# The Impact of Hazardous Industrial Facilities on Housing Prices: A Comparison of Parametric and Semiparametric Hedonic Price Models

Céline Grislain-Letrémy, Arthur Katossky

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Direction des Études et Synthèses Économiques

G 2014 / 05

# The Impact of Hazardous Industrial Facilities on Housing Prices: A Comparison of Parametric and Semiparametric Hedonic Price Models

Céline GRISLAIN-LETRÉMY et Arthur KATOSSKY

Document de travail
INSTITUT NATIONAL DE LA STATISTIQUE ET DES ÉTUDES ÉCONOMIQUES

Série des documents de travail
de la Direction des Études et Synthèses Économiques

G 2014 / 05

# The Impact of Hazardous Industrial Facilities on Housing Prices: A Comparison of Parametric and Semiparametric Hedonic Price Models

Céline GRISLAIN-LETRÉMY* et Arthur KATOSSKY

MAI 2014

Les auteurs remercient Pierre-Philippe Combes, Éric Dubois, Laurent Gobillon, Nicolas Grislain, Anne Lafferère, Claire Lelarge, David Martimort, Philippe Mongin, Corinne Prost, Sandrine Spaeter, Corentin Trevien et Bertrand Villeneuve pour leurs remarques et suggestions. Ce travail a également bénéficié de commentaires de Pauline Charnoz, Amélie Mauroux et des participants au 28ème congrès de l'association économique européenne (EEA), au 12ème atelier international de la statistique et de l'économétrie spatiales, au séminaire de recherche de l'Insee « Immobilier en France : analyses et perspectives », aux conférences de 2012 de l'association européenne de l'économie des ressources et de l'environnement (EAERE) et de l'association de science régionale européenne (ERSA) et aux 11èmes journées Louis-André Gérard Varet. Les auteurs remercient la Direction générale de la Prévention des risques, notamment Sandrine Robert et Grégory Dubois, ainsi que la Direction générale de l'Aménagement, du logement et de la nature du ministère de l'Écologie pour l'obtention des données. Ils remercient tout particulièrement Vincent Binet, Rémi Borel, Olivier Dupret, François Filior, Martine Giloppe, Jeanne-Marie Gouiffès et Brigitte Pouget des centres d'études techniques de l'équipement (CETE) Normandie-Centre, Nord-Picardie et Sud-Ouest pour leur aide dans le recueil des données. Les auteurs remercient enfin Christophe Yon pour avoir réalisé les trois cartes fournies dans ce document de travail.

<footnote>* Département des Études Économiques - Division « Marchés et entreprises » Timbre G230 - 15, bd Gabriel Péri - BP 100 - 92244 MALAKOFF CEDEX</footnote>
# The Impact of Hazardous Industrial Facilities on Housing Prices: A Comparison of Parametric and Semiparametric Hedonic Price Models

## Abstract

Households' willingness to pay for prevention against industrial risks can be revealed by real estate markets. With highly detailed microdata, we study housing prices in the vicinity of hazardous industries located near three important French cities (Bordeaux, Dunkirk and Rouen). We show that the impact of hazardous plants on housing values strongly differs among the three studied areas, even if they all surround chemical and petrochemical industries. We compare the results from standard parametric hedonic property models and from a more flexible, semiparametric hedonic property model. We show that the parametric model may structurally lead to an important bias in the estimated value of the impact of hazardous plants on housing values and in its variations with respect to distance to the plants.

**Keywords:** hedonic analysis, locally-weighted regression, urban housing markets, industrial risk

## L'impact des industries à risques sur le prix des logements : comparaison de modèles paramétriques et semi-paramétriques de prix hédoniques

## Résumé

Le consentement à payer des populations pour des mesures de prévention contre les risques industriels peut être révélé par les marchés immobiliers. Avec une base de données microéconomiques très détaillées, nous étudions les prix des logements à proximité d'industries dangereuses situées près de trois grandes villes françaises (Bordeaux, Dunkerque et Rouen). Nous montrons que l'impact des usines dangereuses sur le prix des logements varie beaucoup entre les trois zones étudiées, même si elles sont toutes à proximité d'industries chimiques et pétrochimiques. Nous comparons les résultats d'un modèle paramétrique standard de prix hédoniques et ceux d'un modèle de prix hédoniques semi-paramétrique, plus flexible. Nous montrons que le modèle paramétrique peut structurellement conduire à un biais important dans la valeur estimée de l'impact des usines dangereuses sur le prix des logements et dans ses variations en fonction de la distance aux usines.

**Mots-clés :** analyse hédonique, régression localement pondérée, marchés du logement urbains, risque industriel

**Classification JEL :** C21, Q51, R52, R21
# 1 Introduction

Real estate markets can reveal households' willingness to pay to reduce their exposure to hazardous industrial facilities. Clearly, hazardous industrial activities generate strong externalities, many of them being negative. Indeed, neighboring populations partly bear the cost of a potential industrial accident,<fnref n="1" /> and may endure day-to-day nuisances associated with the ordinary course of activity. However, these facilities also generate positive externalities (Greenstone et al., 2010), potentially more diffuse: they provide, directly and indirectly, employment; through local taxes, they may contribute to the economic development of the municipalities. Declining the impact of distance to hazardous facilities on housing values with respect to their activities reveals in which extent these activities are perceived as disamenities. We estimate the impact of distance to hazardous plants on housing values by using hedonic price models. The first-order derivative of the hedonic price function with respect to the distance to hazardous plants provides an estimate of the implicit price of the distance to these plants, that is an estimate of the buyers' willingness to pay to live far from these plants. We study housing prices in the vicinity of three important French cities (Bordeaux, Dunkirk and Rouen). These three chosen industrial areas all include chemical and petrochemical industries, but with different socioeconomic characteristics of their neighborhoods and different perceptions of industrial risks.<fnref n="2" />

Our first contribution is to study the impact of hazardous chemical and petrochemical industries on housing prices with highly detailed microdata. Our unique database is much richer than the ones used for studies relative to the impact of similar industrial risks on housing prices. Impact of risk exposure on real estate prices is estimated for oil facilities by Boxall et al. (2005) and Flower and Ragas (1994), for chemical industry by Carroll et al. (1996), and for industrial areas with chemical or petrochemical facilities by Sauvage (1997) and Travers et al. (2009). These studies use data with few (or without) extrinsic characteristics of the dwellings and without information on buyers and sellers.<fnref n="3" /> Our

<footnote n="1">¹Damages certainly imply the liability of the industrialist, but compensation can be delayed and remain partial. In particular, some physical and moral damages cannot be repaired.</footnote>

<footnote n="2">²The industrial areas near Bordeaux and Rouen have become sadly well-known after our data collection. In the gunpowder near Bordeaux, a fire caused one death and two seriously injured persons in December 2013 ("Gironde : un mort et deux blessés dans un incendie sur un site classé Seveso" by Jean-Pierre Tamisier, December 6th, 2013, *Sud-Ouest*). Near Rouen, mercaptan gas escape from one of the factories, the Lubrizol company, caused foul odor in France and in England in January 2013 ("Foul-Smelling Cloud Drifts Over France, Alarming Residents" by Scott Sayare, January 22nd, 2013, *The New York Times*).</footnote>

<footnote n="3">³See Table 13 in Appendix A.2 for a review of all these studies and their data. The impact on housing prices of other industrial activities has been studied, in particular the impact of power plants (Davis, 2011), waste treatment and storage (Greenstone and Gallagher (2008), see Farber (1998) for a review) and of natural gas (Boxall et al., 2005). Air pollution also significantly decreases housing prices (Chay and</footnote>
case study relies on a detailed dwelling-level micro-database, each dwelling’s address be-
ing precisely geocoded. Detailed data relative to their extrinsic characteristics have been
collected: proximity to central business district, shops and public utilities, exposure to
industrial risk, to other risks or pollutions. Dwellings’ price and intrinsic characteristics
come from notarial data; information about buyers and sellers is also provided.

Our second contribution consists in comparing the impacts of distance to hazardous plants on housing prices, as estimated by two models: a standard parametric model and a semi-parametric model. The semiparametric regression is a locally weighted regression that allows implicit prices to vary with respect to space, time of sale and buyer's characteristics, while keeping some smoothness in their distribution.<fnref n="4" /> It enables to relax and to test the assumption of fixed parameters, that is of constant implicit prices across space, time and among buyers. The fixed-parameter assumption is rejected, which confirms the need for flexible forms and pleads for the use of semiparametric models. Such a comparison of parametric and semiparametric models had not been performed until now in the analysis of industrial risks, but for other amenities, such as agricultural pollution on housing prices (Bontemps et al., 2008), light rail access (Redfearn, 2009); all these analyses plead for the use of semiparametric models. Anglin and Gencay (1996) also show that semiparametric hedonic models outperform parametric ones. McMillen (2010), McMillen and Redfearn (2010), Redfearn (2009), Sunding and Swoboda (2010) specifically compare parametric models with locally weighted regression and recommend the use of this semiparametric model. Redfearn (2009) shows that implicit prices vary spatially and temporally and that assuming fixed implicit prices is a misspecification of hedonic model. This misuse of parametric models has important consequences in our case study. If signs and orders of magnitude of effects are similar in the two models for the very wide majority of coefficients, the estimated impacts of the distance to highly hazardous plants on housing prices significantly differ between the two models. The parametric model leads to an important bias (here an overestimation) in the estimated value of this impact near Bordeaux and Rouen and in its variations with respect to distance to the plants near Rouen.

Using the semiparametric model, we show that the impact of hazardous plants on housing
values strongly differs among the three studied industrial areas, even if they all surround

<footnote>Greenstone (2005), see Maslianskaïa-Pautrel (2008) for a review).</footnote>

<footnote n="4">⁴Locally weighted regressions have already been applied in several works by the French National Insti-
tute of Statistics and Economics Studies (INSEE). See Floch (2012) for examples and a methodological
presentation of this model in French.</footnote>
chemical and petrochemical industries. The gunpowder factory near Bordeaux is a former
military plant, not necessarily perceived as hazardous by neighboring population. We find
that its proximity is even valued in its very close vicinity, probably because its neighbor-
hoods are green and very quiet places. Thus, we capture here unobserved amenities which
are spatially correlated with the distance to the plant. Near Dunkirk and Rouen, chemical
activities are clearly identified as hazardous by local populations. However, near Dunkirk,
we find no significant impact of the distance to highly hazardous plants on housing prices,
likely because these industrial risks are overshadowed by the nuclear plant in Gravelines
(located 18 km from Dunkirk). Near Rouen, highly hazardous plants are perceived as
disamenities: on average, households are willing to pay around 1.2% of their dwelling price
to go 100 more meters away from these plants. This marginal willingness to pay decreases
with respect to the distance to the plants, as marginal gains (in terms of exposure re-
duction) of going further away from the hazard source are likely to decrease. Finally, we
show that this marginal willingness to pay increases over time following the AZF accident,
information policies, or the implementation of the technological disasters insurance system.

Our findings have important implications. Inasmuch as households are aware of the differ-
ences in amenities across locations,<fnref n="5" /> the impact of distance to highly hazardous plants on
housing prices can be interpreted as the local populations’ marginal willingness to pay for
prevention against industrial risks. This willingness is used in cost-benefit analyses that
estimate the efficient level of prevention against industrial risks for the civil society (see
Treich (2005) for a review). The benefits, which correspond to the population’s willingness
to pay for this reduction, are often revealed by real estate markets.<fnref n="6" /> Most of the time
this willingness to pay is estimated by parametric hedonic models and for similar types
of industrial activities, over a potentially different study area or period. Our results show
that parametric hedonic models can lead to an important bias in the estimated value of
the marginal willingness to pay. Besides, our results show that estimated willingness to
pay for prevention has a limited external validity: it strongly differs among industrial ar-
eas, even among chemical and petrochemical industries; it also depends on the distance

<footnote n="5">⁵Currie et al. (2013)'s findings do not contradict perfect, or at least unbiased, information about industrial hazardous activities in the housing market, even in the presence of scientific uncertainty about health risks.</footnote>

<footnote n="6">⁶The benefits are the monetary value of the reduction of risk exposure, that is the population's willingness to pay for this reduction. This willingness to pay can be assessed either by stated or revealed preference methods. The stated preference method comes up against numerous biases in the questionnaire of so called contingent valuation studies and the limited incentives of questioned households to reveal their preferences. The revealed preference method consists in observing actual individual decisions on markets, such as real estate market.</footnote>
to these facilities and on time. Thus, our findings plead for a careful use of population's willingness to pay for prevention against industrial risks in the cost-benefit analyses of prevention measures, as it can lead to a significant bias in the estimation of the efficient prevention level.

The paper is organized as follows. The parametric and semiparametric models are presented in Section 2. Section 3 describes the three industrial areas, the delimitation of study areas and the data. Section 4 exposes and compares the results get from parametric and semiparametric models. Section 5 concludes.

## 2 Model

**Hedonic property models.** Real estate markets can reveal households' willingness to pay to reduce their exposure to hazardous industrial facilities. Hedonic property models enable to estimate the implicit price of the distance to highly hazardous plants (which is the first-order derivative of price with respect to the distance to highly hazardous plants). This implicit price is equal to the households' (buyers' or sellers') marginal willingness to go one more meter away from these plants. Indeed, in the framework formalized by Rosen (1974), a dwelling is defined by its distance $d$ to highly hazardous plants and several other characteristics $X$, which determine its price $P(d, X)$. When choosing their location, households equalize their marginal willingness to pay for increasing each characteristic by one unit with the marginal, or implicit, price of this characteristic.<fnref n="7" /> Thus, estimation of the hedonic price function provides an estimation of the implicit price of the distance $d$ to highly hazardous plants $\partial P(d, X)/\partial d$, which can so be interpreted as households' marginal willingness to pay to go one more meter away from highly hazardous plants.

Unless making very specific assumptions, the hedonic price function is not linear and has no known explicit form (see Freeman (2003) for a review of hedonic price methods). This is the reason why we have first performed many parametric models: linear, log-linear, log-log and linear with Box-Cox transformations of the price and continuous regressors - while

$$ \frac{U_d(z, d, X)}{U_z(z, d, X)} = \frac{\partial P(d, X)}{\partial d}, \quad \forall k, \frac{U_{x_k}(z, d, X)}{U_z(z, d, X)} = \frac{\partial P(d, X)}{\partial x_k}. \qquad (1) $$

<footnote n="7">⁷Formally, a household of income $y$ chooses his location by maximizing his utility $U(z, d, X)$, where $z$ denotes the amount of composite consumer good (which includes all consumer goods except land), under his budget constraint $y = z + P(d, X)$. Location choices by households maximize their utility by equalizing their marginal rate of substitution between each characteristic ($d$ or $x_k$) and money with the implicit price $(\partial P(d, X)/\partial d$ or $\partial P(d, X)/\partial x_k$):</footnote>
allowing these Box-Cox coefficients to be different (Grislain-Letrémy and Katossky, 2013). For each of these specifications, we have either added fixed effects (municipality dummies) and tested for different types of spatial dependency (spatial errors and/or spatial lag). Comparison by likelihood ratio tests of these nested models shows that a more flexible form is indeed better (Grislain-Letrémy and Katossky, 2013).

In addition to the baseline log-linear model, we present here two extensions of this model: a parametric model and a semiparametric one.

## 2.1 Parametric model

The parametric model is a log-polynomial model with municipality and time dummies based on ordinary least squares. We estimate the logarithm of the price $P_i$ of dwelling $i$ as a function of the distance to the plants $d_i$, the dwelling's other characteristics $X_i$ and the vector $\eta_{ti}$ of time dummies (for month and year of sale). Near Bordeaux, the square and the cube of the distance to highly hazardous plants are significant and included in the regression. Near Rouen, only the square of the distance to highly hazardous plants is significant and included in the regression.

$$ \text{OLS Bordeaux: } \ln P_i = \alpha + \beta_1 d_i + \beta_2 d_i^2 + \beta_3 d_i^3 + \gamma' X_i + \tau' \eta_{ti}, \quad (2) $$

$$ \text{OLS Dunkirk: } \ln P_i = \alpha + \beta_1 d_i + \gamma' X_i + \tau' \eta_{ti}, \quad (3) $$

$$ \text{OLS Rouen: } \ln P_i = \alpha + \beta_1 d_i + \beta_2 d_i^2 + \gamma' X_i + \tau' \eta_{ti}. \quad (4) $$

## 2.2 Semiparametric model

**Motivation.** The semiparametric model is also based on a log-linear specification. It is a locally weighted regression (LWR), which allows the marginal willingnesses to pay for characteristics to vary with respect to space, time of sale and buyer's characteristics, while keeping some smoothness in their distribution. Indeed, the marginal willingness to pay for each characteristic is a priori not uniform over the study area (McMillen, 2010). It may also vary after events that could have changed risk perception, such as local or national accidents, information policies. Indeed, several empirical works show that real estate prices can be significantly modified by information policies,<fnref n="8" /> or insurance coverage.<fnref n="9" /> Finally, this

<footnote n="8">⁸See Gayer et al. (2000) and Kohlhase (1991) in the case of hazardous waste facilities and Maani (1991) in the case of high-pressure gas pipeline. However, in the case of airport noise disclosure, Pope (2008) shows that publicly available information may not be adequately considered by all buyers.</footnote>

<footnote n="9">⁹For example, flood insurance shapes real estate prices (MacDonald et al. (1990), Harrison et al. (2001), Morgan (2007) and Bin et al. (2008)).</footnote>
willingness to pay may vary among buyers, because of heterogenous preferences.<fnref n="10" />

McMillen (2010), McMillen and Redfearn (2010), Redfearn (2009), Sunding and Swoboda (2010) specifically compare parametric models with locally weighted regression and plead for the use of this semiparametric model. Redfearn (2009) provides two main reasons that plead for the use of locally weighted regression instead of standard hedonic models: the rejected standard assumption of fixed implicit prices and the presence of omitted local amenities. First, Redfearn (2009)'s results show that the standard assumption of fixed implicit prices is rejected and suggest that imposing fixed parameters generates spatial patterns in the errors that leads to parameter estimates highly sensitive to very small changes in sample or in specification. Second, Redfearn (2009) compares regressions with and without a known local amenity and shows that the local regression analysis appears more robust to omitted local amenities.

**Locally weighted regression: formalization.** We estimate the logarithm of the price $P_i$ of dwelling $i$ as a function of the distance to the plants $d_i$, the dwelling's other characteristics $X_i$ and the vector $\eta_{t_i}$ of dummies for time of sale. We allow the coefficients to vary with respect to space (x and y coordinates, $xc_i$ and $yc_i$), time of sale ($t_i$) and buyer's income ($y_i$), while keeping some smoothness in their distribution. We choose here the income as the main buyer's characteristic. Indeed, income is imputed using gender, age, marital status and municipality of origin (Section 3) and it summarizes this way the main observed buyer's characteristics.<fnref n="11" /> By denoting $Z_i = (xc_i, yc_i, t_i, y_i)$, we get

$$ \text{LWR: } \ln P_i = \alpha(Z_i) + \beta(Z_i)d_i + \gamma'(Z_i)X_i + \tau'(Z_i)\eta_{t_i}. \quad (5) $$

More precisely, locally weighted regression is a set of weighted least square regressions, with one regression for each observation (see McMillen and Redfearn (2010) for a review). Each regression estimates the implicit prices at each observation using a subsample of “close” observations. Proximity refers here to spatial proximity, temporal proximity, as well as buyer's characteristics proximity. The set of observations used in each local regression corresponds to the observations within a window around the considered observation

<footnote n="10">¹⁰Proximity in terms of preferences may be already partly captured by geographical proximity, because of a sorting on housing market.</footnote>

<footnote n="11">¹¹We could have also allowed the marginal willingnesses to pay for characteristics to vary with respect to other characteristics of buyers, but the results get from parametric models provide very little hope in this direction. Indeed, in several parametric models we did not get significant coefficients when the distance to the plant was crossed with other characteristics of buyers.</footnote>
j.<fnref n="12" /> Formally, the objective function for estimation of locally weighted regression is, for observation j:

$$ \text{LWR: } \sum_{i=1}^{n} (\ln P_i - \alpha - \beta d_i - \gamma' X_i - \tau' \eta_{t_i})^2 W_{ij}, \qquad (6) $$

where $W_{ij} = f(Z_i, Z_j)$ is the weight for each observation $i$; this weight is a decreasing function of the spatial, temporal and buyer's distance from observation $i$ to the considered observation $j$.

**Weights.** First, we apply the Mahalanobis distance $d^M(Z_i, Z_j)$ between $Z_i$ and $Z_j$ to go from four dimensions to one dimension in our kernel:<fnref n="13" />

$$ d^M(Z_i, Z_j) = \sqrt{(Z_i - Z_j)^T (cov(Z))^{-1} (Z_i - Z_j)}, \qquad (7) $$

where $Z = (Z_1, ..., Z_n)$. Then, weights are generated by a kernel weighting function, here the tri-cubic kernel weighting function, applied to this distance:

$$ W_{ij} = \left( 1 - \left( \frac{d^M(Z_i, Z_j)}{d_{max}^M(Z_i, Z_j)} \right)^3 \right)^3, \qquad (8) $$

where $d_{max}^M(Z_i, Z_j)$ is the largest Mahalanobis distance from the considered observation $j$ to any observation within the window.

**Window.** If the choice of the kernel function has a limited impact on the estimation results (McMillen, 2010),<fnref n="14" /> the choice of the window is crucial. The common window for all regressions can be chosen as a minimizer of the mean difference between the dependent variable and the estimated value over all regressions when excluding the considered observation from the sample.<fnref n="15" /> The size of the window estimated by cross-validation corre-

$$ Y_i = LY_i + u_i. \qquad (9) $$

Thus, a cross-validation estimate of $h$ is a minimizer of the cross-validation measure $CV(h)$:

$$ CV(h) = \frac{1}{n} \sum_{i=1}^{n} \left( \frac{Y_i - \hat{Y}_{h,i}}{1 - l_{ii}} \right)^2, \qquad (10) $$

<footnote n="12">¹²Estimating numerous separate regressions for each observation may lead to think that a high degree of freedom is here used. This is not the case because of the smoothness implied by overlapping samples (Redfearn (2009), see McMillen and Redfearn (2010) for more details).</footnote>

<footnote n="13">¹³Compared to the Euclidean distance, the Mahalanobis distance takes into account the correlations of the data set and is scale-invariant.</footnote>

<footnote n="14">¹⁴Results are robust when using a gaussian kernel function instead of a tri-cubic one.</footnote>

<footnote n="15">¹⁵The LWR model can be written under a linear form (McMillen and Redfearn, 2010). By denoting $Y_i = \ln(P_i)$, we get</footnote>
sponds to 36% of the initial sample near Bordeaux, 40% near Dunkirk, 76% near Rouen.<fnref n="16" />
However, larger windows are needed when the objective is to measure marginal effects and
not to predict the dependent variable (McMillen, 2010). This is the reason why we use
windows that correspond to 80% of the initial sample in the three studied cases.<fnref n="17" />

Thus, locally weighted regression allows coefficients to vary with space, time of sale and buyer's characteristics, while keeping some smoothness in their distribution (implied by overlapping samples). This is why this model enables to relax and to test the assumption of fixed parameters, that is of constant implicit prices across space, time and among buyers. Locally weighted regressions have already been used in several works by INSEE. Floch (2012) provides examples of these works and a methodological presentation of this model in French.

## 3 Industrial areas, study areas and data

We have realized an important work of data collection. We detail here the selection of industrial areas, the delimitation of study areas and the collected variables.

### 3.1 Industrial areas

Many French hazardous industrial plants are surrounded by a high population density and could so be considered for this analysis. However, the important work of data collection limits the number of sites that could be studied. The three industrial areas are chosen for the different socioeconomic characteristics of their neighborhood and for their different perceptions of industrial risks.

**Socioeconomic characteristics.** The three industrial areas studied are located near important French cities: Bordeaux, Dunkirk and Rouen. The three industrial areas present different socioeconomic characteristics (Table 1). Neighborhoods of the gunpowder near Bordeaux are the richest ones; many inhabitants are executives in the aerospace indus-

<footnote>where $\hat{Y}_{h,i}$ is the estimate based on the sample from which observation $i$ is removed and using a window of size $h$ around $i$.</footnote>

<footnote n="16">¹⁶Initial samples include 1,423 observations near Bordeaux, 1,016 near Dunkirk and 571 near Rouen. Thus, the size of the window estimated by cross-validation corresponds to 512 observations near Bordeaux, 406 near Dunkirk and 434 near Rouen.</footnote>

<footnote n="17">¹⁷Here larger windows are all the more needed, as the interest variable (the distance to highly hazardous plants) varies with some kernel variables (the geographic coordinates): small windows may lead to imprecise estimates of its marginal effect. However, when considering smaller windows (60%, 40%) with either cubic or gaussian kernels, the significance and the mean value of the impact of our interest variable on dwelling value appear as robust.</footnote>
try. Dunkirk and its neighborhoods are the densest area; many households are workers. Neighborhoods near Rouen are in an intermediate position between the first two areas: income is higher than near Dunkirk and lower than near Bordeaux; many inhabitants are executives and work far from their home. In the three areas, according to the 2008 French annual declaration of social data, at the most 2% of the population in each municipality is employed by the highly hazardous plants that are described below.

<table><caption>Table 1: Population's socioeconomic characteristics in the three studied cases</caption><thead><tr><th>Municipality</th><th>INSEE code</th><th>Population (a)</th><th>Population density (a) (per sq km)</th><th>Percentage of built area (b)</th><th>Average tax revenue (a) (euro)</th><th>Sports and sociocultural facilities (c)</th><th>Distance to labor pool (d) (km)</th></tr></thead><tbody><tr><td colspan="8" style="text-align: center; font-style: italic;">Near Bordeaux</td></tr><tr><td>Le Haillan</td><td>33200</td><td>8,378</td><td>904</td><td>8</td><td>26,817</td><td>14</td><td>5.9</td></tr><tr><td>Martignas-sur-Jalles</td><td>33273</td><td>6,633</td><td>251</td><td>2</td><td>29,005</td><td>27</td><td>9.2</td></tr><tr><td>Mérignac</td><td>33281</td><td>65,469</td><td>1,359</td><td>9</td><td>23,181</td><td>72</td><td>4.4</td></tr><tr><td>Saint-Aubin-de-Médoc</td><td>33376</td><td>5,550</td><td>159</td><td>1</td><td>41,577</td><td>12</td><td>11.1</td></tr><tr><td>Saint-Médard-en-Jalles</td><td>33449</td><td>26,984</td><td>315</td><td>3</td><td>27,599</td><td>48</td><td>4.8</td></tr><tr><td>Le Taillan-Médoc</td><td>33519</td><td>8,668</td><td>571</td><td>5</td><td>30,412</td><td>14</td><td>4.9</td></tr><tr><td colspan="8" style="text-align: center; font-style: italic;">Near Dunkirk</td></tr><tr><td>Coudekerque-Branche</td><td>59155</td><td>22,994</td><td>2,515</td><td>11</td><td>18,641</td><td>23</td><td>4.3</td></tr><tr><td>Dunkirk</td><td>59183</td><td>69,274</td><td>1,855</td><td>12</td><td>20,218</td><td>18</td><td>4.1</td></tr><tr><td>Fort-Mardyck</td><td>59248</td><td>3,586</td><td>2,543</td><td>13</td><td>18,610</td><td>7</td><td>9</td></tr><tr><td>Saint-Pol-sur-Mer</td><td>59540</td><td>22,100</td><td>4,299</td><td>16</td><td>14,711</td><td>14</td><td>6.2</td></tr><tr><td colspan="8" style="text-align: center; font-style: italic;">Near Rouen</td></tr><tr><td>Grand-Couronne</td><td>76319</td><td>9,346</td><td>552</td><td>5</td><td>20,083</td><td>18</td><td>20.5</td></tr><tr><td>Hautot-sur-Seine</td><td>76350</td><td>9,346</td><td>160</td><td>1</td><td>26,423</td><td>2</td><td>19.3</td></tr><tr><td>Moulineaux</td><td>76457</td><td>881</td><td>238</td><td>2</td><td>21,287</td><td>10</td><td>20.6</td></tr><tr><td>Petit-Couronne</td><td>76497</td><td>8,690</td><td>679</td><td>5</td><td>19,638</td><td>28</td><td>21.1</td></tr><tr><td>Sahurs</td><td>76550</td><td>1,310</td><td>117</td><td>1</td><td>27,017</td><td>5</td><td>26.8</td></tr><tr><td>Val-de-la-Haye</td><td>76717</td><td>751</td><td>74</td><td>1</td><td>23,673</td><td>5</td><td>20.5</td></tr></tbody></table>

Note: Fort-Mardyck and Saint-Pol-sur-Mer municipalities have been associated to Dunkirk municipality in 2010.

Sources: (a) INSEE, (b) building database of the Geographical National Institute, (c) topology database of the Geographical National Institute, (d) Sitranet.

**Risks and perceptions of industrial activities.** Official classification of hazardous plants is defined by regulation. The Seveso II Directive (Council directive 96/82/EC on the control of major-accident hazards) defines hazardous industries according to the presence of hazardous substances or preparations. The degree of hazard is defined with respect to nature and quantities of substances. Two categories of hazardous facilities are so defined: “upper tier” sites and “lower tier” sites. The French legislation is actually harsher than the European one: the French classification includes many other far less hazardous sites, called “authorized” plants.

The three industrial areas here studied all include hazardous chemical and petrochemical
industries. However they present different industrial activities and very different perceptions of associated risks by local populations. The gunpowder factory near Bordeaux was settled in 1660.<fnref n="18" /> Today, it comprises two “upper tier” sites of the Seveso II Directive. By manufacturing gunpowder and explosives, the factory mainly exposes local population to a risk of explosion. However, it is not necessarily perceived as hazardous by neighboring population. Indeed, it is a former military plant; only barbed wire can be seen from some places of the neighborhoods. Furthermore, as the plant is wide (650 buildings over 350 hectares), risk is relatively “contained” within the industrial site. The only nuisance associated to the plant is the transportation of hazardous materials on a precise and limited route.

On the contrary, chemical activities near Dunkirk or Rouen are clearly identified as hazardous by local populations. Near Dunkirk, these activities appeared in the 1970s.<fnref n="19" /> There are now sixteen hazardous plants: fourteen “upper tier” Seveso sites, two authorized. Their activities consist in storage and refining of oil products, metallurgy, manufacture of industrial gases, of chemical and pharmaceutical products and waste treatment. They expose local population to risks of explosion, fire and toxic impacts. The plants (either buildings, chimneys or at least plumes of smoke) can be seen from every neighboring dwelling. However, the presence of a nuclear plant in Gravelines (18 km from Dunkirk) may overshadow the exposure to these industrial risks.

Near Rouen, chemical activities appeared between the 1960s and the 1990s.<fnref n="20" /> There are now thirteen hazardous plants: two “upper tier” Seveso sites, one “lower tier” Seveso site, ten authorized. Their activities are quite diverse: storage and refining of liquid petroleum gas, production of Diester oil (biodiesel) and of liquid carbon dioxide, perfumes storage, paper manufacture, goods transportation and warehouses. They expose local population to the same risks than near Dunkirk: explosion, fire, toxic impacts. However, because of landscape chimneys or silos cannot be seen from some neighboring dwellings.

Although all these highly hazardous plants were established well before the study period, several other events, either local or national, may have modified risk perception during this

<footnote n="18">¹⁸Municipalities in the neighborhoods were initially developed thanks to the activity of the gunpowder. Urban development was then explained by attraction to Bordeaux center.</footnote>

<footnote n="19">¹⁹Near Dunkirk, after World War II, urbanization was realized around industrial activities which were not perceived as hazardous (shipyards, steel industry); hazardous plants (chemistry, petrochemistry) appeared in Dunkirk harbor only in the 1970s.</footnote>

<footnote n="20">²⁰Near Rouen, few plants established in the 1920s and 1930s; the majority of plants progressively appeared between the 1960s and the 1990s and cities get closer and closer to these different plants.</footnote>
period, that is between 2000 and 2008. We detail here all these local and national events (Table 2). Local events are accidents and local policies. Only one accident happened on January 12, 2007 at Rubis Terminal (storage of liquid flammable and agrochemical products) near Dunkirk.<fnref n="21" /> Local policies include the distribution of information leaflets, the update of the emergency plan for households and the implementation of local committees for information and consultation.<fnref n="22" /> National events are the AZF accident in 2001 in Toulouse and implementation of laws during the study period. The raise of public awareness of industrial dangers following the AZF accident could have been all the more important near Rouen that one of the studied plants, Grande Paroisse Normandy, has a very similar activity to the AZF plant and belongs to the same company, Grande Paroisse (a subsidiary of Total group).<fnref n="23" /> Besides, the 2003 law created the technological disasters insurance system,<fnref n="24" /> which improves the coverage of households.<fnref n="25" />

## 3.2 Delimitation of study areas

In order to measure the impact of distance to plants on housing values, the study area has to be broad enough to include dwellings close and far from the plants. However, as Redfearn (2009) notes, “expanding the geographic scope of the sample is likely to draw additional omitted amenities and/or more submarkets”. Indeed, in order to limit the number of collected characteristics of the dwellings and to avoid districts in which neighborhood quality is difficult to capture, the central business district and few atypical districts, cor-

<footnote n="21">²¹Neighboring populations saw flames and plumes of smoke. The accident triggered an emergency plan inside the plant and required the intervention of civil fire brigades. After the study period, other accidents occurred. In the gunpowder near Bordeaux, a fire caused one death and two seriously injured persons in December 2013; near Rouen, mercaptan gas escape from one of the factories, the Lubrizol company, caused foul odor in France and in England in January 2013.</footnote>

<footnote n="22">²²Some future buyers not coming from the study area may not be informed by information releases, in particular by information leaflets about the emergency plan for households. Indeed, information leaflets were distributed in 2006 near Dunkirk and in 2007 near Rouen to current owners (Table 2).</footnote>

<footnote n="23">²³Indeed, the AZF accident appears to have left a particularly deep and long-lasting impression near Rouen (""Grande-Paroisse" à Rouen, un site Seveso hanté par le spectre d'AZF" by Audrey Garric, Febru ary 5, 2013. Le Monde.fr).</footnote>

<footnote n="24">²⁴The 2003 law also created technological risk prevention plans, but they were started after the study period in each study area. In addition, the very same law created mandatory information for buyers and tenants: the seller or the landlord has to precise in writing whether his dwelling is located in an area covered by a technological (or natural) risk prevention plan. This information policy was implemented on June 1st, 2006. As technological risk prevention plans were started after the study period, no such information was released in the three studied areas.</footnote>

<footnote n="25">²⁵Technological disasters insurance aims to manage the basic coverage for victims by avoiding long lit- igation and by covering the residual risk of no responsible identification. Indeed, the guarantee against technological disasters is mandatorily included in home insurance, which is widely purchased in metropoli- tan France (Calvet and Grislain-Letrémy, 2011), and the coverage corresponds to the real estate of main home. Thus, technological disasters premium is included in the home insurance premium; it amounts to a few euros per year. In case of disaster, households are compensated by their home insurer, which then turns against the industrialist liable for the damages (or the industrialist’s insurer).</footnote>
<table><caption>Table 2: Local and national events that may have modified industrial risk perception during the study period (2000-2008)</caption><thead><tr><th>Year</th><th>Bordeaux</th><th>Dunkirk</th><th>Rouen</th><th>National level</th></tr></thead><tbody><tr><td>2001</td><td>-</td><td>-</td><td>-</td><td>AZF accident</td></tr><tr><td>2002</td><td>-</td><td>Information leaflets</td><td>-</td><td>-</td></tr><tr><td>2003</td><td>-</td><td>-</td><td>-</td><td>Law</td></tr><tr><td>2004</td><td>-</td><td>Update of the emergency plan for households</td><td>-</td><td>-</td></tr><tr><td>2005</td><td>-</td><td>-</td><td>Creation of local committee for information and consultation</td><td>-</td></tr><tr><td>2006</td><td>-</td><td>Creation of local committee for information and consultation + information leaflets about the emergency plan for households</td><td>-</td><td>-</td></tr><tr><td>2007</td><td>-</td><td>Accident</td><td>Information leaflets about the emergency plan for households (*)</td><td>-</td></tr></tbody></table>

Note: (*) in all municipalities except Moulineaux.

Source: reports by Technical Studies Center of Public Works of Normandy and Centre, Nord and Picardy, and South West France.

responding to less than 5% of the samples, are excluded.<fnref n="26" />

The chosen study areas include dwellings in the very close vicinity of the plants and also dwellings far from 10 km near Bordeaux, 4 km near Dunkirk and 5 km near Rouen (Table 10 in Appendix A.1 and Figures 1, 2 and 3). The literature studying the impact of similar industrial risks on housing prices uses similar study areas (Table 13 in Appendix A.2).<fnref n="27" />

## 3.3 Data collection

Our analysis requires data on price, characteristics of dwellings, buyers and sellers (Table 3). The price of the dwelling and its intrinsic characteristics (state, living space, number of rooms, etc.) come from PERVAL notarial data in the years 2000, 2002, 2004, 2006 and 2008. Thus, only dwelling purchases (as opposed to rentings) can be studied.<fnref n="28" /> Besides,

<footnote n="26">²⁶The selection of districts is hereafter detailed for Dunkirk, where this selection is the most important. Highly attractive districts that are Téteghem municipality (the select residential suburb of Dunkirk), the historic center and the sandbar in the East of the urban area (Dunkirk-Darses, Dunkirk-Malo-les-Bains) are excluded. Very unattractive districts such as Mardyck village and Grande Synthe municipalities are also excluded. Grande Synthe municipality is an urban renewal zone where housing prices are relatively low because of civil insecurity (petty crime, acts of violence), but this low quality of neighborhood is difficult to capture. Finally, Leffrinckoucke municipality is excluded, since the presence of a little hazardous plant but emitting a black plume of smoke can be a source of confusion for inhabitants with the other hazardous plants here studied.</footnote>

<footnote n="27">²⁷The two exceptions are Carroll et al. (1996), whose study area includes almost 8,000 dwellings, among which some are 35 km far from the plant, and Boxall et al. (2005), who analyze a rural area and are so led to study a wide study area to get a sufficient number of observations.</footnote>

<footnote n="28">²⁸Data do not specify whether buyers intend to live in their new dwelling or to rent it.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: Study area near Bordeaux</figcaption></figure>

Source: base map from the topology database of National Geographic Institute, production by authors.
<figure><img src="image_4.png" /><figcaption>Figure 2: Study area near Dunkirk</figcaption></figure>

Source: base map from the topology database of National Geographic Institute, production by authors.
<figure><img src="image_5.png" /><figcaption>Figure 3: Study area near Rouen</figcaption></figure>

Source: base map from the topology database of National Geographic Institute, production by authors.
we consider transactions only between households. Furthermore, we restrict our analysis to houses, which represent a homogeneous market.<fnref n="29" /> Each dwelling’s address is precisely geocoded; detailed data relative to their extrinsic characteristics are collected: commuting time (by car) to central business district, distance to shops and public utilities, exposure to industrial risk, to other risks or pollutions.<fnref n="30" /> Finally, the database from the French solicitors also provides information about buyers and sellers: their gender, their age, their social and occupational group, their marital status and their municipality of origin.<fnref n="31" /> Annual income is allocated using statements of income from the General Directorate of Public Finances from 2004 to 2008.<fnref n="32" />

Thus, our database is unique and much richer than the ones used for studies relative to the impact of similar industrial risks on housing prices, which use data with few (or without) extrinsic characteristics of the dwellings and without information on buyers and sellers (Table 13 in Appendix A.2). Our samples finally include 1,423 observations near Bordeaux, 1,016 near Dunkirk and 571 near Rouen.<fnref n="33" /> Detailed descriptive statistics are provided in Appendix A.1 (Tables 9, 10 and 11). The literature studying the impact of similar industrial risks on housing prices uses a similar number of observations (Table 13 in Appendix A.2).

Our key variable of interest is the distance from the dwelling to highly hazardous plant (mainly Seveso sites, “upper tier” and “lower tier” ones). We also consider distance to “authorized plants” (subject to the regime of classified plants for the environment protection) and location in risk management areas. These areas are the emergency planning zone for households and two areas of control for future land use: the exclusion area, called “Z1 area”, corresponds to the area with lethal damages in case of accident; the area where new buildings are allowed but limited, called “Z2 area”, corresponds to irreversible damages. As these zones are very limited, they include a small number of transactions during the

<footnote n="29">²⁹Houses are majority in our samples: transactions include 83% of houses near Bordeaux, 87% near Dunkirk and 72% near Rouen.</footnote>

<footnote n="30">³⁰Geographic distance to central business district was also used and gave similar estimation results.</footnote>

<footnote n="31">³¹This information is relative to the buyer (or seller) himself, or to the household reference person. Near Bordeaux, 3% of observations do not include any information on buyers (or only their municipality of origin) and are excluded from the sample. Near Dunkirk, 6% of observations do not provide the main information on buyers (gender, age, marital status) and are excluded; the social and occupational group is missing for 30% of observations. Near Rouen, 9% of observations do not include any information on buyers (or only their municipality of origin) and are excluded from the sample.</footnote>

<footnote n="32">³²Income is allocated using gender, age, marital status and municipality of origin. This leads us to exclude few foreign buyers (less than 1%) for whom we could not allocate income.</footnote>

<footnote n="33">³³In the chosen study areas, transactions within each jurisdiction are relatively uniformly distributed over the study period (Table 12 in Appendix A.1).</footnote>
study period (Table 10 in Appendix A.1). Finally, as the hazardous plants near Rouen present the specificity not to be seen from some dwellings, we consider the dummy for view of plants from the dwelling near Rouen.<fnref n="34" />

<table><caption>Table 3: Data</caption><thead><tr><th colspan="3">Dwelling characteristics</th></tr><tr><th>Intrinsic characteristics (a)</th><th>Extrinsic characteristics</th><th></th></tr></thead><tbody><tr><td>Price (including tax)</td><td>Distance and commuting time (by car) to central business district (*)</td><td>(b), (f)</td></tr><tr><td>House or apartment</td><td>Distance to market square</td><td>(c), (d)</td></tr><tr><td>Less than 5 years old</td><td>Distance to drugstore</td><td>(c), (d)</td></tr><tr><td>Condition</td><td>Distance to food shop</td><td>(c), (d), (e)</td></tr><tr><td>Living space (*)</td><td>Distance to bus stop</td><td>(f)</td></tr><tr><td>Number of rooms</td><td>Distance to park</td><td>(f)</td></tr><tr><td>Number of bathrooms</td><td>Distance to nursery or primary school</td><td>(e), (g)</td></tr><tr><td>Number of parking lots</td><td>Distance to high school</td><td>(e), (g)</td></tr><tr><td>Presence of a terrace</td><td>Distance to the nearest highly hazardous plants (†)</td><td>(h)</td></tr><tr><td>Presence of a balcony</td><td>Distance to the nearest authorized plants (‡)</td><td>(h)</td></tr><tr><td>Presence of an elevator</td><td>View of industrial plants (near Rouen) (◇)</td><td>(g)</td></tr><tr><td>Presence of a swimming pool</td><td>Location in a land use control area (Z1, Z2)</td><td>(i)</td></tr><tr><td>Presence of a basement</td><td>Location in an area of emergency plan for households</td><td>(j)</td></tr><tr><td>Presence of a cellar</td><td>Location in an area exposed to natural hazards (▷)</td><td>(i)</td></tr><tr><td>Presence of annexes</td><td>Location in an area exposed to other hazards (▷)</td><td>(i)</td></tr><tr><td>Presence of outbuildings</td><td>Location in a residual pollution area</td><td>(k)</td></tr><tr><td>Presence of an attic</td><td>Location in environmental protection area</td><td>(i)</td></tr><tr><td>Area of land</td><td>Location in conservation easement area</td><td>(i)</td></tr><tr><td></td><td>Sound exposure to a land transport facility</td><td>(l), (m)</td></tr><tr><td></td><td>Sound exposure to an air transport facility</td><td>(l), (m)</td></tr><tr><th colspan="3">Buyers' and sellers' characteristics (▷)</th></tr><tr><td>Income</td><td></td><td>(n)</td></tr><tr><td>Gender</td><td></td><td>(a)</td></tr><tr><td>Age</td><td></td><td>(a)</td></tr><tr><td>Social and occupational group</td><td></td><td>(a)</td></tr><tr><td>Marital status</td><td></td><td>(a)</td></tr><tr><td>Municipality of origin</td><td></td><td>(a)</td></tr></tbody></table>

Sources: (a) PERVAL, (b) geographic directory of municipalities, (c) Chambers of Commerce and Industry database, (d) municipal database, (e) phone book, (f) topology database of National Geographical Institute, (g) building database of National Geographical Institute, (h) database for classified plants per municipality, (i) land use plan, (j) prefecture, (k) Regional Office for Environment, Planning and Housing, (l) sound map of Departmental Office for Territories and Sea, (m) sound map of Technical Studies Center of Public Works, (n) statements of income from the General Directorate of Public Finances.

Notes: each distant to a facility is built as the distance to the closest facility.

(*) Distance or commuting time to central business district is computed as the distance or commuting time to the town hall of Bordeaux, Dunkirk or Rouen.

(*) Living space is filled in for 81% of observations near Bordeaux, 80% near Dunkirk and 62% near Rouen.

The imputed value for missing values is randomly chosen among the observed distribution of living spaces.

(†) Most hazardous plants among the classified plants for the environment protection (mainly Seveso sites).

(‡) Plants subject to the regime of classified plants for the environment protection.

(◇) View from the dwelling of red and white Petroplus chimney or of Senalia silo.

(▷) Area of servitude or notification.

(▷) This information is relative to the buyer (or seller) himself, or to the household reference person.

<footnote n="34">³⁴The effective distance between the dwelling and the plant is used by all studies relative to the impact of similar industrial risks on housing prices (Table 13 in Appendix A.2). Other variables are added in some studies, such as dummies of location in administrative areas for risk management (emergency plans such as in Boxall et al. (2005) and Travers et al. (2009)) or variables traducing perception of pollutions created by the plant (Boxall et al. (2005), Sauvage (1997)).</footnote>
# 4 Results

We estimate the hedonic price function with three different models: the baseline parametric OLS log-linear model and two extensions of this model, a parametric OLS log-polynomial model and a semiparametric LWR log-linear model (Section 2). Results of these hedonic regressions are presented near Bordeaux in Table 4, near Dunkirk in Table 5 and near Rouen in Table 6.<fnref n="35" /> As expected, recent building, good condition, living space, number of rooms, of bathrooms and of parking lots, area of land, all increase the dwelling value. Presence of outbuildings, of a basement and of a swimming pool can also increase the dwelling price. As variations of commuting time are limited,<fnref n="36" /> once including municipality dummies, the commuting time to central business district does not significantly modify the dwelling value.<fnref n="37" /> In the three models, signs are the same and orders of magnitude are similar.

## 4.1 Different impacts of distance to highly hazardous plants between the studied cases

Even among a same category of industries (chemical and petrochemical industries), the impact of distance to highly hazardous plants on the dwelling value varies between the three cases here studied. Near Bordeaux, in all models, proximity to the gunpowder factory is valued in the very close vicinity of the gunpowder (Table 4), probably because the neighborhoods of the plant are green with many trees and very quiet places.<fnref n="38" /> Thus, we capture here unobserved amenities which are spatially correlated with the distance to the plant. Near Dunkirk, the semiparametric model reveals that the distance to highly hazardous plants does not significantly impact housing prices (Table 5).<fnref n="39" /> Indeed, the presence of

<footnote n="35">³⁵Parametric regressions include municipality dummies and year and month dummies. For the sake of readability, these estimated coefficients are not reported. Some of them are significant at the 1% or the 5% level.</footnote>

<footnote n="36">³⁶All houses are located between a quarter and half an hour from the center, and even between 6 and 14 min near Dunkirk (Table 10 in Appendix A.2).</footnote>

<footnote n="37">³⁷When excluding municipality dummies, commuting time is significant in all parametric models. In the semiparametric ones, this effect is captured by the distribution of intercepts, as intercept is allowed to vary with respect to kernel variables, in particular geographic coordinates. All these results are similar when using the geographic distance to central business district instead of the commuting time.</footnote>

<footnote n="38">³⁸These green areas do not belong to parks. This is why this effect is not captured by the observed distance to parks.</footnote>

<footnote n="39">³⁹On the contrary, the parametric model indicates that the distance to highly hazardous plants increases housing prices near Dunkirk. This difference is due, not to the nature of the model used, but to the size of the sample used for regressions. Near Dunkirk, the main part of the sample is almost uniformly concentrated between 700 m and 3 km from the highly hazardous plants. In the semiparametric regression, the extreme observations are rarely used and weigh less than the others, whereas in the parametric regression, the whole sample is used and each observation has an equal weight. When restricting the sample to the observations between 700 m and 3 km from the highly hazardous plants, the parametric regression shows that the distance to highly hazardous plants does not significantly impact housing prices.</footnote>
a nuclear plant in Gravelines (located 18 km from Dunkirk) is likely to overshadow the exposure to industrial risks here studied. Near Rouen, in all models, the distance to highly hazardous plants increases dwelling values (Table 6): highly hazardous plants are perceived as disamenities. The magnitude of this impact is important: on average, households are willing to pay around 1.2% of their dwelling price to go 100 more meters away from highly hazardous plants near Rouen (Table 7). This order of magnitude is consistent with the other studies relative to the impact of similar industrial risks on housing prices (Table 13 in Appendix A.2).

Reviewing other studies relative to the impact of similar industrial risks on housing prices shows that distance to hazardous plants can increase, decrease or have no significant impact on housing prices, depending on the studied case and in particular on the type of industries (Table 13 in Appendix A.2). Clark and Nieves (1994) also show that the proximity to a petrochemical refinery weighs more on the housing prices than the proximity to coal/gas/oil-fires plants, to hazardous waste or to a liquefied natural gas site. Our results confirm this dependency. It seems here to be mainly due to the differences in the perception of industrial activities and of their neighborhoods.

## 4.2 Testing the fixed-parameter assumption

When comparing the parametric model and the baseline log-linear model, the square or the cube of distance to highly hazardous plants are significant in the parametric model near Bordeaux and Rouen (Tables 4 and 6). This suggests that the fixed-parameter assumption does not hold. The fixed-parameter assumption is properly tested by comparing the semiparametric model and the baseline log-linear model, which corresponds to a nested model under the null hypothesis of fixed coefficients.<fnref n="40" /> This hypothesis is rejected at the 0.01% level, which confirms the need for flexible forms and pleads for the use of this semiparametric model.

## 4.3 Comparing the impacts of distance to plants estimated by the parametric and semiparametric models

We compare the estimated impacts of distance to the plants provided by the parametric log-polynomial model and by the semiparametric model.

<footnote n="40">⁴⁰See McMillen and Redfearn (2010) for a summary of Cleveland and Devlin's *F*-test and its adaptation to the LWR regression.</footnote>
<table><caption>Table 4: Near Bordeaux: parametric and semiparametric hedonic regressions</caption><thead><tr><th rowspan="3">Model</th><th colspan="4">Log-linear, OLS</th><th colspan="4">Log-polynomial, OLS</th><th colspan="4">Log-linear, LWR</th></tr><tr><th colspan="4">Dwelling price</th><th colspan="4">Dwelling price</th><th colspan="4">Dwelling price</th></tr><tr><th>Estimate</th><th>Std. error</th><th>p-value</th><th>Estimate</th><th>Std. error</th><th>p-value</th><th>Estimate</th><th>Std. error</th><th>p-value</th><th>Mean</th><th>Std. error</th><th>p-value (*)</th></tr></thead><tbody><tr><td>Intercept</td><td>11.4</td><td>0.080</td><td>&lt; 2e-16</td><td>11.6</td><td>0.12</td><td>&lt; 2e-16</td><td>11.3</td><td>0.32</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>Less than 5 years</td><td>0.16</td><td>0.028</td><td>6.1e-09</td><td>0.16</td><td>0.028</td><td>9.1e-09</td><td>0.14</td><td>0.040</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>Average condition</td><td>-0.015</td><td>0.014</td><td>0.29</td><td>-0.015</td><td>0.014</td><td>0.28</td><td>-0.032</td><td>0.031</td><td>0.077</td><td></td><td></td><td></td></tr><tr><td>Poor condition</td><td>-0.21</td><td>0.032</td><td>4.3e-11</td><td>-0.22</td><td>0.032</td><td>2.2e-11</td><td>-0.20</td><td>0.051</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>Living space</td><td>0.0020</td><td>2.3e-04</td><td>&lt; 2e-16</td><td>0.0019</td><td>2.3e-04</td><td>&lt; 2e-16</td><td>0.0020</td><td>4.4e-04</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>4 rooms</td><td>0.25</td><td>0.027</td><td>&lt; 2e-16</td><td>0.26</td><td>0.027</td><td>&lt; 2e-16</td><td>0.22</td><td>0.056</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>5 rooms</td><td>0.34</td><td>0.027</td><td>&lt; 2e-16</td><td>0.35</td><td>0.027</td><td>&lt; 2e-16</td><td>0.31</td><td>0.052</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>6 rooms</td><td>0.37</td><td>0.030</td><td>&lt; 2e-16</td><td>0.38</td><td>0.030</td><td>&lt; 2e-16</td><td>0.34</td><td>0.063</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>7 rooms or more</td><td>0.40</td><td>0.036</td><td>&lt; 2e-16</td><td>0.41</td><td>0.036</td><td>&lt; 2e-16</td><td>0.38</td><td>0.071</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>No bathroom</td><td>-0.14</td><td>0.042</td><td>0.0013</td><td>-0.14</td><td>0.042</td><td>8.3e-04</td><td>-0.20</td><td>0.069</td><td>0.018</td><td></td><td></td><td></td></tr><tr><td>2 bathrooms or more</td><td>0.13</td><td>0.016</td><td>7.6e-15</td><td>0.13</td><td>0.016</td><td>1.7e-14</td><td>0.14</td><td>0.030</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>1 parking lot</td><td>0.099</td><td>0.016</td><td>1.1e-09</td><td>0.097</td><td>0.016</td><td>1.7e-09</td><td>0.078</td><td>0.020</td><td>0.0014</td><td></td><td></td><td></td></tr><tr><td>2 parking lots or more</td><td>0.15</td><td>0.025</td><td>1.0e-09</td><td>0.15</td><td>0.025</td><td>2.3e-09</td><td>0.12</td><td>0.046</td><td>3.0e-04</td><td></td><td></td><td></td></tr><tr><td>Presence of outbuildings</td><td>0.052</td><td>0.021</td><td>0.013</td><td>0.051</td><td>0.021</td><td>0.015</td><td>-0.034</td><td>0.023</td><td>1.0</td><td></td><td></td><td></td></tr><tr><td>Presence of a basement</td><td>0.12</td><td>0.030</td><td>2.7e-05</td><td>0.12</td><td>0.030</td><td>2.8e-05</td><td>0.056</td><td>0.048</td><td>0.029</td><td></td><td></td><td></td></tr><tr><td>Presence of a swimming pool</td><td>0.13</td><td>0.019</td><td>1.4e-11</td><td>0.13</td><td>0.019</td><td>2.6e-11</td><td>0.22</td><td>0.032</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>Area of land</td><td>1.6e-05</td><td>6.1e-06</td><td>0.0092</td><td>2.0e-05</td><td>6.5e-06</td><td>0.0016</td><td>5.0e-05</td><td>4.0e-05</td><td>&lt;1e-04</td><td></td><td></td><td></td></tr><tr><td>Commuting time to CBD (‡)</td><td>1.1e-04</td><td>6.6e-05</td><td>0.11</td><td>7.5e-05</td><td>6.7e-05</td><td>0.27</td><td>1.5e-04</td><td>1.7e-04</td><td>0.27</td><td></td><td></td><td></td></tr><tr><td>Distance to plants (‡)<sup>2</sup></td><td>-2.7e-05</td><td>8.9e-06</td><td>0.0025</td><td>-1.4e-04</td><td>5.4e-05</td><td>0.012</td><td>-2.0e-05</td><td>2.0e-05</td><td>0.022</td><td></td><td></td><td></td></tr><tr><td>(Distance to plants)<sup>2</sup></td><td></td><td></td><td></td><td>2.7e-08</td><td>1.2e-08</td><td>0.026</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(Distance to plants)<sup>3</sup></td><td></td><td></td><td></td><td>-1.8e-12</td><td>7.8e-13</td><td>0.019</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Notes: parametric regressions include municipality dummies and year and month dummies. These estimated coefficients are not reported. Some of them are significant at the 1% or 5% level.

(†) CBD = Central Business District; (‡) plant denotes a highly hazardous plant.

(*) See McMillen and Redfearn (2010) for a summary of Cleveland and Devlin's F-test and its adaptation to test the significance of explanatory variables in the LWR regression. Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of South West France, statements of income from the General Directorate of Public Finances. 1,423 observations.
<table><caption>Table 5: Near Dunkirk: parametric and semiparametric hedonic regressions</caption><thead><tr><th rowspan="3">Model</th><th colspan="3">Log-linear, OLS</th><th colspan="3">Log-linear, LWR</th></tr><tr><th colspan="3">Dwelling price</th><th colspan="3">Dwelling price</th></tr><tr><th>Estimate</th><th>Std. error</th><th>p-value</th><th>Mean</th><th>Std. error</th><th>p-value (*)</th></tr></thead><tbody><tr><td>Intercept</td><td>10.9</td><td>0.075</td><td>&lt; 2e-16</td><td>10.9</td><td>0.11</td><td>&lt;1e-04</td></tr><tr><td>Average condition</td><td>-0.071</td><td>0.018</td><td>8.8e-05</td><td>-0.09</td><td>0.023</td><td>0.0098</td></tr><tr><td>Poor condition</td><td>-0.25</td><td>0.021</td><td>&lt; 2e-16</td><td>-0.23</td><td>0.052</td><td>&lt;1e-04</td></tr><tr><td>Living space</td><td>0.0011</td><td>3.3e-04</td><td>6.1e-4</td><td>0.0016</td><td>0.0012</td><td>&lt;1e-04</td></tr><tr><td>4 rooms</td><td>0.15</td><td>0.032</td><td>1.5e-06</td><td>0.12</td><td>0.030</td><td>0.0041</td></tr><tr><td>5 rooms</td><td>0.24</td><td>0.031</td><td>1.1e-14</td><td>0.20</td><td>0.041</td><td>&lt;1e-04</td></tr><tr><td>6 rooms</td><td>0.30</td><td>0.035</td><td>&lt; 2e-16</td><td>0.24</td><td>0.063</td><td>&lt;1e-04</td></tr><tr><td>7 rooms or more</td><td>0.32</td><td>0.044</td><td>7.6e-13</td><td>0.24</td><td>0.099</td><td>&lt;1e-04</td></tr><tr><td>No bathroom</td><td>-0.089</td><td>0.029</td><td>0.0022</td><td>-0.094</td><td>0.044</td><td>0.019</td></tr><tr><td>2 bathrooms or more</td><td>0.14</td><td>0.035</td><td>8.4e-05</td><td>0.13</td><td>0.047</td><td>0.013</td></tr><tr><td>Presence of outbuildings</td><td>-0.038</td><td>0.022</td><td>0.080</td><td>-0.032</td><td>0.032</td><td>0.12</td></tr><tr><td>Presence of a basement</td><td>-0.013</td><td>0.017</td><td>0.45</td><td>0.022</td><td>0.030</td><td>0.43</td></tr><tr><td>Area of land</td><td>2.1e-04</td><td>3.1e-05</td><td>1.1e-10</td><td>5.0e-04</td><td>2.1e-04</td><td>&lt;1e-04</td></tr><tr><td>Commuting time to CBD (†)</td><td>4.6e-05</td><td>1.3e-04</td><td>0.72</td><td>7e-05</td><td>1.8e-04</td><td>1.0</td></tr><tr><td>Distance to plants (‡)</td><td>9.5e-05</td><td>2.2e-05</td><td>1.6e-05</td><td>8e-05</td><td>2e-05</td><td>1.0</td></tr></tbody></table>

Notes: parametric regressions include municipality dummies and year and month dummies. These estimated coefficients are not reported. Some of them are significant at the 1% or 5% level.

(†) CBD = Central Business District; (‡) plant denotes a highly hazardous plant.

(*) See McMillen and Redfearn (2010) for a summary of Cleveland and Devlin's F-test and its adaptation to test the significance of explanatory variables in the LWR regression. Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Nord and Picardy, statements of income from the General Directorate of Public Finances. 1,016 observations.
<table><caption>Table 6: Near Rouen: parametric and semiparametric hedonic regressions</caption><thead><tr><th rowspan="2">Model<br>Explanatory variable</th><th colspan="3">Log-linear, OLS<br>Dwelling price</th><th colspan="3">Log-polynomial, OLS<br>Dwelling price</th><th colspan="3">Log-linear, LWR<br>Dwelling price</th></tr><tr><th>Estimate</th><th>Std. error</th><th>p-value</th><th>Estimate</th><th>Std. error</th><th>p-value</th><th>Mean</th><th>Std. error</th><th>p-value (*)</th></tr></thead><tbody><tr><td>Intercept</td><td>11.1</td><td>0.21</td><td>&lt; 2e-16</td><td>10.8</td><td>0.23</td><td>&lt; 2e-16</td><td>11.0</td><td>0.27</td><td>&lt;1e-04</td></tr><tr><td>Average condition</td><td>-0.061</td><td>0.026</td><td>0.020</td><td>-0.059</td><td>0.026</td><td>0.025</td><td>0.028</td><td>0.040</td><td>0.93</td></tr><tr><td>Poor condition</td><td>-0.31</td><td>0.042</td><td>2.2e-13</td><td>-0.31</td><td>0.041</td><td>2.7e-13</td><td>-0.29</td><td>0.068</td><td>&lt;1e-04</td></tr><tr><td>Living space</td><td>0.0017</td><td>3.6e-04</td><td>3.7e-06</td><td>0.0018</td><td>3.6e-04</td><td>2.0e-06</td><td>0.0019</td><td>5.8e-04</td><td>0.0011</td></tr><tr><td>4 rooms</td><td>0.22</td><td>0.040</td><td>2.9e-08</td><td>0.21</td><td>0.039</td><td>1.2e-07</td><td>0.22</td><td>0.055</td><td>&lt;1e-04</td></tr><tr><td>5 rooms</td><td>0.34</td><td>0.039</td><td>&lt; 2e-16</td><td>0.34</td><td>0.039</td><td>&lt; 2e-16</td><td>0.36</td><td>0.071</td><td>&lt;1e-04</td></tr><tr><td>6 rooms</td><td>0.40</td><td>0.044</td><td>&lt; 2e-16</td><td>0.39</td><td>0.043</td><td>&lt; 2e-16</td><td>0.34</td><td>0.052</td><td>&lt;1e-04</td></tr><tr><td>7 rooms or more</td><td>0.47</td><td>0.049</td><td>&lt; 2e-16</td><td>0.46</td><td>0.048</td><td>&lt; 2e-16</td><td>0.43</td><td>0.065</td><td>&lt;1e-04</td></tr><tr><td>2 bathrooms or more</td><td>0.14</td><td>0.030</td><td>6.0e-06</td><td>0.14</td><td>0.030</td><td>4.9e-06</td><td>0.087</td><td>0.059</td><td>0.0028</td></tr><tr><td>1 parking lot</td><td>0.077</td><td>0.030</td><td>0.010</td><td>0.064</td><td>0.030</td><td>0.030</td><td>0.10</td><td>0.038</td><td>0.29</td></tr><tr><td>2 parking lots or more</td><td>0.12</td><td>0.035</td><td>5e-04</td><td>0.11</td><td>0.035</td><td>0.0017</td><td>0.11</td><td>0.063</td><td>0.046</td></tr><tr><td>Presence of outbuildings</td><td>0.040</td><td>0.029</td><td>0.17</td><td>0.042</td><td>0.029</td><td>0.15</td><td>-0.014</td><td>0.035</td><td>0.59</td></tr><tr><td>Presence of a basement</td><td>0.098</td><td>0.024</td><td>3.5e-05</td><td>0.092</td><td>0.023</td><td>1.0e-04</td><td>0.078</td><td>0.024</td><td>0.0077</td></tr><tr><td>Area of land</td><td>2.4e-05</td><td>9.3e-06</td><td>0.010</td><td>2.5e-05</td><td>9.2e-06</td><td>0.0074</td><td>8.0e-05</td><td>5.0e-05</td><td>&lt;1e-04</td></tr><tr><td>Commuting time to CBD (†)</td><td>-1.5e-04</td><td>1.3e-04</td><td>0.25</td><td>-6.5e-06</td><td>1.4e-04</td><td>0.96</td><td>-1.1e-04</td><td>1.7e-04</td><td>1.0</td></tr><tr><td>Distance to plants (‡)<br>(Distance to plants)<sup>2</sup></td><td>1.3e-04</td><td>2.1e-05</td><td>3.7e-10</td><td>2.6e-04</td><td>4.7e-05</td><td>4.4e-08</td><td>1.2e-04</td><td>5.0e-05</td><td>&lt;1e-04</td></tr></tbody></table>

Notes: parametric regressions include municipality dummies and year and month dummies. These estimated coefficients are not reported. Some of them are significant at the 1% or 5% level.

(†) CBD = Central Business District; (‡) plant denotes a highly hazardous plant.

(*) See McMillen and Redfearn (2010) for a summary of Cleveland and Devlin's F-test and its adaptation to test the significance of explanatory variables in the LWR regression. Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, statements of income from the General Directorate of Public Finances. 571 observations.
**Value.** Parametric log-polynomial regressions lead to a bias in the estimated value of the impact of distance to highly hazardous plants on dwelling prices. Near Bordeaux and Rouen, this implicit price of distance *d* to these plants equals the absolute marginal willingness to pay *AMWTP* to go one more meter away from highly hazardous plants (Section 2). To make comparisons between dwellings or between the three cases easier, we also compute the relative willingness to pay *RMWTP* to go further away from the plants, which expresses this willingness to pay as a percentage of the price of the considered dwellings to go 100 more meters away from the plants.

$$AMWTP = \frac{\partial P(X)}{\partial d}, \quad RMWTP = \frac{\partial P(X)}{\partial d}.$$

Table 7 properly compares the absolute and relative marginal willingnesses to pay, as estimated by the parametric and semiparametric models.

<table><caption>Table 7: Households' marginal willingness to pay to go one more meter away from highly hazardous plants</caption><thead><tr><th rowspan="2"></th><th colspan="3">MWTP estimated by<br>log-polynomial, OLS</th><th colspan="3">MWTP estimated by<br>log-linear, LWR</th></tr><tr><th>Median</th><th>Mean</th><th>Std. Dev.</th><th>Median</th><th>Mean</th><th>Std. Dev.</th></tr></thead><tbody><tr><td><em>Near Bordeaux</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AMWTP (€/m)</td><td>-3.65</td><td>-5.32</td><td>5.24</td><td>-3.36</td><td>-3.17</td><td>3.89</td></tr><tr><td>RMWTP (%/100m)</td><td>-0.19</td><td>-0.25</td><td>0.21</td><td>-0.19</td><td>-0.20</td><td>0.21</td></tr><tr><td><em>Near Dunkirk</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AMWTP (€/m)</td><td>9.45</td><td>10.00</td><td>3.91</td><td>-</td><td>-</td><td>-</td></tr><tr><td>RMWTP (%/100m)</td><td>0.95</td><td>0.95</td><td>0</td><td>-</td><td>-</td><td>-</td></tr><tr><td><em>Near Rouen</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AMWTP (€/m)</td><td>19.33</td><td>20.25</td><td>11.16</td><td>14.39</td><td>15.44</td><td>9.91</td></tr><tr><td>RMWTP (%/100m)</td><td>1.88</td><td>1.67</td><td>0.68</td><td>1.22</td><td>1.20</td><td>0.51</td></tr></tbody></table>

Notes: in the parametric regression, the log of price is explained by the linear expression of dwelling characteristics and by a polynomial of the distance to the plants (Equation 4). In the semiparametric regression, the log of price is explained by the linear expression of dwelling characteristics while allowing the coefficients to vary with respect to $Z_i$, that is with respect to the geographic coordinates, time and the buyer's income (Equation 6). Thus, we get

Log-polynomial OLS Bordeaux: $AMWTP = (\beta_1 + 2\beta_2d + 3\beta_3d^2)P(X)$, $RMWTP = (\beta_1 + 2\beta_2d + 3\beta_3d^2)$,
Log-polynomial OLS Dunkirk: $AMWTP = \beta_1P(X)$, $RMWTP = \beta_1$,
Log-polynomial OLS Rouen: $AMWTP = (\beta_1 + 2\beta_2d)P(X)$, $RMWTP = (\beta_1 + 2\beta_2d)$,
Log-linear LWR: $AMWTP = \beta(Z_i)P(X)$, $RMWTP = \beta(Z_i)$.

Near Dunkirk, willingnesses to pay as estimated by the LWR are not provided, as the distance to the nearest highly hazardous plant is not significant in this model.

Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, statements of income from the General Directorate of Public Finances. 1,423 observations near Bordeaux, 1,016 near Dunkirk and 571 near Rouen.

The parametric model leads to a biased estimation of the average marginal willingness to pay over the sample, be it the absolute willingness or the relative one, near Bordeaux and
Rouen. Near Rouen, it leads to a clear overestimation: the mean of the absolute marginal willingness to pay and the one of the relative marginal willingness to pay are 31% and 39% respectively higher as estimated by the parametric model than by the semiparametric one (Table 7). It is worth underlining that the bias may lead either to an overestimation or to an underestimation of the average marginal willingness to pay. Indeed, the semiparametric model gives a higher weight to sets of “close” observations (proximity being defined by the kernel in terms of location, time and income), whereas the parametric model gives an equal weight to each observation.<fnref n="41" /> If, as here, these sets of “close” observations correspond to a lower marginal willingness to pay than the average, the parametric model leads to an overestimation of the average marginal willingness to pay.

**Variations.** Near Rouen, the parametric model also leads to a biased estimation (here again a clear overestimation) of the variations of the marginal willingness to pay with respect to the distance to plants.<fnref n="42" />

We perform here regressions that correspond to the second step of Rosen (1974)'s method. The first step is the main hedonic price regression: it estimates the dwelling price $P(X)$ depending of the dwelling attributes $X$ (Tables 4, 5 and 6 in Section 4). The second step here conducted consists in using the implicit price $(\partial P(X)/\partial x_k)_k$ estimated in the first step, by either the parametric or the semiparametric model, to recover information relative to demand and supply. More precisely, we explain the marginal willingness to pay as estimated by the semiparametric regression with the distance to plants, year dummies, buyer's income and also his age, gender, social and occupational group, marital status and location of origin (Table 8). We explain the marginal willingness to pay as estimated by the log-polynomial parametric regression with the distance to plants (Table 8).<fnref n="43" />

These regressions must be considered as essentially descriptive. Indeed, the marginal price of each dwelling's characteristic may vary with the quantity of this characteristic. Thus, households choose simultaneously the quantity and the marginal price for each characteristic. This creates an identification problem in this regression as we explain the willingness

<footnote n="41">⁴¹Indeed, OLS can be considered as a special case of LWR: it is a LWR with a windowsize of 100% (the whole sample) and a weight equal to $1/n$ everywhere, $n$ denoting the sample size.</footnote>

<footnote n="42">⁴²We describe here the relative willingness to pay to go further away from highly hazardous plants. Results are similar when using the absolute willingness to pay.</footnote>

<footnote n="43">⁴³Indeed, the log-polynomial parametric model does not allow the marginal willingness to pay to vary with respect to time or buyer's income. When explaining the marginal willingness to pay as estimated by the parametric regression also with respect to time of sale and all buyer's characteristics, we find only a significant impact of time (a significant increase in 2002, 2004 and 2006).</footnote>
to pay to go away from the plants (the marginal price of distance) with the distance to these plants.<fnref n="44" />

<table><caption>Table 8: Explaining households’ relative marginal willingness to pay to go one more meter away from highly hazardous plants near Rouen</caption><thead><tr><th rowspan="2">Explained variable</th><th colspan="3">RMWTP estimated by log-polynomial, OLS</th><th colspan="3">RMWTP estimated by log-linear, LWR</th></tr><tr><th>Estimate</th><th>Std. error</th><th>p-value</th><th>Estimate</th><th>Std. error</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>2.6</td><td>5.7e-17</td><td>&lt;2e-16</td><td>1.46</td><td>0.16</td><td>&lt; 2e-16</td></tr><tr><td>Distance to plants</td><td>-6.7e-04</td><td>3.2e-20</td><td>&lt;2e-16</td><td>-2.4e-04</td><td>1.8e-05</td><td>&lt; 2e-16</td></tr><tr><td>2002</td><td></td><td></td><td></td><td>0.089</td><td>0.054</td><td>0.10</td></tr><tr><td>2004</td><td></td><td></td><td></td><td>0.11</td><td>0.057</td><td>0.046</td></tr><tr><td>2006</td><td></td><td></td><td></td><td>0.26</td><td>0.060</td><td>3.1e-05</td></tr><tr><td>2008</td><td></td><td></td><td></td><td>0.38</td><td>0.058</td><td>1.7e-10</td></tr><tr><td>Income</td><td></td><td></td><td></td><td>-1.3e-07</td><td>1.2e-06</td><td>0.91</td></tr><tr><td>Age</td><td></td><td></td><td></td><td>1.0e-04</td><td>2.2e-03</td><td>0.96</td></tr><tr><td>Gender (men)</td><td></td><td></td><td></td><td>0.064</td><td>0.052</td><td>0.22</td></tr><tr><td>Farmer</td><td></td><td></td><td></td><td>-0.043</td><td>0.31</td><td>0.89</td></tr><tr><td>Self-employed</td><td></td><td></td><td></td><td>-0.092</td><td>0.11</td><td>0.39</td></tr><tr><td>Laborer</td><td></td><td></td><td></td><td>-0.045</td><td>0.091</td><td>0.62</td></tr><tr><td>Manager or professional</td><td></td><td></td><td></td><td>-0.060</td><td>0.094</td><td>0.52</td></tr><tr><td>Intermediate lower occupation</td><td></td><td></td><td></td><td>-0.049</td><td>0.093</td><td>0.60</td></tr><tr><td>Intermediate upper occupation</td><td></td><td></td><td></td><td>-0.062</td><td>0.086</td><td>0.47</td></tr><tr><td>Single</td><td></td><td></td><td></td><td>-0.16</td><td>0.048</td><td>7.4e-4</td></tr><tr><td>From more than 15 km (France)</td><td></td><td></td><td></td><td>-0.050</td><td>0.068</td><td>0.46</td></tr><tr><td>From abroad</td><td></td><td></td><td></td><td>-0.080</td><td>0.43</td><td>0.85</td></tr></tbody></table>

Note: buyer's characteristics are relative to the buyer himself, or to the household reference person.

Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, statements of income from the General Directorate of Public Finances. 571 observations.

Households’ willingness to pay to go further away from highly hazardous plants decreases with respect to the distance to plants over the study area near Rouen (Table 8, Figure 4). This is due to the fact that marginal gains (in terms of exposure reduction) of going further away from the hazard source are likely to decrease. However, two other phenomena may explain the variations of willingness to pay with respect to the distance to plants. First, other spatially-correlated amenities could bias these estimated variations. Second, there may be a sorting on housing market, uncaptured by buyer’s observed characteristics: households with a higher willingness to pay to live further away from the plants may precisely choose a more distant location from these plants. We observe the net result of all three effects; it appears that the first effect dominates the last two ones. The decrease of households’ willingness to pay to go further away with respect to the distance to plants is overestimated by the parametric model: corresponding coefficient is threefold higher

<footnote n="44">⁴⁴Ekeland et al. (2004) offer two methods to implement this second step regression using data from a single market. However, our data do not enable us to apply them, as variability of dwelling attributes with respect to buyers’ observed characteristics is required. This simultaneous choice also creates an endogeneity bias in the first step.</footnote>
(Table 8, Figure 4).

<figure><img src="image_6.png" /><figcaption>Figure 4: Households’ marginal willingness to pay to go one more meter away from highly hazardous plants near Rouen</figcaption></figure>

Caption: The relative marginal willingness to pay to go 100 more meters away from highly hazardous plants, as estimated from the semiparametric model, is a scatter plot. The slope as estimated by the semiparametric model is represented by a dark straight line. The one estimated from the log-polynomial parametric model is represented by a grey straight line.

Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, statements of income from the General Directorate of Public Finances. 571 observations.

In the semiparametric model, households’ willingness to pay to go further away from highly hazardous plants increases over time following the 2001 AZF accident,<fnref n="45" /> the 2003 law,<fnref n="46" /> or 2005 and 2007 information policies (Table 8). Buyer’s characteristics that are income, age, gender, social and occupational group do not significantly explain the willingness to pay to go further away from the plants (Table 8).<fnref n="47" /> The single have a lower relative willingness

<footnote n="45">⁴⁵Recall that Grande Paroisse Normandy, which is settled in near Rouen, has a very similar activity to the AZF plant and belongs to the same company, Grande Paroisse (a subsidiary of Total group). Another analysis of the impact of the AZF accident on housing prices near a French facility similar to the AZF plant (Travers et al., 2009) (Port-Jérôme harbor, Seine-Maritime, France) shows the absence of impact of the AZF accident, while using a parametric model.</footnote>

<footnote n="46">⁴⁶In the short term, this law implemented the technological disasters insurance system, which improves the coverage of households against technological disasters and should have decreased the marginal willingness to pay. However, even if they were not straightaway effective, other measures of this law with a negative impact on homeowners (such as the implementation of technological risk prevention plans or mandatory information about risks) have been probably more mediatized.</footnote>

<footnote n="47">⁴⁷This absence of significant impact holds when considering other specifications (with polynomials or log).</footnote>
to pay to further away from highly hazardous plants, probably as they have no children.
Buyers coming from less than 15 km of the study area have a similar willingness to pay to
go further away from highly hazardous plants.

# 5 Conclusion

Real estate markets can reveal households' willingness to pay to reduce their exposure to hazardous industrial facilities. With highly detailed microdata, we study housing prices in the vicinity of hazardous industries located near three important French cities, Bordeaux, Dunkirk and Rouen. We estimate the impact of distance to hazardous plants on dwelling values by using hedonic price models.

We compare the results from standard parametric hedonic property models and an alternative, more flexible, semiparametric hedonic property model. This semiparametric model is a locally weighted regression that allows this willingness to pay to vary with respect to space, buyers' characteristics and time, while keeping some smoothness in their distribution. If signs and orders of magnitude of effects are similar in the two models for the very wide majority of coefficients, the estimated impacts of the distance to highly hazardous plants on housing prices significantly differ between the two models. The parametric model leads to an important bias in the estimated value of this impact near Bordeaux and Rouen and in its variations with respect to distance to the plants near Rouen.

Using the semiparametric model, we show that this impact strongly differs among industrial areas, even among chemical and petrochemical industries, likely because of different perceptions of industrial risks and dissimilar neighborhoods of these hazardous facilities. We also show that this impact may vary within a study area. Indeed, the impact of distance to hazardous plants on housing prices may significantly decrease with respect to the distance to the plants, as marginal gains in terms of exposure reduction of going further away from the hazard source are likely to decrease; it may also vary over time following accidents or information policies.

There are two substantive lessons that one learns from our analysis. First, our results show that parametric models can lead to an important bias in the estimated value of the marginal willingness to pay. Second, our results show that estimated willingness to pay for prevention strongly differs among industrial areas, even among one category of industries
(here chemical and petrochemical industries), and depends on the distance to these facilities
and on time. This inadequate estimation method and this limited external validity plead
for a careful use of current estimations of population's willingness to pay for industrial risk
reduction in the cost-benefit analyses of prevention measures, whereas most cost-benefit
analyses have until now used the parameters taken from the estimation of parametric
hedonic models on other study area and period than the ones under consideration.

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# A Appendices

## A.1 Descriptive statistics
<table><caption>Table 9: Intrinsic characteristics of the dwellings</caption><thead><tr><th></th><th></th><th>Bordeaux</th><th>Dunkirk</th><th>Rouen</th></tr></thead><tbody><tr><td>N</td><td></td><td>1423</td><td>1016</td><td>571</td></tr><tr><td rowspan="5">Price (current euro, tax. inc.)</td><td>Min</td><td>22090</td><td>7622</td><td>15245</td></tr><tr><td>Q1</td><td>146428</td><td>76222</td><td>92994</td></tr><tr><td>Median</td><td>197000</td><td>99092</td><td>115850</td></tr><tr><td>Q3</td><td>256305</td><td>128000</td><td>154000</td></tr><tr><td>Max</td><td>800000</td><td>285300</td><td>430000</td></tr><tr><td rowspan="2">Less than 5 years old</td><td>Mean</td><td>206887</td><td>104860</td><td>126402</td></tr><tr><td>Standard deviation</td><td>85661</td><td>41021</td><td>54707</td></tr><tr><td rowspan="3">Condition</td><td>Good</td><td>497</td><td>525</td><td>179</td></tr><tr><td>Average</td><td>857</td><td>297</td><td>339</td></tr><tr><td>Poor</td><td>69</td><td>194</td><td>53</td></tr><tr><td rowspan="5">Living space (sq m) (*)</td><td>Min</td><td>30</td><td>46</td><td>30</td></tr><tr><td>Q1</td><td>95</td><td>85</td><td>83</td></tr><tr><td>Median</td><td>110</td><td>95</td><td>100</td></tr><tr><td>Q3</td><td>134</td><td>110</td><td>120</td></tr><tr><td>Max</td><td>350</td><td>300</td><td>300</td></tr><tr><td rowspan="2">Number of rooms</td><td>Mean</td><td>119</td><td>100</td><td>104</td></tr><tr><td>Standard deviation</td><td>36</td><td>25</td><td>34</td></tr><tr><td></td><td>Unspecified</td><td>260</td><td>210</td><td>268</td></tr><tr><td rowspan="5">Number of bathrooms</td><td>3 or less (†)</td><td>109</td><td>77</td><td>71</td></tr><tr><td>4</td><td>361</td><td>268</td><td>131</td></tr><tr><td>5</td><td>506</td><td>441</td><td>177</td></tr><tr><td>6</td><td>288</td><td>161</td><td>115</td></tr><tr><td>7 or more</td><td>159</td><td>69</td><td>77</td></tr><tr><td rowspan="3">Number of parking lots</td><td>0</td><td>34</td><td>80</td><td>0</td></tr><tr><td>1</td><td>917</td><td>881</td><td>450</td></tr><tr><td>2 or more</td><td>472</td><td>55</td><td>121</td></tr><tr><td rowspan="3">Holds...</td><td>0</td><td>304</td><td>1016</td><td>127</td></tr><tr><td>1</td><td>971</td><td>0</td><td>299</td></tr><tr><td>2 or more</td><td>148</td><td>0</td><td>145</td></tr><tr><td rowspan="6">Area of land (sq m)</td><td>... a terrace</td><td>83</td><td>9</td><td>56</td></tr><tr><td>... a swimming pool</td><td>207</td><td>0</td><td>0</td></tr><tr><td>... a basement</td><td>72</td><td>345</td><td>266</td></tr><tr><td>... a cellar</td><td>83</td><td>47</td><td>255</td></tr><tr><td>... outbuildings</td><td>161</td><td>154</td><td>118</td></tr><tr><td>... an attic</td><td>70</td><td>65</td><td>202</td></tr><tr><td rowspan="5">Price (current euro, tax. inc.)</td><td>Min</td><td>30</td><td>0</td><td>30</td></tr><tr><td>Q1</td><td>606</td><td>155</td><td>316</td></tr><tr><td>Median</td><td>816</td><td>207</td><td>500</td></tr><tr><td>Q3</td><td>1052</td><td>305</td><td>848</td></tr><tr><td>Max</td><td>29597</td><td>6599</td><td>18724</td></tr><tr><td rowspan="2">Less than 5 years old</td><td>Mean</td><td>967</td><td>266</td><td>783</td></tr><tr><td>Standard deviation</td><td>1115</td><td>261</td><td>1355</td></tr></tbody></table>

Notes: the unit is the number of dwellings, unless otherwise specified.

(*) The imputed living space for missing values is randomly chosen among the observed distribution of living spaces.

(†) Observations with zero room (stand-alone garages, garden sheds or other outbuildings) are excluded.

Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, Nord and Picardy, and South West France.
<table><caption>Table 10: Extrinsic characteristics of the dwellings</caption><thead><tr><th></th><th></th><th>Bordeaux</th><th>Dunkirk</th><th>Rouen</th></tr></thead><tbody><tr><td>N</td><td></td><td>1423</td><td>1016</td><td>571</td></tr><tr><td rowspan="5">Commuting time (by car) to city center (min)</td><td>Min</td><td>16</td><td>6</td><td>16</td></tr><tr><td>Q1</td><td>24</td><td>8</td><td>19</td></tr><tr><td>Median</td><td>28</td><td>9</td><td>22</td></tr><tr><td>Q3</td><td>31</td><td>10</td><td>25</td></tr><tr><td>Max</td><td>38</td><td>14</td><td>31</td></tr><tr><td rowspan="2">Located close to...</td><td>Mean</td><td>27</td><td>9</td><td>22</td></tr><tr><td>Standard deviation</td><td>4</td><td>2</td><td>4</td></tr><tr><td rowspan="6">Distance to the nearest highly hazardous plant (m)</td><td>... market square (&lt;500m)</td><td>205</td><td>295</td><td>97</td></tr><tr><td>... a drugstore (&lt;250m)</td><td>144</td><td>460</td><td>109</td></tr><tr><td>... a food shop (&lt;250m)</td><td>198</td><td>686</td><td>214</td></tr><tr><td>... a bus stop (&lt;250m)</td><td>676</td><td>880</td><td>448</td></tr><tr><td>... a park (&lt;500m)</td><td>425</td><td>714</td><td>119</td></tr><tr><td>... a nursery or a primary school (&lt;500m)</td><td>428</td><td>924</td><td>172</td></tr><tr><td rowspan="5">Distance to the nearest authorized plant (m)</td><td>Min</td><td>532</td><td>41</td><td>64</td></tr><tr><td>Q1</td><td>3333</td><td>837</td><td>705</td></tr><tr><td>Median</td><td>5141</td><td>1730</td><td>1140</td></tr><tr><td>Q3</td><td>6406</td><td>2451</td><td>2189</td></tr><tr><td>Max</td><td>10379</td><td>4084</td><td>5142</td></tr><tr><td rowspan="2">View of industrial plants</td><td>Mean</td><td>4935</td><td>1711</td><td>1452</td></tr><tr><td>Standard deviation</td><td>1863</td><td>949</td><td>1027</td></tr><tr><td rowspan="5">Location of industrial plants</td><td>Min</td><td>89</td><td>41</td><td>64</td></tr><tr><td>Q1</td><td>1006</td><td>596</td><td>494</td></tr><tr><td>Median</td><td>1657</td><td>872</td><td>771</td></tr><tr><td>Q3</td><td>2663</td><td>1163</td><td>1189</td></tr><tr><td>Max</td><td>5279</td><td>1943</td><td>2884</td></tr><tr><td rowspan="2">Located in an administrative area of...</td><td>Mean</td><td>1848</td><td>877</td><td>943</td></tr><tr><td>Standard deviation</td><td>1061</td><td>369</td><td>624</td></tr><tr><td>View of industrial plants</td><td></td><td>0</td><td>0</td><td>495</td></tr><tr><td rowspan="7">Location of industrial plants</td><td>... land use control (Z1)</td><td>0</td><td>0</td><td>34</td></tr><tr><td>... land use control (Z2)</td><td>0</td><td>0</td><td>56</td></tr><tr><td>... emergency plan</td><td>0</td><td>276</td><td>391</td></tr><tr><td>... natural hazard</td><td>33</td><td>0</td><td>0</td></tr><tr><td>... other hazard</td><td>0</td><td>0</td><td>58</td></tr><tr><td>... environmental protection</td><td>0</td><td>0</td><td>61</td></tr><tr><td>... conservation easement</td><td>0</td><td>39</td><td>200</td></tr><tr><td rowspan="2">Exposed to...</td><td>... land transport noise (★)</td><td>0</td><td>0</td><td>15</td></tr><tr><td>... air transport noise (★)</td><td>79</td><td>0</td><td>0</td></tr></tbody></table>

Notes: the unit is the number of dwellings, unless otherwise specified.

(★) A dwelling is considered as exposed to a land transport facility if sound is above 60 dB / to an air transport facility if sound is above 50 dB.

Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, Nord and Picardy, and South West France.
<table><caption>Table 11: Buyers' characteristics</caption><thead><tr><th></th><th></th><th>Bordeaux</th><th>Dunkirk</th><th>Rouen</th></tr></thead><tbody><tr><td>N</td><td></td><td>1423</td><td>1016</td><td>571</td></tr><tr><td rowspan="6">Income (euro)</td><td>Min</td><td>5024</td><td>3471</td><td>7550</td></tr><tr><td>Q1</td><td>20498</td><td>12134</td><td>13563</td></tr><tr><td>Median</td><td>38815</td><td>23266</td><td>28958</td></tr><tr><td>Q3</td><td>47942</td><td>32544</td><td>35408</td></tr><tr><td>Max</td><td>173016</td><td>160996</td><td>277797</td></tr><tr><td>Mean</td><td>35609</td><td>23781</td><td>27161</td></tr><tr><td rowspan="3">Gender</td><td>Standard deviation</td><td>16061</td><td>12348</td><td>17777</td></tr><tr><td>Unspecified</td><td>112</td><td>64</td><td>114</td></tr><tr><td>Female</td><td>229</td><td>200</td><td>99</td></tr><tr><td rowspan="6">Age (year)</td><td>Male</td><td>1194</td><td>816</td><td>472</td></tr><tr><td>Min</td><td>20</td><td>20</td><td>22</td></tr><tr><td>Q1</td><td>34</td><td>29</td><td>31</td></tr><tr><td>Median</td><td>40</td><td>34</td><td>37</td></tr><tr><td>Q3</td><td>48</td><td>43</td><td>46</td></tr><tr><td>Max</td><td>99</td><td>82</td><td>85</td></tr><tr><td rowspan="3">Social and occupational group</td><td>Mean</td><td>42</td><td>37</td><td>40</td></tr><tr><td>Standard deviation</td><td>11</td><td>11</td><td>11</td></tr><tr><td>Unspecified</td><td>0</td><td>0</td><td>1</td></tr><tr><td rowspan="10">Marital status</td><td>Farmer</td><td>3</td><td>1</td><td>2</td></tr><tr><td>Self-employed</td><td>71</td><td>31</td><td>31</td></tr><tr><td>Managerial or professional occupation</td><td>445</td><td>47</td><td>77</td></tr><tr><td>Intermediate upper occupation</td><td>472</td><td>203</td><td>188</td></tr><tr><td>Employee</td><td>193</td><td>112</td><td>91</td></tr><tr><td>Worker</td><td>103</td><td>180</td><td>131</td></tr><tr><td>Retired</td><td>100</td><td>28</td><td>41</td></tr><tr><td>No occupation</td><td>22</td><td>13</td><td>7</td></tr><tr><td>Unspecified</td><td>14</td><td>401</td><td>3</td></tr><tr><td rowspan="5">Originates from...</td><td>Single</td><td>301</td><td>322</td><td>172</td></tr><tr><td>In civil union</td><td>16</td><td>17</td><td>7</td></tr><tr><td>Married</td><td>884</td><td>538</td><td>309</td></tr><tr><td>Divorced</td><td>158</td><td>101</td><td>50</td></tr><tr><td>Widowed</td><td>34</td><td>18</td><td>18</td></tr><tr><td rowspan="5">Originates from...</td><td>... study area</td><td>633</td><td>758</td><td>199</td></tr><tr><td>... less than 15 km away</td><td>358</td><td>155</td><td>324</td></tr><tr><td>... more than 15 km away (France)</td><td>391</td><td>95</td><td>47</td></tr><tr><td>... abroad</td><td>11</td><td>6</td><td>1</td></tr><tr><td>Unspecified</td><td>30</td><td>2</td><td>0</td></tr></tbody></table>

Notes: buyer's characteristics are relative to the buyer himself, or to the household reference person. The unit is the number of buyers, unless otherwise specified.

Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, Nord and Picardy, and South West France, statements of income from the General Directorate of Public Finances.
<table><caption>Table 12: Transactions by municipality and by year</caption><thead><tr><th>Area</th><th>Municipality (INSEE code)</th><th>2000</th><th>2002</th><th>2004</th><th>2006</th><th>2008</th><th>All years</th></tr></thead><tbody><tr><td rowspan="6">Bordeaux</td><td>Le Haillan (33200)</td><td>36</td><td>31</td><td>34</td><td>38</td><td>24</td><td>163</td></tr><tr><td>Martignas-sur-Jalle (33273)</td><td>42</td><td>40</td><td>42</td><td>32</td><td>30</td><td>186</td></tr><tr><td>Mérignac (33281)</td><td>13</td><td>15</td><td>22</td><td>13</td><td>11</td><td>74</td></tr><tr><td>Saint-Aubin-de-Médoc (33376)</td><td>34</td><td>38</td><td>38</td><td>28</td><td>21</td><td>159</td></tr><tr><td>Saint-Médard-en-Jalles (33449)</td><td>48</td><td>157</td><td>155</td><td>149</td><td>84</td><td>593</td></tr><tr><td>Le Taillan-Médoc (33519)</td><td>52</td><td>50</td><td>55</td><td>49</td><td>42</td><td>248</td></tr><tr><td colspan="2">All municipalities</td><td>225</td><td>331</td><td>346</td><td>309</td><td>212</td><td>1423</td></tr><tr><td rowspan="4">Dunkirk</td><td>Coudekerque-Branche (59155)</td><td>80</td><td>77</td><td>87</td><td>86</td><td>85</td><td>415</td></tr><tr><td>Dunkirk (59183)</td><td>47</td><td>35</td><td>49</td><td>39</td><td>41</td><td>211</td></tr><tr><td>Fort-Mardyck (59248)</td><td>9</td><td>12</td><td>8</td><td>17</td><td>16</td><td>62</td></tr><tr><td>Saint-Pol-sur-Mer (59540)</td><td>50</td><td>67</td><td>86</td><td>56</td><td>69</td><td>328</td></tr><tr><td colspan="2">All municipalities</td><td>186</td><td>191</td><td>230</td><td>198</td><td>211</td><td>1016</td></tr><tr><td rowspan="6">Rouen</td><td>Grand-Couronne (76319)</td><td>48</td><td>58</td><td>57</td><td>42</td><td>50</td><td>255</td></tr><tr><td>Moulineaux (76457)</td><td>6</td><td>10</td><td>4</td><td>7</td><td>5</td><td>32</td></tr><tr><td>Petit-Couronne (76497)</td><td>43</td><td>45</td><td>29</td><td>28</td><td>38</td><td>183</td></tr><tr><td>Sahurs (76550)</td><td>10</td><td>15</td><td>9</td><td>9</td><td>5</td><td>48</td></tr><tr><td>Val-de-la-Haye (76717)</td><td>7</td><td>7</td><td>13</td><td>8</td><td>6</td><td>41</td></tr><tr><td>All municipalities</td><td>118</td><td>140</td><td>113</td><td>94</td><td>106</td><td>571</td></tr><tr><td colspan="2">All areas</td><td>529</td><td>662</td><td>689</td><td>601</td><td>529</td><td>3010</td></tr></tbody></table>

Note: the unit is the number of transactions.

Sources: French solicitors - PERVAL, data collected and standardized by Technical Studies Center of Public Works of Normandy and Centre, Nord and Picardy, and South West France.

## A.2 Other studies on industrial risk and housing prices
<table><caption>Table 13: Other studies on industrial risk and housing prices</caption><thead><tr><th>Study</th><th>Hazardous facilities</th><th>Region</th><th>Time</th><th>Sample size</th><th>Extrinsic characteristics (*)</th><th>Scope of the study area (**)</th><th>Variable(s) for risk exposure</th><th>Effect of risk exposure</th></tr></thead><tbody><tr><td>Boxall et al. (2005)</td><td>Oil and gas facilities</td><td>Central Alberta (Canada)</td><td>1994 - 2001</td><td>532</td><td>Distance to the City, view of mountains</td><td>17 km (mean), 7 km (std) (rural area)</td><td>Distance to the nearest plant, number of plants within 4 km, number of emergency planning zones the property is located in, sum of H2S release within 4 km of property</td><td>Prices decrease for properties located within 4 km of facilities from 150,000 to 450,000 $CDN 2001, i.e. from -4% to -8%</td></tr><tr><td>Carroll et al. (1996)</td><td>PEPCON plant (chemistry)</td><td>Henderson, Nevada (United States)</td><td>1986 - 1990</td><td>7,780</td><td>No</td><td>From 2 to 35 km</td><td>Distance to the plant</td><td>Prior to explosion, prices increase by 4.17% at 3.2 km from PEPCON, i.e. increase by around $11 per meter. Impact of the explosion on implicit price</td></tr><tr><td>Flower and Ragas (1994)</td><td>Petrochemical refineries</td><td>St. Bernard Parish, Louisiana (United States)</td><td>1979 - 1991</td><td>1,999</td><td>No</td><td>From 0 to 3.3 km</td><td>Distances to the refineries or dummies for areas based on these distances</td><td>In one area prices decrease w.r.t distance. Elsewhere prices increase from $210 to $620 per 100 m, i.e. from 1% to 3.4% per 100 m, i.e. from $2 to $6 per m</td></tr><tr><td>Sauvage (1997)</td><td>Chemical and petrochemical facilities</td><td>Waziers, Puget-sur-Argens, Carling, L'Hôpital and St-Gaudens (France)</td><td>1988-1992</td><td>170, 64, 59, 91 and 188</td><td>No</td><td>From 0 to 1.8 km/2 km/4 km</td><td>Distance to the plants</td><td>In Waziers and in Puget-sur-Argens, no significant impact. Elsewhere, prices increase w.r.t. distance. In L'Hôpital for example, prices increase by 110 F per meter, i.e. around 4% per 100 meter</td></tr><tr><td>Travers et al. (2009)</td><td>Chemical facility</td><td>Bradford (United Kingdom)</td><td>1987-1993</td><td>561</td><td>No</td><td>From 0 to 2.5 km</td><td>Distance to the plants, view of plants</td><td>Prices increase by £5.5 per m, i.e. around 2.6% per 100 meter. View over facilities decreases prices by £960, i.e. by 4.6%</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Distances to roads, highway, Seine, play area, town hall</td><td>From 0 to 5 km</td><td>Distance to the plants, location in emergency planning zone</td></tr></tbody></table>

<footnote>(*) Other than variables for risk exposure and dummies for municipalities. (**) The scope of the study area is measured in terms of distance to the nearest plant.</footnote>
G 9001 J. FAYOLLE et M. FLEURBAEY Accumulation, profitabilité et endettement des entreprises

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

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

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

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

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

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

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

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

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

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

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

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

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

G 9110 J.L. BRILLET Micro-DMS

NON PARU

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

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

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

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

G 9202 J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE

Macro-economic import functions with imperfect competition - An application to the E.C. Trade

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G 9311 J. BOURDIEU - B. COLIN-SEDILLOT Les décisions de financement des entreprises

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

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

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

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

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

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

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

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

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

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

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

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

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

G 9406 F. ROSENWALD La décision d'investir

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

G 9408 L. BLOCH, J. BOURDIEU, B. COLIN-SEDILLOT, G. LONGUEVILLE

Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté

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

G 9410 F. ROSENWALD Suivi conjoncturel de l'investissement

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

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

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

G 9414 I. KABLA Le Choix de breveter une invention

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

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

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

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

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

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

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

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

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

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

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

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

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

G 9606 C. DOZ - F. LENGLART Factor analysis and unobserved component models: an application to the study of French business surveys

G 9607 N. GREENAN - D. GUELLEC La théorie coopérative de la firme
<table>
  <tr>
    <td>G 9608</td>
    <td>N. GREENAN - D. GUELLEC<br/>Technological innovation and employment<br/>reallocation</td>
    <td>G 9714</td>
    <td>F. LEQUILLER<br/>Does the French Consumer Price Index Over-<br/>state Inflation?</td>
    <td>G 9808</td>
    <td>A. Mourougane<br/>Can a Conservative Governor Conduct an Ac-<br/>comodative Monetary Policy?</td>
    <td>G 9913</td>
    <td>Division « Redistribution et Politiques Sociales »<br/>Le modèle de microsimulation dynamique<br/>DESTINIE</td>
  </tr>
  <tr>
    <td>G 9609</td>
    <td>Ph. COUR - F. RUPPRECHT<br/>L'intégration asymétrique au sein du continent<br/>américain : un essai de modélisation</td>
    <td>G 9715</td>
    <td>X. BONNET<br/>Peut-on mettre en évidence les rigidités à la<br/>baisse des salaires nominaux ?<br/>Une étude sur quelques grands pays de l'OCDE</td>
    <td>G 9809</td>
    <td>X. BONNET - E. DUBOIS - L. FAUVET<br/>Asymétrie des inflations relatives et menus costs<br/>: tests sur l'inflation française</td>
    <td>G 9914</td>
    <td>E. DUGUET<br/>Macro-commandes SAS pour l'économétrie des<br/>panels et des variables qualitatives</td>
  </tr>
  <tr>
    <td>G 9610</td>
    <td>S. DUCHENE - G. FORGEOT - A. JACQUOT<br/>Analyse des évolutions récentes de la producti-<br/>vité apparente du travail</td>
    <td>G 9716</td>
    <td>N. IUNG - F. RUPPRECHT<br/>Productivité de la recherche et rendements<br/>d'échelle dans le secteur pharmaceutique<br/>français</td>
    <td>G 9810</td>
    <td>E. DUGUET - N. IUNG<br/>Sales and Advertising with Spillovers at the firm<br/>level: Estimation of a Dynamic Structural Model<br/>on Panel Data</td>
    <td>G 9915</td>
    <td>R. DUHAUTOIS<br/>Évolution des flux d'emplois en France entre<br/>1990 et 1996 : une étude empirique à partir du<br/>fichier des bénéfices réels normaux (BRN)</td>
  </tr>
  <tr>
    <td>G 9611</td>
    <td>X. BONNET - S. MAHFOUZ<br/>The influence of different specifications of<br/>wages-prices spirals on the measure of the<br/>NAIRU: the case of France</td>
    <td>G 9717</td>
    <td>E. DUGUET - I. KABLA<br/>Appropriation strategy and the motivations to use<br/>the patent system in France - An econometric<br/>analysis at the firm level</td>
    <td>G 9811</td>
    <td>J.P. BERTHIER<br/>Congestion urbaine : un modèle de trafic de<br/>pointe à courbe débit-vitesse et demande<br/>élastique</td>
    <td>G 9916</td>
    <td>J.Y. FOURNIER<br/>Extraction du cycle des affaires : la méthode de<br/>Baxter et King</td>
  </tr>
  <tr>
    <td>G 9612</td>
    <td>PH. COUR - E. DUBOIS, S. MAHFOUZ,<br/>J. PISANI-FERRY<br/>The cost of fiscal retrenchment revisited: how<br/>strong is the evidence?</td>
    <td>G 9718</td>
    <td>L.P. PELÉ - P. RALLE<br/>Âge de la retraite : les aspects incitatifs du ré-<br/>gime général</td>
    <td>G 9812</td>
    <td>C. PRIGENT<br/>La part des salaires dans la valeur ajoutée : une<br/>approche macroéconomique</td>
    <td>G 9917</td>
    <td>B. CRÉPON - R. DESPLATZ - J. MAIRESSE<br/>Estimating price cost margins, scale economies<br/>and workers' bargaining power at the firm level</td>
  </tr>
  <tr>
    <td>G 9613</td>
    <td>A. JACQUOT<br/>Les flexions des taux d'activité sont-elles seule-<br/>ment conjoncturelles ?</td>
    <td>G 9719</td>
    <td>ZHANG Yingxiang - SONG Xueqing<br/>Lexique macroéconomique français-chinois,<br/>chinois-français</td>
    <td>G 9813</td>
    <td>A.Th. AERTS<br/>L'évolution de la part des salaires dans la valeur<br/>ajoutée en France reflète-t-elle les évolutions<br/>individuelles sur la période 1979-1994 ?</td>
    <td>G 9918</td>
    <td>Ch. GIANELLA - Ph. LAGARDE<br/>Productivity of hours in the aggregate production<br/>function: an evaluation on a panel of French<br/>firms from the manufacturing sector</td>
  </tr>
  <tr>
    <td>G 9614</td>
    <td>ZHANG Yingxiang - SONG Xueqing<br/>Lexique macroéconomique Français-Chinois</td>
    <td>G 9720</td>
    <td>M. HOUBEBINE - J.L. SCHNEIDER<br/>Mesurer l'influence de la fiscalité sur la locali-<br/>sation des entreprises</td>
    <td>G 9814</td>
    <td>B. SALANIÉ<br/>Guide pratique des séries non-stationnaires</td>
    <td>G 9919</td>
    <td>S. AUDRIC - P. GIVORD - C. PROST<br/>Évolution de l'emploi et des coûts par quali-<br/>fication entre 1982 et 1996</td>
  </tr>
  <tr>
    <td>G 9701</td>
    <td>J.L. SCHNEIDER<br/>La taxe professionnelle : éléments de cadrage<br/>économique</td>
    <td>G 9721</td>
    <td>A. Mourougane<br/>Crédibilité, indépendance et politique monétaire<br/>Une revue de la littérature</td>
    <td>G 9901</td>
    <td>S. DUCHÈNE - A. JACQUOT<br/>Une croissance plus riche en emplois depuis le<br/>début de la décennie ? Une analyse en compa-<br/>raison internationale</td>
    <td>G 2000/1</td>
    <td>R. MAHIEU<br/>Les déterminants des dépenses de santé : une<br/>approche macroéconomique</td>
  </tr>
  <tr>
    <td>G 9702</td>
    <td>J.L. SCHNEIDER<br/>Transition et stabilité politique d'un système<br/>redistributif</td>
    <td>G 9722</td>
    <td>P. AUGERAUD - L. BRIOT<br/>Les données comptables d'entreprises<br/>Le système intermédiaire d'entreprises<br/>Passage des données individuelles aux données<br/>sectorielles</td>
    <td>G 9902</td>
    <td>Ch. COLIN<br/>Modélisation des carrières dans Destinie</td>
    <td>G 2000/2</td>
    <td>C. ALLARD-PRIGENT - H. GUILMEAU -<br/>A. QUINET<br/>The real exchange rate as the relative price of<br/>nontrables in terms of tradables: theoretical<br/>investigation and empirical study on French data</td>
  </tr>
  <tr>
    <td>G 9703</td>
    <td>D. GOUX - E. MAURIN<br/>Train or Pay: Does it Reduce Inequalities to En-<br/>courage Firms to Train their Workers?</td>
    <td>G 9723</td>
    <td>P. AUGERAUD - J.E. CHAPRON<br/>Using Business Accounts for Compiling National<br/>Accounts: the French Experience</td>
    <td>G 9904</td>
    <td>Ch. COLIN<br/>Évolution de la dispersion des salaires : un essai<br/>de prospective par microsimulation</td>
    <td>G 2000/3</td>
    <td>J.-Y. FOURNIER<br/>L'approximation du filtre passe-bande proposée<br/>par Christiano et Fitzgerald</td>
  </tr>
  <tr>
    <td>G 9704</td>
    <td>P. GENIER<br/>Deux contributions sur dépendance et équité</td>
    <td>G 9724</td>
    <td>P. AUGERAUD<br/>Les comptes d'entreprise par activités - Le pas-<br/>sage aux comptes - De la comptabilité<br/>d'entreprise à la comptabilité nationale - A<br/>paraitre</td>
    <td>G 9905</td>
    <td>B. CREPON - N. IUNG<br/>Innovation, emploi et performances</td>
    <td>G 2000/4</td>
    <td>Bilan des activités de la DESE - 1999</td>
  </tr>
  <tr>
    <td>G 9705</td>
    <td>E. DUGUET - N. IUNG<br/>R & D Investment, Patent Life and Patent Value<br/>An Econometric Analysis at the Firm Level</td>
    <td>G 9801</td>
    <td>H. MICHAUDON - C. PRIGENT<br/>Présentation du modèle AMADEUS</td>
    <td>G 9907</td>
    <td>B. CREPON - Ch. GIANELLA<br/>Wages inequalities in France 1969-1992<br/>An application of quantile regression techniques</td>
    <td>G 2000/5</td>
    <td>B. CREPON - F. ROSENWALD<br/>Investissement et contraintes de financement : le<br/>poids du cycle<br/>Une estimation sur données françaises</td>
  </tr>
  <tr>
    <td>G 9706</td>
    <td>M. HOUBEBINE - A. TOPIOL-BENSAÏD<br/>Les entreprises internationales en France : une<br/>analyse à partir de données individuelles</td>
    <td>G 9802</td>
    <td>J. ACCARDO<br/>Une étude de comptabilité générationnelle<br/>pour la France en 1996</td>
    <td>G 9908</td>
    <td>C. BONNET - R. MAHIEU<br/>Microsimulation techniques applied to inter-<br/>generational transfers - Pensions in a dynamic<br/>framework: the case of France</td>
    <td>G 2000/6</td>
    <td>A. FLIPO<br/>Les comportements matrimoniaux de fait</td>
  </tr>
  <tr>
    <td>G 9707</td>
    <td>M. HOUBEBINE<br/>Polarisation des activités et spécialisation des<br/>départements en France</td>
    <td>G 9803</td>
    <td>X. BONNET - S. DUCHÈNE<br/>Apports et limites de la modélisation<br/>« Real Business Cycles »</td>
    <td>G 9909</td>
    <td>F. ROSENWALD<br/>L'impact des contraintes financières dans la dé-<br/>cision d'investissement</td>
    <td>G 2000/7</td>
    <td>R. MAHIEU - B. SÉDILLOT<br/>Microsimulations of the retirement decision: a<br/>supply side approach</td>
  </tr>
  <tr>
    <td>G 9708</td>
    <td>E. DUGUET - N. GREENAN<br/>Le biais technologique : une analyse sur don-<br/>nées individuelles</td>
    <td>G 9804</td>
    <td>C. BARLET - C. DUGUET -<br/>D. ENCAOUA - J. PRADEL<br/>The Commercial Success of Innovations<br/>An econometric analysis at the firm level in<br/>French manufacturing</td>
    <td>G 9910</td>
    <td>Bilan des activités de la DESE - 1998<br/>Contrat d'insertion et sortie du RMI<br/>Évaluation des effets d'une politique sociale</td>
    <td>G 2000/8</td>
    <td>C. AUDENIS - C. PROST<br/>Déficit conjoncturel : une prise en compte des<br/>conjonctures passées</td>
  </tr>
  <tr>
    <td>G 9709</td>
    <td>J.L. BRILLET<br/>Analyzing a small French ECM Model</td>
    <td>G 9805</td>
    <td>P. CAHUC - Ch. GIANELLA -<br/>D. GOUX - A. ZILBERBERG<br/>Equalizing Wage Differences and Bargaining<br/>Power - Evidence form a Panel of French Firms</td>
    <td>G 9911</td>
    <td>Ch. COLIN - FI. LEGROS - R. MAHIEU<br/>Bilans contributifs comparés des régimes de<br/>retraite du secteur privé et de la fonction<br/>publique</td>
    <td>G 2000/9</td>
    <td>R. MAHIEU - B. SÉDILLOT<br/>Équivalent patrimonial de la rente et souscription<br/>de retraite complémentaire</td>
  </tr>
  <tr>
    <td>G 9710</td>
    <td>J.L. BRILLET<br/>Formalizing the transition process: scenarios for<br/>capital accumulation</td>
    <td>G 9806</td>
    <td>J. ACCARDO - M. JLASSI<br/>La productivité globale des facteurs entre 1975<br/>et 1996</td>
    <td>G 9912</td>
    <td>G. LAROQUE - B. SALANIÉ<br/>Une décomposition du non-emploi en France</td>
    <td>G 2000/10</td>
    <td>R. DUHAUTOIS<br/>Ralentissement de l'investissement : petites ou<br/>grandes entreprises ? industrie ou tertiaire ?</td>
  </tr>
  <tr>
    <td>G 9711</td>
    <td>G. FORGEOT - J. GAUTIÉ<br/>Insertion professionnelle des jeunes et proces-<br/>sus de déclassement</td>
    <td>G 9807</td>
    <td>J. ACCARDO - M. JLASSI<br/>La productivité globale des facteurs entre 1975<br/>et 1996</td>
    <td>G 9912</td>
    <td>B. SALANIÉ<br/>Une maquette analytique de long terme du<br/>marché du travail</td>
    <td>G 2000/11</td>
    <td>G. LAROQUE - B. SALANIÉ<br/>Temps partiel féminin et incitations financières à<br/>l'emploi</td>
  </tr>
  <tr>
    <td>G 9712</td>
    <td>E. DUBOIS<br/>High Real Interest Rates: the Consequence of a<br/>Saving Investment Disequilibrium or of an in-<br/>sufficient Credibility of Monetary Authorities?</td>
    <td>G 9808</td>
    <td>J. ACCARDO - M. JLASSI<br/>La productivité globale des facteurs entre 1975<br/>et 1996</td>
    <td>G 9912</td>
    <td>Ch. GIANELLA<br/>Une estimation de l'élasticité de l'emploi peu<br/>qualifié à son coût</td>
    <td>G 2000/12</td>
    <td>Ch. GIANELLA<br/>Local unemployment and wages</td>
  </tr>
  <tr>
    <td>G 9713</td>
    <td>Bilan des activités de la Direction des Études<br/>et Synthèses Économiques - 1996</td>
    <td>G 9809</td>
    <td>Bilan des activités de la Direction des Études et<br/>Synthèses Économiques - 1997</td>
    <td>Bis</td>
    <td></td>
    <td>G 2000/13</td>
    <td>B. CREPON - Th. HECKEL<br/>- Informatisation en France : une évaluation à<br/>partir de données individuelles</td>
  </tr>
</table>
- 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 measurement of GDP growth

G2001/02 S. AUDRIC

La reprise de la croissance de l'emploi profonde-t-elle aussi aux non-diplômés ?

G2001/03 I. BRAUN-LEMAIRE

Évolution et répartition du surplus de productivité

G2001/04 A. BEAUDU - Th. HECKEL

Le canal du crédit fonctionne-t-il en Europe ? Une étude de l'hétérogénéité des comportements d'investissement à partir de données de bilan agrégées

G2001/05 C. AUDENIS - P. BISCOURP -

N. FOURCADE - O. LOISEL

Testing the augmented Solow growth model: An empirical reassessment using panel data

G2001/06 R. MAHIEU - B. SÉDILLOT

Départ à la retraite, irréversibilité et incertitude

G2001/07 Bilan des activités de la DESE - 2000

G2001/08 J. Ph. GAUDEMET

Les dispositifs d'acquisition à titre facultatif d'annuités viagères de retraite

G2001/09 B. CRÉPON - Ch. GIANELLA

Fiscalité, coût d'usage du capital et demande de facteurs : une analyse sur données individuelles

G2001/10 B. CRÉPON - R. DESPLATZ

Évaluation des effets des dispositifs d'allégements de charges sociales sur les bas salaires

G2001/11 J.-Y. FOURNIER

Comparaison des salaires des secteurs public et privé

G2001/12 J.-P. BERTHIER - C. JAULENT

R. CONVENEVOLE - S. PISANI

Une méthodologie de comparaison entre consommations intermédiaires de source fiscale et de comptabilité nationale

G2001/13 P. BISCOURP - Ch. GIANELLA

Substitution and complementarity between capital, skilled and less skilled workers: an analysis at the firm level in the French manufacturing industry

G2001/14 I. ROBERT-BOBEE

Modelling demographic behaviours in the French microsimulation model Destinie: An analysis of future change in completed fertility

G2001/15 J.-P. ZOYEM

Diagnostic sur la pauvreté et calendrier de revenus : le cas du "Panel européen des ménages"

G2001/16 J.-Y. FOURNIER - P. GIVORD

La réduction des taux d'activité aux âges extrêmes, une spécificité française ?

G2001/17 C. AUDENIS - P. BISCOURP - N. RIEDINGER

Existe-t-il une asymétrie dans la transmission du prix du brut aux prix des carburants ?

G2002/01 F. MAGNIEN - J.-L. TAVERNIER - D. THESMAR

Les statistiques internationales de PIB par habitant en standard de pouvoir d'achat : une analyse des résultats

G2002/02 Bilan des activités de la DESE - 2001

G2002/03 B. SÉDILLOT - E. WALRAET

La cessation d'activité au sein des couples : y a-t-il interdépendance des choix ?

G2002/04 G. BRILHAULT

- Rétroposition des séries de FBCF et calcul du capital fixe en SEC-95 dans les comptes nationaux français

- Rétroposition 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 vieillesse à l'aide du modèle de microsimulation DESTINIE

G2002/08 J.-P. BERTHIER

Réflexions sur les différentes notions de volume dans les comptes nationaux : comptes aux prix d'une année fixe ou aux prix de l'année précédente, séries chaînées

G2002/09 F. HILD

Les soldes d'opinion résument-ils au mieux les réponses des entreprises aux enquêtes de conjoncture ?

G2002/10 I. ROBERT-BOBÉE

Les comportements démographiques dans le modèle de microsimulation Destinie - Une comparaison des estimations issues des enquêtes Jeunes et Carrières 1997 et Histoire Familiale 1999

G2002/11 J.-P. ZOYEM

La dynamique des bas revenus : une analyse des entrées-sorties de pauvreté

G2002/12 F. HILD

Prévisions d'inflation pour la France

G2002/13 M. LECLAIR

Réduction du temps de travail et tensions sur les facteurs de production

G2002/14 E. WALRAET - A. VINCENT

- Analyse de la redistribution intragénérationnelle dans le système de retraite des salariés du privé - Une approche par microsimulation

- Intragenerational distributional analysis in the french private sector pension scheme - A microsimulation approach

G2002/15 P. CHONE - D. LE BLANC - I. ROBERT-BOBEE

Offre de travail féminine et garde des jeunes enfants

G2002/16 F. MAUREL - S. GREGOIR

Les indices de compétitivité des pays : inter-prétation et limites

G2003/01 N. RIEDINGER - E. HAUVY

Le coût de dépollution atmosphérique pour les entreprises françaises : Une estimation à partir de données individuelles

G2003/02 P. BISCOURP et F. KRAMARZ

Création d'emplois, destruction d'emplois et internationalisation des entreprises industrielles françaises : une analyse sur la période 1986-1992

G2003/03 Bilan des activités de la DESE - 2002

G2003/04 P.-O. BEFFY - J. DEROYON -

N. FOURCADE - S. GREGOIR - N. LAÎB - B. MONFORT

Évolutions démographiques et croissance : une projection macro-économique à l'horizon 2020

G2003/05 P. AUBERT

La situation des salariés de plus de cinquante ans dans le secteur privé

G2003/06 P. AUBERT - B. CRÉPON

Âge, salaire et productivité

La productivité des salariés décline-t-elle en fin de carrière ?

G2003/07 H. BARON - P.O. BEFFY - N. FOURCADE - R. MAHIEU

Le ralentissement de la productivité du travail au cours des années 1990

G2003/08 P.-O. BEFFY - B. MONFORT

Patrimoine des ménages, dynamique d'allocation et comportement de consommation

G2003/09 P. BISCOURP - N. FOURCADE

Peut-on mettre en évidence l'existence de rigidités à la baisse des salaires à partir de données individuelles ? Le cas de la France à la fin des années 90

G2003/10 M. LECLAIR - P. PETIT

Présence syndicale dans les firmes : quel impact sur les inégalités salariales entre les hommes et les femmes ?

G2003/11 P.-O. BEFFY - X. BONNET - M. DARRACQ-PARIES - B. MONFORT

MZE: a small macro-model for the euro area

G2004/01 P. AUBERT - M. LECLAIR

La compétitivité exprimée dans les enquêtes trimestrielles sur la situation et les perspectives dans l'industrie

G2004/02 M. DUÉE - C. REBILLARD

La dépendance des personnes âgées : une projection à long terme

G2004/03 S. RASPILLER - N. RIEDINGER

Régulation environnementale et choix de localisation des groupes français

G2004/04 A. NABOULET - S. RASPILLER

Les déterminants de la décision d'investir : une approche par les perceptions subjectives des firmes

G2004/05 N. RAGACHE

La déclaration des enfants par les couples non mariés est-elle fiscalement optimale ?

G2004/06 M. DUÉE

L'impact du chômage des parents sur le devenir scolaire des enfants

G2004/07 P. AUBERT - E. CAROLI - M. ROGER

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

G2004/08 E. DUGUET - C. LELARGE

Les brevets accroissent-ils les incitations privées à innover ? Un examen microéconométrique

G2004/09 S. RASPILLER - P. SILLARD

Affiliating versus Subcontracting: the Case of Multinationals

G2004/10 J. BOISSINOT - C. L'ANGEVIN - B. MONFORT

Public Debt Sustainability: Some Results on the French Case

G2004/11 S. ANANIAN - P. AUBERT

Travailleurs âgés, nouvelles technologies et changements organisationnels : un réexamen à partir de l'enquête « REPONSE »

G2004/12 X. BONNET - H. PONCET

Structures de revenus et propensions différentes à consommer - Vers une équation de consommation des ménages plus robuste en prévision pour la France

G2004/13 C. PICART

Évaluer la rentabilité des sociétés non financières

G2004/14 J. BARDAJI - B. SÉDILLOT - E. WALRAET

Les retraites du secteur public : projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE

G2005/01 S. BUFFETEAU - P. GODEFROY

Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945 a1974

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

G2005/08 C. L'ANGEVIN - N. LAÎB

Éducation et croissance en France et dans un panel de 21 pays de l'OCDE

G2005/09 N. FERRARI

Prévoir l'investissement des entreprises
Un indicateur des révisions dans l'enquête de conjoncture sur les investissements dans l'industrie.
G2005/10 P.-O. BEFFY - C. L'ANGEVIN
Chômage et boucle prix-salaires : apport d'un modèle « qualifiés/peu qualifiés »

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

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

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

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

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

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

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

G2006/02 C. PICART
Les gazelles en France

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2009/13 R. AEBERHARDT - D. FOUGÈRE - R. RATHELOT

Discrimination à l'embauche : comment exploiter les procédures de testing ?

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

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

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

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

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

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

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

G2010/06 R. AEBERHARDT - J. POUGET
National Origin Differences in Wages and Hierarchical Positions - Evidence on French Full-Time Male Workers from a matched Employer-Employee Dataset

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

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

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

G2010/10 I. BUONO - G. LALANNE
La réaction des entreprises françaises à la baisse des tarifs douaniers étrangers
G2010/11 R. RATHELOT - P. SILLARD
L'apport des méthodes à noyaux pour mesurer la concentration géographique - Application à la concentration des immigrés en France de 1968 à 1999

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

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

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

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

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

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

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

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

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

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

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

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

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

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

G2011/08 M. BACHELET - M. BEFFY - D. BLANCHET
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 entre-

prises sur la base des contrôles fiscaux et son insertion dans les comptes nationaux

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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