumulative response is equal to a given ratio and this ratio should not be lower the 4 quarters ahead. The last three columns reports the ratio between the cumulative response and the long run effect measured at t (date of the shock), t+1 one quarter after the shock and t+2 two quarters after the shock. Using our baseline specification with NMW and sectoral MW, we have reported results for a NMW or inflation shock. "Exogenous Freq." is the case where the shock does not affect the probability of a wage adjustment. "Direct effect" is the case where the shock affects only base wages directly (and not wage floors). "Overall effects" is the case where in the simulations, we allow sectoral and national minimum wages to respond to the shock.
<table><caption>Table 5: Long-Term Aggregate Direct Effects</caption><thead><tr><th></th><th>Direct</th><th>Direct + Indirect</th><th>Overall</th></tr></thead><tbody><tr><td colspan="4"><em>NMW and Industry-Level MW</em><br>(Specification 3)</td></tr><tr><td>CPI Inflation</td><td>0.239<br>(0.004)</td><td>0.286<br>(0.012)</td><td>0.417<br>(0.020)</td></tr><tr><td>NMW</td><td>0.129<br>(0.004)</td><td>0.162<br>(0.011)</td><td>0.172<br>(0.016)</td></tr><tr><td>Wage floors</td><td>0.156<br>(0.004)</td><td>-</td><td>-</td></tr><tr><td>Unemployment</td><td>-0.050<br>(0.002)</td><td>-</td><td>-</td></tr><tr><td colspan="4"><em>NMW only</em><br>(Specification 2)</td></tr><tr><td>CPI Inflation</td><td>0.300<br>(0.008)</td><td>-</td><td>0.397<br>(0.003)</td></tr><tr><td>NMW</td><td>0.142<br>(0.005)</td><td>-</td><td>0.146<br>(0.002)</td></tr><tr><td colspan="4"><em>No Minimum Wage</em><br>(Specification 1)</td></tr><tr><td>CPI Inflation</td><td>0.362<br>(0.007)</td><td>-</td><td>-</td></tr></tbody></table>

Note: This table reports results from the simulation exercise described in section 4.4 where we allow wage floors and the NMW to react to changes in CPI and NMW (indirect effects) but also to aggregate wage changes due to the response to the shock (feedback loop effects). We report the long-run impact of 1% increase in a given variable on wage changes. Column (1) reports direct long-run effects coming from the adjustment of wages to shocks under the assumption that wage floors and the NMW are not responding to shocks in CPI or NMW. Column (2) reports the indirect effect of the shock on base wages coming from the adjustment of wage floors to a given shock. The last column reports the overall effect of the shock on base wages including the direct effect, indirect effect coming from wage floor adjustments and feedback loop effects coming from the adjustment of NMW, wage floor and aggregate wage changes.
Figures

<figure><img src="image_4.png" /><figcaption>Figure 1: Aggregate Wage Dynamics with MW - Calibration Exercizes</figcaption></figure>

Note: we here report aggregate wage response to a shock in a model where the shock affects directly wages but also indirectly through its effect on MW. The red line represents the cumulative aggregate wage response in a model where there is no MW. The top panel reports aggregate wage response where we vary the parameter associated with the shock in the equation describing the probability of a MW adjustment $\alpha^{MW}$ whereas the other panel plots aggregate wage response to a shock where we vary the parameter associated with the shock in the equation describing the frictionless MW adjustment $\beta^{MW}$. See Appendix A for a full description of the calibration exercise.
<figure><img src="image_5.png" /><figcaption>Figure 2: Aggregate Wage Growth, Frequency and Size of Wage Adjustments</figcaption></figure>

Note: we compute for each quarter the average wage growth as the average of all wage changes of our sample (including 0 change), the frequency of wage changes is calculated as the ratio of the number of wage changes over the number of observations in a given quarter, the average size of wage changes is calculated as the average of all wage changes but excluding wage changes equal to 0. Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<figure><img src="image_6.png" /><figcaption>Figure 3: Distribution of Non-Zero Wage Changes</figcaption></figure>

Note: we here compute the distribution of all non-zero wage changes (quarter-on-quarter) (blue histogram) and the distribution of quarter-on-quarter changes in sectoral wage floors (red line). Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<figure><img src="image_7.png" /><figcaption>Figure 4: Aggregate Wage Growth, Sectoral Minimum Wage Increase and Frequency of Firm-Level Wage Agreements</figcaption></figure>

Note: we compute for each quarter the average wage growth calculated as the average of all individual wage changes of our sample (including 0 change) (black line). Top panel: we plot with the average wage change, the average wage floor increase decided in a given quarter for all workers of our sample (including 0 increase when there is no wage bargaining) (dashed red line) and the NMW increase (blue bars - in %, right handside scale). Bottom panel: we plot the frequency of firm-level wage agreements as the ratio between the number of workers covered by a firm-level wage agreement on the total number of workers (proportion, green bars, right handside scale). Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<figure><img src="image_8.png" /><figcaption>Figure 5: Aggregate Wage Adjustment to Shocks (Direct Effects)</figcaption></figure>

Note: We here report the results of our simulation exercise: using our estimated model, we simulate two groups of wage change trajectories, the first one with no shock and the second one with a 1%-increase in macro determinants (see section 4.4 for a full description). The shock is introduced in 2010Q1. We compute the average of all wage change trajectories by date and report the difference between the average calculated using simulations including a shock and the average calculated with simulations without any shock. The red line corresponds to the aggregate average wage response to a given shock. The black line corresponds to the aggregate wage response when we do not allow the probability of a wage change to respond to the shock (i.e. the frequency of wage change is given as exogenous). We also report 95%-confidence intervals (grey shaded area) using bootstrap simulations.
<figure><img src="image_9.png" /><figcaption>Figure 6: Aggregate Response of Wages to NMW and Inflation Shocks (Direct and Second-Round Effects)</figcaption></figure>

Note: We report the results of our simulation exercise when we allow indirect effects of shocks feeding wages through wage floor adjustment and we also allow feedback loop effects: using our estimated model, we simulate two groups of wage change trajectories, the first one with no shock and the second one with a 1%-increase in CPI inflation or NMW. We also allow wage floors and NMW to react to these shocks. Therefore, individual wage changes would also respond to second round effects due to the reaction of NMW and wage floors to the initial increase in aggregate base wages. We compute the average of all wage change trajectories by date and the difference between the average with shock and the average with no shock. We plot on this graph the overall effect (i.e. including direct, indirect and feedback loop effects) (dark blue line) and also direct effects (red dashed line). 95%-confidence intervals are also reported (grey shaded area) they are obtained using bootstrap simulations.
<figure><img src="image_10.png" /><figcaption>Figure 7: Aggregate Wage Adjustment to Shocks Taking into Account or Not Minimum Wages</figcaption></figure>

Note: we report the results of our simulation exercise: using our estimated model, we simulate two groups of wage change trajectories, the first one with no shock and the second one with a 1%-increase in macro determinants. The shock is introduced in 2010Q1. We compute the average of all wage change trajectories by date and report the difference between the average with shock and the average with no shock. The short dashed black line plots the response to the shock in the micro Tobit model ignoring wage-setting institutions (specification (1)). The long dashed line plots the response to the shock using the Tobit model where we only include NMW and not the sectoral wage floors (specification (2)). The blue line plots the IRF when we include NMW and sectoral wage floors in the Tobit model (specification (3)); this also includes indirect and second-round effects. 95%-confidence intervals are also reported (grey shaded area) and are obtained using bootstrap simulations.
<figure><img src="image_11.png" /><figcaption>Figure 8: Aggregate Wage Adjustment to Shocks by Quarter</figcaption></figure>

Note: We here report the results of our simulation exercise: using our estimated model, we simulate two groups of wage change trajectories, the first one with no shock and the second one with a 1%-increase in macro determinants. We compute the average of all wage change trajectories by date and the difference between the average with shock and the average with no shock. We plot on this graph the aggregate response to a shock when we assume that the shock is introduced either in 2010Q1, 2010Q2, 2010Q3, or 2010Q4. The long-run effects incorporate indirect and feedback loop effects.
<figure><img src="image_12.png" /><figcaption>Figure 9: Aggregate Wage Effects of the NMW Along the Wage Distribution</figcaption></figure>

Note: We plot long-run effects of a 1% increase of the NMW on base wages. These effects are obtained using our simulation exercise where we allow for indirect effects through wage floor adjustment, NMW response and feedback loop effects. Simulations are made using parameter estimates from a Tobit model where all exogenous variables interact with dummy variables corresponding to deciles of the wage distribution. We report separately long run effects coming from direct effects of the shock on base wages (dark blue histograms), indirect effects through wage floor adjustment (light blue). The black dashed line also includes feedback loop effects and corresponds to the overall effect of a shock. Vertical lines plot the 95%-confidence intervals.
<figure><img src="image_13.png" /><figcaption>Figure 10: Aggregate Wage Effects of Inflation along the Wage Distribution</figcaption></figure>

Note: We plot long-run effects of a 1% increase of the CPI inflation on base wages. These effects are obtained using our simulation exercise where we allow for indirect effects through wage floor adjustment, NMW response and feedback loop effects. Simulations are made using parameter estimates from a Tobit model where all exogenous variables interact with dummy variables corresponding to deciles of the wage distribution. We report separately long run effects coming from direct effects of the shock on base wages (dark blue histograms), indirect effects through wage floor adjustment (light blue). The black dashed line also includes feedback loop effects and corresponds to the overall effect of a shock. Vertical lines plot the 95%-confidence intervals.
# APPENDIX - Not intended to be published

## A Calibration Exercise

The aim of this appendix is to describe a simple micro wage rigidity set-up where we can provide simple predictions on the shape and duration of the aggregate response of wages to a given shock. These predictions should be considered as qualitative since our aim is not here to reproduce all the patterns of the micro data. In this calibration, we define simple processes for individual wages and minimum wages.

The frictionless wage is defined as:

$$w_{it}^* = \eta_w z_{it} + \gamma t + \alpha \times S1_{\{t \ge 0\}} \quad (9)$$

where $z_{it}$ is the MW, $t$ a time trend, $S$ an exogenous shock to $w^*$. The propensity to increase wage is defined as:

$$R_{it}^* = d1_{\{t-\tau_{it}=4\}} + \eta_p(z_{it} - z_{i\tau_{it}}) + \beta \times S1_{\{\tau_{it} \le 0\}} + \epsilon_{it} \quad (10)$$

where $\tau_{it}$ is the date since the last wage $w_i$ adjustment and $S$ is the shock, we allow the probability of a wage change to be higher every 4 quarters (like in Taylor).

Adjustments in $z_{it}$ are also assumed to be infrequent. The frictionless minimum wage is defined as:

$$z_{it}^* = \gamma^z t + \alpha^{MW} \times S1_{\{t \ge 0\}} \quad (11)$$

and

$$z_{it} = z_{i\tau_{it}^*} + R_{it}^z (z_{it}^* - z_{i\tau_{it}^*}) \quad (12)$$
Minimum wage adjusts when $R_{it}^z = 1$ when $R_{it}^{z*} > 0$ where:

$$R_{it}^{z*} = d^z 1\{t - \tau_{it}^z = 4\} + \beta^{MW} \times S1\{\tau_{it}^z \le 0\} + \epsilon_{it}^z \quad (13)$$

To obtain impulse response functions, we compare the case where $S = 1$ with $S = 0$. In our baseline exercise, we set $(\alpha = 0.3, \beta = 0.0, \gamma = 0.5)$. In the figures below, we allow, $\alpha$ and $\beta$ to vary. To illustrate the role of MW in the transmission of shocks, we also run exercises where the shock only affects the MW and so wages through MW. For that, we set $\eta_w = 0.3$ and $\eta_p = 0.1$ and we allow $\alpha^{MW}$ and $\beta^{MW}$ to vary.

Figure A (see below) plots aggregate response to a shock affecting either the probability of wage adjustment or the frictionless wage $w^*$. When the shock plays a more important role in the probability, the speed of adjustment increases whereas when it does not affect the probability, the speed of adjustment is much slower. When the shock affects the frictionless wage, this only affects the long term effect of the shock and not the speed of aggregate adjustment.
<figure><img src="image_14.png" /><figcaption>Figure A: Aggregate Wage Dynamics without MW - Calibration Exercizes</figcaption></figure>

Note: We here report aggregate wage response to a shock affecting in a model without MW. The top panel reports aggregate wage response where we vary the parameter associated with the shock in the equation describing the probability of a wage adjustment whereas the other panel plots aggregate wage response to a shock where we vary the parameter associated with the shock in the equation describing the frictionless wage
# B Data Appendix

## B.1 Measurement issues

Measurement issues in our individual wage data are very limited here for two reasons. First, wages are reported by firms and not by workers. Second, the statistical office of the French Ministry of Labour is very careful in the conduct of this survey to maintain its high quality since the evolution of base wage partially grounds the NMW increase formula. Surveyors monitor quite closely unusual wage increases or decreases and they can interview the firm several times to check the answer to the questionnaire. One potential measurement issue arises when wage trajectories are not associated with the same employee over time (for instance, a given firm chooses a new employee to report the base wage associated with a given job position). The information on employee substitution is not reported in the data set. We consider here that the wage trajectory is continuous as long as the wage change between two quarters stands between -1% and +7%. If not, we assume that the job is not occupied by the same individual and we assume a new wage trajectory. The proportion of wage changes outside the range -1% to 7% is very small (less than 1% of all initial survey observations) and results are not sensitive to the choice of the threshold.

We also compute a variable reporting the position of the job occupation in the wage distribution based on its position with respect to the value of its base wage relative to the NMW at its first date of observation. Deciles corresponding to the ratio base wage over NMW are used as thresholds defining dummy variables. For that, at the first date the base wage is observed for worker in a given firm, we calculate the ratio of the base wage over the NMW. We then compute the deciles of this ratio over workers and construct dummy variables equal to one if the initial wage of a given worker is between two deciles of this ratio. The deciles are the following: 0.97*NMW, 1.04*NMW, 1.12*NMW,
1.2*NMW, 1.3 NMW, 1.5*NMW, 1.6*NMW, 1.9*NMW, 2.2*NMW, 2.9*NMW. Wages below 0.97*NMW and above 8*NMW are discarded from our data set, they represent less than 1% of our overall sample. These dummy variables allow us to investigate the heterogeneity across workers according to the distance of their wage to the NMW.

Measurement issues on wage agreement data.

- Industry-level agreements

The data set consists of wage floors collected by hand on a governmental web site (https://www.legifrance.gouv.fr/) publishing texts of all wage agreements for almost all industries. Measurement issues are very limited.

- Firm-level agreements

We have removed all firm-level wage agreements dealing with specific bonuses due to Villepin Law 2006 and Sarkozy law in 2008. These two laws have led to a large increase in the number of wage agreements but most of them were signed by small firms and were dealing with a specific annual bonus not monthly base wage increases.

Unemployment: we use unemployment data at the local level (Zone d'Emploi and associate to each firm either the local unemployment rate corresponding to its location or the average (weighted) unemployment rate if this firm has several locations. The cumulated change in unemployment is calculated as the simple difference between date t and the date of the last wage update.

## B.2 Data Matching Procedure

The ACEMO survey does not collect the industry-specific wage floor associated with a given worker or the position of the worker in the industry-specific wage scale. Thus, it is difficult to match the two data sets comparing only levels of actual wages and wage floors.<fnref n="40" /> Thus, we use the following procedure to assign a wage floor growth to every

<footnote n="40">⁴⁰On Portuguese data, Cardoso and Portugal [2005] use the mode of wages to assign a given wage floor to a certain category of employees. This procedure cannot be implemented here since we do not have</footnote>
worker of our sample. We first calculate by bargaining industry (and when possible by
broad job categories in the industry) percentiles of the distribution of individual wage
levels (ACEMO survey) and percentiles of the distribution of wage floors (industry-level
wage agreements data set). We then calculate the wage floor increase associated with
the percentiles of the wage floor distribution. Finally, we assign to actual wages in a
given percentile of the wage distribution the wage floor increase corresponding to the
same percentile in the wage floor distribution. Our main assumption is that in a given
industry and job category, lower actual wages are more likely to be affected by increases
of lower wage floors.<fnref n="41" /> Finally, we match this sample with our data set of firm-level wage
agreements using a common firm identifier. The date at which the wage agreement comes
into effect is not available and we only have information on the date of signature: we here
assume that the wage agreement comes into effect the month after the date of signature.

<footnote>information on the worker's job category (defined by sectoral agreements) in the ACEMO survey.</footnote>

<footnote n="41">⁴¹Most of the variance of wage floor increases in a given industry is however due to variations over time rather than across job occupations in the industry (about 80% of the variance is explained by variations over time and 20% by variations across occupations in the same industry. The variance of wage floor increase across occupations is even smaller when we consider the variance of wage floor increase within a broad job category in a given industry).</footnote>
C Supplementary Empirical Results

<table><caption>Table A: Aggregate Moments of Wage Changes - by firm size</caption><thead><tr><th rowspan="2"></th><th colspan="3">Base Wage changes</th><th colspan="3">Collective wage agreements</th></tr><tr><th>Average (%)</th><th>Freq.</th><th>Size (%)</th><th>Average (%)</th><th>Industry Freq.</th><th>Firm Size Freq. (%)</th></tr></thead><tbody><tr><td>All</td><td>0.47</td><td>0.27</td><td>1.75</td><td>0.38</td><td>0.21</td><td>1.91</td><td>0.15</td></tr><tr><td>Less 20 workers</td><td>0.46</td><td>0.22</td><td>2.06</td><td>0.38</td><td>0.20</td><td>1.94</td><td>0.00</td></tr><tr><td>Btw 20 and 50</td><td>0.45</td><td>0.23</td><td>1.96</td><td>0.39</td><td>0.21</td><td>1.92</td><td>0.01</td></tr><tr><td>Btw 50 and 100</td><td>0.44</td><td>0.24</td><td>1.88</td><td>0.39</td><td>0.21</td><td>1.92</td><td>0.03</td></tr><tr><td>Btw 100 and 200</td><td>0.44</td><td>0.24</td><td>1.84</td><td>0.37</td><td>0.20</td><td>1.88</td><td>0.08</td></tr><tr><td>Btw 200 and 500</td><td>0.46</td><td>0.26</td><td>1.76</td><td>0.39</td><td>0.22</td><td>1.85</td><td>0.13</td></tr><tr><td>More than 500</td><td>0.48</td><td>0.29</td><td>1.68</td><td>0.38</td><td>0.20</td><td>1.92</td><td>0.22</td></tr></tbody></table>

Note: Moments are calculated using the data set matching ACEMO individual data, firm-level and industry-level wage agreements data sets. The first column contains the average quarterly wage changes for all workers of our data set. The second column is the proportion of workers whose wage is modified in a given quarter compare to the previous quarter. The third column is the average wage change conditional on observing a wage change. Columns 4-5-6 are the same statistics but calculated for sectoral minimum wage changes in industry-level agreements. The last column is the proportion of workers covered in a given quarter by a firm-level wage agreement. Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<table><caption>Table B: Aggregate Moments of Wage Changes - by wage level</caption><thead><tr><th rowspan="2"></th><th colspan="3">Base Wage changes</th><th colspan="3">Collective wage agreements</th></tr><tr><th>Average (%)</th><th>Freq.</th><th>Size (%)</th><th>Average (%)</th><th>Freq.</th><th>Size (%)</th><th>Firm Freq.</th></tr></thead><tbody><tr><td>All</td><td>0.47</td><td>0.27</td><td>1.75</td><td>0.38</td><td>0.21</td><td>1.91</td><td>0.15</td></tr><tr><td>Btw 0.99 and 1.04*NMW</td><td>0.47</td><td>0.30</td><td>1.53</td><td>0.41</td><td>0.23</td><td>1.86</td><td>0.12</td></tr><tr><td>Btw 1.04 and 1.12*NMW</td><td>0.45</td><td>0.27</td><td>1.70</td><td>0.40</td><td>0.21</td><td>2.00</td><td>0.15</td></tr><tr><td>Btw 1.12 and 1.2*NMW</td><td>0.46</td><td>0.26</td><td>1.76</td><td>0.40</td><td>0.21</td><td>2.02</td><td>0.14</td></tr><tr><td>Btw 1.2 and 1.3*NMW</td><td>0.48</td><td>0.27</td><td>1.79</td><td>0.39</td><td>0.21</td><td>1.91</td><td>0.14</td></tr><tr><td>Btw 1.3 and 1.5*NMW</td><td>0.47</td><td>0.28</td><td>1.66</td><td>0.38</td><td>0.21</td><td>1.91</td><td>0.17</td></tr><tr><td>Btw 1.5 and 1.6*NMW</td><td>0.48</td><td>0.27</td><td>1.78</td><td>0.38</td><td>0.21</td><td>1.89</td><td>0.16</td></tr><tr><td>Btw 1.6 and 1.9*NMW</td><td>0.48</td><td>0.26</td><td>1.86</td><td>0.37</td><td>0.20</td><td>1.94</td><td>0.16</td></tr><tr><td>Btw 1.9 and 2.2*NMW</td><td>0.48</td><td>0.25</td><td>1.95</td><td>0.35</td><td>0.19</td><td>1.88</td><td>0.17</td></tr><tr><td>Btw 2.2 and 2.9*NMW</td><td>0.47</td><td>0.23</td><td>2.05</td><td>0.33</td><td>0.19</td><td>1.80</td><td>0.15</td></tr><tr><td>More than 2.9*NMW</td><td>0.44</td><td>0.20</td><td>2.16</td><td>0.35</td><td>0.19</td><td>1.80</td><td>0.16</td></tr></tbody></table>

Note: Moments are calculated using the data set matching ACEMO individual data, firm-level and industry-level wage agreements data sets. The first column contains the average quarterly wage changes for all workers of our data set. The second column is the proportion of workers whose wage is modified in a given quarter compare to the previous quarter. The third column is the average wage change conditional on observing a wage change. Columns 4-5-6 are the same statistics but calculated for sectoral minimum wage changes in industry-level agreements. The last column is the proportion of workers covered in a given quarter by a firm-level wage agreement. Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year. The deciles are the following: 0.97*NMW, 1.04*NMW, 1.12*NMW, 1.2*NMW, 1.3 NMW, 1.5*NMW, 1.6*NMW, 1.9*NMW, 2.2*NMW, 2.9*NMW.
<figure><img src="image_15.png" /><figcaption>Figure B: Comparison of Average Wage Changes in our Sample and Aggregate Base Wage Growth (Min of Labour)</figcaption></figure>

Note: We compute for each quarter the average wage growth as the average of all wage changes of our sample (including 0 change) (weighted (red line) or unweighted (dashed black line)) and compare this average to the time-series of aggregate base wage growth released by the Ministry of Labour (yellow bars). Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<figure><img src="image_16.png" /><figcaption>Figure C: Distribution of Wage Changes by Inflation Regime</figcaption></figure>

Note: we here compute the distribution of all non-zero wage changes (quarter-on-quarter). We plot the distribution of wage changes for two periods, the first includes years 2010, 2013-2015 (low inflation) (blue bars) whereas the second one includes 2006-2008, 2009, and 2011-2012 (high inflation) (red bars). We do the same for the distribution of changes in wage floors (blue dashed line and red solid line). Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<figure><img src="image_17.png" /><figcaption>Figure D: Distribution of Durations Between Two Wage Changes</figcaption></figure>

Note: We here compute the distribution of durations between two wage changes. We plot the distribution of durations considering different bargaining regimes (considering whether to a worker is covered or not by a firm-level or an industry-level agreement). Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
<figure><img src="image_18.png" /><figcaption>Figure E: Distribution of Wage Changes by Wage Agreement Regime</figcaption></figure>

Note: We here compute the distribution of all non-zero wage changes (quarter-on-quarter). We plot the distribution of wage changes considering different bargaining regimes (considering whether to a worker is covered or not by a firm-level or an industry-level agreement). Statistics are weighted using the number of workers corresponding to each category of workers within the firm in a given year.
D Supplementary Estimation Results

<figure><img src="image_19.png" /><figcaption>Figure F: Marginal Effects of the Firm's Size on the Probability of a Wage Change: Including or not Wage Bargaining Variables</figcaption></figure>

Note: We plot on this graph the marginal effects associated with the dummy variable for firms' size. These marginal effects are obtained from the Probit regression. We here compare marginal effects obtained using the regression without wage bargaining variables (in grey line, 95%-confidence intervals are in dashed lines) and the ones obtained including these variables (in black line, confidence intervals are in dashed lines).
<table><caption>Table C: Determinants of Wage Changes - Tobit Estimates (1) Selection equation - Robustness</caption><thead><tr><th></th><th colspan="4">Probability of wage change</th></tr><tr><th></th><th>(1a)</th><th>(2a)</th><th>(3a)</th><th>(4a)</th></tr></thead><tbody><tr><td>CPI Inflation</td><td>0.045***<br>(0.000)</td><td>0.026***<br>(0.001)</td><td>0.016***<br>(0.001)</td><td>0.018***<br>(0.000)</td></tr><tr><td>Unemployment</td><td>0.003***<br>(0.001)</td><td>-0.012***<br>(0.001)</td><td>-0.009***<br>(0.001)</td><td>-0.020***<br>(0.001)</td></tr><tr><td>NMW</td><td>0.012***<br>(0.000)</td><td>0.009***<br>(0.000)</td><td>-0.014***<br>(0.001)</td><td>0.026***<br>(0.000)</td></tr><tr><td>Wage floors</td><td>0.030***<br>(0.000)</td><td>0.022***<br>(0.000)</td><td>0.021***<br>(0.000)</td><td>0.025***<br>(0.000)</td></tr><tr><td>Firm agreement</td><td>0.117***<br>(0.001)</td><td>0.110***<br>(0.001)</td><td>0.110***<br>(0.001)</td><td>0.129***<br>(0.001)</td></tr><tr><td>Duration</td><td></td><td></td><td></td><td></td></tr><tr><td>1 quarter</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td></td></tr><tr><td>2 quarters</td><td>0.014***<br>(0.001)</td><td>0.021***<br>(0.001)</td><td>0.037***<br>(0.001)</td><td></td></tr><tr><td>3 quarters</td><td>-0.041***<br>(0.001)</td><td>-0.015***<br>(0.001)</td><td>0.017***<br>(0.001)</td><td></td></tr><tr><td>1 year</td><td>0.346***<br>(0.001)</td><td>0.332***<br>(0.002)</td><td>0.394***<br>(0.002)</td><td></td></tr><tr><td>5 quarters</td><td>-0.029***<br>(0.002)</td><td>0.020***<br>(0.002)</td><td>0.090***<br>(0.003)</td><td></td></tr><tr><td>6 quarters</td><td>-0.119***<br>(0.001)</td><td>-0.077***<br>(0.002)</td><td>-0.014***<br>(0.003)</td><td></td></tr><tr><td>7 quarters</td><td>-0.145***<br>(0.001)</td><td>-0.103***<br>(0.002)</td><td>-0.038***<br>(0.003)</td><td></td></tr><tr><td>2 years</td><td>-0.049***<br>(0.003)</td><td>-0.006*<br>(0.003)</td><td>0.103***<br>(0.005)</td><td></td></tr><tr><td>&gt;2 years</td><td>-0.178***<br>(0.001)</td><td>-0.133***<br>(0.002)</td><td>-0.052***<br>(0.004)</td><td></td></tr><tr><td>Time dummies</td><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Quarter dummies</td><td>No</td><td>Yes*2010</td><td>No</td><td>Yes</td></tr><tr><td>Time linear trend</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Observations</td><td colspan="4">1,986,531</td></tr></tbody></table>

Note: We report in this table the marginal effects calculated from the estimation of the Probit model. Determinants are calculated as cumulative variable since the last wage adjustment. Duration is a dummy variable for durations since the last wage changes. Q1-Q4 are dummy variables for every quarter of the year. Sector, size and wage deciles controls are introduced in all specifications. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
<table><caption>Table D: Determinants of Wage Changes - Tobit Estimates (2) Second equation - Robustness</caption><thead><tr><th></th><th colspan="4">Size of wage change</th></tr><tr><th></th><th>(1b)</th><th>(2b)</th><th>(3b)</th><th>(4b)</th></tr></thead><tbody><tr><td>CPI Inflation</td><td>0.224<sup>***</sup><br>(0.003)</td><td>0.214<sup>***</sup><br>(0.003)</td><td>0.215<sup>***</sup><br>(0.003)</td><td>0.243<sup>***</sup><br>(0.003)</td></tr><tr><td>Unemployment</td><td>-0.085<sup>***</sup><br>(0.004)</td><td>-0.092<sup>***</sup><br>(0.005)</td><td>-0.091<sup>***</sup><br>(0.004)</td><td>-0.068<sup>***</sup><br>(0.004)</td></tr><tr><td>NMW</td><td>0.116<sup>***</sup><br>(0.002)</td><td>0.110<sup>***</sup><br>(0.002)</td><td>0.110<sup>***</sup><br>(0.002)</td><td>0.107<sup>***</sup><br>(0.002)</td></tr><tr><td>Wage floors</td><td>0.137<sup>***</sup><br>(0.002)</td><td>0.136<sup>***</sup><br>(0.002)</td><td>0.136<sup>***</sup><br>(0.002)</td><td>0.146<sup>***</sup><br>(0.002)</td></tr><tr><td>Firm agreement</td><td>0.320<sup>***</sup><br>(0.005)</td><td>0.326<sup>***</sup><br>(0.005)</td><td>0.326<sup>***</sup><br>(0.005)</td><td>0.353<sup>***</sup><br>(0.005)</td></tr><tr><td>Mills ratio</td><td>0.770<sup>***</sup><br>(0.003)</td><td>0.751<sup>***</sup><br>(0.003)</td><td>0.749<sup>***</sup><br>(0.003)</td><td>0.876<sup>***</sup><br>(0.003)</td></tr><tr><td>Time dummies</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Quarter dummies</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Time linear trend</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td colspan="4">466,585</td></tr></tbody></table>

Note: We report in this table the parameter estimates obtained from the second step of the Tobit model. Each specification is associated with a selection equation whose results are reported on the previous table. Determinants are calculated as cumulative variable since the last wage adjustment. Sector, size and wage deciles controls are introduced in all specifications. Time linear trends are interacted with sector, size and wage deciles. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
Robustness Estimation on Annual Wage Growth and Productivity Growth
as a Determinant of Wage Growth

Firm-level productivity growth might be one important determinant of the wage dynamics. However, productivity measures are only available at the annual frequency using firms’ balance sheet data. In this Appendix, we run robustness analysis linking annual wage growth and annual productivity growth.

Using our ACEMO survey micro data, we first calculate for every worker in our sample, the annual log change in base wage (keeping only wages collected in Q4).<sup>42</sup> Using administrative fiscal data (FICUS-FARE) containing information on the balance sheet of the universe of firms in France, we compute a basic productivity measure constructed as the ratio between value added and the number of workers in the firm. Then, we calculate the log annual change of this firm-level productivity measure. Finally, we match our annual ACEMO data set with the administrative data set containing productivity. This new sample contains a little less than 150,000 observations (year×worker). This sample covers mainly workers in large firms because of the sampling design of the ACEMO survey.

In terms of basic wage rigidity statistics, about 20% of annual wage changes are exactly equal to 0 and less than 0.5% of observations are wage decreases. To take into account that there is a large peak of wage change at zero in the distribution of wage changes, we follow the standard empirical strategy in the DNWR literature (see for instance Altonji and Devereux [2000]) and we estimate a type 1 Tobit model. We define $\Delta W^*_{ijt}$ the annual unobserved wage growth which depends on several determinants:

$$ \Delta W^*_{ijt} = \beta \Delta X_{ijt} + \mu_{ij} + \lambda_t + \epsilon_{ijt} \quad (14) $$

where $X_{ijt}$ include the annual wage floor growth for worker $i$ in a given sector, a dummy variable equal to 1 if there is a firm-level wage agreement in the firm in a given year, the

<footnote n="42"><sup>42</sup>Results are robust to the choice of quarter. We here choose to keep Q4 since most wage changes are observed at the beginning of the year (Q1 and to a lesser extent Q2).</footnote>
local unemployment rate and the annual firm-level productivity growth and possibly its lagged value. We also control for year effects $\lambda_t$, wage level effects, sectoral effects and firm size effects ($\mu_{ij}$).$^{43}$ the type 1 Tobit model can be written as:

$$ 
\begin{array}{l}
\text{If } \Delta W_{ijt}^* \le 0 \text{ then } \Delta W_{ijt} = 0 \\
\text{If } \Delta W_{ijt}^* \ge 0 \text{ then } \Delta W_{ijt} = \Delta W_{ijt}^*
\end{array}
$$

The estimation results of the model are presented in Table E below. First, when we do not include productivity growth, wage floors, occurrence of a firm-level agreement and unemployment have all very similar impacts in the annual data model than in the quarterly data model (even if the composition of workers/firms is a little different in this new sample). Productivity growth has a positive but very small on annual wage growth: a 1% increase in the firm productivity will increase wage growth by 0.003 pp.$^{44}$ We also find that lagged productivity growth has a somewhat larger effect than the contemporaneous value. Finally, introducing productivity growth left almost unchanged parameter estimates of sectoral wage floors, firm agreement or unemployment.

<footnote>$^{43}$Using annual data, we cannot use any more cumulated changes in inflation or NMW since the support of distribution is more limited than with quarterly data. We introduce year dummies which will capture macro effects.</footnote>

<footnote>$^{44}$Le Bihan et al. [2012] provide similar evidence using a productivity growth proxy at the sectoral level. They find almost no significant effect of productivity growth on base wage growth.</footnote>
<table><caption>Table E: Determinants of Annual Wage Changes: Type 1 Tobit Estimates</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Productivity growth t</td><td></td><td>0.003***<br>(0.000)</td><td>0.002**<br>(0.001)</td></tr><tr><td>Productivity growth (t - 1)</td><td></td><td></td><td>0.002***<br>(0.001)</td></tr><tr><td>Wage Floors</td><td>0.123***<br>(0.004)</td><td>0.116***<br>(0.005)</td><td>0.115***<br>(0.005)</td></tr><tr><td>Firm Agreement</td><td>0.302***<br>(0.010)</td><td>0.304***<br>(0.010)</td><td>0.302***<br>(0.011)</td></tr><tr><td>Unemployment</td><td>-0.019***<br>(0.003)</td><td>-0.019***<br>(0.004)</td><td>-0.021***<br>(0.004)</td></tr><tr><td>Intercept</td><td>0.467***<br>(0.106)</td><td>0.580***<br>(0.116)</td><td>0.559***<br>(0.119)</td></tr><tr><td>σ<sub>ε</sub></td><td>1.745<br>(0.003)</td><td>1.716<br>(0.004)</td><td>1.707<br>(0.004)</td></tr><tr><td>Observations</td><td>181,315</td><td>146,106</td><td>141,579</td></tr></tbody></table>

Note: We report in this table the parameter estimates of the Tobit 1 model estimated using annual wage growth. Productivity growth and change in wage floors are calculated as annual changes.

Unemployment is introduced in levels and firm agreement is a dummy equal to 1 if there is a wage firm-level agreement in a given year, equal to 0 otherwise. We have also included sector, size, wage deciles and year controls in all specifications. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
<table><caption>Table F: Wage Floor Adjustment: Estimation Results</caption><thead><tr><th></th><th>Probit<br>Marginal Effects</th><th>OLS<br>Param. Estimates</th></tr></thead><tbody><tr><td>CPI inflation</td><td>0.023***<br>(0.005)</td><td>0.253***<br>(0.006)</td></tr><tr><td>Unemployment</td><td>-0.002<br>(0.002)</td><td>0.054***<br>(0.002)</td></tr><tr><td>NMW</td><td>0.029***<br>(0.004)</td><td>0.238***<br>(0.006)</td></tr><tr><td>Past aggregate<br>wage changes</td><td>0.010**<br>(0.005)</td><td>0.312***<br>(0.014)</td></tr><tr><td>Duration 2Q</td><td>0.021**<br>(0.010)</td><td></td></tr><tr><td>Duration 1Year</td><td>0.337***<br>(0.012)</td><td></td></tr><tr><td>Duration 2Years</td><td>0.152***<br>(0.026)</td><td></td></tr><tr><td>Quarter 1</td><td>Ref.</td><td></td></tr><tr><td>Quarter 2</td><td>-0.088***<br>(0.007)</td><td></td></tr><tr><td>Quarter 3</td><td>-0.108***<br>(0.007)</td><td></td></tr><tr><td>Quarter 4</td><td>-0.143***<br>(0.007)</td><td></td></tr><tr><td>Mills ratio</td><td></td><td>0.168***<br>(0.011)</td></tr><tr><td>Time linear trends by industry</td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>14,049</td><td>42,603</td></tr></tbody></table>

Note: we report in this table parameter estimates from the Tobit model estimated on wage floor adjustments. The endogenous variable in the Probit part of the model is a dummy variable for wage agreement in a given industry at date *t* and in the OLS part the endogenous variable is the wage change for position *j* in industry *i* at date *t*. In every industry, there are several positions. Determinants are calculated as cumulative variable since the last wage adjustment, all in nominal terms. Controls for sectors and quarters are included. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
<table><caption>Table G: Firm-level Agreements: Probit Results</caption><thead><tr><th colspan="4">Marginal Effects</th></tr><tr><th colspan="2">Time variables</th><th colspan="2">Firm-level characteristics</th></tr></thead><tbody><tr><td>CPI inflation</td><td>0.004<sup>***</sup><br>(0.001)</td><td>% of NMW earners</td><td>-0.053<sup>***</sup><br>(0.004)</td></tr><tr><td>Unemployment</td><td>0.001<sup>***</sup><br>(0.000)</td><td>% of full-time workers</td><td>0.016<sup>***</sup><br>(0.004)</td></tr><tr><td>NMW</td><td>-0.004<sup>***</sup><br>(0.001)</td><td></td><td></td></tr><tr><td>Wage Floors</td><td>-0.002<sup>***</sup><br>(0.000)</td><td></td><td></td></tr><tr><th colspan="2">Duration</th><th colspan="2">Size</th></tr><tr><td>2Q</td><td>0.015<sup>***</sup><br>(0.002)</td><td>&lt; 20 employees</td><td>Ref.</td></tr><tr><td>3Q</td><td>0.076<sup>***</sup><br>(0.003)</td><td>20 - 50 employees</td><td>0.053<sup>***</sup><br>(0.011)</td></tr><tr><td>4Q</td><td>0.414<sup>***</sup><br>(0.004)</td><td>50 - 100 employees</td><td>0.085<sup>***</sup><br>(0.011)</td></tr><tr><td>5Q</td><td>0.172<sup>***</sup><br>(0.004)</td><td>100 - 200 employees</td><td>0.107<sup>***</sup><br>(0.010)</td></tr><tr><td>6Q</td><td>0.015<sup>***</sup><br>(0.004)</td><td>200 - 500 employees</td><td>0.143<sup>***</sup><br>(0.012)</td></tr><tr><td>7Q</td><td>0.037<sup>***</sup><br>(0.004)</td><td>&gt; 500 employees</td><td>0.176<sup>***</sup><br>(0.012)</td></tr><tr><td>8Q</td><td>0.226<sup>***</sup><br>(0.006)</td><td></td><td></td></tr><tr><td>More than 8Q</td><td>0.017<sup>***</sup><br>(0.004)</td><td></td><td></td></tr><tr><th colspan="2">Seasonal effects</th><td></td><td></td></tr><tr><td>Quarter 1</td><td>Ref.</td><td></td><td></td></tr><tr><td>Quarter 2</td><td>-0.020<sup>***</sup><br>(0.002)</td><td></td><td></td></tr><tr><td>Quarter 3</td><td>-0.093<sup>***</sup><br>(0.001)</td><td></td><td></td></tr><tr><td>Quarter 4</td><td>-0.048<sup>***</sup><br>(0.001)</td><td></td><td></td></tr><tr><td>Observations</td><td colspan="3">326,624</td></tr></tbody></table>

Note: we report in this table marginal effects from the Probit model estimated on the occurrence of a firm-level wage agreement in a given firm at date t. CPI inflation, unemployment, NMW and wage floors are calculated as cumulative change since the last wage adjustment. % of NMW earners is the share of employees paid close to the NMW (less than 1.2× the NMW) in a given firm in a given year (source DADS). % of full-time workers is the share of employees whose contract is an open-ended contract (CDI in French) in a given firm in a given year (source DADS). Controls for sectors and quarters are included. *p<0.1; **p<0.05; ***p<0.01
E Supplementary Simulation Results

<figure><img src="image_20.png" /><figcaption>Figure G: Aggregate Wage Floor Response to NMW and CPI Shocks</figcaption></figure>

Note: We here report the results of our simulation exercise on wage floors in industry-level agreements. Using our estimated model on wage floors, we simulate two groups of wage floor trajectories, the first one with no shock and the second one with a 1%-increase in macro determinants. We compute the average of all wage floor trajectories by date and the difference between the average with shock and the average with no shock. We plot on this graph the aggregate response over time of wage floors to a 1%-increase in NMW and inflation. The red dashed line corresponds to the direct effects of a shock (without feedback loop effects) whereas the blue solid line corresponds to the overall effect (including feedback loop effects). We also report 95%-confidence intervals using bootstrap simulations.
<figure><img src="image_21.png" /><figcaption>Figure H: Aggregate Wage Adjustment to Shocks - Exogenous Frequencies of Sectoral Minimum Wage Changes and Base Wage Changes</figcaption></figure>

Note: We here report the results of our simulation exercise: using our estimated model, we simulate two groups of wage change trajectories, the first one with no shock and the second one with a 1%-increase in macro determinants. The shock is introduced in 2010Q1. We compute the average of all wage change trajectories by date and report the difference between the average with shock and the average with no shock. We also report 95%-confidence intervals using bootstrap simulations. The response to the shock in the case where we assume exogenous frequencies of minimum wage and individual wage adjustment is obtained by assuming that the shock does not affect the probability of a wage adjustment (probabilities of wage changes are taken as predicted by the model without shock). The full response to the shock (with indirect and feedback loop effects) is derived from the multi-level simulated model described in the simulation section.
<table><caption>Table H: Duration Before Long-Term Adjustment</caption><thead><tr><th></th><th colspan="3">Duration (in Q) Before<br>Full Adjustment</th><th colspan="3">% of Long-Term Effect<br>At Date:</th></tr><tr><th></th><th>90%</th><th>95%</th><th>98%</th><th>t</th><th>t+1</th><th>t+2</th></tr></thead><tbody><tr><td colspan="7"><strong>NMW and Industry-Level MW (Specification 3)</strong></td></tr><tr><td><em>Inflation</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct effect</td><td>13</td><td>15</td><td>18</td><td>0.54</td><td>0.75</td><td>0.88</td></tr><tr><td>Overall effect</td><td>17</td><td>19</td><td>&gt; 20</td><td>0.34</td><td>0.49</td><td>0.58</td></tr><tr><td><em>NMW</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct effect</td><td>15</td><td>17</td><td>19</td><td>0.59</td><td>0.82</td><td>0.95</td></tr><tr><td>Overall effect</td><td>18</td><td>&gt; 20</td><td>&gt; 20</td><td>0.52</td><td>0.74</td><td>0.88</td></tr><tr><td colspan="7"><strong>NMW only (Specification 2)</strong></td></tr><tr><td><em>Inflation</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>"Direct"</td><td>13</td><td>16</td><td>17</td><td>0.56</td><td>0.78</td><td>0.9</td></tr><tr><td>Overall</td><td>16</td><td>18</td><td>&gt; 20</td><td>0.41</td><td>0.57</td><td>0.66</td></tr><tr><td><em>NMW</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>"Direct"</td><td>15</td><td>17</td><td>19</td><td>0.61</td><td>0.84</td><td>0.96</td></tr><tr><td>Overall</td><td>17</td><td>18</td><td>&gt; 20</td><td>0.61</td><td>0.82</td><td>0.96</td></tr><tr><td colspan="7"><strong>No Minimum Wage (Specification 1)</strong></td></tr><tr><td><em>Inflation</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>"Direct"</td><td>14</td><td>17</td><td>20</td><td>0.62</td><td>0.86</td><td>0.98</td></tr></tbody></table>

Note: this table reports results on the dynamic aggregate effect of a shock on wages. In the first three columns we report the number of quarters before the cumulative effect is equal to 90, 95 or 98% of the long term effect of a shock on aggregate wages. Our criterion is the following: the first date at which the cumulative response is equal to a given ratio and this ratio should not be lower the four quarters ahead. The last three columns reports the ratio between the cumulative response and the long run effect measured at t (date of the shock), t+1 one quarter after the shock and t+2 two quarters after the shock. We report the results for the three models estimated. For each specification, we have reported results for a NMW or inflation shock. "Direct effect" is the case where the shock affects only base wages directly (and not wage floors). "Overall effects" is the case where we allow sectoral and national minimum wages to respond to the shock.
<figure><img src="image_22.png" /><figcaption>Figure I: Aggregate Wage Effects of the NMW Along the Wage Distribution - Robustness to the Probit Specification</figcaption></figure>

Note: We plot long-run effects of a 1% increase of the NMW on base wages by decile of the wage distribution. These effects are obtained using our simulation exercise where we allow for indirect effects through wage floor adjustment, NMW response and feedback loop effects. Simulations are made using parameter estimates from a Tobit model where all exogenous variables interact with dummy variables corresponding to deciles of the wage distribution. The different lines correspond to different Tobit specifications used for the simulation exercise. In blue, we plot our baseline estimates (including quarter dummies in the Probit model), in brown, the estimates when we include only quarter dummies and no duration dummies, in light green, the estimates when we include no quarter dummies and only duration dummies, in red light we include date dummies, in purple, we include quarter dummies interacted with a dummy before/after 2010.
<table><caption>Table I: Long-Term Aggregate Effects - Robustness to the Timing of the Shock</caption><thead><tr><th></th><th>Direct</th><th>Direct + Indirect</th><th>Overall</th></tr></thead><tbody><tr><td colspan="4"><strong>NMW</strong></td></tr><tr><td>Baseline (Q1 2010)</td><td>0.129<br>(0.004)</td><td>0.162<br>(0.010)</td><td>0.172<br>(0.016)</td></tr><tr><td>(Q1) 2008</td><td>0.114<br>(0.003)</td><td>0.139<br>(0.002)</td><td>0.136<br>(0.003)</td></tr><tr><td>(Q1) 2009</td><td>0.146<br>(0.004)</td><td>0.202<br>(0.002)</td><td>0.209<br>(0.006)</td></tr><tr><td>(Q1) 2011</td><td>0.118<br>(0.003)</td><td>0.155<br>(0.002)</td><td>0.139<br>(0.002)</td></tr><tr><td>Q2 (2010)</td><td>0.112<br>(0.003)</td><td>0.140<br>(0.003)</td><td>0.135<br>(0.005)</td></tr><tr><td>Q3 (2010)</td><td>0.115<br>(0.001)</td><td>0.144<br>(0.001)</td><td>0.160<br>(0.007)</td></tr><tr><td>Q4 (2010)</td><td>0.114<br>(0.001)</td><td>0.146<br>(0.001)</td><td>0.150<br>(0.007)</td></tr><tr><td colspan="4"><strong>CPI Inflation</strong></td></tr><tr><td>Baseline (Q1 2010)</td><td>0.239<br>(0.004)</td><td>0.286<br>(0.012)</td><td>0.417<br>(0.020)</td></tr><tr><td>(Q1) 2008</td><td>0.223<br>(0.006)</td><td>0.260<br>(0.004)</td><td>0.429<br>(0.003)</td></tr><tr><td>(Q1) 2009</td><td>0.263<br>(0.006)</td><td>0.330<br>(0.008)</td><td>0.243<br>(0.008)</td></tr><tr><td>(Q1) 2011</td><td>0.230<br>(0.003)</td><td>0.283<br>(0.008)</td><td>0.519<br>(0.005)</td></tr><tr><td>Q2 (2010)</td><td>0.221<br>(0.005)</td><td>0.258<br>(0.006)</td><td>0.425<br>(0.006)</td></tr><tr><td>Q3 (2010)</td><td>0.225<br>(0.005)</td><td>0.263<br>(0.006)</td><td>0.430<br>(0.018)</td></tr><tr><td>Q4 (2010)</td><td>0.222<br>(0.007)</td><td>0.263<br>(0.007)</td><td>0.425<br>(0.018)</td></tr></tbody></table>

Note: This table reports results from simulation exercise described in section 4.4 where we allow wage floors and the NMW to react to changes in CPI and NMW (indirect effects) but also to aggregate wage changes due to the response to the shock (feedback loop effects). We report the long-run impact of 1% increase in a given variable on wage changes. Column (1) reports direct long run effects coming from the adjustment of wages to shocks under the assumption that wage floors and the NMW are not responding to shocks in CPI or NMW. Column (2) reports the indirect effect of the shock on base wages coming from the adjustment of wage floors to a given shock. The last column reports the overall effect of the shock on base wages including the direct effect, indirect effect coming from wage floor adjustments and feedback loop effects coming from the adjustment of NMW, wage floor and aggregate wage changes.
<table><caption>Table J: Dynamic Effect of Shocks - Sensitivity to Quarter of the Shock</caption><thead><tr><th rowspan="2"></th><th colspan="3">Number of Quarters</th><th rowspan="2">% of Long-Term Effect</th><th colspan="2"></th></tr><tr><th>Before</th><th>Full Adjustment</th><th>After</th><th>t</th><th>t+1</th><th>t+2</th></tr></thead><tbody><tr><td colspan="7"><strong>Direct effect</strong></td></tr><tr><td colspan="7">Inflation</td></tr><tr><td>Q1</td><td>13</td><td>15</td><td>18</td><td>0.54</td><td>0.75</td><td>0.88</td></tr><tr><td>Q2</td><td>13</td><td>16</td><td>&gt; 20</td><td>0.32</td><td>0.52</td><td>0.63</td></tr><tr><td>Q3</td><td>12</td><td>15</td><td>17</td><td>0.25</td><td>0.39</td><td>0.84</td></tr><tr><td>Q4</td><td>11</td><td>14</td><td>16</td><td>0.17</td><td>0.72</td><td>0.91</td></tr><tr><td colspan="7">NMW</td></tr><tr><td>Q1</td><td>15</td><td>17</td><td>19</td><td>0.59</td><td>0.82</td><td>0.95</td></tr><tr><td>Q2</td><td>16</td><td>18</td><td>&gt; 20</td><td>0.37</td><td>0.59</td><td>0.72</td></tr><tr><td>Q3</td><td>14</td><td>16</td><td>&gt; 20</td><td>0.28</td><td>0.44</td><td>0.94</td></tr><tr><td>Q4</td><td>14</td><td>15</td><td>17</td><td>0.19</td><td>0.81</td><td>1.01</td></tr><tr><td colspan="7"><strong>Overall Effect</strong></td></tr><tr><td colspan="7">Inflation</td></tr><tr><td>Q1</td><td>17</td><td>19</td><td>&gt; 20</td><td>0.34</td><td>0.49</td><td>0.58</td></tr><tr><td>Q2</td><td>19</td><td>&gt; 20</td><td>&gt; 20</td><td>0.17</td><td>0.29</td><td>0.36</td></tr><tr><td>Q3</td><td>16</td><td>&gt; 20</td><td>&gt; 20</td><td>0.14</td><td>0.23</td><td>0.76</td></tr><tr><td>Q4</td><td>16</td><td>&gt; 20</td><td>&gt; 20</td><td>0.1</td><td>0.67</td><td>0.88</td></tr><tr><td colspan="7">NMW</td></tr><tr><td>Q1</td><td>18</td><td>&gt; 20</td><td>&gt; 20</td><td>0.52</td><td>0.74</td><td>0.88</td></tr><tr><td>Q2</td><td>&gt; 20</td><td>&gt; 20</td><td>&gt; 20</td><td>0.32</td><td>0.56</td><td>0.69</td></tr><tr><td>Q3</td><td>&gt; 20</td><td>&gt; 20</td><td>&gt; 20</td><td>0.23</td><td>0.37</td><td>0.85</td></tr><tr><td>Q4</td><td>&gt; 20</td><td>&gt; 20</td><td>&gt; 20</td><td>0.16</td><td>0.74</td><td>0.99</td></tr></tbody></table>

Note: this table reports results on the dynamic aggregate effect of a shock on wages. In the first three columns we report the number of quarters before the cumulative effect is equal to 90, 95 or 98% of the long term effect of a shock on aggregate wages. Our criterion is the following: the first date at which the cumulative response is equal to a given ratio and this ratio should not be lower the four quarters ahead. The last three columns reports the ratio between the cumulative response and the long run effect measured at t (date of the shock), t+1 one quarter after the shock and t+2 two quarters after the shock. "Direct effect" is the case where the shock affects only base wages directly (and not wage floors). "Overall effects" is the case where we allow sectoral and national minimum wages to respond to the shock.
<table><caption>Table K: Long-Term Aggregate Effects - Robustness to the Probit Specification</caption><thead><tr><th></th><th>Direct</th><th>Direct + Indirect</th><th>Overall</th></tr></thead><tbody><tr><td colspan="4"><strong>NMW</strong></td></tr><tr><td>Baseline (Quarter effects)</td><td>0.129<br>(0.004)</td><td>0.162<br>(0.010)</td><td>0.172<br>(0.016)</td></tr><tr><td>No Quarter Effects</td><td>0.128<br>(0.005)</td><td>0.191<br>(0.003)</td><td>0.211<br>(0.002)</td></tr><tr><td>Quarter Effects * Before/After 2010</td><td>0.116<br>(0.002)</td><td>0.148<br>(0.003)</td><td>0.157<br>(0.002)</td></tr><tr><td>Time dummies</td><td>0.067<br>(0.002)</td><td>0.107<br>(0.006)</td><td>0.120<br>(0.004)</td></tr><tr><td>No duration effects (only quarters)</td><td>0.123<br>(0.003)</td><td>0.160<br>(0.003)</td><td>0.161<br>(0.004)</td></tr><tr><td colspan="4"><strong>CPI Inflation</strong></td></tr><tr><td>Baseline (Quarter effects)</td><td>0.239<br>(0.004)</td><td>0.286<br>(0.012)</td><td>0.417<br>(0.020)</td></tr><tr><td>No Quarter Effects</td><td>0.270<br>(0.003)</td><td>0.349<br>(0.006)</td><td>0.460<br>(0.008)</td></tr><tr><td>Quarter Effects * Before/After 2010</td><td>0.237<br>(0.004)</td><td>0.286<br>(0.005)</td><td>0.420<br>(0.004)</td></tr><tr><td>Time dummies</td><td>0.257<br>(0.002)</td><td>0.313<br>(0.002)</td><td>0.423<br>(0.002)</td></tr><tr><td>No duration effects (only quarters)</td><td>0.258<br>(0.005)</td><td>0.313<br>(0.005)</td><td>0.430<br>(0.011)</td></tr></tbody></table>

Note: This table reports results from simulation exercise described in section 4.4 where we allow wage floors and the NMW to react to changes in CPI and NMW but also to aggregate wage changes due to the response to the shock (feedback loop effects). Column (1) reports direct long run effects coming from the adjustment of wages to shocks under the assumption that wage floors and the NMW are not responding to shocks. Column (2) reports the indirect effect of the shock on base wages coming from the adjustment of wage floors to a given shock. The last column reports the overall effect of the shock on base wages including the direct effect, indirect effect coming from wage floor adjustments and feedback loop effects coming from the adjustment of NMW, wage floor and aggregate wage changes. 95%-confidence interval are provided in brackets and are obtained using bootstrap simulations.
<figure><img src="image_23.png" /><figcaption>Figure J: Aggregate Wage Effects of the NMW Along the Wage Distribution - Excluding Metalworking, Public Works and Construction Wage Agreements Covering Managers</figcaption></figure>

Note: We plot long-run effects of a 1% increase of the NMW on base wages by decile of the wage distribution. These effects are obtained using our simulation exercise where we allow for indirect effects through wage floor adjustment, NMW response and feedback loop effects. Simulations are made using parameter estimates from a Tobit model where all exogenous variables interact with dummy variables corresponding to deciles of the wage distribution. In blue dashed line, we plot the estimates obtained when we exclude from the sample workers covered by national wage agreements covering managers in the construction, public works and metalworking industries. In light red, we plot our baseline estimates.
# F Direct, Indirect and Feedback Loop Effects

<figure><img src="image_24.png" /><figcaption>Figure K: Direct and Indirect Effects of NMW on Wages</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>Figure L: Feedback Loop Effects of a Base Wage Increase</figcaption></figure>
# G Simulation Exercise: Detailed Algorithms

In this section, we present our simulation setting. We will denote:

* $cpi_t$, $w_t^{NMW}$, $w_{jt}^{WF}$, $w_{it}$ and $W_t$, respectively CPI at quarter $t$, NMW at $t$, sectoral wage floor at $t$ for industry and classification $j$, wage for individual $i$ at quarter $t$, and aggregate wage $W_t$.

* The notation $dX$ stands for the quarter-to-quarter variation of $X$

* The notation $\Delta X$ is the cumulated variation of $X$ since last wage change. The wage considered is either the NMW, sectoral wage floor or individual base wage depending on the wage variation defined by the equation.

We start with the fully simulated set-up without shocks (our benchmark simulation) described below in Algorithm 1. In Algorithm 2, we describe how this algorithm is modified to take into account for indirect effects. To obtain a setting without feedback loop, we use Algorithm 1 without the steps involving the update of $W_t$ and $dw_t^{NMW}$, that are instead taken as given and therefore not affected by the shock$^{45}$. To obtain a setting with only direct effects, we use Algorithm 1 with the previous modification and without updating $w^{WF}$ that is taken as given. In this last case, we only set new individual wages with $w^{WF}$, $W$, $w^{NMW}$ taken as the observed values and therefore not affected by the shock that only enters directly the equation of individual wages through the specified shock.

<footnote>$^{45}$Except when $dw_t^{NMW}$ is explicitly hit by a shock, but it is then computed with observed values plus the value of the shock without further modifications due to the variations of the aggregate wage entering the legal rule.</footnote>
**Algorithm 1** Simulation setting - with indirect effects and feedback loop - NO SHOCK

**Require:** {$dcpi_t$}$_{1\le t\le T}$, initial values at $t = 1$ (set at observed values) for all variables and their cumulated sums.

while $t \neq T$ do

if NMW has to be updated at $t$ then

$$dw_t^{NMW} = max(\Delta cpi_{t-1}, 0) + \frac{1}{2}(\Delta W_{t-1} - \Delta cpi_{t-1})$$

else

$$dw_t^{NMW} = 0$$

end if

(STEP t) Setting of new wage floors and individual wage changes and update cumulated values for $t + 1$

- Update the cumulated structure of wage floors and individual wages due to current minimum wage change:

$$\Delta w_{j,t}^{NMW} = \Delta w_{j,t}^{NMW} + dw_t^{NMW}$$

$$\Delta w_{i,t}^{NMW} = \Delta w_{i,t}^{NMW} + dw_t^{NMW}$$

- Set new wage floors for industry and job classification $j$ at quarter $t$:

$dw_{jt}^{WF} = F(\Delta_j cpi_t, \Delta_j w_t^{NMW}, \Delta_j W_{t-1}, \dots)$ as specified in the Tobit model for wage floors (Table F for parameter estimates)

- Update the cumulated structure of wage floors at the individual level:

$$\Delta w_{i,t}^{WF} = \Delta w_{i,t}^{WF} + dw_{j(i)t}^{WF}$$

- Set new individual wages for $i$ in industry and job classification $j$:

$dw_{it} = G(\Delta cpi_{it}, \Delta w_{it}^{WF}, \Delta w_{it}^{NMW}, \dots)$ as specified by the Tobit model described in Section 4.1 (Table 3 for parameter estimates)

$dW_t$ is computed as the weighted average of all simulated $dw_{it}$

- According to $dw_{jt}^{WF}$, update cumulated structure at $t + 1$ for wage floors for $X_t$ in $CPI_t, W_{t-1}$:

$$\Delta X_{j,t+1} = (\Delta X_{j,t} + dX_{j,t+1}) \times 1\{dw_{jt}^{WF} = 0\} + dX_{j,t+1} \times 1\{dw_{jt}^{WF} \neq 0\}$$

- According to $dw_{jt}^{WF}$, update cumulated structure at $t + 1$ for wage floors for $X_t = w_t^{NMW}$ ($dw_{t+1}^{NMW}$ is still to be determined):

$$\Delta X_{j,t+1} = (\Delta X_{j,t}) \times 1\{dw_{jt}^{WF} = 0\}$$

- According to $dw_{it}$, update cumulated structure at $t + 1$ for individual wages, except for $X \notin w^{WF}, w^{NMW}$:

$$\Delta X_{i,t+1} = (\Delta X_{i,t} + dX_{i,t+1}) \times 1\{dw_{it} = 0\} + dX_{i,t+1} \times 1\{dw_{it} \neq 0\}$$

- For $X \in w^{WF}, w^{NMW}$ ($dw_{t+1}^{WF}$ and $dw_{t+1}^{NMW}$ are still to be determined):

$$\Delta X_{i,t+1} = (\Delta X_{i,t}) \times 1\{dw_{it} = 0\}$$

end while
**Algorithm 2** Simulation setting - with indirect effects and feedback loop - WITH SHOCK

**Require:** {$dcpi_t$}$_{1\le t\le T}$, $t_s$ time of shock, variable potentially hit by a shock $\in \{CPI, NMW\}$, value of the shock $K$, and initial values at $t = 1$ (set at observed values) for all variables and their cumulated sums.

if the shock hits CPI **then**

$$dcpi_{t_s} = dcpi_{t_s} + K$$

end if

while $t \neq T$ do

if NMW is to be updated at $t$ **then**

$$dw_t^{NMW} = max(\Delta cpi_{t-1}, 0) + \frac{1}{2}(\Delta W_{t-1} - \Delta cpi_{t-1})$$

else

$$dw_t^{NMW} = 0$$

end if

if $t = t_s$ and the shock hits NMW **then**

$$dw_t^{NMW} = dw_t^{NMW} + K$$

end if

(STEP t) Setting of new wage floors and individual wage changes and update cumulated values for $t + 1$

as defined in algorithm 1.

end while
# H Long Term Effects of a Shock

In this appendix, we compute in a stylized case the long-term effect of a shock. The long-term effect can be decomposed in three terms: (1) the shock to the notional wage, (2) the effect of an increased frequency of wage changes, (3) a selection effect.

We represent as follows the process of wage adjustment:

$$ 
\begin{aligned}
R_{it}^* &= (X_t - X_{\tau_{it}}) \alpha + Z(t, \tau_{it}) b + \nu_{it} \\
w_{it} - w_{it-1} &= ((X_t - X_{\tau_{it}}) \beta + \varepsilon_{it}) 1\{R_{it}^* > 0\}
\end{aligned}
$$

where $X_t$ are time-varying macro variables affecting the potential wage and $Z(t, \tau_{it})$ are variables affecting the wage change probability (such as calendar effects). $\epsilon_{it}$ and $\nu_{it}$ may be correlated, we denote the correlation $\rho$, and assume $\sigma_\nu = 1$. Both residuals are assumed normal.

We compute the exact long term effect in the following simpler case. First, $Z(t, \tau) = 1$. Second, $X_t = S \times 1\{t \ge t_0\}$ varies only through the introduction of a shock $S$. Our simulation exercizes aim at finding the long term effects which we can not compute analytically in our more complex framework.

There, the model writes simply:

$$ 
\begin{aligned}
R_{it}^{*S} &= \alpha S \times 1\{t_0 > \tau_{it}^S\} + b + \tilde{\nu}_{it} \\
w_{it}^S - w_{it-1}^S &= (a + \beta S \times 1\{t_0 > \tau_{it}^S\} + \varepsilon_{it}) 1\{R_{it}^{*S} > 0\}
\end{aligned}
$$

We introduce a constant $a$ in the wage change equation.<fnref n="46" /> The shock appears in an individual trajectory $i$ until the occurrence of a wage change after $t_0$ (then, $\tau_{it}^S \ge t_0$).

Let us denote the event of no wage change since the shock $C_t = \{t_0 > \tau_{it}^S\}$. We may compute the evolution at $t$ with $S \neq 0$:

<footnote n="46">⁴⁶In our estimated model, it takes rather the form of a linear trend whose length depends on past duration since last wage change: $a(t - \tau_{it})$, which we approximate to simplify computations.</footnote>
$$ 
\begin{aligned}
E[W_t^S] - E[W_{t-1}^S] &= E[w_{it}^S - w_{it-1}^S] \\
&= E[w_{it}^S - w_{it-1}^S | C_t] \mathbb{P}(C_t) + E[w_{it}^S - w_{it-1}^S | C_t] (1 - \mathbb{P}(C_t))
\end{aligned}
$$

With respect to the situation without a shock, the difference of aggregate wage variation may be written as follows:

$$ E[w_{it}^S - w_{it-1}^S] - E[w_{it} - w_{it-1}] = (E[w_{it}^S - w_{it-1}^S | C_t] - E[w_{it} - w_{it-1}]) \mathbb{P}(C_t) $$

This is because after the shock, trajectory $i$ is the same in both situation and thus $E[w_{it}^S - w_{it-1}^S | C_t^C] = E[w_{it} - w_{it-1}]$. With the normality assumptions, we can easily check that:

$$ E[w_{it}^S - w_{it-1}^S] - E[w_{it} - w_{it-1}] = \left[ (a + \beta S)\Phi(\alpha S + b) - a\Phi(b) + \rho\sigma_{\epsilon} \times (\phi(\alpha S + b) - \phi(b)) \right] \times \Phi(-\alpha S - b)^{t-t_0} $$

Summing this difference from $t_0$ to $T$, and letting $T$ going to infinity, we obtain the following long-term effect:

$$ \beta S + \left(1 - \frac{\Phi(b)}{\Phi(\alpha S + b)}\right)a + \rho\sigma_{\epsilon}\frac{\phi(\alpha S + b) - \phi(b)}{\Phi(\alpha S + b)} $$

The first term reflects the shock to the notional wage in all trajectories, the second term corresponds to the effect of increasing the frequency of wage changes and the third term is the selection effect.
# Liste des documents de travail de la Direction des Études et Synthèses Économiques

ii

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

G 9020 G. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE
Macro-economic import functions with imperfect competition - An application to the E.C. Trade

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G 9310 J. BOURDIEU - B. COLIN-SEDILLOT
Les théories sur la structure optimale du capital : quelques points de repère

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G 9406 F. ROSENWALD
La décision d'investir

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

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

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

G 9410 F. ROSENWALD
Suivi conjoncturel de l'investissement

G 9411 C. DEFEUILLEY - Ph. QUIRION
Les déchets d'emballages ménagers : une analyse économique des politiques française et allemande

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

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

G 9414 I. KABLA
Le Choix de breveter une invention

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

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

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

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

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

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

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

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

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

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

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

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

G 9605 P. GENIER - S. JACOBZONE
Comportements de prévention, consommation d'alcool et tabagie : peut-on parler d'une gestion globale du capital santé ?
Une modélisation microéconométrique empirique
G 9606 C. DOZ - F. LENGLART
Factor analysis and unobserved component models: an application to the study of French business surveys

G 9607 N. GREENAN - D. GUELLEC
La théorie coopérative de la firme

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

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

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

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

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

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

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

G 9617 J.L. SCHNEIDER
La taxe professionnelle : éléments de cadrage économique

G 9618 J.L. SCHNEIDER
Transition et stabilité politique d'un système redistributif

G 9619 D. GOUX - E. MAURIN
Train or Pay: Does it Reduce Inequalities to Encourage Firms to Train their Workers?

G 9620 P. GENIER
Deux contributions sur dépendance et équité

G 9621 E. DUGUET - N. IUNG
R & D Investment, Patent Life and Patent Value
An Econometric Analysis at the Firm Level

G 9622 M. HOUDEBINE - A. TOPIOL-BENSAÎD
Les entreprises internationales en France : une analyse à partir de données individuelles

G 9623 M. HOUDEBINE
Polarisation des activités et spécialisation des départements en France

G 9624 E. DUGUET - N. GREENAN
Le biais technologique : une analyse sur données individuelles

G 9625 J.L. BRILLET
Analyzing a small French ECM Model

G 9626 J.L. BRILLET
Formalizing the transition process: scenarios for capital accumulation

G 9627 G. FORGEOT - J. GAUTIÉ
Insertion professionnelle des jeunes et processus de déclassement

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

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

G 9714 F. LEQUILLER
Does the French Consumer Price Index Overstate Inflation?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G 9805 P. CAHUC - Ch. GIANELLA - D. GOUX - A. ZILBERBERG
Equalizing Wage Differences and Bargaining Power - Evidence form a Panel of French Firms

G 9806 J. ACCARDO - M. JLASSI
La productivité globale des facteurs entre 1975 et 1996

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

G 9808 A. MOUROUGANE
Can a Conservative Governor Conduct an Accomodative Monetary Policy?

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

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

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

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

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

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

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

G 9902 Ch. COLIN
Modélisation des carrières dans Destinie

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

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

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

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

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

G 9908 Bilan des activités de la DESE - 1998

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

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

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

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

G 9913 Ch. GIANELLA
Une estimation de l'élasticité de l'emploi peu qualifié à son coût

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

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

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

G 9917 C. PRIGENT
La part des salaires dans la valeur ajoutée : une approche macroéconomique

G 9918 B. SALANIÉ
Estimating price cost margins, scale economies and workers' bargaining power at the firm level

G 9919 Ch. GIANELLA - Ph. LAGARDE
Productivity of hours in the aggregate production function: an evaluation on a panel of French firms from the manufacturing sector

G 9920 S. AUDRIC - P. GIVORD - C. PROST
Évolution de l'emploi et des coûts par qualification entre 1982 et 1996

G 9921 R. MAHIEU
Les déterminants des dépenses de santé : une approche macroéconomique

G 9922 C. ALLARD-PRIGENT - H. GUILMEAU - A. QUINET
The real exchange rate as the relative price of nontrables in terms of tradables: theoretical investigation and empirical study on French data

G 9923 J.-Y. FOURNIER
L'approximation du filtre passe-bande proposée par Christiano and Fitzgerald

G 9924 Bilan des activités de la DESE - 1999

G 9925 B. CREPON - F. ROSENWALD
Investissement et contraintes de financement : le poids du cycle Une estimation sur données françaises

G 9926 A. FLIPO
Les comportements matrimoniaux de fait

G 9927 R. MAHIEU - B. SÉDILLOT
Microsimulations of the retirement decision: a supply side approach

G 9928 C. AUDENIS - C. PROST
Déficit conjoncturel : une prise en compte des conjonctures passées

G 9929 R. MAHIEU - B. SÉDILLOT
Microsimulations of the retirement decision: a supply side approach

G 9930 C. AUDENIS - C. PROST
Déficit conjoncturel : une prise en compte des conjonctures passées

G 9931 R. MAHIEU - B. SÉDILLOT
Équivalent patrimonial de la rente et souscription de retraite complémentaire
G 2000/10 R. DUHAUTOIS
Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertiaire ?

G 2000/11 G. LAROQUE - B. SALANIÉ
Temps partiel féminin et incitations financières à l'emploi

G 2000/12 Ch. GIANELLA
Local unemployment and wages

G 2000/13 B. CREPON - Th. HECKEL
- Informatisation en France : une évaluation à partir de données individuelles
- Computerisation in France: an evaluation based on individual company data

G 2001/01 F. LEQUILLER
- La nouvelle économie et la mesure de la croissance du PIB
- The new economy and the measure ment of GDP growth

G 2001/02 S. AUDRIC
La reprise de la croissance de l'emploi profite-t- elle aussi aux non-diplômés ?

G 2001/03 I. BRAUN-LEMAIRE
Évolution et répartition du surplus de productivité

G 2001/04 A. BEAUDU - Th. HECKEL
Le canal du crédit fonctionne-t-il en Europe ? Une étude de l'hétérogénéité des comportements d'investissement à partir de données de bilan agréées

G 2001/05 C. AUDENIS - P. BISCOURP - N. FOURCADE - O. LOISEL
Testing the augmented Solow growth model: An empirical reassessment using panel data

G 2001/06 R. MAHIEU - B. SÉDILLOT
Départ à la retraite, irréversibilité et incertitude

G 2001/07 Bilan des activités de la DESE - 2000

G 2001/08 J. Ph. GAUDEMET
Les dispositifs d'acquisition à titre facultatif d'annuités viagères de retraite

G 2001/09 B. CRÉPON - Ch. GIANELLA
Fiscalité, coût d'usage du capital et demande de facteurs : une analyse sur données individuelles

G 2001/10 B. CRÉPON - R. DESPLATZ
Évaluation des effets des dispositifs d'allégements de charges sociales sur les bas salaires

G 2001/11 J.-Y. FOURNIER
Comparaison des salaires des secteurs public et privé

G 2001/12 J.-P. BERTHIER - C. JAULENT
R. CONVENEVOLE - S. PISANI
Une méthodologie de comparaison entre consommations intermédiaires de source fiscale et de comptabilité nationale

G 2001/13 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

G 2001/14 I. ROBERT-BOBEE
Modelling demographic behaviours in the French

microsimulation model Destinie: An analysis of future change in completed fertility

G 2001/15 J.-P. ZOYEM
Diagnostic sur la pauvreté et calendrier de revenus : le cas du "Panel européen des ménages »

G 2001/16 J.-Y. FOURNIER - P. GIVORD
La réduction des taux d'activité aux âges extrêmes, une spécificité française ?

G 2001/17 C. AUDENIS - P. BISCOURP - N. RIEDINGER
Existe-t-il une asymétrie dans la transmission du prix du brut aux prix des carburants ?

G 2002/01 F. MAGNIEN - J.-L. TAVERNIER - D. THESMAR
Les statistiques internationales de PIB par habitant en standard de pouvoir d'achat : une analyse des résultats

G 2002/02 Bilan des activités de la DESE - 2001

G 2002/03 B. SÉDILLOT - E. WALRAET
La cessation d'activité au sein des couples : y a-t-il interdépendance des choix ?

G 2002/04 G. BRILHAULT
- Rétropolation des séries de FBCF et calcul du capital fixe en SEC-95 dans les comptes nationaux français
- Retropolation of the investment series (GFCF) and estimation of fixed capital stocks on the ESA-95 basis for the French balance sheets

G 2002/05 P. BISCOURP - B. CRÉPON - T. HECKEL - N. RIEDINGER
How do firms respond to cheaper computers? Microeconometric evidence for France based on a production function approach

G 2002/06 C. AUDENIS - J. DERONYON - N. FOURCADE
L'impact des nouvelles technologies de l'information et de la communication sur l'économie française - un bouclage macro-économique

G 2002/07 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Évaluation de trois réformes du Régime Général d'assurance vieillesse à l'aide du modèle de microsimulation DESTINIE

G 2002/08 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

G 2002/09 F. HILD
Les soldes d'opinion résument-ils au mieux les réponses des entreprises aux enquêtes de conjoncture ?

G 2002/10 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 Familielle 1999

G 2002/11 J.-P. ZOYEM
La dynamique des bas revenus : une analyse des entrées-sorties de pauvreté

G 2002/12 F. HILD
Prévisions d'inflation pour la France

G 2002/13 M. LECLAIR
Réduction du temps de travail et tensions sur les facteurs de production

G 2002/14 E. WALRAET - A. VINCENT
- Analyse de la redistribution intragénérationnelle dans le système de retraite des salariés du privé
- Une approche par microsimulation
- Intragenerational distributional analysis in the french private sector pension scheme - A microsimulation approach

G 2002/15 P. CHONE - D. LE BLANC - I. ROBERT-BOBEE
Offre de travail féminine et garde des jeunes enfants

G 2002/16 F. MAUREL - S. GREGOIR
Les indices de compétitivité des pays : inter-prétation et limites

G 2003/01 N. RIEDINGER - E.HAUVY
Le coût de dépollution atmosphérique pour les entreprises françaises : Une estimation à partir de données individuelles

G 2003/02 P. BISCOURP et F. KRAMARZ
Création d'emplois, destruction d'emplois et internationalisation des entreprises industrielles françaises : une analyse sur la période 1986-1992

G 2003/03 Bilan des activités de la DESE - 2002

G 2003/04 P.-O. BEFFY - J. DERONYON - N. FOURCADE - S. GREGOIR - N. LAÎB - B. MONFORT
Évolutions démographiques et croissance : une projection macro-économique à l'horizon 2020

G 2003/05 P. AUBERT
La situation des salariés de plus de cinquante ans dans le secteur privé

G 2003/06 P. AUBERT - B. CRÉPON
Age, salaire et productivité
La productivité des salariés décline-t-elle en fin de carrière ?

G 2003/07 H. BARON - P.O. BEFFY - N. FOURCADE - R. MAHIEU
Le ralentissement de la productivité du travail au cours des années 1990

G 2003/08 P.-O. BEFFY - B. MONFORT
Patrimoine des ménages, dynamique d'allocation et comportement de consommation

G 2003/09 P. BISCOURP - N. FOURCADE
Peut-on mettre en évidence l'existence de rigidités à la baisse des salaires à partir de données individuelles ? Le cas de la France à la fin des années 90

G 2003/10 M. LECLAIR - P. PETIT
Présence syndicale dans les firmes : quel impact sur les inégalités salariales entre les hommes et les femmes ?

G 2003/11 P.-O. BEFFY - X. BONNET - M. DARRACQ-PARIES - B. MONFORT
MZE: a small macro-model for the euro area

G 2004/01 F. AUBERT - M. LECLAIR
La compétitivité exprimée dans les enquêtes trimestrielles sur la situation et les perspectives dans l'industrie

G 2004/02 M. DUÉE - C. REBILLARD
La dépendance des personnes âgées : une projection à long terme

G 2004/03 S. RASPILLER - N. RIEDINGER
Régulation environnementale et choix de localisation des groupes français

G 2004/04 A. NABOULET - S. RASPILLER
Les déterminants de la décision d'investir : une approche par les perceptions subjectives des firmes

G 2004/05 N. RAGACHE
La déclaration des enfants par les couples non mariés est-elle fiscalement optimale ?

G 2004/06 M. DUÈE
L'impact du chômage des parents sur le devenir scolaire des enfants

G 2004/07 P. AUBERT - E. CAROLI - M. ROGER
New Technologies, Workplace Organisation and the Age Structure of the Workforce: Firm-Level Evidence

G 2004/08 E. DUGUET - C. LELARGE
Les brevets accroissent-ils les incitations privées à innover ? Un examen microéconométrique

G 2004/09 S. RASPILLER - P. SILLARD
Affiliating versus Subcontracting: the Case of Multinationals

G 2004/10 J. BOISSINOT - C. L'ANGEVIN - B. MONFORT
Public Debt Sustainability: Some Results on the French Case

G 2004/11 S. ANANIAN - P. AUBERT
Travailleurs âgés, nouvelles technologies et changements organisationnels : un réexamen à partir de l'enquête « REPONSE »

G 2004/12 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

G 2004/13 C. PICART
Évaluer la rentabilité des sociétés non financières

G 2004/14 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Les retraites du secteur public : projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE

G 2005/01 S. BUFFETEAU - P. GODEFROY
Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945 à 1974

G 2005/02 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G 2005/03 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française

G 2005/04 M. LECLAIR - S. ROUX
Mesure et utilisation des emplois instables dans les entreprises

G 2005/05 C. L'ANGEVIN - S. SERRAVALLE
Performances à l'exportation de la France
et de l'Allemagne - Une analyse par secteur et
destination géographique

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

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

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

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

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

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

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

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

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

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

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

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

G2006/02 C. PICART
Les gazelles en France

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2007/12 V. ALBOUY - B. CRÉPON
Aléa moral en santé : une évaluation dans le
cadre du modèle causal de Rubin

G2008/01 C. PICART
Les PME françaises : rentables mais peu
dynamiques

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

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

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

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

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

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

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

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

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

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

G2008/12 C. AFSA
Analyser les composantes du bien-être et de son
évolution

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

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

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

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

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

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

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

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

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

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

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

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

G2009/11 V. BELLAMY - G. CONSALES - M. FESSEAU - S. LE LAIDIER - É. RAYNAUD

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

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

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

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

G2009/16 C. KLEIN - O. SIMON
Le modèle MÉSANGE réestimé en base 2000
Tome 1 – Version avec volumes à prix constants

G2010/01 C. MINODIER
Avantages comparés des séries des premières
valeurs publiées et des séries des valeurs
révisées - Un exercice de prévision en temps
réel

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

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

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

G2010/05 N. CECI-RENAUD - P.-A. CHEVALIER
Les seuils de 10, 20 et 50 salariés : impact sur la
taille des entreprises françaises
G2010/06 R. AEBERHARDT - J. POUGET
National Origin Differences in Wages and Hierarchical Positions - Evidence on French Full-Time Male Workers from a matched Employer-Employee Dataset

G2010/07 S. BLASCO - P. GIVORD
Les trajectoires professionnelles en début de vie active : quel impact des contrats temporaires ?

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

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

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

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

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

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

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

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

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

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

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

G2010/01 T. LE BARBANCHON - B. OURLIAC - O. SIMON
Les marchés du travail français et américain face aux chocs conjoncturels des années 1986 à 2007 : une modélisation DSGE

G2010/02 C. MARBOT
Une évaluation de la réduction d'impôt pour l'emploi de salariés à domicile

G2010/03 L. DAVEZIES
Modèles à effets fixes, à effets aléatoires, modèles mixtes ou multi-niveaux : propriétés et mises en œuvre des modélisations de l'hétérogénéité dans le cas de données groupées

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

G2011/05 J.-C. BRICONGNE - J.-M. FOURNIER V. LAPÈGUE - O. MONSO
De la crise financière à la crise économique L'impact des perturbations financières de 2007 et 2008 sur la croissance de sept pays industrialisés

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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