# The impact of cultural amenities on inter-urban location: a discrete choice experiment on French students

Olivier Mouate, Muriel Travers

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## To cite this version:

Olivier Mouate, Muriel Travers. The impact of cultural amenities on inter-urban location: a discrete choice experiment on French students. Journal of Cultural Economics, inPress, pp.575-614. 10.1007/s10824-024-09516-y. hal-04670154

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Submitted on 26 May 2025

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# The impact of cultural amenities on inter-urban location: a discrete choice experiment on French students

Olivier Mouate<fnref n="1" /> ID · Muriel Travers<fnref n="2" />

Received: 28 July 2023 / Accepted: 17 July 2024 / Published online: 1 August 2024
© The Author(s) 2024

## Abstract

Since the late 90 s, the literature on the role of culture in the development of cities has grown. A first issue of this literature is to analyse the direction of the causality between the location of people and the one of cultural activities. The second issue is to measure the non-market values produced by cultural assets in cities. The aim of this paper is to investigate the impact of cultural amenities on students’ future inter-urban location choice by using the discrete choice experiment method. The latter is used to address the two issues raised in the literature by modelling a location choice between cities that differ in terms of the living environment offered, and in particu-lar the budget allocated to cultural facilities. Our data concerns students surveyed in 2018 in the city of Angers (France). Our results highlight the existence of a will-ingness to pay for living after graduation in a city with a budget devoted to cultural facilities which is above the average for the medium-sized French cities. We also observe a heterogeneity of students’ preferences according to their index of current cultural awareness created using a cluster analysis. Indeed, the more culturally aware students currently are, the more they will be willing to pay to live in a city offering an increased cultural budget. Finally, we observe a difference in willingness to pay depending on the faculty in which the student is enrolled, with the lowest one for students in Sciences, Engineering and Health.

**Keywords** Location · Cultural amenities · Choice experiment · Random parameter logit

**JEL Classification** Z18 · D12 · D60

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<footnote>Olivier Mouate
olivier.mouate@univ-angers.fr</footnote>

<footnote>Muriel Travers
muriel.travers@univ-nantes.fr</footnote>

<footnote n="1">¹ Department of Economics, University of Angers, Angers, France</footnote>

<footnote n="2">² Department of Economics, Nantes University, Nantes, France</footnote>
# 1 Introduction

The 90 s saw a growing number of cities focusing their development strategy on culture in order to support the transition to a post-industrial economy: Bilbao in Spain, cities in the north of England and Nantes in France<fnref n="1" /> are the best-known cases. In France, the spatial dimension of the cultural economy has appeared recently in the publications of the Ministry of Culture. Thus, a “Regional Atlas of Culture” was launched in 2017 and became in 2021 an online database (“Territorial Culture Atlas”).<fnref n="2" /> Furthermore, according to Eurostats, the proportion of French GDP devoted to financing cultural services is 0.7%, compared with 0.5% in the European Union in 2022.<fnref n="3" /> These different elements show the importance to explore the impact of cultural spending of cities in the interurban location choices in France.

Academic research on the spatial dimension of the cultural economy initially explains why cultural activities tend to cluster in urban areas (Scott, 1997, 2010a). They were then refined through the study of their distribution and specialisation within national territories. For this purpose, Lazzeretti et al. (2008) distinguishes local labour markets specialising in traditional cultural activities (publishing, architecture, music, film and live performance) and non-traditional creative activities (research and development, software and IT services and advertising). Boix et al. (2012) use these categories of activities to show that France, like Italy and Spain, would be mostly specialised in traditional activities or mixed activities, whereas England has based its development strategy on non-traditional ones.<fnref n="4" /> Other research such as Cruz and Teixeira (2015) has shown that concentration in large urban centres mostly applies to knowledge-intensive activities (advertising, publishing, software, TV, radio), whereas traditional activities are better distributed across cities. These findings support the idea that cities are a relevant unit to analyse spatial dimensions of the cultural economy, and that the cultural supply may vary from one place to another. However, it is necessary to remain careful, since there may still be a gap between the location of cultural production and the possibility for inhabitants to consume this production locally.

Alongside this, there is also works that has attempted to explain the location of human capital. Their concentration in cities would facilitate the transition to a new growth model based on knowledge and innovation. Indeed there is a positive

<footnote n="1">¹ Other French cities are also trying to promote their cultural dimension, partly by obtaining international labels such as “European Capital of Culture” (Paris in 1989, Avignon in 2000, Lille in 2004, Marseille in 2013 and Bourges in 2028) or by joining the UNESCO “Creative City network” (Angoulême, Lyon, Cannes, Metz, Toulouse, Limoges, Saint-Etienne, Enghien-les-Bains, Rouen).</footnote>

<footnote n="2">² See the press release from the Ministry of Culture available at the following link: https://www.culture.gouv.fr/en/Press/Press-Archive/Archives-Press-releases-2012-2018/Year-2017/Publication-of-the-2017-edition-of-the-Regional-Atlas-of-Culture.</footnote>

<footnote n="3">³ Source: Eurostat, data last updated in March 2024.</footnote>

<footnote n="4">⁴ In 1997, the Tony Blair government launched the “Creative Industries Task Force” to consider a strategy for the development of the creative industries which can explain the result of Boix et al. (2012).</footnote>
correlation between the level of education<fnref n="5" /> of the regional workforce, income and population growth (Clark & Murphy, 1996; Glaeser et al., 1995; Partridge et al., 2008). Attracting human capital in cities would be a way to enhance the local economic development. The growing interest in amenities of the most highly educated can be explained by the rising of income levels, since amenities are superior goods (Partridge, 2010), and also to improvements in transport technologies that make it easier to travel from one's place to another which encourages people to attach greater importance to their living environment. Authors in economic sociology (e.g. Clark et al., 2002) add that the globalisation of information and its circulation have had the effect of changing consumer preferences in favour of leisure activities at the expense of work and describe the evolution of cities toward "entertainment machines" based on a network of amenities such as parks, museums, art galleries, etc.

It should be noted that the concept of amenities used in the literature refers to elements whose economic nature can be quite diverse since it refers to any “features of urban environments” (Storper & Scott, 2009). According to Partridge (2010), “amenities are simply anything that shifts the household willingness to locate in a particular location. By definition, they are broadly defined and include weather, landscape, public services, public infrastructure, crime, ambiance and so on”. Brueckner et al. (1999) distinguish between exogenous amenities, which correspond to natural amenities such as climate and landscapes, and endogenous or constructed amenities, which correspond to urban amenities whose existence is tied to human activity (Clark, 2003). By its very nature, culture falls into this second category and cultural amenities often refers to built assets such as facilities, events, buildings' architecture. The cultural atmosphere that a cluster of cultural assets may generate in a city, which is more akin to a public good, could therefore correspond to an amenity (Mouate, 2019). This concept refers to a local economic value that can only be enjoyed by locating nearby. Thus, the concept of cultural amenities includes non-use value like prestige, quality of life or ambiance derived from the existence of cultural assets in the city (Seaman, 2011; Snowball, 2008).

Cultural amenities could thus have a positive impact on the well-being of inhabitants and constitute an urban feature which may attract to new ones in the city, in particular educated people. Thus, Glaeser et al. (2001) define four types of amenities that would be valued by educated people when choosing a city: its aesthetic and physical features, the quality of its public services, the speed with which people can move within it, and the variety of consumer goods and services available. This typology of urban amenities may include culture in different ways: through consumption opportunities, architecture of buildings, culture-oriented events hosted by cities. Glaeser et al. (2001) way of classifying amenities also makes it possible to consider urban negative effects of culture (e.g. desamenities), for example when

<footnote n="5">⁵ The creative class theory (Florida 2002a) has raised the debate about measuring human capital in terms of educational attainment or creative occupation (Glaeser, 2005). Empirical studies (Glaeser, 2005; Hansen and Niedomysl, 2009; Marlet and van Woerkens, 2007) have shown the existence of a positive correlation between the two measures respectively in the US (0.75), in Sweden (0.94) and in the Netherlands (0.73), which could be explained by the fact that creativity corresponds to the output of an occupation on the labour market, whereas educational attainment corresponds to an input at the time of entry (Brunow et al., 2018).</footnote>
cultural events generate congestion effects and reduce the ability to move around the city, or when the attractiveness leads to an increase in rents and to gentrification (Florida, 2017; Zukin, 1987).

Furthermore, the literature on the determinants of mobility tends to show that it decreases with age and increases with the level of education (Ehrenberg & Smith, 2012; Glaeser et al., 2002; Kan, 2007) including in France (Détang-Dessendre et al., 2008).<fnref n="6" /> This draws attention to the increasing numbers of young people with college education in France which constitute a stock of human capital that would be worth attracting to cities. Indeed, 35.2% of 25–34 year-olds in France had a college degree in 2002, compared with an EU average of 23.1%. By 2022, these proportions have risen to 50.4% and 42%, respectively.<fnref n="7" />

Two main issues thus emerge from these literatures. The first one is to measure the non-market values produced by cultural assets located in the city. To do so, several methods exist (Navrud & Ready, 2002; Snowball, 2008; Throsby, 2003) and can be distinguished according to their approach regarding individual preferences. On the one hand, there are methods based on revealed preferences as the hedonic price (HP) method which focus on how market prices differ across regions and how these differences can be explained by amenities (Roback, 1982, 1988). On the other hand, there are stated preference methods which comprises the contingent valuation, commonly used in cultural economics,<fnref n="8" /> and the discrete choice experiment (DCE). DCE and HP methods can be used to value simultaneously the individual preferences for different features of a city and thus to compute marginal willingness to pay (WTP) for each of them, including cultural amenities. However, the DCE has the advantage to measure all the economic values while HP only measure direct use value of amenities. The second issue is the direction of the causality between people's location and cultural supply in cities which can be characterised by a circular and cumulative process. As far as we know, only Falck et al. (2011) deal with this issue using instrumental variables. DCE can be another way to address this issue as it compels the location to be made according to the city characteristics, which implies a particular direction to the causality.

The aim of our paper is therefore to give empirical evidence about the impact of cultural amenities on future location choice of French college students using DCE method and to estimate their WTP to live in a culture-oriented city.

The structure of this article is as follows. Section 2 provides a review of the empirical literature on location choices. Section 3 documents the methodology used. Section 4 recalls the econometric specification. Section 5 presents the questionnaire and the sample surveyed. Finally, Sect. 6 presents the estimation results and Sect. 7 concludes.

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<footnote n="6">⁶ Some of the literature has also looked at the degree of mobility of the creative class, showing that they are only marginally more mobile than the rest of the population (Hansen and Niedomysl, 2009; Martin-Brelot et al., 2010).</footnote>

<footnote n="7">⁷ Source: Eurostat, data last updated in September 2023.</footnote>

<footnote n="8">⁸ See Noonan (2003) for a review of the literature using contingent valuation to value cultural assets.</footnote>
# 2 Literature review

## 2.1 Student's location choices

Studies on students' integration into the labour market sometimes underline the role that location can play in terms of economic rewards on the job market. Fagian et al. (2013) thus investigate the heterogeneity of employment conditions between subcategories of bohemian graduates (arts, medias, technology and architecture) compared to non-bohemian ones according to their location in the UK's regions. They show that non-bohemian graduates tend to have higher wages (an average of £4000) compared to bohemians. Also, being located in the great London tends to increase wages and the probability to have a full-time work for both bohemian and non-bohemian graduates. Comunian and Jewell (2018) analyse the impact of previous migration pattern of highly educated people on career opportunities (full-time work, wage and satisfaction) in the UK. Five migration patterns are defined based on migration across three periods: pre-college, college and post-college. The authors also distinguish creative human capital (degrees in art, design or creative media) from three other sets of academic graduation fields (science, technology, engineering and maths – STEM; Social sciences, law, business and education; Humanities). They show that STEM graduates are most likely to migrate before and after college while creative graduates tend to be university stayers (migrating before college and staying in the study city) compared to social sciences, law, business and education graduates. All else being equal, migrating before and after college (repeated migrants) has a significant and positive impact on wages compared to individuals who have never migrated, supporting the human capital theory (Becker, 1962). Having migrated before college and staying on afterwards has also a significant and positive impact on wages compared to non-migrants, but this effect is lower than for repeated migrants.

These two contributions show that students' ability to migrate and the location of their jobs can have an impact on their labour market integration. However, it overlooks at the trade-offs that students might make between economic rewards and the extra-professional advantages that the characteristics of their city may bring to them, particularly in terms of local amenities. Indeed, considering the literature on spatial sorting models, one could assume that some individual could accept a lower wage if compensating by higher quality of life in the city (Roback, 1982, 1988).

When looking at the impact of local characteristics on students' location choice, a first category of factors studied concerns the type of environment in which young adults live. Détang-Dessendre et al. (2008) use French census data and compare the locations in 1982 and 1990 of individuals aged 15–64 in 1982. On both dates, the location can be urban, suburban or rural. They determine 7 typical trajectories between these 3 types of locations and try to explain them according to individuals' position in the life cycle (age, diploma, marital status, and children) and local characteristics (e.g. size of the nearest central city, employment rate). Using a mixed logit model, they show that graduates have a
higher probability of migrating to a central city if they are young. The probability of migrating to urban centres decreases with the size of the nearest central city which supports the idea of a preference of young and educated people for urban environment. They do not intend to measure preferences for cultural amenities, but by highlighting a preference among young graduates for urban centres, they raise the question of the role played by each category of urban amenities in the city's attractiveness. Détang-Dessendre et al. (2008) also show that the effect of local unemployment is unclear what echoes the results of Berck et al. (2016) who find a significant but negative effect of local employment rate on the probability to choose a location among young adults in Sweden, independently of their status of students or young professional.

Weisser (2020) adds the student's personality traits enrolled in economics and business programs among 164 German cities in order to explain their location choice. He also includes in its model quality of life indicators (including the share of recreational areas in the city) and accessibility to urban amenities (via the distance to the city centre). He shows that an increase in the distance to the next regional centre lower the probability to choose the location, supporting the idea that urban amenities play a role in location choice between cities.

The role of cultural amenities in students' location is addressed in two studies. Woldoff et al. (2011) investigate how much preference for cultural amenities influence the preference for an urban environment instead of a suburban or a rural one, when considering location after graduation. This study is made with stated preferences survey on 389 US college students from a city located in a rural state. Results show that preference for cultural amenities (measured by preferences including live music venues, organic food, coffee shops, tattoo shops, and vintage clothing) significantly impact on the choose of a creative major at university, which in turn significantly impact on the preference for urban areas after graduation. Whisler et al. (2008) support this result showing that out-migration of US states is significantly impacted by a rich cultural environment in the destination state for recent graduates and newly-weds without children. The analyses carried out by Woldoff et al. (2011) and Whisler et al. (2008) thus confirms that cultural amenities can play a role in the college students' decision to locate in an urban environment or in a specific state but does not answer the question about the trade-off between cities which is addressed in this article.

## 2.2 Location based on cultural amenities

The literature on the impact of cultural amenities on interurban location choices of non-student people is more extensive and also interesting with regard to the diversity of the methodological approaches used.

First of all, there are macro-territorial studies which explain the distribution of the creative class (Boschma & Fritsch, 2009; Clifton, 2008; Florida & Mellander, 2010; Florida et al., 2008; Florida, 2002a, 2002b; Lorenzen & Andersen, 2009) or the one of the human capital (Bauer et al., 2015; Falck et al., 2011, 2018) according to the characteristics of urban areas. Falck et al., (2011, 2018) show a positive

<figure><img src="image_5.png" /></figure>
impact of cultural amenities on concentration of human capital in German urban areas. Shearmur (2006) draws attention on the fact that the concentration of creative or graduate people cannot be assimilated with explaining location flows. In other words, when these concentrations of people are explained by the existence of city amenities and measured at the same point in time, it provides at best only a correlation between the two phenomena, because dynamics of location flows are ignored.

Alongside, one finds works trying to explain population changes according to the distribution of cultural amenities across urban areas. Glaeser et al. (2001) show that live performance venue per capita between 1977 and 1995 in the US county, has a significant and positive impact on population change but lower than climate. Clark (2003) builds two indicators distinguishing natural (topography, climate) from constructed amenities (libraries, operas, coffee shops etc.) and finds a positive and significant impact of constructed amenities on population change from 1980 to 1990 and from 1990 to 2000. This effect is even higher considering only the change in skilled population while natural amenities are more appropriate to explain retired population change. Backman and Nilsson (2018) also find a positive and significant effect of built heritage on human capital growth in Sweden over the periods 2001–2006 and 2001–2010. Scott (2010b) studies the location choice of US migrant engineers regarding their individual and local destination features using a fractional-response regression model. He distinguishes engineers according to 13 sectors of expertise (nuclear, chemical, etc.) and to two age categories (under and over 60) and analyse the influence of different factors on migration decisions across the 235 US metropolitan areas.<fnref n="9" /> The study shows that only 4 of the 13 categories of engineers, when they are under 60, are significantly and positively impacted by local cultural amenities. This result highlights that preferences for cultural amenities are heterogeneous and cannot be captured only by the main occupation of individuals since the 13 categories are composed of engineers which constitute a homogenous creative occupation according to Florida's approach.

Other studies based on stated preferences try to rank location factors, whether it is hard ones (economic opportunities) or soft ones (e.g. cultural or environmental amenities, quality of life, climate). Economic opportunities (jobs, revenue) tend to be the top factors ranked in surveys (Brown & Scott, 2012; Lawton et al., 2013; Niedomysl & Hansen, 2010), particularly by the most educated individuals (Niedomysl & Hansen, 2010) which can be explained by the human capital theory of Becker (1962). A second important result of this literature concerns the location of the social network in which the individual is embedded, and the former location during the years in higher education (Martin-Brelot et al., 2010). Cultural amenities appear at best secondary in these studies but comparisons between them are not easy because the way the questions are asked differs from one study to another. However, stated preferences studies ensure that the bias in the causality direction between location of people and existence of cultural amenities is disabled, since the questions are formulated in a specific direction.

<footnote n="9">⁹ The explained variable of the model is the share of migrants in one of the 13 categories of engineers in the MSA employment. This variable is computed for each of the two age categories.</footnote>
Finally, we can also find revealed preferences studies based on spatial equilib-
rium, applying sorting models to identify trade-offs between urban amenities and
rents (Roback, 1982, 1988; Rosen, 1974). Van Duijn and Rouwendal (2013) high-
light a positive impact of heritage amenities on housing prices in the Netherlands as
well as a positive neighbourhood effect in surrounding towns. Sheppard et al. (2006)
confirm the capitalisation of cultural amenities on housing prices and so, the posi-
tive effect on attractiveness for US secondary cities, but they only focus on 11 cit-
ies from Massachusetts. However, this result only concerns heritage which may be
longer-lasting and more visible to inhabitants than other sources of cultural ameni-
ties producing cultural atmosphere.

Given the already existing empirical literature, the originality of our article, relies
on two aspects:

- The provision of empirical evidence of the impact of cultural amenities on students' location choices since there are few studies about students' location choices across urban areas, especially in France. Since the literature shows that there can be an impact of cultural amenities on the location of human capital, this result is also expected to be true for college students (H1). Insofar as preferences for cultural amenities and the choice of a specific field of study can be linked, it can be expected in our study that preferences for cultural amenities depend on the field of study chosen by students (H2). We can also expect that the use value associated with the cultural assets of a city will be higher among students already heavily involved in cultural activities compared to that of other students (H3). Likewise, if the city's cultural assets contribute to the creation of specific ambiance, we can expect that students with low cultural participation will also positively value the city's cultural amenities. This could be explained by altruistic purposes, because they expect to attend more cultural activities in the future, or simply to benefit from cultural amenities in terms of social interactions and exchange of ideas that these environments can help to create (H4).

- The use of DCE in order to value the city's cultural dimension, which has not been done yet in the literature. Indeed, the first use of DCE in the cultural field was in the early 80s and focused on cultural events (Louviere & Hensher, 1983). The use of this method increased in the mid-00s with the valuation of museum features (Jaffry & Apostolakis, 2011; Mazzanti, 2003), art galleries (Kinghorn & Willis, 2007), theatre demand (Grisolía & Willis, 2011, 2012), and arts festival (Snowball & Willis, 2006). More recently, we find the use of DCE to value books attributes (Crosby, 2019) or preferences for heritage conservation (Throsby et al., 2021).

## 3 Methodology

We have chosen to model, via the DCE, the student’s location choice (after their graduation) between cities. Indeed, early research showed that cultural activities were subject to agglomeration effects in urban centres (Scott, 1997). Furthermore,

<figure><img src="image_6.png" /></figure>
when we look at the structure of public funding for culture in France, we see that municipalities are an essential territorial unit because they represent 78.0%<fnref n="10" /> of local governments spending in 2017.<fnref n="11" />

The choice experiment method consists of submitting to a sample of individuals a series of hypothetical choices between several alternative projects which are broken down into different main features (called attributes) whose levels may vary between alternatives. By studying the different choices made by individuals, it is possible to compute the attributes that have the greatest impact on their choices. If one of the attributes is expressed in monetary terms, it is also possible to compute a marginal WTP for the other attributes.<fnref n="12" />

Among the different alternatives available to students, we could have chosen their city of residence as the status quo situation. However, this could have introduced a status quo bias (Samuelson & Zeckhauser, 1988) since a known situation (choosing to stay in their current city) would have been in competition with an unknown situation (choosing one of the hypothetical cities). Indeed, the trade-offs made by students would have been possibly impacted by a fear of relocating or of moving away from their family and social network. To avoid this bias, the status quo city has been defined as an “average city” build from statistics on medium-sized French cities.<fnref n="13" /> This latter can be considered as a “statistical reality” where no new policy concerning the living environment is implemented unlike the other hypothetical cities. This status quo has been labelled “reference city” in the survey and information about it was provided to students before they answered.

## 3.1 Attribute and attributes levels selection

Since the literature already shows that employment opportunities and social network are strong predictors of location, we focus on a trade-off between living environment attributes considering all other location factors being equal. The location choice between alternative cities is considered according to five attributes related to living environment on which French municipalities have decision-making power: culture, sports, early childcare, green spaces, and bicycle paths.

The first four attributes are modelled through budget spending within municipalities. The use of budgets has the advantage of maintaining a general approach of these attributes and of not excluding people who are only sensitive to a particular aspect within an attribute. For example, some people can be sensitive only to performing arts and not to fine arts, using a cultural budget not devoted to a specific cultural field makes it possible to encompass these specific preferences in a unique

<footnote n="10">¹⁰ Source: French Ministry of Culture, Key Figs. 2022.</footnote>

<footnote n="11">¹¹ The French system of public funding for culture is structured around a national ministry and three local funding units (Regions, Department and Municipalities). Budget of Municipalities was €7.1 billion in 2017, while the one of the Departments was €1.3 billion and the one of the Regions was 0.7 billion (source: French Ministry of Culture, Key Figs. 2022).</footnote>

<footnote n="12">¹² The general procedure to design DCE is outlined in six key steps by Hanley et al. (2001).</footnote>

<footnote n="13">¹³ Cities from 100,000 to 200,000 inhabitants represent about 30 cities in France. Angers, the city where the study takes place, has 151,056 inhabitants in 2014 (Source: National Census, INSEE).</footnote>
attribute. The fifth attribute is defined by the length of the bicycle path network since the budget of creating and operating new ones is too low compared with the four other budgets. Finally, the sixth attribute is the rent level. This vehicle of payment was chosen because the link between living environment and rent levels in cities is verified theoretically and empirically by hedonic price studies (Bakens et al., 2013; Blomquist et al., 1988; Roback, 1982, 1988; Van Duijn & Rouwendal, 2013). During focus groups with students, we discussed the best way to model this attribute: we have chosen to express it in terms of price by square metres and to add some information about the price that would cost a 25 square meters dwelling since it is a surface that they picture well.

The levels of the five living environment attributes were set up to –10% and +10% to represent a decrease or an increase in terms of budgets. The value of 10% has been chosen in order to illustrate a realistic budget change: we have taken for each attribute the cost of creating a new infrastructure and compute the change it represents regarding the corresponding average budget of the reference city. We homogenised these percentages to +10% to limit the cognitive burden of respondents. The level of –10% was added for each attribute to allow hypothetical cities to offer budget lower than the reference city. With regard to the rent attribute, the average level is €14 per square meter (m²) and was computed with information about mean housing prices found in medium-sized cities. For each level, the information was given in terms of percentage change (–15%, –10%, +10%, +15%) and displayed in euros on the basis of the 25 square meters dwelling to be sure that the amounts of money are understood (–€52, –€35, +€35, +€52 with a reference price of €350). We provided this information to respondents and gave some examples of what could be financed with a change in the corresponding budget (see Table 1).

## 3.2 Experimental design

For each choice set, the individual has the choice between 3 hypothetical alternatives (City A, City B and the Reference City).<fnref n="14" /> An experimental design was used to generate a D-efficient design allowing for main effects. To reduce the cognitive burden, the optimal number of choice sets was divided into 4 blocks of 4 choices which were tested in 4 versions of the survey that only differ by the choice sets tested. Table 2 below shows an example of a choice set included in the survey questionnaire.

To check the quality of the respondent’s answers, an example is presented in the survey. This example reverse the hypothetical cities A and B of the third choice. Thus, this is the same hypothetical cities in these two choice sets, but the names and position in the table are reversed which means that a consistent choice would be to choose city A (respectively B) in the example and city B (respectively A) in the third choice. Another way to be consistent is to choose the reference city in both cases.

<figure><img src="image_7.png" /></figure>

<footnote n="14">¹⁴ People were invited to answer the question: “Among the three cities, in which one would you prefer to live after graduation?”.</footnote>
<table><caption>**Table 1** Attributes and levels of the DCE</caption><thead><tr><th>Attribute</th><th>Levels</th><th>Description</th></tr></thead><tbody><tr><td>Early childcare</td><td>-10%; €10 million (s.q.); +10%</td><td>Budget change due to the addition or the loss of an early childcare centre with respect to the reference city</td></tr><tr><td>Culture</td><td>-10%; €19 million (s.q.); +10%</td><td>Budget change due to the addition or the loss of a cultural facility (e.g. theatre, museum, etc.) with respect to the reference city</td></tr><tr><td>Green spaces</td><td>-10%; €5 million (s.q.); +10%</td><td>Budget change due to the addition or the loss of a green space (equivalent to 4 football ground) with respect to the reference city</td></tr><tr><td>Sport</td><td>-10%; €13 million (s.q.); +10%</td><td>Budget change due to the addition or the loss of a sport facility (e.g. swimming pool) with respect to the reference city</td></tr><tr><td>Bicycle path network</td><td>-10%; 85 km (s.q.); +10%</td><td>Change in the length of the bicycle path network with respect to the reference city</td></tr><tr><td>Monthly rent by square meter (m²)</td><td>-15% (-€52); -10% (-€35); €14/m² (s.q., €350 for 25 m²); +10% (+€35); +15% (+€52)</td><td>Monthly rent changes that the respondent chooses to pay to obtain the level of the five other attributes under the chosen alternative. We indicate in brackets the price variation corresponding to a 25 square meters studio which costs an average of €350 a month</td></tr></tbody></table>

The level corresponding to the status quo of each attribute is given by the “s.q.” in brackets
<table><caption>**Table 2** Example of choice set</caption><thead><tr><th></th><th>City A</th><th>City B</th><th>Reference city</th></tr></thead><tbody><tr><td>Early childcare</td><td>-10%</td><td>+10%</td><td>0% (€10 M)</td></tr><tr><td>Culture</td><td>-10%</td><td>+10%</td><td>0% (€19 M)</td></tr><tr><td>Green spaces</td><td>-10%</td><td>+10%</td><td>0% (€5 M)</td></tr><tr><td>Sport</td><td>+10%</td><td>-10%</td><td>0% (€13 M)</td></tr><tr><td>Bicycle path</td><td>+10%</td><td>-10%</td><td>0% (85 km)</td></tr><tr><td>Monthly rent</td><td>+10%</td><td>-15%</td><td>€350/month for 25 m²</td></tr><tr><td>I choose:</td><td>☐ City A</td><td>☐ City B</td><td>☐ Reference city</td></tr></tbody></table>

## 4 Econometric specification

DCE method is based on new consumer theory (Lancaster, 1966) and on Random Utility Theory (McFadden, 1974). Due to the structure of the choices, the specification below is estimated:

$$U(\text{Alternative}_j) = \text{ASC} + \beta_1 \text{Early Childcare}_j + \beta_2 \text{Culture}_j + \beta_3 \text{Green Spaces}_j + \beta_4 \text{Sport}_j + \beta_5 \text{Bicycle Path}_j + \beta_6 \text{Monthly Rent}_j$$

where:

* Alternative$_j$ is the City A or the City B or the Reference City, ASC (Alternative Specific Constant) is the coefficient associated with the status quo which corresponds to choosing the Reference City,

* Early Childcare$_j$ is the budget change concerning early childcare centres under the alternative$_j$

* Culture$_j$ is the budget change concerning cultural facilities under the alternative$_j$

* Green spaces$_j$ is the budget change concerning green spaces under the alternative$_j$

* Sport$_j$ is the budget change concerning sports facilities under the alternative$_j$

* Bicycle path$_j$ is the change in the length of the bicycle path network under the alternative$_j$

* Monthly rent$_j$ is the rent change that the respondent chooses to pay to obtain the levels of the five living environment attributes under the alternative$_j$

Three types of econometric techniques are used for the estimations: the multinomial logit model (MNL), the random parameter logit model (RPL) and the latent class model (LCM). The Multinomial Logit model assumes homogenous preferences of individuals (McFadden, 1974): the individuals are assumed to assign the same value to an attribute entering their utility function. The random parameter logit (RPL) model relaxes this strong assumption and parameters are allowed to vary randomly among individuals according to a specified distribution $f(\beta|\Omega)$. The coefficient associated with the ASC can be positive or negative depending on whether the
individual derives utility or disutility from choosing the Reference City.<fnref n="15" /> Therefore, it is assumed to be normally distributed. This assumption is also made for the five living environment attributes. The coefficient associated with the monthly rent is supposed to be deterministic.

Then, the probability for an individual *i* to choose the alternative *j* for choice<fnref n="16" /> *t* is calculated as follows:

$$P_i(y_{it} = j | \Omega) = \int_{\beta} P_i(y_{it} = j | \beta) f(\beta | \Omega) d\beta$$

where: $y_{it} = j$ corresponds to the alternative chosen by individual *i* for the choice *t*

And:

$$P_i(y_{it} = j | \beta) = \frac{\exp(\alpha_i + \beta_i' X_{ijt})}{\sum_{j=1}^{J} \exp(\alpha_i + \beta_i' X_{ijt})}$$

where $\alpha$ is the constant associated with the reference city and $X$ corresponds to the attributes.

Consequently, the log-likelihood function associated with the various coefficients to be estimated is defined as follows:

$$\ln L(\Omega) = \sum_{i=1}^{N} \ln \left( \int_{\beta} \left( \prod_{t=1}^{T} P_i(y_{it} = j | \beta) \right) f(\beta | \Omega) d\beta \right)$$

Since the integral of the log-likelihood must be approximated through simulations, the different parameters of the estimate are calculated from different random samples. The RPL model is estimated using 1000 draws.

Nevertheless, in the model presented above, the sources of heterogeneity are assumed to be random. A refinement of this model (Hensher & Greene, 2003) is to allow the means of the parameter distributions to be heterogeneous according to the individual sociodemographic or attitudinal characteristics. For example, Jaffry and Apostolakis (2011) use the previous museum attendance when they estimate preferences for British Museum projects.

Another way to control for preference heterogeneity regarding attributes is the LC model where preference heterogeneity is captured by a discrete distribution. Each individual is assumed to belong to a class *c* and this membership can be explained by sociodemographic and attitudinal individual variables.

The probability that individual *i* chooses the alternative *j* for choice *t* is calculated as follows:

<footnote n="15">¹⁵ ASC=1 if the individual chooses the Reference City, 0 if the individual chooses the City A or the City B.</footnote>

<footnote n="16">¹⁶ Here, the total number of choices *T* by individuals is 4.</footnote>
$$P(y_{it} = j) = \sum_{c=1}^{C} (P(\text{class} = c) \times P_{i}(y_{it} = j|\text{class} = c))$$

where:

$$P(y_{it} = j|\text{class} = c) = \frac{\exp(\alpha_c + \beta_c' X_{ijt})}{\sum_{j=1}^{J} \exp(\alpha_c + \beta_c' X_{ijt})}$$

And:

$$P(\text{class} = c) = \frac{\exp(\gamma_c' z_i)}{\sum_{c=1}^{C} \exp(\gamma_c' z_i)}$$

$z_i$ corresponds to the choice-invariant characteristics having a potential effect on the probability of belonging to class c and $\gamma_c$ corresponds to the coefficients associated with the variables z.

Since each individual make a series of T choices, the probability of choosing alternative j by individual i is:

$$P(y_i = j) = \sum_{c=1}^{C} P(\text{class} = c) \underbrace{\prod_{t=1}^{T} (P(y_{it} = j|\text{class} = c))}_{P_i(j|c)}$$

The log-likelihood function associated with the various parameters to be estimated is then defined as follows:

$$\ln L = \sum_{i=1}^{N} \ln \left( \sum_{c=1}^{C} P(\text{class} = c) \prod_{t=1}^{T} (P(y_{it} = j | \text{class} = c)) \right)$$

The individual marginal WTP for each living environment attribute is calculated as follows in the case of an RPL model:

$$\text{MWTP}_{i, \text{living environment attribute}} = - \frac{\hat{\beta}_{i, \text{living environment attribute}}}{\hat{\beta}_{i, \text{added cost}}}$$

In the case of a LC model, the calculation of the individual marginal WTP for each living environment attribute is calculated in the same way after calculating the coefficients of the different attributes $\hat{\beta}_i$:

$$\hat{\beta}_i = \sum_{c=1}^{C} \hat{\pi}_{ic}^* \hat{\beta}_c$$

With:
$$ \hat{\pi}_{ic}^* = \frac{\hat{\pi}_{ic} \hat{P}_i(j|c)}{\sum_{c=1}^{C} \hat{\pi}_{ic} \hat{P}_i(j|c)} $$

The mean marginal WTP for each living environment attribute are calculated as follows:

$$ \overline{\text{MWTP}} = \frac{1}{N} \sum_{i=1}^{N} \text{MWTP}_i $$

# 5 Survey questionnaire and sample selection

The survey questionnaire was structured in three parts. A first one composed of general questions on the perceived importance of different elements of the living environment to encourage them to think about their preferences. Then, a second part was dedicated to the DCE. The last part focused on sociological characteristics and cultural attendance measures along three dimensions:

* The practice of at least one artistic activity.

* Attendance to cultural facilities during the year before the survey: visits of historic sites, museums, exhibitions, art galleries, regular cinemas, Art-house cinemas,<fnref n="17" /> concerts, classical music concerts, festivals, libraries, theatres, operas, book shops, record shops.

* Opinions about the role of culture in cities in terms of quality of life, economic development, and the need to maintain the public budget for culture despite financial difficulties.

The survey was conducted in face to face between November 2017 and April 2018, with a sample of 737 students from Angers<fnref n="18" /> (France) who were close to completing their degree (3rd year of bachelor or masters)<fnref n="19" /> and therefore, might relocate to enter on the job market after their graduation. Small groups of students we interviewed (fewer than 30 students at a time), mostly during class time which limits self-selection bias. Each faculty, namely Law, Economics, Management and Tourism (LEMT), Humanities and Languages (HL) and Science, Engineering and Health (SEH), are represented in the sample since the choice of a study programme may be linked to the preference for cultural amenities (Woldoff et al., 2011).

Oral presentation of the questionnaire was made for each respondent and explanations were provided about the choice sets. Two examples were given: a very simple

<footnote n="17">¹⁷ “Art-house Cinema” is the equivalent to the French label awarded to cinemas programming independent films often aimed at niche audiences. This label is named in French “Art et Essai”.</footnote>

<footnote n="18">¹⁸ The city of Angers is a medium-size city (151,000 inhabitants in 2017, source: INSEE) in the first 20 French cities according to its population size.</footnote>

<footnote n="19">¹⁹ The French higher education system is structured around 3 main diplomas: 3 years for a bachelor's degree, 2 more years for a master's degree and 3 more years for a PhD.</footnote>
one for pedagogical purpose with a prevailing alternative consisting of a decreasing of rent and an improvement of living environment attributes, and a second one taken from the questionnaire. The aim of this information was to reduce the learning effects that can be observed with DCE (Brouwer et al., 2010).

Of the 737 responses obtained, 267 did not make consistent choices in the DCE (36.2% of the initial sample) according to the consistency test describing in Sect. 3.2 and were therefore removed from the sample. The following were also excluded from the sample: 5 people who declared that they had answered randomly, 6 people who systematically chose the status quo as an exit option and 21 outliers including adults returning to education and people who did not answer all the questions. The final sample thus comprises 442 individuals.

# 6 Results

## 6.1 Descriptive statistics

Women accounted for 66.1% of the sample, compared with 56.9% of French higher education students in 2017–2018. The share of students receiving scholarship is also close to the national average, making up 47.5% of the sample compared with 41.6% of French students in 2017–2018. 46.8% of the students surveyed were enrolled in LMETs, 30.3% in SEHs and 22.9% in HLs. Therefore, the three faculties of higher education which can be found in France are represented in our sample, although there are imbalances compared with the national figures. 48.6% of students were enrolled in the 3rd year of Bachelor’s degree, the rest being divided between the 1st year of Master’s (24.9%) and the 2nd year of Master’s (26.5%) (see Table 3).

Regarding students’ attitude toward culture, 45.0% of the sample take part in at least one artistic activity, which is very close to the national statistics (42.0% of 20–24-year-old people in France take part in at least one artistic activity in 2018). Looking at cultural attendance the year preceding the survey, the most frequent activities are going to a regular cinema (95.5% of the sample), visiting heritage sites (88.7%), museums (72.9%) and exhibitions (63.3%). Conversely, the least frequent activities are going to: opera (10.0%), classical music concerts (16.3%), record shops (18.3%), art galleries (35.5%), theatre (32.4%) and Art-house cinema (36.0%). Opinions on the role of culture in the city are more clear-cut: 22.9% strongly agreeing that culture has a positive impact on the quality of life of inhabitants and 19.2% strongly agreeing that culture is essential to the city’s economic development. Finally, 16.7% strongly believe that public governments should maintain their cultural spending, even in times of financial difficulties (see Table 4).

We can note that the attendance for the cultural activities is higher in the sample than for the 20–24 years-old in France.<fnref n="20" /> It can be explained by the fact that student status gives free or at least reduced rates at many cultural facilities. Students may

<figure><img src="image_8.png" /></figure>

<footnote n="20">²⁰ Some cultural activities have been grouped together in order to be able to compare them with national figures in Table 4.</footnote>
<table><caption>**Table 3** Sociological characteristics of students</caption><thead><tr><th>Characteristic<br>(Variable name into brackets)</th><th>Sample<br>(n=442)</th><th>France<sup>a</sup></th></tr></thead><tbody><tr><td>Women (gender)</td><td>66.1%</td><td>56.9%</td></tr><tr><td>Scholarship (scholarship)</td><td>47.5%</td><td>41.6%</td></tr><tr><td><em>Faculty:</em></td><td></td><td></td></tr><tr><td>Law, economics, management and tourism (LEMT)</td><td>46.8%</td><td>28.4%</td></tr><tr><td>Humanities and languages (HL)</td><td>22.9%</td><td>33.0%</td></tr><tr><td>Science, engineering and health (SEH)</td><td>30.3%</td><td>38.6%</td></tr><tr><td><em>Level of education:</em></td><td></td><td></td></tr><tr><td>3rd year of bachelor (B3)</td><td>48.6%</td><td>41.6%</td></tr><tr><td>1st year of master's degree (M1)</td><td>24.9%</td><td>29.4%</td></tr><tr><td>2nd year of master's degree (M2)</td><td>26.5%</td><td>29.0%</td></tr></tbody></table>

Variables are coded 1 when the characteristic is true, 0 otherwise

also have more free time for cultural activities than the rest of the working population aged 20–24. There is also a close link between education and cultural attendance which can explain these differences (see Table 5). Note also that the city of Angers is characterised by a cultural supply considered average when compared to other French student cities: in fact, according to the annual ranking established by the magazine “L’Etudiant” for the 46 French cities welcoming more than 8000 students, the city of Angers obtained, in 2023–2024, a score of 5/10 for the criterion concerning cultural supply.<fnref n="21" />

A Hierarchical Clustering on Principal Component (HCPC) based on Multiple Correspondence Analysis (MCA) was carried out to synthesise the information gathered from the individuals’ characteristics related to culture: the practice of at least one artistic activity, the attendance of different cultural activities and the opinion on the role of culture in the city. This leads to create three groups of individuals which could explain the heterogeneity of preferences for the cultural attribute in the DCE (see Fig. 1 below). The composition of the groups is described below, and the statistics mentioned are given in Table 9 of this paper.

* First group: L (28.0% of the sample): low cultural awareness (LCA)

The first group includes higher shares of individuals who do not attend cultural facilities compared to the average shares in the sample. This group also includes a higher share of people who do not totally agree with the proposals concerning the

<footnote><sup>a</sup>Figures from the Ministry of Higher Education, Research, and Innovation (France) – Information Systems and Statistical Studies, 2017–2018</footnote>

<footnote n="21">²¹ This cultural criterion takes into account the rate of cultural venues, the quality of the supply (in terms of attendance to museums and to events with more than 10,000 visitors, the number places classified by UNESCO and the number of theatres with more than 2000 seats) and the cultural expenditure per inhabitant of the municipalities of the urban unit.</footnote>
<table><caption>**Table 4** Cultural practices of students</caption><thead><tr><th>Characteristic<br>(Variable name into brackets)</th><th>Sample<br>(n=442)</th><th>France<sup>b</sup></th></tr></thead><tbody><tr><td>Practice at least one artistic activity</td><td>45.0%</td><td>42.0%</td></tr><tr><td colspan="3">Attend the following activities at least once over the year prior to the survey:</td></tr><tr><td>Museums and exhibitions<sup>c</sup>:</td><td>80.3%</td><td>30.0%</td></tr><tr><td>Museums (museum)</td><td>72.9%</td><td>—</td></tr><tr><td>Exhibitions (exhibition)</td><td>63.3%</td><td>—</td></tr><tr><td>Heritage sites (heritage)</td><td>88.7%</td><td>37.0%</td></tr><tr><td>Art galleries (gallery)</td><td>35.5%</td><td>—</td></tr><tr><td>Cinema<sup>c</sup>:</td><td>96.4%</td><td>79.0%</td></tr><tr><td>Regular cinema (cinema)</td><td>95.5%</td><td>—</td></tr><tr><td>Art-house cinema (arthouse)</td><td>36.0%</td><td>—</td></tr><tr><td>Performing arts<sup>c</sup>:</td><td>67.2%</td><td>33.0%</td></tr><tr><td>Theatre (theatre)</td><td>32.4%</td><td>—</td></tr><tr><td>Amplified concert (concert)</td><td>52.3%</td><td>—</td></tr><tr><td>Classical music concert (classic)</td><td>16.3%</td><td>—</td></tr><tr><td>Opera (opera)</td><td>10.0%</td><td>—</td></tr><tr><td>Festivals (festival)</td><td>54.8%</td><td>—</td></tr><tr><td>Multimedia libraries (library)</td><td>49.5%</td><td>36.0%</td></tr><tr><td>Book shops (book)</td><td>41.0%</td><td>—</td></tr><tr><td>Record shops (record)</td><td>18.3%</td><td>—</td></tr><tr><td colspan="3">Totally agree with the following items:</td></tr><tr><td>Culture has a positive impact on quality of life of inhabitants (QoL)</td><td>22.9%</td><td>—</td></tr><tr><td>Culture is essential for economic development of cities (Eco)</td><td>19.2%</td><td>—</td></tr><tr><td>Public cultural spending has to be maintained despite financial difficulties (budget)</td><td>16.7%</td><td>—</td></tr></tbody></table>

Variables are coded 1 when the characteristic is true, 0 otherwise

positive role of culture in quality of life (91.9%) and economic development (91.9%) compared to 77.2% and 80.8% respectively in the sample. Thus, this first group includes individuals with low cultural awareness.

* Second group (52.5% of the sample): moderate cultural awareness (MCA) with a focus on heritage

Individuals in this group are distinguished by a higher proportion of individuals going to museums (93.1%), heritage sites (99.6%) and exhibitions (79.7%) than the sample average of 72.9%, 88.7% and 63.4% respectively. Conversely, the share of people going to the opera (1.3%) or classical music concerts (4.7%) is lower than

<footnote><sup>b</sup>Practices among 20–24-year-old according to the survey on the cultural practices of the French 2018, Ministry of Culture. Research, Forecasting and Statistics Department</footnote>

<footnote><sup>c</sup>Cultural practices listed have been aggregated to allow comparisons with national figures. For example, the 80.3% of museum and exhibition goers include those who go to visit both, those who only go to visit museums, and those who only go to visit exhibitions outside museums</footnote>
<table><caption>**Table 5** Percentage of French people who visited cultural facilities at least once over 1 year in 2018 according to their level of Education</caption><thead><tr><th>Education level</th><th>Libraries</th><th>Cinema</th><th>Museums and exhibitions</th><th>Heritage</th><th>Performing arts</th></tr></thead><tbody><tr><td>No diploma</td><td>12</td><td>33</td><td>9</td><td>13</td><td>20</td></tr><tr><td>School certificate or professional aptitude certificate</td><td>22</td><td>59</td><td>19</td><td>27</td><td>38</td></tr><tr><td>High school</td><td>31</td><td>72</td><td>30</td><td>33</td><td>46</td></tr><tr><td>Higher education</td><td>39</td><td>81</td><td>52</td><td>55</td><td>61</td></tr></tbody></table>

Survey on the cultural practices of the French 2018, Ministry of Culture, Research, Forecasting and Statistics Department

33% of people with no diploma went to the cinema at least once during the year, compared with 81% of people with higher education diploma

the sample average (9.9% and 16.3% respectively). These individuals are moderately aware of culture, and their preferences are more inclined toward activities related to heritage and the fine arts.

* Third group (19.5% of the sample): high cultural awareness (HCA)

This group has a higher share of individuals who attend to the various cultural activities compared to the sample, and a higher share of individuals who take part in at least one artistic activity (67.4%) compared to the average of the sample (45.0%). Lastly, this group contains a higher proportion of individuals who strongly agree with the positive role of culture in quality of life (51.2%), economic development (38.4%) and also strongly agree with maintaining cultural spending in cities despite financial difficulties (29.1%) compared to the corresponding shares in the sample (22.9%, 19.2% and 16.7% respectively). This group is made up of students with a relatively high level of cultural awareness.

By observing the dependence relations that may exist between cultural awareness and individual characteristics, we can see a link between faculty and cultural awareness (see Table 10). Indeed, the share of LEMT students is significantly lower in the moderate cultural awareness group (42.7%, see Table 10 column (3)) than in the sample (46.8%). Their share is significantly higher in the high cultural awareness group (57.0%) than in the sample. Conversely, the share of SEH students is significantly higher in the moderate cultural awareness group (34.9%) compared with the sample (30.3%) and significantly lower in the high cultural awareness group (17.4%) compared with the average in the sample. This result confirms what Woldoff et al. (2011) observe with US students about the preferences for cultural amenities and their dependence on enrolment in specific academic fields of study.

Despite the independence between the three cultural groups and the individual characteristics, there are multiple significant dependencies between faculty and individual characteristics (see Tables 11, 12). Thus, among students enrolled in SEH faculty, the share of third-year bachelor students is higher than in the sample (60.4% compared with 46.8%). The share of students in their 1st year master's degree is
<figure><img src="image_9.png" /><figcaption>**Fig. 1** Projection of individuals according to the first two dimensions of the multiple component analysis and according to their level of cultural awareness</figcaption></figure>

significantly higher than the average in LEMT faculty (33.3%) compared with the sample (22.9%). Among students enrolled on HL faculty, the share of the 2nd year master's students is significantly higher than in the sample (39.6% compared with 30.3%). The share of women in SEH faculty is significantly lower than in the sample (52.2% compared with 66.1%), as is the share of students with scholarship (35.1% compared with 47.5%).

## 6.2 Estimation results

The estimations are conducted using various models including MNL, RPL and LCM. The McFadden's pseudo-R2 is equal to 0.202 for the MNL model, 0.259 for the RPL model and 0.240 for the 2-class LCM model. Therefore, this paper does not present the estimation results for the LCM.

Table 6 summarises the results of MNL and RPL estimations. All living environment attributes are significant at the 1% level and enter the utility function with a positive sign. The standard deviations are both significant indicating the presence of unobserved heterogeneity in the preferences for the five living environment attributes. The coefficient associated with the monthly rent is negative and significant suggesting that *ceteris paribus*, students prefer alternatives with a lower rent. The ASC is positive and significant implying that students prefer, on average, the budget of the reference city, but there is large heterogeneity.

The results also indicate that the two attributes most valued by students for their future city are respectively the budget allocated to green spaces and cultural facilities. Our first hypothesis H1 (namely the impact of cultural amenities on students’

<figure><img src="image_10.png" /></figure>
<table><caption>**Table 6** MNL and RPL estimates</caption><thead><tr><th rowspan="3">Parameters</th><th colspan="3">Dependant variable: choice</th></tr><tr><th>MNL</th><th>RPL</th><th rowspan="2">Standard deviation (S.E.)</th></tr><tr><th>Coefficient (S.E.)</th><th>Coefficient (S.E.)</th></tr></thead><tbody><tr><td>ASC</td><td>0.287*** (0.058)</td><td>1.100*** (0.145)</td><td>0.994*** (0.153)</td></tr><tr><td>Early childhood</td><td>0.013*** (0.004)</td><td>0.027*** (0.008)</td><td>0.089*** (0.015)</td></tr><tr><td>Culture</td><td>0.052*** (0.004)</td><td>0.100*** (0.011)</td><td>0.081*** (0.016)</td></tr><tr><td>Sport</td><td>0.034*** (0.004)</td><td>0.071*** (0.010)</td><td>0.102*** (0.015)</td></tr><tr><td>Green spaces</td><td>0.059*** (0.004)</td><td>0.117*** (0.012)</td><td>0.102*** (0.015)</td></tr><tr><td>Bicycle paths</td><td>0.008** (0.004)</td><td>0.027*** (0.007)</td><td>0.042** (0.019)</td></tr><tr><td>Rent</td><td>-0.018*** (0.001)</td><td>-0.031*** (0.002)</td><td></td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.202</td><td>0.259</td><td></td></tr><tr><td>Log-likelihood</td><td>-1525.767</td><td>-1439.148</td><td></td></tr><tr><td>N</td><td>442</td><td>442</td><td></td></tr><tr><td>Observations</td><td>1768</td><td>1768</td><td></td></tr></tbody></table>

Significance levels: 1%: **; 5%: **; 10%: *; S.E., standard error

choice of location) is therefore validated. The Sports budget ranks 3rd. These results are consistent with those of Liu et al. (2022) which demonstrates the importance of the green spaces existence on campus on the students' mental health and which could therefore explain why this dimension is highly valued by students in their choice of a future city. Our results are also consistent with these of Weisser (2020) who shows that the share of recreational areas is significant in explaining study location choices. However, his article does not explain how this indicator is constructed, whether it includes elements relating to green spaces, cultural and sports facilities, or other consumer amenities. The work by Whisler et al. (2008) about the determinants of migration across the life cycle are the closest of ours. They highlight the importance of rich cultural environment and the other recreational amenities in attracting young graduates and newly-weds without children. The cost of living (including housing costs) plays a significant and negative role for young graduates which is not the case for newly-weds without children. The latter are also sensitive to climate amenities, which is not the case for young graduates who value more cultural amenities than recreational ones.

Two RPL models with interactions are estimated in order to control for heterogeneity of preferences. In the first model, all the attributes are interacted with the faculty of the student (RPL2). In the second model, it is assumed that the current cultural profile of the student can act on the preferences concerning the cultural budget of his future city (RPL3). Indeed, the faculty and the cultural awareness indicators are not introduced simultaneously in the same estimation because there is a significant link between the two (see Sect. 6.1).

The RPL2 model (see Table 7) has a higher quality of adjustment than the RPL model without interactions (0.267 versus 0.259). From the likelihood ratio test (2×(Log-likelihood (RPL2)-Log-likelihood (RPL without interactions))) ~ khi2 (ddl:
difference in the number of estimated parameters between the two models)), we reject at the 1% risk threshold the null hypothesis that the quality of the RPL2 model is no better than that of the RPL model without interactions.<fnref n="22" /> The same is true for the RPL3 model.<fnref n="23" />

The results obtained by the RPL2 model indicate a positive and significant interaction between the culture attribute and the faculty to which the respondent belongs and thus validates our hypothesis H2: a student from Law, Economics, Management and Tourism (LEMT) or a student from Humanities and Languages (HL) will prefer, when choosing a future location, a city where the budget dedicated to cultural facilities will be greater compared to a student from Science, Engineering and Health (SEH). It should also be noted that students from Humanities and Languages value the budget allocated to early childhood negatively compared to the students from Science, Engineering and Health. There are no other significant effects between the other 3 living environment attributes (sport, green spaces, and bicycle paths) and the faculty. However, for students from Science, Engineering and Health, the sports budget becomes the 2nd criterion before culture and after green spaces.

The results obtained by the RPL3 model indicate a positive interaction between the culture attribute and the current cultural awareness of students. Indeed, the more the student is considered to be aware of culture, the more he wants to locate in a city where the budget for cultural facilities is high. This is consistent with our third hypothesis (H3): students with high cultural awareness have a high rate of cultural participation at the moment of the survey: they probably have a higher use value for cultural assets offered by their future city. This is also consistent with our fourth hypothesis (H4) because even low cultural awareness students express a positive coefficient to live in a more culture oriented city. One can assume that they attach an altruistic value to culture or they could simply value the cultural atmosphere of cities. They also can anticipate attending more in cultural facilities in their future cities, which correspond to an option demand.

Our results can be put into perspective with those of Woldoff et al. (2011). Our data does not allow us to verify whether the cultural preference prior to enrolment at university has an impact on the choice of study, but we do observe a significant link between cultural awareness and faculty (see Sect. 6.1) and a significant impact of the faculty on the cultural attribute value (see results of RPL2). However, our result complete it by showing that field of study explains heterogeneity of preferences for culture across cities.

## 6.3 Willingness-to-pay

Table 8 presents the marginal WTP estimates obtained from the RPL models (RPL, RPL2 and RPL3). The respondents show a positive and significant WTP for all living environment attributes.

<footnote n="22">²² The calculated value of the test is equal to 29.82 (2×(-1424.239 + 1439.148)). At the 1% risk threshold, the theoretical value of $khi^2(12)$ being equal to 26.22, the H0 hypothesis is not accepted.</footnote>

<footnote n="23">²³ The calculated value of the test is equal to 40.86 (2×(-1418.719 + 1439.148)). At the 1% risk threshold, the theoretical value of $khi^2(2)$ being equal to 9.21, the H0 hypothesis is not accepted.</footnote>
<table><caption>**Table 7** Estimations of RPL with interactions</caption><thead><tr><th rowspan="3">Parameters</th><th colspan="4">Dependant variable: choice</th></tr><tr><th colspan="2">RPL2<sup>d</sup></th><th colspan="2">RPL3<sup>e</sup></th></tr><tr><th>Coefficient (S.E.)</th><th>Standard deviation (S.E.)</th><th>Coefficient (S.E.)</th><th>Standard deviation (S.E.)</th></tr></thead><tbody><tr><td>ASC</td><td>1.051*** (0.192)</td><td>0.987*** (0.151)</td><td>1.126*** (0.146)</td><td>1.008*** (0.154)</td></tr><tr><td>Early childhood</td><td>0.042*** (0.014)</td><td>0.084*** (0.015)</td><td>0.029*** (0.008)</td><td>0.090*** (0.015)</td></tr><tr><td>Culture</td><td>0.059*** (0.014)</td><td>0.076*** (0.016)</td><td>0.042*** (0.014)</td><td>0.066*** (0.017)</td></tr><tr><td>Sport</td><td>0.074*** (0.016)</td><td>0.100*** (0.014)</td><td>0.072*** (0.010)</td><td>0.103*** (0.015)</td></tr><tr><td>Green spaces</td><td>0.098*** (0.017)</td><td>0.099*** (0.015)</td><td>0.119*** (0.012)</td><td>0.107*** (0.015)</td></tr><tr><td>Bicycle paths</td><td>0.040*** (0.013)</td><td>0.038 (0.020)</td><td>0.028*** (0.008)</td><td>0.044** (0.018)</td></tr><tr><td>Rent</td><td>-0.031*** (0.002)</td><td></td><td>-0.032*** (0.002)</td><td></td></tr><tr><td>Culture x MPA</td><td></td><td></td><td>0.061*** (0.018)</td><td></td></tr><tr><td>Culture x HPA</td><td></td><td></td><td>0.149*** (0.026)</td><td></td></tr><tr><td>ASC x HL</td><td>0.105 (0.244)</td><td></td><td></td><td></td></tr><tr><td>Childhood x HL</td><td>-0.059*** (0.022)</td><td></td><td></td><td></td></tr><tr><td>Culture x HL</td><td>0.054** (0.022)</td><td></td><td></td><td></td></tr><tr><td>Sport x HL</td><td>-0.025 (0.023)</td><td></td><td></td><td></td></tr><tr><td>Green spaces x HL</td><td>0.023 (0.024)</td><td></td><td></td><td></td></tr><tr><td>Bicycle paths x HL</td><td>-0.029 (0.019)</td><td></td><td></td><td></td></tr><tr><td>ASC x LEMT</td><td>0.018 (0.207)</td><td></td><td></td><td></td></tr><tr><td>Childhood x LEMT</td><td>-0.003 (0.018)</td><td></td><td></td><td></td></tr><tr><td>Culture x LEMT</td><td>0.059*** (0.019)</td><td></td><td></td><td></td></tr><tr><td>Sport x LEMT</td><td>0.005 (0.020)</td><td></td><td></td><td></td></tr><tr><td>Green spaces x LEMT</td><td>0.030 (0.020)</td><td></td><td></td><td></td></tr><tr><td>Bicycle paths x LEMT</td><td>-0.017 (0.016)</td><td></td><td></td><td></td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.267</td><td></td><td>0.270</td><td></td></tr><tr><td>Log-likelihood</td><td>-1424.239</td><td></td><td>-1418.719</td><td></td></tr><tr><td>N</td><td>442</td><td></td><td>442</td><td></td></tr><tr><td>Observations</td><td>1768</td><td></td><td>1768</td><td></td></tr></tbody></table>

Significance levels: 1%; **; 5%; **; 10%: *; S.E., standard error

<sup>d</sup>The reference level is that corresponding to the academic sector “Science, Engineering and Health” (SEH)

<sup>e</sup>The reference level is that corresponding to students with a low cultural awareness (LPA)

The average WTP to locate in a city, whose budget allocated to early childcare centres is 10% higher than the reference city, is €8.72 per student. The highest willingness to pay is that associated with an increase in the budget for green spaces: in fact, students are willing, on average, to pay €37.42 to locate in a city with a budget of + 10% higher than that of the reference city. The willingness to pay for an increase
<table><caption>Table 8 Marginal willingness to pay estimates for the five living environment attributes (for a 10% increase compared to the budget of the reference city, €/student)</caption><thead><tr><th rowspan="2"></th><th colspan="2">RPL</th><th colspan="2">RPL.2</th><th colspan="2">RPL.3</th></tr><tr><th>WTP</th><th>Standard error</th><th>WTP</th><th>Standard error</th><th>WTP</th><th>Standard error</th></tr></thead><tbody><tr><td>Childhood</td><td>8.72*** [3.48;13.96]</td><td>2.67</td><td>13.35*** [4.41; 22.29]</td><td>4.56</td><td>9.09*** [3.95; 14.24]</td><td>2.62</td></tr><tr><td>Culture</td><td>32.05*** [26.22;37.89]</td><td>2.98</td><td>18.81*** [9.95; 27.67]</td><td>4.52</td><td>13.25*** [4.53; 21.98]</td><td>4.45</td></tr><tr><td>Sport</td><td>22.71*** [17.00;28.41]</td><td>2.91</td><td>23.78*** [13.99; 33.56]</td><td>4.99</td><td>22.80*** [17.07; 28.53]</td><td>2.93</td></tr><tr><td>Green spaces</td><td>37.42*** [30.98;43.87]</td><td>3.29</td><td>31.28*** [21.13; 41.42]</td><td>5.18</td><td>37.55*** [31.10; 44.01]</td><td>3.29</td></tr><tr><td>Bicycle paths</td><td>8.54*** [3.99;13.10]</td><td>2.32</td><td>12.86*** [5.04; 20.68]</td><td>3.99</td><td>8.85*** [4.35; 13.36]</td><td>2.30</td></tr><tr><td>Culture x MCA</td><td></td><td></td><td></td><td></td><td>19.41*** [8.40; 30.42]</td><td>5.62</td></tr><tr><td>Culture x HCA</td><td></td><td></td><td></td><td></td><td>47.00*** [31.99; 62.01]</td><td>7.66</td></tr><tr><td>Childhood x HL</td><td></td><td></td><td>-18.86*** [-32.39; -5.32]</td><td>6.91</td><td></td><td></td></tr><tr><td>Culture x HL</td><td></td><td></td><td>17.46** [3.94; 30.98]</td><td>6.90</td><td></td><td></td></tr><tr><td>Sport x HL</td><td></td><td></td><td>-7.95 [-22.65; 6.75]</td><td>7.50</td><td></td><td></td></tr><tr><td>Green spaces x HL</td><td></td><td></td><td>7.31 [-7.60; 22.21]</td><td>7.61</td><td></td><td></td></tr><tr><td>Bicycle paths x HL</td><td></td><td></td><td>-9.40 [-21.11; 2.31]</td><td>5.97</td><td></td><td></td></tr><tr><td>Childhood x LEMT</td><td></td><td></td><td>-0.92 [-12.32; 10.49]</td><td>5.82</td><td></td><td></td></tr><tr><td>Culture x LEMT</td><td></td><td></td><td>18.94*** [7.47; 30.41]</td><td>5.85</td><td></td><td></td></tr><tr><td>Sport x LEMT</td><td></td><td></td><td>1.50 [-10.94; 13.94]</td><td>6.35</td><td></td><td></td></tr><tr><td>Green spaces x LEMT</td><td></td><td></td><td>9.48 [-3.11; 22.07]</td><td>6.42</td><td></td><td></td></tr><tr><td>Bicycle paths x LEMT</td><td></td><td></td><td>-5.47 [-15.34; 4.39]</td><td>5.03</td><td></td><td></td></tr></tbody></table>

Significance levels: 1%: **; 5%: **; 10%: *; S.E., standard error, []: 95% confidence interval for the value estimated by the

<figure><img src="image_11.png" /></figure>
in the budget for cultural facilities is quite close (€32.05 on average) to that of green spaces.<fnref n="24" />

The results also show that a student from Science, Engineering and Health would be willing to pay, on average, €18.81 to locate in a city with a budget for cultural facilities 10% higher than the one of the reference city. This value is lower than the average value of the entire population surveyed. It represents an increase of 5.4% compared to the rent in the reference city. Conversely, the students from Humanities and Languages and from Law, Economics, Management and Tourism are willing to pay more than students from Science, Engineering and Health, namely an extra €17.46 and €19.94 respectively.<fnref n="25" />

We also note that students with a low awareness of culture during their studies are ready to pay an average of €13.25, which represents an increase of 3.9% compared to the rent in the reference city. Students with a moderate cultural awareness are willing to pay an extra €19.41 compared to students with low cultural awareness. This extra is even more important for students with a strong cultural awareness: + €47.00.<fnref n="26" />

Furthermore, surveys carried out in 2018 by the University of Angers indicate that students who studied at the SEH faculty have a higher average gross monthly income (€2351) than students at the LEMT faculty (€2037) and those of the HL faculty (€2054) 6 months after the end of their 2nd year master's degree. Consequently, if we assume that the students were aware, when making their choices, of this hierarchy of future wages, the anticipated income differential does not seem to be an explanatory factor of the WTP differential for culture. Indeed, the SEH students have the lowest WTP for an increase in the budget allocated by the city to cultural facilities (e.g. theatre, museum) while they will earn on average the highest wages. Furthermore, if we think in relative terms (share of WTP/anticipated income), this difference will be even greater because future wages for SEH students will be higher than those of the two other faculties. Consequently, the relative share devoted to culture by SEH students will be much lower than that of other students.

The analysis of the impact of the student's standard of living at the time of the survey (proxied by the fact that the student benefits or not from a scholarship based on social criteria) also shows that the standard of living of the student is not a determining criterion to explain the coefficient and WTP differentials for culture (see Tables 13, 14 and Fig. 5). Therefore, it seems that the field of study is a more decisive criterion to explain this differential, and that a part of the WTP differential according to the faculty can be explained by the cultural awareness at the time of the survey.

<footnote n="24">²⁴ The distributions of individual marginal WTP for the 5 living environment attributes are presented in Fig. 2.</footnote>

<footnote n="25">²⁵ The distributions of individual marginal WTP for the culture attribute according to students' faculty are presented in Fig. 3.</footnote>

<footnote n="26">²⁶ The distributions of individual marginal WTP for the culture attribute according to students' cultural awareness at the time of the survey are presented in Fig. 4.</footnote>
<figure><img src="image_12.png" /><figcaption>Fig. 2 Distributions of individual marginal WTP for the 5 living environment attributes</figcaption></figure>

# 7 Discussion and conclusion

The aim of this research was to study the impact of the cultural amenities offered by cities on the location choice of French students after graduation, using the DCE method. We focused on young adults enrolled in college, since they are most mobile than the rest of the population. The cultural dimension of the living environment has been modelled by a variation in the budget devoted to cultural facilities, considering that the more cultural facilities a city offers, the more culture-oriented ambiance (and thus cultural amenities) the city provides. This is what makes our study original compared to what has been done in terms of DCE applied to cultural economics, but also in terms of the methodology employed in empirical studies about location led by cultural amenities. Moreover, there is little literature on the location choice of students according to the cities’ characteristics. This study helps to understand the trade-offs that students would make between living environment dimensions when migrating after college and thus, complements the literature highlighting students’ preference for urban spaces (Détang-Dessendre et al., 2008; Woldoff et al., 2011).

Our results show that, on average, all the dimensions of the living environment presented in the DCE (culture, sports, early childcare, green spaces, and bicycle paths) have a significant impact on the future location choice and that the budget
<figure><img src="image_13.png" /><figcaption>Fig. 3 Distributions of individual marginal WTP for the culture attribute according to students' faculty</figcaption></figure>

dedicated to cultural facilities ranks second, after the green spaces one and before the sports one. Thanks to the creation of an indicator measuring the cultural awareness of students at the time of the survey, we were able to explain a part of the heterogeneity of preferences regarding the cultural budget. Indeed, estimates indicate that the more people are currently aware of culture, the higher the WTP for living after their studies in a city offering an increase in the budget dedicated to cultural facilities. It could be explained by the attachment of a higher use value to the cultural facilities in the future city. Our results also show that even if there are differences in WTP for cultural amenities according to the enrolment of students in different facilities, the WTP is always positive and therefore support theories on the positive role played by culture in cities for the educated young people. Even the least culturally aware students express a positive WTP for culture oriented city, which supports the idea that there could be non-use values attached to cultural amenities in cities, for example, altruism value or option value in the case people doubt about their future needs for these kinds of facilities.

Many criticisms have been made of urban growth models based on amenities and more specifically of policies based on cultural amenities. Thus, Florida (2017) speaks of a “new urban crisis” due to the emergence of an economic hierarchy between “superstar cities” which concentrate value-added activities, and poorer cities. A second aspect of this crisis is at the intra-urban scale where spatial segregation
<figure><img src="image_14.png" /><figcaption>Fig. 4 Distributions of individual marginal WTP for the culture attribute according to students' cultural awareness at the time of the survey</figcaption></figure>

and gentrification are emerging as a result of rising rents linked to the increased attractiveness of urban areas. Brueckner et al. (1999) already drew attention to the link between amenities and the spatial sorting of the population. They explained that the centre of Paris (France) attracted the richest people because it concentrated most of the endogenous amenities, particularly cultural ones, leaving the less attractive and less expensive peripheral areas to the poorest people. Conversely, the city centre of Detroit (United States) is devoid of attractive amenities and is occupied by the poorest individuals, while the peripheries offering natural amenities are occupied by the richest. According to Storper and Scott (2009), these phenomena of inter- and intra-urban spatial sorting are dangerous since the ecosystem of cities is based on the complementarity between high and low-skilled people. Finally, a third limit that we can see in the implementation of public policies oriented toward culture corresponds to conflicts over the use of urban space which can emerge within cities between new populations attracted by culture and already established populations whose preferences may differ. Some cultural activities, particularly night-time ones, can indeed generate noise pollution and therefore, disutility for people who are not the target audience, jeopardising social cohesion within the city (Mouate, 2019).

<figure><img src="image_15.png" /></figure>
<figure><img src="image_16.png" /><figcaption>**Fig. 5** Distributions of individual marginal WTP for the culture attribute according to scholarship-holder status</figcaption></figure>

These considerations about the limits of these policies lead us to the implica-
tions in terms of public policy for local authorities. On the one hand, investment in
culture may have an impact on the location of young graduates. In this respect, we
can only encourage decision-makers to take this type of impact into account when
evaluating their cultural policy. This also confirms that it can contribute to changing
the composition of the local population toward more skilled people. However, our
results do not say whether or not this impact compensates for the prior presence of a
dynamic economy enabling a good matching between labour supply and demand of
new city incomers. Furthermore, spatial planning policies must also be in line with
the preferences of established inhabitants to avoid spatial inequalities, gentrification
and preserve social cohesion. Rent control and social housing construction policies
could help to maintain a certain social mix within cities. Investment in public trans-
port infrastructure is also an avenue recommended by Florida (2017) to open up
the periphery and which can at the same time contribute to limiting the increase
in real estate prices by facilitating urban sprawl. Finally, regulating the opening
hours of night-time cultural establishments and helping to bring soundproofing up to
standard can also accompany these developments through culture policies in order to preserve social cohesion within cities.

Several implications for future research emerge from this study. First, further research is needed to explore on the articulation of use and non-use values associated with cultural assets in cities. Second, we think that it is necessary to better understand the relationship between cultural awareness and choices made during studies (e.g. orientation and career choices) since cultural preferences can change during this period. This analysis seems to us all the more important to carry out because France has several public policies intended to encourage cultural consumption by students and young adults in general. The implementation of the “Pass Culture” by the French government in 2019 being, among other purposes, to promote the local offer of cultural goods and services through the use of a location-based application software, it would be interesting to measure the evolution of the WTP of young people for culture and to confirm (or not) the effect of public cultural policies aimed at young people.

Since our data date 2018, the question of the stability of our results also arises. Indeed, the Covid-19 pandemic has had a major impact on people’s daily lives. The various confinements that have taken place in France have thus been able to modify the preferences of students with regard to the different aspects of their living environment. We can therefore wonder if the preferences for green spaces or for the cultural amenities of their city are still the same. Moreover, the growing attention paid to the ecological impact of lifestyles and the energy crisis may lead us to questions about the hierarchy of attributes obtained in this work, particularly with regard to the importance given to the network of bicycle paths in cities.

## Appendix

See Tables 9, 10, 11, 12, 13 and 14.

<table><caption>**Table 9** Description of the three HCPC clusters by active variables related to culture</caption><thead><tr><th>Characteristic</th><th>Cluster/characteristic</th><th>Characteristic/cluster</th><th>Characteristic/full sample</th><th>v.test</th></tr></thead><tbody><tr><td colspan="5"><em>Cluster 1: low cultural awareness (LCA), 28.0% of the sample</em></td></tr><tr><td>Museum = 0</td><td>83.3%</td><td>80.7%</td><td>27.2%</td><td>15.6</td></tr><tr><td>Exhibition = 0</td><td>64.2%</td><td>83.9%</td><td>36.7%</td><td>12.9</td></tr><tr><td>Heritage = 0</td><td>92.0%</td><td>37.1%</td><td>11.3%</td><td>10.2</td></tr><tr><td>Galery = 0</td><td>40.7%</td><td>93.6%</td><td>64.5%</td><td>8.7</td></tr><tr><td>Arthouse = 0</td><td>37.8%</td><td>86.3%</td><td>64.0%</td><td>6.4</td></tr><tr><td>Concert = 0</td><td>42.2%</td><td>71.8%</td><td>47.7%</td><td>6.4</td></tr><tr><td>Fest = 0</td><td>42.5%</td><td>68.6%</td><td>45.3%</td><td>6.1</td></tr></tbody></table>

<figure><img src="image_17.png" /></figure>
<table><caption>**Table 9** (continued)</caption><thead><tr><th>Characteristic</th><th>Cluster/characteristic</th><th>Characteristic/cluster</th><th>Characteristic/full sample</th><th>v.test</th></tr></thead><tbody><tr><td>Theatre = 0</td><td>35.5%</td><td>85.5%</td><td>67.7%</td><td>5.2</td></tr><tr><td>BookShp = 0</td><td>36.8%</td><td>77.4%</td><td>59.1%</td><td>5.0</td></tr><tr><td>RecordShp = 0</td><td>32.7%</td><td>95.2%</td><td>81.7%</td><td>5.0</td></tr><tr><td>QoL = 0</td><td>33.4%</td><td>91.9%</td><td>77.2%</td><td>4.9</td></tr><tr><td>Library = 0</td><td>36.8%</td><td>66.1%</td><td>50.5%</td><td>4.1</td></tr><tr><td>Opera = 0</td><td>30.7%</td><td>98.4%</td><td>90.1%</td><td>4.1</td></tr><tr><td>Classic = 0</td><td>31.6%</td><td>94.4%</td><td>83.7%</td><td>4.0</td></tr><tr><td>Eco = 0</td><td>31.9%</td><td>91.9%</td><td>80.8%</td><td>3.9</td></tr><tr><td>Cinema = 0</td><td>70.0%</td><td>11.3%</td><td>4.5%</td><td>3.9</td></tr><tr><td>Budget = 0</td><td>30.7%</td><td>91.1%</td><td>83.3%</td><td>2.9</td></tr><tr><td>Art_Activity = 0</td><td>33.3%</td><td>65.3%</td><td>55.0%</td><td>2.7</td></tr><tr><td colspan="5"><em>Cluster 2: moderate cultural awareness (MCA), 52.5% of the sample</em></td></tr><tr><td>Museum = 1</td><td>67.1%</td><td>93.1%</td><td>72.9%</td><td>10.4</td></tr><tr><td>Heritage = 1</td><td>58.9%</td><td>99.6%</td><td>88.7%</td><td>8.3</td></tr><tr><td>Exhibition = 1</td><td>66.1%</td><td>79.7%</td><td>63.4%</td><td>7.6</td></tr><tr><td>Classic = 0</td><td>59.7%</td><td>95.3%</td><td>83.7%</td><td>7.1</td></tr><tr><td>Opera = 0</td><td>57.5%</td><td>98.7%</td><td>90.1%</td><td>6.7</td></tr><tr><td>RecordShp = 0</td><td>55.7%</td><td>86.6%</td><td>81.7%</td><td>2.8</td></tr><tr><td>Cinema = 1</td><td>53.8%</td><td>97.8%</td><td>95.5%</td><td>2.5</td></tr><tr><td colspan="5"><em>Cluster 3: high cultural awareness (HCA), 19.5% of the sample</em></td></tr><tr><td>Classic = 1</td><td>75.0%</td><td>62.8%</td><td>16.3%</td><td>11.6</td></tr><tr><td>Opera = 1</td><td>88.6%</td><td>45.4%</td><td>10.0%</td><td>10.6</td></tr><tr><td>Arthouse = 1</td><td>40.9%</td><td>75.6%</td><td>36.0%</td><td>8.4</td></tr><tr><td>RecordShp = 1</td><td>54.3%</td><td>51.2%</td><td>18.3%</td><td>8.0</td></tr><tr><td>Theatre = 1</td><td>39.9%</td><td>66.3%</td><td>32.4%</td><td>7.2</td></tr><tr><td>Fest = 1</td><td>30.6%</td><td>86.1%</td><td>54.8%</td><td>6.8</td></tr><tr><td>Concert = 1</td><td>31.2%</td><td>83.7%</td><td>52.3%</td><td>6.7</td></tr><tr><td>Galery = 1</td><td>36.9%</td><td>67.4%</td><td>35.5%</td><td>6.7</td></tr><tr><td>QoL = 1</td><td>43.6%</td><td>51.2%</td><td>22.9%</td><td>6.5</td></tr><tr><td>Museum = 1</td><td>25.5%</td><td>95.4%</td><td>72.9%</td><td>5.8</td></tr><tr><td>Exhibition = 1</td><td>26.8%</td><td>87.2%</td><td>63.4%</td><td>5.4</td></tr><tr><td>BookShp = 1</td><td>31.5%</td><td>66.3%</td><td>41.0%</td><td>5.3</td></tr><tr><td>Eco = 1</td><td>38.8%</td><td>38.4%</td><td>19.2%</td><td>4.7</td></tr><tr><td>Art_Activity = 1</td><td>29.2%</td><td>67.4%</td><td>45.0%</td><td>4.6</td></tr><tr><td>Library = 1</td><td>27.9%</td><td>70.9%</td><td>49.6%</td><td>4.4</td></tr><tr><td>Budget = 1</td><td>33.8%</td><td>29.1%</td><td>16.7%</td><td>3.2</td></tr><tr><td>Heritage = 1</td><td>21.2%</td><td>96.5%</td><td>88.7%</td><td>2.7</td></tr></tbody></table>

Only characteristics whose v.test is greater than 1.96 are displayed in the table

The first cluster includes 83.3% of students who did not go to the museum during the year. It is made up of 80.7% of students who did not go to the museum, whereas in the sample this was the case for only 27.2% of students
<table><caption>**Table 10** Sociological characteristics of students and cultural practices of the sample and by cultural awareness group</caption><thead><tr><th>Column<br>Characteristic (variable name into brackets)</th><th>(1)<br>Sample (n=442)</th><th>(2)<br>LCA group 28.0%</th><th>(3)<br>MCA group 52.5%</th><th>(4)<br>HCA group 19.5%</th></tr></thead><tbody><tr><td>Women (gender)</td><td>66.1%</td><td>65.3%</td><td>64.7%</td><td>70.9%</td></tr><tr><td>Scholarship (scholarship)</td><td>47.5%</td><td>46.8%</td><td>47.0%</td><td>50.0%</td></tr><tr><td><em>Faculty:</em><br>Law, economics, management and tourism (LEMT)</td><td>46.8%</td><td>47.6%</td><td>42.7%</td><td>57.0%</td></tr><tr><td>Humanities and languages (HL)</td><td>22.9%</td><td>21.8%</td><td>22.4%</td><td>25.6%</td></tr><tr><td>Science, engineering and health (SEH)</td><td>30.3%</td><td>30.6%</td><td>34.9%</td><td>17.4%</td></tr><tr><td><em>Level of education</em><br>3rd year of bachelor (B3)</td><td>48.6%</td><td>49.2%</td><td>50.4%</td><td>43.0%</td></tr><tr><td>1st year of master's degree (M1)</td><td>24.9%</td><td>29.0%</td><td>20.7%</td><td>30.2%</td></tr><tr><td>2nd year of master's degree (M2)</td><td>26.5%</td><td>21.8%</td><td>28.9%</td><td>26.7%</td></tr><tr><td>Practice at least one artistic activity</td><td>45.0%</td><td>34.7%</td><td>42.2%</td><td>67.4%</td></tr><tr><td><em>Attend the following activities at least once over the year prior to the survey:</em><br>Museums and exhibitions:<br>Museums (museum)</td><td>80.3%</td><td>33.1%</td><td>99.1%</td><td>97.7%</td></tr><tr><td>Exhibitions (exhibition)</td><td>72.9%</td><td>19.4%</td><td>93.1%</td><td>95.3%</td></tr><tr><td>Heritage sites (heritage)</td><td>63.3%</td><td>16.1%</td><td>79.7%</td><td>87.2%</td></tr><tr><td>Art galleries (gallery)</td><td>88.7%</td><td>62.9%</td><td>99.6%</td><td>96.5%</td></tr><tr><td>Cinema:</td><td>35.5%</td><td>6.5%</td><td>39.2%</td><td>67.4%</td></tr><tr><td>Regular cinema (cinema)</td><td>96.4%</td><td>88.7%</td><td>99.1%</td><td>100.0%</td></tr><tr><td>Art-house cinema (arthouse)</td><td>95.5%</td><td>88.7%</td><td>97.8%</td><td>98.8%</td></tr><tr><td>Performing arts:</td><td>36.0%</td><td>13.7%</td><td>33.2%</td><td>75.6%</td></tr><tr><td>Theatre (theatre)</td><td>67.2%</td><td>42.7%</td><td>68.1%</td><td>100.0%</td></tr><tr><td>Amplified concert (concert)</td><td>32.4%</td><td>14.5%</td><td>29.3%</td><td>66.3%</td></tr><tr><td>Classical music concert (classic)</td><td>52.3%</td><td>28.2%</td><td>53.4%</td><td>83.7%</td></tr><tr><td></td><td>16.3%</td><td>5.6%</td><td>4.7%</td><td>62.8%</td></tr></tbody></table>

<figure><img src="image_18.png" /></figure>
<table><caption>Table 10 (continued)</caption><thead><tr><th>Column</th><th>(1)<br>Sample (n=442)</th><th>(2)<br>LCA group 28.0%</th><th>(3)<br>MCA group 52.5%</th><th>(4)<br>HCA group 19.5%</th></tr></thead><tbody><tr><td>Opera (opera)</td><td>10.0%</td><td>1.6%</td><td>1.3%</td><td>45.3%</td></tr><tr><td>Festivals (festival)</td><td>54.8%</td><td>31.5%</td><td>55.6%</td><td>86.0%</td></tr><tr><td>Multimedia libraries (library)</td><td>49.5%</td><td>33.9%</td><td>50.0%</td><td>70.9%</td></tr><tr><td>Book shops (book)</td><td>41.0%</td><td>22.6%</td><td>41.4%</td><td>66.3%</td></tr><tr><td>Record shops (record)</td><td>18.3%</td><td>4.8%</td><td>13.4%</td><td>51.2%</td></tr><tr><td colspan="5"><em>Totally agree with the following items:</em></td></tr><tr><td>Culture has a positive impact on quality of life of inhabitants (QoL)</td><td>22.9%</td><td>8.1%</td><td>20.3%</td><td>51.2%</td></tr><tr><td>Culture is essential for economic development of cities (Eco)</td><td>19.2%</td><td>8.1%</td><td>18.1%</td><td>38.4%</td></tr><tr><td>Public cultural spending has to be maintained despite financial difficulties (Budget)</td><td>16.7%</td><td>8.9%</td><td>16.4%</td><td>29.1%</td></tr></tbody></table>
<table><caption>Table 11 Table of p-values for khi2 tests of independence between students' characteristics</caption><thead><tr><th rowspan="2"></th><th colspan="3">Cultural awareness</th><th colspan="3">Faculty</th><th colspan="3">Education level</th><th colspan="3">Individual characteristics</th></tr><tr><th>LCA</th><th>MCA</th><th>HCA</th><th>LEBT</th><th>HL</th><th>SEH</th><th>B3</th><th>M1</th><th>M2</th><th>Scholarship</th><th>Gender</th></tr></thead><tbody><tr><td rowspan="3">Cultural awareness</td><td>LCA</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.928</td><td>0.833</td><td>1.000</td><td>0.969</td><td>0.256</td><td>0.201</td><td>0.930</td><td>0.925</td></tr><tr><td>MCA</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.081</td><td>0.907</td><td>0.035</td><td>0.487</td><td>0.042</td><td>0.272</td><td>0.890</td><td>0.578</td></tr><tr><td>HCA</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.048</td><td>0.597</td><td>0.006</td><td>0.298</td><td>0.255</td><td>1.000</td><td>0.693</td><td>0.350</td></tr><tr><td rowspan="3">Faculty</td><td>LEBT</td><td>0.928</td><td>0.081</td><td>0.048</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.543</td><td>0.000</td><td>0.004</td><td>0.325</td><td>0.119</td></tr><tr><td>HL</td><td>0.833</td><td>0.907</td><td>0.597</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.008</td><td>0.868</td><td>0.001</td><td>0.017</td><td>0.019</td></tr><tr><td>SEH</td><td>1.000</td><td>0.035</td><td>0.006</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.000</td><td>0.994</td><td>0.001</td><td>0.000</td></tr><tr><td rowspan="3">Education level</td><td>B3</td><td>0.969</td><td>0.487</td><td>0.298</td><td>0.543</td><td>0.008</td><td>0.002</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.797</td><td>0.130</td></tr><tr><td>M1</td><td>0.256</td><td>0.042</td><td>0.255</td><td>0.000</td><td>0.868</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.351</td><td>0.671</td></tr><tr><td>M2</td><td>0.201</td><td>0.272</td><td>1.000</td><td>0.004</td><td>0.001</td><td>0.994</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.189</td><td>0.236</td></tr><tr><td rowspan="2">Individual characteristics</td><td>Scholarship</td><td>0.930</td><td>0.890</td><td>0.693</td><td>0.325</td><td>0.017</td><td>0.001</td><td>0.797</td><td>0.351</td><td>0.189</td><td>0.000</td><td>0.722</td></tr><tr><td>Gender</td><td>0.925</td><td>0.578</td><td>0.350</td><td>0.119</td><td>0.019</td><td>0.000</td><td>0.130</td><td>0.671</td><td>0.236</td><td>0.722</td><td>0.000</td></tr></tbody></table>

<figure><img src="image_19.png" /></figure>
<table><caption>**Table 12** Sociological characteristics of students by faculty</caption><thead><tr><th></th><th>Sample</th><th>Law. economics. management and tourism (LEMT)</th><th>Humanities and languages (HL)</th><th>Science. engineering and health (SEH)</th></tr></thead><tbody><tr><td>Women</td><td>66.1%</td><td>70.0%</td><td>76.2%</td><td>52.2%</td></tr><tr><td>Scholarship</td><td>47.5%</td><td>50.2%</td><td>58.4%</td><td>35.1%</td></tr><tr><td>Level of education:</td><td></td><td></td><td></td><td></td></tr><tr><td>3rd Year of Bachelor</td><td>46.8%</td><td>46.9%</td><td>36.6%</td><td>60.4%</td></tr><tr><td>1st year of master's degree</td><td>22.9%</td><td>33.3%</td><td>23.8%</td><td>12.7%</td></tr><tr><td>2nd year of master's degree</td><td>30.3%</td><td>19.8%</td><td>39.6%</td><td>26.9%</td></tr></tbody></table>

<table><caption>**Table 13** Estimation of RPL according to scholarship-holder status</caption><thead><tr><th rowspan="3">Parameters</th><th colspan="2">Dependant variable: choice</th></tr><tr><th colspan="2">RPL4</th></tr><tr><th>Coefficient (S.E.)</th><th>Standard deviation (S.E.)</th></tr></thead><tbody><tr><td>ASC</td><td>1.093*** (0.170)</td><td>1.006*** (0.153)</td></tr><tr><td>Early childhood</td><td>0.036*** (0.012)</td><td>0.097*** (0.015)</td></tr><tr><td>Culture</td><td>0.105*** (0.017)</td><td>0.078*** (0.016)</td></tr><tr><td>Sport</td><td>0.084*** (0.014)</td><td>0.105*** (0.015)</td></tr><tr><td>Green spaces</td><td>0.096*** (0.014)</td><td>0.102*** (0.015)</td></tr><tr><td>Bicycle paths</td><td>0.017* (0.010)</td><td>0.039* (0.020)</td></tr><tr><td>Rent</td><td>-0.031*** (0.002)</td><td></td></tr><tr><td>ASC x scholarship</td><td>0.060 (0.181)</td><td></td></tr><tr><td>Childhood x scholarship</td><td>-0.012 (0.016)</td><td></td></tr><tr><td>Culture x scholarship</td><td>-0.005 (0.016)</td><td></td></tr><tr><td>Sport x scholarship</td><td>-0.026 (0.018)</td><td></td></tr><tr><td>Green Spaces x scholarship</td><td>0.045** (0.018)</td><td></td></tr><tr><td>Bicycle Paths x scholarship</td><td>0.024* (0.014)</td><td></td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.262</td><td></td></tr><tr><td>Log-likelihood</td><td>-1432.488</td><td></td></tr><tr><td>N</td><td>442</td><td></td></tr><tr><td>Observations</td><td>1768</td><td></td></tr></tbody></table>

Significance levels: 1%; **; 5%; **; 10%: *; S.E., standard error
<table><caption>**Table 14** Marginal willingness to pay estimates for the five living environment attributes according to scholarship-holder status</caption><thead><tr><th rowspan="2"></th><th colspan="2">RPL4</th></tr><tr><th>WTP</th><th>Standard error</th></tr></thead><tbody><tr><td>Childhood</td><td>11.47***<br>[4.22;18.73]</td><td>3.70</td></tr><tr><td>Culture</td><td>33.40***<br>[25.69;41.12]</td><td>3.94</td></tr><tr><td>Sport</td><td>26.84***<br>[19.03;34.64]</td><td>3.98</td></tr><tr><td>Green spaces</td><td>30.57***<br>[22.50;38.64]</td><td>4.12</td></tr><tr><td>Bicycle paths</td><td>5.25*<br>[-0.83;11.33]</td><td>3.10</td></tr><tr><td>Childhood x scholarship</td><td>-3.74<br>[-14.00;6.52]</td><td>5.24</td></tr><tr><td>Culture x scholarship</td><td>-1.54<br>[-11.69;8.62]</td><td>5.18</td></tr><tr><td>Sport x scholarship</td><td>-8.34<br>[-19.25;2.58]</td><td>5.57</td></tr><tr><td>Green spaces x scholarship</td><td>14.40**<br>[3.34;25.44]</td><td>5.64</td></tr><tr><td>Bicycle paths x scholarship</td><td>7.53*<br>[-1.21;16.27]</td><td>4.46</td></tr></tbody></table>

Significance levels: 1%: ***; 5%: **; 10%: *; S.E., standard error, []: 95% confidence interval for the value estimated by the model

**Acknowledgements** We would like to thank the anonymous referees for their comments and suggestions, which helped to improve the quality of the paper substantially.

**Funding** Open access funding provided by Université d'Angers.

**Declarations**

**Conflict of interest** The authors declare that they have no conflict of interest. The authors did not receive support from any organization for the submitted work.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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