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Happy Wuppertal – Measuring Individual and Community Well-Being on the Urban Scale

  • Hans Haake
  • Kai LudwigsEmail author
Case Article

Abstract

In this paper we will present lessons learned and examples of empirical findings from an app-based survey in the City of Wuppertal, Germany. The app consists of several complementing modules that look into the individual well-being and community wellbeing of the citizens of Wuppertal. Within 12 months the central questionnaire has been answered 1799 times in full by different individuals. The app is very well known within the city due to partnerships with local enterprises and media. Some first steps towards using the app in decision making have been taken and will be extended. The experience shows great potential of such an approach both for research and new forms of participation but also some challenges in consistently motivating citizens to participate. The results of the survey confirmed some known factors influencing happiness but also showed some interesting patterns relevant both to local decision making and further research.

Keywords

Community well-being App-research Happiness ESM DRM 

Introduction

New Indicator Sets

In recent years there have been many attempts to better measure progress for nations, regions or cities. Most of these attempts can be categorized as stemming from the “beyond GDP” debate (Constanza et al. 2009; European Commission 2013; Fleurbaey and Blanchet 2013), recognizing that GDP and other economic measures are often used as primary measures of progress while never designed and often unfit for that purpose. Some of these indicators try to complement GDP with environmental and social metrics (e.g. United Nations 2014), others adjust GDP by economic valuation of non-market goods (McGuire et al. 2012; Talberth et al. 2007) or build entire new sets of indicators based on concepts of human needs or capabilities (Deutscher Bundestag 2013; OECD 2013a, b). The most prominent overview of such concepts was provided by Nobel laureates Stiglitz and Sen (Stiglitz et al. 2009). While important advances come from the “beyond GDP” direction, it is important not to forget earlier work on sustainability indicators that shared many of the goals and concrete data foundations with newer concepts. There are sustainability indicators for any level of governance as well as companies, with the most recent development being the UN Sustainable Development Goals with their accompanying indicators (UN General Assembly 2030). They are currently being integrated into strategies and decision making at all levels, despite some contradictions and shortcomings (Easterly 2015; Lane 2016).

Another important development in that context is the availability of more and better data on the subjective well-being of people, with happiness seen by some as the ultimate goal of all these other indicator sets.1 While single questions on subjective well-being have been asked in international surveys for a while, deeper analysis of subjective well-being was usually limited to small groups. New technologies, as used in our study, are providing previously unavailable insights. Some institutions have already tried to include “happiness” into broader indicator sets, such as the Happy Planet Index (Jeffrey et al. 2016) and countries are trying to use this data in decision-making (Centre for Bhutan Studies and GNH Research 2016; UK Cabinet Office 2013).

It is important to note that the motivation for creating all these indicator sets is not merely academic or the attempt to fine-tune a generally acceptable economic system. There is a clear need to move humanity off a pathway that is deeply unsustainable, as it is currently creating present well-being for some at the expense of others and even more at the expense of future generations. Finding better metrics for well-being beyond material wealth is essential in the necessary transformation to a sustainable society.

Methodological Advances in Measuring Subjective Well-Being

Conceptual and methodological advances have moved Happiness Research forward. More and more researchers are asking for better data displaying people’s subjective well-being in various moments, enabling them to analyze differences from moment to moment, thereby understanding differences due to life changes and interventions. This goes far beyond analyzing differences from person to person, which usually only shows differences due to demographic factors. Based on work from Kahneman et al. (2004) and Killingsworth and Gilbert (2010), Ludwigs (2018) developed an efficient method to collect such data using an app called the “Happiness Analyzer”. In various studies this app showed its potential to collect meaningful data to understand subjective well-being in greater detail (Hendriks et al. 2016) but a bigger data collection on an urban level was still lacking which should be changed with the Happy Wuppertal project.

Urban Indicators

Many of the developments in indicator design and calculation eventually find their way from the national to the local level, fulfilling one of the characteristics of Community Well-Being (Kim and Lee 2014). While Kim and Lee (2014) provide a valuable survey of existing indicators of Community Well-Being in cities, the list should really be much longer since most of the indicators that were developed during the “Local Agenda 21” movement or even common city rankings include many elements relevant to Community Well-Being. Very few of these indicator sets include a satisfactory, if any, component of subjective well-being. Happy City UK, a program focused on Bristol (www.happycity.org.uk) and some work in Santa Monica, California (wellbeing.smgov.net) are notable exceptions where at least annual surveys including more than one question on subjective well-being were included. However, more detailed data on how satisfied citizens are with their life and their city is costly to compile.

The Happy Wuppertal Project

Wuppertal is a city in western Germany with a population of around 350,000. Historically one of the most important industrial centers of Europe, it shared the common downturn of industrial cities in the second half of the twentieth century. It lost many citizens, factories closed, the city is highly indebted and is struggling to keep services and infrastructure at the level of surrounding wealthier cities. However, the downturn has also provided open spaces and cheap rents for a lively art and culture scene, with citizens taking a major role in developing the city where the administration is unable to. With a large university and one of the foremost think-tanks on sustainability, the Wuppertal Institute, Wuppertal is a fitting testing ground for new measures of Community Well-Being. In a parallel project, a set of dimensions and indicators based on the OECD Better Life Index has been adapted to Wuppertal but was lacking the important subjective components. With the availability of the “Happiness Analyzer” developed by Ludwigs (2018) and support from local businesses it was possible to use an App-based approach to fill this gap. The aim of the study was exploratory, as no central research questions were formulated. It was both a proof-of-concept, testing a new technology in a new context and the urban setting, and a chance to gather data to connect with existing work. Subjective data is not only relevant for individual life satisfaction, but also to judge perceptions of the city. To give an example, objective crime data only tells part of the story of how secure people feel. Contrasting these two angles could lead to new policy options.

We define (community) well-being as a broad set of dimensions that influence peoples’ lives (in a city), using the Better Life Index of the OECD in a form that was adapted to Wuppertal (Better Life Index Urban, BLI-U).

For our definition of subjective well-being we also follow the OECD: „Good mental states, including all of the various evaluations, positive and negative, that people make of their lives and the affective reactions of people to their experiences.” (OECD 2013a, b). This includes three elements of subjective well-being: The cognitive evaluation of one’s life, the feelings or emotional states (affect) as well as whether people feel they are leading a good and meaningful life.

What sets “Happy Wuppertal” apart from many previous studies is the combination of a concept of community well-being, including many categories relevant to people, with a subjective approach. Previous studies were often either pure happiness surveys or measured community well-being through existing statistical data, as pointed out by Sirgy (2018) or Anderson (2017).

Aim of the Paper

The aim of this paper is not easily summed up by a single research question, as the underlying research has many exploratory elements, especially in the direction of mixing a research tool with a participation tool. The Focus, however, is on the lessons learned from adapting the app-based approach to Happiness Research (Ludwigs and Erdtmann 2019) to the city level, adding both questions appropriate to cities in general (community well-being) and one specific city. Considering the developments outlined above, bringing Happiness Research to cities can be valuable both to inform local decision makers and to add more localized data to broader research on subjective well-being. As a case article, the paper aims to support other researchers interested in using app-based approaches in similar settings. However, the paper also shows some selected insights into the results that can be generated. Some of these results confirm previous findings on determinants of happiness, others provide new insights on the relationships between domain satisfaction and overall well-being and others again provide insights relevant to local decision making.

Method

Project and Survey Design

The first phase of the project “Happy Wuppertal” from May 2017 to June 2018 was centered around an adaptation of the smartphone app, server infrastructure and live dashboard for presenting results of the Happiness Analyzer (Ludwigs 2018). However, it goes beyond simply adapting the app to Wuppertal, bringing together a network of partners in order to motivate a large number of citizens to participate repeatedly and accurately share their subjective assessments of the city. This included local businesses (A mutual savings bank and the energy and transport utility), close communications with city administration and policy makers, a strong partnership with the local newspaper and various forms of outreach as described below.

The App consisted of the following four modules, some of which were used throughout the study, some only in the beginning:
  1. 1.

    Basic survey, repeated every three months:

     

This survey provided the key dataset, requiring about ten minutes to respond to about 100 questions. All items can be seen in Appendix Table 1. It combined (mostly) questions on a scale of one to seven, with some free form texts. In its first half it asked mostly questions on subjective individual well-being, following the OECD guidelines for measuring subjective well-being, directly relating to our definition of well-being (see above). This included core questions as used by the European Social Survey, a scale of positive and negative experience (Diener et al. 2010), a satisfaction with life scale (Diener et al. 1985), a flourishing scale (Diener et al. 2010) as well as a domain evaluation questionnaire (OECD 2013a, b) (for more details see Ludwigs and Erdtmann 2019).

The second half of the survey consisted of questions on Wuppertal: “How happy are you with various infrastructure, with environmental quality, with politics and the administration?” It included some very specific current projects as well as the ability to mention aspects that should be included as a stand-alone item in future surveys. Many aspects of Community Well-Being as listed elsewhere were included, and all dimensions of the BLI-U for Wuppertal were also integrated.

The domain evaluation questionnaire presents an interesting overlap between the two halves. It is considered a part of measuring subjective well-being, but its domains strongly overlap with the dimensions of quality of life. While the latter partly uses objective statistics, e.g. on unemployment or air quality, in some cases it relies on surveys that would ask questions very similar to the domain evaluation questionnaire. This is an important consideration for integrating city-related questions into the logic of the OECD guidelines as outlined below.

Demographic questions were included and used later to check how representative the sample was compared to the population of Wuppertal.
  1. 2.

    Activity Based Survey using the Day Reconstruction Method (DRM)

     
Following Ludwigs (2018), based on the work of Kahneman et al. (2004), participants had the option of participating in an additional module that helped them to reconstruct their days over the course of the week. In what can be considered a journal they were able to define time periods in which they pursued certain activities, who they were with and how happy they felt at the time.
  1. 3.

    Experience Based Survey using the Experience Sampling Method (ESM)

     
This optional module went one step further, based on the work of Killingsworth and Gilbert (2010) in being as close as possible to the moment where an emotion was felt. Here participants were sent a notification four times a day for one week, asking them what they were doing, who they were with and how happy they were feeling at that moment.
  1. 4.

    Individual Feedback

     

At any time, participants could submit an impression of things that made them happy or unhappy or that they would like to draw attention to.

The four modules and their elements contribute in different ways to the aim of participation or policy advice on the one hand and the aim of better understanding community well-being on the other hand. The results generated by the study are intended to be of direct use to decision makers and public debate, showing strengths and weaknesses and possible paths towards higher well-being. However, very detailed information on personal well-being was also collected to create a base for both better understanding the connection between community- and individual well-being and for future evaluations of policy decisions. This specific combination of academic interests and very concrete local topics and needs can create specific challenges, e.g. were the value in asking about happiness using five different scales is unclear to local citizens or where links between concrete projects and happiness are difficult to evaluate precisely. But it also provides the opportunity of exploring the use of happiness research beyond its simplest form in the urban context.

Incentive Structure and Local Networks

A survey of this scale, ideally leading to a panel of participants repeatedly taking part, will require various incentives for people to take part. Since the project was framed as serving the common good and wanted to engage people, direct financial incentives were not considered a valuable option. Therefore, three forms of incentives were included:
  1. 1.

    Intrinsic Social incentives: The project was framed as a way to provide feedback on life in Wuppertal, and how the city should develop. Through close cooperation with policy makers, who provided quotes and were present at events, the public presentation of results and an intensive presence in local media, it was plausible that the results would have an impact on the city. In addition, the combination of “Wuppertal” and “Happy” was often seen as something benefitting the city in improving its somewhat mediocre image in the rest of Germany.

     
  1. 2.

    Intrinsic Individual Incentives: The app allowed people to review their responses, also comparing them over time. This helped them in consciously thinking about their own happiness and finding out more about what makes them happy. This alone has been shown to somewhat increase happiness (Ludwigs et al. 2018).

     
  1. 3.

    Extrinsic Social Incentives: After finishing the main survey, participants were given the opportunity to pick a charitable project in Wuppertal from an existing platform (“Good for Wuppertal”). These projects were then provided with five Euros from a fund donated by the two local cooperating businesses (see below).

     

To strengthen these incentives, it was essential for the project to be “present” in the city, for two major reasons: participation and relevance. Citizens needed to be aware of the app and the various reasons for participating to get a high enough return rate. But they also had to see the app and its results as being relevant to the public debate, to decision makers and concrete decisions. The latter will be essential for the next steps of the project.

The development of the project and its communication, as described in the next section, depended on local networks, especially some key partners. From an early stage, the local energy and transport utility and a local savings bank (Sparkasse) were chosen as partners, providing some project funding and the funds to be donated by participants. But more importantly, they shared their perspective on the app, the central questionnaire and the communication strategy. They also supported the advertising for the app by providing design input and using their various channels such as newsletters, employee bulletins and print products. The app was even advertised on hundreds of ATM machines in Wuppertal. The core partners were augmented by a large network of civil society organizations and members of the city administration already formed through previous local projects of the Wuppertal Institute and the Center for Transformation Research and Sustainability (TransZent). They provided additional publicity, but also feedback on various stages of the project in two workshops held in December 2016 and January 2017.

Communication Strategy

As laid out above, the project had to be visible in the city to assure participation and relevance. This was achieved by using various channels. A project website (www.happy-wuppertal.com) was designed, including a professional animated video, showing the basic workings and potential impact of the app. A Facebook page shared regular updates and Twitter Channels of the Wuppertal Institute were used. 5000 postcards were printed and placed in restaurants and cafes throughout the city. Posters were also printed and placed along the stops of the famous hanging monorail. As mentioned, the partners used their channels including advertising on ATM machines. The project was very present in local media, with around 50 articles in various newspapers reaching thousands of readers. The main local newspaper (Westdeutsche Zeitung) launched a weekly series asking well-known citizens to share their perspective on happiness. Local radio and TV also covered the app several times.

Public events were also organized, with around 100 participants each at the launch of the app and the first presentation of results. Results are also made visible via a public dashboard on the website, allowing anyone to dig into the data and run some analysis.

The scale of the communication efforts and the strong media interest shows how topics such as happiness or well-being are popular in principle. Without these efforts the numbers of participants would not only have been lower, but any use of the results in decision making would have been unlikely. However, the necessary activities were substantial and difficult to maintain for a longer panel study and were only possible with strong local partners, showing that monitoring community happiness and well-being at the urban level will always require significant resources.

Sample

After four rounds of the surveys being pushed to installed apps, supported by media coverage and social media activities, well over 2000 citizens have participated. After rejecting suspicious entries potentially only filled out to donate the five Euros, 2372 of the main questionnaires remained, filled out by 1799 individuals. 277 participants used the journal function and over 4000 experience samples were submitted. The spontaneous feedback function was hardly used.

The rounds of the survey differ in participation, mostly due to the amount of media coverage at the time, with participation dropping off over time. Two of the modules of the survey, the activity- and experience- based methods, were dropped after the second round. While they provided valuable data, they largely showed effects already known from previous studies and little that was specific to the city.

Participation

Round

Questionnaires (after cleaning)

Journal entries

Experience samples

Feedback

1

1103

10,325 (Individuals: 277)

4084 (Individuals: 484)

56

2

564 (repeated: 133)

997 (Individuals:25)

755 (Individuals: 31)

7

3

359 (repeated: 236)

Module not used

Module not used

8

4

252 (repeated:204)

Module not used

Module not used

3

Participation was, as expected, not representative of the general population but did cover a range of different groups. The male to female ratio was 46% to 54%, similar to the ratio in the city of 48% to 52%. All age groups from 21 to 65 were well-represented, youth and those over 65 were not. The different districts of Wuppertal all had adequate participation, with some bias towards the more central areas. Minorities were under-represented, around 30% of the population of Wuppertal has a migrant background, but not even 8% of the survey participants. Similarly, citizens with lower educational achievement were less likely to participate. This reproduces some of the biases common in any form of participation, where those interested in the development of the city and its politics are better off financially and more highly educated. Altogether, participation yielded more than enough data to evaluate the viability of measuring subjective well-being and community well-being this way. A more detailed comparison between sample and city demographics can be found in Appendix Table 2.

Results

Overview

The large datasets compiled in the study allow various forms of descriptive and more advanced analysis. In the online-dashboard (www.happy-wuppertal.com) all data can be analyzed in an interface to get descriptive results and compare groups for their significance using analysis of variance techniques (ANOVA).

Here we only want to outline some main findings primarily relevant to the city of Wuppertal to give examples of the kind of information that can be found in the data, showing the potential of such studies for better understanding subjective well-being and community well-being anywhere. Correlation analysis was chosen as a preferred method, as causalities are often not clear with different variables influencing each other. The detailed descriptive results and the correlation table can be seen in Appendix Table 3 and Appendix Table 4.

General Results

The survey confirms known correlations on demographic and social factors: Those engaged and feeling connected to their city are more content. The same goes for those in a relationship, with a university degree, an income over 4000 Euro per month or over the age of 50 (see e.g. Easterlin 2003; Cuñado and de Gracia 2012; Frijters and Beatton 2012).

Wuppertal-Specific Results

In principle, Wuppertal is happy. The questions asking “All in all, how happy would you say you are?” and “All in all, how satisfied are you presently with your life?” were both answered with an average of 5.41 on a scale of 7. With an average of Globally (4.19), within Germany (5.17) and for the province of Northrhine-Westfalia (5.28) that is above average (Helliwell et al. 2017; Raffelhüschen and Krieg 2017).

Looking at the various domains of well-being shows some expected and some unexpected results. Shortage of time scores lowest (M = 4.58) in the personal domains, in community well-being satisfaction with the infrastructure for cyclists and motorists is lowest (M = 3.62 and M = 3.70), noise (M = 3,90) and lack of cleanliness (M = 3,60) cause the highest dissatisfaction with environmental quality. Services for people with disabilities had a relatively low rating (M = 3,97), the energy and water grid were at the other end of the scale (M = 5,52).

Appendix Table 3 shows the satisfaction with all domains, both personal and city-focused.

The satisfaction with transportation infrastructures provides some interesting examples of further analyses that are possible when some of the demographic data are used. First off, cycle infrastructure and car infrastructure are rated the lowest, however, the latter improved significantly after the closure of one major road was lifted. Interestingly, those without a driver’s license are more satisfied with car infrastructure (M = 4.18) than those with one (M = 3.66). This could actually point to a form of criticism, with non-drivers pointing out the dominance of infrastructure they do not even use. Going into more detail, those who use their cars every day are actually least happy with the infrastructure they use. In contrast, users of public transit are more content with “their” infrastructure the more often they use it.

The satisfaction with different dimensions of environmental quality could also provide valuable insights. Complaints about cleanliness are most prominent, also replicated in free-form answers. This is a common theme in any workshop or meeting in a city, however hardly a topic of much political debate. While air quality (M = 4.34) was expected to be an issue with prominent media coverage of particle and nitrogen pollution in German cities, noise (M = 3.90) is rated as a larger problem. The noise pollution in central parts of the city where the main roads run is visible in our data as well as official statistics in the city. This provides another indication of the importance of mobility and traffic for community well-being.

Correlation analysis between general life satisfaction and other responses is the next logical step and also yielded some interesting results. The known limitations need to be kept in mind, with possible influences from variables not included in the survey.

There is a general correlation of the satisfaction with any of the characteristics of the city with personal satisfaction. This could be the result of the general attitude of a person, however different strengths of the correlations are still relevant. Correlation analysis was done using Pearson’s r, as all variables are on a metric scale. Due to the high number of cases, the correlations used are all significant on the level of p < .01.

For further analysis, especially towards policy advice, it makes sense to target domains with interesting combinations of satisfaction scores and correlations with overall happiness. The combinations depend on the domains and might require inputs from other research on specific domains. In this case, both the infrastructure for cars (r = .147) and public transport (r = .140) are more highly correlated, with the latter scoring higher for satisfaction (M = 4.22 vs. M = 3.70). Looking at the big picture of a city that has historically primarily focused on cars, an underfunded public transport system still scores relatively well and might be the thing to focus on. Bicycle infrastructure scores very low for correlation (r = .056), however this is almost entirely driven by a low number of cyclists in the city and the study sample. For those cycling every day or almost every day, the correlation is .210.

Looking at the environmental topics, noise emerges as an interesting issue. In principle, connections between noise and health are well established, with some work on noise and happiness (Weinhold 2008). In our sample, there is both a low satisfaction with the levels of noise (M = 3.90) and a high correlation to happiness (r = .224). At the same time in Wuppertal noise is very unevenly distributed, with the happiest parts of the city being least affected.2 While in current political debates in Germany particle and nitrogen emissions are most present, noise is hardly considered. Reducing traffic in residential areas would benefit both domains.

While the GPS capabilities of the app were not used to bypass issues of privacy, even the comparison of different districts of the city is valuable. Those districts outside the city center with more green spaces and less noise consistently score higher in both the subjective well-being scales and the community well-being scales. There is an obvious connection with income, which is also higher in those districts (vgl. Empirica 2007, p. 12).

While single projects are not likely to have a large influence on community well-being, they were an important element of the app to test its potential as a tool for participation. This provided some clear results on infrastructure projects, the building of factory outlet centers or public participation. Again, it was possible to get some deeper insights by looking at the responses in different districts, e.g. with negative values for the building of a cable car from those directly affected and indifference from more distant districts. At least in some cases the results were taken up by media.

This paper focuses on the results from the key questionnaire, as it was taken most often and included many of the elements of community well-being in addition to personal well-being. However, the additional modules of the app also provided valuable data that might be used to further explore links between happiness, different activities and resource use. They also confirmed previous findings on how being outside, being with friends or being active improves life satisfaction. However, strictly for the purpose of better informing policy in a specific city, the ESM and DRM methods have not yet added significant value relative to the time required of participants.

The free-form fields included in the app were also analyzed in depth but are not considered here. They largely confirm the findings from the quantitative data but show potential to be used for public participation as many concrete proposals to improve well-being were given.

Discussion

Lessons Learned

While the data from the app is mostly relevant for local use, pointing out ways to improve community well-being locally, the process of establishing and using the app in Wuppertal provided some key lessons that might inform similar efforts elsewhere and will be summarized in the following:
  • An app is (one) appropriate tool for assessing community well-being at the urban level.

As the study showed, using an app to assess community well-being in a city works and has some significant advantages (not all of which were used during this study) and few shortcomings. This is especially true since with the right server system in the background it has become quite easy to deploy the same survey through a stand-alone app as well as on a (optimized for mobile) website and even as a part of other existing apps. In this app it is possible to track the well-being of individuals over time while keeping them anonymous through the use of a random code attached to each installation. This makes the development of anonymous panel studies possible, even though a large enough panel has not been recruited for Happy Wuppertal yet. Smartphones are carried around all day by most persons, making a survey of current experiences possible. An app can also present a much smoother user experience, e.g. by automatically advancing to the next question or only asking specifics depending on a previous reply. Informal feedback by users and a low number of aborted questionnaires confirm this for Happy Wuppertal. A list of around 100 questions in print form or even on a website would have seemed impossibly extensive.

Concerns about an app only using younger participants proved to be unfounded, however an overrepresentation of those who are well-educated, and wealthy remains problematic. This is a well-known issue in any form of public participation. Nonetheless it will need to be addressed going forwards. Especially those with a migration background need to be included, making up 30% of the population of Wuppertal. Working with gatekeeper organizations such as clubs or schools is one important strategy.
  • Combining Happiness and Community Well-Being works

Happy Wuppertal combines questions on happiness at the individual level and satisfaction with aspects of the city known to drive community well-being. Both components benefited from the other, in public perception as well as the analysis. Including subjective well-being made the ultimate aim of improving happiness visible and provided a much easier base for communication than a complex “Better Life Index” or similar. Happiness requires much less explanation, even if it is understood differently by different people. On the other hand, the questions on the city grounded the research and made it specific to Wuppertal, motivating people to participate that might not have done so in a generic happiness study. While it would be overly ambitious at this point to expect clear relationships between aspects of the city and happiness, some correlations were found, informing current and future debates on the best measures to improve a city. The exact combination of happiness-research and city-specific topics in such a survey will need further balancing. While the core questions on happiness provided important context, the more extensive modules for journaling and experience sampling did not provide any insights specific to the City yet. At this point, the survey for Wuppertal is likely to be shortened mostly by reducing the resolution on happiness, shifting the focus more towards community well-being and city development.
  • Timing and Size of survey rounds need to be optimized

The survey consisted of four rounds taking place every three months. This led to long breaks between surveys, causing some participants to be unsure if the project was still running. The module offering the ability to provide “city feedback” at any time was hardly used, some participants stated that they simply “forgot” about it. The main questionnaire was too long to reply “in between”, requiring some level of commitment. This leads to a planned improvement of the method, shifting from four large survey rounds to more frequent, shorter questionnaires. Some would focus on the basic questions around personal happiness and key aspects of the city, others would look more in depth at topics like the environment, work or health, others again at current proposals and developments in the city.
  • Incentive structures work, but not alone

It is not possible to quantify which incentives (intrinsic, extrinsic, individual, social) were most important. Combining the motivation of improving the city with a donation to local projects was clearly attractive to certain groups of citizens. Those groups benefitting from the donations clearly advertised the project. Several projects that are not visible in the media or public discourse attracted large numbers of donations, so members and supporters must have been asked to take part. In one case the donations as an incentive backfired, with almost all entries of supporters of one project deleted from the data. Students of one school had been asked to support a project for refugees but were told to “just do it quickly”, leading to implausible response patterns or many skipped questions.
  • A strong Communication Strategy is necessary

Installing an app and responding to over 100 questions, ideally several times, requires effort. As explained above, that was not the only reason for an ambitious communication strategy. Active communication can also strengthen the relevance of the results to local decision makers. The visibility of the app in local media was clearly beneficial to the project. Many thousands of citizens were reached and are now aware of some of the concepts behind community well-being and happiness research, even though they may never have installed the app. The challenge for similar projects will be achieving this level of visibility with reasonable effort and cost. Local partners will be key here and can be easily recruited due to the inherent appeal of happiness.
  • Results can be used for city development, but this takes time

Happy Wuppertal was always intended to not only provide scientific insights but to also be relevant to local decision-makers, especially in policy and the city administration. Ideally, there would be some continuous flow of information from the app into city hall, leading to public responses and concrete measures taken to improve well-being. Some success can be shown here. The mayor was consistently positive on the project, as were other politicians, all agreeing on the need to further integration. However, implementation of this has been difficult as administration staff would need to invest significant amounts of time, most likely only possible if some additional funds are acquired. Results from the app did enter public debate, e.g. on specific projects such as a planned cable car connecting the train station and the university. Happy Wuppertal was also included in the process towards a city development concept that will be presented in 2019. Participation formats were informed by app data, leading to interesting combinations of digital and analogue input.

Making indicators relevant to decision making is not a new problem and is encountered in many other areas. Direct exchange between citizens and politicians / administration officials and relevance to local issues could make the difference between data that is used or not.
  • A strong local network is necessary

In the form described here and considered valuable for increasing community well-being, Happy Wuppertal can only work through a strong network of local partners. This includes a research organization with a strong presence in the city, which could be an institute or a university and local partners from business, politics and civil society. If such a network can be formed, the study becomes relevant to the city, leading to higher participation, better results, and actual influence on decision-making.

Conclusion and Next Steps

The study presented in this paper is the first attempt to measure a combination of subjective well-being and perceptions of community well-being at the city level using a smartphone app. While significant improvements will be needed for the next rounds of the survey, it showed the general viability of such a study, generating a large enough dataset for various analyses at the local level. The improvements are listed in the „lessons learned “above and include a more modular design of shorter surveys and a stronger connection to decision-making. Some of the key factors of success were identified as strong local networks, efforts at communication on all possible channels and the use of a variety of incentives motivating different groups of participants.

In future research, the perceptions on community well-being will be compared and contrasted with the available statistical data on different dimensions of well-being. Reproductions or similar Studies in other cities will be needed to create a stronger basis for comparisons, while running continuous surveys in Wuppertal will enable comparisons over time.

Footnotes

  1. 1.

    There are conflicting views on the relationship between broader sets of well-being indicators and happiness. Some see the dimensions of well-being (health, education, etc.) mainly in their contribution to happiness and try to measure that contribution (Veenhoven 2010; Stiglitz et al. 2009:145–151), while work based on the capability approach considers the capabilities as their own ends (Stiglitz et al. 2009: 151–153).

    In practical applications, subjective well-being is sometimes included as one of several indicators (e.g. in the Better Live Index, OECD 2013a, b), sometimes considered the outcome of the factors measured (Santa Monica Well-Being, as described in OECD 2017). However, Veenhoven (2010) finds that both a focus on capabilities and one on happiness lead to similar policy prescriptions.

  2. 2.

    Noise maps are available at geoportal.wuppertal.de

Notes

Acknowledgements

The work presented in this paper was supported and partly funded by the Forschungsinstitut für Gesellschaftliche Weiterentwicklung, the Stadtsparkasse Wuppertal and the Wuppertaler Stadtwerke. We would like to thank two anonymous reviewers for their suggestions. Both Prof. Uwe Schneidewind and Anna Lohmann provided significant input into the underlying project.

Compliance with Ethical Standards

Conflict of Interest

We hereby confirm that no one of the authors has any conflict of interest with this publication. Additionally, we declare that this research was conducted in line with the Declaration of Helsinki which explains all main rules for human research ethics.

References

  1. Anderson, R. E. (2017). Community functioning that fosters sustainable social well-being. In R. Phillips & C. Wong (Eds.), Handbook of community well-being research (pp. 3–10). Dordrecht: Springer Netherlands.  https://doi.org/10.1007/978-94-024-0878-2_1.CrossRefGoogle Scholar
  2. Deutscher Bundestag. (2013). Schlussbericht der Enquete-Kommission “Wachstum, Wohlstand, Lebensqualität - Wege zu nachhaltigem Wirtschaften und gesellschaftlichem Fortschritt in der Sozialen Marktwirtschaft.” Berlin.Google Scholar
  3. Centre for Bhutan Studies & GNH Research (2016). A compass towards a just and harmonious society: 2015 GNH survey report.Google Scholar
  4. Constanza, R., Hart, M., Posner, S., & Talberth, J. (2009). Beyond GDP. The need for new measures of progress. The Pardee Papers no. 4. http://www.bu.edu/pardee/files/documents/PP-004-GDP.pdf. Accessed 15 November 2018.
  5. Cuñado, J., & de Gracia, F. P. (2012). Does education affect happiness? Evidence for Spain. Social Indicators Research, 108, 185.  https://doi.org/10.1007/s11205-011-9874-x.CrossRefGoogle Scholar
  6. Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49, 71–75.CrossRefGoogle Scholar
  7. Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D.-W., Oishi, S., & Biswas-Diener, R. (2010). New well- being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143–156.CrossRefGoogle Scholar
  8. Easterlin, R. A. (2003). Explaining happiness. Proceedings of the National Academy of Sciences, 100(19), 11176–11183.  https://doi.org/10.1073/pnas.1633144100.CrossRefGoogle Scholar
  9. Easterly, W. (2015). The trouble with the sustainable development goals. Current History, 3.Google Scholar
  10. Empirica (2007). Stadtumbau-West: Städtebauliches Entwicklungskonzept Wuppertal. available from www.wuppertal.de/wirtschaft-stadtentwicklung/medien/dokumente/Endfassung_25230_Wuppertal_Endbericht1_pdf.pdf, Accessed March 28 2019.
  11. European Commission (2013). Commission staff working document: Progress on “GDP and beyond” actions. Brussels.Google Scholar
  12. Fleurbaey, M., & Blanchet, D. (2013). Beyond GDP: measuring welfare and assessing sustainability. Oxford. New York: Oxford University Press.CrossRefGoogle Scholar
  13. Frijters, P., & Beatton, T. (2012). The mystery of the U-shaped relationship between happiness and age. Journal of Economic Behavior & Organization, 82(2), 525–542.  https://doi.org/10.1016/j.jebo.2012.03.008.CrossRefGoogle Scholar
  14. Helliwell, J., Layard, R., & Sachs, J. (2017). World happiness report 2017. New York: Sustainable Development Solutions Network.Google Scholar
  15. Hendriks, M., Ludwigs, K., & Veenhoven, R. (2016). Why are locals happier than internal migrants? The role of daily life. Social Indicators Research, 125(2), 481–508.  https://doi.org/10.1007/s11205-014-0856-7.CrossRefGoogle Scholar
  16. Jeffrey, K., Wheatley, K., & Abdallah, S. (2016). The Happy Planet Index 2016: A global index of sustainable wellbeing. London: New Economics Foundation https://static1.squarespace.com/static/5735c421e321402778ee0ce9/t/57e0052d440243730fdf03f3/1474299185121/Briefing+paper+-+HPI+2016.pdf. Accessed 15 November 2018.
  17. Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science (New York, N.Y.), 306(5702), 1776–1780.  https://doi.org/10.1126/science.1103572.CrossRefGoogle Scholar
  18. Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science, 330(6006), 932–932.  https://doi.org/10.1126/science.1192439.CrossRefGoogle Scholar
  19. Kim, Y., & Lee, S. J. (2014). The development and application of a community wellbeing index in Korean metropolitan cities. Social Indicators Research, 119(2), 533–558.  https://doi.org/10.1007/s11205-013-0527-0.CrossRefGoogle Scholar
  20. Lane, J.-E. (2016). Decabonisation and sustainable development goals: A Critique of J. Sachs. International Journal of Economics, Commerce and Management, 4(12), 113–141.Google Scholar
  21. Ludwigs, K. (2018). The happiness analyzer. A new technique for measuring subjective well-being. Erasmus University Rotterdam.Google Scholar
  22. Ludwigs, K., & Erdtmann, S. (2019). The happiness analyzer – Developing a new technique for measuring subjective well-being. International Journal of Community Well-Being, 1(2), 101–114.CrossRefGoogle Scholar
  23. Ludwigs, K., Lucas, R., Burger, M., Veenhoven, R., & Arends, L. (2018). How does more attention to subjective well-being affect subjective well-being? Applied Research in Quality of Life, 13(4), 1055–1080.Google Scholar
  24. McGuire, S., Posner, S., & Haake, H. (2012). Measuring prosperity: Maryland’s genuine Progress Indicator. Solutions, 3(2), 50–58.Google Scholar
  25. OECD. (2013a). OECD guidelines on measuring subjective well-being. Paris: OECD Publishing.CrossRefGoogle Scholar
  26. OECD. (2013b). How’s life? 2013 - measuring well-being. Paris: OECD Publishing.CrossRefGoogle Scholar
  27. OECD. (2017). Embracing innovation in government. Global Trends., (February 2017).Google Scholar
  28. Raffelhüschen, B., & Krieg, O. (2017). Deutsche Post Glücksatlas 2017. Albrecht Knaus Verlag.Google Scholar
  29. Sirgy, M. J. (2018). What types of indicators should be used to capture community well-being comprehensively? International Journal of Community Well-Being.  https://doi.org/10.1007/s42413-018-0002-4.
  30. Stiglitz, J., Sen, A., & Fitoussi, J.-P. (2009). Report by the Commission on the Measurement of Economic Performance and Social Progress. Paris.Google Scholar
  31. Talberth, J., Cobb, C. W., & Slattery, N. (2007). The Genuine Progress Indicator 2006. Oakland: Redefining Progress.Google Scholar
  32. UK Cabinet Office (2013). Wellbeing policy and analysis: An update of wellbeing work across Whitehall. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/224910/Wellbeing_Policy_and_Analysis_FINAL.PDF. Accessed 15 November 2018.
  33. UN General Assembly (2030) A/RES/71/313: Work of the statistical commission pertaining to the 2030 agenda for sustainable development.Google Scholar
  34. United Nations (2014). System of environmental-economic accounting - 2012 central framework. New York. https://seea.un.org/sites/seea.un.org/files/seea_cf_final_en.pdf. Accessed 15 November 2018.
  35. Veenhoven, R. (2010). Capability and happiness: Conceptual difference and reality links. The Journal of Socio-Economics, 39(3), 344–350.CrossRefGoogle Scholar
  36. Weinhold, D. (2008). How big a problem is noise pollution? A brief happiness analysis by a perturbable economist. MPRA Working Paper, No., 10660.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of WuppertalWuppertalGermany
  2. 2.Wuppertal InstitutWuppertalGermany
  3. 3.Happiness Research OrganisationDüsseldorfGermany

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