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

Julien Silhol, Lionel Wilner

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Julien Silhol, Lionel Wilner. Teachers' desired mobility to disadvantaged schools: Do financial incentives matter?. 2023. hal-05354432

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

## Documents de travail

N° 2023-09 - Avril 2023

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>
Institut national de la statistique et des études économiques

2023/09

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

Julien SILHOL* Lionel WILNER**

Avril 2023

Département des Études Économiques – Timbre G201
88, avenue Verdier – CS 70 058 – 92 541 MONTROUGE CEDEX – France
Tél. : 33 (1) 87 69 59 54 – E-mail : d3e-dg@insee.fr – Site Web Insee : http://www.insee.fr

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

<footnote>* Insee, AMSE. Email: julien.silhol@insee.fr.</footnote>

<footnote>** Insee, Crest, Ined. 88 avenue Verdier, 91120 Montrouge, France. Phone: (+33)187695917.
Please address correspondence to lionel.wilner@insee.fr.</footnote>
# Quel effet des primes spécifiques à l'éducation prioritaire sur les vœux de mobilité des enseignants ?

## Résumé

Cette étude s'appuie sur le doublement de la prime annuelle spécifique à l'exercice dans un établissement REP+, vu comme une expérience naturelle, afin de déterminer le rôle des incitations financières sur les souhaits de mobilité des enseignants envers ces établissements. Au moyen des données de vœux exprimés dans le cadre du mouvement intra-académique de Montpellier entre 2015 et 2019, ce travail montre que les enseignants les plus sensibles à cette hausse de la prime REP+ ont moins d'expérience et travaillent déjà dans un REP+. Des simulations contrefactuelles suggèrent que cette réforme ne s'est toutefois pas effectuée au détriment des REP ; elle aurait plutôt incité davantage d'enseignants à émettre un vœu de mobilité ou à choisir moins fréquemment des établissements non classés en éducation prioritaire.

**Mots-clés** : Mobilité des enseignants ; Incitations financières ; Préférences révélées ; Listes de vœux ordonnés ; Éducation prioritaire.

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

## Abstract

This paper exploits a 2018 reform of teachers' financial incentives to work in some French disadvantaged schools, and determines the role of incentives in teachers' stated preferences to move towards such schools based on this quasi-natural experiment. Using data from the internal human resource management of an educational authority, we find that most responsive teachers are less qualified, have less experience and are already working in such areas. Counterfactual simulations suggest that the policy has not hurt other disadvantaged schools, but rather induced some teachers not to remain in their current school or to opt less for regular schools.

**Keywords** : Teacher mobility; Financial incentives; Stated preferences; Rank-ordered choices; Disadvantaged schools.

**Classification JEL** : I21, I22, J45.
# 1 Introduction

To attract and retain good teachers is a challenge that disadvantaged schools have to face in many countries. In France, the allocation of public-tenured teachers results from a centralized mechanism, which helps mitigate the unequal distribution of high-quality teachers (e.g., with high qualification and more experience) over the territory. Nevertheless, teachers working in disadvantaged schools are less qualified, have less experience and are more likely to be contract staff (Benhenda, 2020). In turn, this allocation has adverse consequences on students’ outcomes: the link between teacher quality and student achievement has been widely studied, and on the whole, the literature suggests that teachers’ experience has a positive impact on pupils’ skills (Rockoff, 2004). In contrast, disadvantaged students are often taught by low-quality teachers (Chetty, Friedman, and Rockoff, 2014).

What can the policy maker do in order to temper such inequality? A commonly used public policy instrument consists in offering extra money to teachers working in disadvantaged schools. In practice though, since public teachers are civil servants, their wages can hardly vary within a grade, given a certain level of experience. The Department of Education may provide teachers working in those areas with supplementary financial incentives, namely an extra pecuniary bonus on top of their base salary. The effects of such programs on effective exit mobility have been studied, and the empirical evidence is rather mixed. Clotfelter et al. (2008) found a positive and significant effect of offering teachers an €1,800 annual bonus, in terms of turnover rates at disadvantaged schools in North Carolina. According to Prost (2013), an annual €960 bonus program in France was insufficient to retain teachers in disadvantaged schools. These differences in impact doubtlessly arise from differences in incentive levels.

This study proposes an empirical analysis which aims at answering different research questions related to that issue. What is the causal impact of financial incentives on teachers’ desired mobility? Are some teachers more responsive to such incentives? Which annual amount of bonus should be offered to compensate the lack of attractiveness of disadvantaged schools? What are teachers’ preferences for schools, which includes: how much do they dislike travel time, and how much do they value amenities with respect to income?

Our contribution is twofold. First, we rely on a substantial variation of the annual bonus granted to severely disadvantaged schools from 2018 onwards. We take advantage of this nationwide change decided by the policy maker as a quasi-natural experiment, which allows us to exploit the corresponding source of exogenous variation, and thus to infer the causal impact of financial incentives. In 2014, about
730 middle-schools were labelled Réseau d'Éducation Prioritaire (REP), and 360 middle-schools, Réseau d'Éducation Prioritaire renforcé (REP+). Both types of schools are disadvantaged, but REP+ schools are considered the most challenging in terms of working conditions. From that time onwards, teachers working in REP schools have received a €1,732 annual bonus, this amount remaining unchanged, while in REP+ schools the bonus was upgraded from €2,312 between 2014 and 2017, to €3,479 in 2018 and to €4,646 in 2019. These financial incentives represent nearly 13% of teachers' annual wage, and up to 20% as regards teachers less than 30. Compared with other settings where such pecuniary incentives have been provided, the current framework provides with a high-intensity treatment that is also more salient to the concerned individuals. Second, we investigate whether this gradual but large increase of the bonus at the intensive margin has an impact on desired teacher mobility, as opposed to effective mobility, i.e. to actual assignments. This is made possible because we dispose of unique application data issued by the internal human resource management of the French Department of Education in which we observe the rank-ordered lists of teachers requested for their school choices. Any teacher asking for a transfer has to fill out such lists indicating her stated preferences, including those who seek a placement in disadvantaged schools. We examine not only occurrences, but also absolute rankings of REP+ schools in those lists as our main outcomes.

To address those issues, we rely on rank-ordered lists filled out by teachers working in Montpellier's educational authority from 2015 to 2019. First, we conduct a reduced-form analysis that consists in a difference-in-differences approach based on the comparison between REP+ schools, the treatment group, and REP schools viewed as the comparison group. Both types of schools are disadvantaged: their proximity as well as their different exposure to the reform make them natural candidates for such an analysis. We empirically assess the validity of our empirical strategy by performing an event study analysis that supports the absence of any pre-trend before the reform; hence the common trend assumption (CTA) cannot be rejected in our data. To alleviate any concern about treatment spillovers on REP schools, hence to support the Stable Treatment Unit Value Assumption (SUTVA), we resort to a structural model of school choice.<fnref n="1" /> Importantly, this model leads us to conclude to the absence of negative externalities

<footnote n="1">¹This discrete-choice model takes the rivalry of alternatives into account, which the reduced-form does not. Indeed, the increased bonus in REP+ schools may indirectly affect the comparison group (REP schools) by making it relatively less attractive: it is therefore likely to decrease its share since school shares sum up, mechanically, to one every year. A discrete-choice model avoids this shortcoming of the reduced-form approach which may overestimate the pure effect of financial incentives.</footnote>
on those schools: we find that stronger financial incentives have enhanced the attractiveness of REP⁺ schools without hurting REP schools’ one, a yet recurrent critique made against the reform. We interpret this further result as empirical evidence against a possible contamination of the treatment to REP schools, which comforts the identification strategy based on REP schools as a valid comparison group. Our results point out to some positive, significant impact of the REP⁺ bonus. The larger the bonus, the higher the share of REP⁺ schools among top 1 choices: our point estimate of the impact of a supplementary €1,000 per year amounts to +1.4pp on the corresponding share (slightly less than 4% before 2018), resulting in an overall average treatment effect (ATE) of +3.1pp over the period, most of the change having intervened between 2017 and 2018 as confirmed by the event study. This reduced-form evidence is comforted by our results from the structural model, which point out to a +3.5pp effect.

Second, based on the school choice model, we estimate that an annual bonus of about €3,000 compensates the lower attractiveness of REP⁺ schools with respect to regular schools. From that viewpoint, the 2018 reform has resulted in some kind of net subsidy targeted toward the former since the current bonus, €4,646, exceeds that amount. We then simulate counterfactual scenarios in which the bonus would not have been increased, or without any bonus at all -another way to measure the causal impact of financial incentives. In this approach, we explain the annual share of each school among top 1 choices, relative to the fraction of teachers who decide to stay in their current school, by a set of covariates that includes school fixed effects, year dummies and financial incentives (namely the bonus, in euros). Our model performs quite well: when assessing how it fits observed variations in the share of REP⁺ schools among top 1 choices, simulated changes related to the pecuniary bonus alone explain a large part of these variations, while the remaining, unobserved change (due to residuals) is fairly small, suggesting that confounding factors, if any, would have been neutralized. Counterfactual experiments point out to a +2pp effect of the first €2,300 on that share (i.e. moving from no bonus to a +€2,300 annual bonus) but nearly a +3.5pp for the following €2,300 (i.e. moving from a +€2,300 bonus to a + €4,600 annual bonus). These results suggest that financial incentives need to be substantial in order to truly induce teachers join disadvantaged schools. Remember that, contrary to other frameworks where incentives are weak, the current programs involve annual bonuses which represent a non-negligible part of teachers’ annual wage. As a result, this paper sheds new light on the level of financial incentives -namely, substantial- that is required for such policies to reach their target. Finally, our counterfactual simulations show that 90% of the rise in REP⁺ share among top 1 choices is due to their higher
relative attractiveness with respect to the outside option (i.e. the option of not requesting any transfer) while the remaining 10% stems from their higher relative attractiveness with respect to regular schools.

Third, we estimate teachers' preferences for schools, including their travel costs and their marginal utility of income, based on a rank-ordered Logit. Contrary to previous approach, we take advantage of the richness of our data at the most disaggregated level, namely individual-year (i.e. list)-alternative (i.e. school), which enables us to adjust our previous estimates for travel costs, for instance. By definition, such a model is appropriate to infer preferences from ordinal choices; it also allows us to investigate whether teachers respond differently to financial incentives depending on their observed characteristics (qualification, experience, current workplace being a disadvantaged school already, etc.). Moreover, the model somehow embeds the previous difference-in-differences approach in that the source of identifying variation of the response to financial incentives relies on the differential change of the annual bonus over time, depending on the type of disadvantaged school.<fnref n="2" /> Results point out to an average cost of travel time equal to the opportunity cost of time, namely market hourly wage. The ranking of school quality (regular, REP and REP⁺) can therefore be inferred from the data. We also estimate that an annual bonus of +€2,600 would compensate for the relative disadvantage in terms of attractiveness of REP+ vis-à-vis regular schools, which builds upon previous estimates since the latter did not account for teachers' disutility for travel time. Last, we find that teachers' reaction to such incentives is in fact very heterogeneous and more pronounced for those at the beginning of their career, and already working in a disadvantaged school. Since our model includes travel time as an explanatory variable, we are able to quantify the effect of the bonus and express it in terms of equivalent commuting time saved daily.

The current article connects to a wide literature devoted to teacher recruitment in disadvantaged schools. Numerous empirical studies have shown that teachers highly value students' characteristics in their decision to remain in a school. For example, Hanushek, Kain, and Rivkin (2004), Falch and Strøm (2005) and Scafidi, Sjoquist, and Stinebrickner (2007), on the basis of panel data on post holders in public schools in the '90s, brought to light the factors which increase teachers' probability of leaving their schools, in Texas, Norway and Georgia respectively. Results are quite similar in these three settings. In Texas, teachers' mobility turns out to be much more related to students' characteristics (race and achievement) rather than to their own wage. In Norway, teachers tend to leave schools with

<footnote n="2">²Once again, this structural, discrete-choice model enables us to address any concern about SUTVA.</footnote>
a high proportion of minority students and a high share of students with special needs. In Georgia, teachers are more likely to leave schools with lower test scores, lower family incomes, or a higher proportion of minorities -the latter matters most in their decision. Although the French educational system has some specific features (teachers are civil servants and school assignment is centralized), similar patterns can be found in the data. On the basis of exhaustive mobility data on French teachers from 1987 to 1992, Prost (2013) found that teachers tend to switch schools when they face a high proportion of less able students, students from minority groups or from economically disadvantaged backgrounds.

In order to enhance the attractiveness of disadvantaged schools, policy tools include offering both pecuniary and non-pecuniary incentives to teachers in order to attract and retain a high-quality staff, so that school heads do not have to fill vacancies with inexperienced or less qualified teachers.<fnref n="3" /> This is all the more important that teacher quality, especially experience, has apparently a positive impact on students’ achievement (Rockoff, 2004; Rivkin, Hanushek, and Kain, 2005). The effects of these policies are yet not clear-cut and depend on the institutional background, the design and the extent of incentives. For instance, the selection of disadvantaged schools by authorities in such programs could have stigmatising effects, as suggested by Beffy and Davezies (2013). On the basis of a three-year panel from North-Carolina, Clotfelter et al. (2008) found a significant effect of an annual $1,800 bonus on disadvantaged schools’ turnover rates. In Norway, from 1992 to 2003, teachers got a premium of about 10% when they worked in schools with a high degree of teacher vacancies; Falch (2011) found that this wage premium reduced the probability of resignation. On the contrary, Prost (2013), studying a program implemented in September 1989, found that the bonus amounts, which increased from €25⁴ per month in 1990 to €79 in 1992, were insufficient to retain teachers in these schools. Programs can also aim at improving teacher quality. Cowan and Goldhaber (2018) documented an increase in the share of certified teachers in disadvantaged schools consecutive to a Washington state program. The latter awarded some bonus (15% of the annual salary) to national board certified teachers working in those schools. Last, some programs are not based on financial incentives. A French program implemented in 2005 consists in offering teachers who accept to spend more time in disadvantaged schools higher chances of being assigned next to their preferred area, and of choosing their next

<footnote n="3">³In France where assignment system is highly centralized, Benhenda (2020) estimates that the share of contract-staff among teachers in disadvantaged middle-schools in 2014 is about 12%, against less than 8% in other middle-schools.</footnote>

<footnote n="4">⁴This amount corresponds namely to an increase of about 2% for a novice teacher, and only 1% for the most qualified or experienced one.</footnote>
school. Benhenda and Grenet (2020) evaluated that this program had a statisti-
cally significant and positive impact on the number of consecutive years teachers
remain in these schools.

The rest of the paper is organized as follows. Section 2 is devoted to the teacher labour market in France and contains a brief history of disadvantaged schooling programs including REP and REP+ bonuses. The data containing stated preferences is described in section 3. Section 4 presents the reduced-form evidence. Counterfactual simulations which quantify the impact of stronger financial incentives on the share of REP+ schools among top 1 choices are exposed in section 5. Section 6 explores the heterogeneity of responses and quantifies the relative importance of teachers’ preferences for money and time. Section 7 concludes.

2 Institutional background

2.1 Teacher allocation

In France, public-tenured teacher allocation to middle- and high-schools is fully
centralized. The procedure takes place each year. To match teachers’ requests and
human resources needs, two rounds are based on a system with a priority index
called *barème*. At the end of a first round called *mouvement inter* (namely a trans-
fer between educational authorities<fnref n="5" />), beginners and tenured teachers who request
a transfer are assigned to some regional educational authority. A second round
called *mouvement intra* (namely a transfer within a given educational authority)
allocates then teachers into the schools themselves. Three types of teachers par-
ticipate to that second round: beginners, teachers coming from another region but
allocated to this very region at the issue of the first round, and teachers who are
already assigned to a position in the region, but who are willing to work in another
school. In the latter case, teachers have a supplementary option: it is possible for
them to remain in the same position if no match can occur at the issue of the
second round. By contrast, beginners and teachers coming from another region
who were not assigned to any school after that round are allocated where there is
a vacancy. During the second round, teachers submit a rank-ordered list (ROL)
that comprises between one and twenty choices. Stated choices may correspond
either to a school, a teaching supply area or a geographical area; in that latter
case, a teacher’s choice is considered as fulfilled whenever she is assigned to any
school in the desired area. We assume that this centralized mechanism induces
teachers to reveal sincerely their preferences, hence available in the corresponding

<footnote n="5">⁵There are 30 regional educational authorities, or rectorates, all over the French territory.</footnote>
rank-ordered lists, given that (i) the mechanism includes no application cost, (ii) teachers are aware of injunctions coming by both the Department of Education and unions, (iii) they have a good knowledge of the matching process, but also (iv) they face some uncertainty at the time of application. Further theoretical details about the algorithm in charge of teachers' allocation to schools are provided in Appendix C.1.

## 2.2 Disadvantaged schools programs

Since 1982, the French Department of Education has developed programs targeted towards disadvantaged schools. It aimed at tempering social inequality in educational outcomes, which the policy maker thus acknowledged. Disadvantaged schools were labeled *Zones d'Éducations Prioritaires (ZEP)* at the time, and selected according to socioeconomic criteria. This compensatory educational policy includes both financial and non-financial components: it allocates more resources to these schools (for instance, the ratio of the number of students over the teaching staff is lower), and supports local pedagogical initiatives. As they tend to avoid schools with underprivileged socioeconomic background pupils, teachers working in disadvantaged schools have received a bonus, the expected objectives of which are, admittedly, to attain a greater stability of pedagogical teams and to attract a higher proportion of tenured teachers. The justification for this “affirmative action”, or compensatory scheme, which contrasted at the time with centralized principles that usually prevail in French policy rules, is that an “unequal” investment should help recover equality.

ZEP concern primary schools (1st to 5th grades) and lower secondary schools (6th to 9th grades), as well as upper secondary schools (10th to 12th), but to a lesser extent. The number of ZEP schools has been regularly increasing from about 10% of secondary schools in 1982 to nearly 15% at the end of the '90s.

In the mid-'00s, the efficiency of ZEP policies was put into question (Bénabou, Kramarz, and Prost, 2009). A program was then launched, which included non-financial incentives, and which was targeted towards a short list of middle-schools, in order to encourage teachers to stay longer in the same school (Benhenda and Grenet, 2020).

## 2.3 The 2018 reform

In 2014, a new list of schools was published by the Department of Education, which was deemed to remain unchanged for several years. These schools received a label that was changed from ZEP to Réseau d'Éducation Prioritaire (REP),
and divided further into REP and REP⁺. The word réseau (network) puts the
emphasize on teamwork within educational staff. Furthermore, schools became
systematically networked in the sense that, when a secondary school was labeled
as REP or REP⁺, that label also applied to the primary schools from which their
pupils come. In the end, this new program includes fewer schools than the initial
ZEP program, but financial resources are now targeted in such a way that public
funding increased for each of the concerned schools -especially in REP⁺ schools.

Since 2014, REP and REP⁺ schools as well as the specific resources allocated
to those schools have remained remarkably stable. However, an important public
policy change occurred from 2018 onwards: the annual bonus granted to each
and any teacher working in REP⁺ schools has been gradually increased. Its face
value finally doubled in 2019 when compared with 2017. This paper precisely
exploits this variation viewed as a quasi-natural experiment, an exogenous shock
on financial incentives decided and implemented by the policy maker. In 2015,
annual bonuses in REP (resp. REP⁺) schools were equal to €1,734 (resp. €2,312)
and remained unchanged until 2017. During the 2018-2019 period, bonuses did
not vary in REP schools while they increased linearly in REP⁺ schools: €3,479
in 2018 (+50% with respect to baseline year 2017, up to inflation), and €4,646
in 2019 (+100% with respect to baseline year 2017, up to inflation), as shown by
Figure 1.<fnref n="6" /> Compared with the average annual wage of a teacher aged less than
30,<fnref n="7" /> these annual bonuses represent an incremental salary of about 7% in REP,
but 9% in REP⁺ between 2015 and 2017, 14% in 2018 and about 19% in 2019.
An advantage of the current setting lies therefore in such a bonus being large and
salient to the concerned teachers. Another evidence in favor of the salience of this
policy lies in that this increase of the REP⁺ bonus was announced by candidate
Emmanuel Macron during his 2017 presidential campaign. On top of that, there
has been no other reform related to priority education in secondary schools which
could have acted as a confounding factor during this period. There was a much
publicized contemporary reform, though, which consisted in the reduction of class
sizes, but this change concerned primary schools only.

<footnote n="6">⁶from 4/3 to 2 and finally to 8/3 times the bonus granted in REP schools.</footnote>

<footnote n="7">⁷Thanks to the *Système d'Information sur les Agents du Servive Public (SIASP)*, an admin-
istrative database on civil servants' wages, we can compute that wage for teachers under 30 and
working in a middle-school of Montpellier's educational authority: about €24,500 on average,
including overtime and all types of bonus at the exception of REP- and REP⁺-specific bonus.
For teachers over 40 working in the same schools, a REP-specific bonus constitutes a 5% increase,
against 7% for REP⁺-specific bonus between 2015 and 2017, 10% in 2018 and 14% in 2019.</footnote>
# 3 Data

The application data used here is provided by the human resources department of the Montpellier's educational authority. It contains all assignment preferences stated in the form of rank-ordered lists by teachers in this rectorate between 2015 and 2019. As regards teachers, the data contains information about their gender, their age, their location, the subject they taught, and the school where they currently work (the latter information being available neither for beginners nor for supply teachers). We also know their qualification, i.e. whether they hold the CAPES or an *Agrégation*,<fnref n="8" /> and their seniority. For each school-specific choice, the data includes the school's education level (i.e. whether it is a middle-school or a high-school), its location, its labeling as a REP or a REP⁺, and the corresponding bonus.

On top of the previous database, we resort to administrative data in order to get an exhaustive set of teachers working within the Montpellier's educational authority between 2015 and 2019. Thanks to that database, we are able to compute annual shares of teachers applying to other schools within the authority (see Table 7). These shares decrease over time: at first sight, the 2018 reform might have encouraged some teachers to join REP⁺ schools (from nearly 200 choices before to 300 choices after), but had seemingly little impact, at the extensive margin, on the number of teachers requesting any transfer at all -hence on the probability of requesting some transfer since the headcount of teachers in the authority had remained rather stable all over that period. Meanwhile, the average number of choices per rank-ordered list also diminished, which suggests that the reform didn't lead teachers to simply add up REP⁺ schools in their application files. Moreover, the number of choices toward REP schools did not decrease over that period while regular schools were less often elicited on those lists. On the whole, this empirical evidence is consistent with the hypothesis of the reform having an impact at the intensive margin: a same pool of teachers would now favor REP⁺ as opposed to regular schools. However, it could also be that, in the absence of the reform, more teachers would have requested some transfers: in that case, the reform would have induced more teachers to stay in place -perhaps it would have retained teachers in REP⁺ schools, remember Prost (2013) for instance. The model developed in section 5 will enable us to disentangle among the two concurring explanations, namely a crowding-out of choices toward regular schools by REP⁺ schools, as opposed to less transfer requests. Moreover, by breaking down the shares of teachers

<footnote n="8">⁸CAPES accounts for *Certificat d'Aptitude au Professorat dans l'Enseignement Secondaire* and is the main teaching certification level. The *Agrégation* is an advanced qualification obtained at the issue of a more competitive examination.</footnote>
requiring some transfer according to the type of school where they are currently
assigned at the time when they apply, Table 8 actually suggests that the sharpest
decrease of teachers willing some transfer is found among REP+ schools (about
one half). This further empirical evidence is therefore also consistent with the
reform retaining teachers in those disadvantaged areas, hence with a higher sta-
bility of pedagogical teams. However, the focus of this paper is the causal impact
of financial incentives on attracting teachers in disadvantaged schools -not on re-
taining them; on top of that, we lack data on teacher allocation into schools, hence
we do not address the latter question.

Moreover, this external dataset enables us to analyze the characteristics of
teachers requesting some transfer within the rectorate, i.e. participating to the
second round of the process. The SIASP file contains administrative information
on each and any civil servant in France, including teachers; it includes usual socio-
demographic characteristics (gender, age, residence location, qualification) on top
of the annual wage and the current workplace, i.e. school of assignment. Moreover,
by comparing variables present both in our database and in SIASP, we are able
to proceed to a statistical matching, hence to identify in SIASP the teachers who
are requesting a transfer and those who are not. This procedure allows us to
characterize the sub-sample of teachers requesting a transfer (mostly the share
among all teachers and observed characteristics), which enables us (i) to define
the outside option of the model used in section 5 for counterfactual simulations,
and (ii) to address possible selection concerns. Appendix Table 10 shows actually
that those teachers are more often young (hence they are more likely to be in the
early stage of their career), male, hold more frequently the Agrégation,

When they fill out their ROL, teachers have to indicate between 1 and 20
choices. There is substantial dispersion about the number of stated choices, namely
the length of these lists: see the corresponding pattern displayed by Figure 2. The
capacity constraint of the lists turns out to bind for about 10% of ROLs. In that
case, the weakly truth-telling (WTT) assumption (see Appendix C.1) casts doubts,
which questions sincere revelation of preferences. On top of cognitive costs among
other application costs, the capacity constraint inherent to the mechanism at stake
may refrain individuals from truthfully reporting their complete preferences. In
a conservative approach, we select therefore these ROLs out of our estimation
sample.

Since both teachers' and school locations' zipcodes are available in the data,
we are able to compute commuting times thanks to the Metric App' based on
centers of corresponding zipcodes.<fnref n="9" /> Appendix Table 11 shows an apparently strong correlation between the rank in the list and the commuting time, which suggests that the latter is a key determinant of school choice. It will be therefore necessary to take it into account when modelling teachers’ preferences in a structural fashion.

Our working sample is defined as follows. Table 12 in Appendix depicts in details the successive selection steps from the original data provided by the Montpellier's educational authority. This process leaves us with 5,902 ROLs (about 41% of the ROLs included originally) after selecting out:

* ROLs filled out by beginners and teachers coming from another educational authority. Remember from section 2.1 that those teachers are allocated to fill up vacancies when none of their choices can be afforded. This stressful situation is likely to induce strategic behavior here (those ROLs might not reveal preferences);

* ROLs filled out by vocational teachers, vocational schools being unaffected by REP/REP⁺ programs;

* ROLs where WTT may not hold, mostly lists with 20 stated choices and lists filled out by beginners who can get an extra non-pecuniary bonus, expressed in points on their priority index, as regards their top 1 choice in the list; revealing truthfully one's preferences is then not a dominant strategy.

* ROLs with geographical preferences only. When applying to schools, teachers may opt for geographical choices, which, by definition, correspond to any school within some administrative area -either a municipality, a group of municipalities, a *département* or the whole educational authority. We exclude those choices from our main analysis, but provide with a robustness check in which we split those choices into as many schools as they correspond to, see Appendix C.4 on that topic.

We also select out lists such that a move is suspected, for instance when the minimal commuting time within a list exceeds one hour. We do not consider either specific positions like school counsellors. Last, some specific stated choices (teaching in a jail, for instance) are eliminated, too -but the rest of the list, if any, is not. After this process, we obtain our working sample when we keep remaining school-specific choices only (cf. row 6 of Table 12).

<footnote n="9">⁹When a teacher ranks a school located in her own municipality of residence, the average travel time from this municipality is imputed.</footnote>
Table 9 in Appendix provides with some descriptive statistics on teachers from the working sample. 60% of them are women. The median age is 42. 85.5% of second-round lists are filled out by holders of the CAPES while the remaining 14.5% correspond to a teacher detaining an *Agrégation*. From year to year, the priority index fluctuates, which is both related to changes in the rules defining its computation and to changes in the composition of teachers asking for a different assignment than their current one. The average expected commuting time is about 28 minutes, but this variable exhibits substantial dispersion; nearly a third of stated choices (32.2%) correspond to schools being located in the same municipality as the one where teachers live.

Figure 3 displays the evolution of the share of REP⁺ schools among all choices: that share increased from 2.9% in 2017 to 4.9% in 2018 and to 6.9% in 2019. Meanwhile, the share of REP schools hardly changed over that period. The same diagnosis holds more or less as regards that share among top 1 choices: Figure 4 shows that this share moved from 3.7% in 2017 to 6.2% in 2018 and finally to 9.3% in 2019, hence an increase by +2.5pp between 2017 and 2018, and by +5.6pp between 2017 and 2019. Last, much heterogeneity is at stake: Table 13 in Appendix details the corresponding changes in shares depending on teachers’ level of education, seniority and qualification.

# 4 Reduced-form evidence

We propose here a reduced-form evaluation of the impact of stronger financial incentives in REP⁺ schools from 2018 onwards. To that aim, we consider two outcomes related to teachers’ desired mobility towards these schools: (i) the share of REP⁺ schools among all school choices, and (ii) the share of REP⁺ schools among top 1 choices only. In the Online Appendix, we also focus on the rank of REP⁺ schools in teachers’ rank-ordered lists.

## 4.1 Share of REP⁺ schools among all choices

First, we investigate whether the policy change had an impact on the share of REP⁺ schools among all school choices made explicitly by teachers when filling out their rank-ordered lists. The question we address here is whether observed changes have been truly induced by the policy, and by how much.

Our identification strategy relies on a difference-in-differences approach where REP⁺ schools constitute the treatment group and REP schools are the comparison group. This method takes advantage of the reform, which we view as a
quasi-natural experiment that provides us with exogenous variation in the determinants of teachers’ school choices. Our choice of the comparison group is guided (i) by the fact that the annual bonus in REP schools did not vary over the period of study while it increased linearly in REP⁺ schools from 2018 onwards, and (ii) by the proximity of REP and REP⁺ schools in terms of observed (and probably unobserved) characteristics as regards the quality of education, teachers and pupils, including their headcount within Montpellier’s educational authority (17 REP⁺ schools and 16 REP schools) and their observed shares (about 3-4%). Based on Figure 3, the share of REP⁺ schools among all choices increased from 2.9% in 2017 to 4.9% in 2018 and to 6.9% in 2019; meanwhile, the share of REP schools hardly varied from 2017 (3.6%) to 2018 (3.8%), and increased slightly to 4.5% in 2019 (+0.8pp). As a result, the unconditional difference-in-differences estimates of the average treatment effects (ATE) induced by the change in teachers’ incentives is (4.9 − 2.9) − (3.8 − 3.6) = +1.8pp in 2018, and (6.9 − 2.9) − (4.5 − 3.6) = +3.1pp in 2019.

Removing regular schools from the estimation sample here, and denoting by $s_{ikt}$ the share of $k$-type schools ranked by teacher $i$ on year $t$ where $k \in \{\text{REP}, \text{REP}^+\}$, we consider the following estimating equation to implement a difference-in-differences approach with controls:

$$s_{ikt} = a\text{REP}_k^+ + b\text{REP}_k^+ \times 1\{t \ge 2018\} + \nu_t + \mu_{ik} + u_{ikt}, \quad (1)$$

where REP⁺ₖ is a dummy that equals one when $k = \text{REP}^+$, $\nu_t$ are year dummies, $\mu_{ik}$ is a fixed-effect accounting for unobserved teachers’ taste for $k$-type schools, and $u_{ikt}$ is some idiosyncratic error term, which we assume normally distributed. Standard errors are clustered at the teacher level, allowing for possible autocorrelation of individual preferences over time. The coefficient $a$ refers to the average difference in attractiveness between REP and REP⁺ schools over the period while $b$ is the main coefficient of interest, i.e. the ATE.

The results are provided by columns 1 and 2 of Table 1. The estimated ATE is significant at usual levels. Column 1 points out to an estimated ATE of nearly +2.4pp, close to the average of unconditional estimates. After controlling for teacher × $k$-type of school fixed-effects, i.e. for teachers’ unobserved taste for either REP schools or REP⁺ schools, the point estimate falls to +2.1pp (column 2) while remaining statistically significant at usual levels. Figure 5 displays the event study analysis.<fnref n="10" /> The corresponding point estimates are fairly close to

<footnote n="10">¹⁰Replacing $b$ with $b_t$ in equation (1) normalizing point estimates to zero in 2017, i.e. one year before the implementation of the reform.</footnote>
unconditional estimates in 2018 (a significant +1.7pp) and in 2019 (a significant +2.5pp).

It is possible that previous estimates do not capture the mere effect of financial incentives alone, since non-financial incentives promoting REP⁺ schools may have also been provided by the policy maker over the period. For instance, the reduction of class size in REP⁺ primary schools has been implemented from 2017 onwards, through a duplication of first and second grades. Though this public policy did not impact directly REP⁺ middle-schools nor high-schools, it may have been interpreted as a positive signal towards these disadvantaged areas, and such changes might be viewed as confounding factors in the above estimating equations. To disentangle the impact of financial from non-financial incentives -in other words, to identify teachers’ marginal benefit of income in their elicited preferences for schools-, we now rely on the gradual increase in the pecuniary bonus implemented in REP⁺ schools from 2018 onwards. Hence we consider the following econometric specification:

$$s_{ikt} = \tilde{a}\mathrm{REP}_{k}^{+} + \tilde{b}\mathrm{BONUS}_{kt} + \tilde{\nu}_{t} + \tilde{\mu}_{ik} + \tilde{u}_{ikt}, \quad (2)$$

which helps us separate the impact of pecuniary incentives alone (encompassed in the coefficient $\tilde{b}$) from the effect of non-pecuniary incentives (by comparing the corresponding magnitude implied by this estimate $\tilde{b}$ with $\hat{b}$).

The results are displayed by columns (3) and (4) of Table 1. An increase of €1,000 would imply a significant increase of +1.15pp in the share of REP⁺ schools among all choices. Remembering that the pecuniary bonus increased in REP⁺ schools by €1,167 both in 2018 and in 2019, this result suggests that financial incentives alone explain most of the effect found above (about +1.3pp every year, to be compared with previous +1.7pp in 2018 and +2.5pp in 2019).

Importantly, the shares of disadvantaged schools have experienced rather similar variations before 2018, which could already be observed based on descriptive statistics, and which the event study analysis confirms. Such empirical evidence claims in favor of the absence of any differential pre-trend that would jeopardize the plausibility of the identification strategy. Put differently, the common trend assumption (CTA), according to which our outcomes would have experienced parallel trends in both comparison and treatment groups, cannot be discarded in the data.

Our identification strategy relies also on the Stable Unit Treatment Value Assumption (SUTVA), which might be violated if the comparison group, REP⁺ schools here, was indirectly affected by the treatment. Such spillovers are likely
with outcomes based on shares among choices because shares of REP, REP⁺ and regular schools sum up to one every year, due to rivalry of mutually exclusive alternatives. Hence, when the share of REP⁺ increases, it follows mechanically that either the share of REP schools or the share of regular schools decreases - perhaps both of them. Previous estimates might then suffer from a contamination bias which might lead to overestimate the true ATE: the treatment is likely to decrease the share of the comparison group, which would result in a larger differential with the share of the treatment group. To overcome this limitation of the reduced-form approach, we believe that a structural model relaxing SUTVA is needed in order to empirically assess the causal impact of incentives on teachers' desired mobility, which we will be doing in section 5.

## 4.2 Share of REP⁺ schools among top 1 choices

Second, we consider another outcome, replacing previous outcome $s$, i.e. the share of REP⁺ schools among all choices, with $s_1$, i.e. that very share among top 1 choices only, while adopting the same identification strategy as above. Note that this outcome is likely closer to the realized mobility, i.e. to the actual match between a teacher and a school, but that in the absence of data on teacher assignment, this hypothesis cannot be tested. At the individual level, $s_1$ is simply a dummy equal to one when a teacher ranks some REP⁺ school first on her list.<fnref n="11" /> The results are given in Table 2¹² while Figure 6 reports the estimates from the event study. Remember from Figure 4 that unconditional estimates were equal to $(6.2-3.7)-(3.0-3.9) = +3.4pp$ in 2018, and to $(9.3-3.7)-(5.5-3.9) = +4.0pp$ in 2019. The event study estimates turn out to be close ($+3.6pp$ and $+3.0pp$ respectively) -if any, slightly lower in 2019. Finally, the estimated ATE is $+3.2pp$, and still $+3.1pp$ after controlling for teacher × $k$-type of school fixed effects. All those point estimates are significant at usual levels, which suggests that the attractiveness of REP⁺ schools has increased consecutive to the reform with respect to REP schools' one.

Columns 3 and 4 of Table 2 focus on the impact of financial incentives alone. A €1,000 bonus would induce a supplementary increase of +1.4pp of the share of REP⁺ schools among top 1 choices with respect to the corresponding share of REP schools, after controlling for teachers' unobserved taste for REP and REP⁺

<footnote n="11">¹¹A binary model could be used here, instead of a linear specification, which would nevertheless lead to similar conclusions; corresponding estimates are available upon request.</footnote>

<footnote n="12">¹²The number of observations differs slightly from the ones in Table 1 because our estimation sample contains some rank-ordered lists from which the top 1 choice has been removed, see Appendix C.4 for details.</footnote>
schools. Hence the sole €1,167 increase would have been responsible for +1.6pp
each year, i.e. +1.6pp in 2018 and +3.2pp in 2019. Non-financial incentives would
then matter: financial incentives alone would not fully explain both observed and
estimated differentials in shares in 2018, immediately consecutive to the reform.
A concurring explanation would lie in teachers anticipating future increase as soon
as 2018.

Overall, our estimations suggest that the policy was efficient at enhancing the attractiveness of REP⁺ schools. Both descriptive evidence and causal analysis based on difference-in-differences and event study approaches point out to shares of REP⁺ schools among all (or top 1) choices being multiplied by about two within only two years. Raw statistics show that those shares rose from 2.9 to 6.9% and from 3.7 to 9.3%, respectively; unconditional DinD estimates amount to nearly +3.1pp and up to +4.0pp; event study estimates yield +2.5pp and +3.0pp. The effect of financial incentives alone would be +1.15pp per extra €1,000 as regards the share among all choices, and +1.4pp per extra €1,000 as regards the share among top 1 choices: in other words, those incentives would be the source of almost the whole effect (+2.7pp and +3.3pp), given the observed variation in bonus (+€2,334) induced by the public policy at stake.

Another interesting conclusion that can be drawn from previous reduced-form
analysis has to do with the plausibility of the WTT assumption in our setting.
The proximity of estimated ATEs relative to shares among all choices, on the
one hand, and to shares among top 1 choices, on the other hand, suggests that
restricting our attention to the top of the list can be done without bias, compared
with considering the whole list -as should be the case under the WTT assumption.
Indeed, according to the latter, teachers rank their most-preferred schools by their
true preference order, but not necessarily rank all schools: remember that the
median observed length of rank-ordered lists is slightly above 4, which means that
teachers rank about 4 schools out of 266 only. Overall, this empirical evidence
supports the WTT assumption in the current context, and in what follows, we
restrict our attention to top choices only.

5 Counterfactual analysis

We seek here to simulate counterfactual 2018 and 2019 years such that bonuses
would be kept unchanged at their 2017 level in REP⁺ schools. By comparison
with observed outcomes on those years, these simulations yield an evaluation of
the causal impact of the change in financial incentives decided by the policy maker.
Other thought experiments can be simulated, including a series of counterfactual years 2015 to 2019 in which bonuses in either REP or REP+ schools would be removed, for instance. Those exercises allow us to disentangle finely the role played by each and any variation in corresponding pecuniary amounts, and help us quantify the possibly nonlinear effect of income. All over that section, we aggregate the data at the school-year level and our outcome of interest is schools' share among top 1 choices.

## 5.1 Econometric specification

We adapt a competition model introduced by Berry (1994). In his model, consumers face a number of differentiated products and purchase the one that maximizes their utility; they may also decide not to purchase at all, i.e. choose the outside option. This model can straightforwardly be applied to our setting by letting teachers play the role of consumers and schools the role of products. Such a discrete-choice model is more appropriate to deal with shares that sum up to one: the rivalry of mutually exclusive alternatives needs to be taken into account, especially in order to address any concern about SUTVA. Each year, we consider a single market, namely the whole Montpellier's educational authority, with a fairly large number of schools.<fnref n="13" /> The outside option corresponds to teachers in the educational authority who do not request any transfer at all. Every year t from 2015 to 2019, the share of a given school j among top 1 choices, i.e. the analogue of the product market share $s_{jt}$, divided by $s_{0t}$ the share of the outside option, i.e. the fraction of teachers whose top 1 choice is their current school (they do not fill out any ROL in that case), depends only on characteristics of that school:

$$ \log s_{jt} - \log s_{0t} = \delta_{jt} \equiv \beta \text{BONUS}_{jt} + \alpha_j + \gamma_t + \xi_{jt}, \quad (3) $$

where $\delta_{jt}$ is the mean utility level, specified as a linear index of (i) attractiveness of school j, encompassed here by the fixed-effect $\alpha_j$, (ii) financial incentives $\text{BONUS}_{jt}$, (iii) aggregate shocks common to all schools in the educational authority, captured by year dummies $\gamma_t$ as well as (iv) unobserved local demand shocks $\xi_{jt}$ that account for any residual change in the reputation of school j on year t. This formula is derived from the theoretical share in a multinomial Logit:

$$ s_{jt} = \frac{e^{\delta_{jt}}}{\sum_{k=0}^{J} e^{\delta_{kt}}}, \quad (4) $$

<footnote n="13">¹³266, remember Table 12 for instance. In Berry's model, the asymptotics is in the number of products/schools.</footnote>
where $j = 1, \dots, J$ indexes schools. As usual, $\delta_{0t} = 0$ is the normalization of the mean utility level provided by the outside option.<fnref n="14" /> This approach assumes that teachers’ preferences are uncorrelated with their current school. Put differently, we allow for that current school to affect the probability of filling out a ROL, i.e. of requesting some transfer, but not to shape preferences over schools. This exclusion restriction enables us to identify preferences<fnref n="15" /> under WTT since elicited preferences are rank-ordered at the top of the lists by assumption. The current model does not include any taste heterogeneity, it can rather be interpreted as a model of pure vertical differentiation whereby teachers all agree on school quality. By contrast, next section will relax this assumption by allowing more heterogeneity at the individual level. We view those approaches as complement: the current one adopts the centralized viewpoint of the educational authority, hence at an aggregate level, while the model developed in next section departs from this approach and focuses on determinants of school choice at the teacher level.

The estimation procedure exploits estimating equation (3) and consists in in-
verting the empirical counterparts of theoretical shares $\hat{s}_{jt}$ in order to recover the
mean utility levels $\hat{\delta}_{jt}$ which rationalize those observed shares. This equation is
particularly convenient since it is linear in the parameters of interest. Under this
approach, unobserved local demand shocks $\xi_{jt}$ are the residuals of a regression
where the dependent variable, a logarithmic transform of shares, is explained by a
set of covariates that comprises the bonus on top of year- and school- fixed effects.
Standard errors are clustered at the school level. In order to perform counter-
factual experiments described below, though, this method requires to dispose of
schools that are always present, i.e. chosen, in the sample, hence to deal with the
problem of zeroes. This standard problem in empirical IO, and more generally the
selection problem in applied econometrics, turns out to have no straightforward
solution, especially in this setting, see for instance D'Haultfœuille, Durrmeyer,
and Février (2019) for an attempt of solving that issue. We follow here the ap-
proach developed by Gandhi, Lu, and Shi (2021) and replace observed shares
by their Laplace transform $s_{jt}^L = \frac{N_t s_{jt} + 1}{N_t + J + 1}$,<fnref n="16" /> which allows us to handle the case
where $s_{jt} = 0$.

The point estimate $\hat{\beta}$ is 0.239 with a standard error of 0.076 (Table 3): it
is positive and significant at usual levels, suggesting once again that financial
incentives did increase schools’ perceived attractiveness. The inclusion of school

<footnote n="14">¹⁴Such a normalization is required for identification purposes since $(\beta, \alpha_j, \gamma_t, \delta_{0t}, \xi_{jt})$ and $(\beta, \alpha_j, \gamma_t - \delta_{0t}, 0, \xi_{jt})$ are observationally equivalent.</footnote>

<footnote n="15">¹⁵for schools and for money (among other amenities) as well.</footnote>

<footnote n="16">¹⁶$N_t$ designates the total number of teachers in the educational authority, requesting a transfer or not on year $t$.</footnote>
fixed effects in that equation is nevertheless crucial to obtain that positive sign, as was already the case in the reduced-form analysis: otherwise, the bonus captures merely the relative distaste for disadvantaged schools, and the sign of $\hat{\beta}$ is negative.

From previous estimates, it can already be assessed whether the program has reached its target from an “equality of opportunity” viewpoint. Comparing the estimated attractiveness in regular schools and in disadvantaged schools gives a proxy of the comparative advantage of the former over the latter, the goal of the bonus being to compensate for such a differential. Disposing of estimated schools’ attractiveness, net of any pecuniary bonus, enables us to determine whether the policy has attained its primary objective. More precisely, we compute $\hat{\alpha}_j$—regular, on the one hand, and $\hat{\alpha}_j$—REP$^+$ + $\hat{\beta}$BONUS$_{jt}$, on the other hand, both in 2018 and 2019. First, the average difference $\hat{\alpha}_j$—REP$^+$ — $\hat{\alpha}_j$—regular is negative, consistently with the current segmentation of schools.<fnref n="17" /> To some extent, our model is therefore able to infer unobserved school quality from the data<fnref n="18" />. Second, the magnitude of the difference in attractiveness between REP$^+$ schools and regular schools is about −0.71, which means that teachers should be compensated for by about €3,000¹⁹ annually in order to be indifferent between a regular school and a REP$^+$ school.<fnref n="20" /> Remembering that the annual bonus was €2,312 in 2017, €3,479 in 2018 and €4,646 in 2019, the policy has more than compensated the attractiveness differential from 2018 onwards. In 2018, the compensation is almost perfect: their attractiveness, proxied by the average share among top 1 choices, 6.2%, is roughly equal to the overall fraction of REP$^+$ schools (17/266 ≈ 6.4%) within the educational authority. In 2019, some over-compensation is at stake: our estimates suggest that, on average, REP$^+$ schools became more attractive than regular schools, with a positive differential of +.4, consistently with a higher observed share among top 1 choices (9.3%). In that sense, our model performs quite well when replicating shares at the aggregate level, and enables us to infer preferences for both schools and money accurately.<fnref n="21" />

<footnote n="17">¹⁷The same holds as regards $\hat{\alpha}_j$—REP — $\hat{\alpha}_j$—regular.</footnote>

<footnote n="18">¹⁸The rank-ordered Logit in next section will provide with similar results, from a qualitative viewpoint.</footnote>

<footnote n="19">¹⁹i.e. approximately 1,000 × (−0.71/0.239).</footnote>

<footnote n="20">²⁰Including geographical choices as well would not alter the diagnosis, see Table 19 in section C.4.</footnote>

<footnote n="21">²¹In the same vein, the observed share of REP schools is always below 16/266 ≈ 6% between 2015 and 2019, consistently with our estimates of net attractiveness being less than regular schools’ average.</footnote>
## 5.2 Breaking down the evolution of shares

We implement next a series of thought experiments that allow us to break down the observed changes in school shares from 2017 to 2019 along two channels: (a) financial incentives, and (b) demand (or attractiveness) shocks. For the sake of readability, we focus on aggregate shares (the shares of REP, REP+ and regular schools) that are conditional on requesting some transfer: by definition, these shares sum up to one annually. We start with the environment that prevailed in 2018 and 2019 (same demand conditions and same bonus in disadvantaged schools) and successively replace certain parameters with their values in 2017. Specifically, we simulate the following counterfactual situations:

(a) We change the bonus, that is, we replace its current value in 2018 or 2019 with its value in 2017. We compute the shares that prevail after the change, thus assessing the pure effect of financial incentives by computing the shares in the absence of the 2017 reform. The counterfactual mean utility levels are:

$$ \tilde{\delta}_{jt}^{a} = \hat{\delta}_{jt} - \hat{\beta} (\text{BONUS}_{jt} - \text{BONUS}_{j2017}) \quad \forall t = 2018, 2019, \quad (5) $$

which neutralizes the reform implemented by the policy maker from 2018 onwards. The counterfactual shares are:

$$ \tilde{s}_{jt}^{a} = \frac{e^{\tilde{\delta}_{jt}^{a}}}{\sum_{k=1}^{J} e^{\tilde{\delta}_{kt}^{a}}}, \quad (6) $$

paying attention to the fact that the denominator starts at $k = 1$ (instead of $k = 0$) since the shares are conditional on not choosing the outside option.

(b) We change the demand parameters, namely the time-varying, local attractiveness shocks $\hat{\xi}_{jt}$ and replace them in 2018 and 2019 with their value in 2017. Note that changing aggregate demand shocks $\hat{\gamma}_t$ on year $t$ would not change anything at all since such terms cancel out from conditional shares (6) which are, in fact, independent from such common shocks to all schools. To isolate the sole role of local demand shocks, we compute conditional shares $\tilde{s}_{jt}^b$ issued from counterfactual mean utility levels $\tilde{\delta}_{jt}^b$ as before:

$$ \tilde{\delta}_{jt}^{b} = \hat{\delta}_{jt} - (\hat{\xi}_{jt} - \hat{\xi}_{j2017}) \quad \forall t = 2018, 2019. \quad (7) $$

By definition, combining (a) and (b) yields exactly the same observed shares as in 2017 since corresponding mean utility levels are identical. The fit of the model is perfect, by definition, once it has been fed up with the correct residuals, because these residuals are designed to match observed conditional shares. As
a result, in order to break down observed changes in school shares among top
1 choices, and given the specification (3) adopted here, it is both necessary and
sufficient to disentangle financial incentives from unobserved local demand shocks
only.

Table 4 displays the results of this decomposition exercise.<fnref n="22" /> It confirms that financial incentives play a role in explaining the sharp increase in the demand for REP⁺ schools after 2017 (+2.0pp in 2018 and +4.4pp in 2019) in a context that would have been favorable to these schools even in the absence of the reform. Indeed, both 2018 and 2019 local demand shocks turned out to enhance REP⁺ schools’ attractiveness when compared with 2017 ones: neutralizing the change in local demand shocks yields lower shares (5.9 and 7.4%) than the observed ones (6.2 and 8.6%) on this balanced sample. The reform has thus acted as a supplementary force on top of good reputation shocks in 2018 and 2019. This empirical evidence looks all the more plausible that the role played by unobserved local demand shocks, i.e. residuals in equation (3), is limited.<fnref n="23" /> Besides, this decomposition reveals that the share of REP schools would have not been much affected by the reform: in the absence of the reform, their counterfactual share in 2019 amounts to 5.8% whereas their observed share is 5.6%.

## 5.3 Counterfactual experiments: the role of financial incentives

To explore further the role played by the sole financial incentives, one can imagine many other counterfactual experiments corresponding to various amounts of financial incentives $\tilde{\text{BONUS}}_{jt}$, compute counterfactual mean utility levels $\tilde{\delta}_{jt} = \hat{\delta}_{jt} - \hat{\beta}(\text{BONUS}_{jt} - \tilde{\text{BONUS}}_{jt})$ and simulate conditional schools’ shares among top 1 choices. For instance, remembering Figure 1, and in order to gradually decompose the impact of each and any change in financial incentives, one may consider four distinct $\tilde{\text{BONUS}}_{jt}$:

(0) $\tilde{\text{BONUS}}_{jt} = \text{BONUS}_{jt}$, i.e. the observed bonus, which yields $\tilde{\delta}_{jt}^0 = \hat{\delta}_{jt}$; this experiment corresponds to the observed situation.

<footnote n="22">²²Minor differences between Figure 4 and Table 4 (“Observed” rows) are due to the Laplace transform.</footnote>

<footnote n="23">²³By contrast, omitting school fixed-effects in equation (3) would lead to diametrically opposite results: a minor role played by financial incentives, and unobserved demand shocks acting as confounding factors, i.e. explaining the vast majority of the seemingly higher attractiveness of REP⁺ schools -a scenario which is harder to believe in. More precisely, corresponding counterfactual shares would be 6.2% in 2018 and 8.4% in 2019 when neutralizing the reform, i.e. quite close to observed shares (6.2% and 8.6%). Holding unobserved local demand shocks at their 2017 level would yield shares of 4.4% in 2018 and 4.5% in 2019.</footnote>
(1) $\widetilde{\text{BONUS}}_{jt} = \text{BONUS}_{j2017}$, i.e. the amount prevailing in 2017 in each type of schools (€1,734 in REP schools and €2,312 in REP$^+$ schools), which yields $\tilde{\delta}_{jt}^1$; this experiment neutralizes the reform and corresponds exactly to previous experiment (a), penalizing REP$^+$ schools only from 2018 onwards;

(2) $\widetilde{\text{BONUS}}_{jt} = \text{BONUS}_{jt}$, i.e. the (minimum) amount prevailing in REP schools over the period (€1,734), regardless of the REP/REP$^+$ status, which yields $\tilde{\delta}_{jt}^2$; this experiment penalizes REP$^+$ vis-à-vis REP schools;

(3) $\widetilde{\text{BONUS}}_{jt} = 0$: we remove any bonus everywhere, which yields $\tilde{\delta}_{jt}^3$ -this experiment penalizes unambiguously disadvantaged schools (both REP and REP$^+$) vis-à-vis regular schools.

All those experiments are such that $\widetilde{\text{BONUS}}_{jt} \le \text{BONUS}_{jt}, \forall j, t$: as a result, given formula (4) and since $\hat{\beta} > 0$, (i) each counterfactual share of REP$^+$ schools will be unambiguously smaller than (or equal to) its observed value, and (ii) each counterfactual share of regular schools will be unambiguously higher than (or equal to) its observed value. Equivalently, the counterfactual share of disadvantaged schools, i.e. the sum of the shares of REP and REP$^+$ schools, will be unambiguously lower. The magnitudes of such changes are, however, to be quantified by the simulations. Furthermore, the relative position of the counterfactual share of REP schools with respect to its observed value is ambiguous: it empirically depends on the magnitude of $\hat{\beta}$. In practice, we will see that it hardly changes at all, even in experiment (1).

Figure 7 displays the contribution of each experiment to the differential between observed data (experiment 0) and complete removal of incentives (experiment 3). An eyeball analysis is sufficient to convince oneself that those effects are credibly in line with Figure 1. Due to the functional form adopted, our simulations point out to a nonlinear impact of money. Moving from €2,312 to €4,646 in 2019, a difference of €2,334, would increase the share of REP$^+$ schools by 3.5pp from 5.1% to 8.6%. Yet moving from €0 to €2,312 would increase it by 2pp only from 3.1% to 5.1%. As an intermediate experiment, increasing that bonus from €1,734 to €2,312, a change of €578, would cause the corresponding share to rise by 0.6pp from 4.5% to 5.1%. Those results suggest nevertheless that substantial amounts of money shall be offered to teachers so that they are truly encouraged to ask for disadvantaged schools: remember that financial incentives at stake may represent up to 20% of an annual wage, as opposed to the smaller shares of 8-10% found, e.g., in Clotfelter et al. (2008).

Last, Table 5 focuses on experiment (1) -the same as experiment (a) above- and displays the corresponding changes in unconditional shares, i.e. taking the
outside option into account. This exercise enables us to answer a concern raised
by teachers’ unions: the reform would benefit to REP+ schools but hurt REP
schools, which would therefore not be Pareto-improving. According to such an
hypothesis, the share of REP+ schools may well increase, but the share of REP
schools would decrease and the total share of disadvantaged schools might even
be reduced. However, based on the current sample and the assumptions of our
model, we find no empirical evidence in favor of such an hypothesis. First, Table 8
confirms that teachers working in REP schools do not make more transfer requests
consecutive to the reform. Second, the share of REP+ schools would have increased
consecutive to stronger financial incentives, but 91% of this change would be due
to business stealing from the outside option, i.e. to the fact that teachers not
requesting any transfer in the absence of the reform would change their mind
consecutive to supplementary incentives; put differently, the number of transfer
requests has decreased over time, but we estimate that in the absence of the reform
this number would have been even lower. The remaining 8% would be explained
by business stealing from regular schools, i.e. to teachers otherwise willing to join
some regular school, and who would opt for a REP+ school instead. The fraction
of teachers who trade off a REP school against a REP+ school in their rank-order
list due to these supplementary incentives would be negligible (less than 1%).

On top of tempering this public policy concern, the fact that REP schools
have not suffered -even indirectly- from the reform alleviates other methodolog-
ical concerns regarding a possible violation of SUTVA, which would endanger
our reduced-form approach. Previous results suggest that REP schools constitute
an appropriate comparison group,<fnref n="24" /> since adverse spillovers of the treatment can
apparently be neglected. Interestingly, the previous difference-in-differences has
concluded to a +1.4pp impact of supplementary €1,000 on the share of REP+
schools among top 1 choices, a figure that is completely in line with the current
+3.5pp estimate obtained from the counterfactual simulations, and corresponding
to a €2,334 change. All those results look pretty consistent with one another,
though obtained thanks to different methods, which comforts us in our evalua-
tion of the reform at stake. They are also weakly suggestive of the reform having
enhanced the stability of the pedagogical team in REP+ schools, through both
higher incoming and lower outgoing mobility, though further data on teacher as-
signment (on top of a longer period of observation, probably) would be required
to empirically assess the latter statement.

<footnote n="24">²⁴This would not be the case of regular schools, which we consider as such when looking at
the rank in lists, though.</footnote>
# 6 Heterogeneous responses to financial incentives

Why does heterogeneity in teachers’ response to the policy change matter? An important policy concern with such compensatory programs has to do with selection, i.e. the risk of attracting teachers who already work in disadvantaged schools, or younger teachers, with less experience, etc. By contrast, enhancing teaching quality might require to recruit older teachers, with more seniority and some experience in regular schools. Unfortunately, disadvantaged schools are often composed of young teachers seeking to escape as soon as possible, i.e. as soon as their priority index enables them to leave, and we will show that those teachers who actually respond to such incentives belong rather to that category. As a result, it is to fear that this reform has not achieved at improving teaching quality; at the very least, it has not changed notably the composition of teaching staff in REP⁺ schools. Another important outcome to look at would be the stability of the pedagogical team, as mentioned above.<fnref n="25" />

To deal with that heterogeneity, we make best use of our micro-data available at a very detailed level, namely teachers’ rank-ordered lists including multiple school choices viewed as alternatives. This comparative advantage of our data helps us learn more about teachers’ preferences and about their heterogeneous attitudes towards money and time. Based on elicited preferences, we resort to a structural model of school choice, which enables us to infer teachers’ travel costs but also their marginal utility of income. To identify the latter, we still use the reform as a source of exogenous variation in financial incentives: we encompass previous difference-in-differences approach within a rank-ordered discrete-choice model. We are then able to quantify the opportunity cost of travel time, or equivalently to express the marginal benefit of income in terms of saved daily commuting time. We estimate a model that is designed to handle rank-ordered lists, namely a rank-ordered Logit model (see, e.g., Abdulkadiroğlu et al., 2020; Fack, Grenet, and He, 2019). Relying on both cross-sectional and longitudinal variation, the estimation will lead to document a substantial heterogeneity in teachers’ responses to financial incentives.

In what follows, we assume that teacher $i$ working in current school $s$ opting for school $j$ among the $J_t$ schools in Montpellier’s educational authority on year $t^{26}$ derives the following net utility, denoting by $B_{ist}$ the benefit enjoyed when he works in his current school and by $C_{ist}$ the mobility cost incurred when he decides

<footnote n="25">²⁵The ultimate outcome would be students’ achievement as proxied by test scores -a valuable information which, unfortunately, we do not have.</footnote>

<footnote n="26">²⁶We drop hereafter the index $t$ and denote $J_t$ by $J$ only.</footnote>
to leave that school:

$$U_{ijst} = V_{ijst} + B_{ist} - C_{ist} + \varepsilon_{ijst} = \pi_j + \eta_i \text{BONUS}_{jt} - \lambda_i d_{ij} + B_{ist} - C_{ist} + \varepsilon_{ijst}. \quad (8)$$

By contrast, if this teacher does not request any transfer, he gets his reserve utility, his outside option, and does not incur any mobility cost. It is however useless to specify any outside option in the current setting since it won't play any role in teachers' ranking among alternatives/schools j. Indeed, we adopt a conditional approach by focusing on schools ranked in ROLs only, as opposed to ROLs that would be augmented by the outside option, namely his current school s, which would lie somewhere at the bottom of his ROL -but the researcher would not know exactly where (i.e. before or after other unranked schools?). We focus rather on the top of those lists, i.e. on schools that are actually observed in our application data. This approach is valid and exempt from any selection bias under the WTT assumption, some empirical support in favor of which has already been provided by previous reduced-form evidence. Doing so guarantees that the outside option won't play any role in our estimation procedure. On top of that, teachers' choice among alternatives j is independent from $B_{ist} - C_{ist}$ due to this term canceling out from the corresponding discrete choice. This is conditional on the fact that the worst elicited alternative yields a higher utility than the outside good $U_{i0st}$ -or equivalently, that those schools are ranked according to true preference ordering, which must, again, be the case since under WTT those alternatives are revealed preferred to the outside good.

The above specification states that teachers make a trade-off between school attractiveness $\pi_j$, unobserved to the econometrician, and distance $d_{ij}$. For the sake of simplicity, we assume linear travel costs, but we perform a robustness check by considering a quadratic specification (see Table 16 in Appendix). Moreover, when school-specific bonuses are available, as is the case in disadvantaged schools, the previous trade-off is mitigated by financial incentives, increasing the global attractiveness to the level $\pi_j + \eta_i \text{BONUS}_{jt}$ if $\eta_i$ designates teacher's marginal utility of income.

Without loss of generality, i.e. since $B_{ist} - C_{ist}$ cannot be identified from the sole choice among alternatives j, we denote by

$$\bar{V}_{ijt} \equiv \pi_j + \eta_i \text{BONUS}_{jt} - \lambda_i d_{ij} \quad (9)$$

the mean utility level obtained when choosing school j. The idiosyncratic error terms $\varepsilon_{ijst}$ are supposed to follow some extreme-value (type I) distribution. From
previous assumptions, the conditional probability of observing the rank-ordered
list $r_{it} = (r_{i1t}, \dots, r_{iJt})$, given observed and unobserved school- and teacher- char-
acteristics, writes:

$$
\mathbb{P}(U_{ir_{i1}st} > \dots > U_{ir_{iJ}st} | U_{ir_{iJ}st} \ge U_{i0st}, \text{BONUS}_{jt}, d_{ij}; \theta_{ij}) = \prod_{j=1}^{J-1} \frac{\exp(\bar{V}_{ir_{ij}t})}{\sum_{k=j}^{J} \exp(\bar{V}_{ir_{ik}t})} \quad (10)
$$

where the right-hand side of equation (10) accounts for the standard closed-form
used in the rank-ordered Logit literature -a model being sometimes called the
"exploded Logit".

Structural parameters of the model $\theta_{ij} = (\eta_i, \lambda_i, \pi_j)$ include the aversion to travel time $\lambda_i$, the marginal benefit of income $\eta_i$, two parameters which possibly vary across individuals, and school fixed-effects $\pi_j$. The identification of the model, i.e. of the vector $\theta$, stems from (i) the exogeneity of distance at the time of teachers’ reporting their preferences, as far as $\lambda_i$ is concerned; (ii) the exogenous variation in financial incentives provided by the 2018 reform, this source of identifying variation allowing us to recover $\eta_i$; and (iii) parametric assumptions (the additive separability of the utility into the mean utility level and the error term, the distribution of errors, and the specification of the mean utility level itself) leading to the monotonicity of formula_10 with respect to $\bar{V}_{ijt}$ and thus to $\theta_{ij}$. Those parameters can be estimated consistently by a conditional likelihood maximization procedure: from Beggs, Cardell, and Hausman (1981), equation (10) provides with a concave log-likelihood, which guarantees the existence and uniqueness of the maximizer. Importantly, we do not face any incidental parameter problem here: should have $B_{ist} - C_{ist}$ not been normalized to zero, would this term have disappeared from the individual likelihood, which renders that normalization unimportant. As regards inference, standard errors are clustered at the teacher level, which accounts therefore for possible autocorrelation of residuals over time in teachers’ reporting their preferences.

Our econometric specification is motivated by the desire to account for het-
erogeneity in teachers’ responses, which may be relevant from a public policy
perspective. First, travel costs may be higher for older individuals and females,
as both the literature and Table 10 in Appendix suggest.<fnref n="27" /> As a result, we posit

<footnote n="27"><sup>27</sup>Teachers with high travel costs, including older individuals and women, according to that Table, may less be prone to request some transfer. It is then fair to assume that, when requesting a transfer, those individuals still incur high travel costs.</footnote>
the following observed heterogeneity on the coefficient $\lambda_i$:

$$ \lambda_i \equiv \lambda_0 + \lambda_a f(Age_i) + \lambda_f Female_i, \qquad (11) $$

where $f(.)$ is some flexible functional form. Second, teachers may be more or less responsive to financial incentives, depending on their qualification, their professional experience, and on whether they already work in a disadvantaged school:

$$ \eta_i \equiv \eta_0 + \eta_n \text{Novice}_i + \eta_b \text{Beginner}_i + \eta_r \text{REP}_i + \eta_+ \text{REP}_i^+ + \eta_a \text{Agrégation}_i + \eta_c \text{Classe normale}_i \qquad (12) $$

where Novice$_i$ (resp. Beginner$_i$) is a dummy equal to 1 when teacher $i$'s seniority is less than 3 years (resp. comprised between 4 and 8 years), and REP$_i$ (resp. REP$_i^+$) is another dummy equal to 1 when the teacher $i$ is already working in a REP (resp. REP$^+$) school.<fnref n="28" />

In alternative specifications, one could allow for further heterogeneity, for instance in perceived school attractiveness which may itself differ according to teachers' characteristics (i.e. $\pi_{ij}$): Abdulkadiroğlu et al. (2020) estimate a similar model separately for each cell $c(i)$ such that all teachers within a cell share common observed characteristics like age, gender, seniority, etc. This approach would yet face here the problem of estimating the $\pi_{c(i)j}$ parameters consistently, a difficult task when the cell size becomes small.

Table 6 confirms that teachers dislike commuting time, i.e. that travel costs matter in their school choice. Yet no significant heterogeneity is found as regards age, probably because of composition effects -oldest individuals being scarce in our data because they are less likely to request any transfer. On the whole, financial incentives matter in the sense that the point estimate $\hat{\eta} \approx 0.260$ is significantly positive (column 1) in the homogeneous model where $\eta_i = \eta$ and $\lambda_i = \lambda, \forall i$. Provided that teachers have on average 10 trips per week and work 36 weeks a year, this figure points out to an opportunity cost of time of nearly 11.5€ per hour -about €1,600 per month, which compares with teachers' median monthly wage. On average, the €2,334 increase between 2017 and 2019 would have represented an equivalent gain of almost 68 minutes saved daily for the concerned individuals.

However, those average numbers mask substantial heterogeneity in teachers' responses to financial incentives, see column 2. For instance, novice teachers, i.e. teachers with at most 3 years of seniority, are almost twice more sensitive to the

<footnote n="28">²⁸This variable is measured with error: for instance, teachers who replace temporarily absent teachers may opt for the school where they are currently working in. However, this has a marginal impact: removing those teachers from our estimation does not affect our results.</footnote>
bonus: for them, an extra €1,000 is associated with an extra and highly significant point estimate of 0.219, which means that it is worth saving up to 8 more minutes per commuting trip (16 minutes per day). Similarly, teachers already working in a REP⁺ school may be reluctant to lose their bonus when opting for a school outside of the REP⁺ network: this would happen, for instance, if they were subject to loss aversion, or simply due to downwards nominal wage rigidity.

It is also worth noting that our estimates of the average gross<fnref n="29" /> unobserved taste $\pi_j$ for REP⁺ schools lies behind the one prevailing in REP schools, which is itself dominated by regular schools’ one. This inferred ranking suggests that our model is able to properly account for the implicit hierarchy in school quality. Equipped with such estimates $\hat{\pi}_j$, we can go further and quantify the equivalent surplus brought by an extra €1,000. Put differently, since the reform has increased the bonus by €2,312, it has enhanced the attractiveness of REP⁺ schools by about 2.312×0.260 ≈ 0.6, i.e. by about one third of a standard deviation in attractiveness (1.852). It is thus possible to evaluate whether the policy has been successful in filling the gap between disadvantaged and regular schools. Before 2018, in REP⁺ schools that estimated net attractiveness amounted to about −0.16+0.26× 2.312 ≈ 0.45 while in REP schools it was close to −0.09 + 0.26 × 1.734 ≈ 0.39, to be compared with a point estimate of 0.53 in regular schools. In 2019, the compensatory scheme implemented in REP⁺ schools is such that the perceived attractiveness of those schools is virtually increased by the policy maker at a level equal to −0.16 + 0.26 × 4.646 ≈ 1.05, which now exceeds the one prevailing in all other schools -a diagnosis which is largely reminiscent of the results found in previous section. Also, consistently with previous section, we estimate that a €2,600 annual bonus would compensate for the relative disadvantage in terms of attractiveness between REP⁺ and regular schools, after disutility for travel time have been accounted for.<fnref n="30" />

Last, the fit of this structural model, defined at the individual level, cannot be perfect, due to the ordered nature of the outcome, among others.<fnref n="31" /> This is the reason why we chose to aggregate the data at the school-year level in section 5 and to consider schools’ shares among top 1 choices -a continuous variable- as our preferred outcome. This procedure, combined with the discrete-choice model

<footnote n="29">²⁹i.e. in the absence of any pecuniary bonus, as opposed to the net (perceived) attractiveness $\pi_j + \eta_i \text{BONUS}_{jt}$, see below.</footnote>

<footnote n="30">³⁰This result is robust to considering geographical choices: the equivalent annual bonus would then amount to nearly €2,200, see Table 22 in section C.4.</footnote>

<footnote n="31">³¹It would also be the case with another partial outcome out of the whole rank-ordered list, namely the dummy equal to one when teacher $i$ ranks school $j$ first on her list at time $t$.</footnote>
developed in section 5, has enabled us to achieve a perfect fit<fnref n="32" /> and therefore to simulate desired counterfactuals.

# 7 Conclusion

This article evaluates the impact of stronger financial incentives on desired teachers' mobility towards disadvantaged schools, relying on a substantial increase of a pecuniary bonus offered in French REP⁺ schools from 2018 onwards. First, the empirical evidence based on both a difference-in-differences approach and a structural model suggests that the public policy change has induced a significant +3pp to +3.5pp effect on schools' shares among top 1 choices; financial incentives would explain almost all of it, with an equivalent +1.4pp for each supplementary €1,000 in the annual bonus granted to those schools. Second, we show thanks to counterfactual simulations that teachers reporting a preference for a REP⁺ school consecutive to the reform do not come from REP schools but rather from the outside option (i.e. they would not have requested any transfer in the absence of the public policy shock), and, to a lesser extent, from regular schools. Third, the estimation of teachers preferences for schools reveals that teachers had in fact reacted very differently to pecuniary incentives, and that those who actually respond most are novice and already work in disadvantaged schools. Overall, to compensate for the lack of attractiveness of REP⁺ schools vis-à-vis regular schools, the decision maker should offer an annual bonus that ranges from €2,200 to €3,000 (depending on whether teachers' aversion for travel has been taken into account or not, and on whether geographical choices are included or not). The problem of the lump-sum bonus being mechanically more attractive for low-paid, hence less qualified teachers, could be circumvented by offering instead some fraction of the annual wage, as it has been recently implemented in the Netherlands for instance.<fnref n="33" /> Last, on top of pointing to higher incoming mobility towards REP⁺ schools, the descriptive evidence at stake is weakly suggestive of a slightly refrained outgoing mobility, which could mean a higher stability of pedagogical teams in those disadvantaged schools; however, a more detailed investigation on this topic is left for further research.

The most salient limit of the current study is external validity since we rely on data from a single educational authority; on top of that, disadvantaged schools are

<footnote n="32">³²Once unobserved residual components $\xi_{jt}$ have been recovered and taken into account. By contrast, the conditional likelihood estimation does not permit to recover the idiosyncratic shocks $\varepsilon_{ijt}$ at the individual level.</footnote>

<footnote n="33">³³https://www.dutchnews.nl/news/2021/08/top-up-pay-in-schools-with-children-with-learning-chal-</footnote>
not numerous (only 17 in that rectorate), which restrains the identifying variation
and might explain why some results are not significant -on top of the heterogene-
ity in responses, though. One would benefit most of supplementary data issued
from other educational authorities, especially those with a high number of REP+
schools, or even nationwide, which would enlarge our source of identifying varia-
tion, on top of conferring a higher external validity. Further research should also
investigate the impact of such policies on realized teacher mobility, on the stability
of the pedagogical teams, as well as on students' achievement.
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A Figures

<figure><img src="image_4.png" /><figcaption>Figure 1: The change in financial incentives in REP+ schools from 2018 onwards</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 2: Length of rank-ordered lists</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 3: Share of REP/REP+ schools among all choices (%)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 4: Share of REP/REP+ schools among top 1 choices (%)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 5: Event study approach (share of REP+ schools among all choices)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 6: Event study approach (share of REP+ schools among top 1 choices)</figcaption></figure>
Share of REP+ schools among top 1 choices

<figure><img src="image_10.png" /><figcaption>Figure 7: Impact of financial incentives on shares of REP+ among top 1 choices</figcaption></figure>
B Tables

<table><caption>Table 1: DinD estimates (outcome: share of REP+ among all choices)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>REP<sup>+</sup></td><td>-0.526*<br>(0.307)</td><td></td><td>-1.258***<br>(0.424)</td><td></td></tr><tr><td>REP<sup>+</sup> × 1{t ≥ 2018}</td><td>2.356***<br>(0.585)</td><td>2.083***<br>(0.587)</td><td></td><td></td></tr><tr><td>Bonus (in €1,000)</td><td></td><td></td><td>1.317***<br>(0.338)</td><td>1.151***<br>(0.355)</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Teacher × REP<sup>+</sup> FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>11,804</td><td>11,804</td><td>11,804</td><td>11,804</td></tr><tr><td>R<sup>2</sup></td><td>0.004</td><td>0.005</td><td>0.008</td><td>0.005</td></tr></tbody></table>

Note. Comparison group: REP schools. Shares: in %.

Robust standard errors clustered at the teacher level.
<table><caption>Table 2: DinD estimates (outcome: share of REP⁺ among top 1 choices)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>REP⁺</td><td>-0.201<br>(0.407)</td><td></td><td>-0.986*<br>(0.564)</td><td></td></tr><tr><td>REP⁺ × 1{t ≥ 2018}</td><td>3.164***<br>(0.819)</td><td>3.096***<br>(0.862)</td><td></td><td></td></tr><tr><td>Bonus (in €1,000)</td><td></td><td></td><td>1.591***<br>(0.468)</td><td>1.395***<br>(0.503)</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Teacher × REP⁺ FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>11,631</td><td>11,631</td><td>11,631</td><td>11,631</td></tr><tr><td>R²</td><td>0.005</td><td>0.006</td><td>0.009</td><td>0.004</td></tr></tbody></table>

*Note.* Comparison group: REP schools. Shares: in %.
Robust standard errors clustered at the teacher level.

<table><caption>Table 3: Estimates from the school choice model</caption><tbody><tr><td>Bonus (in €1,000)</td><td>0.239***<br>(0.076)</td></tr><tr><td>Year FE</td><td>Yes</td></tr><tr><td>School FE</td><td>Yes</td></tr><tr><td>average among 233 regular schools</td><td>0.07</td></tr><tr><td>average among 16 REP schools</td><td>-0.43</td></tr><tr><td>average among 17 REP⁺ schools</td><td>-0.64</td></tr><tr><td>Observations</td><td>1,330</td></tr><tr><td>R²</td><td>0.779</td></tr></tbody></table>

*Note.* Sample at the school-year level: 266 schools, 2015-2019.
Robust standard errors clustered at the teacher level.
<table><caption>Table 4: Breaking down variations in shares of top 1 choices</caption><thead><tr><th></th><th>Year</th><th>2017</th><th>2018</th><th>2019</th></tr></thead><tbody><tr><td rowspan="3">REP<sup>+</sup> schools</td><td>Observed</td><td>4.2</td><td>6.2</td><td>8.6</td></tr><tr><td>(a) 2017 financial incentives</td><td>4.2</td><td>4.8</td><td>5.1</td></tr><tr><td>(b) 2017 local demand shocks</td><td>4.2</td><td>5.9</td><td>7.4</td></tr><tr><td rowspan="3">REP schools</td><td>Observed</td><td>4.4</td><td>3.7</td><td>5.6</td></tr><tr><td>(a) 2017 financial incentives</td><td>4.4</td><td>3.8</td><td>5.8</td></tr><tr><td>(b) 2017 local demand shocks</td><td>4.4</td><td>4.4</td><td>4.3</td></tr><tr><td rowspan="3">Regular schools</td><td>Observed</td><td>91.4</td><td>90.1</td><td>85.8</td></tr><tr><td>(a) 2017 financial incentives</td><td>91.4</td><td>91.4</td><td>89.1</td></tr><tr><td>(b) 2017 local demand shocks</td><td>91.4</td><td>89.7</td><td>88.3</td></tr></tbody></table>

Note. Shares of schools among top 1 choices: in % of rank-ordered lists.

<table><caption>Table 5: Counterfactual experiment: neutralizing the 2018 reform</caption><thead><tr><th></th><th>Year</th><th>2017</th><th>2018</th><th>2019</th></tr></thead><tbody><tr><td rowspan="3">No transfer request (outside option)</td><td>Observed</td><td>89.32</td><td>90.09</td><td>90.53</td></tr><tr><td>Simulated</td><td>89.32</td><td>90.22</td><td>90.84</td></tr><tr><td>Difference</td><td>0.00</td><td>0.14</td><td>0.31</td></tr><tr><td rowspan="3">Regular school ranked first</td><td>Observed</td><td>9.76</td><td>8.93</td><td>8.13</td></tr><tr><td>Simulated</td><td>9.76</td><td>8.94</td><td>8.16</td></tr><tr><td>Difference</td><td>0.00</td><td>0.01</td><td>0.03</td></tr><tr><td rowspan="3">REP school ranked first</td><td>Observed</td><td>0.47</td><td>0.37</td><td>0.53</td></tr><tr><td>Simulated</td><td>0.47</td><td>0.37</td><td>0.53</td></tr><tr><td>Difference</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td rowspan="3">REP<sup>+</sup> school ranked first</td><td>Observed</td><td>0.45</td><td>0.62</td><td>0.81</td></tr><tr><td>Simulated</td><td>0.45</td><td>0.47</td><td>0.47</td></tr><tr><td>Difference</td><td>0.00</td><td>-0.15</td><td>-0.34</td></tr><tr><td colspan="5">Part of difference coming from REP schools (%)</td></tr><tr><td colspan="5">Part of difference coming from regular schools (%)</td></tr><tr><td colspan="5">Part of difference coming from outside option (%)</td></tr></tbody></table>

Top panels: Unconditional shares (in %).

Bottom panel: Baseline = Difference in REP<sup>+</sup> schools (in absolute).
<table><caption>Table 6: Teachers’ preferences</caption><thead><tr><th>Dependent variable</th><th colspan="2">rank-ordered list (ROL)</th></tr></thead><tbody><tr><td>Travel time (in minutes)</td><td>-0.018<sup>***</sup><br>(0.002)</td><td>-0.027<sup>*</sup><br>(0.015)</td></tr><tr><td>Travel time × female</td><td></td><td>0.000<br>(0.004)</td></tr><tr><td>Travel time × aged 40-</td><td></td><td>0.012<br>(0.015)</td></tr><tr><td>Travel time × aged 41-50</td><td></td><td>0.005<br>(0.016)</td></tr><tr><td>Travel time × aged 51-60</td><td></td><td>0.002<br>(0.016)</td></tr><tr><td>Bonus (in €1,000)</td><td>0.260<sup>**</sup><br>(0.108)</td><td>0.274<br>(0.210)</td></tr><tr><td>Bonus × (0 ≤ Seniority ≤ 3)</td><td></td><td>0.219<sup>***</sup><br>(0.085)</td></tr><tr><td>Bonus × (4 ≤ Seniority ≤ 8)</td><td></td><td>0.127<br>(0.097)</td></tr><tr><td>Bonus × currently in REP school</td><td></td><td>0.139<br>(0.093)</td></tr><tr><td>Bonus × currently in REP<sup>+</sup> school</td><td></td><td>0.245<sup>***</sup><br>(0.093)</td></tr><tr><td>Bonus × Agrégation</td><td></td><td>0.096<br>(0.159)</td></tr><tr><td>Bonus × Classe normale</td><td></td><td>-0.200<br>(0.188)</td></tr><tr><td># of observations (N)</td><td>18,222</td><td>18,222</td></tr><tr><td>log(L)/N</td><td>-0.835</td><td>-0.834</td></tr><tr><td># of ROLs</td><td>5,902</td><td>5,902</td></tr><tr><td># of teachers</td><td>3,251</td><td>3,251</td></tr><tr><td># of school FE (π<sub>j</sub>)</td><td>266</td><td>266</td></tr><tr><td>π̄<sub>j</sub> (REP<sup>+</sup> schools)</td><td>-0.160</td><td>-0.156</td></tr><tr><td>π̄<sub>j</sub> (REP schools)</td><td>-0.090</td><td>-0.019</td></tr><tr><td>π̄<sub>j</sub> (regular schools)</td><td>0.532</td><td>0.561</td></tr><tr><td>1 estimated s.d. of π<sub>j</sub></td><td>1.852</td><td>1.850</td></tr></tbody></table>

Note. Rank-ordered Logit model (ML estimation).
Robust standard errors clustered at the teacher level.
# C Supplementary material

## C.1 Institutional background details

From a theoretical viewpoint, the underlying algorithm is a slight modification of a deferred-acceptance (DA) mechanism and proceeds in two steps. First, every teacher already present in a school is ranked above any other teacher external to that school. Second, the matching process results from a DA-algorithm à la Gale and Shapley (1962). Importantly, this two-step procedure preserves the strategy-proofness property of the DA mechanism. Fack, Grenet, and He (2019) prove the existence of a pure strategy bayesian Nash equilibrium in such a setting, yet they emphasize the role played by application costs which may refer to cognitive burden and institutional restrictions on ROLs' length. In the absence of any application cost, the mechanism induces a truthful revelation of preferences, but multiple equilibria may arise. In contrast, with positive application costs, and as long as teachers play undominated strategies, they need only submit some partial order of their true preferences. Weakly truth-telling (WTT) is a strategy that consists in ranking her most preferred schools only; it may not be dominant. The plausibility of truthful implementation is an empirical issue: when studying the first round of teacher allocation in France, Combe, Tercieux, and Terrier (2021) argue that the WTT assumption is all the more likely when the mechanism includes no application cost, when teachers are aware of injunctions coming from both the Department of Education and unions, when they have a good knowledge of the allocation process (it depends more generally on teachers' information set), and when they face some uncertainty. *De facto*, at the time of application, teachers know their own priority index for each school, but ignore their colleagues' choices, and can hardly predict school-specific cutoffs. Indeed, teachers are ranked by schools according to their single-dimensional priority index, the so-called *barème*; this framework corresponds to a strict priority environment. The *barème* depends on experience, seniority, individual- and school- characteristics. Uniform rules are applied, which is common knowledge; in particular, schools directors are not involved in this centralized matching procedure. Every teacher can be allocated to a school whenever her priority index exceeds its school-specific cutoff. Those cutoffs cannot easily be predicted: they are not disclosed publicly: in particular, they are not available on unions websites (only the cutoffs related to the first round can be found on those websites).
## C.2 Supplementary descriptive statistics

<table><caption>Table 7: Extensive margin</caption><thead><tr><th></th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th></tr></thead><tbody><tr><td>Share of teachers requesting some transfer (%)</td><td>15.3</td><td>15.1</td><td>13.5</td><td>12.6</td><td>11.8</td></tr><tr><td><i>Among beginners (age ≤ 30)</i></td><td>27.8</td><td>30.7</td><td>31.1</td><td>28.3</td><td>26.1</td></tr><tr><td>Average number of choices per list</td><td>7.3</td><td>6.8</td><td>6.6</td><td>6.6</td><td>6.6</td></tr><tr><td><i>Among beginners (age ≤ 30)</i></td><td>11.4</td><td>11.1</td><td>9.9</td><td>9.6</td><td>10.7</td></tr><tr><td>Number of choices to:</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>REP+ schools</td><td>224</td><td>206</td><td>195</td><td>307</td><td>329</td></tr><tr><td><i>Among beginners (age ≤ 30)</i></td><td>57</td><td>66</td><td>63</td><td>84</td><td>79</td></tr><tr><td>REP schools</td><td>202</td><td>242</td><td>200</td><td>215</td><td>213</td></tr><tr><td><i>Among beginners (age ≤ 30)</i></td><td>40</td><td>50</td><td>53</td><td>45</td><td>31</td></tr><tr><td>Regular schools</td><td>4,990</td><td>5,025</td><td>4,234</td><td>3,722</td><td>3,314</td></tr><tr><td><i>Among beginners (age ≤ 30)</i></td><td>415</td><td>456</td><td>420</td><td>283</td><td>308</td></tr></tbody></table>

**Sample:** Teachers from Montpellier educational authority already assigned to a school.

<table><caption>Table 8: Shares of teachers requesting some transfer (depending on current school assignment, %)</caption><thead><tr><th></th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th></tr></thead><tbody><tr><td>REP+ schools</td><td>21.9</td><td>22.0</td><td>17.4</td><td>15.1</td><td>10.5</td></tr><tr><td>REP schools</td><td>23.0</td><td>22.3</td><td>17.5</td><td>13.9</td><td>13.2</td></tr><tr><td>Regular schools</td><td>14.6</td><td>14.4</td><td>13.1</td><td>12.4</td><td>11.8</td></tr></tbody></table>

**Sample:** Teachers from Montpellier educational authority, already assigned to a school.

**Lecture:** In 2015, 21.9% of tenured teachers assigned to a REP+ school asked for a transfer.
<table><caption>Table 9: Teachers’ characteristics</caption><thead><tr><th></th><th>mean</th><th>sd</th><th>min</th><th>D1</th><th>Q1</th><th>med</th><th>Q3</th><th>D9</th><th>max</th><th># of obs.</th></tr></thead><tbody><tr><td>Age</td><td>42.4</td><td>8.4</td><td>23</td><td>31</td><td>36</td><td>42</td><td>48</td><td>55</td><td>67</td><td>5,902</td></tr><tr><td>Seniority</td><td>13.2</td><td>7.9</td><td>1</td><td>2</td><td>4</td><td>10</td><td>15</td><td>20</td><td>41</td><td>5,900</td></tr><tr><td>Commuting time</td><td>27.9</td><td>20.6</td><td>1</td><td>8</td><td>15</td><td>25</td><td>36</td><td>50</td><td>208</td><td>5,902</td></tr><tr><td>Priority index (2015)</td><td>477</td><td>543</td><td>21</td><td>73</td><td>185</td><td>331</td><td>504</td><td>785</td><td>3,213</td><td>1,300</td></tr><tr><td>Priority index (2016)</td><td>578</td><td>839</td><td>21</td><td>68</td><td>186</td><td>368</td><td>570</td><td>931</td><td>7,894</td><td>1,340</td></tr><tr><td>Priority index (2017)</td><td>602</td><td>1,165</td><td>21</td><td>62</td><td>131</td><td>350</td><td>589</td><td>849</td><td>8,593</td><td>1,155</td></tr><tr><td>Priority index (2018)</td><td>644</td><td>1,310</td><td>21</td><td>66</td><td>114</td><td>346</td><td>538</td><td>856</td><td>8,540</td><td>1,097</td></tr><tr><td>Priority index (2019)</td><td>744</td><td>1,231</td><td>14</td><td>95</td><td>215</td><td>476</td><td>734</td><td>1,062</td><td>8,444</td><td>1,010</td></tr></tbody></table>

**Sample:** Restricted sample, i.e. teachers (non-vocational education) from Montpellier educational authority, already assigned to a school, requesting a transfer, and whose rank-ordered list includes at least a school-specific choice. Full-filled lists and teachers suspected to move are removed.

**Note** Commuting time is measured during rush hours between teacher residence location and school choice. The average commuting time per ROL is reported here.

<table><caption>Table 10: Probability of requesting some transfer</caption><thead><tr><th>Dependent variable</th><th>Dummy for transfer request</th></tr></thead><tbody><tr><td>Male</td><td>1</td></tr><tr><td>Female</td><td>0.852<sup>***</sup><br>(0.024)</td></tr><tr><td>CAPES</td><td>1</td></tr><tr><td><em>Agrégation</em></td><td>1.112<sup>***</sup><br>(0.042)</td></tr><tr><td>2015</td><td>1</td></tr><tr><td>2016</td><td>1.047<sup>***</sup><br>(0.044)</td></tr><tr><td>2017</td><td>0.910<sup>***</sup><br>(0.040)</td></tr><tr><td>2018</td><td>0.866<sup>***</sup><br>(0.038)</td></tr><tr><td>2019</td><td>0.810<sup>***</sup><br>(0.037)</td></tr><tr><td>Age dummies</td><td>Yes</td></tr><tr><td>Observations</td><td>57,426</td></tr><tr><td>log(L)/N</td><td>-0.316</td></tr></tbody></table>

*Note.* Odds ratios from a Logit model. Robust standard errors.
<table><caption>Table 11: Rank in list and commuting time</caption><thead><tr><th>Rank</th><th>Fraction reporting</th><th>Commuting time</th></tr><tr><th></th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>1</td><td>100.00</td><td>22</td></tr><tr><td>2</td><td>79.51</td><td>23</td></tr><tr><td>3</td><td>67.29</td><td>24</td></tr><tr><td>4</td><td>57.87</td><td>26</td></tr><tr><td>5</td><td>49.86</td><td>28</td></tr><tr><td>6</td><td>44.22</td><td>28</td></tr><tr><td>7</td><td>38.83</td><td>29</td></tr><tr><td>8</td><td>34.65</td><td>31</td></tr><tr><td>9</td><td>30.94</td><td>31</td></tr><tr><td>10</td><td>27.93</td><td>33</td></tr><tr><td>11</td><td>25.20</td><td>35</td></tr><tr><td>12</td><td>22.88</td><td>36</td></tr><tr><td>13</td><td>20.93</td><td>34</td></tr><tr><td>14</td><td>19.36</td><td>36</td></tr><tr><td>15</td><td>17.79</td><td>37</td></tr><tr><td>16</td><td>16.28</td><td>37</td></tr><tr><td>17</td><td>14.77</td><td>39</td></tr><tr><td>18</td><td>13.43</td><td>40</td></tr><tr><td>19</td><td>11.86</td><td>49</td></tr></tbody></table>

*Sample*: Rank-ordered lists of school choices filled out by teachers from Montpellier educational authority already assigned to a school. 18,222 choices in 5,902 lists.

*Notes*: (1) Fractions of teachers applications listing each choice. (2) Average commuting time between a teacher's home and each choice, measured in minutes during rush hours.
<table><caption>Table 12: Definition of working samples: selection steps</caption><thead><tr><th></th><th># of teachers</th><th># of lists</th><th># of choices</th><th># of school-specific choices</th><th># of municipality choices</th><th># of other geographical choices</th><th># of other choices</th><th># of schools</th></tr></thead><tbody><tr><td>Raw data</td><td>7,532</td><td>14,482</td><td>115,386</td><td>38,321</td><td>42,092</td><td>25,349</td><td>9,624</td><td>405</td></tr><tr><td>Deletion of lists from vocational teachers and specific staff (1)</td><td>5,426</td><td>10,676</td><td>89,751</td><td>30,191</td><td>33,882</td><td>18,778</td><td>6,900</td><td>364</td></tr><tr><td>Deletion of lists from teachers of another educational authority</td><td>4,360</td><td>8,913</td><td>60,387</td><td>23,618</td><td>24,773</td><td>8,919</td><td>3,077</td><td>356</td></tr><tr><td>Deletion of lists when WTT assumption is questionable (2)</td><td>3,980</td><td>7,918</td><td>42,548</td><td>19,713</td><td>16,808</td><td>4,449</td><td>1,578</td><td>351</td></tr><tr><td>Deletion of choices for specific schools and teacher positions</td><td>3,952</td><td>7,848</td><td>40,434</td><td>19,177</td><td>16,808</td><td>4,449</td><td>0</td><td>266</td></tr><tr><td>Deletion of lists when teacher is suspected to move</td><td>3,825</td><td>7,537</td><td>38,644</td><td>18,222</td><td>16,200</td><td>4,222</td><td>0</td><td>266</td></tr><tr><td>Deletion of lists with no school-specific choice (Working sample)</td><td>3,251</td><td>5,902</td><td>31,164</td><td>18,222</td><td>10,270</td><td>2,672</td><td>0</td><td>266</td></tr><tr><td>Exploding geographical choices into schools</td><td>3,825</td><td>7,537</td><td>117,066</td><td>117,066</td><td>0</td><td>0</td><td>0</td><td>269</td></tr><tr><td>Adding up unranked schools located less than 60 minutes from teacher home (Exploded sample)</td><td>3,825</td><td>7,537</td><td>610,520</td><td>610,520</td><td>0</td><td>0</td><td>0</td><td>269</td></tr></tbody></table>

Notes. (1) Vocational schools are not concerned by REP/REP+ programs.

School librarians, school counselors and school psychologists are not in the scope of the current analysis.

(2) 932 lists are filled with 20 choices, i.e. the maximal length authorized.

106 lists are detected as benefiting from a priority index point bonus on their first choice.
<table><caption>Table 13: Share of REP/REP+ among school choices (%)</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Share among all choices</th><th colspan="2">At least once among first three choices</th><th colspan="2">Share among first choices</th><th rowspan="2">N</th></tr><tr><th>REP</th><th>REP+</th><th>REP</th><th>REP+</th><th>REP</th><th>REP+</th></tr></thead><tbody><tr><td rowspan="5">Middle-school choices</td><td><strong>2015</strong></td><td>6.3</td><td>6.5</td><td>9.4</td><td>7.5</td><td>7.1</td><td>7.5</td><td>1,486</td></tr><tr><td><strong>2016</strong></td><td>9.2</td><td>7.5</td><td>14.6</td><td>10.4</td><td>12.7</td><td>11.1</td><td>1,631</td></tr><tr><td><strong>2017</strong></td><td>9.2</td><td>7.5</td><td>13.0</td><td>9.3</td><td>13.0</td><td>12.3</td><td>1,380</td></tr><tr><td><strong>2018</strong></td><td>8.8</td><td>11.3</td><td>10.4</td><td>21.3</td><td>8.4</td><td>17.2</td><td>1,402</td></tr><tr><td><strong>2019</strong></td><td>9.8</td><td>14.8</td><td>16.3</td><td>22.3</td><td>13.1</td><td>22.3</td><td>1,429</td></tr><tr><td rowspan="5">Novices seniority ≤ 3 y.</td><td><strong>2015</strong></td><td>5.4</td><td>6.9</td><td>13.0</td><td>10.5</td><td>5.2</td><td>12.5</td><td>533</td></tr><tr><td><strong>2016</strong></td><td>7.8</td><td>6.7</td><td>13.2</td><td>12.6</td><td>6.4</td><td>8.0</td><td>628</td></tr><tr><td><strong>2017</strong></td><td>6.3</td><td>7.1</td><td>6.5</td><td>12.0</td><td>5.3</td><td>11.5</td><td>617</td></tr><tr><td><strong>2018</strong></td><td>5.9</td><td>8.1</td><td>4.9</td><td>13.3</td><td>3.6</td><td>15.3</td><td>596</td></tr><tr><td><strong>2019</strong></td><td>8.4</td><td>12.4</td><td>16.9</td><td>22.4</td><td>9.5</td><td>16.1</td><td>622</td></tr><tr><td rowspan="5">Beginners. seniority 4-8 y.</td><td><strong>2015</strong></td><td>2.8</td><td>2.4</td><td>2.1</td><td>2.5</td><td>2.8</td><td>2.2</td><td>850</td></tr><tr><td><strong>2016</strong></td><td>4.7</td><td>5.4</td><td>10.5</td><td>10.0</td><td>5.7</td><td>8.3</td><td>763</td></tr><tr><td><strong>2017</strong></td><td>4.9</td><td>4.9</td><td>6.7</td><td>7.4</td><td>6.2</td><td>7.3</td><td>699</td></tr><tr><td><strong>2018</strong></td><td>6.0</td><td>8.8</td><td>6.4</td><td>17.4</td><td>7.1</td><td>10</td><td>719</td></tr><tr><td><strong>2019</strong></td><td>5.4</td><td>9.2</td><td>7.1</td><td>15.9</td><td>8.7</td><td>12.0</td><td>793</td></tr><tr><td rowspan="5">Teachers. seniority >8 y.</td><td><strong>2015</strong></td><td>1.5</td><td>1.5</td><td>3.0</td><td>2.2</td><td>1.4</td><td>0.8</td><td>2,659</td></tr><tr><td><strong>2016</strong></td><td>2.2</td><td>1.3</td><td>4.4</td><td>2.0</td><td>2.8</td><td>1.4</td><td>2,926</td></tr><tr><td><strong>2017</strong></td><td>2.4</td><td>1.2</td><td>5.2</td><td>1.1</td><td>3.0</td><td>1.2</td><td>2,217</td></tr><tr><td><strong>2018</strong></td><td>2.3</td><td>2.4</td><td>4.8</td><td>5.2</td><td>1.7</td><td>2.9</td><td>1,921</td></tr><tr><td><strong>2019</strong></td><td>2.7</td><td>3.7</td><td>6.0</td><td>6.8</td><td>3.3</td><td>6.5</td><td>1,679</td></tr><tr><td rowspan="5">CAPES</td><td><strong>2015</strong></td><td>2.5</td><td>2.7</td><td>4.5</td><td>3.9</td><td>2.0</td><td>2.5</td><td>3,499</td></tr><tr><td><strong>2016</strong></td><td>3.9</td><td>3.2</td><td>7.6</td><td>5.7</td><td>4.1</td><td>3.7</td><td>3,608</td></tr><tr><td><strong>2017</strong></td><td>4.0</td><td>3.0</td><td>6.9</td><td>4.3</td><td>4.6</td><td>4.0</td><td>2,893</td></tr><tr><td><strong>2018</strong></td><td>4.4</td><td>6.2</td><td>6.0</td><td>10.9</td><td>3.4</td><td>8.9</td><td>2,663</td></tr><tr><td><strong>2019</strong></td><td>5.4</td><td>7.9</td><td>9.9</td><td>14.1</td><td>6.4</td><td>10.1</td><td>2,498</td></tr><tr><td rowspan="5">Agrégation</td><td><strong>2015</strong></td><td>1.1</td><td>0.2</td><td>1.8</td><td>0.0</td><td>1.7</td><td>0.0</td><td>543</td></tr><tr><td><strong>2016</strong></td><td>1.4</td><td>1.0</td><td>2.5</td><td>1.3</td><td>1.5</td><td>0.5</td><td>709</td></tr><tr><td><strong>2017</strong></td><td>1.7</td><td>2.8</td><td>0.3</td><td>4.1</td><td>1.1</td><td>2.7</td><td>640</td></tr><tr><td><strong>2018</strong></td><td>0.9</td><td>1.9</td><td>1.4</td><td>2.6</td><td>1.3</td><td>1.9</td><td>573</td></tr><tr><td><strong>2019</strong></td><td>1.0</td><td>2.5</td><td>2.7</td><td>4.7</td><td>1.3</td><td>5.7</td><td>596</td></tr></tbody></table>

*Sample*: Restricted sample of school-specific choices by teachers from Montpellier educational authority already assigned to a school.
## C.3 Supplementary robustness checks

### C.3.1 Rank of REP⁺ schools in teachers’ lists

We focus here on another outcome, the rank of REP⁺ schools in teachers’ lists. We thus exploit the richness of our dataset that contains the exact ranking of teachers’ school choices. Instead of aggregating the data at the rank-ordered list-type of school (regular, REP or REP⁺) level, we base our analysis on the raw data at the rank-ordered list-school level. We seek to understand the impact of the policy change on that ranking, which leads us to consider the following estimating equation:

$$ \text{RANK}_{ijt} = e\text{REP}_j^+ \times 1\{t \ge 2018\} + X_{ijt}f + r_j + \zeta_t + v_{ijt}, \quad (13) $$

where RANK$_{ijt}$ designates the order of school j in the rank-ordered list filled out by teacher i on year t, $r_j$ is a school fixed-effect, $\zeta_t$ account for annual dummies, and $X_{ijt}$ includes control variables, especially commuting time by car between home and school denoted by $d_{ij}$, measured in minutes during rush hours. The error term $v_{ijt}$ is normally distributed, and standard errors are clustered at the teacher level in order to take autocorrelation of residuals into account.

<figure><img src="image_11.png" /><figcaption>Figure 8: Average rank of schools in lists</figcaption></figure>
We adopt once again a difference-in-differences approach based on a treatment group, still composed of REP⁺ schools, and a comparison group. However, there is no reason why regular schools should be disregarded as a valid comparison group here: contrary to shares, rank do not sum up mechanically to one, and the only necessary condition for the identification strategy to be valid is whether the CTA holds. Figure 8 shows actually that there is much imprecision as regards the average rank (about 5 before 2018) of both REP and REP⁺ schools in rank-ordered lists, due to the scarcity of such schools in the educational authority.<fnref n="34" /> As a result, it is not really possible to validate nor to invalidate our empirical strategy based on such eyeball evidence. Note also that the average rank of regular schools, about 4, remains fairly constant over time, and that there is much less imprecision on that rank due to the higher number of such schools, 233. We decide therefore to adopt three different identification strategies by considering three different comparison groups: (i) REP schools, (ii) regular schools, and (iii) both REP and regular schools. Remember from Figure 1 that in both REP and regular schools, financial incentives did not vary over the period, though the bonus amounts to €1,734 in REP schools while there is none in regular schools. Also, the aggregation (iii) is made possible by the inclusion of school fixed-effects $r_j$ in equation (13) which capture this time-invariant pecuniary amount, among other.

It is in fact crucial to include school fixed-effects in equation (13). An interpretation of those unobserved school-specific factors has to do with “attractiveness” or “school quality” as perceived by teachers, but not by the econometrician. In the absence of unobserved school-specific effects, the coefficient $e$ would certainly capture a selection effect due to the differential attractiveness of REP⁺ schools with respect to schools in the comparison group -this selection effect being probably less pronounced in the first identification strategy since the intuition suggests that the gap with REP schools is smaller than the one with regular schools. However, conditional on those fixed effects, hence on the classification into $k$-types of schools which has remained mostly unchanged over the period,<fnref n="35" /> the coefficient $e$ measures the desired ATE.

As before, when willing to disentangle financial from non-financial incentives, we may consider the alternative specification:

$$ \text{RANK}_{ijt} = \tilde{e}\text{BONUS}_{jt} + X'_{ijt}\tilde{f} + \tilde{r}_j + \tilde{\zeta}_t + \tilde{v}_{ijt}, \quad (14) $$

<footnote n="34">³⁴This is the reason why we wish to replicate the current analysis on a larger dataset including possibly other educational authorities with more disadvantaged schools like Créteil or Versailles, or even on data at the nationwide level.</footnote>

<footnote n="35">³⁵A single REP⁺ school has closed, a REP school was turned into a REP⁺ school, and a regular school became a REP school all over the period considered here.</footnote>
The estimation of the latter model relies on the following exclusion restriction:

$$ \mathbb{E}[\text{BONUS}_{jt}, d_{ij}|\tilde{r}_j, \tilde{\zeta}_t, X_{ijt}, \tilde{v}_{ijt}] = 0. \quad (15) $$

Though not testable, the plausibility of this identification assumption hinges on the exogeneity of the policy change. The absence of measurement error in travel time, the exogeneity of location choices as well as the linearity in travel costs are nevertheless more tricky assumptions, and we provide below with some robustness checks, especially by considering alternative measures of travel time.

<table><caption>Table 14: DinD estimates (outcome: rank of REP⁺ schools in teachers’ lists)</caption><thead><tr><th>Comparison group</th><th>REP schools</th><th>regular schools</th><th>REP &amp; regular schools</th></tr></thead><tbody><tr><td>REP<sup>+</sup> × 1{t ≥ 2018}</td><td>-0.720<br>(0.499)</td><td>-1.238<sup>***</sup><br>(0.447)</td><td>-1.196<sup>***</sup><br>(0.432)</td></tr><tr><td>Bonus (in €1,000)</td><td>-0.094<br>(0.243)</td><td>-0.453<sup>**</sup><br>(0.228)</td><td>-0.447<sup>**</sup><br>(0.215)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>School FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,327</td><td>1,327</td><td>17,588</td></tr><tr><td>R<sup>2</sup></td><td>0.106</td><td>0.104</td><td>0.144</td></tr></tbody></table>

Note. Controls: travel time (in minutes).

Robust standard errors clustered at the teacher level.

The estimation results are given in Table 14. On the one hand, the first identification strategy suffers from a small sample size, in relation with the scarcity of disadvantaged schools in the educational authority. We find an effect of -0.7, not significant at usual levels, on the average rank of REP⁺ schools in lists. On the other hand, according to both second and third identification strategies, REP⁺ schools were ranked better consecutive to the reform, the estimated change being -1.2, and significant at usual levels. According to the event study displayed by Figure 9, the effect is mostly driven by year 2018.

We proceed to some falsification test by considering a fake treatment group composed of all schools in a given *département* of Montpellier’s educational authority: we do as if those schools were all REP⁺, hence dropping REP⁺ schools from the estimation sample for the sake of this exercise only. The results are displayed by Table 15. Reassuringly, in each of the five *départements*, the point estimate is not significant at usual levels.

To assess the robustness of our results, we investigate whether they depend on possible measurement error as regards a key determinant of school choice, namely
<table><caption>Table 15: Fake treatment groups (départements)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Département</td><td>Aude</td><td>Gard</td><td>Hérault</td><td>Lozère</td><td>Pyrénées-Orientales</td></tr><tr><td>Département × 1{t ≥ 2018}</td><td>-0.733<br>(0.901)</td><td>1.604*<br>(0.876)</td><td>-0.912<br>(0.726)</td><td>-0.354<br>(2.818)</td><td>0.906<br>(2.017)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>School FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>17,529</td><td>17,529</td><td>17,529</td><td>17,529</td><td>17,529</td></tr><tr><td>R<sup>2</sup></td><td>0.109</td><td>0.110</td><td>0.109</td><td>0.108</td><td>0.109</td></tr></tbody></table>

Note. Controls: travel time (in minutes).

Robust standard errors clustered at the teacher level.

<table><caption>Table 16: Estimated ATEs under various specifications of travel costs (outcome: rank of REP+ schools in lists)</caption><thead><tr><th>Comparison group</th><th>(1)<br>REP schools</th><th>(2)<br>regular schools</th><th>(3)<br>REP & regular schools</th></tr></thead><tbody><tr><td>Travel time (baseline)</td><td>-0.720*<br>(0.499)</td><td>-1.238***<br>(0.447)</td><td>-1.196***<br>(0.432)</td></tr><tr><td>Travel time + travel time squared</td><td>-0.768<br>(0.493)</td><td>-1.266***<br>(0.440)</td><td>-1.228***<br>(0.425)</td></tr><tr><td>Travel time - off-peak hours</td><td>-0.716<br>(0.500)</td><td>-1.239***<br>(0.449)</td><td>-1.197***<br>(0.434)</td></tr><tr><td>Euclidean distance</td><td>-0.161<br>(0.811)</td><td>-1.338*<br>(0.782)</td><td>-1.204<br>(0.762)</td></tr><tr><td>Effective distance</td><td>-0.709<br>(0.502)</td><td>-1.246***<br>(0.453)</td><td>-1.195***<br>(0.438)</td></tr><tr><td>Time FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>School FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Robust standard errors clustered at the teacher level.

travel time used as a covariate above. We consider alternative concepts of travel time (measured during off-peak hours instead of during rush hours, Euclidean distance and effective distance, the last two ones being expressed in kilometers instead of in minutes), as well as a quadratic specification of travel costs instead of a linear one. Results remain very close to those found above, see Table 16 on this topic. This robustness check may be viewed as some kind of sensitivity analysis with respect to the exclusion restriction (15).
Event study analysis (outcome: rank in ROL)

<figure><img src="image_12.png" /><figcaption>Figure 9: Event study approach (outcome: average rank of REP+ schools in lists)</figcaption></figure>
### C.3.2 Censoring

One may worry that WTT is less likely when ROLs have exactly 20 choices, that is, when the length constraint is binding, due to possible censoring issues. This is the reason why we have excluded those lists from the estimation. Including them as a robustness exercise would not dramatically alter our results. Corresponding estimates are available upon request.

### C.3.3 Nonlinearity of the effect

Moving from rank 20 to rank 19 is not equivalent to moving from rank 2 to rank 1. More generally, one may suspect heterogeneous, nonlinear effects with respect to the rank of the choice itself. Restricting our attention to choices ranked 10 or less would yet not change much previous diagnosis. Corresponding estimates are available upon request.

### C.3.4 Other outcomes

Figures 10 and 11 below show two supplementary event studies realized on different outcomes: the share of REP+ schools among top 2 choices and that same share among top 3 choices, respectively. From both qualitative and quantitative viewpoints, our previous results remain unchanged.

<figure><img src="image_13.png" /><figcaption>Figure 10: Share of REP+ schools (in % of top 2 choices)</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure 11: Share of REP+ schools (in % of top 3 choices)</figcaption></figure>
## C.4 Considering geographical choices

**Exploding geographical choices: an example** In this example, we consider a teacher who ranks 4 choices: two schools (M1 and A), then a municipality (M) with three schools (the already ranked M1, but also M2 and M3) and a school called B. We assume further that this teacher is located less than one hour from five other unranked schools, called U1 to U5.

As regards the sample restricted to school-specific choices, the municipality choice is simply not considered, while in the exploded sample that choice is replaced with schools M2 and M3. More precisely, it would have been replaced with schools M1, M2 and M3 if M1 had not been ranked above the latter two. Since teachers are assumed to be indifferent between schools within the same geographical area, both M2 and M3 are ranked third. The fourth choice, namely school B, becomes fifth. Finally, we add up schools U1 to U5 at the bottom of the ROL, which is in fact unimportant, with a rank equal to 0.

<table><thead><tr><th>Initial ROL</th><th>Restricted sample</th><th>Exploded sample</th></tr></thead><tbody><tr><td>1. School M1</td><td>1. School M1</td><td>1. School M1</td></tr><tr><td>2. School A</td><td>2. School A</td><td>2. School A</td></tr><tr><td>3. Municipality M</td><td></td><td>3. School M2</td></tr><tr><td>4. School B</td><td>4. School B</td><td>3. School M3</td></tr><tr><td></td><td></td><td>5. School B</td></tr><tr><td></td><td></td><td>0. School U1</td></tr><tr><td></td><td></td><td>0. School U2</td></tr><tr><td></td><td></td><td>0. School U3</td></tr><tr><td></td><td></td><td>0. School U4</td></tr><tr><td></td><td></td><td>0. School U5</td></tr></tbody></table>

**Exploding geographical choices: the general case** We assume that when opting for a geographical choice a teacher is indifferent among all schools within the corresponding area. Hence we explode such a choice in each and any school located in that area, with the same rank: as a result, this procedure leads to numerous ties. If a school appears more than once consecutive to this imputation, we keep its first occurrence only (in terms of rank ordering). Also, the ranks of stated choices following a geographical choice are shifted appropriately as explained above.
We assume further that any unranked school located less than one hour away from teachers' residence is revealed less preferred than those in the list. A convenient feature of the rank-ordered Logit model, which we use to estimate preferences in section 6, is precisely to deal with such unranked alternatives in an ordinal fashion: without loss of generality, these schools may receive a rank equal to 0.

To sum up, a second “exploded sample” is obtained as follows:

1. geographical choices are exploded into schools located in the corresponding area; when a school appears more than once in a ROL after this procedure, we select out all occurrences but the top-ranked one;

2. ranks are shifted accordingly: if a geographical choice has rank $n$ in a ROL and is turned into $k$ schools, we impute a rank equal to $n$ to each and any of these schools, and $n + k + 1$ to the next choice; the latter rule is however adjusted to take schools which may have already been ranked into account;

3. we add all schools not included in the list, provided that they are located in an one hour radius from the teacher's residence; such schools are ranked at the bottom of each ROL, and receive a rank equal to 0.<fnref n="36" />

While the restricted sample had 18,222 observations at the teacher-year-school level, the size of the exploded sample has now 610,520 observations at the very same level.

Based on that exploded sample, Figure 12 suggests a positive but smaller effect of the 2018 reform, i.e. of stronger financial incentives provided to teachers working in REP⁺ schools, on the share of those schools among all schools. From 2017 to 2019, the proportion of REP⁺ schools among all (explicit or implicit) choices increased by 0.4pp on that sample (from 5.9% to 6.3%).

<footnote n="36">³⁶Ranking these alternatives at the bottom is neither necessary nor important; it is however essential to attribute them a rank (here, 0) that is common to all unranked alternatives, including those coming from different lists.</footnote>
<figure><img src="image_15.png" /><figcaption>Figure 12: Share of REP/REP+ schools in teachers' lists (in % of all schools)</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Figure 13: Share of REP/REP+ schools (in % of top 1 choices)</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Figure 14: Event study approach (share of REP/REP+ schools)</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Figure 15: Event study approach (share of REP/REP+ schools among top 1 choices)</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>Figure 16: Impact of financial incentives on shares of REP+ schools among top 1 choices</figcaption></figure>
<table><caption>Table 17: DinD estimates (outcome: share of REP⁺ schools among all choices)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>REP<sup>+</sup></td><td>-0.057<br>(0.165)</td><td></td><td>-0.167<br>(0.223)</td><td></td></tr><tr><td>REP<sup>+</sup> × 1{t ≥ 2018}</td><td>0.453<br>(0.292)</td><td>0.323<br>(0.335)</td><td></td><td></td></tr><tr><td>Bonus (in €1,000)</td><td></td><td></td><td>0.228<br>(0.175)</td><td>0.116<br>(0.206)</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Teacher × REP<sup>+</sup> FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>15,074</td><td>15,074</td><td>15,074</td><td>15,074</td></tr><tr><td>R<sup>2</sup></td><td>0.001</td><td>0.001</td><td>0.004</td><td>0.001</td></tr></tbody></table>

*Note.* Comparison group: REP schools. Shares: in %.
Robust standard errors clustered at the teacher level.

<table><caption>Table 18: DinD estimates (outcome: share of REP⁺ schools among top 1 choices)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>REP<sup>+</sup></td><td>-0.312<br>(0.332)</td><td></td><td>-0.890*<br>(0.454)</td><td></td></tr><tr><td>REP<sup>+</sup> × 1{t ≥ 2018}</td><td>2.382***<br>(0.641)</td><td>1.748***<br>(0.672)</td><td></td><td></td></tr><tr><td>Bonus (in €1,000)</td><td></td><td></td><td>1.191***<br>(0.360)</td><td>0.609<br>(0.385)</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Teacher × REP<sup>+</sup> FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>14,864</td><td>14,864</td><td>14,864</td><td>14,864</td></tr><tr><td>R<sup>2</sup></td><td>0.003</td><td>0.003</td><td>0.006</td><td>0.002</td></tr></tbody></table>

*Note.* Comparison group: REP schools. Shares: in %.
Robust standard errors clustered at the teacher level.
<table><caption>Table 19: Estimates from the structural model</caption><tr><td>Bonus (in €1,000)</td><td>0.127***<br>(0.047)</td></tr><tr><td>Year FE</td><td>Yes</td></tr><tr><td>School FE</td><td>Yes</td></tr><tr><td>average among 236 regular schools</td><td>0.05</td></tr><tr><td>average among 16 REP schools</td><td>-0.4</td></tr><tr><td>average among 17 REP+ schools</td><td>-0.38</td></tr><tr><td>Observations</td><td>1,345</td></tr><tr><td>R<sup>2</sup></td><td>0.868</td></tr></table>

*Note.* Sample at the school-year level: 266 schools, 2015-2019.

Robust standard errors clustered at the teacher level.

<table><caption>Table 20: Breaking down variations in shares of top 1 choices</caption><thead><tr><th></th><th>Year</th><th>2017</th><th>2018</th><th>2019</th></tr></thead><tbody><tr><td rowspan="3">REP<sup>+</sup> schools</td><td>Observed</td><td>5.8</td><td>6.7</td><td>7.6</td></tr><tr><td>(a) 2017 financial incentives</td><td>5.8</td><td>5.8</td><td>5.8</td></tr><tr><td>(b) 2017 local demand shocks</td><td>5.8</td><td>6.8</td><td>7.8</td></tr><tr><td rowspan="3">REP schools</td><td>Observed</td><td>4.7</td><td>4.5</td><td>5.3</td></tr><tr><td>(a) 2017 financial incentives</td><td>4.7</td><td>4.6</td><td>5.4</td></tr><tr><td>(b) 2017 local demand shocks</td><td>4.7</td><td>4.7</td><td>4.7</td></tr><tr><td rowspan="3">Regular schools</td><td>Observed</td><td>89.4</td><td>88.8</td><td>87.1</td></tr><tr><td>(a) 2017 financial incentives</td><td>89.4</td><td>89.6</td><td>88.8</td></tr><tr><td>(b) 2017 local demand shocks</td><td>89.4</td><td>88.5</td><td>87.5</td></tr></tbody></table>

*Note.* Shares of schools ranked first: in % of rank-ordered lists.
<table><caption>Table 21: Counterfactual experiment: neutralizing the 2018 reform</caption><thead><tr><th>Year</th><th></th><th>2017</th><th>2018</th><th>2019</th></tr></thead><tbody><tr><td rowspan="3">No transfer request (outside option)</td><td>Observed</td><td>75.09</td><td>76.67</td><td>75.99</td></tr><tr><td>Simulated</td><td>75.09</td><td>76.84</td><td>76.35</td></tr><tr><td>Difference</td><td>0.00</td><td>0.17</td><td>0.36</td></tr><tr><td rowspan="3">Regular school ranked first</td><td>Observed</td><td>22.29</td><td>20.71</td><td>20.91</td></tr><tr><td>Simulated</td><td>22.29</td><td>20.71</td><td>21.01</td></tr><tr><td>Difference</td><td>0.00</td><td>0.04</td><td>0.10</td></tr><tr><td rowspan="3">REP school ranked first</td><td>Observed</td><td>1.18</td><td>1.06</td><td>1.26</td></tr><tr><td>Simulated</td><td>1.18</td><td>1.06</td><td>1.27</td></tr><tr><td>Difference</td><td>0.00</td><td>0.00</td><td>0.01</td></tr><tr><td rowspan="3">REP<sup>+</sup> school ranked first</td><td>Observed</td><td>1.45</td><td>1.56</td><td>1.83</td></tr><tr><td>Simulated</td><td>1.45</td><td>1.35</td><td>1.37</td></tr><tr><td>Difference</td><td>0.00</td><td>-0.21</td><td>-0.46</td></tr><tr><td colspan="2">Part of difference coming from REP schools (%)</td><td></td><td>1.08</td><td>1.28</td></tr><tr><td colspan="2">Part of difference coming from regular schools (%)</td><td></td><td>21.03</td><td>21.30</td></tr><tr><td colspan="2">Part of difference coming from outside option (%)</td><td></td><td>77.89</td><td>77.41</td></tr></tbody></table>

Top panels: Unconditional shares (in %).

Bottom panel: Baseline = Difference in REP<sup>+</sup> schools (in absolute).
<table><caption>Table 22: Teachers' preferences</caption><thead><tr><th>Dependent variable</th><th colspan="2">rank-ordered list (ROL)</th></tr></thead><tbody><tr><td>Travel time (in minutes)</td><td>-0.038***<br>(0.002)</td><td>-0.029***<br>(0.005)</td></tr><tr><td>Travel time × female</td><td></td><td>-0.003<br>(0.003)</td></tr><tr><td>Travel time × aged 40-</td><td></td><td>-0.004<br>(0.005)</td></tr><tr><td>Travel time × aged 41-50</td><td></td><td>-0.016***<br>(0.006)</td></tr><tr><td>Travel time × aged 51-60</td><td></td><td>-0.005<br>(0.006)</td></tr><tr><td>Bonus (in €1,000)</td><td>0.048***<br>(0.018)</td><td>-0.004<br>(0.029)</td></tr><tr><td>Bonus × novice</td><td></td><td>0.163***<br>(0.016)</td></tr><tr><td>Bonus × beginner</td><td></td><td>0.125***<br>(0.018)</td></tr><tr><td>Bonus × currently in REP school</td><td></td><td>0.055*<br>(0.028)</td></tr><tr><td>Bonus × currently in REP+ school</td><td></td><td>0.131***<br>(0.027)</td></tr><tr><td>Bonus × Agrégation</td><td></td><td>-0.206***<br>(0.030)</td></tr><tr><td>Bonus × Classe normale</td><td></td><td>-0.038<br>(0.027)</td></tr><tr><td># of observations (N)</td><td>610,520</td><td>610,520</td></tr><tr><td>log(L)/N</td><td>-0.725</td><td>-0.724</td></tr><tr><td># of ROLs</td><td>7,537</td><td>7,537</td></tr><tr><td># of teachers</td><td>3,825</td><td>3,825</td></tr><tr><td># of school FE (π<sub>j</sub>)</td><td>269</td><td>269</td></tr><tr><td>π̄<sub>j</sub> (REP+ schools)</td><td>0.221</td><td>0.220</td></tr><tr><td>π̄<sub>j</sub> (REP schools)</td><td>0.236</td><td>0.241</td></tr><tr><td>π̄<sub>j</sub> (regular schools)</td><td>0.329</td><td>0.338</td></tr><tr><td>1 estimated s.d. of π<sub>j</sub></td><td>0.682</td><td>0.673</td></tr></tbody></table>

Note. Rank-ordered Logit model (ML estimation).

Robust standard errors clustered at the teacher level.
## Liste des documents de travail de la Direction des Études et Synthèses Économiques

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

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

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

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

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

<table>
  <tr>
    <td>G 9806</td>
    <td>J. ACCARDO - M. JLASSI</td>
    <td>J. ACCARDO - M. JLASSI</td>
  </tr>
  <tr>
    <td>G 9807</td>
    <td>Bilan des activités de la Direction des Études et Synthèses Économiques - 1997</td>
    <td>La productivité globale des facteurs entre 1975 et 1996</td>
  </tr>
  <tr>
    <td>G 9808</td>
    <td>A. MOUROUGANE</td>
    <td>A. MOUROUGANE</td>
  </tr>
  <tr>
    <td>G 9809</td>
    <td>X. BONNET - E. DUBOIS - L. FALVET</td>
    <td>Asymétrie des inflations relatives et menus costs - tests sur l'inflation française</td>
  </tr>
  <tr>
    <td>G 9810</td>
    <td>E. DUGUET - N. IUNG</td>
    <td>Sales and Advertising with Spillovers at the firm level: Estimation of a Dynamic Structural Model on Panel Data</td>
  </tr>
  <tr>
    <td>G 9811</td>
    <td>J.P. BERTHIER</td>
    <td>J.P. BERTHIER</td>
  </tr>
  <tr>
    <td>G 9812</td>
    <td>C. PRIGENT</td>
    <td>Congestion urbaine : un modèle de trafic de pointe à courbe débit-vitesse et demande élastique</td>
  </tr>
  <tr>
    <td>G 9813</td>
    <td>A.Th. AERTS</td>
    <td>A.Th. AERTS</td>
  </tr>
  <tr>
    <td>G 9814</td>
    <td>B. SALANIE</td>
    <td>L'évolution de la part des salaires dans la valeur ajoutée en France refiletée-telle les evolutions individuelles sur la période 1979-1994 ?</td>
  </tr>
  <tr>
    <td>G 9901</td>
    <td>S. DUCHÈNE - A. JACQUOT</td>
    <td>Guide pratique des séries non-stationnaires</td>
  </tr>
  <tr>
    <td>G 9902</td>
    <td>Ch. COLIN</td>
    <td>S. DUCHÈNE - A. JACQUOT</td>
  </tr>
  <tr>
    <td>G 9903</td>
    <td>Modélisation des carrières dans Destinie</td>
    <td>Une croissance plus riche en emplois depuis le début de la dédenne ? Une analyse en comparaison internationale</td>
  </tr>
  <tr>
    <td>G 9904</td>
    <td>Ch. COLIN</td>
    <td>Ch. COLIN</td>
  </tr>
  <tr>
    <td>G 9905</td>
    <td>B. CREPON - N. IUNG</td>
    <td>Ch. COLIN</td>
  </tr>
  <tr>
    <td>G 9906</td>
    <td>B. CREPON - Ch. GIANELLA</td>
    <td>Ch. COLIN</td>
  </tr>
  <tr>
    <td>G 9907</td>
    <td>B. CREPON - R. MAHIEU</td>
    <td>Ch. COLIN</td>
  </tr>
  <tr>
    <td>G 9908</td>
    <td>B. CREPON - F. ROSENWALD</td>
    <td>Ch. COLIN - R. MAHIEU</td>
  </tr>
  <tr>
    <td>G 9909</td>
    <td>B. CREPON - F. ROSENWALD</td>
    <td>Ch. COLIN - R. MAHIEU</td>
  </tr>
  <tr>
    <td>G 9910</td>
    <td>Bilan des activités de la DESE - 1999</td>
    <td>Bilan des activités de la DESE - 1999</td>
  </tr>
  <tr>
    <td>G 9911</td>
    <td>G. LAROQUE - B. SALANIE</td>
    <td>Une décomposition du non-emploi en France</td>
  </tr>
</table>

<table>
  <tr>
    <td>G 9912</td>
    <td>B. SALANIÉ</td>
    <td>B. SALANIÉ</td>
  </tr>
  <tr>
    <td>G 9913</td>
    <td>Ch. GIANELLA</td>
    <td>Une maquette analytique de long terme du marché du travail</td>
  </tr>
  <tr>
    <td>G 9914</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
    <td>Ch. GIANELLA</td>
  </tr>
  <tr>
    <td>G 9915</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
    <td>Une estimation de l'élasticité de l'emploi peu qualifié à son coût</td>
  </tr>
  <tr>
    <td>G 9916</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
  </tr>
  <tr>
    <td>G 9917</td>
    <td>B. CREPON - R. DESPLATZ - J. MAIRESSÉ</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
  </tr>
  <tr>
    <td>G 9918</td>
    <td>Estimating price cost margins, scale economies and workers' bargaining power at the firm level</td>
    <td>Estimation de l'élasticité de l'emploi peu qualifié à son coût</td>
  </tr>
  <tr>
    <td>G 9919</td>
    <td>Ch. GIANELLA - Ph. LAGARDE</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
  </tr>
  <tr>
    <td>G 9920</td>
    <td>Productivity of hours in the aggregate production function: an evaluation on a panel of French firms from the manufacturing sector</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
  </tr>
  <tr>
    <td>G 9921</td>
    <td>S. AUDRIC - P. GIVORD - C. PROST</td>
    <td>Division « Redistribution et Politiques Sociales »</td>
  </tr>
  <tr>
    <td>G 9922</td>
    <td>Évolution de l'emploi et des coûts par qualification entre 1982 et 1996</td>
    <td>R. MAHIEU</td>
  </tr>
  <tr>
    <td>G 9923</td>
    <td>R. MAHIEU</td>
    <td>Les déterminants des dépenses de santé : une approche macroéconomique</td>
  </tr>
  <tr>
    <td>G 9924</td>
    <td>C. ALLARD-PRIGENT - H. GUILMEAU - A. QUINET</td>
    <td>C. ALLARD-PRIGENT - H. GUILMEAU - A. QUINET</td>
  </tr>
  <tr>
    <td>G 9925</td>
    <td>The real exchange rate as the relative price of nontrables in terms of tradables: theoretical investigation and empirical study on French data</td>
    <td>J.-Y. FOURNIER</td>
  </tr>
  <tr>
    <td>G 9926</td>
    <td>L'approximation du titre passe-bande proposée par Christiano et Fitzgerald</td>
    <td>J.-Y. FOURNIER</td>
  </tr>
  <tr>
    <td>G 9927</td>
    <td>Bilan des activités de la DESE - 1999</td>
    <td>Bilan des activités de la DESE - 1999</td>
  </tr>
  <tr>
    <td>G 9928</td>
    <td>Investissement et contraintes de financement : le poids du cycle</td>
    <td>Bilan des activités de la DESE - 1999</td>
  </tr>
  <tr>
    <td>G 9929</td>
    <td>Une estimation sur données françaises</td>
    <td>A. FLIPO</td>
  </tr>
  <tr>
    <td>G 9930</td>
    <td>Les comportements matrimoniaux de fait</td>
    <td>A. FLIPO</td>
  </tr>
  <tr>
    <td>G 9931</td>
    <td>R. MAHIEU - B. SÉDILLOT</td>
    <td>R. MAHIEU - B. SÉDILLOT</td>
  </tr>
  <tr>
    <td>G 9932</td>
    <td>Microsimulations of the retirement decision: a supply side approach</td>
    <td>R. MAHIEU - B. SÉDILLOT</td>
  </tr>
  <tr>
    <td>G 9933</td>
    <td>C. AUDENIS - C. PROST</td>
    <td>R. MAHIEU - B. SÉDILLOT</td>
  </tr>
  <tr>
    <td>G 9934</td>
    <td>Déficit conjointuel : une prise en compte des conjonctures passées</td>
    <td>R. MAHIEU - B. SÉDILLOT</td>
  </tr>
  <tr>
    <td>G 9935</td>
    <td>Équivalent patrimonial de la rente et souscription de retraite complémentaire</td>
    <td>R. MAHIEU - B. SÉDILLOT</td>
  </tr>
  <tr>
    <td>G 9936</td>
    <td>R. DUHAUTOIS</td>
    <td>R. DUHAUTOIS</td>
  </tr>
  <tr>
    <td>G 9937</td>
    <td>Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertière ?</td>
    <td>Ralentissement de l'investissement : petites ou grandes entreprises ? industrie ou tertière ?</td>
  </tr>
</table>
V

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

G2000/12 Ch. GIANELLA
Local unemployment and wages

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

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

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

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

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

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

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

G2001/07 Bilan des activités de la DESE - 2000

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

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

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

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

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

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

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

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

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

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

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

G2002/01 B. LEQUILLER
- La nouvelle économie et la mesure de la croissance du PIB
- The new economy and the measure of GDP growth

G2002/02 B. LEQUILLER - E. WALRAET
La cessation d'activité au sein des couples : y a-t-il interdépendance des choix ?

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

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

- Retropagation of the investment series (GFCF) and estimation of fixed capital stocks on the ESA-95 basis for the French balance sheets

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2003/03 Bilan des activités de la DESE - 2002
G2003/04 P.-O. BEFFY - J. DEROYON - N. FOURCADE - S. GREGOIR - N. LAIB - B. MONFORT

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

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

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

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

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

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

G2003/11 P.-O. BEFFY - X. BONNET - M. DARRACQ-PARIES - B. MONFORT
Mise et utilisation des emplois instables dans les entreprises

G2003/12 C. PÉCART
Évalueur la rentabilité des sociétés non financières

G2003/13 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Les retraites du secteur public : projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE

G2003/14 S. BUFFETEAU - P. GODEFROY
Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945 à 1974

G2003/07 S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

vi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2010/13 D. BLANCHET - S. BUFFETEAU - E. CRENNER S. LE MINEZ Le modèle de microsimulation Destinie 2 : 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 Mesange réestimé en base 2000

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2014/12 F. ARNAUD - J. BOUSSARD - A. POISSONNIER - H. SOUAL