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SATISFIED BUT THINKING ABOUT LEAVING: THE
REASONS BEHIND RESIDENTIAL SATISFACTION AND
RESIDENTIAL ATTRACTIVENESS IN SHRINKING
PORTUGUESE CITIES
Ana Paula Barreira, Luís Catela Nunes, Maria Helena Guimarães, and Thomas Panagopoulos
Corresponding author: Ana Paula Barreira (e-mail: [email protected]; voice: +351 289 800
900). Centre for Advanced Studies in Management and Economics (CEFAGE), University of
Algarve, Campus de Gambelas, Building 9, P–8005–139 Faro, Portugal.
Luís Catela Nunes (e-mail: [email protected]). Nova School of Business and Economics,
Campus de Campolide, P-1099-032 Lisbon, Portugal.
Maria Helena Guimarães (e-mail: [email protected]). Landscape Dynamics and Social
Processes Group of Institute of Mediterranean Agricultural and Environmental Sciences
(ICAAM), University of Évora, Núcleo da Mitra, Edifício Principal, Apartado 94, P-7002-554
Évora, Portugal.
Thomas Panagopoulos (e-mail: [email protected]). Research Centre for Spatial and
Organizational Dynamics (CIEO), Universidade do Algarve, Campus de Gambelas, Building 9,
P–8005–139 Faro, Portugal.
Acknowledgements
This work was funded by the European Regional Development Fund through the Operational
Programme for Competitiveness Factors and by national funding from the Foundation for
Science and Technology under project EXPL/ATP-EUR/0464/2013 entitled ‘Policy guidelines
for regeneration in shrinking cities’.
Abstract
Creating liveable cities is a policy priority, especially for cities that are experiencing population loss. A decline in the number of inhabitants is commonly associated with low levels of residential satisfaction. However, such a supposition does not often find empirical support in shrinking cities. In the present study, we identify variables that influence the level of residential satisfaction, as well as those influencing residential attraction (captured by the intention of current residents to leave their city in the near future). The study is based on a face-to-face questionnaire administered to 701 residents in four shrinking Portuguese cities. As expected, lower levels of residential satisfaction lead to an increased intention to leave the city. The results also show that the variables explaining residential satisfaction mostly differ from those explaining residential attractiveness. The specific characteristics of each city influence citizens’ assessment of residential satisfaction, but the variables impacting residential attractiveness are universal.
Keywords: shrinking cities; residential satisfaction; residential attractiveness; intention to move
out; Portuguese cities
Word count: 8833
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1. IntroductionUntil recently, the dominant view of researchers, planners, and politicians was that urban success
required urban growth because growth was a guarantee of individuals’ well-being and standard
of living (International Organization for Migration [IOM], 2011). However, contrary to some
predictions, empirical studies have shown that residents in shrinking cities (i.e., those undergoing
population decline) experience satisfaction from living in such cities (Delken, 2008; Hollander,
2011). The attachment of residents to their city, along with improvements in the quality of life
resulting from a city becoming smaller (Pallagst, Schwarz, Popper & Hollander, 2009), may
explain their high levels of satisfaction with their city. However, and despite evidence for
residential satisfaction, certain American and German shrinking cities are continuing to lose
inhabitants (Wiechmann & Pallagst, 2012). The observed decline emphasizes the need to identify
the characteristics of such cities that can contribute to residential attractiveness, that is, the
characteristics that counter or reinforce inhabitants’ intention to leave. However, studies that
have investigated how the attributes of cities influence both residents’ satisfaction and their
assessment of cities’ attractiveness are scarce, with the work of McCrea, Shyy, and Stimson
(2014) being an exception. Identifying the attributes that explain both residential satisfaction and
the attractiveness of the place where individuals live is crucial for informing policies and for
defining planning strategies aimed at achieving sustainable development.
Research into the characteristics and dynamics of shrinking cities has captured the attention of
scholars as the phenomenon spreads in Europe. Only very recently has urban population decline
begun to be analysed in Portugal (Panagopoulos & Barreira, 2012; Sousa, 2010), but the number
of publications is increasing (e.g., Alves, Barreira, Guimarães & Panagopoulos, 2016;
Guimarães, Barreira & Panagopoulos, 2015; Guimarães, Nunes, Barreira & Panagopoulos, 2016;
Panagopoulos, Guimarães & Barreira, 2015; Sousa and Pinho, 2015). However, little is known
about the main attributes that either help to secure individuals in a place or force them to leave
(residential attractiveness), or about the level of residential satisfaction that individuals obtain
from cities whose populations are declining. Through analysing data obtained from a face-to-
face questionnaire survey conducted in four shrinking Portuguese cities, this study aims to gain a
deeper understanding of the above issues by identifying the characteristics that influence
inhabitants’ level of satisfaction with their city of residence and also the factors that influence
their intention to leave in the near future.
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2. Literature Review
2.1. Inhabitants’ Assessment of Residential Satisfaction
The ways in which individuals in a society obtain satisfaction is a topic of interest in several
research areas, including economics, sociology, and psychology (Lambiri, Biagi & Royuela,
2007; Sirgy et al., 2006). Satisfaction can be related to different aspects of life such as health,
familial relationships, work, income, and standard of living (e.g., Lee, 2008; Sirgy, Gao &
Young, 2008). One aspect that impacts an individual’s satisfaction is the satisfaction obtained
from the place of residence, which implies a confrontation between the individual’s
achievements in that place and his/her aspirations or needs (McCrea, Stimson & Western, 2005;
Sirgy, Rahtz, Cicic & Underwood, 2000). Residential satisfaction is a multi-dimensional
construct (Francescato, 2002) that involves assessment of the spatial, human, and functional
aspects of a place (Amérigo, 2002), and is defined as the level of pleasure or gratification that
individuals obtain from the place in which they live (Bonaiuto, Fornara & Bonnes, 2003).
The literature describes residential satisfaction as being influenced by inhabitants’
sociodemographic characteristics as well as by the social and physical attributes of the place
where they live. Residents’ characteristics such as age (Bonaiuto, Aiello, Perugini, Bonnes &
Ercolani, 1999; Lu, 2009), income (Dekker, de Vos, Musterd & van Kempen, 2011; Grinstein-
Weiss et al., 2011), and education (Lee, 2008; Lu, 2009) are commonly identified as positively
influencing residential satisfaction (i.e., older, wealthier, and more educated individuals are more
satisfied with their place of residence). However, some investigations have failed to find a
relationship between income and residential satisfaction (e.g. Mellander, Florida & Stolarick,
2011). Moreover, some studies have reported a negative relationship between education and
residential satisfaction, as individuals with higher levels of education may have more difficulty
in fulfilling their expectations in particular places (Filkins, Allen & Cordes, 2000; Hur &
Morrow-Jones, 2008). Gender also makes a difference, with women stating that they are more
satisfied with their place of residence than are men (Kamalipour, Yeganeh & Alalhesabi, 2012;
Perez, Fernandez-Mayoralas, Rivera & Abuin, 2001).
Typically, the following have also been shown to positively influence inhabitants’ assessment
of residential satisfaction: a longer duration of residence (Amérigo & Aragonés, 1997; Bonaiuto
et al., 1999); being a homeowner (Dekker et al., 2011; Perez et al., 2001); being surrounded by a
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pleasant and aesthetic urban environment (Florida, Mellander & Stolarick, 2011; Parkes, Kearns
& Atkinson, 2002); and being part of a social network (Bonaiuto et al., 1999; Parkes et al.,
2002). However, a longer period of residence has been identified by some authors (e.g. Dekker et
al., 2011; Lu, 1999) as affecting residential satisfaction negatively, because a longer contact with
the environment may heighten inhabitants’ perception of the negative aspects of the locality.
Homeownership has also been found by some researchers as having limited or no influence on
citizens’ assessment of residential satisfaction (e.g. Florida et al., 2011; Parkes et al., 2002).
However, residential satisfaction is positively related to the attributes of people’s homes (Azimi
& Esmaeilzadeh, 2017; Perez et al., 2001) and to inhabitants’ level of civic engagement
(Manturuk, Lindblad & Quercia, 2010; Perez et al., 2001).
The existence of certain features in the place where individuals live can increase or decrease
their level of residential satisfaction and can be combined into pull or push factors, respectively,
generally through a statistical technique such as factor analysis. The most commonly reported
features associated with residential satisfaction are the absence of noise, the existence of green
spaces, and the proximity to certain facilities such as commercial areas and schools as well as to
employment and recreational activities (Andersen, 2008; Cao & Wang, 2016; Dekker et al.,
2011; McCrea et al., 2005; Neal & Neal, 2012; Parkes et al., 2002), and such features can be
aggregated into pull factors encapsulating living conditions and accessibility to facilities. In
contrast, features such as the need to maintain homes, a lack of safety, or poor access to work
(Cao & Wang, 2016; McCrea et al., 2005; Woo & Morrow-Jones, 2011) constitute push factors,
which decrease the level of residential satisfaction.
2.2. Inhabitants’ Assessment of Residential Attractiveness
Residential attractiveness is a new concept and still requires a consensual conceptualisation
(Miot, 2015; Niedomysl, 2010). However, in general terms, residential attractiveness can be
defined as place-based preconditions that make prospective inhabitants desire to move there or
that make those already living there desire not to move to another location (Fertner, Groth,
Herslund & Carstensen, 2015). Residential attractiveness can derive from public strategies
aiming to restore the residential functions of a place, thus promoting both the attachment and
attraction of populations (Miot, 2015). Some authors have argued that differences in the
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available features explain differences between urban areas in their capacity to be attractive to
people (Buch, Hamann, Niebuhr & Rossen, 2014; Mellander, Florida & Stolarick, 2011).
Places are viewed as attractive if the available features fulfil the needs, demands, and
preferences of inhabitants (van den Berg, van de Meer & Oligaar, 2006; Niedomysl, 2010). Such
features can be combined through factor analysis into pull factors. In contrast, the absence of
certain features such as educational institutions and hospitals (van den Berg et al., 2006; Braun,
2008) decrease the attractiveness of a place and can be regarded as push factors. The European
Commission (2006) considers that an attractive place has to provide accessibility and mobility,
public services and institutions, an effective economic structure, and an appealing natural and
physical environment, as well as stimulating technological, cultural, and touristic environments.
An increase in the attractiveness of a place tends to be related to job opportunities (van den
Berg et al., 2006; Braun, 2008). A causal relationship between employment and population
movements is found in the literature (IOM, 2011; Seo, 2002). Localities that are more
economically active generate more job opportunities, typically attracting the younger generations
(Lutz, 2001; Zimmermann, 2005) and the more highly skilled workers (Buch et al., 2014; Krabel
& Flöther, 2012). Jointly with economic attractors, housing affordability has also been revealed
as a factor that positively influences migration flows (van den Berg et al. 2006; Seo, 2002).
The attractiveness of a place can also result from place attachment, which derives from the
affective bonds developed with the place of residence (Hidalgo & Hernández, 2001; Sampson &
Goodrich, 2009). Place attachment translates into feelings of pride and identity about the place
(Hernández, Martín, Ruiz & Hidalgo, 2010), into trust in the community (Raymond, Brown &
Weber, 2010), and into a sense of stability and security (Billig, 2006), which reduce the incentive
for residents to move out (Andersen, 2008; Hidalgo & Hernández, 2001). Factors that positively
influence place attachment are the length of residence (Bonaiuto et al., 1999; Lewicka, 2011),
age (Hidalgo & Hernández, 2001; Kamalipour et al., 2012), and homeownership and education
(Abellán & Rojo, 1997; Hidalgo & Hernández, 2001). Women develop greater levels of place
attachment feelings compared with men (Kamalipour et al., 2012).
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2.3. Residential Satisfaction, Residential Attractiveness, and Intention to Stay or To Move Out
Residential satisfaction has an ambiguous relevance in the literature in explaining individuals’
decisions to stay in or move out from a certain location. Low residential satisfaction is
commonly identified as a predictor of out-migration (Andersen, 2008; Kearns & Parkes, 2003).
However, empirical evidence (e.g. Fang, 2006; Livingston, Bailey & Kearns, 2010) also shows
that even when individuals are dissatisfied with the place in which they live, this does not often
extend to an actual move. Moreover, and despite reports of residential satisfaction, when
individuals identify places that they feel are more attractive, such individuals are willing to move
there. Ogu (2002) has emphasized the need to distinguish between ‘real’ problems and uniform
reports of residential satisfaction, as urban areas can possess unpleasant characteristics, which
are not captured only through the assessment of residential satisfaction. This means that
assessing the attractiveness of a place is at least as important as assessing residential satisfaction
in explaining individuals’ decisions to stay or leave.
By examining four different urban environments, including disadvantaged urban areas,
McCrea et al. (2014) found using survey data that inhabitants in those environments reported
similar levels of residential satisfaction, but their evaluations of the attributes making those areas
attractive places differed between environments. In this sense, the measurement of a city’s
attractiveness, rather than the measurement of residential satisfaction, might better characterize
the subjective quality of life. The work of McCrea et al. suggested that the existence of certain
features that ensure residential satisfaction may not be sufficient to prevent inhabitants from
moving out.
2.4. The Context of Shrinking Cities
The issue of shrinking environments has been steadily gaining interest from researchers as the
phenomenon of declining populations spreads to a larger number of countries, regions, and cities
(Khavarian-Garmsir, Pourahmad, Hataminejad & Farhoudi, 2017; Oswalt & Rieniets, 2006;
2007; Turok & Mykhnenko, 2007). Economic transformation and suburbanization processes are
the most cited reasons for the reduction in the number of inhabitants in cities (Haase, Bernt,
Grobmann, Mykhnenko & Rink, 2013). The existence of satellite cities and harsh climatic
conditions are also identified as factors in the Portuguese case (see Guimarães et al., 2015). The
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departure of residents from cities generates housing degradation and brownfields (Hollander &
Németh, 2011; Wiechmann & Pallagst, 2012), which create conditions that favour disorder and a
lack of safety (Dekker et al., 2011; Kleinhans & Bolt, 2013) and a consequent reduction in the
appeal of such cities. In contrast, shrinking cities also provide attracting attributes compared with
growing cities, such as the affordability of housing, the presence of natural amenities, an absence
of traffic congestion, and less pollution (Hollander, 2011; Pallagst et al., 2009).
Given the above, in some areas showing population decline, and in which reports of high
levels of residential dissatisfaction may be expected, individuals reveal that they are in fact
satisfied with their place of residence (Delken, 2008; Hollander, 2011). McCrea et al. (2005)
examined urban domains such as neighbourhoods and metropolitan regions and noted that
population size was not particularly important for the determination of residential satisfaction.
Accordingly, a willingness to change city of residence would not be expected in shrinking cities
because inhabitants report satisfaction with their city; however, in apparent contradiction, data
show decreasing numbers of inhabitants in such cities. This may be due to the fact that as the
number of inhabitants decreases, actual or perceived changes occur in the residential context that
may trigger inhabitants’ wishes to move to another location. The changed or new characteristics
of the place may no longer meet their preferences and needs (Bonaiuto et al., 1999; 2003). As
such, in a context of population decline, although assessments of residential satisfaction may
provide some insights, it is just as critical to understand which features make the city sufficiently
attractive to explain inhabitants’ desire to stay.
In shrinking cities, where the exodus of people may lead to the remaining inhabitants feeling a
psychological sense of low worth, it is crucial to value the attractive features of the city that
might balance negative characteristics, even in cases where reports of residential satisfaction are
found. In shrinking contexts, two strategies can be chosen to deal with it: to accept population
decline but ensure an acceptable level of quality of life for the residents who stay, or to take
actions aimed at reversing the population loss (Haase, Hospers, Pekelsma & Rink, 2012;
Hospers, 2014). Under the first strategy, residential satisfaction seems more important, but under
the second strategy, residential attractiveness assumes critical relevance.
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3. Case Study: The Shrinking Cities of PortugalPortugal has 158 cities, which in 2011 contained 44% of the national resident population. When
records from 1991 to 2011 are compared, 31 cities showed population loss, with 8 of them losing
more than 10% of their inhabitants and a further 6 showing a persistent decline, corresponding to
an overall reduction in the number of inhabitants in these 31 cities of 13.2% between 1991 and
2011 (census data – National Statistics [INE]). Several overlapping causes (see details in
Guimarães et al. 2015) have contributed to the reduction in the number of inhabitants in these
cities. In conjunction with negative rates of natural population change, cases of city shrinkage
due to suburbanization, economic transformation, climatic drivers, or the satellite effect have
been identified. Because the different causes of shrinkage may influence the assessments of
residential satisfaction and of a city’s residential attractiveness, four case-study cities were
selected, namely Oporto, Barreiro, Moura, and Peso da Régua, as cases typifying
suburbanization, economic transformation, climatic drivers, and the satellite effect, respectively.
For a detailed description of the population growth in shrinking cities in Portugal see Alves et al.
(2016).
Oporto, the second most populous city in the country, is located in northwestern Portugal and
was the city that registered the greatest relative population loss (21.5%) between 1991 and 2011
(from 302,500 to 237,600 inhabitants; INE). While Oporto was losing inhabitants, the four
neighbouring municipalities: Gondomar, Maia, Matosinhos and Vila Nova de Gaia gained in the
same period (1991-2011) 133,800 inhabitants (+20.6%), which explains that the main cause of
population decline has been suburbanization. Oporto has consistently gained inhabitants until
1981, moment in which reached its peak, having double the number of inhabitants since the
beginning of the century as the result of the increasing movement of citizens from rural into
urban areas (Alves et al. 2016). Since 1981, the city lost ~ 90,000 inhabitants, putting population
in 2011 at the level registered in 1930.
Barreiro, located on the southern bank of the Tejo River and facing Lisbon (the capital city),
showed the second-largest population loss in relative terms between 1991 and 2011 (21.2%,
from 47,900, its pick, to 37,700 inhabitants; INE). The highest growth of the city occurred
between 1960 and 1981, in which the population almost doubled (an increase of ~ 23,000
inhabitants), due the rapid industrialization of the country occurred during these period,
becoming Barreiro the most important city for the chemical industry (Alves et al. 2016). The
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growth of population was explained by the job opportunities generated by the industry with
higher wages than agricultural and phishing activities. However, such population growth, built
on a mono-activity, led lately to its decline due the lower diversification of the economic basis.
Therefore, the main cause of the population decline was the abrupt closure of factories, which
left behind brownfields and abandoned facilities.
Moura has slowly grown between 1900 and 1930 (average growth of 8.3% per decade),
accelerating its growth until 1960, when the city had its peak with 12,130 inhabitants (an average
growth of 17.6% per decade). Since then the city has been losing inhabitants with a recovery of
7% between 1991 and 2001, returning to decline afterwards (Alves et al. 2016). Accordingly,
Moura exhibited recent city shrinkage, with a decrease in the number of inhabitants of 9% (from
9200 to 8400 inhabitants; INE) between 2001 and 2011. Moura, which is located in the country’s
interior, in the Alentejo region, still relies today on agricultural activities that are less appealing
to the younger as better employment opportunities in the tertiary sector are available in the coast.
Additionally, the harsh environmental conditions in Moura compared with the coast, namely the
maximum average temperature of 32.2ºC in August and the minimum average temperatures of
5.7ºC in January, does not help to retain inhabitants. Further, Moura is subject to intense heat
weaves (e.g., 44.8ºC at 8 of August of 2016).
Peso da Régua, located in the Douro region in northern-central Portugal, showed a persistent,
although small, population decline between 1991 and 2011 (from 10,300 to 10,000 inhabitants;
INE). The nearest town (25 km away) is Vila Real, whose population increased by 33% between
1991 and 2011 (INE). Peso da Régua does not offer higher-education opportunities, whereas
Vila Real does and therefore attracts many young people. Therefore, Peso da Régua is
considered to be a satellite city of Vila Real. The satellite effect explains its growth until 1981
when reached 10,600 inhabitants, exhausting at that time its potential for attracting inhabitants.
The purchasing power in Peso da Régua of 79.2, in 2011, below the national average of 100,
whereas in Vila Real the value was 101.5 (INE), helps explain the decline in inhabitants. The
primary sector is the main economic activity in Peso da Régua which might contribute to the
decrease of the city’s appeal.
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4. Aims and Method
4.1. Research Goals
Although recent empirical studies have demonstrated that inhabitants of shrinking cities report
high levels of residential satisfaction (Delken, 2008; Hollander, 2011), population decline often
tends to become aggravated in such cities. The results of those studies highlight the need to
jointly assess residential satisfaction and the attractiveness of the city as a place in which to live.
The main goal of the present work is to provide insights into the influences on residential
satisfaction and residential attractiveness in the context of shrinking Portuguese cities.
Understanding the drivers of satisfaction and its influence on population movement can be of
great utility for policymakers, who are frequently concerned with the impacts of population
decline. Therefore, we also try to understand how residential satisfaction influences the
willingness to move away from a shrinking city in the near future as well to identify the factors
that influence inhabitants’ intention to leave those cities. Moreover, the paper examines whether
the demographic, socio-economic, and civic engagement characteristics of inhabitants affect
their assessments of the level of residential satisfaction and of their willingness to leave the city
in the near future.
Thus, this work poses the following research questions:
1) Which pull/push factors of shrinking cities influence inhabitants’ assessments of
residential satisfaction and residential attractiveness?
2) Are inhabitants’ assessments of residential satisfaction and residential attractiveness
affected by their demographic, socio-economic, and civic engagement characteristics?
3) Is there a relationship between inhabitants’ residential satisfaction and their intention
to leave a shrinking city?
4.2. Materials, Sample, and Procedure
Data to answer the research questions posed were obtained by conducting a survey with a
questionnaire entitled ‘How to deal with population loss in your city,’ which was conducted
face-to-face in the four selected shrinking cities during July 2014. A total of 701 completed
questionnaires were obtained from individuals aged 18 years or over. A random stratified
sampling scheme was used, which ensured a maximum margin of error of 7.45% for the 95%
confidence interval on the population proportion. The sampling scheme was stratified in two
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steps. First, a stratification was made according to the number of inhabitants in each city and
their distribution in the parishes that comprise each city, namely, Oporto (15 parishes), Barreiro
(3), Moura (3), and Peso da Régua (2), according to data from the 2011 national census. Second,
a stratification was made according to the typology of households characterizing each city1, again
using 2011 national census data. The survey was applied to the following types of households: 1)
one person (15–64 years old) with or without other(s) (<15 years old); 2) one person (>64 years
old); 3) two persons (both 15–64 years old) with or without other(s) (<15 years old); 4) two
persons in which at least one is >64 years old; and 5) three persons (>15 years old) with or
without other(s) (<15 years old).
The questionnaire was composed of three parts. The first part identified the demographic
characteristics of each respondent, including questions regarding age, gender, education level,
and household composition. In this part, respondents were also asked to assess their degree of
residential satisfaction as scored on a five-point scale from 1 (very dissatisfied) to 5 (very
satisfied). The first part also included three other questions regarding the respondent’s perception
of the evolution of the city’s population (declining, stable, or increasing), the respondent’s
intention of leaving the city of residence within one year (answered with ‘1 – yes’ or ‘0 – no’),
and the respondent’s willingness to be involved in activities to deal with the shrinkage
phenomenon. The binary variable ‘intention of living the city of residence within one year’ was
used as a measure of the residential attractiveness of the studied shrinking cities.
The second part of the questionnaire listed the main attributes to be assessed by each
respondent concerning their relevance to the attractiveness of the city. The selection of the listed
attributes was based on the literature and on the specific characteristics of the studied cities. This
part was composed of 24 attributes that may attract inhabitants (pull attributes) and 24 attributes
that may repel inhabitants (push attributes). Respondents were asked to assess the importance of
each attribute according to the following five-point Likert scale: 5 – crucial, 4 – very important,
3 – moderately important, 2 – weakly important, and 1 – irrelevant.
The third part asked about the socio-economic characteristics of the respondent, namely,
homeownership, type and era of construction of his/her house, years of residence in the city,
income, and the number of employed family members.
1 Details can be found in Panagopoulos et al. (2015).
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Table 1 presents descriptive statistics regarding the responses to the first and third parts of the
questionnaire. The distribution of the sample matches the demographic characteristics of each
city as the sampling method took into account the distribution of households by parish and by the
typology of households living in each city. For the purpose of comparing the sample with the
population characteristics of each city, Table 1 also presents the available 2011 census data for
the four studied cities for age, gender, educational level, household size, household income, the
number of people employed in the family, homeownership, and era of construction of the house.
(INSERT TABLE 1 HERE)
The survey showed that individuals’ levels of residential satisfaction were high in shrinking
cities (Figure 1), thus confirming the findings of previous studies (Delken, 2008; McCrea et al.,
2014). For the full sample (all cities), 78% of the respondents were either satisfied or very
satisfied with their city of residence, thus supporting the proposition that shrinkage is not
detrimental to residential satisfaction.
(INSERT FIG. 1 HERE)
Almost all of the respondents of Oporto, the city with the greatest population loss in relative
terms, reported that they were satisfied living there, with 89% expressing satisfaction (scores of 4
or 5). This finding is reinforced by the fact that only a small proportion of the city’s inhabitants
(3%) revealed an intention to leave. Residents of Peso da Régua were also found to be satisfied
with their city, with 80% scoring 4 or 5 for residential satisfaction, and to have a moderate
intention of leaving the city. In contrast, Barreiro, the city with the second-largest decline in
population in relative terms, showed the lowest level of residential satisfaction of the four cities,
with only 59% of respondents assigning a score of 4 or 5. The intrinsic characteristics of Barreiro
as an old industrial city might explain the below-average result. The surveyed inhabitants of this
city were revealed to have the highest intention of leaving (16%) compared with the surveyed
inhabitants of the other cities. The inhabitants of Moura indicated high levels of residential
satisfaction: 89% reported scores of 4 or 5, but they also had a high intention of leaving the city
(10%).
Table 2 presents the 24 pull attributes and 24 push attributes contained in the second part of
the questionnaire. The table also presents five pull factors and four push factors, which were
derived using factor analysis, as detailed in Guimarães et al. (2016). The five pull factors and
four push factors are included in the data analysis presented in the following sections.
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(INSERT TABLE 2 HERE)
4.3. Statistical Models
A logistic regression model was used to assess the relative influence of the respondents’
demographic and socio-economic characteristics, including residential satisfaction, on their
intention of leaving the city of residence (residential attractiveness). Defining the dichotomous
variable yi = 1 if a respondent i intends to leave the city, and yi = 0 if not, the probability that a
respondent intends to leave the city corresponds to the probability that an estimated linear
function of the respondent’s characteristics, plus a random error, exceeds an estimated threshold:
Prob (yi = 1 ) = Prob (1 x1,i +...+ k xk,i +i > c1 ) (1)
where x1,i,..., xk,i are k explanatory variables capturing the characteristics of respondent i, 1 ,..., k
are coefficients, c1 is the threshold, and the random error i is assumed to be logistically
distributed. It follows that the probability of observing yi = 1 is given by:
Prob (yi = 1 ) = F ( -c1 + 1 x1,i +...+ k xk,i ) (2)
where F(z) = 1 / (1 + e-z) is the cumulative logistic distribution.
The parameters of the model, c1, 1,..., k, are estimated by maximum likelihood. Average
marginal effects were calculated to better interpret the estimated coefficients. These effects
capture how the predicted probability of leaving the city is affected by each of the explanatory
variables (see StataCorp, 2013).
The variable ‘residential satisfaction’ was measured on a Likert scale ranging from ‘very
dissatisfied’ to ‘very satisfied’. Because there were very few responses for the two lowest levels
of satisfaction (only 1.3% answered ‘very dissatisfied’, and only 2.6% answered ‘moderately
dissatisfied’), these levels were merged with the third level ‘neither satisfied nor dissatisfied’. To
simplify notation, the levels ‘very dissatisfied’, ‘moderately dissatisfied’, and ‘neither satisfied
nor dissatisfied’ were recoded to 1, ‘moderately satisfied’ was recoded to 2, and ‘very satisfied’
was recoded to 3. An ordinal logistic regression model was used to identify the respondents’
demographic and socio-economic characteristics explaining residential satisfaction. This model
is a simple extension of the logistic regression model. The probability of observing a respondent
stating a satisfaction level j corresponds to the probability that the estimated linear function of
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the respondent’s characteristics, plus the random error, is within the range of thresholds defined
for that level j:
Prob (yi = j ) = Prob (cj-1 <1 x1 +...+ k xk +i < cj ) (3)
where c0, c1, c2, and c3 are the thresholds, with c0 = and c3 = . In this case, the average
marginal effects capture how the predicted probability of the highest level of residential
satisfaction is affected by the explanatory variables. The explanatory variables considered were
respondent’s characteristics, including age, gender, educational level, household size, household
income, number of people employed in the family, homeownership, era of construction of the
house, type of building, years of residence in the city, perception of population change, and
willingness to participate in urban regeneration programs, as well as the five pull and four push
factors described in Table 2. We present only the final estimated models after sequentially
discarding variables that were not statistically significant at the 10% level of significance.
One key feature of our analysis is the fact that the survey data include responses from
residents in four different cities, with each city having a particular shrinkage type. Therefore, we
present the results of two variants of the regression model: one that does not control for the city
of residence (‘non-controlled model’), and one that includes a dummy variable for each city as
explanatory variables (‘controlled model’).
4. Results
4.1. Variables Influencing the Level of Residential Satisfaction
Estimation results for the ordered logistic regression model without including the ‘city’ dummy
variables are presented in Table 3. Older (compared with younger) respondents tend to be more
satisfied. Respondents with higher (compared with lower) education levels are less satisfied, as
are respondents who have lived in the city for longer (compared with shorter) durations.
Regarding the pull factors, high scores for the city’s ‘living conditions’, ‘recreational and
environmental amenities’, and ‘social ties’, all imply high satisfaction levels. In contrast, low
satisfaction levels are reported when high scores are given to push factors related to the city’s
‘shrinking atmosphere’, ‘surroundings and visual attributes’, and ‘working conditions’.
(INSERT TABLE 3 HERE)
14
Estimation results for the model in which the ‘city’ dummy variables were included as
explanatory variables are reported in Table 4. Only the ‘city’ dummy variable for Barreiro is
statistically significant, with its negative sign indicating lower residential satisfaction levels in
this city even after controlling for all the other respondents’ characteristics. The impacts of age
and education level in this controlled model are similar to those in the non-controlled model. The
effect of years of residence is no longer significant, but this variable already had a weak
significance level in the non-controlled model. The major difference between the two models is
the influence of the pull and push factors. In the controlled model, the only significant pull factor
is related to ‘accessibility’ conditions, and it has a positive impact on the level of residential
satisfaction. The pull factors reported as significant in the non-controlled model (i.e., ‘living
conditions’, ‘recreational and environmental amenities’, and ‘social ties’) lose their significance
in the controlled model. For the push factors, the impact of the ‘surroundings and visual
attributes’ push factor is no longer significant, while the remaining factors maintain their
negative impact on satisfaction. We conclude that the ‘surroundings and visual attributes’ push
factor is essentially related to the specificities of the cities. Both the ‘shrinking atmosphere’ and
the ‘working conditions’ push factors negatively affect satisfaction levels, even after controlling
for city-specific characteristics.
(INSERT TABLE 4 HERE)
4.2. Variables Influencing Residential Attractiveness
Table 5 presents the estimated logistic regression model explaining the intention to leave the city
of residence in the near future as a function of the respondents’ characteristics and also including
dummy variables for each city. In this case, the characteristics that are specific to each shrinking
city appear to play no role in explaining the intention to leave after controlling for respondents’
characteristics. The results for the non-controlled model are practically the same and have been
omitted to save space.
(INSERT TABLE 5 HERE)
As expected, the more satisfied respondents report a lower probability of leaving the city in
the near future. However, even after controlling for satisfaction, there are other factors that
influence the intention to leave shrinking cities. As expected, older respondents are less likely to
leave the city. Those more willing to participate in urban regeneration programs have a higher
15
intention of moving out, although the statistical significance of this variable is the weakest (p-
value = 0.064). Employment also plays a role in the intention to leave: the higher the number of
employed people in the household, the lower the likelihood that the respondent will leave the city
in the near future. Similarly, respondents assigning higher scores to the ‘live and work’ pull
factor also have a lower intention of leaving the city. In contrast, respondents assigning higher
scores to the push factors ‘lack of services’ and ‘shrinking atmosphere’ have a higher intention
of moving out.
5. DiscussionThe empirical findings support the literature identifying age as a predictor of residential
satisfaction (Bonaiuto et al., 1999; Lu, 2009), with older inhabitants of the shrinking cities
studied here reporting higher levels of satisfaction compared with younger residents. Moreover,
age also positively influences the desire to remain in those cities, which is related to the higher
degree of place attachment (Hidalgo & Hernández, 2001; Kamalipour et al., 2012). However,
education and length of residence both negatively influence residential satisfaction, which is in
accordance with the findings of Hur and Morrow-Jones (2008) and Dekker et al. (2011),
respectively. Longer durations of living in a shrinking city increase inhabitants’ awareness of
living in a city that is declining in population, which in turn decreases their level of residential
satisfaction. Further, since the level of residential satisfaction decreases with increasing
education level, shrinking cities may no longer fulfil the needs of the more educated inhabitants.
However, neither education level nor the length of residence plays any role in the willingness of
inhabitants to leave those cities, contradicting the findings of Hidalgo and Hernández (2001) and
Lewicka (2011). This result may be because even though some inhabitants may feel dissatisfied
with the place in which they live, this may not eventuate in an actual intention to move (Fang,
2006; Livingston et al., 2010).
Neither income nor gender appears to influence residential satisfaction or the desire to leave
the surveyed shrinking cities. The irrelevance of income in explaining residential satisfaction
supports the finding of Mellander et al. (2011), while the absence of gender significance
contradicts the finding of Kamalipour et al. (2012). Similarly, there are no gender differences in
explaining the residential attractiveness of the studied cities, in contrast to the study of
16
Kamalipour et al. (2012), which suggested that women (compared with men) are more attached
to place and are therefore less prone to changing residential location.
Contrary to previous studies, homeownership and the characteristics of homes do not explain
residential satisfaction (Azimi & Esmaeilzadeh, 2017; Dekker et al., 2011) or residential
attractiveness (Abellán & Rojo, 1997; Seo, 2002). Our results also contradict the finding of
Manturuk et al. (2010) that inhabitants’ civic engagement increases residential satisfaction.
However, the ‘willingness of inhabitants to participate in urban regeneration programs’
negatively influences the desire to remain in the studied shrinking cities, thus contradicting the
predictions that those more actively involved in the community feel more pride about the city
and have greater feelings of trust and security (Billig, 2006; Hernández et al., 2010; Raymond et
al., 2010). This result is of concern because retaining the more engaged inhabitants in shrinking
cities is crucial for finding ways to deal with the population loss.
The ‘living conditions’ pull factor, which comprises attributes such as the cities’ safety as
well as their appearance, the affordability of houses, and access to certain facilities like good
schools and transportation, is the most influential factor explaining residential satisfaction. The
relevance of such attributes is in accordance with previous studies (e.g. Neal & Neal, 2012;
Parkes et al., 2002; Woo & Morrow-Jones, 2011). This result shows that shrinking cities provide
residential satisfaction so long as the cities do not exhibit social and physical degradation that
may undermine living conditions (Hollander, 2011; Pallagst et al., 2009). The ‘recreational and
environmental amenities’ pull factor, which comprises the existence of spaces for open-air sports
as well as good environmental quality attributes, influences the assessment of residential
satisfaction in a positive manner, as found by previous studies (Cao & Wang 2016; Dekker et al.,
2011). The ‘social ties’ pull factor also has a positive effect on residential satisfaction, supporting
earlier investigations (Bonaiuto et al., 1999; Parkes et al., 2002). However, when the model is
controlled for city of residence, the relevance of these pull factors disappears, and they are
replaced by the ‘accessibility’ pull factor. This result reveals that these pull factors, namely,
‘living conditions’, ‘recreational and environmental amenities’, and ‘social ties’, seem to be
intrinsically related to the city of residence, given that their effect is lost in the controlled model.
The ‘accessibility’ pull factor includes the proximity to leisure and green areas and emerges as
the main influential factor for residential satisfaction in the controlled model. The proximity of
17
areas that are aesthetically appealing has been previously mentioned by Florida et al. (2011) and
Parkes et al. (2002) as positively influencing residential satisfaction.
However, when the analysis considers the intention of leaving shrinking cities, none of the
above pull factors is relevant; instead, ‘live and work’ is the only pull factor influencing
residential attractiveness. Given that the number of employed family members is another
variable that also explains residential attractiveness, the results confirm that economic activity is
central to capturing or retaining inhabitants (van den Berg et al., 2006; Braun, 2008). Thus, to
reduce inhabitants’ intention of leaving, shrinking cities must rely on the maintenance of jobs;
otherwise, they will continue to lose inhabitants, despite being reported as good places in which
to live (high residential satisfaction).
The ‘shrinking atmosphere’ and ‘working conditions’ push factors impact negatively on
residential satisfaction; however, only the first of these factors influences the intention to leave
the cities. The perception that cities are losing population impels the current inhabitants to find
other places to live, which means that those cities are no longer entirely capable of fulfilling the
needs and desires of their residents (van den Berg et al., 2006; Niedomysl, 2010). The
‘surroundings and visual attributes’ push factor also reduces the level of residential satisfaction
in the non-controlled model, but this effect is not significant in the controlled model. The ‘lack of
services’ push factor is not a determinant of residential satisfaction, contradicting previous
findings (e.g. Neal & Neal, 2012; Parkes et al., 2002), but this factor does increase the desire of
inhabitants of shrinking cities to move out, as cities with poor services are not regarded as
appealing places in which to live. Both van den Berg et al. (2006) and European Commission
(2006) have pointed out that the absence or low level of certain services tends to push current
inhabitants out. High levels of residential satisfaction reduce the intention to leave the studied
cities, supporting the earlier studies of Kearns and Parkes (2003) and Andersen (2008).
6. ConclusionsShrinking cities represent a particular context, one that is generally associated with less
appealing living conditions and lower levels of residential satisfaction compared with other
urban contexts. When inhabitants of shrinking cities report high levels of residential satisfaction,
this does not mean that they are not confronted with residential issues that may impel them to
18
move out of the cities. As such, assessing residential satisfaction may be different from
measuring the attractive features of a city as a place to live.
In the presented case study of four shrinking cities in Portugal, residents indicated overall
satisfaction with their respective cities. The empirical findings presented show that with the
exception of age, no common demographic or socio-economic characteristics explain residential
satisfaction and residential attractiveness.
The pull factors that determine residential satisfaction are ‘living conditions’, ‘recreational
and environmental amenities’, and ‘social ties’ in the model uncontrolled for city of residence, or
‘accessibility’ when city of residence is included as an explanatory variable. However, ‘live and
work’ is the only pull factor that explains the intention to leave a shrinking city (i.e., residential
attractiveness). The push factors that explain residential satisfaction are ‘working conditions’ and
‘surroundings and visual attributes’, whereas the ‘lack of services’ explains the intention to leave
shrinking cities. The exception is the push factor ‘shrinking atmosphere’, which has a negative
effect on both residential satisfaction and residential attractiveness.
With regard to the intention to leave the city, economic aspects emerged as the most
important of the cities’ characteristics that may impel individuals to move out, with an emphasis
on the availability of services and jobs. Therefore, a major conclusion of this study is that city-
specific characteristics influence the assessment of residential satisfaction whereas the factors
that influence the intention to leave are common to all the cities. These findings support the
proposition that residential satisfaction and residential attractiveness are predominantly different
and reinforce the argument of McCrea et al. (2014) that there is a need to measure what is most
important. If shrinkage is a condition accepted by the local government, then attributes
associated with recreational and environmental amenities as well as with accessibility factors that
ensure residential satisfaction should be prioritized. One possible strategy is to create conditions
that could support an economy oriented towards providing health, recreational, and care services
for the elderly, the so-called ‘grey economy’. In contrast, if the strategy taken by decision-
makers is aimed at stabilizing or even reversing shrinkage, then the focus should be placed on
those attributes that improve the city’s attractiveness, which are related to increases in the
provision of services and job opportunities.
Regardless of the strategy adopted, because residential satisfaction has a positive effect on
residential attractiveness, governments cannot neglect the former. Accordingly, shrinking cities
19
must work to reduce the perception that the cities are becoming smaller, given that a perception
of a ‘shrinking atmosphere’ negatively affects both residential satisfaction and attractiveness,
thus requiring a planning approach that strengthens the resilience of such cities.
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