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Citation: Preisendörfer, P.; Bruderer
Enzler, H.; Diekmann, A.; Hartmann,
J.; Kurz, K.; Liebe, U. Pathways to
Environmental Inequality: How
Urban Traffic Noise Annoyance
Varies across Socioeconomic
Subgroups. Int. J. Environ. Res. Public
Health 2022,19, 14984. https://
doi.org/10.3390/ijerph192214984
Academic Editor: Paul B. Tchounwou
Received: 11 October 2022
Accepted: 10 November 2022
Published: 14 November 2022
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International Journal of
Environmental Research
and Public Health
Article
Pathways to Environmental Inequality: How Urban Traffic
Noise Annoyance Varies across Socioeconomic Subgroups
Peter Preisendörfer 1, * , Heidi Bruderer Enzler 2, Andreas Diekmann 3,4, Jörg Hartmann 5, Karin Kurz 6
and Ulf Liebe 7
1Institute of Sociology, University of Mainz, Jakob-Welder-Weg 12, D-55128 Mainz, Germany
2School of Social Work, Zurich University of Applied Sciences, Pfingstweidstr. 96,
CH-8037 Zurich, Switzerland
3Environmental Research Group, ETH Zurich, WEP H18, CH-8092 Zurich, Switzerland
4Institute of Sociology, University of Leipzig, Beethovenstr. 15, D-04107 Leipzig, Germany
5Research Centre Global Dynamics, University of Leipzig, Strohsackpassage, D-04109 Leipzig, Germany
6Institute of Sociology, Georg-August-University Göttingen, Platz der Göttinger Sieben 3,
D-37073 Göttingen, Germany
7Department of Sociology, University of Warwick, Coventry CV4 7AL, UK
*Correspondence: preisendoerfer@uni-mainz.de
Abstract:
The article investigates how socioeconomic background affects noise annoyance caused
by residential road traffic in urban areas. It is argued that the effects of socioeconomic variables
(migration background, education, and income) on noise annoyance tend to be underestimated
because these effects are mainly indirect. We specify three indirect pathways. (1) A “noise exposure
path” assumes that less privileged households are exposed to a higher level of noise and therefore
experience stronger annoyance. (2) A “housing attributes path” argues that less privileged households
can shield themselves less effectively from noise due to unfavorable housing conditions and that this
contributes to annoyance. (3) Conversely, an “environmental susceptibility path” proposes that less
privileged people are less concerned about the environment and have a lower noise sensitivity, and
that this reduces their noise annoyance. Our analyses rest on a study carried out in four European
cities (Mainz and Hanover in Germany, Bern and Zurich in Switzerland), and the results support the
empirical validity of the three indirect pathways.
Keywords:
noise annoyance; noise exposure; housing attributes; environmental susceptibility;
socioeconomic background
1. Introduction
Since the emergence of the environmental justice movement in the US in the 1980s,
the unequal social distribution of environmental risks has become an important research
topic [
1
–
4
]. To investigate environmental inequalities, empirical studies usually concentrate
on testing the hypothesis that less privileged population groups (ethnic minorities, low-
income households, etc.) are confronted with more serious environmental threats in their
everyday life than privileged ones. Whether this so-called social gradient hypothesis of
environmental bads holds true, can be examined by referring to objective risk exposure data
and/or subjective risk evaluations. Most studies in the tradition of environmental inequality
follow an approach that focuses on objective risks. Nevertheless, the joint evaluation
of objective and subjective risks is a preferable research strategy because objective and
subjective representations may not go hand in hand [5,6].
In this article, we consider both objective and subjective data, but focus on subjective
risks. The research question is whether and how social background variables affect per-
ceived environmental disamenities. For our empirical analyses, we look at annoyance due
to residential road traffic noise as a prominent example of local environmental risks. The
Int. J. Environ. Res. Public Health 2022,19, 14984. https://doi.org/10.3390/ijerph192214984 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022,19, 14984 2 of 18
selection of noise as environmental threat can be justified both substantively and—in terms
of previous research—strategically.
Noise is an important and growing stress factor in urban life. It is well known
that long-term noise exposure has negative impacts on people’s subjective well-being
and personal health [
7
,
8
]. Furthermore, noise is often connected with air pollution and
other environmental risks, and is thus a proxy for unfavorable environmental conditions.
Compared to other environmental risks, noise can more easily be perceived by individual
actors, and therefore seems particularly suitable for an assessment of potential discrepancies
between objective conditions and subjective evaluations.
In terms of scientific knowledge and evidence, noise and sound research is a highly
developed and sophisticated research field [
9
,
10
]. Bearing little relation to research on
environmental inequality, noise research is a relatively independent subfield of applied
psychology—with a close affiliation to public health. We intend to show in this article that
both sides, environmental inequality research on the one hand and noise research on the
other, could benefit from being brought into a closer relationship.
Against this background, our article aims in two directions. First, it contributes to
the study of noise annoyance within noise and sound research. Our innovative element
is that we elaborate—in a more detailed way than previous studies—on the effects of
socioeconomic variables and inequality dimensions on annoyance due to urban traffic
noise. Second, the article contributes to environmental inequality research: a recourse to
the stock of knowledge, theoretical models, and the methodology of noise research could
be useful for the study of environmental risks in general, including the unequal social
distribution of such risks.
The article is structured as follows. In Section 2, we highlight the theoretical and empir-
ical background and develop our hypotheses. The background section reviews experiences
of noise annoyance research, including a focus on the role of socioeconomic variables
and environmental inequality. Our hypotheses will differentiate between the direct and
indirect effects of socioeconomic variables on noise annoyance. Section 3introduces our
data gathered in four European cities (Mainz and Hanover in Germany, Bern and Zurich
in Switzerland). It further describes the dependent and independent variables, and sets
out the statistical methods. In the results Section 4, we present our findings concerning
the total, direct, and indirect effects of socioeconomic variables on noise annoyance. A
discussion and conclusions Section 5closes the article.
2. Background and Hypotheses
2.1. Theoretical and Empirical Background
Noise annoyance, i.e., adverse feelings of resentment, anger, discomfort, or offense
about ambient noise that interferes with daily activities [
11
], is a major topic in noise
research. Feelings of annoyance serve as the main indicator of the subjective assessment
of objective noise exposure conditions. There are standardized methods for measuring
noise annoyance in surveys, and relatively well-confirmed empirical models of its most
important determinants.
Concerning these determinants, research shows that annoyance depends on both
acoustical and non-acoustical factors [
12
–
17
]. The most important acoustical factor is the
objective exposure to noise; so-called dose–response models try to depict its relation to
subjective noise annoyance [
18
,
19
]. The most prominent non-acoustical factor is noise
sensitivity, usually seen as a personality trait.
In both groups of factors, however, there are additional influences that have been
proved or suggested to be significant predictors of noise annoyance. When we are interested
in indoor noise annoyance caused by outdoor noise exposure, it seems particularly relevant
to take the attributes of the housing into account. For living comfort, subjective well-
being, and health effects, indoor rather than outdoor noise is crucial, and there are more
or less effective ways of preventing external noise from intruding into the building and
thus becoming subjectively annoying. Housing and dwelling attributes, which can be
Int. J. Environ. Res. Public Health 2022,19, 14984 3 of 18
subsumed within the group of acoustical factors [
16
], include the size of the residence, the
sound insulation of the building, the quality of the windows, and the internal functional
arrangement of rooms.
The list of additional non-acoustical factors, most often personal and social attributes,
is even longer than that of acoustical ones [
12
,
13
,
20
]. Besides socio-demographic variables
(gender, age, etc.), general attitudes, such as environmental concern, and specific attitudes
toward the noise source have been shown to be relevant for annoyance responses. Fear
of harm connected with the noise source, individual coping capacity, and expectations of
the future development of the noise situation are other non-acoustical factors that feature
within the debate and corresponding research.
Socioeconomic variables, which belong to this group of non-acoustical factors and
constitute the main interest of this article, do not play an important role within noise
research. Relatively few empirical studies have focused on socioeconomic inequalities in
noise annoyance [
6
,
21
,
22
]. Fyhri and Klaeboe [
23
] summarize: “within noise research there
is rarely any discussion of the relevance of SES [socioeconomic status] for the impacts of
noise, nor of the possible mechanisms involved in producing differences of annoyance”
(p. 28). This applies not only to SES, but also to income and education. Education,
income, and SES are usually grouped under the rubric of social background variables, and
reviews of empirical studies regularly conclude that—after controlling for other influence
factors—they do not have significant effects on noise annoyance [
12
,
13
,
16
]. This means that
regression models, which include a set of proposed determinants of annoyance in a single
step, yield insignificant direct effects of social background variables. Nevertheless, some
noise researchers, including Fields and Miedema in their review articles, concede that there
may be indirect effects, and they recommend more detailed studies of these effects.
A study following this recommendation is Fyhri and Klaeboe [
23
]. Based on surveys
in Norway, the authors investigated the direct and indirect effects of income on urban road
traffic noise annoyance. The indirect effect that Fyhri and Klaeboe concentrate on is the
mediation via noise exposure. They started with the hypothesis that privileged population
groups “buy themselves out of noisy neighbourhoods” (p. 27), i.e., high-income people
move out of areas with noise exposure, and this results in their experiencing lower noise
annoyance. In accordance with previous research, Fyhri and Klaeboe did not find a direct
effect of income on noise annoyance. Contrary to their expectations, however, they also
found only partial confirmation for the hypothesis that high-income households have a
lower noise exposure. The hypothesis was confirmed in small-to-medium size cities, but
not for the large city of Oslo, the capital of Norway. The authors explain their Oslo finding
by the fact that Oslo is a highly attractive city for young urban professionals. This group has
a preference for living in the capital city, and they trade off urban noise and air pollution
against the advantages of living there.
Taking a broader perspective, Fyhri and Klaeboe’s “residential buy-out hypothesis”
can be embedded in the field of environmental inequality research. Besides describing
environmental social disparities, this research tries to explain what causal mechanisms
generate the social gradient of exposure to environmental risks [
4
,
24
,
25
]. When it comes to
residential decisions, two mechanisms of selective migration have received most attention
in environmental inequality research. (1) The inhabitants of areas with unfavorable envi-
ronmental conditions may move out when they achieve a higher income. This corresponds
to the “residential buy-out hypothesis”. (2) Lower-income groups may settle in areas with
unfavorable environmental conditions because rents are lower than in less exposed neigh-
borhoods. Furthermore, minority or migrant groups may experience discrimination in the
housing market and may thus be forced into low-quality neighborhoods. This mechanism
describes a selective moving-in process.
Indeed, the majority of environmental inequality studies in the US and European coun-
tries report less favorable environmental conditions for ethnic minorities and low-income
or low-status groups [
1
–
3
,
21
,
26
,
27
]. However, there are also remarkable contradictory
results in line with Fyhri and Klaeboe’s Oslo finding. In a study of four French cities,
Int. J. Environ. Res. Public Health 2022,19, 14984 4 of 18
Padilla et al. [
28
] were puzzled by the seemingly paradoxical phenomenon of a positive
social gradient in Paris. Exposure to air pollution in the French capital is more severe in
city districts with a population of high SES. Pertaining to Rome, Italy, Forastiere et al. [
29
]
also observed a positive association between exposure to traffic-induced air pollution and
both income and SES. Rüttenauer [
30
] explored the association between industrial sites, air
pollution, and environmental inequality in German cities. He found that environmental
risks for foreigners and migrant workers are higher than for German citizens in most cities.
However, there are also cities where this relation is reversed.
Despite these caveats, research on environmental inequalities suggests for our em-
pirical analyses the indirect two-step path: socioeconomic variables
→
noise exposure
→
noise annoyance. We expect that privileged social groups are less exposed to road
traffic noise, but this does not necessarily spill over to subjective noise annoyance, i.e., they
do not necessarily feel less annoyed. The reason for this potential discrepancy between
exposure and annoyance may be one of the two additional indirect pathways proposed in
the following section on our hypotheses.
2.2. Hypotheses: The Direct and Indirect Effects of Socioeconomic Variables
Since it is relevant for our hypotheses, we begin this section with a specification
of our “socioeconomic-group variables”. We use three measures to capture the social
background and socioeconomic resources of our respondents: migration background,
education, and income. Migration background denotes whether the respondent or at least
one of his/her parents was born abroad. Persons with a migration background often have
several disadvantages in a new host country—devalued educational credentials, lower
skills in the new language, a restricted social network, and direct forms of discrimination.
Education aims at a person’s labor market as well as sociocultural resources. Income can
be seen as the most direct measure of economic resources and captures the respondent’s
financial constraints and opportunities.
Figure 1can serve as a road map for our theoretical argumentation and hypotheses.
Taken together, we will test four broad hypotheses. The first concerns the direct effects
of socioeconomic variables on annoyance from urban road traffic, while hypotheses 2–4
specify indirect, i.e., mediating, effects.
Based on the results of previous research and the theoretical arguments presented
above, we predict that—after controlling for the other influencing factors in Figure 1and a
set of additional control variables (not shown in Figure 1)—there are no direct effects of
migration background, education, and income on annoyance due to road traffic noise. If we
unexpectedly observe significant direct effects, we predict that the strength of these effects
is weak, much weaker than the effect of other well-known predictors of noise annoyance
(noise exposure, noise sensitivity, etc.).
The preceding Section 2.1 also introduced the indirect “noise exposure path” (path 1
in Figure 1). Research on environmental inequality postulates that processes of selective
migration lead to lower levels of noise exposure for persons with higher socioeconomic
resources; and from research on noise annoyance, we know (as mentioned above) that noise
exposure is the most important predictor of noise annoyance. We expect that migration
background yields a significant positive effect on noise exposure, while income yields a
significant negative effect. Education usually correlates with income, and our expectation
is that the income effect dominates the education effect in the context of noise exposure.
A second indirect path (path 2 in Figure 1) should run through housing attributes.
In Section 2.1, we mentioned that housing attributes are important factors influencing
indoor noise annoyance and that there are several ways to prevent external noise from
intruding into the building. It seems reasonable to assume that privileged households live
in dwellings that shield more effectively against residential noise. Diekmann et al. [
31
]
developed this “environmental shielding hypothesis” in more detail. The hypothesis states
that better-off social groups reside in dwellings that tend to serve as “structural coping
devices” against outdoor noise. Their dwellings are larger, and have more rooms and
Int. J. Environ. Res. Public Health 2022,19, 14984 5 of 18
better noise protection appliances. In our analyses, we will specifically look at four housing
attributes: dwelling size; whether or not the bedroom faces the street; window quality; and
whether the dwelling has an outdoor garden. Our prediction is that—in a first step—these
four attributes will be significantly influenced by socioeconomic variables, and—in a second
step—they will have significant effects on noise annoyance. Specifically, we expect that
households with a higher income will live in bigger dwellings, less often have a bedroom
facing the street, more often have a residence with high-quality soundproofed windows,
and more often enjoy an outdoor garden. The reverse should be true for respondents with a
migration background, because independent of financial constraints ethnic discrimination
is a persistent feature of the housing market in many countries [
32
]. Contrary to this, when
we control for income, additional effects of education on the housing attributes would be
surprising. Having a bigger residence (with several rooms) usually means that household
members have better opportunities to arrange daily activities in a noise-evading way, and
this should reduce noise annoyance. We further assume that those who have a bedroom
facing the street are more often annoyed by road traffic noise. On the other hand, both
soundproofed windows and an outdoor garden that most often is backyard can be expected
to contribute to lower annoyance.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 5 of 20
Figure 1. Potential direct and indirect effects of socioeconomic variables on noise annoyance.
Based on the results of previous research and the theoretical arguments presented
above, we predict that—after controlling for the other influencing factors in Figure 1 and
a set of additional control variables (not shown in Figure 1)—there are no direct effects of
migration background, education, and income on annoyance due to road traffic noise. If
we unexpectedly observe significant direct effects, we predict that the strength of these
effects is weak, much weaker than the effect of other well-known predictors of noise an-
noyance (noise exposure, noise sensitivity, etc.).
The preceding Section 2.1 also introduced the indirect “noise exposure path” (path 1
in Figure 1). Research on environmental inequality postulates that processes of selective
migration lead to lower levels of noise exposure for persons with higher socioeconomic
resources; and from research on noise annoyance, we know (as mentioned above) that
noise exposure is the most important predictor of noise annoyance. We expect that migra-
tion background yields a significant positive effect on noise exposure, while income yields
a significant negative effect. Education usually correlates with income, and our expecta-
tion is that the income effect dominates the education effect in the context of noise expo-
sure.
A second indirect path (path 2 in Figure 1) should run through housing attributes. In
Section 2.1, we mentioned that housing attributes are important factors influencing indoor
noise annoyance and that there are several ways to prevent external noise from intruding
Figure 1. Potential direct and indirect effects of socioeconomic variables on noise annoyance.
Int. J. Environ. Res. Public Health 2022,19, 14984 6 of 18
In addition to the “noise exposure path” and the “housing attributes path,” we assume
a third indirect path—an “environmental susceptibility path” (path 3 in Figure 1). This
path proposes that privileged people are environmentally more concerned and have a
higher general noise sensitivity, and this tends to lead to a higher level of noise annoy-
ance. As has been described in Section 2.1, there is strong evidence that noise sensitivity
affects noise annoyance, and some evidence that environmental attitudes have a positive
influence. The increasing public attention to environmental protection in recent years
has contributed to a framing that subsumes noise issues under the broader umbrella of
environmental problems [
22
,
33
]. This justifies the inclusion of environmental concern in
our study. Looking at the socioeconomic variables, there is good reason to assume that
educational achievements, rather than migration background and income, mainly matter
in this context. Education is usually connected with a higher level of environmental knowl-
edge and knowledge about the negative health effects of environmental bads, and this
should stimulate “environmental susceptibility” (for this concept, e.g., [
34
,
35
]). Numerous
studies in environmental social sciences confirm that education is a robust predictor of
environmental concern [
36
,
37
]. The evidence concerning the effect of education on noise
annoyance, however, is mixed [21,38–40].
Whereas the “noise exposure path” and the “housing attributes path” suggest that
privileged groups will be less annoyed by road traffic noise, the “environmental suscep-
tibility path” runs in the opposite direction: it suggests that privileged people complain
more about noise. These contradictory indirect effects may be one of the reasons why most
previous studies did not find that socioeconomic background variables have significant
effects on noise annoyance.
3. Data, Variables, and Methods
3.1. Empirical Data
The main data for our analyses come from surveys in two German cities, Mainz
and Hanover, and two Swiss cities, Bern and Zurich. Mainz is located in the middle of
Germany near Frankfurt and has about 220,000 inhabitants. Hanover is more in the north
of Germany and has about 530,000 inhabitants. Bern is the capital of Switzerland, and
about 130,000 people live there. Zurich is the biggest city of Switzerland and has about
430,000 inhabitants. Pragmatic considerations were important for the selection of these
cities. Members of our research group were affiliated with the universities in three of the
four cites, and this proved to be helpful both for the survey sampling and for access to the
“objective” noise exposure data.
With the exception of a few local adaptations, the surveys in the four cities were strictly
comparable in terms of research design (sampling procedure, etc.) and question program.
The surveys were carried out as mail questionnaires and were conducted between October
2016 and March 2017. They were based on random samples of the adult population
(aged 18–70) in the four cities. The addresses of the random samples, which specified
individual persons, came from the official population registers managed and maintained
by the city administrations. Because we had the exact addresses of our respondents, we
were additionally able to locate the spatial coordinates of their places of residence. These
coordinates enabled us to match administrative noise data to the survey data (Section 3.2).
Subjects selected for participation in the study were approached following the tailored
design method of Dillman et al. [
41
]. That is, they received a first invitation to participate
in the survey, a postcard after one week, a second invitation after three weeks, and a
third invitation after seven weeks. It is important to note that the surveys were not
introduced as an environmental survey, but as a survey titled “Housing and Living in [City
Name]”. Starting with 4000 addresses in each city, the surveys yielded a response rate of
45.2 per cent in Mainz, 35.9 per cent in Hanover, 55.2 per cent in Bern, and 48.4 per cent
in Zurich (standard RR2 for postal surveys to specifically named persons [
42
]). In total,
7540 respondents participated in the survey (for further details of the study, including
issues of sample selectivity, see [43]).
Int. J. Environ. Res. Public Health 2022,19, 14984 7 of 18
For our analyses, we excluded some cases from the beginning because the answers
showed considerable inconsistencies and/or gave hints that the data would be unreliable.
Furthermore, we use only complete cases—that is, cases with valid values for all variables.
This reduces the number of cases to 5301.
3.2. Variables and Their Operationalization
In Figure 1, we have five “boxes” of variables for which we need empirical measures:
noise annoyance; socioeconomic variables; noise exposure; housing attributes; and envi-
ronmental susceptibility. A further group not shown in Figure 1is a set of covariates that
serve as statistical controls. In this section, we merely describe the measurement of these
variables—without descriptive statistics, which will be given in Section 3.3.
Noise annoyance. Our crucial dependent variable is annoyance resulting from residen-
tial road traffic noise. For its measurement we used the standard 11-point scale, ranging
from “0 = not annoyed at all” to “10 = very much annoyed” [
44
]. However, we modified
the standard item. We did not ask respondents to think about the last 12 months when
they were at home, but—without specifying a timeframe—to think about their situation
at home under four different conditions. Our question wording was as follows: “When
you are at home in your dwelling, how strongly do you feel annoyed by road traffic noise,
(1) during the day when the dwelling’s windows are open, (2) during the day when win-
dows are closed, (3) during the night when windows are open, and (4) during the night
when windows are closed?” Adding up the answers for the four constellations and dividing
the sum by 4 yields our dependent variable “noise annoyance,” with a range from 0 to 10.
Socioeconomic variables. Based on the topic of interest in this article, socioeconomic
characteristics are the most important independent variables. As already described in
Section 2.2, we refer to migration background, education, and income for their measure-
ment. Depending on the country of origin, a respondent is assigned a migration status
independently of citizenship. We distinguish (a) no migration background, (b) European
and other Western countries (North America, Australia), and (c) Africa, Asia, and South
America. Although group (c) is very heterogeneous, shortages of socioeconomic resources
as well as difficulties of social integration can be assumed to be more pronounced in this
group. Education is measured by years of schooling typically needed to achieve a specific
educational level. A household’s income situation is captured by the monthly net equiv-
alent household income, using the modified OECD scale. To make incomes comparable
between Germany and Switzerland, we converted Swiss Francs into Euros and adjusted
the income according to purchasing power parity (PPP). This means that (monthly net
equivalent) “household income” is measured in PPP-adjusted Euros.
Noise exposure. The noise exposure data were not gleaned from the survey, but from
external sources, i.e., from administrative noise registers in the four cities. The addresses
of our respondents denoted their exact place of residence. We first determined the spatial
coordinates for these locations. Based on these coordinates, fine-grained data on local road
traffic noise were merged with the survey data. Fine-grained means the data focus directly
on the building where the respondents lived. To capture the level of noise exposure we
refer to the day-evening-night level (Lden). This measures noise exposure in decibels dB,
gives a weighted 24 h average, and applies the usual penalties for evening and nighttime
noise [45]. Appendix A.1 provides additional information about our noise exposure data.
Housing attributes. In Section 2.2, we introduced the four housing attributes “dwelling
size,” “bedroom facing the street,” “window quality,” and “dwelling with outdoor garden”.
Dwelling size is measured in m
2
(divided by 10). Whether or not the respondent’s bedroom
faces the street is a dummy variable. The respondents assessed the quality of the windows of
their dwelling on a 5-point scale, ranging from “1 = very bad” to “5 = very good”. Dwelling
with outdoor garden registers the binary information on whether the respondent’s residence
has a private garden.
Environmental susceptibility. Environmental concern and noise sensitivity are the two
variables in this group. The measurement of environmental concern refers to six items of
Int. J. Environ. Res. Public Health 2022,19, 14984 8 of 18
the environmental concern scale of Diekmann and Preisendörfer [
46
]. Noise sensitivity was
measured by an additive index of five items, adapted from Weinstein’s noise sensitivity
scale [47,48]). Details on the measurement of environmental concern and noise sensitivity
are summarized in Appendix A.2.
Control variables. As statistical controls, we use five variables: gender; age; labor force
participation; household size; and city. Gender is included as a dummy with “1 = woman”.
Age is measured in years (divided by 10). Labor force participation is another dummy
variable with “1 = currently active in the labor market”. Household size registers the
number of persons in the household. With Mainz as reference category, “city” captures the
urban context in the form of three further dummy variables.
3.3. Statistical Procedures and Descriptive Statistics
To examine whether and how socioeconomic background affects annoyance resulting
from traffic noise in the neighborhood, we follow a standard three-step procedure of
mediation analysis [
49
]. This procedure recommends three different regression models that
allow a separation of the total, direct, and indirect effects of our socioeconomic variables:
(1) a regression that includes the socioeconomic variables, but excludes the supposed
mediators to get the total effects; (2) a regression that includes both the socioeconomic
variables and the supposed mediators to get the direct effects; and (3) regressions of the
mediators on the socioeconomic variables to get the first step of the indirect effects.
The descriptive statistics of the variables relevant for these regressions are given in
Table 1. Concerning the socioeconomic variables, the table shows that 9% of the respondents
have a Western and 16% a non-Western migration background. The mean of education is
15.2 years. The average PPP-adjusted household income is 2930 Euros.
Table 1. Variables and their descriptive statistics.
Variable Obs. Mean Std. Dev. Min. Max.
Noise annoyance 5301 2.26 2.29 0 10
Migration background
No 3997 0.75 0.43 0 1
Yes, European/Western 445 0.09 0.28 0 1
Yes, non-Western 859 0.16 0.37 0 1
Education in years 5301 15.2 2.73 8 18
Household income/1000 5301 2.93 1.47 0.20 10.00
Road traffic noise Lden 5301 52.96 7.29 32.17 81.09
Dwelling size in m2/10 5301 9.36 4.22 0.80 30.00
Bedroom facing the street 5301 0.52 0.50 0 1
Window quality 5301 3.71 1.06 1 5
Dwelling with outdoor garden 5301 0.48 0.5 0 1
Environmental concern 5301 3.53 0.78 1 5
Noise sensitivity 5301 3.16 0.87 1 5
Woman 5301 0.54 0.50 0 1
Age in years/10 5301 4.25 1.36 1.80 7.00
Labor force participation 5301 0.75 0.44 0 1
Household size 5301 2.46 1.19 1 8
City
Mainz 1219 0.23 0.42 0 1
Hanover 906 0.17 0.38 0 1
Bern 1686 0.32 0.47 0 1
Zurich 1490 0.28 0.45 0 1
Although noise annoyance, our final dependent variable, is not normally distributed
(mean = 2.3 and median = 1.5) and therefore OLS regressions do not fit exactly, we decided
in favor of OLS models—instead of binary logistic regressions (with % highly annoyed
as dependent variable). OLS models have the advantage that they exploit the data of the
11-point scale more fully than 0/1 logistic regressions. To account for the city contexts, all
Int. J. Environ. Res. Public Health 2022,19, 14984 9 of 18
the models incorporate the city dummies Hanover, Bern, Zurich as fixed effects (Mainz
serves as reference). Furthermore, all models use gender, age, labor force participation, and
household size as statistical controls.
4. Empirical Results
4.1. Total Effects of Socioeconomic Variables
Models 1a to 1c in Table 2—the so-called reduced-form regressions that leave out
the endogenous mediator variables—are appropriate to capture the total effects of our
socioeconomic variables (i.e., their direct and indirect effects together). The table displays
unstandardized regression coefficients and—in parentheses—their absolute t-values. Model
1a shows significant positive total effects of the two migration background dummies. This
means that (as expected) respondents with a migration background complain more often
about road traffic noise in their neighborhood. According to Model 1b, education tends to
have a negative total effect, but the effect is not significant. Household income, however,
yields a highly significant negative total effect on annoyance, and this is in line with
expectations from environmental inequality research.
Table 2. Factors affecting annoyance due to road traffic noise (OLS regressions).
Model 1a Model 1b Model 1c Model 2
Migration background
European/Western 0.25 *
(2.10)
0.08
(0.76)
Non-Western 0.26 **
(3.03)
0.03
(0.34)
Education in years −0.02
(1.55)
0.01
(0.15)
Household income/1000 −0.17 ***
(7.08)
−0.06 *
(2.36)
Road traffic noise Lden 0.12 ***
(33.41)
Dwelling size in m2/10 −0.01
(0.81)
Bedroom facing the street 0.82 ***
(15.35)
Window quality −0.43 ***
(17.16)
Dwelling with outdoor garden
−0.26 ***
(4.55)
Environmental concern 0.19 ***
(5.54)
Noise sensitivity 0.50 ***
(16.15)
Woman −0.03
(0.45)
−0.03
(0.51)
−0.07
(1.14)
−0.10
(1.88)
Age in years/10 −0.14 ***
(5.96)
−0.14 ***
(6.07)
−0.12 ***
(5.22)
0.01
(0.17)
Labor force participation −0.02
(0.23)
0.01
(0.05)
0.13
(1.72)
0.09
(1.48)
Household size −0.02
(0.71)
−0.01
(0.45)
−0.03
(1.33)
0.03
(1.02)
Int. J. Environ. Res. Public Health 2022,19, 14984 10 of 18
Table 2. Cont.
Model 1a Model 1b Model 1c Model 2
City
Hanover −0.13
(1.27)
−0.12
(1.22)
−0.16
(1.55)
−0.46 ***
(5.44)
Bern −0.33 ***
(3.79)
−0.31 ***
(3.55)
−0.19 *
(2.16)
0.01
(0.14)
Zurich −0.02
(0.27)
0.04
(0.41)
0.20 *
(2.17)
0.18 *
(2.23)
Constant 2.99 ***
(19.60)
3.30 ***
(13.84)
3.32 ***
(21.02)
−5.11 ***
(15.45)
Adj. R21.1% 1.0% 1.8% 31.8%
No. of cases 5301 5301 5301 5301
Notes: Unstandardized regression coefficients with absolute t-values in parentheses. * p< 0.05, ** p< 0.01, *** p< 0.001.
Nevertheless, the fit values (adj. R
2
) of Models 1a–1c are very low. Thus, when it
comes to annoyance caused by traffic noise, social background variables play a certain role,
but it is evident that they are not dominant predictors.
4.2. Direct Effects of Socioeconomic Variables
The effect pattern of the socioeconomic variables clearly changes when Model 2 in
Table 2additionally takes the supposed mediators (noise exposure, housing attributes, and
environmental susceptibility) into account and thus shifts the analysis to the direct effects
of the socioeconomic variables. Both migration dummies are no longer significant. Supple-
mentary analyses (not shown here) reveal that it is mainly the inclusion of noise exposure
and environmental concern that is responsible for the dropping away of the migration
background effects. Whereas the total education effect pointed in a negative direction, there
is definitely no direct effect of education on noise annoyance. Supplementary analyses
(again not shown here, but see Table 3below) suggest that if education plays a role, it is
mainly via its influence on environmental concern and on noise sensitivity. Contrary to our
prediction, the direct income effect remains statistically significant in Model 2. However,
compared to the total income effect, the direct effect is much lower. The total income
effect is nearly three times as strong as the direct effect (Model 1c versus Model 2). This
implies that the indirect income effects contribute more to the reduced noise annoyance of
high-income households than the direct effect. It further implies that empirical studies that
restrict their interest to the direct income effects (as is the case with most studies in noise
research) underestimate the importance of income and financial resources for subjective
annoyance due to road traffic noise.
Gauged by the size of the t-values of the regression coefficients in Model 2, noise exposure
is the most important influence factor on noise annoyance, followed by window quality, noise
sensitivity, and whether or not the bedroom faces the street. The findings with respect to
noise exposure and noise sensitivity correspond to prior studies in noise research (Section 2.1).
The effect of the quality of the windows is remarkably strong, and this means in practice
that high-quality soundproofed windows are a very effective way to reduce residential noise
annoyance. The significant positive effect of environmental concern on noise annoyance is
a finding that up until now has not been a prominent topic in noise research. Contrary to
our expectations, dwelling size shows no direct effect in Model 2. With adj. R
2
= 31.8%,
Model 2 fits the data much better than Models 1a–1c. Compared to the direct effects of noise
exposure, noise sensitivity and the two housing attributes of window quality and location of
the bedroom, the direct effect of income is small and much weaker.
Taken together, the regression coefficients in Model 2 confirm the hypothesis of miss-
ing or at least only weak direct effects of socioeconomic variables on noise annoyance.
Although this more or less corresponds to “the prevailing view” in noise research (see
Int. J. Environ. Res. Public Health 2022,19, 14984 11 of 18
again, Section 2.1), we can claim for our study that our measurement of social background
characteristics has been more refined than that used in other noise studies. Additionally,
based on this refined measurement, we have found that if there are direct effects of socioe-
conomic variables, they mainly hinge on income and financial resources shielding against
noise annoyance.
4.3. Indirect Effects of Socioeconomic Variables
This “shielding capacity” of financial resources becomes more evident when we
look at the indirect pathways theoretically suggested and explained above. Figure 1
specified seven potential mediator variables: noise exposure, four housing attributes, and
two environmental susceptibility variables. Using them as dependent variables, Table 3
presents the results of OLS regressions; each includes the socioeconomic variables and
our set of control variables. According to Model 2 in Table 2, six of the seven supposed
mediators show significant direct effects on noise annoyance, and we therefore focus on
these mediators. The exception is the housing attribute “dwelling size” that—contrary to
our expectations—does not (at least not directly and independent of the other housing
attributes) contribute to a significant reduction of noise annoyance.
Table 3.
Factors affecting noise exposure, housing attributes, and environmental susceptibility
(OLS regressions).
Road Traffic
Noise Lden
Dwelling
Size
Bedroom Facing
the Street
Window
Quality
Dwelling with
Outdoor Garden
Environmental
Concern
Noise
Sensitivity
Migration
background
European/Western
0.77 *
(2.08)
−0.71 ***
(4.21)
0.04
(1.49)
0.01
(0.18)
−0.05 *
(2.16)
−0.08 *
(2.00)
0.09 *
(2.06)
Non-Western 1.00 ***
(3.60)
−1.11 ***
(8.82)
0.06 **
(3.22)
−0.14 ***
(3.37)
−0.04 *
(2.40)
−0.31 ***
(10.58)
−0.07 *
(2.05)
Education in years −0.01
(0.37)
0.03
(1.85)
−0.01
(1.48)
0.01
(1.65)
0.01 ***
(3.71)
0.04 ***
(9.36)
0.03 ***
(6.06)
Household
income/1000
−0.29 ***
(3.54)
1.11 ***
(30.44)
−0.03 ***
(4.68)
0.10 ***
(8.77)
0.03 ***
(5.42)
−0.07 ***
(7.77)
0.02 *
(2.24)
Woman −0.32
(1.63)
0.32 ***
(3.62)
−0.01
(0.13)
0.03
(0.97)
0.04 **
(2.99)
0.20 ***
(9.47)
0.12 ***
(4.82)
Age in years/10 −0.61 ***
(8.18)
0.70 ***
(20.81)
−0.01 *
(2.57)
0.07 ***
(6.76)
0.07 ***
(13.41)
−0.02 *
(2.55)
0.04 ***
(4.20)
Labor force
participation
−0.06
(0.27)
−0.46 ***
(4.24)
−0.01
(0.42)
−0.04
(1.10)
−0.01
(0.88)
−0.01
(0.07)
0.03
(1.01)
Household size −0.31 ***
(3.66)
1.93 ***
(51.00)
0.02 ***
(4.24)
0.03 *
(2.51)
0.11 ***
(19.08)
0.03 **
(2.89)
−0.04 ***
(3.64)
City
Hanover 2.44 ***
(7.78)
0.23
(1.60)
−0.04
(1.92)
−0.04
(0.79)
0.09 ***
(4.24)
0.02
(0.60)
0.07
(1.73)
Bern −0.61 *
(2.19)
−0.80 ***
(6.38)
−0.01
(0.27)
0.07
(1.75)
0.06 ***
(3.49)
0.14 ***
(4.76)
−0.26 ***
(7.66)
Zurich 0.47
(1.59)
−1.14 ***
(8.53)
−0.06 **
(2.75)
0.02
(0.57)
−0.12 ***
(6.38)
0.10 **
(3.25)
−0.19 ***
(5.30)
Constant 57.00 ***
(73.81)
−1.18 ***
(3.38)
0.68 ***
(12.60)
2.88 ***
(25.58)
−0.29 ***
(5.76)
3.01 ***
(37.14)
2.63 ***
(28.43)
Adj. R24.1% 41.7% 1.9% 3.8% 11.3% 6.6% 4.0%
No. of cases 5301 5301 5301 5301 5301 5301 5301
Notes: Unstandardized regression coefficients with absolute t-values in parentheses. * p< 0.05, ** p< 0.01, *** p< 0.001.
With respect to the “noise exposure path”—the indirect pathway rooted in environ-
mental inequality research—Table 3reveals that respondents with a migration background
are exposed to higher noise levels at their place of residence, while respondents with a
Int. J. Environ. Res. Public Health 2022,19, 14984 12 of 18
higher income are exposed to lower noise levels. Education, however, is not significant.
This is in line with our hypotheses.
Concerning the “housing attributes path”—inspired by the environmental shielding
hypothesis [
31
] —we also find a confirmation of our expectations. Respondents with a
migration background, and particularly those with an origin in a non-Western country, live
more often in dwellings with a bedroom facing the street, reside less often in dwellings
with high-quality windows, and less often have dwellings with a garden. The opposite is
true for those with a high income. The income effects tend to be stronger than the effects of
migration background. Education shows in the context of the housing attributes only one
significant effect: respondents with more years of schooling live more often in dwellings
with a garden.
The third indirect pathway suggested in Figure 1, the “environmental susceptibility
path,” argues in favor of a mechanism that runs counter to the overall tendency for priv-
ileged social groups to experience lower noise annoyance. For education, the results in
Table 3clearly support the conjecture that highly educated respondents are both environ-
mentally more concerned and more sensitive toward noise. The effects of education stay
significant when we additionally control for noise exposure in the regression equations of
environmental concern and noise sensitivity (not shown in Table 3). Less clear-cut are the
effects of income and migration background. Income yields the expected positive effect
on noise sensitivity, but its effect on environmental concern is negative. As predicted,
respondents with a migration background are environmentally less concerned than those
without such a background. With respect to noise sensitivity, the regressions in Table 3
show a negative effect for respondents with a non-Western, but a positive effect for those
with a Western migration background.
To check the robustness of our results, Appendix A.3 discusses further empirical
analyses, including an estimation of all regressions in the form of a structural equation
model (SEM).
5. Discussion and Conclusions
To understand whether and how social background variables relate to individual
environmental risks, it seems reasonable to begin with two basic insights. (1) Objective
exposure to environmental risks should be separated from their subjective perceptions and
assessments. These two sides often do not go hand in hand, and—given the same level
of exposure—education, income, and other socioeconomic variables may induce different
subjective reactions. (2) The effects of socioeconomic variables on the subjective perceptions
and evaluations of environmental risks tend to be underestimated, because they are not
direct, but predominantly indirect. They are mediated by other variables, implying indirect
paths, which have to be taken into account in adequate empirical appraisals.
We have demonstrated the validity of these two basic and more general propositions
in an empirical application to noise annoyance caused by residential road traffic. From
ample noise research, we know that besides objective noise exposure several other factors
influence subjective noise annoyance. Our results clearly support this insight. However,
whereas mainstream noise research regularly states that education, income, social status,
etc. do not have significant effects on noise annoyance, we find that they do have such
effects, albeit mainly indirect. Based on our theoretical model and our empirical results,
three indirect paths and mechanisms deserve special attention: a noise exposure path; a
housing attributes path; and an environmental susceptibility path.
Socioeconomic resources create opportunities for individuals to choose a place of
residence with lower noise exposure, and this usually also reduces noise annoyance. When
there is road traffic noise in the neighborhood, individuals and households with more
socioeconomic resources can shield themselves better against this noise via more favorable
housing conditions. They have larger dwellings, less often a bedroom facing the street,
more often high-quality soundproofed windows, and more often a backyard garden. Our
analyses demonstrate that high-quality windows in particular effectively reduce indoor
Int. J. Environ. Res. Public Health 2022,19, 14984 13 of 18
noise and thus noise annoyance. These structural advantages notwithstanding, if road
traffic noise characterizes a neighborhood, privileged households tend to react with more
feelings of anger than less privileged ones. They have and “can afford” a higher noise
sensitivity and are environmentally more concerned, and this stimulates—at the same
level of noise exposure—more annoyance. The finding of an increased environmental
susceptibility on the part of privileged people is well in line with the assumption of local
environmental quality as a “luxury good” [50].
Looking at the relative strength of migration background, education, and income
as separate aspects of the socioeconomic standing, our results suggest that migration
background and income mainly unfold along the noise exposure and housing attributes
paths. Thereby, income tends to be more important than migration background. Education,
on the other hand, is most relevant to the environmental susceptibility path.
Like other studies, our study has limitations and weaknesses. All our findings pertain
to annoyance due to urban road traffic noise. It would be desirable to apply our model
with its three indirect pathways to other local environmental risks. If the results also hold
for the subjective assessments of other risks, this could strengthen the conclusion that the
effects of unequal socioeconomic resources are more profound than is often presumed in
noise research.
Our study pertains to four European cities and thus has a local restriction. At best, the
selected cities can be seen as more or less typical European cities, but surely do not represent
cities in less developed countries. Of course, it would be preferable to investigate additional
cities—cities in other parts of the world and cities with more pronounced social inequalities.
The noise exposure path rests on the assumption of selective migration processes in
reaction to traffic noise. Our cross-sectional data do not allow us to test this assumption,
but our literature review revealed that the empirical validity of selective move-out and
move-in processes, induced by local environmental bads, is far from trivial.
The relationship between outdoor road traffic noise and subjective noise annoyance in the
dwelling is mediated by the indoor noise level, which certainly differs from the outdoor noise
level. Particularly with respect to the housing attributes path, it would be helpful to know
this level of indoor noise, but we did not have data on indoor noise exposure. Furthermore, it
would be useful to take other characteristics of the buildings (e.g., whether they have façade
insulation) and the dwellings (e.g., the internal arrangement of the rooms) into account to
gain a better understanding of social differences in private noise prevention strategies.
Our finding that people with lower education and lower income and with a non-
Western migration background have a lower noise sensitivity should not be misunderstood.
It does not necessarily mean that less privileged people personally suffer less from a given
level of noise and that the negative health consequences of noise are less serious. It is well
known that the objective noise level is detrimental to health even when people seemingly
adapt to it. Privileged social groups are more eager to voice protests against noise and
more engaged in active opposition, and this may actually spill over into stronger feelings
of annoyance. Consequently, it seems to be a challenging research topic to elaborate the
details of the interrelationships between socioeconomic variables, noise sensitivity, and
other subjective reactions to noise.
Author Contributions:
All authors contributed equally to the manuscript. All authors have read and
agreed to the published version of the manuscript.
Funding:
This work was supported by the Swiss National Science Foundation SNSF (project
100017E
−
154251) and the German Research Foundation DFG (projects PR 237/7-1 and KU 1926/3-1).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data are accessible upon request from the authors (see also https:
//search.gesis.org/research_data/SDN-10.7802-1993, accessed on 10 October 2022).
Conflicts of Interest: The authors declare no conflict of interest.
Int. J. Environ. Res. Public Health 2022,19, 14984 14 of 18
Appendix A
Appendix A.1. Administrative Data on Road Traffic Noise in the Four Cities
For big cities such as Mainz and Hanover, the Environmental Noise Directive of
the European Union 2002/49/EC mandates the publication of strategic noise maps [
51
].
According to this directive, noise is modelled at the noisiest façade of every building at 4
m above ground. The noise data are made available as a raster map on a 10 m grid. For
Mainz, such data for 2012 was provided by the Grün- und Umweltamt Mainz [
52
]. No new
maps for road traffic noise have been created up to the time of our study, as changes since
2012 have been deemed minor. For the purpose of our analyses, the original Lden values
in categories of 5 dB are replaced by category midpoints. The city of Hanover provided
road traffic noise data as LAeq values for 2015 for daytime (06.00–22.00) and nighttime
(22.00–6.00). These were then used to estimate Lden values by computing Ldn (with a
penalty of 10 dB for nighttime noise) and adjusting it according to the conversion term of
+1 dB suggested by Brink et al. [45].
For the Swiss cities of Bern and Zurich, the Swiss Federal Office for the Environ-
ment [
53
] provided road traffic noise data from the national noise monitoring database
sonBASE for 2015. In sonBASE, noise is modelled at multiple façade points on every
floor of a building. We received noise values for all façade points of the buildings on our
address lists. However, in some cases, the addresses did not correspond to buildings for
which noise was modelled but had been matched to nearby but different buildings, to a
group of adjacent buildings, or to buildings that had been demolished and replaced in
the meantime. In order to reduce the impact of these issues, only façade points both with
a radius of 20 m from our respondents’ coordinates and on the same building floor as
the respondents’ apartments were considered relevant. For this purpose, the respondents’
floors were adopted from the Swiss Register of Buildings and Dwellings [
54
]. If no façade
points on the given floor were found, a nearby floor was assigned (at a maximum distance
of
±
2.5 floors from the original floor). All cases without any points within the 20 m radius
were inspected visually using QGIS. This revealed that for 20 buildings, the reason for
the points being at a greater distance was the geometry of the building (typically a very
large building). In these cases, all of the buildings’ façade points on the relevant floor were
considered meaningful. In the next step, Lden values were computed based on A-weighted
long-term average sound levels (LAeq) for daytime (7.00–19.00), evening (19.00–23.00), and
nighttime (23.00–7.00), applying the usual penalties for evening and nighttime noise of 5 dB
and 10 dB, respectively [
45
]. Given the methodological differences in the measurement of
residential noise exposure in the two German and the two Swiss cities, direct comparisons to
the German and Swiss administrative noise data should be handled with reasonable care.
Appendix A.2. Measurement of Environmental Concern and Noise Sensitivity
Environmental concern. Question wording: Using a scale from 1 (strongly disagree)
to 5 (strongly agree), what is your position with respect to the following statements?
(1) I am afraid when I think about the future environmental conditions for our children and
grandchildren. (2) If we continue our current lifestyle, we run the risk of an environmental
catastrophe. (3) The majority of people do not act in an environmentally responsible way.
(4) In my opinion, environmental problems are greatly exaggerated by proponents of the
environmental movement. (5) It is still true that politicians are doing far too little to protect
the environment. (6) To protect the environment, we should be willing to constrain our
current standard of living. An additive index of environmental concern was constructed
with a range from 1 to 5 (item 4 reversed, sum of the six items, divided by six). A principal
components analysis shows that the environmental concern items load on a single factor
(eigenvalue = 3.12; explained variance = 52%; all factor loadings > 0.50). Cronbach’s alpha
for the environmental concern scale is 0.81.
Noise sensitivity. Question wording: Please answer on a scale from 1 (strongly disagree)
to 5 (strongly agree) whether you agree with the following statements. (1) I get annoyed
when my neighbors are noisy. (2) I get used to most noises without much difficulty.
Int. J. Environ. Res. Public Health 2022,19, 14984 15 of 18
(3) I find it hard to relax in a place that is noisy. (4) I get mad at people who make noise that
keeps me from falling asleep or getting work done. (5) I am sensitive to noise. An additive
index of noise sensitivity was constructed with a range from 1 to 5 (item 2 reversed, sum
of the five items, divided by five). A principal components analysis shows that the noise
sensitivity items load on a single factor (eigenvalue = 2.72; explained variance = 54%; all
factor loadings > 0.50). Cronbach’s alpha for the noise sensitivity scale is 0.79.
Appendix A.3. Supplementary Robustness Analyses
(1) Missing values. In total, 7540 respondents participated in our survey. Our complete
case regression analyses, however, include only 5301 cases. The main reason for this
considerable reduction of the number of cases are missing values of the income variable.
We employed multiple imputation considering missing values on all key variables (not
shown here), but this did not change the results of the regression models. As the imputation
of missing values is also based on several (strong) assumptions, we finally worked with the
observed values in our article.
(2) Binary logistic regression instead of OLS regressions. As a robustness check, we re-ran
all OLS regressions, which had noise annoyance as a dependent variable, also as binary
logistic regressions with 1 = % highly annoyed (not shown here), and can report that the
results are similar, with the general tendency that the effects of the covariates are more
clear-cut for the OLS than for the logistic regressions.
(3) Variables “bedroom facing the street” and “dwelling with outdoor garden”. Although the
dependent variables “bedroom facing the street” and “dwelling with outdoor garden” are
0/1 variables, Table 3of the main text gives OLS regressions for them. This means that the
regression equations are linear probability models. Additional analyses (not shown here)
reveal that the results of corresponding binary logistic regressions are very similar.
(4) Structural equation model. In Section 4, we presented regressions aiming separately
at the total, direct, and indirect effects of socioeconomic variables. Our results are stable
and robust when we estimate a structural equation model (SEM) considering all paths si-
multaneously. Table A1 below shows the coefficients of this SEM. The model has acceptable
goodness of fit measures when including three error covariances.
Table A1. Path model of factors affecting annoyance due to road traffic noise.
Noise
Annoyance
Road Traffic
Noise Lden
Dwelling
Size
Bedroom
Facing the
street
Window
Quality
Dwelling
with Outdoor
Garden
Environmental
Concern
Noise
Sensitivity
Standardized coefficients
Migration background
European/Western 0.01
(0.74)
0.03 *
(2.14)
−0.06 ***
(4.96)
0.02
(1.42)
0.01
(0.14)
−0.03 *
(2.45)
−0.03 *
(2.05)
0.03 *
(2.12)
Non-Western 0.01
(0.35)
0.05 ***
(3.50)
−0.09 ***
(7.60)
0.05 ***
(3.31)
−0.05 ***
(3.33)
−0.03
(1.88)
−0.15 ***
(10.54)
−0.03 *
(2.18)
Education in years 0.01
(0.14)
−0.01
(0.37)
0.02
(1.68)
−0.02
(1.50)
0.03
(1.65)
0.05 ***
(3.69)
0.14 ***
(9.36)
0.09 ***
(6.06)
Household income/1000 −0.04 *
(2.37)
−0.06 ***
(3.47)
0.38 ***
(29.24)
−0.08 ***
(4.72)
0.14 ***
(8.76)
0.08 ***
(5.02)
−0.13 ***
(7.81)
0.04 *
(2.34)
Road traffic noise Lden 0.39 ***
(33.47)
Dwelling size in m2/10 −0.01
(0.84)
Bedroom facing the street 0.18 ***
(15.39)
Window quality −0.20 ***
(17.19)
Dwelling with
outdoor garden
−0.06 ***
(4.55)
Int. J. Environ. Res. Public Health 2022,19, 14984 16 of 18
Table A1. Cont.
Noise
Annoyance
Road Traffic
Noise Lden
Dwelling
Size
Bedroom
Facing the
street
Window
Quality
Dwelling
with Outdoor
Garden
Environmental
Concern
Noise
Sensitivity
Environmental concern 0.07 ***
(5.55)
Noise sensitivity 0.19 ***
(16.17)
Woman −0.02
(1.88)
−0.02
(1.62)
0.04 ***
(3.48)
−0.01
(0.14)
0.01
(0.97)
0.04 **
(2.89)
0.13 ***
(9.47)
0.07 ***
(4.85)
Age in years/10 0.01
(0.16)
−0.11 ***
(8.09)
0.21 ***
(19.20)
−0.04 **
(2.67)
0.09 ***
(6.72)
0.17 ***
(12.76)
−0.04 **
(2.62)
0.06 ***
(4.34)
Labor force participation 0.02
(1.48)
−0.01
(0.29)
−0.05 ***
(4.07)
−0.01
(0.42)
−0.02
(1.10)
−0.01
(0.80)
−0.01
(0.07)
0.01
(0.99)
Household size 0.02
(1.14)
−0.04 **
(3.09)
0.51 ***
(46.71)
0.06 ***
(4.01)
0.03 *
(2.39)
0.21 ***
(16.15)
0.04 **
(2.65)
−0.04 **
(2.84)
City
Hanover −0.08 ***
(5.46)
0.13 ***
(7.82)
0.02
(1.19)
−0.03
(1.95)
−0.01
(0.81)
0.06 ***
(4.01)
0.01
(0.58)
0.03
(1.78)
Bern 0.01
(0.14)
−0.04 *
(2.18)
−0.09 ***
(6.33)
−0.01
(0.28)
0.03
(1.75)
0.06 ***
(3.40)
0.08 ***
(4.76)
−0.14 ***
(7.65)
Zurich 0.04 *
(2.22)
0.03
(1.60)
−0.13 ***
(8.53)
−0.05 **
(2.77)
0.01
(0.56)
−0.11 ***
(6.37)
0.06 **
(3.25)
−0.10 ***
(5.30)
Variances of errors
Noise annoyance 0.69
Road traffic noise Lden 0.96
Dwelling size in m2/10 0.62
Bedroom facing the street 0.98
Window quality 0.96
Outdoor garden 0.90
Environmental concern 0.93
Noise sensitivity 0.96
Covariances of errors
Road traffic noise, bedroom
facing the street
0.13 ***
(9.59)
Dwelling size in m2/10,
outdoor garden
0.19 ***
(14.60)
Environmental concern,
noise sensitivity
0.12 ***
(9.00)
Goodness of fit measures
Chi2 (model vs. saturated) 205.82 ***
Root mean squared error of
approximation (RMSEA) 0.04
Comparative fit index (CFI) 0.97
Standardized root mean
squared residual (SRMR) 0.02
Notes: Standardized coefficients with absolute z-values in parentheses. * p< 0.05, ** p< 0.01, *** p< 0.001. The
model was estimated using Stata’s sem command for structural equation modeling.
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