Cahier De Recherche

Abel Brodeur
email: abrodeur@uottawa.ca.

Sarah Flèche
email: sarah.fleche@univ-amu.fr.

Clément Bosquet

Andrew Clark

Jan-Emmanuel De Neve

Xavier Fontaine

Pierre- Yves Geoffard

Erzo Luttmer

Thomas Piketty

Steve Pischke

Nattavudh Powdthavee

Claudia Senik

Conal Smith

Malgorzata Switek

Ekaterina Zhuravskaya

Judy Bass

Brandi L Bennett

Michelle L Cook

Joseph Grandpre

Mark Gildemaster

Kip Neale

Samara Viner-Brown

Keywords: neighbors' income, public goods, amenities, relative deprivation, well-being. JEL Classification: C25, J01, R23

published or not. The documents may come

Neighbors' Income, Public Goods, and Well-Being

The rationale for this is that the notion of neighborhood can refer to various geographical scales. Individuals are engaged in social relationships within their immediate neighborhood (e.g. block/street peers), the local community (e.g. schools, hospitals, parks and restaurants' consumers in their ZIP code of residence) and a broader metropolitan area (e.g. potential co-workers in their county or MSA of residence). The challenge for empirical research on neighborhood effects is hence to take into account these multiple scales simultaneously and understand at which scale and how neighbors' income effects operate [START_REF] Galster | Quantifying the Effect of Neighbourhood on Individuals: Challenges, Alternative Approaches, and Promising Directions[END_REF]).

Neighbors' income may affect well-being through many channels (relative deprivation, social capital, amenities and fellow-feeling) and the strength of those channels may depend on the scale of neighborhood, e.g. as the definition of the reference group is broaden. Arguably, we tend to care more about individuals close to us and less about those further away. Feelings of relative deprivation or fellow-feeling may than be stronger at more disaggregated levels such as the local community than at the metropolitan area level. Similarly, the effects of public goods and amenities might be stronger within our local community level since we are more likely to share them with close neighbors. Having a single framework with multiple reference neighbors' income can allow us to (i) investigate whether the relative size of the income spillovers varies with the different levels of aggregation and (ii) reconcile seemingly divergent results from the literature.

Our analysis suggests some interesting results. We first test whether neighbors' income and self-reported well-being are positively or negatively related at the county, MSA and state levels. [START_REF] Luttmer | Neighbors as Negatives: Relative Earnings and Well-Being[END_REF] provides evidence that subjective well-being is positively associated with own income and negatively correlated with average/median income at the Public Use Microdata Areas (PUMAs). PUMAs have on average 150,000 inhabitants and represent travel-to-work areas. [START_REF] Luttmer | Neighbors as Negatives: Relative Earnings and Well-Being[END_REF] argues that this negative relationship between income and well-being is consistent with individuals having utility functions that depend on relative consumption, i.e. relative income hypothesis. Using the BRFSS, we find the finding that self-reported well-being is positively related to own income and negatively associated with median income at the county, MSA and state levels.

We then use more disaggregated data to test whether the relationship between median income and subjective well-being varies as we use a smaller level of aggregation (ZIP code). We rely on proprietary data from the BRFSS identifying the ZIP code of residence of respondents. In our sample, ZIP codes have on average 24,000 inhabitants. Residents of a similar ZIP code may thus share similar public goods and amenities. By contrast, conditional on own and county median incomes, we find a positive relationship between median income and well-being at the ZIP code level. This result suggests that the negative income effects identified at the PUMA and county levels might be counterbalanced at the ZIP code level by positive spillovers. We also distinguish between individuals below and above ZIP code median income. We provide evidence that the positive effect of neighbors' income on well-being at the ZIP code level is larger for poorer individuals [START_REF] Clark | Who Compares to Whom? The Anatomy of Income Comparisons in Europe[END_REF][START_REF] Ferrer-I-Carbonell | Income and Well-Being: An Empirical Analysis of the Comparison Income Effect[END_REF]).

We test explicitly whether local public goods and amenities drive the association between ZIP code median income and subjective well-being. We include in our specifications local area variables such as the economic environment, poverty rate, neighbors' socioeconomic characteristics and the number of school and health establishments. Adding these variables in our model makes the relationship between ZIP code median income and respondents' satisfaction statistically insignificant. The estimate is a well-estimated 0 when the full set of controls is included. We then test whether only a subset of public goods explain the positive effect of neighbors' income. We show that neighbors' income affects well-being through environmental and geographical public goods such as local unemploy-ment, criminality and poverty rates. On the other hand, institutional variables such as the number of school and health establishments, the number of child care services and local government expenditures do not seem to explain the positive income spillovers.

Overall, our findings suggest that the direction of the relationship between others' income and well-being depends on the geographical reference group that is included: e.g. local community at the ZIP code level, broader metropolitan area at the county and MSA levels, and state of residence. At the most disaggregated level (ZIP code) we find that social and economic features of the local environment drive the association. The ZIP code income effect is as large as one-quarter the effect of own income on self-reported wellbeing. At the different levels of aggregation, however, we cannot conclude on whether relative deprivation and income comparisons vary with the literal distance from people.

The next section reviews the literature on neighbors' income and well-being. Section 2. provides a conceptual framework. In Section 3., we discuss the empirical strategy. Section 4. details the BRFSS and presents the findings. Section 5. concludes.

Previous Literature

An important and debated theme in the literature is the link between income and subjective well-being. 1 We are more interested in the idea that life satisfaction may depend on others' income. Various researchers argue that continuous income growth does not lead to ever happier individuals. [START_REF] Easterlin | Does Economic Growth Improve the Human Lot? Some Empirical Evidence[END_REF][START_REF] Easterlin | Will Raising the Incomes of all Increase the Happiness of all?[END_REF][START_REF] Easterlin | Income and Happiness: Towards a Unified Theory[END_REF] claims that while within most societies, richer people are happier than poorer people, over time the population does not become happier on average when the country's income rises. To reconcile these two facts, 1 Investigating the impact of income on self-reported well-being is difficult because of reverse causality. Income affects a person's well-being but well-being also affects the ability to succeed. There is some evidence that the positive correlation between money and well-being goes from the former to the latter [START_REF] Frijters | Money Does Matter! Evidence from Increasing Real Income and Life Satisfaction in East Germany Following Reunification[END_REF]; [START_REF] Gardner | Money and Mental Wellbeing: A Longitudinal Study of Medium-Sized Lottery Wins[END_REF]; De [START_REF] De Neve | Estimating the Influence of Life Satisfaction and Positive Affect on Later Income Using Sibling Fixed-Effects[END_REF]; [START_REF] Pischke | Money and Happiness: Evidence from the Industry Wage Structure[END_REF]; [START_REF] Pischke | A Cautionary Note on Using Industry Affiliation to Predict Income[END_REF]). See among others Clark et al. (2008), Di Tella and[START_REF] Di Tella | Some Uses of Happiness Data in Economics[END_REF] and [START_REF] Frey | What Can Economists Learn from Happiness Research?[END_REF] for an overview of the relevant papers.

Easterlin proposes, among other explanations, the relative income hypothesis. People compare themselves with other people: it is relative income rather than absolute income that matters. If own income rises, but the income of people with whom we compare increases as well, then the ratio may stay unchanged. These results have been challenged by [START_REF] Stevenson | Economic Growth and Subjective Well-Being: Reassessing the Easterlin Paradox[END_REF] and [START_REF] Sacks | The New Stylized Facts About Income and Subjective Well-Being[END_REF] who find that economic growth is associated with rising well-being. A recent paper by [START_REF] Proto | A Reassessment of the Relationship Between GDP and Life Satisfaction[END_REF] finds a positive relationship between growth and satisfaction for countries with a GDP below 15,000 USD but shows that this relationship is approximately flat in richer countries suggesting a gap between aspiration and realized income.

The economic analysis of relative income effects can be traced back to at least Veblen (1899) and [START_REF] Duesenberry | Income, Saving and the Theory of Consumer Behavior[END_REF]. There is by now a substantial empirical literature regarding this relationship [START_REF] Card | Inequality at Work: The Effect of Peer Salaries on Job Satisfaction[END_REF]; [START_REF] Clark | Satisfaction and Comparison Income[END_REF]; [START_REF] Easterlin | Will Raising the Incomes of all Increase the Happiness of all?[END_REF][START_REF] Ferrer-I-Carbonell | Income and Well-Being: An Empirical Analysis of the Comparison Income Effect[END_REF]; [START_REF] Frank | The Demand for Unobservable and Other Nonpositional Goods[END_REF]; [START_REF] Kapteyn | The Relative Utility: Evidence from Panel Data[END_REF]; [START_REF] Van Praag | The Welfare Function of Income in Belgium: An Empirical Investigation[END_REF]). In support of the importance of relative income, [START_REF] Knight | Subjective Well-Being and its Determinants in Rural China[END_REF] note that two-thirds of respondents in a survey of Chinese households report that their main comparison group consists of residents in their own village. On the other hand, [START_REF] Clark | Who Compares to Whom? The Anatomy of Income Comparisons in Europe[END_REF] find that the large majority of Europeans compare with their work colleagues and also their friends. This is confirmed by [START_REF] Mayraz | Life Satisfaction and Relative Income: Perceptions and Evidence, Working Paper: CEP Discussion Paper[END_REF], who report that comparisons are mainly generic or work-related.

There are few papers that address how neighbors' income affects well-being. Blanchflower and [START_REF] Blanchflower | Well-being over time in Britain and the USA[END_REF], [START_REF] Helliwell | New Measures of the Costs of Unemployment: Evidence from the Subjective Well-Being of 3.3 Million Americans[END_REF] and [START_REF] Luttmer | Neighbors as Negatives: Relative Earnings and Well-Being[END_REF] report that subjective well-being is positively associated with own income and negatively associated with average/median income in the region of residence. They argue that the negative effect of neighbors' earnings on well-being is due to relative consumption. [START_REF] Luttmer | Neighbors as Negatives: Relative Earnings and Well-Being[END_REF]Luttmer ( ) used the 1987Luttmer ( -1988Luttmer ( and 1992Luttmer ( -1994 waves of the National Survey of Families and Households and matched this dataset to the PUMAs. [START_REF] Blanchflower | Well-being over time in Britain and the USA[END_REF] provided a similar analysis using the General Social Survey and state income per capita while [START_REF] Helliwell | New Measures of the Costs of Unemployment: Evidence from the Subjective Well-Being of 3.3 Million Americans[END_REF] relied on county level data. In each of these papers, the lack of large and rich datasets and finely disaggregated data makes the identification of the presence of income comparisons difficult, and subject to potential omitted variable bias.

Other studies rely on more disaggregated data from Europe. [START_REF] Clark | Economic Satisfaction and Income Rank in Small Neighbourhoods[END_REF], [START_REF] Dittmann | Your House, Your Car, Your Education: The Socioeconomic Situation of the Neighborhood and its Impact on Life Satisfaction in Germany[END_REF], [START_REF] Knies | Keeping Up With the Schmidt's: An Empirical Test of Relative Deprivation Theory in the Neighbourhood Context[END_REF] and [START_REF] Knies | Income Comparisons Among Neighbours and Satisfaction in East and West Germany[END_REF] use, respectively, Danish and German data. [START_REF] Knies | Keeping Up With the Schmidt's: An Empirical Test of Relative Deprivation Theory in the Neighbourhood Context[END_REF] rely on German ZIP codes (roughly 9,000 inhabitants on average) and find no statistically significant associations between neighbors' income and life satisfaction. [START_REF] Clark | Economic Satisfaction and Income Rank in Small Neighbourhoods[END_REF] rely on a geographical grid of 10,000 square meters and provide evidence that respondents are more satisfied when their neighbors are richer which is consistent with a public goods interpretation. [START_REF] Dittmann | Your House, Your Car, Your Education: The Socioeconomic Situation of the Neighborhood and its Impact on Life Satisfaction in Germany[END_REF] use microgeographic data on the households' immediate neighborhoods. The building level covers eight households on average and the street section level 25 households on average. They find that respondents living in a neighborhood with a higher socioeconomic status report higher levels of life satisfaction.2 Last, [START_REF] Knies | Income Comparisons Among Neighbours and Satisfaction in East and West Germany[END_REF] relies on very disaggregated data at the street level in Germany. The author provides evidence that neighborhood income effect for West Germany is negative. In contrast, the effect is not statistically significant for respondents living in East Germany. While it is possible that Americans and Europeans are affected differently by neighbors' income, our intuition is that the disparities in those results are driven by the size of the geographical reference group, the definition of the neighborhood income and omitted variable bias, i.e. local public goods.

The main contribution of our paper is to look at the relationship between neighbors' income and satisfaction in the US for different geographical levels: state, MSA, county and ZIP code. Using different geographical areas simultaneously will give us the possibility to test whether this association is driven by the size of the locality that is included, i.e. whether income spillovers vary with geographical distance. In addition, we specifically test some of the mechanisms behind this relationship. The correlation between well-being and neighbors' income may be driven by omitted variables or selection. Moreover, there could be interpretations other than income comparisons such as public goods or social proximity (see Section 2.). In this paper, we try to overcome some of these complications by controlling for potentially omitted factors such as differences in local prices and public goods. An alternative approach would be to identify fairly random shocks to incomes in particular neighborhoods. Unfortunately, this methodology requires information on migration status and on whether the respondent is affected or not by the shocks [START_REF] Galster | The Mechanism (s) of Neighbourhood Effects: Theory, Evidence, and Policy Implications[END_REF]; [START_REF] Ludwig | Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults[END_REF]).

Two other studies similar to ours are [START_REF] Kingdon | Community, Comparisons and Subjective Well-Being in a Divided Society[END_REF] and [START_REF] Barrington-Leigh | Empathy and Emulation: Life Satisfaction and the Urban Geography of Comparison Groups[END_REF]. 3 Kingdon and Knight (2007) use 366 randomly selected clusters covering 2,900 people on average and districts in South Africa. Their findings indicate a positive relationship for clusters and a negative association between neighbors' income and household satisfaction at the district level. Their results suggest evidence of empathy for close neighbors and comparisons for those further away. Note that their paper analyzes a particularly segregated society in a developing country. Their results could thus differ from ours. In an unpublished paper, [START_REF] Barrington-Leigh | Empathy and Emulation: Life Satisfaction and the Urban Geography of Comparison Groups[END_REF] rely on different Canadian surveys and show the relationship between measures of well-being and income spillovers for different geographic scales. Their findings suggest that the overall spillover effect is negative.

These studies, however, do not test directly whether the positive relationship at the most disaggregated level is due to public goods. We will revisit their results by showing a positive and statistically significant partial correlation between well-being and median income at the ZIP code level and a negative association at the county level. We will argue that the former result cannot be explained only by income comparisons. Arguably, the strength of income comparisons should be decreasing with distance. Nearby neighbors are more likely to compare their earnings with each other than with faraway neighbors.

Conceptual Framework

The effect of neighbors' income on well-being is, a priori, ambiguous since many channels are at work [START_REF] Galster | Quantifying the Effect of Neighbourhood on Individuals: Challenges, Alternative Approaches, and Promising Directions[END_REF][START_REF] Galster | The Mechanism (s) of Neighbourhood Effects: Theory, Evidence, and Policy Implications[END_REF]). In this section, we present different mechanisms which could explain the relationship between neighbors' spillovers and self-reported well-being.

We then derive specific hypotheses about the strength of the mechanisms captured in the different aggregations, i.e. state, MSA, county or ZIP code.

Mechanisms

Galster (2012) reviews the literature and identifies 15 potential pathways of neighborhood effects. He regroups these pathways in four broad rubrics: social-interactive, environmental, geographical and institutional mechanisms. We focus on the "economic" pathways in what follows and regroup the last three rubrics for space consideration.

(i) Social-Interactive Mechanism: This rubric includes feelings of relative deprivation (see [START_REF] Veblen | The Theory of the Leisure Class[END_REF], [START_REF] Duesenberry | Income, Saving and the Theory of Consumer Behavior[END_REF] and [START_REF] Stigler | The Development of Utility Theory, I and II[END_REF]). Well-being depends partly on individuals' absolute income and, arguably, partly on individuals' relative income, i.e. the gap between own income and some reference benchmark. One reason for feelings of relative deprivation could be a sense of unfairness, envy or jealousy towards others in the reference group. Moreover, [START_REF] Galster | Quantifying the Effect of Neighbourhood on Individuals: Challenges, Alternative Approaches, and Promising Directions[END_REF] points out that neighbors may also compete for local resources such as public park.

Another pathway proposed in the literature is the "Tunnel Effect" [START_REF] Hirschman | The Changing Tolerance for Income Inequality in the Course of Economic Development; with a Mathematical Appendix[END_REF]). The income of others contains potential information about own social status, but also about future prospects. 4 The relative increase of others' income is seen as promising evidence about the individual's own chance of success. Note that both the tunnel effect and relative deprivation could also be affected by (social) distance to others.

There would thus be an interaction effect between the different channels.

Proximity in a social network or psychological distance due to class distinctions may also matter. The social distance channel may work through altruism towards individuals close to us or through prestige from how well people socially close do.5 Social networks may also affect individuals through interpersonal communication of resources and information or social norms [START_REF] Galster | The Mechanism (s) of Neighbourhood Effects: Theory, Evidence, and Policy Implications[END_REF]).

Hence, the social-interactive mechanism suggests that neighbors' income may have negative effects on individuals' well-being through relative deprivation, competition and "bad" future prospects. On the contrary, the social-interactive mechanism may affect positively well-being through altruism, prestige, communication and "good" future prospects. We come back in the next subsection on whether the strength of these pathways is related to the size of the neighborhoods.

(ii) Environmental, Geographical and Institutional Mechanisms: Environmental and geographical mechanisms include exposure to violence, public infrastructure, job opportunities and toxic exposure. We test this channel explicitly by including in our model variables such as the local unemployment, criminality and poverty rates. On the other hand, the institutional pathway includes the quality of private or public institutions such as day care, restaurants, hospitals and schools. We test this pathway by including in our model the number of school and health establishments, the number of child care services, and the number of physicians' and dentists' offices.

It is possible that we do not necessarily want to have rich neighbors per se, but we like to have neighbors that have specific demographic characteristics, e.g. young and parents. Note that our empirical framework will control for a broad range of demographic characteristics of the area that we study (see Section 3.). We thus test separately the demographic composition of the neighborhood and the local public goods mechanisms, i.e.

environmental, geographical and institutional mechanisms.

Overall, the net effect of neighbors' income may be negative or positive depending on the relative size of these mechanisms [START_REF] Knies | Trying the Neighbourhood for Size, Neighbourhood Effects in Germany[END_REF]). Note that there are other plausible interpretations which do not involve neighborhood effects. For example, there could be a negative correlation between neighbors' income and well-being because of differences in prices. In this case, the negative effect of neighbors' income would simply reflect that higher housing prices reduce well-being. One may also worry that neighbors' income simply proxies for respondents' income. The positive correlation between neighbors' income and well-being would reflect the positive association between own income and happiness. We will address these complications in the next sections.

Aggregations

One critique of the economic literature on neighbors' income and well-being is the use of large neighborhood units [START_REF] Knies | Trying the Neighbourhood for Size, Neighbourhood Effects in Germany[END_REF]). This is often the result of relying on datasets with statistically defined neighborhood units. For instance, [START_REF] Luttmer | Neighbors as Negatives: Relative Earnings and Well-Being[END_REF] relies on PUMAs which have on average 150,000 inhabitants. In this study, we rely on the following statis-tical units: states, MSAs, counties and ZIP codes. We believe that studying the impact of neighbors' income on well-being requires disaggregated and multi-scaled data to dig out whether the net effect of neighbors' income varies with (i) the scale of neighborhood included and (ii) the relative size of the mechanisms discussed above.

Online Appendix A.1 provide descriptive statistics on the statistical units. ZIP codes cover about 61 square miles on average against 2,298 square miles for counties. ZIP codes have on average 24,000 inhabitants, while counties have approximately 394,000 inhabitants in our sample. 6 In Online Appendix Table A.2, we provide evidence that the median ZIP code household income is highly correlated with many public goods included in the environmental, geographical and institutional mechanisms. In our sample, the median ZIP code household income is negatively correlated with unemployment and poverty rates and positively associated with the number of schools and dentist offices. The percentage of married couples and residents with a high school diploma is also much higher in richer ZIP codes. Moreover, there is a positive correlation between the median county household income and unemployment rate, although the magnitude is much smaller. These correlations suggest that the median ZIP code income is more likely to capture the quality of public goods and amenities than the median county income.

Controlling for the ZIP code median income, we argue that the median county and MSA household incomes are proxies for the travel-to-work area characteristics. MSA represent a core area having a high degree of economic integration. MSA have at least one urbanized area of 50,000 or more inhabitants. About 81% of the respondents in our dataset work in their county residence and solely 4% work outside of their state of residence. Similarly, only 16% of the respondents in our dataset work in their state of residence but outside their county of residence (see Online Appendix A.1). Residents of a similar county or MSA may be potential co-workers. A county or MSA are geographical areas with a relatively high population density and close economic ties throughout.

We derive two hypotheses concerning the strength of the mechanisms captured in the four aggregations:

(i) Arguably, we tend to care more about individuals close to us and less about those in a faraway city (i.e. literal distance from people). Residents of our ZIP code have similar social characteristics or at least closer characteristics than those of individuals in our county or state. We thus assume that feelings of relative deprivation, altruism, prestige and future prospects (i.e. social-interactive mechanisms) are stronger at the ZIP code level than at the county, MSA and state levels. In other words, we hypothesize that relative deprivation or income comparisons should decrease with distance since individuals are more likely to observe consumption of close neighbors.

(ii) Similarly, we assume that the effect of public goods, i.e. environmental, geographical and institutional mechanisms, should also be stronger at the ZIP code level since we are more likely to share hospitals, child care establishments and public schools with closer neighbors than with faraway neighbors. Moreover, crime and poverty rates are likely to affect individuals' well-being at the ZIP code level since we tend to care more about the degree of public safety and deprivation in our immediate neighborhood than in faraway cities.

Empirical Strategy

In order to test these different mechanisms, we conduct a multi-scale approach at the state, MSA, county and ZIP code levels. Traditionally, the effect of income spillovers on well-being is tested through the coefficient of y, the mean or median reference group income.

In our contextual framework, y is the place of residence median income. We follow the literature by using median household income since it is less sensitive to outliers than the mean [START_REF] Clark | Relative Income, Happiness, and Utility: An Explanation for the Easterlin Paradox and Other Puzzles[END_REF]). Note that using the mean instead yields similar findings.

The following relation is assumed:

U i = U (y i , y, X) (1)
where U is the economic concept of utility which depends on y, the household income, and y, the place of residence median income. X is a set of individual covariates.

The discussion so far has focused on a general notion of well-being, which is different than the notion of utility. In economics, utility is a device for representing revealed preferences. This paper does not intend to give a survey of research on the link between utility and well-being (see [START_REF] Frey | What Can Economists Learn from Happiness Research?[END_REF]). We propose to use self-reported well-being data as proxies for utility. Note that many issues remain unresolved regarding self-reported measures of well-being. For instance, [START_REF] Bertrand | Do People Mean What they Say? Implications for Subjective Survey Data[END_REF] discuss how cognitive factors may affect the way people answer survey questions. (For a more detailed discussion see [START_REF] Benjamin | What Do You Think Would Make You Happier? What Do You Think You Would Choose?[END_REF], [START_REF] Benjamin | Beyond Happiness and Satisfaction: Toward Well-Being Indices Based on Stated Preference[END_REF] and [START_REF] Deaton | Two Happiness Puzzles[END_REF].)

Our econometric model is as follows:

SW B isczt = α + δln(y it ) + θln(y zt ) + λln(y ct ) + µln(y st ) + γX it + ε isczt (2)
where SWB is the outcome variable (life satisfaction) for respondent i in year t living in ZIP code z, county c and state s and δ is the coefficient associated with household income. 7 θ, λ and µ are the coefficients of interest in this framework. The well-being function is believed to be concave in household income, which explains our choice to introduce income in logarithmic form [START_REF] Stevenson | Subjective Well-Being and Income: Is There Any Evidence of Satiation?[END_REF]). 8 We rely throughout on ordered probit response models where the dependent variable life satisfaction is discrete and defined on a finite ordinal scale. We cluster the standard errors at the county level. 9

We are aware that the association between well-being and neighbors' income may be spurious due to omitted socioeconomic and local area variables. We thus include in the basic econometric model region*year fixed effects in order to absorb most of the crossregion variations and month*year dummies to control for seasonal influences. We provide robustness checks where we include county-and state-year fixed effects and replace the household income variable by the household income per equivalent adult. 10

We include individual covariates in our basic model and a broad set of local area characteristics at the ZIP code, county and state levels. The individual covariates include gender, age, employment, marital status, education, number of children and race of respondent. Note that the area characteristics at the ZIP code, county and state are the percentage of non-Hispanic black, the percentage of Hispanic, the percentage of elderly and the percentage of children (less than 19 years old). 11 The basic model also includes the percentage of rural population and the natural log of the total population since it is possible that social interactions are more prevalent in smaller cities. Last, we include the natural log of the area.

county and MSA median income is very high (0.794).

8 Note that using a dummy for each income category in the data instead of the log of own income yields similar findings (available upon request). There are eight income categories in the BRFSS.

9 We cluster at the county level and not at the county-year level because the error for a given county in 2010 is likely to be correlated with the error for the same county in 2009.

10 This measure proposed by the OECD takes into account the number of individuals in the household. We limit the number of other adults and kids to three. Household income per equivalent adult is equal to the real family income divided by (1 + 0.5 (other adults) + 0.3 kids).

11 [START_REF] Poterba | Demographic Structure and the Political Economy of Public Education[END_REF] finds that a larger fraction of elderly in a jurisdiction leads to lower public spending on education.

We examine whether the inclusion of housing prices and additional variables at the ZIP code and county levels affects our estimates. At the ZIP code level, we further include the unemployment rate, the poverty rate, the percentage of high school graduates (among individuals older than 25), the percentage of married people and the percentage of women 15 to 50 years old who had a birth in the past 12 months. We also include the number of health establishments, the number of child day care services, the number of offices of physicians, the number of offices of dentists and the number of schools. At the county level, we include other controls such as the number of murders and non-negligent manslaughters known to police per capita, local government direct expenditures on health, education, welfare and total expenditures.

BRFSS: Data and Analysis

Data

In our econometric analysis, we rely on the Behavioral Risk Factor Surveillance System (BRFSS) which was established in 1984 by the Centers for Disease Control and Prevention but did not include a question on life satisfaction before 2005. 12 The time period covered by this dataset is thus 2005-2010. The BRFSS contains repeated cross sections, has a total sample of around 1,750,000 and contains information on state, county, household income and life satisfaction. It covers more than two-thirds of the counties in the US: county codes are suppressed for counties with fewer than 10,000 residents for confidentiality reasons and statistical reliability. 13The public version does not identify ZIP codes of residence. We obtained this infor-mation from the BRFSS state coordinators. We managed to gather ZIP codes for respondents of eight states: Arizona, Maine, Ohio, Rhode Island, South Dakota, Texas, Utah and Wyoming. Fortunately, there is at least one state per region (Northeast, Midwest, South and West). The period covered is 2005-2010 for all these states except Texas (2007)(2008)(2009)(2010). We follow the recommendation of BRFSS coordinators and restrict the sample to ZIP codes where the number of respondents is greater than 50 for statistical reliability.

We combine all years when doing such an exercise, which increases the number of ZIP codes that we may use. This technique gives us a sample size of 216,546 respondents and, respectively, 364, 399, 1160, 70, 381, 1866, 278 and 169 ZIP codes for Arizona, Maine, Ohio, Rhode Island, South Dakota, Texas, Utah and Wyoming. 14 We present descriptive statistics in Appendix Table 5.

In our sample, counties do not span across states. On the other hand, some ZIP codes span across counties. ZIP codes that span across counties are usually remote areas.

Remote areas are not included in our analysis since the BRFSS do not report the ZIP code of residence if there are less than 50 respondents per wave.

In the BRFSS, subjective well-being is assessed through the following question: "In general, how satisfied are you with your life?" where respondents have four choices (4=very satisfied, 3=satisfied, 2=dissatisfied and 1=very dissatisfied). Table 1 presents means and standard deviations of the variables coming from the BRFSS and shows the distribution of life satisfaction. 46% of the respondents reported that they were very satisfied with their life. On the other hand, 1% answered that they were very dissatisfied. Figure 1 illustrates the average life satisfaction for each county for which we have ZIP code data.

The question on household income is the following: "What is your annual household income from all sources?", where respondents have eight different choices ranging from "Less than 10,000" to "75,000 or more." Respectively, 5% and 26% report having less than $10,000 and more than $75,000. We linearized these income categories by dividing/multiplying bottom-/top-coded categories by standard factors. We use different factors at the end of this paper in order to test the robustness of our results (see Appendix Table A.4 for more details). The BRFSS also contains information on gender, age, employment status, education, marital status, number of children and race of respondents (see Table 1).

We match this dataset with administrative data from the US Census Bureau. Note that the US Census Bureau does not provide statistics at the ZIP code level (fivedigit) since the land area covered is not always well identified. Instead, the 2010 Census reports statistics for ZIP Code Tabulation Areas (ZCTAs). ZCTAs are "generalized area representations of US Postal Services (USPS) ZIP code service areas". 16 There are 33,120 five-digit ZCTAs and 3,033 counties in the US as of 2010. Summary statistics for the eight states are presented in Appendix Table 5.

Our main variables coming from the US Census Bureau are the state, MSA, county 15 http://censtats.census.gov/usa/usa.shtml. There are, on average, 62 counties per state. The states with respectively the smallest and greatest number of counties are Delaware (3) and Texas (254).

16 ZCTAs represent the most frequently occurring five-digit ZIP codes found in a given area (www.census.gov/geo/ZCTA.html). In most cases, the ZCTA is the same as the ZIP code for an area. We thus rely on the ZCTAs and obtain the median household income at the ZCTA level from the Census. and ZIP code median household incomes. The time period covered for the state, MSA, county median household incomes is 2005-2010. Additionally, we obtain ZIP code median household income from the 2010 Census. The state, MSA, county and Zip code median household income is total money income before deductions by all household members 15 years and over. Total money income is the sum of amounts for income from wages, selfemployment, social security, public assistance and pensions. 17 The maximum value for the ZIP code median household income is $250,000. We use different multiplication factors throughout. When not specified, we multiply $250,000 by 1.5. We check that this has no effect on our findings by using the following factors: 1, 2 and 2.5.

Main Results

This section reports the empirical results for the BRFSS. We rely on data at the state, county and ZIP code levels and simultaneously present the associations between neighbors' incomes and well-being in Table 2. We show ordered probit regressions where the dependent variable is self-reported life satisfaction. Note that the regressions presented here include region-year fixed effects, month-year fixed effects and state, county and ZIP code controls, which are the percentage of non-Hispanic black, the percentage of Hispanic, the percentage of elderly and the percentage of children (less than 19 years old). We present standard errors clustered at the county level.

We also control for local housing prices. Note that housing prices vary because of the price and the quantity of housing services [START_REF] Luttmer | Neighbors as Negatives: Relative Earnings and Well-Being[END_REF]). To isolate the quantity and the quality at the ZIP code level, we use the 2011 American Community Survey 5-year estimates to run a hedonic regression of the log home value on a set of ZIP code housing characteristics. 18 We then include the ZIP code housing prices adjusted for housing characteristics to control directly for some components of local prices.

The first column shows the relationship between own household income and respondents' well-being, controlling for socioeconomic characteristics. As expected, the coefficient of own household income is positive and statistically significant at the 1% level. We include the following socioeconomic characteristics: age, age-squared, gender, 8 dummies for employment status, 5 education dummies, 6 dummies of marital status, 4 child dummies and 7 race dummies. We display the coefficients for these variables in Online Appendix Table A.5. They attract signs that are consistent with those of the literature. For instance, there is a positive relationship between life satisfaction and being employed or married [START_REF] Stevenson | Subjective and Objective Indicators of Racial Progress[END_REF]).

In columns 2, 3 and 4, we include respectively the natural log of the "median ZIP code household income," the natural log of the "median county household income" and the natural log of the "median state household income." The second column shows that ZIP code median income is positively correlated with well-being, suggesting that respondents report higher levels of satisfaction when their ZIP code neighbors are richer. On the other hand, columns 3 and 4 report a negative relationship between satisfaction and county and state median incomes but the estimates are not statistically significant. These results indicate that the association between well-being and income spillovers depends on the size of the reference group, i.e. the local area considered.

Column 4 shows the baseline specification where both median ZIP code and median county income variables are included simultaneously. The coefficient of our variables of interest "median household income" at the county and ZIP code levels are statistically significant at the 5% level, but they attract different signs. First, there is a negative vacant units, home ownership vacancy rate, rental vacancy rate, eight dummies of construction dates (e.g. percentage built between 1940 and 1949), eight dummies of the average number of rooms, three dummies for vehicles, nine dummies for the type of heating fuel, one dummy for mortgage status and one dummy for whether a telephone service is available.

relationship between satisfaction and county median income. In other words, controlling for demographic factors and ZIP code median income, individuals living in richer counties report being less satisfied. This result is consistent with previous findings reported by [START_REF] Luttmer | Neighbors as Negatives: Relative Earnings and Well-Being[END_REF]. Second, the natural log of the "median ZIP code household income" remains positively related to respondents' satisfaction. The coefficient of this variable is approximately one-quarter the coefficient of the variable own income.

Interestingly, the coefficients of interest are larger in column 4 than in columns 1 and 2 and the coefficient of the variable "median ZIP code household income" is now slightly smaller than the coefficient of the variable "median county household income." This means that if county and ZIP code median incomes all rise by the same percentage, a person would be slightly less satisfied. Yet the overall impact of neighbors' income is not significantly different from zero when considering both median incomes. 19Column 5 confirms these results by presenting the baseline specification with the three median income variables included simultaneously. The estimates for "median household income" at the county and ZIP code levels are very similar to column 4 and remain statistically significant. On the other hand, "median state household income" is negative and statistically insignificant.

Robustness Checks

Table 3 provides robustness checks. In column 1, we turn to presenting OLS estimates instead of relying on an ordered probit response model. This specification check confirms that "median ZIP code household income" is positively correlated to self-report life satisfaction while "median county household income" is negatively related to satisfaction. In column 2, we replace the variable "median county household income" by the variable "me-dian MSA household income." The correlation between county and MSA median incomes is very high (0.794) and we thus prefer not to include these two variables simultaneously.

The coefficient estimate for "median household income" at the MSA level is negative and statistically significant at the 1% level. The coefficient of this variable is approximately twice the coefficient of the variable "median county household income" and our estimate for the ZIP code median income remains positive and significant at the 1% level.

In columns 3 and 4, we include respectively state*year fixed effects and county-year fixed effects in order to absorb most of the cross-state and cross-county variations. These specification checks confirm the positive and statistically significant relationship between ZIP code median income and self-reported life satisfaction. Last, we show in column 5 that replacing the household income variable by the household income per equivalent adult has no effect on our main results.

In columns 6 and 7, we examine if the economic position of the respondent affects the relationship between well-being and income spillovers. The last two columns of Table 4 restrict the sample respectively to respondents having a household income smaller and larger than the median income in their ZIP code of residence. We find that respondents poorer than the median enjoy living in richer ZIP codes and poorer counties. Moreover, the size of the coefficients of interest is much larger than for the whole sample with coefficients of interests that are half the size of the coefficient of own income. On the other hand, the relationship between life satisfaction and ZIP code neighbors' income is much smaller for individuals having a household income larger than the median income. The estimates in columns 6 and 7 (sample of respondents poorer and richer than the median) are significantly different at the 5% level for the effects of ZIP code neighbors' income on respondents' life satisfaction.

These results are consistent with the idea that neighborhood effects are more impor-tant for poorer individuals as they cannot get away from their neighborhood [START_REF] Chetty | The Effects of Exposure to Better Neigh-borhoods on Children: New Evidence from the Moving to Opportunity Experiment[END_REF]). Another plausible explanation is that richer people spend less time in their neighborhood and their social capital investment with their neighbors is supposedly lower.

Alternatively, individuals having higher incomes could substitute environmental goods by private goods if they are not publicly provided in their neighborhood. The next subsection will examine whether local public goods and amenities could explain the pattern observed so far.

Local Public Goods and Amenities

Table 4 investigates whether public goods could be driving our previous results. We upgrade our basic model (equation ( 2)) by including additional ZIP code and county level variables. Column 1 includes the following environmental and geographical ZIP code variables: unemployment rate, poverty rate, neighbors' socioeconomic characteristics. Adding these controls in the model does affect the size of the coefficients of interest substantially.

Controlling for environmental and geographical amenities decreases the size of the effect of ZIP code neighbors' income on well-being. The estimate is now statistically insignificant and the coefficient is very close to zero. This is an indication that richer ZIP code neighbors have a positive effect on residents' well-being not because they are rich per se but because they bring public goods and amenities that are valuable. Note also that the variables "median ZIP code household income" and "median county household income" are not significantly different from each other.

In column 2, we include the institutional ZIP code variables: the number of schools, dentist offices, physician offices, health establishments and child care establishments. Including these variables has no effect on the size and magnitude of the variable of interest, "median ZIP code household income." These results suggest that the positive association between well-being and median income at the ZIP code level is driven entirely by public goods in the rubrics environmental and geographical mechanisms. Column 3 includes simultaneously the environmental, geographical and institutional ZIP code variables. The coefficient of interest is a well-estimated zero confirming that ZIP code neighbors' income affects life satisfaction through public goods and amenities.

In columns 4, 5 and 6, we include public goods and amenities at the county level instead.

Column 4 controls for the unemployment rate, the number of murders and nonnegligent manslaughters known to police per capita and the percentage of high school graduates.

Only the coefficient of the variable "median county income" changes when we include those controls. We find that the negative effect becomes more negative suggesting a positive role for public goods also at the county level.

In column 5, we include the following public goods in the rubric institutions: local government direct expenditures on health, education, public welfare, total revenue and general expenditures per capita. The coefficients of interest "median ZIP code household income" and "median county household income" remain unchanged suggesting that these public goods do not affect well-being through neighbors' income. We include all the county controls in our model in column 6. The estimate is similar to column 4 indicating that the negative impact of median county income on satisfaction goes through the environmental and geographical variables.

Last, in column 7, we include the full set of amenities at the ZIP code and county levels. The coefficient of the variable "median ZIP code household income" is very small and statistically insignificant. This is suggestive evidence that local area characteristics such as unemployment and poverty explain the positive relationship between the variable "median ZIP code household income" and self-reported well-being presented in Table 2.

Conclusion

This paper shows that the effect of neighbors' income on individuals' self-reported wellbeing varies with the size of the neighborhood. In smaller areas such as ZIP codes, we find a positive relationship between median income and individuals' life satisfaction, whereas it is the opposite at the county, MSA and state levels. The size of these effects is not negligible.

The neighbors' income effects are mainly concentrated among poorer individuals and are as large as one-quarter the effect of own income on self-reported well-being.

We provide evidence that public goods in the local area drives the positive association between satisfaction and neighbors' income at the ZIP code level. Our findings suggest that neighbors' income affects well-being mainly through local area characteristics such as unemployment, criminality and poverty. On the other hand, institutional variables such as schools, hospitals and local government expenditures do not seem to explain the positive income spillovers at the ZIP code level. These findings suggest that such externalities have important implications. A better understanding of these externalities would help us, for example, to improve optimal policies in income taxation and public goods provision.

We believe further research is needed in at least two dimensions. From our results, we cannot conclude on whether relative deprivation and income comparisons have a negative effect on well-being. In order to answer this question, more disaggregated data at the street level may be better suited [START_REF] Brodeur | Where the Streets Have a Name: Income Comparisons in the US[END_REF]; [START_REF] Knies | Income Comparisons Among Neighbours and Satisfaction in East and West Germany[END_REF]). In addition, future research should try to identify random income shocks in particular neighborhoods in order to get the causal effect of neighbors' income on well-being. Blank means there were no data for this county or ZIP code. This figure illustrates the BRFSS county life satisfaction distribution. We report the average life satisfaction for each county. Table 1: (2007)(2008)(2009)(2010)). Life satisfaction is assessed through the following question: "In general, how satisfied are you with your life?" where respondents have four choices (4=very satisfied, 3=satisfied, 2=dissatisfied and 1=very dissatisfied). Columns (1)-( 2) and ( 5)-( 7) include region-year fixed effects, month-year fixed effects and socioeconomic controls (described in the text). Column (3) includes state-year fixed effects and column (4) includes county-year fixed effects. In column 5, we replace the natural log of the real household income by the natural log of the real household income per equivalent adult. Columns ( 6) and ( 7) restrict the sample respectively to respondents having a household income smaller and larger than the median income in their ZIP code of residence. Household income has 8 categories. The log of the real household income is calculated using the middle point of each category (see Online Appendix). We include the following variables at the ZIP code, county and state levels: natural log of population, natural log of area, percentage of rural population, percentage of elderly (more than 65 years old), percentage of children (less than 19 years old), percentage of non-Hispanic black and percentage of Hispanic (fraction other races omitted). Significance levels: * 10 percent; **: 5 percent, ***: 1 percent. Household income of respondents is available in categories. There are eight categories in the BRFSS. Our strategy for computing the "Log of Household Income" is the following.

Figures

Tables

We give the middle point of the household income category to the respondents. For instance, if a respondent answered between $50,000 and $60,000, then $55,000 is his/her household income. Top-and bottom-coded categories receive a special treatment. All top-coded income are replaced by 1.5 the value of the topcode. We verify that this has no effect on our findings by using also 1, 2 and 2.5 (See Appendix Table A.4). For the bottomcoded income category, the value was divided by 2. Once again, this does not affect the main conclusions of this paper. The median household income at the county level and the household income of respondents are in 2005 dollars. We also used in some specifications (when it is mentioned) the household income per equivalent adult. This measure proposed by the OECD takes into account the number of individuals in the household. We limit the number of other adults and kids to three. Household income per equivalent adult is equal to the real family income divided by (1 + 0.5 (other adults) + 0.3 kids).

List of variables coming from US Census and USA Counties

When there was no information for some years, the last year available is used as a replacement. For instance, the number of violent crimes known to police in 2008 is used for the years 2009-10.

• County Median Household Income (2005-10) US Census Bureau (e.g. IPE010209D)

• Size of County • Size of ZCTAs (2010), Square Meter, GIS: projection is North America Albers Equal Area Conic

• Rural Population (ZCTAs)

• Total Population (ZCTAs)

• Population between 60 and 65 years, between 65 and 75 years, between 75 and 85 years and over -percent (ZCTAs)

• Population less than 19 years -percent (ZCTAs)

• Non-Hispanic black -percent (ZCTAs)

• Hispanic -percent (ZCTAs)

• Unemployment rate (ZCTAs)

• Education (e.g. high school graduate) -percent (ZCTAs)

• Married people -percent (ZCTAs)

• Women 15 to 50 years old who had a birth in the past 12 months -percent (ZCTAs)

• Poverty rate (ZCTAs) (2007)(2008)(2009)(2010)). Life satisfaction is assessed through the following question: "In general, how satisfied are you with your life?" where respondents have four choices (4=very satisfied, 3=satisfied, 2=dissatisfied and 1=very dissatisfied). We include region-year fixed effects, month-year fixed effects. Household income has 8 categories. The log of the real household income is calculated using the middle point of each category (see Appendix). 44

Online Appendix Tables: NOT FOR PUBLICATION

  15 Our methodology has the advantage of combining a rich survey and reliable administrative data. USA Counties collect thousands of data items from a variety of sources such as the Bureau of Economic Analysis, the Department of Education, the Federal Bureau of Investigation and the 2010 Census of Population and Housing. The Online Appendix gives a definition of the county and ZIP code level variables used in the analysis coming from this source.

Figure 1 :

 1 Figure 1: BRFSS County Life Satisfaction Distribution. Data came from the BRFSS, 2005-2010. Note that we restrict the sample to counties for which we have ZIP code data.Blank means there were no data for this county or ZIP code. This figure illustrates the BRFSS county life satisfaction distribution. We report the average life satisfaction for each county.

  Summary Statistics.

	BRFSS	Mean	Std. Dev.
	Life Satisfaction	3.402	0.619
	[4] Very Satisfied	0.460	0.497
	[1] Very Dissatisfied	0.010	0.101
	Household Income
	[0, 10000[	0.050	0.218
	[10000, 15000[	0.059	0.237
	[15000, 20000[	0.075	0.263
	[20000, 25000[	0.093	0.291
	[25000, 35000[	0.122	0.328
	[35000, 50000[	0.164	0.371
	[50000, 75000[	0.177	0.381
	More Than 75,000	0.259	0.438
	Male	0.398	0.489
	Age	53.62	16.42
	Elementary School	0.021	0.144
	Att. High School	0.050	0.217
	Grad. High School	0.285	0.451
	Att. College	0.284	0.451
	Grad. College	0.360	0.479
	Married	0.593	0.492
	Divorced	0.143	0.351
	Single	0.105	0.309
	Separated	0.017	0.130
	Widowed	0.119	0.324
	Couple	0.023	0.150
	No Child	0.671	0.471
	One Child	0.119	0.324
	Two Children	0.121	0.327
	Three Children or More	0.089	0.287
	Employed	0.477	0.499
	Unemployed (Less 1Y)	0.023	0.152
	Unemployed (More 1Y)	0.018	0.133
	Self-Employment	0.095	0.293
	Retired	0.239	0.426
	Disabled	0.053	0.227
	Student	0.016	0.124
	Full-Time Homemaker	0.079	0.270
	White	0.893	0.330
	Black or African American	0.033	0.182
	Asian	0.007	0.088
	Pacific Islander (Hawaiian)	0.002	0.040
	American Indian or Alaska	0.023	0.151
	Other Race	0.033	0.186
	Multiracial	0.009	0.097
	Observations	216,546
	Note: Data from the Behavioral Risk Factor Surveillance System (BRFSS). The period covered is 2005-2010 (except
	for Texas (2007-2010)). Life satisfaction is assessed through the following question: "In general, how satisfied are
	you with your life?" where respondents have four choices (4=very satisfied, 3=satisfied, 2=dissatisfied and 1=very
	dissatisfied).

Table 3 :

 3 Robustness Checks: Life Satisfaction and Income Spillovers.

	Life Satisfaction	OLS	Ordered	Ordered	Ordered	Ordered	Ordered	Ordered
			Probit	Probit	Probit	Probit	Probit	Probit
		(1)	(2)	(3)	(4)	(5)	(6)	(7)
		baseline	with MSA with state with county	equiv.	poorer	richer
				-year FE	-year FE	income	median	median
	Ln (Real HH Income)	0.137***	0.271***	0.271***	0.274***		0.223***	0.337***
		(0.003)	(0.005)	(0.005)	(0.005)		(0.007)	(0.016)
	Ln (Real Equiv. HH					0.264***
	Income )					(0.005)
	Ln (Median	0.030***	0.071***	0.064***	0.073***	0.071***	0.105***	0.047*
	ZIP code HH Income)	(0.008)	(0.016)	(0.018)	(0.016)	(0.018)	(0.022)	(0.026)
	Ln (Median	-0.037**		-0.068**		-0.071*** -0.117***	0.021
	County HH Income)	(0.014)		(0.029)		(0.029)	(0.033)	(0.046)
	Ln (Median		-0.138**
	MSA HH Income)		(0.047)
	Ln (Median	-0.042	-0.101			-0.100	-0.017	-0.244
	State HH Income)	(0.073)	(0.154)			(0.154)	(0.195)	(0.233)
	Control Variables
	Socioeconomic controls
	ZIP code controls
	County controls
	State controls
	Quantity adjusted
	housing price
	Region-year fixed effects
	State-year fixed effects
	County-year fixed effects
	Month-year fixed effects
	P (θ = λ)	0.005	0.000	0.001		0.000	0.000	0.677
	Observations	216,546	216,546	216,546	216,546	216,546	130,111	86,426
	Pseudo R2		0.073	0.073	0.078	0.072	0.061	0.037
	R2	0.129
	Note: Data from the Behavioral Risk Factor Surveillance System (BRFSS). Robust standard errors are in paren-
	theses, clustered by county. The period covered is 2005-2010 (except for Texas

Table 4 :

 4 Life Satisfaction and Local Public Goods. Data from the Behavioral Risk Factor Surveillance System (BRFSS). Robust standard errors are in parentheses, clustered by county. The period covered is2005 -2010 (except for Texas (2007 -2010))). Life satisfaction is assessed through the following question: "In general, how satisfied are you with your life?" where respondents have four choices (4=very satisfied, 3=satisfied, 2=dissatisfied and 1=very dissatisfied). All columns include region-year fixed effects, month-year fixed effects, socioeconomic controls (described in the text), ZIP code, county and state controls (see Table2). Household income has 8 categories. The log of the real household income is calculated using the middle point of each category (see Appendix).

	Ordered	Probit	(7)	Alll	0.272***	(0.005)	-0.003	(0.028)	-0.072***	(0.030)	-0.072	(0.152)																														0.158	213,375	0.073
	Ordered	Probit	(6)	Alll	0.272***	(0.005)	0.069***	(0.018)	-0.107***	(0.031)	-0.082	(0.153)																														0.001	213,375	0.073
	Ordered	Probit	(5)	Alll	0.272***	(0.005)	0.067***	(0.018)	-0.074***	(0.028)	-0.078	(0.152)																														0.001	213,375	0.073
	Ordered	Probit	(4)	Alll	0.271***	(0.005)	0.066***	(0.018)	-0.101***	(0.031)	-0.100	(0.156)																														0.001	216,546	0.072
	Ordered	Probit	(3)	Alll	0.271***	(0.005)	-0.006	(0.027)	-0.049*	(0.027)	-0.088	(0.155)																														0.346	216,546	0.073
	Ordered	Probit	(2)	All	0.271***	(0.005)	0.064***	(0.019)	-0.074**	(0.029)	-0.097	(0.155)																														0.001	216,546	0.072
	Ordered	Probit	(1)	All	0.271***	(0.005)	-0.008	(0.027)	-0.049*	(0.026)	-0.088	(0.155)																														0.359	216,546	0.073
	Life Satisfaction				Ln (Real HH Income)		Ln (Median	ZIP code HH Income	Ln (Median	County HH Income)	Ln (Median	State HH Income)	Additional ZIP Code Controls	Environmental and geographical mechanisms:	Unemployment rate	Poverty rate	% of High school graduate	% of Married people	% Baby past 12 months	Institutional mechanisms:	# Health establishments	# Child day care services	# Offices of physicians	# Offices of dentists	# School establishments	Additional County Controls	Environmental and geographical mechanisms:	Unemployment rate	% of High school graduate	Criminality	Institutional mechanisms:	Gvt. Direct Exp: Educ., Health and Welfare	Gvt. Direct Exp: Revenue and Direct Expenditure	Baseline Control Variables	Socioeconomic controls	ZIP code controls	County controls	State controls	Quantity adjusted housing price	Region-year fixed effects	Month-year fixed effects	P (θ = λ)	Observations	Pseudo R2	Note:

•

  Number of health establishments (ZCTAs), 2007 Economic Census • Number of child day care services (ZCTAs), 2007 Economic Census • Number of offices of physicians (ZCTAs), 2007 Economic Census • Number of offices of dentists (ZCTAs), 2007 Economic Census • Number of schools (ZCTAs), 2007 Economic Census8.4. HousingZIP code level data: Total housing units, median housing price, urban housing units, percentage of vacant units, home ownership vacancy rate, rental vacancy rate, eight dummies of construction dates (e.g. percentage built between 1940 and 1949), eight dummies of the average number of rooms, three dummies for vehicles, nine dummies for the type of heating fuel, one dummy for mortgage status and one dummy for whether a telephone service is available.

Table A .

 A 1: Selected ZIP Code, County and State Characteristics. Data from the US Census Bureau. See Sections 8.2-8.3 of the Online Appendix for additional information.

	US Census	Mean	SD
	Zip code area (sq miles)	61.26	79.81
	County area (sq miles)	2297.269	2860.31
	ZIP code total population (in thousands)	23.81	17.10
	County total population (in thousands)	394.05	618.33
	Work in the state of residence	96.04 %	5.22
	Work in the state and county of residence	80.50 %	14.93
	Work in the state of residence but outside the county of residence	15.54 %	14.26
	Work not in the state of residence	3.95 %	5.22
	# Health establishments : ZIP code	341.60	322.34
	# Child day care service : ZIP code	19.49	16.84
	# Offices of physicians : ZIP code	37.75	49.85
	# Offices of dentists : ZIP code	31.99	34.77
	# School establishments : ZIP code	15.43	16.46
	Note:

  Table A.5: Life Satisfaction and Covariates. Data from the Behavioral Risk Factor Surveillance System (BRFSS). Robust standard errors are in parentheses, clustered by county. The period covered is 2005-2010 (except for Texas

	Life Satisfaction	Ordered	Probit
		Coeff.	S.E.
	Ln (Real HH Income)	0.271***	(0.005)
	Ln (Median ZIP code HH Income)	0.064***	(0.018)
	Ln (Median County HH Income)	-0.067**	(0.029)
	Ln (Median State HH Income)	-0.095	(0.155)
	Control Variables
	Male	0.033	(0.020)
	Age	-0.019***	(0.001)
	Age Squared	0.021***	(0.001)
	Age*Male	-0.002***	(0.000)
	Black	0.095***	(0.018)
	Asian	-0.119***	(0.032)
	Hawaiian	-0.001	(0.067)
	Indian	0.108	(0.025)
	Other Race	0.056***	(0.023)
	Multiracial	-0.092***	(0.029)
	Elementary School	-0.026	(0.018)
	Attend High School	-0.035**	(0.012)
	Attend College	0.024*	(0.007)
	College	0.175***	(0.009)
	Married	0.337***	(0.010)
	Divorced	-0.001	(0.012)
	Separated	-0.201***	(0.032)
	Widowed	0.040***	(0.012)
	Couple	0.127***	(0.021)
	One Child	-0.055***	(0.009)
	Two Children	-0.035**	(0.010)
	Three Children	0.011	(0.012)
	Unemployed More Than 1 Year	-0.405***	(0.017)
	Unemployed Less Than 1 Year	-0.363***	(0.016)
	Self-Employed	0.084***	(0.011)
	Retired	0.098***	(0.011)
	Disabled	-0.571***	(0.016)
	Student	0.134***	(0.022)
	Homemaker	0.095***	(0.016)
	Ln Population County	-0.018**	(0.007)
	Percent Rural Pop. County	-0.083*	(0.030)
	Percent Age 0-19 County	0.170	(0.193)
	Percent Age 65 and more County	-0.000	(0.002)
	Percent Hispanic County	-0.022	(0.062)
	Percent Black County	-0.044	(0.083)
	Ln Area County	0.005	(0.007)
	Ln Population Zip Code	-0.001	(0.007)
	Ln Area Zip Code	0.000	(0.001)
	Percent Rural Pop. Zip Code	0.080***	(0.018)
	Percent Age 0-19 Zip Code	-0.001	(0.001)
	Percent Age 0-65 Zip Code	0.002***	(0.001)
	Percent Black Zip Code	-0.000	(0.000)
	Percent Hispanic Zip Code	0.001	(0.001)
	Ln Population State	-0.382*	(0.195)
	Ln Area State	1.815	(1.237)
	Percent Rural Pop. State	-4.351	(3.237)
	Percent Age 0-19 State	-13.729	(11.679)
	Percent Age 65 and more State	0.000	(0.000)
	Percent Black State	0.128*	(0.060)
	Percent Hispanic State	-0.038	(0.027)
	Region-year fixed effects
	Month-year fixed effects
	Observations	216,546
	Pseudo R2	0.072
	Note:

Socioeconomic status is measured using a nine-point scale index based mainly on education and income.

In a recent working paper,[START_REF] Ifcher | Dost Thou Covet Thy Neighbor's Wife, Servant, Animals, or Anything That Is Their's? Competing Channels in the Relationship Between Well-Being and Others' Income[END_REF] replicate our analysis at the ZIP code level using data from the U.S. Gallup-Healthways Well-Being Index. Their estimates are also positive when using ZIP code median income as the reference income.

[START_REF] Hirschman | The Changing Tolerance for Income Inequality in the Course of Economic Development; with a Mathematical Appendix[END_REF] put forward the idea that societies experiencing economic development may show, at the beginning of the process, more tolerance towards inequality.

 5 Many researchers, including[START_REF] Helliwell | Social Capital, the Economy and Well-Being[END_REF], argue that social capital can have a positive effect on well-being.

Five-digit ZIP codes are a system of postal codes. The main city in a region typically has the first ZIP codes. Note that the vast majority of ZIP codes in our sample do not span across states. ZIP codes that span across states are usually remote areas and are thus not included in our analysis. See Section 4. for more information on the sample restrictions.

Note that we do not include MSA median income in the main specification as the correlation between

Other economic papers using the BRFSS include[START_REF] Brodeur | Smoking, Income and Subjective Well-Being: Evidence from Smoking Bans[END_REF],[START_REF] Glaeser | Unhappy Cities[END_REF] and[START_REF] Oswald | Objective Confirmation of Subjective Measures of Human Well-being: Evidence from the USA[END_REF].

States have different rules for the data files. It seems, though, that states report county/ZIP code level data when the number of respondents is greater than fifty in a given geographic location.

The remaining states were excluded for three reasons. First, some states did not answer our request. The second reason is simply that many states refused to provide the data in order to protect the confidentiality of respondents. Lastly, we did not have the funding to pay for the fees asked by a few states.

See http://www.census.gov/support/USACdata.html for more details.

We include the following housing variables: total housing units, urban housing units, percentage of

We calculate the standard error on the coefficient sum, and find that it is not significantly greater than zero.

 (2007)(2008)(2009)(2010)). Life satisfaction is assessed through the following question: "In general, how satisfied are you with your life?" where respondents have four choices (4=very satisfied, 3=satisfied, 2=dissatisfied and 1=very dissatisfied). All columns include region-year fixed effects, month-year fixed effects and socioeconomic controls (described in the text). Household income has 8 categories. The log of the real household income is calculated using the middle point of each category (see Online Appendix). We include the following variables at the ZIP code, county and state levels: natural log of population, natural log of area, percentage of rural population, percentage of elderly (more than 65 years old), percentage of children (less than 19 years old), percentage of non-Hispanic black and percentage of Hispanic (fraction other races omitted). Significance levels: * 10 percent; **: 5 percent, ***: 1 percent. (2007)(2008)(2009)(2010)). Life satisfaction is assessed through the following question: "In general, how satisfied are you with your life?" where respondents have four choices (4=very satisfied, 3=satisfied, 2=dissatisfied and 1=very dissatisfied). All columns include region-year fixed effects, month-year fixed effects, socioeconomic controls (described in the text), ZIP code, county and state controls (see Table 2). Household income has 8 categories. The log of the real household income is calculated using the middle point of each category (see Appendix). . All columns include region fixed effects, month fixed effects and socioeconomic controls (described in the text). Household income has 8 categories. The log of the real household income is calculated using the middle point of each category (see Online Appendix). We include the following variables at the ZIP code, county and state levels: natural log of population, natural log of area, percentage of rural population, percentage of elderly (more than 65 years old), percentage of children (less than 19 years old), percentage of non-Hispanic black and percentage of Hispanic (fraction other races omitted). Significance levels: * 10 percent; **: 5 percent, ***: 1 percent.

Appendix Table


Alexandra Oddo

Introduction " Crisol

Keywords: Crisol, série numérique, n°26, La variation linguistique en espagnol: approches plurielles

come

Introduction

ALEJANDRA BARRIO CRILLASH, UNIVERSITÉ D E S ANTILLES

alejandra.barrio@univ-antilles.fr

ALEXANDRA ODDO ÉTUDES R O M A N E S -CRIIA, U N I V E R S I T É P A R I S NANTERRE

alexandra.oddo@parisnanterre.fr 1.

Le projet qui se dessine dans le titre de cette publication La variation linguistique en espagnol : approches plurielles s'ancre pour nous dans un questionnement profond et dans un temps long qui a déjà permis de produire une série de travaux consacrés au domaine de la variation. Celle-ci entend donc compléter de façon cohérente une réflexion initiée par les deux coordinatrices de ce volume depuis quelques années avec la publication en 2018 du n° 8 de la revue Archipélies (Pour la visibilisation de la variation linguistique, Alejandra Barrio et Élodie Cocote (éds.) ; en 2019 du n° 6 de la revue Crisol (Migrations, mutations, métamorphoses, Caroline Lepage, Françoise Martinez et Alexandra Oddo (éds.), puis, en 2020, du numéro de Cahiers de lexicologie consacré à Variation et phraséologie (Jean-Claude Anscombre, Bernard Darbord et Alexandra Oddo (éds.) et du numéro de la revue Pandora consacré aux Enjeux et conséquences des mutations discursives dans l'espace Espagne-Amériques (Caroline Lepage, Françoise Martinez et Alexandra Oddo (éds.). Nous avions pu, grâce à ces publications, évaluer l'importance de la variation non seulement en diachronie et en diatopie, mais aussi dans des emplois sociaux, institutionnels et pragmatiques qui nous ont fait prendre conscience du pouvoir fédérateur de ce domaine de recherche qui se pose comme un outil indispensable pour mieux connaître et comprendre les rapports entre la langue et la société.

2.

Si ces travaux ont pu éclairer et dynamiser de nombreux pans de la recherche dans des domaines variés allant de la langue à la littérature et à la civilisation dans les pays de l'aire hispanique, ils sont loin d'avoir épuisé la question et ont, bien au contraire, ouvert de nouvelles perspectives de recherche et de nouveaux questionnements. C'est que la notion de variation peut être abordée sous plusieurs angles et peut donc intéresser des cher-Crisol, série numérique -26 1 cheurs provenant de différentes disciplines. Elle peut être étudiée d'un point de vue linguistique, c'est le cas notamment des travaux qui s'inscrivent dans le courant variationniste de la sociolinguistique, angle grâce auquel on peut s'intéresser aux relations qu'entretiennent des facteurs divers (tels que les facteurs géographiques, sociaux et diachroniques, parmi d'autres) avec des phénomènes de variation dans la langue, ces variations étant également complexes puisqu'elles surviennent à tous les niveaux linguistiques (phonétique, lexical et morphosyntaxique). À cet égard, on doit également noter le rôle prépondérant de ces relations dans les processus de changement linguistique, dans lesquels la variation se présente comme l'étape préalable indispensable à tout renouvellement d'une langue. La variation intéresse aussi en général l'enseignement, la didactique et les politiques linguistiques (quelle variété de langue enseigner ? Quelle place accorder à la variation dans le domaine public ? Comment étudier et comment considérer les phénomènes de variation dus aux contacts de langues ? sont autant de questions que l'on peut se poser). Des éléments de variation peuvent également être constatés et analysés dans d'autres manifestations linguistiques et culturelles : dans les textes, les proverbes et les locutions, dans la tradition orale (par exemple dans les romances) -et faire intervenir la notion d'une variation diamésique différenciant les emplois écrits et oraux de la parole -ainsi que dans toutes les manifestations littéraires (signalons, par exemple, les phénomènes de variation que nous pouvons rencontrer dans un roman qui présente des diatopismes -ces mots propres à une variété linguistique régionale -ou les études qui s'intéressent à la littérature écrite par des femmes -existe-t-il d'ailleurs une forme de variation justifiant cette catégorie ? Ces quelques lignes mettent en évidence l'importance en langue de la diversité des emplois géographiques et dialectaux, de l'hétérogénéité des emplois sociaux et collectifs impliqués dans la variation diastratique et enfin, plus récemment et dans le contexte plus vaste des « études de genre », de la question d'une variation diagénique subordonnée à la variation diastratique.

3.

Ce numéro de Crisol, consacré à la variation dans les domaines de la langue et de la littérature, se donne pour objectif d'observer la variation au sens large cette fois, dans un monde hispanique d'une très grande diversité au-delà du concept générique qui englobe les peuples de langue et de culture espagnole sous la bannière de l'hispanidad. Il nous aura encore une fois permis de rassembler des travaux inédits d'une très grande qualité autour de cette notion féconde qu'est la variation.

4.

Mise à l'honneur dans ce volume, elle nous permet dans une première partie d'observer le diasystème de l'espagnol dans sa diversité. À travers les notions de diatopie, de diachronie, de diastratie et de diaphasie, des manifestations linguistiques qui touchent les signifiants en synchronie et en diachronie sont mises en évidence et montrent la diversité d'une langue espagnole parlée dans une aire géographique très vaste, par des locuteurs marqués par leur environnement géographique et social. Les travaux réunis dans cette première partie rendent compte, par ailleurs, des phénomènes de variation qui se produisent aux différents niveaux de l'analyse linguistique : ils évoquent la phonologie, la syntaxe, la morphologie, etc.

5.

L'article de Corinne Mencé-Caster se propose ainsi de revenir sur « La problématique de la 'disparition' et/ou de la co-existence des phonèmes : variations synchroniques et diachroniques et approches phonologiques en espagnol ancien et contemporain (Espagne et Amérique) » en analysant les modalités selon lesquelles sont abordées et traitées les problématiques de disparition et/ou de coexistence de phonèmes au sein d'une synchronie donnée ou à travers le temps, avec une attention particulière portée au phénomène du yeísmo. C'est aussi l'espace différencié de l'Espagne et de l'Amérique qui sert de cadre à l'étude d'Olivier Iglesias « Si pudiera o pudiese: la variation dans l'emploi des formes en -ra et en -se depuis une perspective idiolectale », qui propose une approche novatrice des facteurs motivant la variation morphologique des deux formes de l'imparfait du subjonctif en espagnol à travers le recours à la méthode idiolectale. Au niveau lexical et dans une perspective diachronique, Carmen Quintero observe dans « Rosa y rosado: cuestiones de variación lingüística » certains aspects de la variation linguistique autour du terme chromatique rosa en espagnol grâce à un corpus de dictionnaires monolingues représentatifs et aux grands corpus mis à la disposition du public par la RAE (CORDE, CREA et CORPES XXI). La contribution d'Ana Ramos Sañudo « Variación pragmática regional de marcadores discursivos de (des)acuerdo en el español de Andalucía » recoupe deux axes de la variation : la variation géographique régionale et la variation pragmatique des marqueurs d'accord et de désaccord afin d'observer le fonctionnement discursif des marqueurs de la variété andalouse de l'espagnol. Au niveau discursif, l'article d'Alejandra Barrio « Fenómenos de variación y alternancia en el español de hablantes bilingües en Martinica » permet de clore cette première partie en s'intéressant aux pratiques linguistiques des hispanophones en Martinique à travers l'analyse des phénomènes de code-switching induits par le contact avec le français et le créole.

6.

La deuxième partie de ce volume prend en compte d'autres coordonnées de la variation, celles de la phrase, à travers les locutions et les proverbes, très liés à l'oralité, et dans une approche qui lui est complémentaire, celle du texte, de l'écrit, à travers des productions littéraires, historiques et artistiques. Alexandra Oddo aborde ainsi dans « Quelques observations sur la variation des unités phraséologiques espagnoles en diachronie » la question de la variation morphologique dans le temps des locutions (orthographe, genre, nombre, syntaxe) et réfléchit à l'existence de traits saillants du système phraséologique qui seraient à même de conditionner l'évolution de ces unités. L'article de Jean-Claude Anscombre « Tabús, censura y variaciones en el refranero soez » s'attache à décrire la variation diaphasique grâce à un corpus de proverbes grossiers de l'espagnol contemporain patiemment collectés par l'auteur dans les proverbiers et dictionnaires d'argot. Cette deuxième partie du volume est également consacrée à la variation textuelle. Elle offre l'occasion à Bernard Darbord de se pencher sur une strophe présente dans deux des trois principaux manuscrits du Libro de buen amor. Dans « Variation autour de la strophe 61 du Libro de buen amor », il aborde ainsi différents aspects de la notion philologique de variante, de tradition, de modèle culturel au Moyen Âge. La contribution de Clara Dauler « De La novela de mi vida à Regreso a Itaca ou les variations du genre romanesque dans l'oeuvre de Leonardo Padura », interroge quant à elle les formes de variation mises en oeuvre dans la ré-écriture d'un roman en scénario, ou plutôt en « relato novelesco », afin de représenter l'identité cubaine. Toujours dans la réflexion sur la variation à partir d'un texte littéraire, Raquel Gómez Pintado analyse, dans « Traduire l'hétérolinguisme de la littérature des Petites Antilles francophones en espagnol. Études de cas : Maryse Condé et Raphaël Confiant », les stratégies pratiquées par les traducteurs de deux écrivains antillais et met en évidence les difficultés et les enjeux de traduction des hétérolinguismes, ces éléments appartenant à une langue ou à une variété différente de la langue principale du texte.

7.

Les articles réunis dans ce numéro nous ont permis d'observer à quel point la richesse de la langue espagnole repose sur sa très grande capacité de variation, un moteur qui lui permet d'évoluer, de se réinventer. La notion de variation, ici comprise dans un sens large, en offre une vision plu-rielle, avec des travaux d'une très grande complémentarité. En abordant des aspects synchroniques et diachroniques, des phénomènes relevant des différents niveaux d'analyse linguistique (phonologique, morphologique, lexical, pragmatique et discursif) et en s'intéressant aux versants oraux et écrits de la langue espagnole dans les espaces américains et péninsulaires, ce numéro a le mérite de mettre en avant un panorama représentatif non seulement de la diversité de la langue espagnole, mais aussi de la pluralité des approches et des questions que se posent des chercheurs d'horizons divers travaillant en synergie autour d'une même notion. Nous espérons que cette diversité de questionnements autour de la variation sera du goût de nos lecteurs et surtout, qu'elle invitera d'autres chercheurs à continuer d'approfondir ce domaine et à proposer de nouvelles pistes d'exploration qui pourront favoriser de nouveaux échanges et de futures collaborations.

8 .

 8 Nous remercions, enfin, les collègues de Nanterre et d'autres universités (Université de Bourgogne, Université Lyon III, Sorbonne Université, Université Paris 8, Universidad Complutense de Madrid) qui ont participé à l'évaluation scientifique de ce volume et l'ont enrichi grâce à leurs remarques bienveillantes. Alejandra Barrio et Alexandra Oddo Comité scientifique du volume : Alejandra Barrio ; Thomas Faye ; Hélène Fretel ; Sonia Gómez-Jordana Ferary ; David Macías Barrés ; Marta López Izquierdo ; Clémentine Lucien ; Alexandra Oddo. Comité de rédaction : Caroline Lepage ; Amélie Piel Crisol, série numérique -26 5

Crisol, série numérique -26

Crisol, série numérique -26 3

Crisol, série numérique -26


est destinée au dépôt

hospital, to gradually replace the ward stock distribution system (WSDS), which allowed a low level of pharmaceutical monitoring.

In 2011, UDDDS was used for 374 beds. UDDDS allows named "ready-to-use" treatments to be dispensed daily, avoiding nurse preparation of pillboxes, necessary with WSDS. Purpose To assess the impact of a robotic UDDDS on the incidence of medicines administration errors and to assess the cost of this system. A binary logistic regression with odds ratio (OR) was performed using SPSS v.15.0. Results TDM was performed for 39 (81%) of 48 patients receiving vancomycin. The median age was 57 years (95%CI: 52-62); 26 were male (68%); 21(54%) had mild to moderate RI.

Materials and Methods

There were 76 PIs [median 2/patient (IQR: 2)], 51(67%), 4 (5%) and 21 (28%) of high, medium and low importance, respectively; 67(88%) were accepted.

TCH-048

medicine safety of parenteral ready-to-use all-in-one mixtures, e.g. TPN bags in neonatology.

No conflict of interest.

The advanTageS of uv-raman SPecTroScoPy for checking The STrengTh of nalbuPhine PreParaTionS

doi:10.1136/ejhpharm-2013-000276.237 1 F Nardella, 1 P Collart-Dutilleul, 1 C Weyant, 1 L Perello, 2 AC Gairard-Dory, 3 B Gourieux, 1 G Ubeaud-Sequier. Background A paediatric nalbuphine formulation is prepared in the hospital pharmacy of the Nouvel Hôpital Civil of Strasbourg. It was previously checked by HPLC. Following the acquisition of an UV-Raman spectrometer, a method was developed in order to improve the monitoring of nalbuphine preparation. Purpose To cheque paediatric nalbuphine formulations with a simple, fast and reliable method by using UV-Raman spectroscopy.

Materials and Methods

In order to validate a method using the QC-prep (a UV-Raman spectrometer), we prepared three concentration ranges, prepared by diluting three different samples of nalbuphine reconstituted in 0.9% NaCl. Each range was composed of 5 points of calibration. The linearity was validated from the average of the three ranges. The fidelity of the method is tested by repeatability (one solution was sampled five times by the QC-prep) and reproducibility (five different solutions were sampled at one time). The method is considered as valid if the linearity is good enough (r² > 0.999) and the coefficient of variation (CV) and relative error of repeatability and reproducibility are below 5%.

Results

The QC-prep method for nalbuphine 1 mg/ml in 0.9% NaCl is valid in terms of:

• •
Linearity: the calibration is linear from 0.2 to 2.0 mg/mL (r² = 0.9997)

• •

Repeatability: the CV is less than 0.25% Accuracy: the relative error is less than 5% Five different batches have been checked in routine work. No mistakes have been identified, either in the concentration of the drug (quality control and sample), or in identification of the solvent. Conclusions Calibration of the QC-prep is simple thanks to easyto-use software. This is a powerful tool that enables us to determine the concentration of nalbuphine more quickly, easily and safely than the HPLC method previously used. The UV-Raman spectroscopy method could be extended to the analysis of other formulations such as paediatric antibiotics preparations.

No conflict of interest.

The effecT of a roboTic uniT doSe drug diSPenSing SySTem on medicineS adminiSTraTion errorS and

The

  Effect of a Robotic Unit Dose Drug Dispensing System on Medicines Administration Errors and the Cost of Drug Dispensing M Viprey, C Burgos Leon-Djian, X Dode, G Aulagne To cite this version: M Viprey, C Burgos Leon-Djian, X Dode, G Aulagne. The Effect of a Robotic Unit Dose Drug Dispensing System on Medicines Administration Errors and the Cost of Drug Dispensing. 18th Congress of the EAHP, Mar 2013, Paris -Palais des congrès de la MaillotPorte, France. 20, pp.A85.3 -A86, 2013, 10.1136/ejhpharm-2013-000276.239. hal-04331952 Technology Eur J Hosp Pharm 2013;20(Suppl 1):A1-238 A85

  The coST of drug diSPenSing doi:10.1136/ejhpharm-2013-000276.238 M Viprey, C Burgos Leon-Djian, X Dode, G Aulagner. Groupement Hospitalier Est, Pharmacy, Bron Cedex, France Background A Unit Dose Drug Dispensing System (UDDDS) by a robot (PillPick system, Swisslog) with daily pharmaceutical monitoring of medical prescriptions is being implemented in our TCH-046 TCH-047 on December 8, 2023 by guest. Protected by copyright. http://ejhp.bmj.com/ Eur J Hosp Pharm: first published as 10.1136/ejhpharm-2013-000276.238 on 12 March 2013. Downloaded from