Abstract
This study investigates in three steps whether there is an association between happiness and living in one of Europe’s capital cities. Making use of the European Social Survey, the first step is a raw unadjusted correlation assessment which, overall, finds a negative and statistically significant effect on happiness of living in one of Europe’s capitals. The second step is the addition of socio-economic controls which (overall) increases the happiness penalty associated with living in a European capital city. The third step adds environmental factors and perceptions (safety of local area, worries about crime, for example) to control for further potential confounding factors. Tentative evidence is also presented that this is not just a big city effect. Overall, there is a happiness penalty associated with living in Europe’s capitals though this result is dominated by a few particularly unhappy capitals.
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Notes
As is quite common within the economic analysis of well-being, I use life satisfaction, happiness and (subjective) well-being as synonyms.
A key motivation for this was the fear that tourism may be negatively affected by the apparent grumpiness of Berliners, and grumpiness was to be alleviated by the appointment of goodwill ambassadors (for example civil servants and train drivers). More explanation is provided by the news report: “As part of the campaign, civil servants will hand out ironic postcards printed with legendary grumpy Berlin phrases such as “What do you think you're looking at?” and “Do I look like an information desk?”. The idea is to poke fun at the stereotype of the grumpy Berliner… The cards are also meant to show that it is possible to be courteous and friendly on a day-to-day basis. “These cards are fantastic, they show that we don't always take ourselves seriously and that we can laugh at ourselves too,” [city spokesman Richard] Meng said (The Local 2009).
Recently, a London based organisation, ‘Talk to Me London’, has been set up with very similar aims and methods to the Berlin-based initiative. The organisation aims to get people in the capital talking to each other, and several times links this explicitly with an expected outcome of increased happiness and well-being for individuals and societies. See http://www.talktomelondon.org/why-talk for these claims. The website for the organisation explicitly state that they chose London because it is where the greatest need is for such an initiative in the UK. A youth organisation connected to the European Union has provided some of the organisation’s funding.
There are even arguments found within evolutionary psychology, with, for example, one study in this area linking city living to “greater activation of the amygdala—an area of the brain associated with anger, aggressive behaviour, and perceiving environmental threat—when experiencing social stress.” (Fitzgerald and Danner 2012). Grinde (2002) discusses ‘discords’ when modern life clashes with our evolutionary heritage and may cause unhappiness. He suggests one such discord as loneliness, because people live lives often separated from extended kin (which was not the case in the ancestral environment). However, these discords offer a potential reason for every modern phenomena investigated: we did things differently back then.
Changes in how the region is coded between ESS rounds 4 and 5, mean that this study can only take advantage of the first 4 ESS rounds which cover 2002–2008.
What the ESS calls Great Britain is really the United Kingdom because it includes Northern Ireland, and London has been used as the capital. If the analysis is restricted to England, the results are qualitatively the same as the ones presented throughout for ‘Great Britain’.
An alternative capital city dummy variable was created with the inclusion of four more countries (Italy, Netherlands, Norway, and Slovakia) where the data allowed at least a partial possibility of highlighting the capital city, for example there is data for ‘groot-Amsterdam’ or greater Amsterdam. This is not fully satisfactory, so the outcome for these four countries will be briefly presented in footnotes.
Additionally, the polarities of the response to the happiness question in each country were also investigated. Differences between those near the extremes of the distribution who live in the capital and those who do not were noteworthy in just one country. In Bulgaria, but outside of the capital there is a substantial tail of low happiness scores when compared to distribution of those who live in Sofia.
Furthermore, an additional single equation model was employed including country dummies and interactions between the capital city and country dummies to compare the size of the happiness differentials. Broadly, they follow the picture presented by the individual country estimations. Bulgaria has the biggest differential of approximately 0.75, followed by the Ukraine with just under 0.5. Both of these are positive differentials, representing happier individuals in the capital than elsewhere in the capital. The following countries have comparable negative differentials of just under 0.5: Austria, Belgium, Cyprus Denmark, Greece, Portugal. Other countries have very small differentials.
For the additional countries, Italy is in the positive association category, the Netherlands negative (the p value is 0.063, however), Norway negative, and Slovakia had no significant difference.
Not shown, but this analysis was also undertaken restricting the sample to females only, and then males only. The size of the effect does vary by gender, and in three cases the association with happiness is different with gender: females who live in Copenhagen (Kiev) are less happy (happier) than females who live in the rest of Denmark (Ukraine); in Paris, males are happier than males who live elsewhere in France. Similar restrictions were made for young people (age less than or equal to 30) and older people (age 55 or higher). As predicted in the introduction, the results are consistent with young people being happier than older people in the capital compared to their peers elsewhere (however, both groups of capital city inhabitants are less happy than the comparator group. For the individual countries, the most striking differences are found in Austria and Germany (in both cases, older people unhappier than elsewhere but the young are not), Bulgaria (young people happier, but older people are not), Cyprus (young people unhappier than elsewhere but the old are not), and France (older people happier than elsewhere but the young are not).
It should be noted, too, that given the cross-sectional nature of the data set, these controls could also be a result of happiness (for example marriage, Stutzer and Frey 2006).
The London-weighting paid to new starters in many jobs reflects a perception that the cost of living is higher in the UK’s capital than elsewhere.
The reason is to do with space, as a complete presentation of these results would require substantially more pages.
Remember that the question utilised for the dependent variable asks ‘how happy are you’ with values from extremely unhappy and extremely happy, so a variable with a negative coefficient can be viewed as having an association with unhappiness.
Just as a recap, three of these countries (Denmark, Great Britain, and Sweden) had previously a negative association with happiness and living in the capital and for the other three (Bulgaria, France, and the Ukraine) the unmediated effect was positive.
Rearding the single equation model enabling comparison of the differentials, Greece has the biggest differential being −0.6, then the following countries have a negative differential of roughly a 0.35: Belgium, Cyprus, the Czech Republic, Denmark and Portugal. The differentials in the other countries are smaller.
For the additional countries, there is only one substantial change when the socio-economic controls are added. Citizens of groot-Amsterdam do not report significantly different happiness than citizens in the rest of the Netherlands, when they did in the estimate without controls.
A check was made with socio-economic controls comparing Berlin, Hamburg and the rest of Germany using the SOEPlong panel file. Berlin is associated with significantly less happiness than the rest of Germany, whereas Hamburg is associated with more happiness.
There was also little change in the case in the single equation estimation. One notable change is the shrink in the differential for Greece which, with these additional controls, is now 40 % of what it was. A comparison of the differentials for the other countries retains the pattern of step 2 briefly summarised in footnote 17 above.
For the additional countries, both the Netherlands and Norway have areas that include the capital where individuals are significantly less happy than people who live elsewhere. For Slovakia, there is no significant difference and for Italy, no estimation was possible.
Caution needs to accompany the analysis here because of the question’s subjectivity (a big city to one person may not be to another), and because any results are not necessarily representative results. The countries chosen for this analysis can be adequately used for capital cities with, in most cases, little concern. However, ‘‘big cities’, even if objectively defined, were not always employed as primary sampling units for sample design in the individual countries.
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Acknowledgments
I am very grateful to Nick Adnett, Geoff Pugh, Nils Saniter, Stefan Zins (member of the Sampling Expert Panel of the ESS), and two anonymous referees for helpful suggestions and comments. Also useful was discussion with participants of the informal research seminar series at Universität Flensburg. The Norwegian Social Science Data Services (NSD) is the data archive and distributor of the ESS data. Neither the original collectors of the data nor the Archive bear any responsibility for the analyses or interpretations presented here.
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Appendices
Appendix 1
About the sample:
The ESS is a biannual survey that contains a random sample from, in the latest round, approximately 30 countries. The sample from each country is representative for that country, and in many cases the individual country samples have been constructed to allow representative analysis at a more disaggregated level. This appendix gives more detail regarding the sampling procedures of the ESS and provides evidence about which level of regional disaggregation is possible for statistical inference.
There are some general principles guiding the individual country samples, but how the sample of each country is collected can (must, really) be different. These differences reflect the availability or otherwise of good sampling frames. For example Denmark has the Danish Central Persons Register (covering approximately 99 % of people resident in Denmark), while the UK and the Netherlands has reliable lists of addresses (postal delivery points) which enable almost full coverage. However countries such as Portugal and Greece do not, for every round, have reliable lists. With these different national starting points the ESS draws its sample.
Within most countries, a first stage in the sampling was stratification by region. Sometimes the regional stratification for a country in a particular round meant that it was impossible to identify the capital city adequately, and these countries were no longer included in the analysis. For the countries where isolation of the capital is possible, there are varying degrees of acceptability for statistical inference.
Essentially, the analyses of the paper are national analyses, employing a capital city dummy variable to separate inhabitants of the capital city to other citizens of that country (no different for dummy variables for unemployed individuals, married individuals et cetera). However, with some of the nationally representative samples, further disaggregation is possible. For example, Germany: Berlin is selected with certainty in the first strata along with the other NUTS level 1 regions (see Table 7 below for more information), and can be considered a representative sample of Berlin. It is randomly chosen at an early stage in the stratification procedure like other Länder. Separate regional analysis can take place at the level of the Länder though this is not necessary for the analysis of this investigation. Some countries are suitable for statistical inference at a further degree of disaggregation, such as Denmark which, impressively for a national sample in a cross-national project, is disaggregated and appropriately sampled to allow for inference at NUTS level 3. Other countries have minor problems with regional disaggregation. For example, in Spain for one round, there was a problem with the field work in Valencia, which necessitates a bit of caution with results. Arguably this does not affect the analyses made in the paper for Spain which employs a dummy variable (1 for Madrid, 0 otherwise) with the national sample. Details regarding all the countries in the sample follow in two tables. Table 7 is an estimate of the level of caution that should be attached to necessary for regional analysis, which is based on the information presented in Table 8, which is itself and a summary follows of the disaggregation possible for statistical analysis possible for the different countries.
Appendix 2
Happiness question and local area/environmental questions from the ESS.
The happiness dependent variable comes from this question:
Taking all things together, how happy would you say you are? Please use this card.
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Piper, A.T. Europe’s Capital Cities and the Happiness Penalty: An Investigation Using the European Social Survey. Soc Indic Res 123, 103–126 (2015). https://doi.org/10.1007/s11205-014-0725-4
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DOI: https://doi.org/10.1007/s11205-014-0725-4