17Pap. Eur. 34(2021): 17-28 Urban or Rural: Where are people happier and why? Abilio Henrique Berticelli de Freitas1 Fecha de recepción: 2 de septiembre de 2021 / fecha de aceptación: 30 de noviembre de 2021 Abstract. Using data from a worldwide sample, we investigate how happy people look like and if these “happiness characteristics” are more present in big urban towns or in small rural villages. We found evidence that (i) people seem to be slightly happier in rural settlements, (ii) happier people have some particular characteristics (e.g., higher levels of trust in others and being more interested in politics) and (iii) these positive attitudes are slightly more present in rural contexts. Then, we discuss some conceivable explanations to what we have seen. Keywords: happiness economics; urban planning; subjective well-being. [es] Urbano o rural: ¿Dónde es más feliz la gente y por qué? Resumen. Utilizando datos de una muestra mundial, investigamos cómo es la gente feliz y si estas “características de felicidad” están más presentes en las grandes ciudades urbanas o en los pequeños pueblos rurales. Encontramos pruebas de que (i) la gente parece ser ligeramente más feliz en los asentamientos rurales, (ii) las personas más felices tienen algunas características particulares (por ejemplo, mayores niveles de confianza en los demás y estar más interesados en la política) y (iii) estas actitudes positivas están ligeramente más presentes en los contextos rurales. A continuación, se discuten algunas explicaciones concebibles a lo que hemos visto. Palabras clave: economía de la felicidad; planificación urbana; bienestar subjetivo. Sumario. 1. Introduction. 2. Methodology. 3. Results.4. Discussion. 5. Conclusion. 6. References. Cómo citar: Berticelli de Freitas, A. H. (2021). Urban or Rural: Where are people happier and why?, en Papeles de Europa 34, 17-28. JEL: D63, I31, J19, R58 1. Introduction Quite a few studies have talked about the impact of urbanisation on well-being, with mixed results. Winters and Li (2016) show that large and densely populated urban areas are associated with lower subjective well-be- ing and suggest policies like reducing congestion, pollution and time spent commuting in cities to raise peo- ple’s well-being. Rukumnuaykit (2015) found that this result holds water even after controlling for individual socio-economic factors. Other works nuance these results, as in Coldwell and Evans (2018), who suggest it is not urbanisation that affects well-being, but rather green-space visits. Lenzi and Perucca (2020) claim that this relation overlooks positive externalities generated by cities, finding actually the opposite conclusion: urbanisa- tion is positively associated with individual well-being. Studying different regions of Spain and Italy, Navarro et al. (2020) found no relevant relation in most Spanish regions and an effect that was variable according to the region in Italy – for instance, in Lombardia people are happier in cities or towns, while in Campania it is the opposite. In the wake of that literature, the present paper intends to shed some light on three questions. How does the size of town or the kind of settlement one lives in affect his visions and attitudes towards a series of subjects? How is size of town or kind of settlement linked to subjective well-being? How are the variables that correlate to them linked to subjective well-being? They will be called “direct” and “indirect” analyses. The direct analy- sis is quick and simple; business as usual. The indirect analysis, on the other hand, is unhackneyed. Being able 1 Faculté d’Économie. Master Économie et Management Publics. Université de Montpellier. abiliofreitas2@gmail.com ORCID: 0000-0002-6344-283X ARTÍCULOS Papeles de Europa ISSN-e 1989-5917 https://dx.doi.org/10.5209/pade.77764 mailto:abiliofreitas2%40gmail.com?subject= https://dx.doi.org/10.5209/pade.77764 18 Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 to link sundry trends to either urban settings or rural ones, we were able to characterise how, on average and for a global sample, happy people look like. Then, we went back to that first step to see if these characteristics were typical of urban or rural settings, bigger or smaller cities, important or ordinary towns. It is all about ex- ploring data and trying to find meaning through it; it is definitely not an extensive work, to exhaust the matter in any way. The paper is organised as follows: first, a section on methodology, which explains where data comes from and how it was treated; second, a section on results, showing answers from data to the three questions men- tioned above along with a quick stroll down separate-countries data; third, a section discusses these results, considering possible explanations, already thought on the literature or not; finally, in the conclusion, some of the implications for policymakers are concisely presented. 2. Methodology Subjective data follows the idea that people themselves are the best judges of their own happiness (Ferrer-i-Car- bonell, 2013). Its main advantage is being more feasible in large scale (Ribeiro, 2015), while economists have found evidence that subjective measures go along with objective ones (Graham & Felton, 2006) – consistent patterns within a country and among them, since most people interpret questions in a similar way –, like the number of smiles or changes in facial muscles during an interview (Ferrer-i-Carbonell, 2013). Since it presents intertemporal and international consistency (Graham & Felton, 2006), subjective data is used here. The inter- viewer does not define exactly what is to be considered in each question, which would render the operational part impractical. We used, mostly, data from the World Values Survey (WVS) in its seventh wave (2017-2020), with material from 49 countries already made available, such as Australia, Brazil, China, Germany, Japan, Nigeria and the US (Haerpfer et al., 2020). They are considered aggregately. Cross-section data was the choice for the present work, even though WVS has data for seven different waves, beginning in 1981, which would enable time-se- ries research. All relevant details about WVS methodology are available on their website (https://www.world- valuessurvey.org/)2. Selected questions are an arbitrary choice, following which appears on relevant literature and some guesses from the researchers. Now, here they are (codes in parentheses follow the documentation available on the WVS website): how do people in different sized towns put value in different things, like family, friends, leisure time, politics, work and religion (Q1-Q6)? What do they say about changes in society (Q42)? How happy or satisfied do they say they are (Q46 and Q49)? How much hardship do they face (Q51-Q55)? Do they trust others (Q57) and how does this trust vary for each mentioned group (Q58-Q63) and for each mentioned institution (Q64-Q89)? What do they think about economic values (Q106-Q110) and what do they think about environmental protection in relation to economic growth (Q111)? Views on corruption (Q112), on migration (Q121 and Q130), on security (Q131-Q138), on science and technology (Q158 and Q163), on religion (Q164 and Q171-Q172), on politics (Q199, Q234, Q240, Q250, Q254) and on their finances (Q286-Q287) were also considered. Answers to these questions were crossed with information about the size of the town where the interview was conducted (G) and settlement type (H and H1). Traditionally, rural areas are less populated. Specifically for question G, options included eight different ranges: under 2000 inhabitants, 2-5k, 5-10k, 10-20k, 20-50k, 50-100k, 100-500k and over 500000. After a first analysis, we chose to consider this question with five aggregate ranges: under 5000, 5-20k, 20-100k, 100-500k and over 500000 inhabitants. Question H offered five options: capital city (national capital), regional centre (capital/centre/seat of the region), district centre, another city/town (not a national, regional or district centre) or village, while H1 coded settlement type for either urban (city, town) or rural (village). All analyses were made using Stata. Then, as a complementary tool, data from the Pew Research Center (PRC) was used to try and see some other relations – namely, Spring 2019 Survey Data, their most recent global dataset available, which includes 34 countries, such as Canada, France, India and Kenya. All relevant details about PRC methodology are availa- ble on their website (https://www.pewresearch.org/). Since PRC data does not have anything specific on urban- isation, we use variables that were highly associated with it in the previous analysis as proxies. Only after the mentioned analysis will we be able to name them, but following our possibilities of study in the WVS dataset, we would probably consider questions Q1 (subjective well-being), Q30, Q55a and Q66a-Q66b (views on religion), Q40b, Q40d and Q50f-Q50h (views on immigration and military policy), Q50a-Q50b, Q55b, Q55e (views on politics), Q3 and Q50c-Q50e (views on economic values) and Q67c (being proud of your nation- ality). In this stage, the analysis will be even more exploratory, but making the distinction between countries. 2 Since we are looking for answers considering a range of countries scattered across several continents, in order to have a global view of the matter, we chose WVS; otherwise, datasets available in Latinobarómetro (https://www.latinobarometro.org/), for Latin American countries, or in European Values Survey (https://europeanvaluesstudy.eu/) and in European Social Survey (http://www.europeansocialsurvey.org/), for European countries, could be useful. https://www.worldvaluessurvey.org/ https://www.worldvaluessurvey.org/ https://www.pewresearch.org/ https://www.latinobarometro.org/ https://europeanvaluesstudy.eu/ http://www.europeansocialsurvey.org/ 19Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 3. Results Before anything else, some descriptive statistics about urbanisation variables are shown and correlations be- tween them and answers to other questions are verified. Then, results from WVS data are presented following two divisions already announced: first, a direct analysis (how size of town or settlement type are linked to well-being), then, an indirect analysis (how variables correlated to these two are linked to well-being). After that, the exploratory analysis from PRC data is presented. 3.1. Descriptive statistics and correlations Beginning with H1, we can see that in the aggregate sample, there are 66.4% of people living in urban areas and 33.6% living in rural ones – that means roughly two thirds of our sample comes from urban areas. Going to question H, which goes a little further on the division, almost 28.7% of our sample – which on the whole accounts for more than 70000 people – live in a village. The percentages of people living in capitals, regional centres, district centres, and other cities are, respectively, 19.6%, 21.4%, 15.1% and 15.3%. For question G, which marked town size by classifying it in five different ranges, already specified in the previous section, we got, respectively, from the smallest to the biggest range, 20.4%, 16.7%, 21.7%, 17.9% and 23.5%. Outwardly, there are no huge discrepancies here that could bias our analysis. After these very brief descriptive statistics on urbanisation data, we will see which of the questions men- tioned in Methodology (apart from subjective well-being, which will be treated later) are significantly corre- lated with a particular group on the urbanisation variables. The way to do that was by doing t tests to compare mean responses according to H1 (for instance, we compared average responses for Q1 by settlement type, the null hypothesis being that means are equal. In Q1, the smaller the answer, the bigger the importance put on the subject in question by the respondent; however, to simplify for the reader, results were “translated”, which means that “bigger” below means “bigger importance”, not “bigger value”). For concision, p-values are not mentioned, but we considered a 0.1% type one error (α = 0.001), since the sample size is really huge. We sup- pose that variables follow a normal distribution to use the t test – otherwise, it would be possible to do another test, like Mann-Whitney (nonparametric). Table 1 presents the results. The best way to read it is by having WVS documentation under your eyes. Table 1. Comparison of means for different variables on each urbanistic group Code/Correlation H1 (urban/rural) G (town size) H (kind of city) Q1 (family importance) bigger in rural bigger for R1, R2 and R3 smaller for R5 smaller for K1 (capitals) bigger for K5 (villages) Q2 (friends importance) no difference bigger for R5 bigger for K1 smaller for K5 Q3 (leisure time importance) bigger in urban smaller for R1 and R2 bigger for R4 and R5 bigger for K1, K2, K3 and K4 and smaller for K5 Q4 (politics importance) bigger in rural bigger for R1 and R3 smaller for R5 smaller for K2 (regional centres) Q5 (work importance) bigger ir rural bigger for R1, R2 and R3 smaller for R5 smaller for K1 bigger for K5 Q6 (religion importance) bigger in rural bigger for R1, R2 and R3 smaller for R4 and R5 smaller for K1, K2 and K3 bigger for K5 Q42 (changes in society) no difference smaller desire for changes in R5 bigger desire for changes in K5 Q51 (going without food to eat) bigger in rural bigger for R1 and R2 smaller for R4 and R5 smaller for K1, K2 and K4 bigger for K5 Q52 (felt unsafe in home) bigger in urban smaller for R2 and R3 bigger for R4 bigger for K1 and K2 smaller for K4 and K5 Q53 (going without needed medicine) bigger in rural bigger for R1 and R2 smaller for R4 and R5 smaller for K1, K2 and K4 bigger for K5 Q54 (going without a cash income) bigger in rural bigger for R1 and R2 smaller for R4 and R5 smaller for K1 bigger for K4 and K5 Q55 (going without a safe shelter) bigger in rural bigger for R1 smaller for R3 and R4 smaller for K1 and K4 bigger for K2 and K5 Q57 (trust in general) bigger in urban smaller for R1, R2 and R3 bigger for R5 bigger for K1 and K3 smaller for K2 and K5 Q58 (trust in family) bigger in rural bigger for R1 and R2 smaller for R5 smaller for K3 and K4 bigger for K5 Q59 (trust in neighbours) bigger in rural bigger for R1 and R2 smaller for R4 and R5 smaller for K1, K2 and K3 bigger for K5 Q60 (trust in known people) bigger in urban smaller for R1, R2 and R4 bigger for R5 bigger for K1, K2 and K4 smaller for K5 20 Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 Code/Correlation H1 (urban/rural) G (town size) H (kind of city) Q61 (trust in new acquaintances) bigger in urban smaller for R2 and R3 bigger for K1 smaller for K2 and K5 Q62 (trust in people of another reli- gion) bigger in urban smaller for R1 and R2 bigger for R5 bigger for K1 and K4 smaller for K5 Q63 (trust in people of another natio- nality) bigger in urban smaller for R1 and R2 bigger for R5 bigger for K1 and K4 smaller for K5 Q106 (more equality or greater incen- tives for individual effort) more equality bigger in urban less equality for R1 and R2 more equality in K4 less equality in K5 Q107 (privatisation or nationalisation of companies) more privatisation bigger in urban less privatisation in R1 and R2 more privatisation in R4 and R5 more privatisation in K1 and K4 less privatisation in K5 Q108 (government should take more responsibility or people should do that) no difference government should in R1 and R2 people should in R5 government should in K3 people should in K1 Q109 (competition is good or harm- ful) competition better viewed in urban bad viewed in R1 and R5 better viewed in R3 and R4 bad viewed in K2 and K5 better viewed in K1, K3 and K4 Q110 (hard work or luck, what brings success?) hard work better viewed in urban better viewed in R1, R2 and R3 bad viewed in R5 better viewed in K3 and K5 bad viewed in K1 and K2 Q111 (economic growth or environ- ment protection) no difference jobs are more important in R2 environment is more important in R4 jobs are more important in K3 environment is more important in K1 Q112 (corruption in your country) more corruption viewed in rural more corruption viewed by R1, R2 and R3 less corruption viewed by R5 more corruption viewed by K3, K4 and K5 less corruption viewed by K1 and K2 Q121 (impact of immigration in your country) better viewed in urban better viewed in R3 and R4 bad viewed in R5 better viewed in K3 bad viewed in K2 Q130 (open borders for immigration) better viewed in urban bad viewed in R1 and R2 better viewed in R5 bad viewed in K5 better viewed in K1, K3 and K4 Q131 (how secure do you feel?) more security in rural more secure in R1, R2 and R3 less secure in R4 and R5 more secure in K5 less secure in K1, K2 and K3 Q158 (views on science and techno- logy) better in rural better in R2 and R3 worse in R5 better in K5 worse in K1, K2 Q163 (views on science and techno- logy) better in rural better in R1 and R2 worse in R5 better in K5 worse in K2 Q164 (importance of God) bigger in rural bigger for R1, R2 and R3 smaller for R4 and R5 bigger for K4 and K5 smaller for K1 and K3 Q171 (attendance to religious servi- ces) bigger in rural bigger for R1, R2 and R3 smaller for R4 and R5 bigger for K5 smaller for K1, K2 and K3 Q172 (how often do you pray?) bigger in rural bigger for R1, R2 and R3 smaller for R4 and R5 bigger for K4 and K5 smaller for K1 and K3 Q199 (interest in politics) no difference bigger for R1 smaller for R3 and R5 bigger for K3 smaller for K5 Q234 (importance of honest elec- tions) bigger in rural bigger for R1, R2 and R3 smaller for R4 and R5 bigger for K4 and K5 smaller for K2 Q240 (political spectrum) more right-wing in rural more right-wing in R1 and R2; more left-wing in R4 and R5 more right-wing in K5 more left-wing in K1 and K2 Q250 (importance of democratic go- vernment) bigger in rural bigger for R3 and R4 smaller for R5 no difference Q254 (proud of your nationality) prouder in rural prouder in R1, R2 and R3 less proud in R4 and R5 prouder in K3 and K5 less proud in K1 and K2 Q286 (saving or borrowing money) more borrowing in rural more in R1 and R2 less in R4 and R5 more borrowing in K5 less borrowing in K1 Q287 (social class) lower in rural lower for R1, R2 and R3 bigger for R5 lower for K5 bigger for K1 and K2 Source: own elaboration using data from World Values Survey (2017-2020) After tests for H1, we proceeded to testing for G and then to testing for H. For tests considering H, we took the average in Q for each group in H and then compared it to the general average answer, using again a t test, with the same parameters (kinds are abbreviated with a K). Same is true for G (ranges are abbreviated with an R). Ranges not mentioned were not significant. Note that we are not interested in absolute values, but only in a possible difference according to urbanisation characteristics. There are not two different species of human beings; saying, for example, that people in rural areas put more value in work does not mean everybody there is a workaholic and everybody in cities has a perfect work-life balance. These are just trends differentiating them. Note, also, that R and K are in inverse scales (the bigger R is, the bigger the city; the bigger K is, the more modest the kind of city). Questions Q64-Q89, that asked about trust in several institutions, like banks or the World Health Organiza- tion, were not reported in the table, since they added a precision that would not be worth much in the sequence. 21Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 However, it is worth noting that in all t tests we did, for every single one of these questions people in rural areas showed more trust than people in urban areas. Questions Q132-Q138, asking respondents how often they see a series of crimes and violent behaviour in their neighbourhood, were not reported in the table, but it is worth noting that in all t tests we did for them, results had a pattern: they were always higher in urban areas. Since this is perfectly in line with what is reported for Q131, we chose to keep just that one for the following analyses. Questions Q158-Q163, concerning views on science and technology, elicited mixed answers. For questions Q158 and Q163, which asked directly if science and technology make our lives easier and if the world is better off because of them, people in rural areas were more favourable. After that, things are quite blurred: Q159 shows people in rural areas more favourable to science and Q161 and Q162 shows people in urban areas more favourable to it, while Q160 showed no difference. We will keep the first two mentioned, but having this is mind when discussing. 3.2. Direct analysis Just to mention a couple descriptive statistics, Q46 asked “taking all things together, would you say you are…”, giving out four possible answers (“very happy”, “rather happy”, “not very happy”, “not at all happy”), having the majority of people placing themselves in the “rather happy” group (53.9%). For the three other categories, percentuals were, respectively, 31.7%, 12.1% and 2.3%. Moreover, Q49 asked how satisfied the respondent was with his or her life, in a range going from 0 to 10. Whereas the mean response was roughly 7, most people placed themselves with a level of satisfaction of 8 or 10 (19.6% and 18.1%, respectively). Categories over 5 had a participation of at least 10% of the sample in each of them, while under 5 categories summed together roughly 12%. Using the same method employed in the last subsection, we analyse how answers to these two questions on subjective well-being vary according to size and type of the town respondents live in. As before, unmen- tioned results correspond to t tests where the null hypothesis (equal means) could not be rejected (for the same α = 0.001). Stata coding for Q46 is available in the Appendix; obviously, the procedure is the same for all the other questions in Table 1 and Table 2. Table 2. Comparison of means for subjective well-being on each urbanistic group Code/Correlation H1 (urban/rural) G (town size) H (kind of city) Q46 (how happy are you?) happier in rural happier in G1, G2 and G3 less happy in G5 less happy in K1 (capitals) happier in K5 (villages) Q49 (how satisfied are you?) no difference happier in G3 less happy in G5 less happy in K2 happier in K4 Source: own elaboration using data from World Values Survey (2017-2020) 3.3. Indirect analysis In the present subsection, we aim to analyse which of the variables studied in subsection 3.1 are sig- nificantly (always with α = 0.001) correlated with questions Q46 and Q49 (on subjective well-being). We did not choose to build an econometric model that would explain happiness. With a consistent exploratory basis, work will be easier for those who choose to continue assorted lines of research in the future – or even for us to continue it ourselves. Again, Stata was used (specifically the command pwcorr). The goal is to identify which variables are correlated, in one way or another, with subjective well-being; then, in the next section, comments will be made concerning the relation between these variables and their relation with urbanistic variables depicted before – measuring their impact here is off table. Table 3 shows the results. Keep in mind scales are sometimes inverted (as is the case for Q46), and in binary choices rather than scale choices, classifying the effect as positive or negative does not have great meaning. Explanations in parentheses and having WVS documentation in hand, however, will make everything simpler. When questions are mentioned in the Q49 column, omitted parentheses mean the conclusion is the same as in the Q46 column; otherwise, indications are made. Of course, we are not assuming any causal links here, and words like “happier” and “less happy” are used only to get the message across. 22 Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 Table 3. Correlations between different variables and subjective well-being Correlation/Code Q46 (how happy are you?) Q49 (how satisfied are you?) Positively correlated (happier) Q1, Q2, Q3, Q4, Q5, Q6 (importance of…) Q57, Q58, Q59, Q60, Q61, Q62, Q63 (trust) Q110 (people who believe in hard work) Q111 (environment over jobs people) Q112 (“no corruption” people) Q130 (pro-immigration people) Q131 (people who feel more secure) Q171 (people who attend religious services) Q172 (people who pray more often) Q199 (people more interested in politics) Q234 (honest elections people) Q254 (people proud of their country) Q286 (saving money people) Q287 (people in upper classes) Q1, Q2, Q3, Q4, Q5 Q42 (being more conservative) Q57, Q58, Q59, Q60, Q63 Q109 (pro-competition people) Q110, Q111 Q112 (“abundant corruption” people) Q130, Q131 Q199, Q234 Q254 Q286, Q287 Negatively correlated (less happy) Q51, Q52, Q53, Q54, Q55 (hardship) Q106 (pro-equality people) Q107 (more privatisation people) Q108 (government should take more responsibility) Q121 (anti-immigration people) Q158, Q163 (anti-science people) Q164 (God less important) Q240 (left-wing people) Q250 (people that do not value democracy) Q51, Q52, Q53, Q54, Q55 Q106, Q107, Q108 Q121 Q158, Q163 Q164 Q240 Q250 Uncorrelated Q42 (changes in society) Q109 (views on competition) Q6 (religion importance) Q61, Q62 (trust in people you meet for the 1st time and in people of another religion) Q171, Q172 (religious attendance and praying frequency) Source: own elaboration using data from World Values Survey (2017-2020) As one can see, changes from Q46 to Q49 are mainly due to significance levels (appearing as uncorrelated and then as correlated or the opposite), rather than changing the correlation to the antipodal direction – Q112 being an exception. 3.4. PRC exploratory ramble To support what have been done in the previous subsections, we present now an exploratory ramble on PRC data, considering the following variables: Q1 (subjective well-being), Q30, Q55a and Q66a-Q66b (views on religion), Q40b, Q40d and Q50f-Q50h (views on immigration and military policy), Q50a-Q50b, Q55b, Q55e (views on politics), Q3 and Q50c-Q50e (views on economic values) and Q67c (being proud of your national- ity). We calculated a weighted mean for all the countries in Q1 (0-10 scale). With the exception of Lithuania, the percentage of people who respond “don’t know” or refuse to answer is usually very low, so we did not consider it. Table 4 shows the results. Countries appear in the same order they appear in the PRC table. The happiest countries in the sample are Israel (7.46), Mexico (7.30), Netherlands (7.27), Sweden (7.14) and Brazil (7.10). The least happy countries in the sample are Lebanon (4.90), India (4.93), Ukraine (5.09), Bulgaria (5.31) and Tunisia (5.42). When we consider these five happiest and these five least happy countries in the sample, comparing them against each other (aggregating categories, if needed), we see no evidence of what we had previously seen in subsection 3.3 for questions asking about views on religion (Q30, Q55a, Q66a and Q66b). Speaking about views on immigration and military policy, happier countries would seem to be more favourable to immigration (Q40b, Q50g) and more pacific towards other countries (Q50f), however Q40d and Q50h do not support that conclusion. Valuing democracy, feeling represented, giving importance to freedom of press and the importance of honest elections (Q50a, Q50b, Q55b and Q55e, respectively) are stronger in happier countries. People in happier countries say the current economic situation in their country is better than people in unhappy countries (Q3), they support more a free market economy even if it brings inequality up (Q50c), they believe in hard 23Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 work over luck (Q50d); they also see less corruption in the State gears (Q50e). Lastly, being proud of one’s nationality could not be considered, since PRC presents data only for six countries for this question. Table 4. Weighted means on subjective well-being for a sample of countries Country Weighted means Country Weighted means United States 6.92 Ukraine 5.09 Canada 7.02 Australia 7.06 France 6.38 India 4.93 Germany 6.81 Indonesia 6.47 Greece 5.86 Japan 5.79 Italy 6.51 Philippines 5.77 Netherlands 7.27 South Korea 6.46 Spain 6.36 Israel 7.46 Sweden 7.14 Lebanon 4.90 United Kingdom 6.53 Tunisia 5.42 Bulgaria 5.31 Turkey 5.59 Czech Republic 6.62 Kenya 5.96 Hungary 6.20 Nigeria 6.13 Lithuania 6.37 South Africa 6.67 Poland 6.65 Argentina 6.72 Slovakia 6.23 Brazil 7.10 Russia 5.53 Mexico 7.30 Source: own elaboration using data from Pew Research Center (Spring 2019) Nevertheless, the main point of this ramble, which serves as a parentheses in the article and could well be in the appendix, is to show that our conclusions from subsection 3.3 are valid as long as we are considering countries aggregately. Readers should be very attentive to the fact that the conclusions we have seen there do not say anything about any particular country. There is no formula for happiness. Not to talk about outliers – for instance, Israel is itself a big outlier in some matters, like views on immigration and military policy, given its peculiar geopolitical features; the same is true for Sweden or India, for example, when considering other questions. 4. Discussion In this section, we discuss the results, elaborating some possible explanations for them. Since there is not a specific “theoretical predictions” section before results (where we would put hypotheses), relevant literature already existent on each identified relation is mentioned here. Results discussed here will mainly concern the so-called indirect analysis. However, before getting there, just a couple words about the direct analysis. For Q46, we see evidence that people are happier in rural areas, in smaller cities and in villages, while they are unhappier in urban areas, in cities over 500000 inhabitants and in capital cities. This result is in line with most traditional research in this field, like Winters and Li (2016) and Rukumnuaykit (2015), mentioned in the Introduction. For Q49, though, things become a bit blurred. We see no difference according to the type of set- tlement, which would suggest the approach used by Coldwell and Evan (2018) may have its merits. Moreover, people are happier in regular cities and unhappier in regional centres; they are also happier in medium-sized towns (20-100k) and, again, unhappier in big towns (over 500000). 4.1. Trust and things that matter Commencing with trust (Q57-Q63), we have seen, in Table 3, that people who trust others report higher levels of subjective well-being. Similar trends have already been well established by other authors, like Helliwell and Wang (2010). Trust is a positive predictor of self-rated health, happiness, life satisfaction and quality of life and a negative predictor of illness and sedentary behaviour (Chan et al., 2017). Then, looking into Table 1, trust is usually bigger in urban settlements (barring trust in family and in neighbours, which are bigger in rural settings), with size and classification of town following the same pattern. Point for urban areas! A possible explanation can be the higher level of connectedness bigger towns have, a higher level of spe- cialisation that enhances mutual dependence, while things are a lot more independent or based on family bonds 24 Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 or neighbours’ relations in rural settings. This result goes in the opposite direction of what Yang and Zheng (2016) found considering data from China. Adjaye-Gbewonyo et al. (2019) found results aligned with ours when looking into data from South Africa and opposite ones when diving into Ghanaian data. Considering things people put value in (Q1-Q6), Table 2 shows that those who think family, friends, leisure time, politics, work and religion are important are happier. This is congruent with Brannan et al. (2013), who identified the relation between family and friends, and subjective well-being, using data from the US, Iran and Jordan. Brajša-Žganec et al. (2011) support with Croatian data the positive impact of leisure activities on well-being, whilst Shimazu et al. (2014) show that workaholism decreases life satisfaction and increases ill-health, but work engagement has the reverse effect. Political participation (Frey, 2011) and religious in- volvement (Lim, 2015) have also been shown to positively correlate with happiness. The first and the last three factors are more valued in smaller and rural places, while the two others appear to be more valued in urban and bigger towns. Point for rural areas! A possibility to understand this is to think about preferences. People in rural areas may have a higher pref- erence for work as a generator of well-being, and people in urban areas may have it for leisure time. Regarding family, religion and politics, the same factor used to explain differences in trust may be used here: people in rural settlements are more tied to family, to a religious community and to politics (since it is easier to know the mayor if you live in a 4000 inhabitants place than in a one hundred times larger town). Amato (1993) shows that rural people rely relatively more on family and urban people rely relatively more on friends, which is in compliance with our results. 4.2. Hardship, personal finances and security Vis-à-vis going through hardship (Q51-Q55), we would intuitively think it is linked with lower levels of well-being; that intuition is confirmed in Table 3. Equal results are found in regard to Q286 and Q287, showing that people who save more money and are in the upper classes are happier. Annink et al. (2016) showed, using samples from the European Social Survey, that people going through financial hardship experience lower lev- els of well-being. Financial hardship (difficulty paying bills, food insecurity and medication needed) and debt (credit card and medical debt) are linked to depressive symptoms and anxiety (Marshall et al., 2020). Then, getting to Table 1, we see that people in small and rural places go through more hardship than others. Point for urban areas! An intuitive deciphering would be to imagine that rural settlements are usually poorer than urban ones. According to Macours and Swinnen (2008), poverty is, indeed, higher in the rural context. The percentage, out of the total of poor people, that live in rural areas can get as high as 85% (Alkire et al., 2014). Amato et al. (1992) highlight that, overall, the urban poor are higher in perceived health than the rural poor. That could be due to the fact that, generally speaking, a city has more public infrastructure, available to everyone, like parks, natural sites, public concerts and events. Isolation can be an aggravating factor to being poor. Apropos of security (Q131), Table 3 points us clearly to consider that those who feel more secure are happi- er. This is compatible with Lucchesi et al. (2020), who, using data from São Paulo, demonstrated that security and walkability positively influence subjective well-being in all age cohorts, proposing, at the end, that cities should be rethought to respond to people’s emotional needs and behaviour. They suggest that a way to do so is by providing attractive neighbourhoods and increasing people’s perception of security. Afterwards, Table 1 shows people in smaller and more modest kinds of settlements feel safer. Point for rural areas! Again, the explanation can be pretty straightforward: crime incidence may be simply higher in urban set- tings. Or it can be more violent when it happens as well. Such reasoning is backed up by Sager et al. (2019), who show that air pollution is positively associated with crime incidence. An alternative explanation would be to think – in the light of Parker et al. (2017), who, using PRC data, showed that 46% among those in the US who live in urban areas own guns, a number that drops to 19% in urban areas – that, considering protests we have seen worldwide recently, people in general do not trust the police; rural people, however, choose to own guns to protect themselves and their property, while urban people are hold hostage to organised order forces. 4.3. Science and religion As to views on science and technology (Q158 and Q163), Table 3 presents a clear result: people who fa- vour science and technology are happier. We see studies which show the mixed effects technology has in our well-being. But, can we say that people who favour technology necessarily use more of it in their daily life? It would be quite hard to associate this result with anything already published, if it were not for the study conducted by Stavrova et al. (2016), which, using a Dutch sample, linked the belief in scientific-technological progress to higher levels of life satisfaction. Besides, Table 1 puts rural people more in favour of science and technology. Point for rural areas! A reasonable explanation to that would be to envisage that productivity of labour increases faster in the countryside, making work easier, with the advent of new machinery and new techniques. Maybe some things, 25Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 like having access to modern medical treatments and new innovative smartphones, are taken for granted in bigger towns. Respecting views on religion (Q164 and Q171-Q172), Table 3 points in the direction of religious people being happier. That is a link well-established in the empirical literature, as can be seen in Krause et al. (2017), who, using American data, showed that people who are more committed to faith tend to be happier. Then, Table 1 reveals that rural and small town people are more religious than people in bigger and urban cities. Point for rural areas! Some possible reasons why people in rural areas are more religious can be tradition – the role the family and the community have over daily life, existing villages in which a religious leader is the most notable person – and less options of leisure or endeavours in which to put time than there are in a bigger city. Another way to view this is to think people in urban towns are often overloaded with tasks in work or university, spend more commuting time and, therefore, may have less time for things that would make them happier. 4.4. Politics and corruption As regards views on politics (Q199, Q234, Q240, Q250), according to Table 3, people who are more interested in politics, who care more about honest elections, who value democracy and who identify themselves more as right-wing electors are happier. Empirical research from Switzerland has already shown that direct democracy and political decentralization raise subjective well-being (Frey & Stutzer, 2000). Inversely, those more satis- fied with their lives are more likely to turn out to vote in the US (Flavin & Keane, 2011). Weitz-Shapiro and Shapiro (2011) discuss the direction of this effect, using data from Latin American countries; the correlation between life satisfaction and political participation is clear, even if it is attenuated in countries that have en- forced compulsory voting. Results from Table 1 are not clear for Q199; for the other questions, though, people in rural and small villages are more interested in politics and more right-wing. Point for rural areas! Our explanation on subsection 4.1 is still pertinent here: since in small towns everybody knows everybody and their representatives, they may feel like politicians’ decisions have a bigger impact on their life. If you live in a place with 4000 other people, probably almost every decision made will impact you somehow; if you live in a 400000 inhabitants place, that will not necessarily be the case. As for the right-wing relation, Napier and Jost (2008), who found evidence that conservatives are happier than liberals in the US, propose that the rationalization of inequality, which forms part of the core of conservative ideology, is the mediator. That will resonate with our section 4.6. Nevertheless, it is worth mentioning that such a concept as right or left on a political scale is anything but universal. Since our results come from a global aggregate sample, they should be taken with a grain of salt. Regarding corruption (Q112), we found, in Table 3, contradictory results. For Q46, people who see less corruption were happier (which, through Table 1, would benefit urban people); for Q49, it was people who see abundant corruption (which would benefit rural people). Since results do not match, it is not possible to continue the analysis here. However, it urges us to point out that perceived corruption may not match actual corruption, which, in its turn, is negatively associated with subjective well-being (Tavits, 2008). We call it a tie! 4.5. Immigration and patriotism Pro-immigration people are happier than anti-immigration folks (Table 3, Q121 and Q130). Welsch et al. (2020), inspecting data from 35 European countries, found that immigration-friendliness, through a mecha- nism of moral satisfaction, is associated with greater subjective well-being. Moreover, looking specifically into German data, Akay et al. (2014) identified the same relation, specifying that the positive effect of immigration on natives’ life satisfaction depends on the assimilation of immigrants in the region. Analysing British data, we also see that, in general, groups anti-immigration are made of relatively older individuals, those with be- low-average household incomes, the unemployed and those without any formal education as well (Howley et al., 2019). Table 1 reveals that immigration is better viewed in bigger centres. Point for urban areas! Such a difference is in agreement with Zahl-Thanem and Haugen (2019), who found Norwegians in the countryside express more negative attitudes towards immigrants and immigration. If, as we have seen in subsection 4.2, rural people go through more financial hardship in life, then, considering Howley et al. (2019), it would make sense to expect them to be less favourable to immigration. Isolation can play a role too: maybe we are afraid of what we do not know. Since exposure to different groups of migrants – and even to different social groups or “urban tribes” – is lower in smaller cities, fear of the unknown can be a reason for that attitude. Concerning preferences on changes in society (Q42), both Table 1 and Table 3 do not present clear results. Thus, no comment will be made. As for being proud of your nationality (Q254), Table 3 reports that those who experience that feeling are happier. This is in assent with Morrison et al. (2011), who found, in a worldwide survey, that national satisfaction predicts individual life satisfaction. Reeskens and Wright (2011), nonetheless, showed a crucial factor: greater civic nationalism is associated with higher subjective well-being, whereas 26 Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 greater ethnic nationalism is linked to lower subjective well-being levels. There is a split between patriotism and bigotry. From Table 1, we see that people in rural small villages are prouder of their country. Point for rural areas! That is, in fact, a puzzling result. If people in rural areas are less favourable to immigration (which is itself linked to greater happiness), we would think their kind of nationalism is the ethnic one (which is, in its turn, associated with lower happiness). Some possibilities: the conclusions from Reeskens and Wright (2011) may not be globally applicable or rural people might show higher levels of the two kinds of nationalism – that would suppose that people in urban areas are not very proud of their nationality or even that ethnic nationalism is relatively larger than civic nationalism in urban areas. It is, for sure, an open field. 4.6. Economic values About economic values (Q106-Q111), Table 3 exhibits that pro-equality people, those who are pro priva- tisation and those who think government should take more responsibility (instead of individuals taking it) are unhappier. At the same time, pro-competition people, people who believe in hard work over luck and those who think environmental protection trumps the need to create and maintain jobs are happier. Self-efficacy – the conviction one has that they can do a specific task, believing in oneself and believing we are the captains of our destiny – has already been proven to be related to subjective well-being (Strobel et al., 2011). Naturally, those people will not fear competition either. Preferences on equality influencing life satisfaction were already seen in subsection 4.4 (Napier & Jost, 2008). It is already well-established in the empirical literature that economic freedom relates to happiness (Gropper et al., 2011). Having found the same relation, Ovaska and Takashima (2006) suggest economic growth and income, always in vogue in the media, do not raise happiness as much as economic freedom does. Finally, Sulemana (2015), using data from fifteen countries, found that happier people are more willing to make income sacrifices to protect the environment. Going to Table 1, we see no clear results for two questions (Q108 and Q111), two results that favour urban people (Q109-Q110) and two results that do the same for rural people (Q106-Q107). It is a draw! Inhabitants of bigger towns have a better view of hard work and competition, which could be either a symptom of living in a big city, where there is plenty of competition and you have to be good to make the cut, or a cause to explain that choice. Luck can also play a major role in a rural context, inasmuch as a lot depends on weather conditions. If you trust in yourself, you will probably not think equality is fair, which would explain why pro-equality people are unhappier. As for preferences on privatisation, it is not evident how to conciliate that result with the others; a possible explanation would be, like it was for the political scale in subsection 4.4, that such a word has a wide variability of interpretations, depending on the individual and on the context of the country at the time. 5. Conclusion In this article, through mean comparisons and correlation tests, using a recent worldwide sample, three groups of results were found. First, people seem to be happier in rural settlements, smaller cities and villages. Second, some characteristics are more tied to how happy people think and act than others, for instance: higher levels of trust in others, self-efficacy, saving more, experiencing less hardship, being pro-immigration, religious, pro-science, proud of their country and interested in politics. Third, looking into prevalent trends in different urbanistic contexts and linking them to the second result, we see that people in rural areas appear to be slightly happier than others, through indirect effects. Further research can deeper investigate how different attitudes and visions are related to subjective well-be- ing. Most of all, they can look into how these “happiness variables” – some of them already well-established in the literature – appear more or less in one urbanistic context than in the other and what explains that difference. Another line of research consists in splitting these results to see which of them are universal and which are tied to countries with specific particularities (and what are they). Finally, if some features make people happier, and they are present in one context and absent in the other, they could, in some cases, be brought up by policymakers. Such an endeavour can be accomplished by economic policy (e.g., increasing economic freedom), migration policy (e.g., integrating migrants all over the country), changes in the political system (e.g., increasing direct political participation and giving more transparency to public decisions), by increasing financial education in rural areas and by increasing public security in urban ones, just to mention a few traditional policies. Policymakers can, however, and that is the tone with which we want to finish this article, use nudge policies, which are based on behav- ioural insights and advocate minimal intervention, to make people’s lives easier and a bit happier in daily situations. 27Berticelli de Freitas, A. H. Pap. Eur. 34(2021): 17-28 Bibliography Adjaye-Gbewonyo, D., Rebok, G. W., Gross, A. L., Gallo, J. J., & Underwood, C. R. (2019). Assessing urban-rural differences in the relationship between social capital and depression among Ghanaian and South African older adults. 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