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# Atmospheric Pollution, Environmental Justice and Mortality Rate: a Spatial Approach

Emmanuelle Lavaine

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Atmospheric Pollution, Environmental Justice and
Mortality Rate : a Spatial Approach

Emmanuelle LAVAINE

2010.72

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# Atmospheric Pollution, Environmental Justice and Mortality

## Rate: a Spatial Approach

Emmanuelle LAVAINE*

Université Paris 1 Panthéon Sorbonne, Paris School of Economics

September 2010

## Abstract

This paper presents the first study of environmental inequality related to health in France at the national scale. Through an econometric analysis based on a panel data from 2000 to 2004 at a department level, we investigate total mortality rate in relation to socioeconomic status and air pollution. Concentration level of CO, SO₂, NO₂, NO, O₃ and PM₁₀ are estimated by spatial interpolation from local observations of a network of monitoring stations. By running a multivariate model, we first investigate the relationship between socioeconomic factors and total mortality rate; then, we make the link with environmental air quality measured within the department. Unemployment plays an important role in affecting the mortality rate. Pollutant concentration level are divided into two risk categories (low and high) at the median. We find a positive and significative relationship between NO₂ and mortality rate especially at high concentration level of NO₂ with a relative risk more important for women. Besides, NO₂ level tends to modify the effect of unemployment on mortality rate. These results not only confirm the existence of short term relationships between current air pollution levels and mortality but also raise questions about environmental justice in France.

Keywords: Inequality, Air pollution, Air quality, Environmental Economics, Environmental Health and Safety, Environmental Impact, Environmental Equity, Mortality rate, Spatial Autocorrelation.

JL classification: R12, Q5, I12, R15, C1

<footnote>*CES, Maison des sciences Economiques, 106 112 boulevard de l'hôpital, 75013 Paris cedex 13, France.
Email: emmanuelle.lavaine@univ-paris1.fr</footnote>
# 1 Introduction

Many pollutants are declining throughout the industrialized world. However, exposure to air pollution, even at the levels commonly achieved nowadays in European countries, still leads to adverse health effects. In this context, there has been increasing global concern over the public health impacts attributed to environmental pollution.

Multilevel modelling has been previously used to assess the negative correlation between pollution exposure and socioeconomic status, such as unemployment, education, and working class in Canada (Premji & al. [2007]), ethnic group, and income in England (Mc Leod & al. [1999]) and in the US (Grineski & al. [2007], Morello-Frosch & al. [2002]) where the concept of environmental justice has been the object of increasing attention. Lucie Laurian [2008] emphasizes that towns with high proportions of immigrants tend to host more hazardous sites even controlling for population size, income, degree of industrialization of the town, and region. In Germany, Schikowski & al. [2008] show the existence of social differences in respiratory health among women population and Kolhuber & al. [2006] acknowledge social inequality in perceived environmental exposures in relation to housing conditions. Pearce & al. [2007] for New Zealand point out that industrial pollution is more important in wealthy places, whereas overall pollution takes place in poorer zones.

In addition, multiple models also estimate the relation between health and pollution, showing the impact of outdoor air pollution on mortality rate in England (Kunzli & al. [2000], Janke & al. [2009]), on allergic sensitization on primary schoolchildren in France (Maesano & al. [2007]), on asthma (Wilhelm & al. [2009]), or on cancer risks among schoolchildren in the US (Morello-Frosch & al. [2002]). Finally, Finkelstein & al. [2003] point out that mean pollutant levels tend to be higher in lower income neighbourhoods in Ontario and both income and pollutant levels are associated with mortality differences.

Besides, we observe a growing epidemiologic literature about air pollution effects on health by sex. The most recent study for gender analysis from Clougherty [2010] shows that most of studies for adults report stronger effects among women, particularly where using residential exposure assessment. For instance, Marr [2010] evaluates marathon race results, weather data and air pollution concentrations for seven major U.S. marathons in cities such as New York, Boston and Los Angeles, where pollution tends to be highest. Higher levels of particles in the air — also known as smog — were associated with slower performance times for women. Men, however, showed no significant impact from pollution. Smaller size of the trachea has been argued to be a reason which makes women more sensitive to particulates in the air. Franklin & al. [2007] studied 130,000 respiratory deaths in 27 U.S. communities and found that community air pollution
better predicted death among women than among men. However, it remains unclear whether observed modification is a result of sex-linked biological differences (e.g., hormonal complement, body size) or gender differences in activity patterns, socially derived gender exposures. The meta-Analysis of Annesi-Maesano [2003] points out that among 14 studies, eight conclude to a risk more important for women than for men.

Our analysis offers several contributions to the existing literature. Most of international empirical eco-
nomic studies estimate either the relation between health and pollution or correlation between pollution
exposure and socioeconomics status. We aim to gather both literatures to assess the impact of air pollution
on health according to the social status. To our knowledge, environmental factors affecting health, such as
exposure to atmospheric air pollution have not been yet studied in France at a national scale in the context
of social inequalities. Laurent & al. [2007] emphasize the importance of continuing to investigate this topic
due to the tendancy to show greater effects among the more deprived. Furthermore, we examine recent rela-
tionships between pollution and health for the entire country. We also account for unobserved confounders
using fixed effects suggesting that the previous epidemiologic French literature may have mistaken the true
health effects of atmospheric pollutants.

This paper investigates the relationship between ambient air pollutant concentrations, social class, and population mortality at the scale of the department in France. The French air quality monitoring system is composed of 38 air quality control organizations (AASQA) certified by the ministry of urban and rural planning and the environment (ADEME) with a regional, departmental or district competency. The common air pollutant Ozone ($O_3$), Carbon monoxide (CO), Nitrogen monoxide (NO), Sulphur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$) and Particles of less than 10 micrometers in aerodynamic diameter ($PM_{10}$) are subject to their control, so that we consider the distribution of those pollutants to construct our dataset. We estimate a multi-level model to allow regression coefficients to be examined simultaneously at several spatial scales. First, we consider a standard model considering the main determinants of all causes mortality. Second, we study the impact of atmospheric pollutants on mortality rates by also estimating a model comparing gender. Finally, we will focus on the interaction between air pollution and socioeconomic status. This study takes part of a new research about environmental justice, and provides an overview about the distribution of environmental risks focusing mainly on $NO_2$.

Results for $NO_2$ are particularly robust especially at high concentration level of $NO_2$ with a relative risk more
important for women. Besides, $NO_2$ level tends to interact with socioeconomics factors. Unemployment has
a positive and significative impact on mortality rate.
## 2 Data

We use data on concentration of pollutants and mortality rates available at a local level for all the French territory.

Detailed data on atmospheric pollution come from the information system of air quality measure of the French Environment and Energy Management Agency (ADEME). The contamination of the atmosphere by pollutants at the local and regional level is the result of three processes: emission, transmission, and air pollution concentration. Pollutants are first released at the source. Then, pollutants emitted are dispersed, or sometimes they can be chemically transformed in the atmosphere, creating new, secondary pollutants (transmission); Ozone is an example of this process as it is formed when Hydrocarbons (HC) and Nitrogen oxides ($NO_x$) combine in the presence of sunlight. Having combined with air and become diluted, atmospheric pollutants are finally inhaled by humans, animals and plants, thus completing the cycle. The L.A.U.R.E (Law on Air and Rational Use of Energy) and the different European directives give priority to control common air pollutants. ADEME gathers information coming from the 38 associations (AASQA) which control the quality of air united within the ATMO federation. A large number of monitoring stations compose the federation<fnref n="1" />. The French nomenclature identifies seven classes of stations, consistent with the various classification defined on the European level : roadside, urban, industrial, near city background, national rural, regional rural, specific observation at a number of 84, 286, 119, 138, 10, 62, 13, and 12 respectively. Most of the monitoring stations take place where the density of population is significant, apart from rural national monitoring stations. The measure taken into consideration in our study is the annual mean of concentration for pollutant within a civil year (1st of January to the 31st of December) calculated by each AASQA for each captor and measured in micrograms per cubic metre of air. This unit of concentration is mostly used to control for outdoor air quality<fnref n="2" />.

For spatial interpolation between monitoring stations, we use Universal Kriging, a stochastic geostatistical method that takes into account spatial dependence. Following Currie & Neidell [2005] and Janke and al. [2009], we assign annual pollutant concentrations to the 95 French departments. We consider a model with spatial dependence and express this dependence by constructing a contiguity matrix. The contiguity matrix is based on the distance between two entities. Using the geographical coordinates of the headquarters of a

<footnote n="1">¹See appendix 9</footnote>

<footnote n="2">²Air pollutant concentrations do not necessarily produce accurate predictions of exposure levels. People may be resident in one area, they may work in another. Nevertheless, the geographical level used in this article reduces the bias related to population mobility. The department surface represents an average of 570 000 hectares and we know from INSEE data that the average distance between the place of residence and the place of work is nearby 20km, so the accuracy of the exposure levels seems reasonable. It is also important to emphasize that death is registered in the commune of birth although most of people do not live in their place of birth.</footnote>
local authority, we calculate the distance between the headquarters (prefecture) and all monitoring stations. This distance is given as the great-circle distance between the two points that is, the shortest distance over the earth's surface giving an 'as-the-crow-flies' distance. Let $(L_i, N_i)$ be the latitude and longitude in degrees of monitoring station $i$ and $(L_j, N_j)$ of headquarters $j$. The distance between the monitoring station $i$ and the headquarter $j$ is given by:

$$d_{ij} = \arccos(G_{ij})R \qquad (1)$$

where $R$ is the radius of the earth, measured around the equator ($R = 6378$) and

$$G_{ij} = \sin(aL_i)\sin(aL_j) + \cos(aL_i)\cos(aL_j)\cos(aN_j - aN_i) \qquad (2)$$

$$\text{with } a = \pi/180$$

From this distance we calculate a weighted mean of pollutant concentration. The weight attributed to a monitoring station corresponds to the inverse of the distance between the prefecture and the station so that every elements $C_{ij}$ of the distance matrix $C$ is given by:

$$C_{ij} = \frac{1/d_{ij}}{\sum_{i=1}^{n} 1/d_{ij}} \qquad (3)$$

Matrix $C$ is a stochastic matrix of size $N \times N$ where elements in the main diagonal are zero. It is normalized in order to have each row summing to 1. Such normalization allows considering the relative distance instead of the absolute one. Then, we calculate the average weighted mean of pollutant concentration $\bar{P}$ within the entire department:

$$\bar{P} = \sum_{i=1}^{n} C_{ij} P_{iu} \qquad (4)$$

$P_{iu}$ corresponds to the annual mean concentration measured by the monitoring station $i$ for the pollutant $u$.

The top panel of Table 1 presents descriptive statistics for pollution data. It is important to stress that air pollutant concentration used to be below the limit value fixed by European and national institution over which health can be harmed. And the average concentration from our measure is lower than the level used by studies in United States and even lower than in England where it is already considered to be quite low to harm health. However, E.R.P.U.R.S project [2003] in France shows recently that $NO_2$ and $PM_{10}$ have a negative impact on health, even at low air pollutant concentration, considering hospitalization number as the explicative variable. Pascal & al. [2009] in France obtain similar results considering also different mortality rates in nine polluted cities.
NO, NO₂ and PM₁₀ are positively correlated with correlation coefficients between 0.5 and 0.8. They are negatively correlated with O₃ which may be due to the fact that Ozone is rapidly destroyed to form NO₂ within cities. In addition, correlation between both NO₂ and NO is high (0.85), so that we choose to keep NO₂ as an explanatory variable and drop NO to prevent from autocorrelation. We do not include observations for SO₂ and CO as few monitoring stations measure those pollutants. Other pollutants are also likely to be associated with differences in mortality, but data were unavailable to perform intra urban interpolations for these pollutants.

The second panel of Table 1 presents mortality rates. A large range of pollutants are responsible for
outdoor air pollution, so that it is difficult to assign them a specific health effect that's why we use all causes
mortality rate. Data on health come from the national federation of regional health observatories (ORS).
We use directly age standardized rates to control for different population age structures across department.
The year corresponds to mid-year of the triennial period used. Data on mortality are available from 1980
to 2004 whereas data on pollution from 1985 to 2005 with very few values before 2000, so that we consider
a period of 5 years (2000-2004). The standard deviation is quite high showing us that our data are spread
out over a large range of values<fnref n="3" />. In addition, the variation within each department is significative as well
as for pollutant concentration <fnref n="4" />.

The third block describes the control variables. Data on weather come from Meteo France through the French Institute of the Environment (IFEN). Smoking rate fell by 35% between 2000 and 2004, probably due to the "Loi Évin" since 1991 and the tax increase (INSEE 2006). Road accident rate fell 29% according to the data from the national federation of regional health observatories (ORS). We also collect from the ORS, the number of people per 1 hospital bed to measure health care system and the availability of medical care resources in a particular department from 2000 to 2004. We add the share of industry to control for industrialization, as a time invariant variable for each department coming from the 2005 census of the French Institute of Statistics (INSEE).

The fourth panel shows the socioeconomic variables: education and unemployment coming from the 2007 census of (INSEE) and the French Ministry of Labour (DARES). Definitions of the variables are given in the Appendix 3.

Finally, the last row of descriptive statistics corresponds to an air pollution Index. To capture peaks of
pollution, we use the ATMO index calculated by the AASQA. The Atmo outlook varies daily according to

<footnote n="3">³The degree of dispersion (spread) and skewness in the data are presented graphically in Appendix 2.</footnote>

<footnote n="4">⁴We do not include in this paper specific cause of mortality due to the weak variability of these data in France for the 2000-2004 period which does not allow any estimation.</footnote>
air quality using a scale of 1-10 (1 = very good air quality, 10 = very bad air quality). This index takes into consideration the concentration of four subindexes characterizing Nitrogen dioxide (NO₂), Sulphur dioxide (SO₂), Particles in suspension (PS) and Ozone (O₃). It considers pollution measured only by urban and industrial monitoring stations for main agglomerations for a period from 2000 to 2003. After 2003, the construction of the index has been changed, so that we cannot consider it for 2004. We retain 41 agglomerations and we are associating each one to a department. We construct a yearly variable summing the number of days above indices 8, 9 and 10 which corresponds to a poor air quality according to the definition of Atmo index<fnref n="5" />.

This index variable is positively correlated with the previous measure of NO₂, NO, PM₁₀ and O₃. In other words, peaks of pollution are correlated positively with ambient air pollution which gives more credence to our measure. However, we will prefer further on in our estimation to use real concentration of pollution instead of indices. Few days correspond to peaks of pollution, and fixing a threshold below which pollution does not have any impact is highly arguable. Pollution is indeed fluctuating, low level can be active and the toxic level of perception is variable even among healthy population. Within a population, some people are more sensitive than others and will suffer from atmospheric pollution even at really low level - level which does not go beyond the actual threshold fixed by public authorities.

<table><caption>Table 1 : Descriptive statistics for the estimation sample ( n = 220, groups = 44)</caption><thead><tr><th>Variable</th><th>Mean</th><th>Std. Dev.</th><th>Min</th><th>Max</th><th>Between department<br>Std. Dev.</th><th>Within department<br>Std. Dev.</th><th>Mean in 2000</th><th>Mean in 2004</th></tr></thead><tbody><tr><td><strong>Pollutants</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM10 (µg/m3)</td><td>21.46066</td><td>5.507876</td><td>8.817638</td><td>57.38416</td><td>3.56479</td><td>4.226236</td><td>20.66536</td><td>22.9148</td></tr><tr><td>NO2(µg/m3)</td><td>31.71435</td><td>11.37481</td><td>12</td><td>74.04559</td><td>10.53944</td><td>4.509464</td><td>32.39159</td><td>29.79553</td></tr><tr><td>NO(µg/m3)</td><td>23.11989</td><td>18.70101</td><td>3.62616</td><td>145.7256</td><td>15.84246</td><td>10.16506</td><td>22.66962</td><td>27.73054</td></tr><tr><td>O3 (µg/m3)</td><td>53.02869</td><td>15.39018</td><td>30.60137</td><td>99.65313</td><td>6.579284</td><td>13.94135</td><td>43.26784</td><td>78.57994</td></tr><tr><td><strong>All causes mortality rates</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>All causes mortality rate (per 100 000)</td><td>819.7591</td><td>64.61492</td><td>620</td><td>1000</td><td>63.91677</td><td>12.82</td><td>835.75</td><td>806.9091</td></tr><tr><td>Male mortality rate (per 100 000)</td><td>1092.595</td><td>96.55129</td><td>792</td><td>1393</td><td>93.63266</td><td>26.74333</td><td>1127.659</td><td>1058.75</td></tr><tr><td>Female mortality rate (per 100 000)</td><td>626.5364</td><td>47.62308</td><td>499</td><td>756</td><td>46.77091</td><td>10.97244</td><td>631.0227</td><td>626.2727</td></tr><tr><td><strong>Control variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>industry (%)</td><td>16.17478</td><td>4.77803</td><td>5.930366</td><td>24.47606</td><td>4.822272</td><td>0</td><td>16.17478</td><td>16.17478</td></tr><tr><td>Smoking rate (per 1000)</td><td>1218.513</td><td>274.1815</td><td>483.5</td><td>2298.7</td><td>192.2561</td><td>197.2009</td><td>1372.991</td><td>883.8295</td></tr><tr><td>Accident (per 100 000)</td><td>12.05455</td><td>4.106652</td><td>3</td><td>22</td><td>3.765045</td><td>1.716958</td><td>13,77273</td><td>9.75</td></tr><tr><td>PPHB</td><td>136.1738</td><td>37.84015</td><td>71.69604</td><td>268.8203</td><td>38.15236</td><td>1.691371</td><td>134.3395</td><td>137.9933</td></tr><tr><td>Precipitation (mm)</td><td>2.211417</td><td>.5709846</td><td>.854918</td><td>3.865206</td><td>.4134218</td><td>.3969113</td><td>2.434172</td><td>1.984284</td></tr><tr><td>Sun (C°)</td><td>5.422066</td><td>.9889944</td><td>3.736066</td><td>8.115891</td><td>.8865025</td><td>.446114</td><td>5.194088</td><td>5.397749</td></tr><tr><td><strong>Socioeconomics variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Unemployment (%)</td><td>8.436023</td><td>2.033194</td><td>4.575</td><td>14.625</td><td>1.976955</td><td>.5448954</td><td>8.753409</td><td>8.867045</td></tr><tr><td>Education (%)</td><td>15.67797</td><td>5.044896</td><td>10.24137</td><td>37.48089</td><td>5.087249</td><td>.2087244</td><td>15.89803</td><td>15.4632</td></tr><tr><td><strong>Others</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Atmo index 8 to 10</td><td>3.524096</td><td>5.531948</td><td>0</td><td>28</td><td>3.639727</td><td>4.184507</td><td>.</td><td>.</td></tr></tbody></table>

<footnote n="5">⁵See appendix 6</footnote>
# 3 Model and Econometrics Issues

## 3.1 Specification

The focus of this study is the relationship between average pollution, socioeconomic status, and mortality. Our unit of analysis is the department, which is the main administrative unit below the national level. There are 95 departments in France with an average population of 620 000 people, ranging from over 70 000 to over two million. Departments are grouped over 22 metropolitan known as region.

In the analysis, we start by estimating a standard model with total mortality rate as the explicative variable without consideration of environmental quality. After doing preliminary regression for various functional form and following the result from overall normality test based on skewness and on kurtosis for each of them, we estimate an equation of the following form to ensure that errors are normally distributed<fnref n="6" /> $\varepsilon \sim N(0, \sigma^2)$:

$$X_{it}^{k} = \alpha_{k} + Socioeconomic_{it}\beta_{k} + PPHB_{it}\gamma_{k} + Industry_{i}\zeta_{k} + Lifestyle_{it}\eta_{k} + \varepsilon_{it}^{k} \quad (5)$$

where *i* indexes the local authority, *t* indexes the year, *k* the kind of mortality rate. $X_{it}^k$ is a vector of all causes mortality rates (overall mortality rate, male and female mortality rate). Socioeconomic variables such as unemployment rate and education are included as the main explanatory variables. An individual's living situation and quality of life have a very high degree of correlation with whether or not he or she is employed (Lin [2009]). There is also, most likely, a direct positive effect of education on health (Groot & Maassen van den Brink [2007]). While the exact mechanism underlying this link is unclear, the differential use of health knowledge and technology is almost certainly important parts of the explanation. Due to multicollinearity issue <fnref n="7" />, we were not able to include both the average revenue and education. The number of people per 1 hospital bed $PPHB_{it}$ in each department, was included as a proxy to measure the health care system and the availability of medical care resources in a particular department. We also include the % of industry added value over the total added value $Industry_{i}$ for each department as a time invariant variable. The vector $Lifestyle_{it}$ accounts for lifestyle which refers to the regular activities and habits a person has that could have an effect on its health. We include accident and smoking rate variables. $\varepsilon_{it}$ is the error term. To address the possibility that omitted variables account for some of the heterogeneity among French de-

<footnote n="6">⁶There is no evidence that the log transform is the best fit for mortality time trends (Bishai & Opuni [2009]).</footnote>

<footnote n="7">⁷The squared correlation between education and the average revenue is above 0.8.</footnote>
partments, an error component model is estimated:

$$ \varepsilon_{it} = c_i + \delta_t + u_{it} \quad (6) $$

$c_i$ and $\delta_t$ are residual differences where $c_i$ is a department effect which accounts for differences across departments that are time-invariant (e.g lifestyle differences that we cannot take into account), $\delta_t$ is a year effect which controls for factors that vary uniformly across departments over time, and $u_{it}$ is the remaining error term. We weight all of our observations by the square root of mid year population estimates. We consider that this weight takes also into account the land surface, knowing the high correlation between both land area and average population. Ramsey test confirms the robustness of our specification.

The next step of our empirical work is to choose the right estimation model. It is likely that population's health affects unemployment through productivity, education and other factors. This potential simultaneity can be a source of endogeneity, making standard estimators inconsistent. We need to test this hypothesis so that we consider the lag of the endogenous variable, unemployment, as an instrument. The F-test on the excluded instruments in the first stage regression confirms the validity of the instrument<fnref n="8" />. Then, Hausman test rejects the endogeneity of the model<fnref n="9" />. However, OLS estimator is not consistent due to unobserved factors $f_{it}$ which determine both $X_{it}^k$ and $P_{it}$ with:

$$ E(P_{it}; f_{it}) \neq 0 \text{ and } E(\varepsilon_{it}; f_{it}) \neq 0 \text{ such that } E(\varepsilon_{it}; P_{it}) \neq 0. $$

Thus, a next decision in performing a multilevel analysis is whether the explanatory variables considered in the analysis have fixed or random effect. Independent variables have explained most of differences about department and year, but there is probably some unmodeled heterogeneity. Hausman test considers the null hypothesis that the coefficients estimated by the efficient random effects estimator are the same as the ones estimated by the consistent fixed effects estimator. By running this test, and apart when we consider the sample below the median of pollution for NO₂, fixed effect model appears to be the most efficient one. In fact, we think about each department as having its own systematic baseline. We also calculate robust variance estimator in order to prevent for heteroskedasticity that we found by running Breush-Pagan test: this test checks if squared errors are explicited by explanatory variables. Our estimation will also take into account autocorrelation because Wooldridge test shows that disturbances exhibit autocorrelation, with the values in a given period depending on values of the same series in previous periods.

<footnote n="8">⁸To avoid the weak instruments pathology, we look at the F-test on the excluded instruments in the first stage regression and check whether the test statistic is larger than 10. F(1,192) = 28.91</footnote>

<footnote n="9">⁹P=0.810</footnote>
In the second model, mortality rate is expressed as a function of environmental variables added to the previous variables. We will estimate the model :

$$X_{it}^{k} = \lambda_{k} + P_{it}\theta_{k} + Socioeconomic_{it}\psi_{k} + PPHB_{it}\varsigma_{k} + Industry_{i}\mu_{k} + Lifestyle_{it}\sigma_{k} + Z_{it}\phi_{k} + \varepsilon_{it}^{k} \quad (7)$$

$P_{it}$ is a vector of air pollutants concentration for $O_3$, $NO_2$ and $PM_{10}$. We also include a vector of weather variables at department level $Z_{it}$ as a control for average pollution levels. We consider the annual mean of precipitation to capture the effect of very wet years and the annual mean of the daily maximum temperature as time varying control. In this model, the main coefficient of interest is $\theta$ representing the mean parameter estimates for all 95 departments for explanatory variables $P_{it}$. It also represents the effect of air quality on the health outcome. We will again use the fixed effect estimator to control for heterogeneity between departments. The fixed effects are contained in the error term, $\varepsilon_{it}$, in equation (7), which consists of the unobserved department-specific effects, $c_i$, the year effect $\delta_t$, and the observation-specific errors, $u_{it}$:

$$\varepsilon_{it} = c_{i} + \delta_{t} + u_{it} \qquad (8)$$

Finally, we will add an interactive term $P_{it}$ between socioeconomic and environmental quality to provide a better description of the relationship between the mortality rate and the independent variables such that:

$$X_{it}^{k} = \lambda_{k} + P_{it}\theta_{k} + Socioeconomic_{it}\psi_{k} + P_{it}Socioeconomic_{it}\varpi_{k} + PPHB_{it}\varsigma_{k} + Industry_{i}\mu_{k} + Lifestyle_{it}\sigma_{k} + Z_{it}\phi_{k} + \varepsilon_{it}^{k} \quad (9)$$

$$= \lambda_{k} + (\theta_{k} + Socioeconomic_{it}\varpi_{k})P_{it} + Socioeconomic_{it}\psi_{k} + PPHB_{it}\varsigma_{k} + Industry_{i}\mu_{k} + Lifestyle_{it}\sigma_{k} + Z_{it}\phi_{k} + \varepsilon_{it}^{k}$$

where $(\theta_k + Socioeconomic_{it}\varpi_k)$ represents the effect of environmental quality on mortality rate at specific level of socioeconomic variable and $\varpi_k$ indicates how much the slope of $P_{it}$ changes as socioeconomic goes up or down one unit.
## 3.2 Results

### 3.2.1 Impact of environment quality on health

We start by examining a standard model without consideration of environmental quality. We will then add NO₂, O₃ and PM₁₀ in the specification controlling for precipitation and temperature to see if considering pollutant variables improves the global fit of the model. To capture the department effect, both fixed effects and random effects are estimated. Approximately, seventy percent of the variation in the response variable may be attributed to explanatory variables. High R² in all fixed effect models confirm the global good fit of the models when considering characteristics of departments. Moreover, the values of Baltagi-Wu LBI and Durbin-Watson are above or close to 2, showing that the autocorrelation is not an issue.

<table><thead><tr><th></th><th>OLS</th><th>Fixed effect</th><th>Random effect</th></tr></thead><tbody><tr><td>Ed</td><td>-10.354***<br>(11.20)</td><td>-0.466<br>(0.03)</td><td>-8.079***<br>(6.55)</td></tr><tr><td>Accident</td><td>-4.665***<br>(3.79)</td><td>-1.975**<br>(2.49)</td><td>-1.045<br>(1.38)</td></tr><tr><td>Sm</td><td>0.058***<br>(4.28)</td><td>0.046***<br>(5.67)</td><td>0.059***<br>(7.77)</td></tr><tr><td>Industry</td><td>4.388***<br>(6.14)</td><td></td><td>4.813***<br>(3.82)</td></tr><tr><td>PPHB</td><td>-0.231**<br>(2.40)</td><td>-2.392<br>(1.43)</td><td>-0.351**<br>(2.44)</td></tr><tr><td>Unemployment</td><td>7.274***<br>(3.76)</td><td>5.912***<br>(3.77)</td><td>10.617***<br>(7.02)</td></tr><tr><td>Constant</td><td>867.247***<br>(28.28)</td><td>1,070.844**<br>(2.38)</td><td>768.756***<br>(18.41)</td></tr><tr><td>Observations</td><td>220</td><td>220</td><td>220</td></tr><tr><td>R-squared</td><td>0.67</td><td>0.98</td><td></td></tr><tr><td>DW</td><td>1.0691343</td><td></td><td></td></tr><tr><td>Baltagi-Wu LBI</td><td>1.6427049</td><td></td><td></td></tr></tbody></table>

Note: observations weighted by the square root of mid-year population estimates. * significant at 10%; ** significant at 5%; ***significant at 1%. Absolute value of z statistics in parentheses. Robust t statistics in parentheses.

<caption>Table 2: Estimates of the association between all causes mortality rate and the standard determinants of mortality rate without consideration of environmental quality</caption>

We first estimate the standard model with OLS trying to test a most complete model and we observe in the first column from table 2 that all the coefficients of the determinants of mortality are significative. However, we also use the within estimator as we assume that the unobserved factors $f_{it}$ between departments determine both mortality rate and explanatory variables. All the coefficients remain significative when considering fixed effect estimator apart for PPHB and education. This loss of significativity may be due to the correlation between department specific effect and both explanatory variables. Fixed effect imposes time independent effects for each entity that are possibly correlated with the regressors that's why Industryᵢ, time invariant
variable, is not taken into account.

We then study the relationship between NO₂, O₃, PM₁₀ and mortality rates in both a single pollutant model (table 3) and in a multi-pollutant one (table 4). The multi-pollutant model allows coefficients to be examined at the same time, so as to not overestimate the impact of one pollutant. In addition, we divide our panel in two: departments above and those below the median of air pollutant concentration to compare results when facing high atmospheric pollution.

All the determinants of mortality remain significative when adding the environmental variable in an OLS model. Nevertheless, apart for Ozone, the environmental variables are not significative probably due to multiple correlation existing between explanatory variables. As we explained previously, OLS estimator is not a consistent estimator, so that we need to consider a fixed effect model.

As it is shown in the first two models, coefficients are not significantly different from zero when we consider fixed and random effect for PM₁₀ in all specifications. We may think that the impact of PM₁₀ on morbidity would be more likely. This result is opposed to the French study by the Sanitary Health Institute (Pascal & al. [2009]) which founds a positive effect of PM₁₀ on mortality in a panel of nine different French cities. However, this article does not precise the type of estimator used. Furthermore, Pascal & al. [2009] do not take into account the influence of lifestyle or socioeconomic factors on health as their model strictly includes weather data whereas the robustness of our model is not verified if we take education out. Finally, the average concentration from our measure is probably lower than the level used by the Sanitary Health Institute which considers 9 urban cities.
<table><thead><tr><th></th><th colspan="3">NO2</th><th colspan="3">O</th><th colspan="3">PM10</th></tr><tr><th></th><th>OLS</th><th>Fixed effect</th><th>Random effect</th><th>OLS</th><th>Fixed effect</th><th>Random effect</th><th>OLS</th><th>Fixed effect</th><th>Random effect</th></tr></thead><tbody><tr><td>NO2</td><td>-0.100<br>(0.28)</td><td>0.333**<br>(2.17)</td><td>0.229<br>(1.38)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>O</td><td></td><td></td><td></td><td>-0.653***<br>(2.81)</td><td>-0.166<br>(1.23)</td><td>-0.349***<br>(2.69)</td><td></td><td></td><td></td></tr><tr><td>PM10</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.032<br>(0.05)</td><td>0.154<br>(0.93)</td><td>0.104<br>(0.54)</td></tr><tr><td>Pr</td><td>-14.005***<br>(3.11)</td><td>-0.405<br>(0.15)</td><td>-2.805<br>(1.11)</td><td>-16.033***<br>(3.49)</td><td>-0.586<br>(0.21)</td><td>-2.731<br>(1.11)</td><td>-14.229***<br>(3.14)</td><td>-0.085<br>(0.03)</td><td>-2.714<br>(1.06)</td></tr><tr><td>Sun</td><td>-17.268***<br>(5.46)</td><td>2.631<br>(1.23)</td><td>0.652<br>(0.35)</td><td>-16.021***<br>(5.52)</td><td>1.983<br>(0.86)</td><td>-0.548<br>(0.29)</td><td>-17.436***<br>(5.77)</td><td>3.226<br>(1.50)</td><td>1.039<br>(0.56)</td></tr><tr><td>Ed</td><td>-12.895***<br>(14.55)</td><td>30.908*<br>(1.75)</td><td>-8.878***<br>(7.83)</td><td>-12.806***<br>(17.00)</td><td>36.896**<br>(2.09)</td><td>-8.451***<br>(7.61)</td><td>-12.967***<br>(15.84)</td><td>34.369*<br>(1.93)</td><td>-8.637***<br>(7.73)</td></tr><tr><td>Accident</td><td>-6.808***<br>(6.16)</td><td>0.398<br>(0.44)</td><td>-0.772<br>(0.97)</td><td>-6.572***<br>(6.66)</td><td>0.270<br>(0.29)</td><td>-0.875<br>(1.12)</td><td>-6.694***<br>(6.34)</td><td>0.354<br>(0.39)</td><td>-0.881<br>(1.10)</td></tr><tr><td>Sm</td><td>0.083***<br>(6.66)</td><td>0.038***<br>(4.34)</td><td>0.056***<br>(6.81)</td><td>0.058***<br>(3.95)</td><td>0.030**<br>(2.42)</td><td>0.035***<br>(2.99)</td><td>0.082***<br>(6.87)</td><td>0.040***<br>(4.34)</td><td>0.059***<br>(7.09)</td></tr><tr><td>Industry</td><td>2.058***<br>(2.64)</td><td></td><td>4.450***<br>(3.73)</td><td>1.655**<br>(2.39)</td><td></td><td>4.181***<br>(3.48)</td><td>1.969***<br>(2.77)</td><td></td><td>4.659***<br>(3.94)</td></tr><tr><td>PPHB</td><td>0.191**<br>(2.42)</td><td>3.419<br>(1.45)</td><td>-0.169<br>(1.00)</td><td>0.154**<br>(2.00)</td><td>4.279*<br>(1.83)</td><td>-0.187<br>(1.10)</td><td>0.194**<br>(2.50)</td><td>3.803<br>(1.60)</td><td>-0.176<br>(1.04)</td></tr><tr><td>Unemployment</td><td>10.787***<br>(6.04)</td><td>6.331***<br>(3.54)</td><td>10.655***<br>(6.86)</td><td>11.596***<br>(6.76)</td><td>6.462***<br>(3.42)</td><td>11.194***<br>(7.32)</td><td>10.659***<br>(5.84)</td><td>6.301***<br>(3.34)</td><td>10.752***<br>(6.89)</td></tr><tr><td>Constant</td><td>983.934***<br>(32.37)</td><td></td><td>757.774***<br>(275.267)</td><td>1,043.919***<br>(27.56)</td><td></td><td>813.404***<br>(453.933)</td><td>986.288***<br>(31.79)</td><td></td><td>751.962***<br>(381.116)</td></tr><tr><td>Observations</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td></tr><tr><td>R-squared</td><td>0.77</td><td>0.99</td><td></td><td>0.78</td><td>0.98</td><td></td><td>0.77</td><td>0.98</td><td></td></tr><tr><td>DW</td><td>.9813505</td><td></td><td></td><td>.93997743</td><td></td><td></td><td>.97930161</td><td></td><td></td></tr><tr><td>Baltagi-Wu LBI</td><td>1.5939381</td><td></td><td></td><td>1.5603009</td><td></td><td></td><td>1.5895372</td><td></td><td></td></tr></tbody></table>

Note: observations weighted by the square root of mid-year population estimates. * significant at 10%; ** significant at 5%; ***significant at 1%. Absolute value of z statistics in parentheses. Robust t statistics in parentheses.

<caption>Table 3: Estimates of the association between all causes mortality rate and air pollutant concentrations in single pollutant models</caption>

Ozone is negatively correlated with mortality rate and it is only significative with fixed effect estimator when considering the sample above the median of air pollutant concentration in a multi-pollutant model. The relationship between Ozone and temperature remains a complex phenomenon and may be the cause of the negative coefficient also pointed out in England by Janke & al. [2009]. Temperature, humidity, winds, and the presence of other chemicals in the atmosphere influence Ozone formation, and the presence of Ozone, in turn, affects those atmospheric constituents. French data show this positive correlation between temperature and Ozone<fnref n="10" />. Using two time-series Poisson regression models, Ren & al. [2008] indicate that Ozone positively modified the temperature associations across different regions in the USA. In addition, both

<footnote n="10">¹⁰See appendix 7</footnote>
ambient Ozone and temperature are associated with human health. From an epidemiologic point of view, Sanitary Health Institute [2009] in France insists about the complexity of studying the interaction between Ozone and sanitary variables.

In contrast, NO₂ appears to have a significative and positive effect in both single and multi-pollutant models whether we consider the fixed effect estimator. In addition, the impact is greater with higher significativity when we consider the sample above the median of pollution in the multi-pollutant model. We observe from the OLS estimator that the effect of NO₂ on mortality rate tends to lead to erroneous conclusion if the fixed-effects problems are neglected. As a result, we give more credence to fixed and random effect estimators for the rest of the study and we will focus on the unique pollutant, NO₂, as it has a relevant significativity in both models.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Overall panel</th><th colspan="3">Above the median of pollution</th></tr><tr><th>OLS</th><th>Fixed effect</th><th>Random effect</th><th>OLS</th><th>Fixed effect</th><th>Random effect</th></tr></thead><tbody><tr><td>NO2</td><td>-0.377<br>(1.01)</td><td>0.361*<br>(1.73)</td><td>0.234<br>(1.08)</td><td>-0.096<br>(0.11)</td><td>0.860***<br>(2.82)</td><td>0.865**<br>(2.06)</td></tr><tr><td>O</td><td>-0.711***<br>(3.02)</td><td>-0.158<br>(1.15)</td><td>-0.349***<br>(2.65)</td><td>-1.047***<br>(2.66)</td><td>-0.362*<br>(1.82)</td><td>-0.569**<br>(2.52)</td></tr><tr><td>PM10</td><td>0.491<br>(0.78)</td><td>-0.052<br>(0.27)</td><td>-0.006<br>(0.02)</td><td>0.030<br>(0.04)</td><td>-0.148<br>(0.38)</td><td>-0.469<br>(1.00)</td></tr><tr><td>Pr</td><td>-15.212***<br>(3.30)</td><td>-0.779<br>(0.28)</td><td>-2.783<br>(1.13)</td><td>-25.794***<br>(2.99)</td><td>-6.608*<br>(1.68)</td><td>-6.702*<br>(1.71)</td></tr><tr><td>Sun</td><td>-15.641***<br>(4.98)</td><td>1.402<br>(0.60)</td><td>-0.993<br>(0.51)</td><td>-20.229***<br>(4.22)</td><td>-8.333**<br>(2.18)</td><td>-9.719***<br>(2.87)</td></tr><tr><td>Ed</td><td>-12.595***<br>(15.09)</td><td>32.763*<br>(1.85)</td><td>-8.752***<br>(7.70)</td><td>-12.728***<br>(9.45)</td><td>3.102<br>(0.10)</td><td>-10.223***<br>(6.24)</td></tr><tr><td>Accident</td><td>-6.869***<br>(6.57)</td><td>0.329<br>(0.36)</td><td>-0.782<br>(0.99)</td><td>-3.150<br>(1.55)</td><td>-1.475<br>(0.65)</td><td>-2.237<br>(1.42)</td></tr><tr><td>Sm</td><td>0.061***<br>(4.07)</td><td>0.029**<br>(2.39)</td><td>0.033***<br>(2.82)</td><td>0.021<br>(0.95)</td><td>0.025<br>(1.55)</td><td>0.028<br>(1.38)</td></tr><tr><td>Industry</td><td>1.876**<br>(2.47)</td><td></td><td>3.934***<br>(3.23)</td><td>-0.832<br>(0.53)</td><td></td><td>1.351<br>(0.71)</td></tr><tr><td>PPHB</td><td>0.148*<br>(1.87)</td><td>3.808<br>(1.62)</td><td>-0.176<br>(1.03)</td><td>0.060<br>(0.38)</td><td>-0.191<br>(0.04)</td><td>-0.107<br>(0.40)</td></tr><tr><td>Unemployment</td><td>11.948***<br>(6.94)</td><td>6.457***<br>(3.59)</td><td>11.019***<br>(7.18)</td><td>9.365***<br>(3.04)</td><td>4.784<br>(1.62)</td><td>8.926***<br>(3.52)</td></tr><tr><td>Constant</td><td>1,035.776***<br>(27.06)</td><td>-330.199<br>(0.55)</td><td>818.452***<br>(17.34)</td><td>1,199.314***<br>(11.51)</td><td>750.746<br>(0.59)</td><td>970.668***<br>(10.47)</td></tr><tr><td>Observations</td><td>205</td><td>205</td><td>205</td><td>74</td><td>74</td><td>74</td></tr><tr><td>R-squared</td><td>0.78</td><td>0.99</td><td></td><td>0.88</td><td>0.99</td><td></td></tr><tr><td>DW</td><td>.96615973</td><td></td><td></td><td>1.2117275</td><td></td><td></td></tr><tr><td>Baltagi-Wu LBI</td><td>1.5807708</td><td></td><td></td><td>1.9954329</td><td></td><td></td></tr></tbody></table>

Note: observations weighted by the square root of mid-year population estimates. * significant at 10%; ** significant at 5%; ***significant at 1%. Absolute value of z statistics in parentheses. Robust t statistics in parentheses.

<caption>Table 4 : Estimates of the association between all causes mortality rate and air pollutant concentrations in multi-pollutant models</caption>

Nitrogen oxides (NOx) is the main indicator of transportation vehicles and stationary combustion sources, such as electric utility and industrial boilers contamination<fnref n="11" />. NOx forms when fuels are burned at high

<footnote n="11">¹¹The spatial distribution of NO₂ is generally not homogeneous within individual metropolitan areas. The primary reason for the observed heterogeneity in concentrations across an urban area is the substantially higher concentrations of NO₂ near sources, such as roadways. [Electric power research institute 2009].</footnote>
temperatures and includes various Nitrogen compounds like Nitrogen dioxide (NO₂) and Nitric oxide (NO).
These compounds play an important role in the atmospheric reactions that create harmful particulate matter, ground-level Ozone (smog), acid rain, and eutrophication of coastal waters. NO₂ is produced by chemical transformation with NO and Ozone (NO + O₃ = NO₂ + O₂). Not only particle filters but also the rise of Ozone in the atmosphere, increase NO₂ emissions (AFSSET [2009]). As a consequence, NOₓ is a powerful oxidizing gas, linked with a number of adverse effects on the respiratory system (EPA [2010]).

Then, we divide our panel in the departments above and those below the median of NO₂ as shown in table 5. The within group and the long difference estimates are quite similar although we prefer fixed effect model for the reason we described previously. The first block presents the panel below the median of pollution for NO₂. The association is no significant, whereas when we consider departments above the median of pollution for NO₂, the coefficient remains significantly positive with fixed and random effect. We confirm results found with previous models. The fixed effect estimate suggests that per 1 µg/m³ increase in NO₂, there is almost one more death a year. Concentration of NO₂ varies from 12 to 74 µg/m³ suggesting a difference of nearby 50 deaths a year according to the department. This result confirms in France the existence at a high level of pollution of a short-term relationship between current air pollution levels and mortality.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Below the median of pollution for NO2</th><th colspan="3">Above the median of pollution for NO2</th></tr><tr><th>OLS</th><th>Fixed effect</th><th>Random effect</th><th>OLS</th><th>Fixed effect</th><th>Random effect</th></tr></thead><tbody><tr><td>NO2</td><td>-0.125<br>(0.17)</td><td>-0.532<br>(1.70)</td><td>0.038<br>(0.07)</td><td>0.374<br>(0.60)</td><td>0.771***<br>(3.89)</td><td>0.625**<br>(2.03)</td></tr><tr><td>Pr</td><td>-7.231<br>(1.05)</td><td>5.282<br>(1.47)</td><td>0.641<br>(0.14)</td><td>-22.886***<br>(3.11)</td><td>-5.002<br>(1.42)</td><td>-9.151**<br>(2.30)</td></tr><tr><td>Sun</td><td>-14.130**<br>(2.48)</td><td>7.722***<br>(3.12)</td><td>2.228<br>(0.73)</td><td>-20.084***<br>(4.89)</td><td>-3.366<br>(1.09)</td><td>-7.933**<br>(2.52)</td></tr><tr><td>Ed</td><td>-16.136***<br>(8.37)</td><td>54.712<br>(1.62)</td><td>-12.453***<br>(4.36)</td><td>-12.254***<br>(10.18)</td><td>-4.456<br>(0.17)</td><td>-9.281***<br>(6.28)</td></tr><tr><td>Accident</td><td>-9.241***<br>(7.07)</td><td>1.185<br>(1.02)</td><td>-2.375**<br>(2.17)</td><td>-4.192**<br>(2.30)</td><td>0.570<br>(0.34)</td><td>-1.286<br>(0.91)</td></tr><tr><td>Sm</td><td>0.127***<br>(6.88)</td><td>0.043***<br>(3.81)</td><td>0.063***<br>(4.60)</td><td>0.064***<br>(3.56)</td><td>0.039***<br>(3.47)</td><td>0.062***<br>(4.62)</td></tr><tr><td>Industry</td><td>3.886***<br>(3.02)</td><td></td><td>4.873***<br>(3.38)</td><td>1.394<br>(1.04)</td><td></td><td>3.914**<br>(2.33)</td></tr><tr><td>PPHB</td><td>-0.057<br>(0.48)</td><td>8.059**<br>(2.27)</td><td>-0.231<br>(1.05)</td><td>0.316**<br>(2.43)</td><td>-1.628<br>(0.38)</td><td>-0.013<br>(0.05)</td></tr><tr><td>Unemployment</td><td>11.665***<br>(5.76)</td><td>15.973***<br>(5.86)</td><td>13.208***<br>(5.40)</td><td>10.158***<br>(3.60)</td><td>4.908**<br>(2.32)</td><td>9.620***<br>(4.19)</td></tr><tr><td>Constant</td><td>976.640***<br>(19.01)</td><td>-1,226.252<br>(1.33)</td><td>790.633***<br>(13.40)</td><td>985.378***<br>(16.07)</td><td>1,001.842<br>(0.95)</td><td>803.099***<br>(11.47)</td></tr><tr><td>Observations</td><td>106</td><td>106</td><td>106</td><td>99</td><td>99</td><td>99</td></tr><tr><td>R-squared</td><td>0.64</td><td>0.99</td><td></td><td>0.83</td><td>0.99</td><td></td></tr><tr><td>DW</td><td>1.0747863</td><td></td><td></td><td>1.350698</td><td></td><td></td></tr><tr><td>Baltagi-Wu LBI</td><td>1.6960147</td><td></td><td></td><td>2.0349193</td><td></td><td></td></tr></tbody></table>

Note: observations weighted by the square root of mid-year population estimates. * significant at 10%; ** significant at 5%;

**significant at 1%. Absolute value of z statistics in parentheses. Robust t statistics in parentheses.

<caption>Table 5: Estimates of the association between all causes mortality rate and NO₂ for different level of air pollutant concentration</caption>
**Gender Analysis** Hence, we consider male and female mortality rates as explicative variables related to NO₂. We observe a significative effect for female as we do not find any for male. Road accident, smoking rate and unemployment have a significative and positive effect on male mortality rate. Lifestyle seems to be more prevalent than air pollution concentration on male mortality rate. The female fixed effect estimate suggests that per 2 µg/m³ increase in NO₂, another death for women is registered suggesting a difference of 30 deaths a year for women between departments.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Ms</th><th colspan="3">Mr</th></tr><tr><th>OLS</th><th>Fixed effect</th><th>Random effect</th><th>OLS</th><th>Fixed effect</th><th>Random effect</th></tr></thead><tbody><tr><td>NO2</td><td>0.150<br>(0.41)</td><td>0.513***<br>(2.95)</td><td>0.369**<br>(2.44)</td><td>-0.550<br>(1.35)</td><td>0.152<br>(0.65)</td><td>0.096<br>(0.37)</td></tr><tr><td>Pr</td><td>-14.290***<br>(3.53)</td><td>-1.158<br>(0.44)</td><td>-2.435<br>(1.05)</td><td>-12.294*<br>(1.91)</td><td>0.738<br>(0.18)</td><td>-5.118<br>(1.28)</td></tr><tr><td>Sun</td><td>-7.557***<br>(2.74)</td><td>7.407***<br>(3.47)</td><td>5.868***<br>(3.43)</td><td>-34.166***<br>(7.33)</td><td>-6.845**<br>(2.08)</td><td>-11.245***<br>(3.86)</td></tr><tr><td>Ed</td><td>-8.568***<br>(11.50)</td><td>33.133**<br>(2.23)</td><td>-5.471***<br>(5.24)</td><td>-19.415***<br>(14.70)</td><td>28.547<br>(1.01)</td><td>-13.819***<br>(8.76)</td></tr><tr><td>Accident</td><td>-6.162***<br>(6.49)</td><td>-1.106<br>(1.33)</td><td>-1.740**<br>(2.39)</td><td>-9.782***<br>(6.13)</td><td>2.902*<br>(1.81)</td><td>-0.438<br>(0.36)</td></tr><tr><td>Sm</td><td>0.051***<br>(4.33)</td><td>0.008<br>(1.08)</td><td>0.022***<br>(2.98)</td><td>0.141***<br>(8.53)</td><td>0.090***<br>(6.11)</td><td>0.120***<br>(9.45)</td></tr><tr><td>Industry</td><td>2.265***<br>(3.09)</td><td></td><td>3.962***<br>(3.59)</td><td>2.620***<br>(2.62)</td><td></td><td>5.768***<br>(3.55)</td></tr><tr><td>PPHB</td><td>0.203***<br>(2.96)</td><td>3.976*<br>(1.89)</td><td>-0.036<br>(0.23)</td><td>0.115<br>(0.96)</td><td>3.156<br>(0.85)</td><td>-0.377*<br>(1.65)</td></tr><tr><td>Unemployment</td><td>7.281***<br>(4.94)</td><td>2.596<br>(1.63)</td><td>7.338***<br>(5.19)</td><td>16.599***<br>(6.01)</td><td>11.657***<br>(4.14)</td><td>14.604***<br>(6.33)</td></tr><tr><td>Constant</td><td>717.745***<br>(25.92)</td><td>-521.682<br>(1.00)</td><td>548.218***<br>(14.65)</td><td>1,381.733***<br>(34.14)</td><td>-11.379<br>(0.01)</td><td>1,075.123***<br>(18.65)</td></tr><tr><td>Observations</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td></tr><tr><td>R-squared</td><td>0.65</td><td>0.98</td><td></td><td>0.81</td><td>0.98</td><td></td></tr><tr><td>DW</td><td>1.1149423</td><td></td><td></td><td>.96666977</td><td></td><td></td></tr><tr><td>Baltagi-Wu LBI</td><td>1.6190883</td><td></td><td></td><td>1.6268575</td><td></td><td></td></tr></tbody></table>

Note: observations weighted by the square root of mid-year population estimates for male and female respectively. * significant at 10%; ** significant at 5%; ***significant at 1%. Absolute value of z statistics in parentheses. Robust t statistics in parentheses.

<caption>Table 6 : Estimates of the association between NO2 and all causes mortality rate according to gender</caption>

### 3.2.2 Interaction between socioeconomic status and environment quality

Hence, we might suspect that exposure to air pollution related to health varies with economic status in France. People with low incomes may be disproportionately exposed to environmental contamination that threatens their health.

Thus, to be more precise, we want to analyze whether the effect of the socioeconomic variables has been moderated or modified by the introduction of the environmental variable. To do so, we include an interaction variable to look at the unemployment and NO₂ interact. Critics assert that increased level of collinearity in
models including a multiplicative term distorts the beta coefficients. To reduce multicollinearity, we subtract
the mean from each observation of unemployment and NO2 so that the new mean is equal to zero (Cronbach
[1987]).

The effects of unemployment rate on health status with consideration of NO₂ are summarized in table 7. Coefficients for unemployment, NO₂ and the interactive variable are significative with both explicative variables "all causes mortality rate" and female mortality rate when using the within estimator. The significativity of the interaction coefficient suggests that the effect of unemployment has been modified by the environmental variable. In other words, the effect of NO₂ (θₖ + *Socioeconomic*ᵢₜ *ω*ₖ) at some value of unemployment *Socioeconomic*ᵢₜ has a significant effect on the mortality rate. If unemployment is positive, the effect of NO₂ on mortality rate is also positive.

We want to check the robustness of our previous result about gender, so that we consider separately female
and male mortality rate as it is shown in the two last blocks. Unemployment and NO2. are again positively
and significatively correlated with female mortality rate with the fixed effect estimator. Unemployment has a
positive and significative impact on the three different types of mortality rates. In contrast, the coefficient of
NO2 does not appear significative when considering male mortality rate. We also notice an higher coefficient
of unemployment for men than for women which suggests a relative impact of having a job on health, more
important for men. This result leads us to think about the significance of taking into account the individual
degree of exposure including demographics, type of activities or personal health situation.
<table><thead><tr><th rowspan="2"></th><th colspan="3">All causes mortality rates</th><th colspan="3">Ms</th><th colspan="3">Mr</th></tr><tr><th>OLS</th><th>Fixed effect</th><th>Random effect</th><th>OLS</th><th>Fixed effect</th><th>Random effect</th><th>OLS</th><th>Fixed effect</th><th>Random effect</th></tr></thead><tbody><tr><td>Unemployment CT</td><td>10.693***<br>(6.17)</td><td>7.209***<br>(4.04)</td><td>10.742***<br>(6.91)</td><td>7.300***<br>(5.10)</td><td>3.510**<br>(2.13)</td><td>7.433***<br>(5.25)</td><td>16.658***<br>(6.39)</td><td>12.526***<br>(4.54)</td><td>14.712***<br>(6.36)</td></tr><tr><td>NO2 CT</td><td>-0.105<br>(0.30)</td><td>0.469**<br>(2.59)</td><td>0.335*<br>(1.80)</td><td>0.151<br>(0.41)</td><td>0.654***<br>(3.43)</td><td>0.482***<br>(2.85)</td><td>-0.547<br>(1.35)</td><td>0.288<br>(1.11)</td><td>0.212<br>(0.72)</td></tr><tr><td>NO2 CT *Unemployment CT</td><td>0.030<br>(0.30)</td><td>-0.115*<br>(1.78)</td><td>-0.084<br>(1.23)</td><td>-0.006<br>(0.06)</td><td>-0.119**<br>(2.13)</td><td>-0.088<br>(1.42)</td><td>-0.019<br>(0.13)</td><td>-0.114<br>(1.01)</td><td>-0.096<br>(0.90)</td></tr><tr><td>Pr</td><td>-13.984***<br>(3.09)</td><td>-0.891<br>(0.33)</td><td>-3.190<br>(1.25)</td><td>-14.294***<br>(3.51)</td><td>-1.663<br>(0.63)</td><td>-2.832<br>(1.21)</td><td>-12.307*<br>(1.91)</td><td>0.257<br>(0.06)</td><td>-5.507<br>(1.36)</td></tr><tr><td>Sun</td><td>-17.262***<br>(5.46)</td><td>2.381<br>(1.09)</td><td>0.314<br>(0.17)</td><td>-7.558***<br>(2.74)</td><td>7.148***<br>(3.29)</td><td>5.557***<br>(3.22)</td><td>-34.170***<br>(7.33)</td><td>-7.094**<br>(2.14)</td><td>-11.590***<br>(3.94)</td></tr><tr><td>Ed</td><td>-12.883***<br>(14.51)</td><td>27.495<br>(1.60)</td><td>-9.066***<br>(7.96)</td><td>-8.570***<br>(11.53)</td><td>29.557**<br>(2.11)</td><td>-5.659***<br>(5.39)</td><td>-19.423***<br>(14.70)</td><td>25.195<br>(0.88)</td><td>-14.010***<br>(8.81)</td></tr><tr><td>Accident</td><td>-6.812***<br>(6.16)</td><td>0.440<br>(0.49)</td><td>-0.778<br>(0.97)</td><td>-6.161***<br>(6.48)</td><td>-1.063<br>(1.28)</td><td>-1.739**<br>(2.40)</td><td>-9.780***<br>(6.10)</td><td>2.944*<br>(1.83)</td><td>-0.423<br>(0.35)</td></tr><tr><td>Sm</td><td>0.083***<br>(6.59)</td><td>0.038***<br>(4.36)</td><td>0.056***<br>(6.82)</td><td>0.051***<br>(4.30)</td><td>0.009<br>(1.16)</td><td>0.022***<br>(2.97)</td><td>0.141***<br>(8.48)</td><td>0.090***<br>(6.09)</td><td>0.120***<br>(9.43)</td></tr><tr><td>Industry</td><td>2.064***<br>(2.64)</td><td>-</td><td>4.331***<br>(3.63)</td><td>2.264***<br>(3.08)</td><td>-</td><td>3.841***<br>(3.48)</td><td>2.616***<br>(2.61)</td><td>-</td><td>5.644***<br>(3.47)</td></tr><tr><td>PPHB</td><td>0.193**<br>(2.42)</td><td>3.070<br>(1.33)</td><td>-0.170<br>(1.01)</td><td>0.203***<br>(2.92)</td><td>3.610*<br>(1.82)</td><td>-0.038<br>(0.25)</td><td>0.114<br>(0.94)</td><td>2.815<br>(0.75)</td><td>-0.380*<br>(1.66)</td></tr><tr><td>Constant</td><td>1,071.287***<br>(31.07)</td><td>-<br>107.263<br>(0.18)</td><td>863.210***<br>(22.06)</td><td>784.020***<br>(24.83)</td><td>-<br>374.311<br>(0.76)</td><td>630.267***<br>(17.48)</td><td>1,504.627***<br>(32.26)</td><td>193.743<br>(0.20)</td><td>1,209.984***<br>(21.83)</td></tr><tr><td>Observations</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td><td>205</td></tr><tr><td>R-squared</td><td>0.77</td><td>0.99</td><td></td><td>0.65</td><td>0.98</td><td></td><td>0.81</td><td>0.98</td><td></td></tr><tr><td>DW</td><td>1.0204769</td><td></td><td></td><td>1.145104</td><td></td><td></td><td>.99439627</td><td></td><td></td></tr><tr><td>Baltagi-Wu LBI</td><td>1.6209387</td><td></td><td></td><td>1.6414211</td><td></td><td></td><td>1.6460637</td><td></td><td></td></tr></tbody></table>

<caption>Table 7: An interaction model</caption>

## 4 Conclusion

The objective of this paper has been first to investigate whether both department's environmental quality and socioeconomic status relative to its neighbors have an impact on its mortality rate. The second purpose has been to analyze the link between inequalities and air quality between departments. We test these hypothesis by using a multivariate model and taking fixed effects into account.

Results are strongly supportive of the hypothesis that NO₂ has a positive impact on mortality with the effect being larger when considering the subsample with the highest level of pollution. In addition we show that even relatively low concentrations of air pollutants are related to a range of adverse health effects. We also find that higher inequality and unemployment rate in a department are associated with more uncontrolled air pollution in a given department.

<footnote>Note: observations weighted by the square root of mid-year population estimates. * significant at 10%; ** significant at 5%; ***significant at 1%. Absolute value of z statistics in parentheses. Robust t statistics in parentheses.</footnote>
Finally, we point out that women health is more impacted than men health by NO₂.

This finding is consistent with the results of international studies that have examined the relationship
between economic inequality, environmental quality and health. It also confirms the importance of ambient
air pollution and reinforces the need for politics to take into account environmental justice in France.

Our paper suggests that further research on environmental inequality in France focusing on smaller geographical level and individual characteristics is essential. It would be consistent to examine the impact of atmospheric pollution focusing on a individual-level data. It would also be interesting to dispose of morbidity data, especially professional diseases to shed light on productivity loss implications.

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# Appendices

<figure><img src="image_6.png" /><figcaption>## Appendix 2 : The yearly distribution of all causes mortality rates for the 2000 - 2004 period, in all departments</figcaption></figure>

<figure><img src="image_7.png" /></figure>
Appendix 3 : Definition of variables

<table><thead><tr><td>Variable</td><td>Definition</td><td>Sources</td></tr></thead><tbody><tr><td>M</td><td>Total mortality rate, age standardized rates 2000-2004 calculated using data on registered deaths from INSERM, CEPIDc and INSEE. The year corresponds to mid-year of the trienal period used. Unit: per 100 000 people</td><td>National federation of regional health observatories (ORS)</td></tr><tr><td>Mr</td><td>Total male mortality rate, age standardized rates 2000-2004 calculated using data on registered deaths from INSERM, CEPIDc and INSEE. The year corresponds to mid-year of the trienal period used. Unit: per 100 000 people</td><td>National federation of regional health observatories (ORS)</td></tr><tr><td>Ms</td><td>Total female mortality rate, age standardized rates 2000-2004 calculated using data on registered deaths from INSERM, CEPIDc and INSEE. The year corresponds to mid-year of the trienal period used. Unit: per 100 000 people</td><td>National federation of regional health observatories (ORS)</td></tr><tr><td>NO<sub>2</sub>, NO, PM<sub>10</sub>, O<sub>3</sub></td><td>Annual mean of NO<sub>2</sub>, NO, PM<sub>10</sub>, O<sub>3</sub> concentration (µg/m<sup>3</sup>) respectively 2000-2004</td><td>French Environment and Energy Management Agency (ADEME)</td></tr><tr><td>Pr</td><td>High precipitation totals 2000-2004</td><td>Météo France</td></tr><tr><td>Sun</td><td>Annual mean of the daily maximum temperature 2000-2004</td><td>Météo France</td></tr><tr><td>Sm</td><td>Number of cigarettes sold for 1000 residents 2000-2004</td><td>French Monitoring Centre for Drugs and Drug Addictions (OFTD)</td></tr><tr><td>Accident</td><td>Road accident rate, age standardized rates 2000-2004 calculated using data on registered road accident. Unit: per 100 000 people</td><td>National federation of regional health observatories (ORS)</td></tr><tr><td>PPHB</td><td>Number of people per 1 hospital bed 2000-2004</td><td>National federation of regional health observatories (ORS)</td></tr><tr><td>Industry</td><td>Share of industry in the total value added of a department (in %).</td><td>French National Institute for Statistics (INSEE), census 2005</td></tr><tr><td>Ed</td><td>Population from 15 years (without students) with minimum BAC+2 divided by population within department in 2006</td><td>French National Institute for Statistics (INSEE), census 2007</td></tr><tr><td>Un</td><td>The unemployment rate is the percentage of unemployed people in the labour force (occupied labour force + the unemployed) 2000-2004.</td><td>French Ministry of Labour (DARES)</td></tr></tbody></table>
Appendix 4 : Atmo subindex determination grid

<table><thead><tr><th rowspan="2">Index scale</th><th rowspan="2">Subindexes</th><th>PS scale</th><th>NO2 scale</th><th>SO2 scale</th><th>O3 scale</th></tr><tr><th colspan="4">Average of mean daily concentrations for the various sites</th></tr></thead><tbody><tr><td>Very good</td><td>1</td><td>0 to 9 µg/m3</td><td>0 to 29 µg/m3</td><td>0 to 39 µg/m3</td><td>0 to 29 µg/m3</td></tr><tr><td>Very good</td><td>2</td><td>oct-19</td><td>30 - 54</td><td>40 - 79</td><td>30 - 54</td></tr><tr><td>Good</td><td>3</td><td>20 - 29</td><td>55 - 84</td><td>80 - 119</td><td>55 - 79</td></tr><tr><td>Good</td><td>4</td><td>30 - 39</td><td>85 - 109</td><td>120 - 159</td><td>80 - 104</td></tr><tr><td>Moderate</td><td>5</td><td>40 - 49</td><td>110 - 134</td><td>160 - 199</td><td>105 - 129</td></tr><tr><td>Poor</td><td>6</td><td>50 - 64</td><td>135 - 164</td><td>200 - 249</td><td>130 - 149</td></tr><tr><td>Poor</td><td>7</td><td>65 - 79</td><td>165 - 199</td><td>250 - 299</td><td>150 - 179</td></tr><tr><td>Bad</td><td>8</td><td>80 - 99</td><td>200 - 274</td><td>300 - 399</td><td>180 - 209</td></tr><tr><td>Bad</td><td>9</td><td>100 - 124</td><td>275 - 399</td><td>400 - 599</td><td>250 - 359</td></tr><tr><td>Very bad</td><td>10</td><td>125 and more</td><td>400 and more</td><td>500 and more</td><td>240 and more</td></tr></tbody></table>

Appendix 5 : Simple correlation coefficients

<table><thead><tr><th></th><th>NO2</th><th>PM10</th><th>O</th><th>NO</th><th>Atmo index (8-10)</th><th>Temperature</th></tr></thead><tbody><tr><td>NO2</td><td>1.0000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM10</td><td>0.7691</td><td>1.0000</td><td></td><td></td><td></td><td></td></tr><tr><td>O</td><td>-0.1777</td><td>0.1226</td><td>1.0000</td><td></td><td></td><td></td></tr><tr><td>NO</td><td>0.9338</td><td>0.6687</td><td>-0.2476</td><td>1.0000</td><td></td><td></td></tr><tr><td>Atmo index (8-10)</td><td>0.3210</td><td>0.5058</td><td>0.3229</td><td>0.1853</td><td>1.0000</td><td></td></tr><tr><td>Temperature</td><td>0.1711</td><td>0.3603</td><td>0.6602</td><td>0.1268</td><td>0.2420</td><td>1.0000</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Appendix 6 : Quantile plots of annual pollutant concentrations for every department</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Appendix 7 : Map of French monitoring stations for NO₂</figcaption></figure>